Application of nanotechnology for the

discovery of circulating proteins as novel

biomarkers of breast cancer

Antonio Castro López

Doctoral Thesis UDC 2021

Supervisors: Dr. Benigno Acea Nebril, Dra. Cristina Núñez González

Doctoral Program in Health Sciences

Application of nanotechnology for the

discovery of circulating proteins as novel

biomarkers of breast cancer

Antonio Castro López

Doctoral Thesis UDC 2021

Supervisors: Dr. Benigno Acea Nebril, Dra. Cristina Núñez González

Doctoral Program in Health Sciences

ACKNOWLEDGEMENTS

Simplemente, a la VIDA.

Esta tesis se ha realizado con el esfuerzo continuado de muchas personas que me

han ayudado y que, gracias a su apoyo y motivación, he conseguido terminarla.

Al Dr. Benigno Acea Nebril, por la co-dirección de esta tesis, por su confianza,

por su persistencia y por su capacidad de enseñarme.

A la Dra. Cristina Núñez González, por la co-dirección de la tesis, por su ayuda,

su trabajo y su apoyo.

A la Dra. Mª Carmen Cereijo Garea, por su disponibilidad, su entusiasmo y su

amistad.

A todas las pacientes, cuya colaboración han hecho posible este trabajo.

SHORT ABSTRACTS

Abstract

Breast cancer is one of the most common cancers in women and accounts for about

14% of cancer-related deaths in females around the world. Breast cancer is a

heterogeneous disease that presents a wide variety of molecular and clinical

characteristics, as well as variability in clinical progression. For the treatment choice,

patients are classified according to intrinsic biological subtypes within the breast

spectrum, using clinical-pathological criteria, i.e. the recognition of amplification and/or

overexpression of the human epidermal growth factor receptor 2 (HER2) oncogene, the

immunohistochemical classification of the estrogen receptor (ER) and the progesterone

receptor (PR), and Ki-67 labelling index. This classification allows for a more

personalized approach to medical treatments, with favorable results. However, despite

that, almost 10-15% of these patients still experience local or distant recurrences in the

first 5 years from diagnosis.

Classification of breast cancer might be markedly improved if new biomarkers

identified with the use of high-throughput “omics” approaches could support diagnosis

based on histopathological patterns. In this way, nanomaterials have been introduced into

the field of proteomics to establish a new and rapidly evolving research area termed

nanoproteomics.

It is well known that the dispersion of a nanomaterial in physiological fluid results in

the formation of a protein shell named “protein corona” (PC). PC varies depending on the

characteristics of the biological media, the physical (size, shape, curvature) and chemical

properties (composition, surface charge/chemistry, hydrophobicity/hydrophilicity) of the

nanomaterial, and the incubation time. Disease-associated biomarkers comprise less than

1% of serum proteins. In this way, through the formation of the PC, nanoparticles could

act as sorbent materials for the enrichment of low-abundance peptides/proteins presented

in serum samples before the biomarker identification by mass spectrometry (MS)

analysis. Importantly, otherwise undetectable changes in protein concentration at an early

stage of the disease (as breast cancer), after any treatment (chemotherapy,

immunotherapy) or surgery could be detected analyzing the PC composition. Thus,

characterization of the PC around NPs offers distinct advantages over sole proteomic

approaches and increases the success of identifying molecular targets.

Firstly, this thesis aims to optimize the formation of the bio-corona formed around

the surface of gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), platinum

nanoparticles (PtNPs) and magnetic nanoparticles (MNPs) after their interaction with

proteins present in human serum. After that, it was developed an exhaustive qualitative

and quantitative analysis of composition of the PC through electrophoretic separation

(SDS-PAGE) in combination with liquid chromatography tandem-mass spectrometry

(LC-MS/MS). This methodology was applied for the identification of serum biomarkers

of the different breast cancer subtypes. This study shows that nanoproteomics is a

valuable tool that can facilitate comprehensive and systematic identification of the serum

proteome and the molecular classification of breast cancer.

Resumen

El cáncer de mama es uno de los tipos de cáncer más común en mujeres y supone

aproximadamente el 14% de las muertes relacionadas con el cáncer en mujeres de todo el

mundo. Se trata de una enfermedad heterogénea con una amplia variedad de

características moleculares y clínicas, así como una variabilidad en la progresión clínica.

Para la elección del tratamiento adecuado, las pacientes con cáncer de mama se clasifican

en distintos subtipos empleando criterios clínico-patológicos que se basan en los niveles

de expresión del oncogén del receptor 2 del factor de crecimiento epidérmico humano

(HER2), la clasificación inmunohistoquímica del receptor de estrógeno (RE) y el receptor

de progesterona (RP), y el índice Ki-67. La clasificación del cáncer de mama en los

distintos subtipos permite un abordaje más personalizado de los tratamientos médicos,

con resultados favorables. Sin embargo, a pesar de esto, casi el 10-15% de estos pacientes

todavía experimentan recidivas locales o distantes en los primeros 5 años tras el

diagnóstico.

Actualmente, el uso de nuevas herramientas “ómicas” permite la identificación de

nuevos biomarcadores que respalden el diagnóstico basado en patrones histopatológicos,

lo que se traducirá en una mejora en la clasificación del cáncer de mama.

Con esta finalidad se han comenzado a introducer los nanomateriales en el campo de

la proteómica, dando lugar a una nueva área de investigación denominada

nanoproteómica.

La nanoproteómica se basa en que la dispersión de un nanomaterial en un fluido

fisiológico da como resultado la formación de una capa de proteinas denominada

"corona". Esta corona proteica varía según las características del medio biológico, las

propiedades físicas (tamaño, forma, curvatura) y químicas (composición, carga

superficial/química, hidrofobicidad/hidrofilicidad) del nanomaterial y el tiempo de

incubación.

Los biomarcadores asociados a un determinada enfermedad suponen menos del 1%

de las proteínas presentes en el suero sanguíneo. Con la formación de la corona de

proteínas, los nanomateriales actuan como materiales absorbentes con los que se lleva a

cabo el enriquecimiento de péptidos/proteínas de baja abundancia presentes en las

muestras de suero sanguíneo. El análisis de estas proteínas ancladas a la superficie de los

nanomateriales mediante técnicas de espectrometría de masas permitirá la identificación

de nuevos biomarcadores asociados con una determinada enfermedad, como el cancer de

mama. Así, mediante este tipo de análisis se podrán detectar cambios en la concentración

de proteínas en una fase temprana de una enfermedad, tras cualquier tratamiento

(quimioterapia, inmunoterapia) o una intervención quirúrgica. Por lo tanto, la

caracterización de la corona de proteínas que se forma alrededor de los nanomateriales

ofrece distintas ventajas en relación con los análisis proteómicos convencionales, y es

más eficaz a la hora de llevar a cabo la identificación de nuevas dianas moleculares.

En primer lugar, esta tesis tiene como objetivo optimizar la formación de la bio-

corona formada alrededor de la superficie de nanopartículas de oro (AuNPs),

nanopartículas de plata (AgNPs), nanopartículas de platino (PtNPs) y nanopartículas

magnéticas (MNPs) tras su interacción con las proteínas presente en suero sanguíneo

humano. Posteriormente, se lleva a cabo un análisis cualitativo y cuantitativo exhaustivo

de la composición del corona proteica formada mediante la combinación de las técnicas

de separación en gel (SDS-PAGE) en combinación con la espectrometría de masas en

tándem (MS/MS) acoplada a la cromatografía de líquidos (LC-MS/MS). Tras la

optimización de esta metodología, se aplica en la identificación y cuantificación de

biomarcadores de los diferentes subtipos de cáncer de mama presentes en muestras de

suero sanguíneo.

Este estudio muestra que la nanoproteómica es una herramienta valiosa que puede

facilitar la identificación integral y sistemática del proteoma sérico y la clasificación

molecular del cáncer de mama.

Resumo

O cancro de mama é un dos cancros máis comúns nas mulleres, xa que representa

aproximadamente o 14% das mortes relacionadas co cancro en mulleres de todo o mundo.

É unha enfermidade heteroxénea cunha gran variedade de características moleculares e

clínicas, así como variabilidade na progresión clínica. Para escoller o tratamento axeitado,

as pacientes con cancro de mama clasifícanse en diferentes subtipos empregando criterios

clínicopatolóxicos baseados nos niveis de expresión do oncóxeno receptor de factor de

crecemento epidérmico humano 2 (HER2), a clasificación inmunohistoquímica do

receptor de estróxenos (RE) e o receptor de proxesterona (RP), e o índice Ki-67. A

clasificación do cancro de mama nos diferentes subtipos permite unha abordaxe máis

personalizada dos tratamentos médicos con resultados favorables. Non obstante, a pesar

diso, case o 10-15% destas pacientes aínda experimentan recorrencias locais ou distantes

nos primeiros 5 anos despois do diagnóstico.

Actualmente, o uso de novas ferramentas "ómicas" permite a identificación de

novos biomarcadores que apoien o diagnóstico baseado en patróns histopatolóxicos, o

que se traducirá nunha mellora na clasificación do cancro de mama.

Para iso, comezaron a introducirse nanomateriais no campo da proteómica, dando

lugar a unha nova área de investigación chamada nanoproteómica.

A nanoproteómica baséase no feito de que a dispersión dun nanomaterial nun

fluído fisiolóxico resulta na formación dunha capa de proteínas chamada "coroa". Esta

coroa proteica varía segundo as características do medio biolóxico, as propiedades físicas

(tamaño, forma, curvatura) e químicas (composición, carga superficial/química,

hidrofobicidade / hidrofilicidade) do nanomaterial e o tempo de incubación.

Os biomarcadores asociados a unha determinada enfermidade representan menos

do 1% das proteínas presentes no soro sanguíneo. Coa formación da coroa proteica, os

nanomateriais actúan como materiais absorbentes cos que enriquecen os

péptidos/proteínas de baixa abundancia presentes nas mostras de soro sanguíneo. A

análise destas proteínas ancoradas á superficie de nanomateriais mediante técnicas de

espectrometría de masas permitirá a identificación de novos biomarcadores asociados a

unha determinada enfermidade, como o cancro de mama. Así, a través deste tipo de

análise, pódense detectar cambios na concentración de proteínas nunha fase inicial dunha

enfermidade, despois de calquera tratamento (quimioterapia, inmunoterapia) ou

intervención cirúrxica. Polo tanto, a caracterización da coroa proteica que se forma ao

redor dos nanomateriais ofrece distintas vantaxes sobre as análises proteómicas

convencionais e é máis eficiente na identificación de novas dianas moleculares.

En primeiro lugar, esta tese ten como obxectivo optimizar a formación da bio-

coroa formada ao redor da superficie de nanopartículas de ouro (AuNPs), nanopartículas

de prata (AgNPs), nanopartículas de platino (PtNPs) e nanopartículas magnéticas (MNPs)

despois da súa interacción coas proteínas presente no soro sanguíneo humano.

Posteriormente, lévase a cabo unha análise cualitativa e cuantitativa completa da

composición da coroa proteica formada combinando técnicas de separación de xel (SDS-

PAGE) en combinación con espectrometría de masas en tándem (MS/MS) unida a

cromatografía líquida (LC-MS / MS). Despois de optimizar esta metodoloxía, aplícase na

identificación e cuantificación de biomarcadores dos diferentes subtipos de cancro de

mama presentes en mostras de soro sanguíneo.

Este estudo demostra que a nanoproteómica é unha valiosa ferramenta que pode

facilitar a identificación completa e sistemática do proteoma sérico e a clasificación

molecular do cancro de mama.

CONTENTS

Contents

Abbreviations…………………………………………………………………………...1

I. Introduction…………………………………………………………………………..7

1. Problematic: breast cancer…………………………………………………......7

2. Non-modifiable risk factors…………………………………………………....7

3. Modifiable risk factors………………………………………………………..10

4. Protective factors……………………………………………………………..11

5. Actions with sufficient evidence of benefit…………………………………...12

6. Actions without sufficient evidence of relationship………………………......14

7. Factors with sufficient evidence of no or little relationship……………….......15

8. Locoregional and systemic spread of breast cancer…………………………..15

9. Early detection of breast cancer……………………………………………....18

10. Protein biomarkers for breast cancer screening and diagnosis……………....20

11. Protein biomarkers for breast cancer prognosis……………………………..38

12. Biomarkers for response prediction and treatment monitoring of breast

cancer…………………………………………………………………………………...40

13. References…………………………………………………………………..42

II. Objectives…………………………………………………………………………..65

III. Results and Discussion…………………………………………………………....67

Results and Discussion. Chapter 1…………………………………………………....67

Abstract………………………………………………………………………....67

Keywords……………………………………………………………………….67

1. Introduction…………………………………………………………………..68

2. Experimental………………………………………………………………....70

3. Results and discussion……………………………………………………….74

4. Conclusions…………………………………………………………………..84

5. References…………………………………………………………………....85

Results and Discussion. Chapter 2…………………………………………………...93

Abstract………………………………………………………………………....93

Keywords……………………………………………………………………….94

1. Introduction…………………………………………………………………..94

2. Experimental………………………………………………………………....97

3. Results and discusión……………………………………………………….106

4. Conclusions………………………………………………………………....135

5. References…………………………………………………………………..135

Results and Discussion. Chapter 3…………………………………………………....143

Abstract………………………………………………………………………..143

Keywords……………………………………………………………………...144

1. Introduction………………………………………………………………....144

2. Experimental………………………………………………………………..146

3. Results and discusión……………………………………………………….149

4. Conclusions………………………………………………………………....163

4. References…………………………………………………………………..165

IV. Conclusions……………………………………………………………………....173

ANNEX A. Supplemental Material Chapter 1…………………………………….175

ANNEX B. Supplemental Material Chapter 2……………………………………..205

ANNEX C. Supplemental Material Chapter 3…………………………………….283

ANNEX D. Extended abstract………………………………………………………325

ANNEX E. List of publications

ABBREVIATIONS

1

AAL: aleuria aurantia lectin

ACM: antibody colocalization microarray

ACN: acetonitrile

AD: area-based breast density

AFP: α-fetoprotein

AgNPs: silver nanoparticles

AHSG: alpha 2HS-glycoprotein

ANX A3: annexin A3

APOA1: apolipoprotein A-I

APOA2: apolipoprotein A-II

APOC1: apolipoprotein C-I

APOC2: apolipoprotein CII

APOC3: apolipoprotein C-III

APOE: apolipoprotein E

APOH: apolipoprotein H

ATII: angiotensin II

AUC: area under the curve

AuNPs: gold nanoparticles

BBC: BRCA1 mutant breast cancer

BBD: benign breast disease

BCa: breast cancer

BH: BRCA1 mutant healthy

BM: bone metastasis

BTD: biotinidase

CA15-3: carbohydrate antigen 15-3

CA19-9: carbohydrate antigen 19-9

CA27.29: carbohydrate antigen 27.29

CA125: carbohydrate antigen 125

C3a desArg: C3a des-arginine anaphylatoxin

C4BPB: complement component 4 binding protein β

CDH5: cadherin-5

CEA: carcinoembryonic antigen

CFHR3: complement factor H-related 3

CGB: champedak galactose binding

2

COL10A1: collagen 10a1

COL11A1: collagen11a1

COMP: collagen oligomeric matrix protein

1CTP: pyridinoline crosslinked carboxyterminal telopeptide of type I collagen

DCIS: ductal carcinoma in situ

DE: differentially expressed

DEP: differentially expressed proteins

DL: detection limit

DM: distant metastases

dPC: digital ProteomesChip

DPV: differential pulse voltammetry

DR: dynamic range

2D-DIGE: 2-dimensional difference gel electrophoresis

2D-nanoLC-MS/MS: two-dimensional nano-liquid chromatography coupled with

tandem mass spectrometry

ECL: electrochemiluminescent

ELISA: enzyme-linked immunosorbent assay

ELLA: enzyme-linked lectin assay

ER: estrogen receptor

ESI: electrospray ionization

EVs: extracellular vesicles

FGA: fibrinogen alpha

GO: graphene oxides

HC: healthy controls

HER2: human epidermal growth factor receptor-2

HIC: hydrophobic interaction chromatography

HILIC: hydrophilic interaction chromatography

HP: human plasma

HPA: helix pomatia agglutinin

HS: human serum

HSPs: heat shock proteins

HSP90A: heat shock protein 90A

IAP: inhibitor of apoptosis

IDC: invasive ductal carcinoma

3

IEF: isoelectric focusing

IHC: immunohistochemistry

IGFBP3: insulin-like growth factor-binding protein 3

IgG Fc: immunoglobulin G crystallizable fragment

IMAC: immobilized metal affinity chromatography

ITIH4: inter-alpha trypsin inhibitor heavy chain family member H4

iTRAQ: isobaric tags for relative and absolute quantification

LA: luminal A

LAC: lectin affinity chromatography

LB: luminal B

LC: label-free quantification

LC-MS/MS: liquid chromatography tandem-mass spectrometry

LNM: lymph node metastasis

LR: local recurrence

LTA: lotus tetragonolobus agglutinin

MALDI-TOF MS: matrix-assisted laser desorption/ionization time-of-flight mass

spectrometry

MBs: magnetic beads

MIF: migration inhibitory factor

M-LAC: multi-lectin affinity chromatography

MLR: multiple logistic regression

MNPs: magnetic nanoparticles

MRM: multireaction monitoring

MWCNTs: multiwalled carbon nanotubes

m/z: mass/charge ratio

NCIs: non-cancerous individuals

NFX1: nuclear transcription factor, X box-binding protein 1

NSR: no sign of recurrence

NTNBC: non-triple-negative breast cancer

NTX: N-terminal crosslinking telopeptides of type I collagen

OPN: osteopontin

ORM-1: α-glycoprotein orosomucoid 1

OS: overall survival

PAI-1: plasminogen activator inhibitor-1

4

PAPPA: pappalysin-1

PCA: perchloric acid

PDD: primary disseminated disease

PDGF: platelet-derived growth factor

PEP: protein elution plate

pIgR: polymeric immunoglobulin receptor

PKG1: cGMP dependent protein kinase1

PR: progesterone receptor

PRM: parallel reaction monitoring

PTMs: post-translational modifications

PTHrP: parathyroid hormone-related protein

PtNPs: platinum nanoparticles

PZP: pregnancy zone protein

RALGAPA2: Ral GTPase-activating protein subunit alpha-2

RANTES/CCL5: regulated on activation normal T cell expressed and

secreted/chemokine (C-C motif) ligand 5

REC: recurrent breast cancer

ROC: receiver operating characteristic

RP: reverse phase

RPC: reversed phase chromatography

SAP: serum amyloid protein

SBA: antibody suspensión bead array

SBC: sporadic breast cancer

SDS-PAGE: sodium dodecyl sulfate polyacrylamide gel electrophoresis

SELDI-TOF MS: surface-enhanced laser desorption/ionization time-of-flight mass

spectrometry

SEREX: serological analysis of recombinant cDNA expression libraries

SISCAPA: stable isotope standards and capture by anti-peptide antibodies

SLPI: secretory leukocyte protease inhibitor

SPB: serum protein biomarker

sTfR: soluble form of transferrin receptor

TAA: tumor-associated antigens

TAAb: tumor associaced autoantibodies

TJP2: tight junction protein 2

5

TNBC: triple-negative breast cancer

TPA: tissue polypeptide antigen

TPS: tissue polypeptide specific antigen

UPLC: ultraperformance liquid chromatography

WCX: weak cation exchange

WT: wild type

6

I. INTRODUCTION

I. Introduction

______________________________________________________________________

7

1. Problematic: breast cancer

Incidence is defined as the number of new cases of a disease in a population and

at a given time. It can be expressed as the absolute number of new cases in a year or as

rates (number of new cases per-100000 people/year).

Cancer continues to be one of the main causes of death with approximately 18.1

million new cases/year in the world (SEOM, year 2018) [1]. Regarding the European

Union and breast cancer, in that year 404,900 new cases were diagnosed, which

represents 29.2 percent of all cancers in women and an incidence rate of 108.8 per-

100000. Spain has an average incidence rate in relation to its neighboring countries

(84.9 per-100000 women) (SEOM, año 2018) [1].

Breast cancer is the leading cause of cancer death in the E.U. In 2018, 98800

women died from this cause, with an age-standardized mortality rate of 21.4 per-100000

(in Spain, 15.4 per-100000) (SEOM, año 2018) [1].

The estimate of new cases of breast cancer in Spain in 2020 is 32953. It is

estimated that one in every 8 women will have breast cancer at some point in her life

[2].

Breast cancer continues to be a major problem and, at present, we do not have

effective primary prevention measures, given that the most important risk factors (age,

family history of breast cancer, sex, history of breast disease, …) are not modifiable.

It is undoubted, therefore, that our efforts should go towards secondary

prevention activities, such as a diagnosis as early as possible, ... etc. In this way, we will

be able to carry out a treatment with a much greater chance of cure, since survival is

closely linked to the stage in which the cancer is, at the time of diagnosis. (Survival in

stage I is 98%, while in stages III it drops to 25%). In 2018, 6534 women died of breast

cancer in Spain [3].

2. Non-modifiable risk factors

2.1. Age. Sex. Race and Size

The risk increases with increasing age. It is very rare, before the age of 30 (and

is usually associated with genetic alterations) [4]. In Spain, the percentage of women

with breast cancer over 65 years of age represents 0.24% of the population in that age

group, while in the range of 15 to 65 years, it represents 0.12% of that age group.

I. Introduction

______________________________________________________________________

8

population. This explains why the incidence is higher in developed countries, given that

life expectancy is higher than in underdeveloped countries.

Only 1 in 100 breast cancers occurs in men. This is explained by hormonal

differences, since exposure to sex hormones is the most determining difference.

Estrogen levels (but also progestogens and androgens) have shown a strong

association with breast cancer (more pronounced in postmenopausal women) and

especially with luminal tumors. This association is not so evident in triple negative

tumors; therefore, there are studies that report that they can even be protective against

this subtype of breast cancer.

White women are at higher risk (and within these, Hispanics have a lower risk

than Caucasians). African American women have a higher risk of "triple negative" and

"younger ages" than Caucasians of the same characteristics. And this is significant. But

lifestyle and migrant populations may see their initial risk modified in relation to race

[5].

Women who are taller than 1.75 cm are 20% more likely to develop breast

cancer than those who measure 1.60 or less (bias in relation to lifestyle and diet, in the

growth stage) [6].

2.2. Breast tissue density

Women with mammographically dense breasts have a 2- to 6-fold increased risk

of developing breast cancer compared to women with the lowest breast density.

The increase in risk is proportional to the degree of density. In a report that

groups three studies of cases (1112 pairs of cases) and controls (similar number), it was

observed that women with a density of 75% or more, compared to women with a

density lower than 10%, which ranges between 1.79 and 4.74. This increase persisted

for a minimum of 8 years and was greater in younger women [7, 8].

An increased risk of mortality has not been shown in women with dense breast

tissue.

2.3. Bone mineral density

There is a correlation between high bone mineral density (which leads to a high

hormonal load) and an increased risk of breast cancer [9]. In the Women's Health study,

it is observed that for each unit that increases the T-score, this risk increases [10].

I. Introduction

______________________________________________________________________

9

2.4. Reproductive factors

Early menarche and late menopause are higher risk factors for breast cancer; due

to increased reproductive cycles and therefore increased circulating estrogens. Even

higher, from 55 years of age and independent of the tumor phenotype [11].

2.5. Personal history of breast cancer

There is an increased risk of developing contraletral breast cancer, but it varies

depending on the age of the woman or the histological type of the tumor (in both DCIS

and ICD). LCIS is considered a risk marker for developing ipsilateral or contralateral

breast carcinoma, with a RR between 8-10 [6].

2.6. Personal history of proliferative injuries

Non-proliferative lesions (simple cysts, fibrosis, simple fibroadenoma, simple

columnar or apocrine alteration, and mild ductal hyperplasia) have not been shown to

significantly increase the risk of developing breast cancer (RR, 1.2 - 1.4).

Proliferative lesions without atypia (ductal or columnar hyperplasia, sclerosing

adenosis, papilloma and radial scar), a RR, between 1.7-2.1.

Proliferative lesions with atypia (HLA, CLIS, HDA), a clear increase in risk has

been demonstrated (RR greater than or equal to 4).

The increased risk due to flat epithelial atypia, aprocrine atypia, and secretory

atypia is unclear [12].

2.7. Heredity

Only 5-10% of women with breast cancer have inherited mutations that are the

main cause of tumors. The most common are in the BRCA1 and BRCA2 genes (55-

65%) that pose an approximate risk of developing cancer before age 80, of 70%. Other

genes also involved are: PALB2, TP53, ATM, CHEK2, PTEN or STK11.

There are populations with characteristic mutations such as the Icelandic and the

Ashkenazi Jewish community [13].

I. Introduction

______________________________________________________________________

10

3. Modifiable risk factors

3.1. Hormone therapy

Multiple population studies (Heart and estrogen/progestin replacement study,

WHY, Million Women study, etc.) have shown that long-term use of combined

estrogen-progestin HRT increases the risk of breast cancer (very low, with less use of

five years and marked with use greater than 10 years). However, the relationship of

increased risk with the use of estrogen-only HRT is controversial; since there are studies

that show a higher risk and others that show protection (it is possible that the time

elapsed between the start of E therapy and menopause, is very important) [4].

3.2. Obesity

In postmenopausal women there is a clear relationship between weight gain and

an increased risk of breast cancer. Mainly due to the fact of a greater production of

estrogens by adipose tissue (between 50 to 100%, higher in women with obesity). The

relationship between obesity, diabetes and insulin concentration and the risk of breast

cancer has been studied; but this has not been possible to define clearly [14-16].

3.3. Alcohol

Alcohol use increases the risk of breast cancer and has been shown to be

dose/dependent in many studies and is a global risk for all tumor subtypes. In a British

meta-analysis of 53 studies, comparing women with breast cancer who did not consume

alcohol, the RR was 1.32 (95% CI, 1.19-1.45; P <0.001) in women who consumed 35 to

44 g of alcohol per day and 1.46 (95% CI, 1.33-1.61; P <0.001) among those who

consumed 45 or more. The RR of breast cancer increases 10% for every 10 grams of

alcohol (one drink/day) [17].

3.4. Exposure to ionizing radiation

Although it appears that there is no increased risk of developing breast cancer, in

women exposed to ionizing radiation for diagnosis, there appears to be a certain

predisposition, especially in carriers of BRCA mutations, especially if the exposure

occurs at ages younger than 40 years.

On the other hand, exposures to radiation of a therapeutic nature (Hodking's disease,

etc.) in young women (during their breast development), if it represents an increased

risk of up to 35% of developing breast cancer around the age of 40. Higher doses of

I. Introduction

______________________________________________________________________

11

radiation and treatment between 10 and 16 years of age are associated with a higher risk

(and this risk is not reduced with follow-up time, persisting up to 25 years after

treatment). Ovarian suppression secondary to chemotherapy or targeted radiation does

seem to have a protective effect [14-27].

3.5. Nulliparity

It is a perfectly established risk factor (especially in the development of tumors

in ages over 70 years). Obesity acts synergistically for the development of cancer [28].

4. Protective factors

4.1. Pregnancy

In general, it is considered a protective factor for breast cancer. The early age in

gestation confers greater protection (there is a 12% decrease in risk for each full-term

pregnancy, in menopausal women and 3% for each one in premenopausal women). This

protective effect does not occur in women over 35 years of age, compared to nulliparous

women [28-30].

4.2. Breastfeeding

Breastfeeding is associated with a lower risk of breast cancer (this decrease in

risk is especially significant, for triple negative tumors). In a review of 47

epidemiological studies from 30 countries, with 50302 women with breast cancer and

96000 controls. It was shown that the decrease in risk was greater in women who had

children and breastfed than in those who had children and did not breastfeed. It was also

proportional to the duration of lactation. The RR decreased by 4.3% for every 12

months of lactation and by 7%, for every delivery [31].

4.3. Physical activity

Regular physical exercise can reduce the risk of breast cancer, especially in

young women who have had children. In a meta-analysis with 123574 cases, it was

observed that physical exercise decreased the risk of breast cancer, as well as the events

arising from the neoplasia [32-35].

I. Introduction

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12

4.4. Diet

The analyzes on the impact of the Mediterranean diet, as well as the intake of

fruits and vegetables seem to show a decrease in the risk of breast cancer. In a statistical

analysis carried out on 62000 women in the Netherlands, a statistically significant

association was observed between diet and a decrease in breast cancer (especially in ER

negative tumors). Although all this is contradictory, due to the existence of other studies

(Women's Healthy Eating and living randomized trial, a branch of the WHI, ... etc) that

have not found a relationship [36].

5. Actions with sufficient evidence of benefit

5.1. Selective estrogen receptor modulators

Several trials have shown that tamoxifen reduces the recurrence rate and the

appearance of new contralateral primary breast cancers and protects bone mineral

density (BMD) in postmenopausal women. [37-42].

The Breast Cancer Prevention Trial (BCPT) randomized more than 13,000

women at high risk of breast cancer to receive treatment with tamoxifen versus placebo,

finding a 49% decrease in the incidence in the tamoxifen group, also accompanied by a

reduction in the number of fractures (as side effects, more endometrial cancers and more

thrombotic phenomena were observed) [43, 44]. An update confirmed similar results at

7 years of follow-up [45].

A meta-analysis was conducted with three other trials: one in the UK (2471

women at high risk of breast cancer due to family history) [45], another Italian (5,408

women undergoing hysterectomy, low or normal risk) [46] and the Breast Cancer

Intervetion study, with 7152 women at increased risk of breast cancer; where a 38%

reduction in the incidence of breast cancer was demonstrated (greater decrease in ER

positive tumors, up to 48%), confirming a similar incidence of adverse effects [47].

The NSABP-24 showed that in women with DCIS (who have a higher risk of

contralateral breast cancer), who had added tamoxifen to local radiotherapy, compared

to those who had not been added, a decrease was shown, statistically significant,

invasive and in situ cancers, as well as contralateral breast cancers [48].

Raloxifene is a selective estrogen receptor modulator (SERM) that acts as an

antiestrogen at the level of the breasts and endometrium, with an estrogenic effect at the

bone, coagulation and lipid levels. In the MORE study, with a sample of close to 8.000

I. Introduction

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13

postmenopausal women with osteoporosis, treated with raloxifene, vertebral fractures

were reduced and, as a collateral benefit, the incidence of invasive breast cancer (mainly

positive for ER) was reduced, and no found an increase in the incidence of endometrial

cancer and hyperplasia [49].

In the CORE trial (MORE trial extension), in which 80% of the MORE study

participants were followed for a further 4 years, there was a 66% reduction in invasive

breast cancer and a reduction in breast cancer positive for ER was 76% [50].

Similar results were observed in the Raloxifene User for the heart (study to

evaluate coronary effects and invasive breast cancer with raloxifene) [51].

The Star study compared about 20000 women at risk to tamoxifen versus

raloxifene treatment, with a 4-year follow-up and demonstrated a similar decrease in

incidence in both groups; but fewer invasive cancers in tamoxifen. There were no

significant differences in coronary events, stroke, or fractures. Episodes of venous

thrombosis and cataracts were more frequent on tamoxifen [52, 53].

5.2. Aromatase inhibitors

These drugs interfere with the adrenal enzyme (anastrozole and letrozole inhibit

its activity and exemestane inactivates it) that allows the production of estrogens in

postmenopause. The most notable side effect is the reduction in bone mineral density

(BMD) and the increase in fractures.

Assays: Arimidex, Tamoxifen Alone or in combination (compared anastrozole

and tamoxifen as adjuvant therapy for breast cancer) [54], another 5000 women taking

tamoxifen adjuvant for five years were randomized to chance, letrozole placebo vs [55];

another controlled with placebo in which 1900 women participated who had received

adjuvant tamoxifen followed by 5 years, followed by letrozole, taking it for another five

years [56] and, another of about 4700 women with neoadjuvant tamoxifen for two years

randomized to continue tamoxifen or switch to exemestane; all demonstrated a

decreased risk of recurrence and new breast cancers in women with previous breast

cancers [57].

Both an RCT of primary prevention (comparing exemestane with placebo in

4500 women) [58], and in IBIS II (studying 3800 women with increased risk of

developing breast cancer, who were alatorized to anastrozole and placebo) [59], which

aromatase inhibitors decrease the incidence of breast cancer in patients at increased risk.

I. Introduction

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14

5.3. Risk-reducing mastectomy (RRM)

The studies are retrospective. Bilateral RRM reduces the risk of breast cancer by

around 90%, depending on the type of surgery performed and the clinical study. There is

no clear demonstration of reduced survival in these women. But it is an option in

women carrying Brca [60].

Contralateral RRM may be an indication in patients with a previous diagnosis of

breast cancer in some circumstances (diagnosed before the age of 41, or triple negative

tumors diagnosed before the age of 50). An impact on survival of contralateral RRM in

patients at low or moderate risk of breast cancer has not been demonstrated [61].

5.4. Risk-reducing salpingo-oophorectomy (RRSO)

High-risk patients and BRCA mutation carriers are at increased risk for breast,

ovarian, tube, and primary peritoneal cancer. Since there are no reliable methods of

early detection and the poor prognosis of advanced ovarian cancer, RRSO has been

recommended, after ending the birth desire (this is associated with a decrease in the risk

of carcinoma of the ovary, tube and primary peritoneum (in carriers of the mutation) and

77% of all-cause mortality. This surgery has been associated with a reduction in the risk

of breast cancer, especially if performed in premenopausal women. In risk

assessment/benefit, the impact on reproduction, the risk of breast and ovarian cancer,

and the risks associated with premature menopause must be considered [62-65].

6. Actions without sufficient evidence of relationship

6.1. Taking hormonal contraceptives

Studies have linked a small increased risk of breast cancer in current consumers,

which decreases over time [66, 67]. Another study, in Denmark [68], also found it

among those who take them now or had recently taken them (and it increased, the

longer they were taken); but, in absolute terms, this effect was very low.

In other cases and controls, well carried out, no relationship was observed

between its consumption and the increased risk of breast cancer with respect to each

use, the duration of use or when it was used [69].

6.2. Environmental factors

In general, studies and evidence supporting a relationship between

environmental factors and specific exposures and the increase in breast cancer are often

I. Introduction

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15

weak. Because many factors have to be considered, which leads to many difficulties in

interpreting that relationship [70-71].

7. Factors with sufficient evidence of no or little relationship

7.1. Shift work

Attempts have been made to link the nocturnal production of melatonin,

secondary to night shift work, with the development of breast cancer. There are multiple

contradictory and light-weight studies; but in 2016, the results of three prospective

studies from the United Kingdom and several additional prospective studies were

combined, with a total of 800,000 women, and it is objective that there are no data that

allow associating the incidence of breast cancer and night shift work [72].

7.2. Geographical residence

It has not been shown that geographic influence could have something to do with

the risk of breast cancer. However, a variation has been seen in the prevalence and

incidence of breast cancer in population groups that migrate (especially in the second

generation), which seems to demonstrate the incidence of lifestyle in this cancer.

The combination of the polygenic risk score (PRS) with family history and other

risk factors allows better risk stratification and the development of prediction models,

which require further studies for their validation and adaptation to other populations

[73].

8. Locoregional and systemic spread of breast cancer

(Extracted, with permission, from the doctoral thesis of Dra. Alejandra García

Novoa. https://www.researchgate.net/publication/317638293)

Between 25-30% of breast cancer patients may have a recurrence [74,75].

The metastases of any carcinoma are the spread of tumor cells to other organs.

Genetic heterogeneity makes it possible for some tumor cells to survive in other organs.

These cells, starting from the local invasion, through the blood and the lymphatics

colonize in the distance [76].

In breast cancer, tumor spread can occur by embolization or permeation through

the bloodstream, the lymphatic system, or by direct invasion through the chest wall.

Systemic spread is usually mixed: lymphovascular. Thus through the small

I. Introduction

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16

intramammary veins a neoplastic invasion can occur. Cancer can invade the vasa-

vasorum or perivascular lymphatics, leading to intravascular cancer invasion with

consequent neoplastic embolization through the bloodstream [77].

Pathophysiological studies present indirect evidence that tumor cells in breast

cancer tend to invade lymphatic vessels first than blood vessels. However,

hematogenous spread can occur without clear identification of regional lymph node

invasion. In fact, most patients do not present with simultaneous lymphatic and bone

marrow involvement [78] and up to 40% of patients without lymph node metastases

present with bone marrow micrometastases. This supports the theory that breast cancer

spread does not occur simultaneously and that it can use different pathways.

Halsted's Mechanistic Theory. William Halsted proposed that breast cancer is a

local disease that spreads systemically in a predictable way. The disease begins in the

primary tumor in the breast, later spreading to regional lymph nodes and then

systemically to distant organs. Unfortunately, only 12% of patients treated with the

classic Halsted radical mastectomy survived 10 years, so this theory did not explain the

failure of local treatment [79].

Alternative Theory: systemic disease. In contrast to the “Halstedian” theory,

Bernard Fisher proposed a concept of systemic disease, defending the idea that tumor

cells can directly invade lymphatic or hematic capillaries and spread systemically

without passing through regional nodes [80, 81]. Therefore, the hypothesis that

metastases occur as a late "additional event" in carcinogenesis is questioned. One of the

reasons for rethinking this theory is the evidence that 10-20% of patients with metastatic

breast carcinoma at the time of surgery do not have infiltrated lymph nodes. In addition,

it has been reported that more than 30% of patients without lymph node involvement

will relapse in the next 10 years [82].

Spectrum theory. It is based on the biological heterogeneity of the tumor and its

genetic expression. Postulates that the ability to metastasize is acquired in early stages

of carcinogenesis, although it manifests much later, after mutation of other genes [64].

Other groups defend that tumor cells develop their metastatic potential as the tumor

grows and evolves clinically. Therefore, lymph node dissection is important for the

prognosis and control of the disease [82].

I. Introduction

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17

“Seed and Soil” hypothesis. Each carcinoma has a different ability to

metastasize to each organ. In breast cancer, the bones, lung, liver, lymph nodes, chest

wall and brain are the most frequent sites of metastasis, however, cases of metastatic

invasion have been reported in almost any organ. Tumors with hormone receptors (HR)

usually metastasize initially to the bones, and those negative for HR, HER2 (human

epidermal growth factor receptor 2) positive more commonly metastasize to the viscera.

[83]. The "Seed and Soil" hypothesis could explain this fact. This hypothesis proposed

by Paget [83, 84] in 1889 explained that each cancer ("seed" or seed) has a specific

tropism for each organ ("soil" or soil).

Based on animal models, it has been shown that thousands of epithelial tumor

cells diffuse daily into the bloodstream; Most of these cells are very short-lived, some

are already apoptotic, while others are supposed to be removed by shear forces from the

bloodstream. However, in up to 30% of patients, tumor cells are able to persist in the

bloodstream after removal of the primary tumor, which can lead to late disease relapse

[84]. This theory is the subject of much modern research, which focuses on determining

the molecular environment that allows the metastatic cascade of cancer.

Plumbing theory. In contrast to the above, the "plumbing" or anatomical theory

was proposed, which defends that the ability to metastasize in certain tissues is

secondary to the anatomical relationships and the circulation that the tumor tissue

presents [77]. Thus, for example, colon cancer patients metastasize to the liver through

the portal system. Both arguments are currently defended as contributors to the tropism

of cancer cells. The dissemination of metastatic cells can occur in the early stages of the

disease, even before the tumor acquires the maximum phenotypic expression of

malignancy. This is the reason because the primary and metastatic tumors can evolve

independently with genetic diversity, acquiring different phenotypes.

Tumor Cells in the Bone Marrow. It has recently been reported that 30% to

40% of breast cancer patients may present viable tumor cells in the bloodstream after

surgery, and may even persist in the blood or bone marrow after adjuvant treatment is

completed [83, 84]. But despite the high incidence of bone marrow micrometastases in

breast cancer patients, bone marrow metastases are rare. This persistence of malignant

cells is associated with a worse prognosis, with 40% to 60% of these patients suffering a

I. Introduction

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18

relapse [83]. Gruber et al [84] demonstrated that the persistence of tumor cells after

chemotherapy is an independent marker of residual disease and therefore reduces the

disease-free period and OS. This fact supports the "sleeping cell" hypothesis, which

proposes that the tumor cell survives in a latent state until it finds the optimal conditions

to proliferate. Disseminated tumor cells are characterized by a low expression of

proliferation markers such as Ki-67, which could explain their ability to survive as a

latent cell to antiproliferative cytotoxic treatment. In addition, these cells express few

molecules of the major histocompatibility complex class I (MHC I), which allows an

immune escape. Up to 87% of bone marrow tumor cells are HER2 positive, as opposed

to 15% to 30% of HER2 positive primary tumors [84]. Consequently, studies have been

carried out indicating specific therapy (for example, Trastuzumab) in patients with

tumor cells in the bone marrow that are positive for this marker, successfully

eliminating them. However, the clinical value of eliminating these tumor cells is

uncertain. [84]. This phenotypic difference between primary tumor cells and circulating

cells also occurs with hormone receptors; being the majority of tumor cells in bone

marrow negative for estrogen receptors and therefore resistant to TH. Consequently, for

routine clinical practice, biopsy and phenotypic identification of metastases contribute

to the choice of the appropriate specific treatment.

9. Early detection of breast cancer

Early detection of BC is important for improvement of prognosis and survival

rate. Until now, mammography has been one of the most important early diagnostic

methods for BC, but it is less effective for young women, with a sensitivity of 25-59%

[85].

On the other hand, the main cause of mortality after BC is metastatic

dissemination of the primary tumour to distant sites in the body. New markers capable

of identifying metastatic breast cancer are required to aid clinical decision making for

individual patients [86,87]. The prognostic tests in current clinical use require tumour

tissue to be obtained by biopsy or other surgical approaches.

It is desirable to minimise such invasive procedures, and new validated

serum/plasma biomarkers are urgently necessary for the early detection of BC in

asymptomatic individuals, precise prognosis and prediction of response to treatment,

and clinical detection of breast cancer metastasis [88].

I. Introduction

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19

Whilst several serum biomarkers have been evaluated over the past three

decades, they lack the sensitivity to detect early primary BC [89] and none of these

possess sufficient accuracy in predicting recurrence [90]. Therefore, it is imperative to

find potential blood-based biomarkers in breast cancer.

Proteomics has become an attractive approach to search for novel biomarkers in

biological fluids of cancer patients using protein and peptide profiling [91]. In this way,

mass spectrometry (MS) has been used to compare proteomic patterns in cancer patients

and healthy controls [92]. The detection of early-stage cancer is based on the paradigm

that the disease develops by increasing deviations from the normal status. Thus,

potential biomarkers could be found among the specific proteins or peptides that are up

or down-regulated in serum proteomic profiling in cancer patients compared with

controls [93]. Furthermore, proteomics analysis could also complement gene analyses in

its use in the prognosis and evaluation of disease [94].

This revision summarizes studies linked to the application of proteomics in the

field of early BC detection, prognosis, and response to therapeutic treatments (see

Figure 1).

Figure 1. Classification of proteomic studies carried out in breast cancer focused on

early detection, prognosis and evaluation of response to treatment.

I. Introduction

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20

10. Protein biomarkers for breast cancer screening and diagnosis

Besides the prevention, detection at an prompt stage continues to be the way to

decrease breast cancer associated mortality. Even though importante advances in breast

imaging, the capacity to precisely detect breast cancer (BC) remains a challenge. With

the discovery of protein signatures and strategic biomarkers for BC, proteomic

technologies are prepared to work as a perfect diagnostic adjunct to imaging (see Table

1).

Some blood-borne tumour markers have showed capability to detect malignancy

before clinical diagnosis and are presently being evaluated in screening trials for certain

cancers; for example, CA125 for screening ovarian cancer [95]. However, there are

currently no blood-borne biomarkers suggested for breast cancer diagnosis or screening.

Though candidate markers such as carcinoembryonic antigen (CEA) [96], the soluble

form of MUC1 protein (CA15-3, CA27.29), the oncogenic protein RS/DJ-1 [97], the

human epidermal growth factor receptor-2 (HER2) [98] and circulating cytokeratin

fragments (TPA, TPS and CYFRA 21-1) have been recommented as diagnostic

markers, they were defficient in sensitivity and specificity for early disease detection.

Thus, breast cancer markers in clinical practice are used for predicting response to

therapy, monitoring after primary therapy or as prognostic indicators [99].

Table 1. Summary of proteomic studies in plasma/serum to identify proteins related to

breast cancer diagnosis.

Type of samples Enrichment

strategy

Techniques

(determination)

Candidate

biomarkers Results Ref.

HS samples with

concentrations of

CA 15-3 ranging

from 0.02 to 100

U/mL

(GO/Py-COOH)

as sensor probe

MWCNTs-

supported

numerous ferritin

as labels

DPV CA 15-3

DR: 0.05 and 100 U/mL

DL: 0.009 ± 0.0006

U/mL

[102]

HS PCA isolates of

HC (n = 105) and

BC patients with

stage 0 (n = 31) and

stage I (n = 48)

Enrichment of

serum

glycoproteins

using PCA

ELLA

Ratio of serum

proteoglycan 4

to protease C1

inhibitor

Significant inverse

altered abundance of

proteoglycan 4 and

plasma protease C1

inhibitor in BC patients

compared to HC

[103]

HS from 68 women

diagnosed with BC

up to three years

after enrollment

and 68 matched HC

-

Quantitative

bead-based

multiplexed

assay

Biomarker

panel: OPN,

haptoglobin,

CA15-3, CEA,

CA-125,

prolactin,

CA19-9, AFP,

leptin, MIF

This panel cannot be

used for diagnosis of

early breast cancer

[104]

I. Introduction

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21

HS from 239

women who

subsequently

developed BC and

239 matched HC

- ELISA

Biomarker

panel:

CA15-3,

RANTES,

IGFBP3, OPN,

PAI-1, SLPI,

HSP90A,

PAPPA and

APOC1

Potential as early

prognostic markers:

CA15-3, HSP90A and

PAI-1

[105]

HS from 100 BC

patients and 50 HC -

SEREX in

combination

with phage

display

technology

Biomarker

panel: B11

(LGALS3), B18

(PHB2), B119

(MUC1), B130

(GK2), and

CA15-3

The complex

autoantigens identified

along with CA15-3

significantly increased

the sensitivity (87 %),

specificity (76 %), and

OS in the diagnosis of

BC at as early as stage

T1N0M0, compared with

CA15-3 alone

[106]

HS samples from

62 patients with

IDC and 47 NCIs

(16 HC and 31

patients with BBD)

IMAC SELDI-TOF

MS

4 protein peak

set: m/z 3,972,

6,850 and 8,115

(BC2) and

8,949 (BC3)

The identified 4 peaks

combined with CA15-3

expression may be used

as a protein profiling

test to diagnose BC

[107]

HS from 107

patients with

recurrence after

BC: 15 with PDD,

9 developed LR

during the follow-

up period and 83

developed DM

-

ADVIA Centaur

automated assay

(two-site

sandwich

immunoassay

using direct

chemiluminesce

nt Technology)

CA 15-3, CEA

and HER2

For the detection of

metastatic breast

cancer: combination of

CEA and HER2 (in

tissue HER2+ tumours)

and CA 15-3 and CEA

(in tissue HER2−

tumours)

[108]

HS samples from

27 BC patients, 24

women with BBD

and 37 HC

IMAC30 protein

chips loaded with

Cu2+ metal

SELDI-TOF

MS Bc1, Bc2, Bc3

Bc2 possesses the

highest individual

diagnostic power

[109]

HS from 50 BC

patients and 26 HC

Protein

immobilization

Multiplex

immunoassays

on micro-

structured

protein

microarray

Seven proteins

belonging to the

HSPs family

(HSPB1,

HSPD1,

HSP70, HSP90,

HSP110,

HSPA5,

HSP90B1) and

one oncoprotein

(p53)

Discrimination of BC

patients (50) from HC

(26) with a sensitivity

of 86 % and a

specificity of 100 %

[117]

HS from 36 newly

diagnosed patients

with stage II BC

and those from 36

HC

Proteoprep® 20

Plasma

Immunodepletion

kit

2D

electrophoresis,

Western blot

and MALDI-

TOF MS

AHSG

Detection of

autoantibodies against

AHSG in BC patients

with a sensitivity of

91.7%

[118]

HS samples from

18 HC, 92

participants

diagnosed with

BBD and 100

participants

diagnosed with BC

- ECL-based

ELISA

22 SPB and 24

TAAb

The benefit of the

integration of SPB and

TAAb data in a

combinatorial

proteomic approach for

detecting BC

[119]

HS samples from

15 BC with

estrogen receptor

(ER+) histological

staining harboring

one or more lymph

node metastases

- ACM

ENG, LEP,

OPN, IL-1B,

TNF-α, and

uPAR

The ability of the ACM

to distinguish between

healthy and breast

disease using protein

levels in patient sera

[120]

I. Introduction

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22

and 11 HC

40 HP samples

from women with

BC (diagnosed with

a stage II or III or

earlier breast

cancer) and 40 HP

samples from HC

-

Identification by

LC/MS/MS and

quantification

using the

LC/MS-based

label-free

protein

quantification

software

licensed from

Eli Lilly and

Company

90 alternative

splicing

isoforms in 38

genes were

found, which

showed

statistically

significant (q <

0.05)

differences

between BC

and HC samples

The signature identified

92.5 % BC samples and

72.5 % of normal

samples

[125]

HS samples for BC

patients and HC

were pooled with

equal volumen (100

μL each)

AlbuvoidTM beas

2-D gel

separation and

subsequent PEP

Metabolic

enzymes

(hexokinases)

and proteases

Qualitative and

quantitative differences

between BC patients

and HC

[126]

Among all, carbohydrate antigen 15-3 (CA 15-3), is the most widely used serum

marker in patients with BC [100]. CA 15-3 has been used for routine breast cancer

screening, monitoring and follow-up of patients with breast cancer [7]. The median

level of CA 15-3 is 17 U/mL (range 3.9-99.5 U/mL) in patients with primary untreated

breast cancer [101].

To develop novel strategies for the ultrasensitive detection of CA 15-3, an

electrochemical nanostructured immunosensor was fabricated using non-covalent

functionalized graphene oxides (GO/Py-COOH) and multiwalled carbon nanotube

(MWCNTs)-supported numerous ferritins as labels [102]. CA 15-3 was selectively

detected as low as 0.01 ± 0.07 U/mL in human serum samples. This system showed an

excellent selectivity, and it can be regenerated for multiple uses, having a great potential

for future development of the point-of-care cancer diagnostics. On this way, perchloric

acid (PCA) was used to improve the detection of serum O-glycosylated proteins (such

as CA 27.29 and CA 15-3) using an earlier developed sandwich enzyme-linked lectin

assay (ELLA) [103]. By subjecting pre-coated champedak galactose binding (CGB)

lectin-captured glycoprotein fractions of serum PCA isolates of the stage 0 (n = 31) and

stage I (n = 48) breast cancer patients and those of controls (n = 105) to SDS-PAGE,

substantial inverse altered abundance of plasma protease C1 inhibitor and proteoglycan

4 were detected in both the early stages of breast cancer patients related to the controls.

Although it needed further validation in clinically representative populations, ratio of

serum proteoglycan 4 to protease C1 inhibitor couldy be exploited for screening of early

breast cancer.

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23

Furthermore, the potential of different biomarker panels (containing the cancer

antigen 15-3 (CA15-3)) were explored for the diagnosis of early breast cancer. It was

found that the set of ten potential breast cancer serum biomarkers and cancer antigens

(haptoglobin, osteopontin (OPN), cancer antigen 15-3 (CA15-3), cancer antigen 125

(CA-125), cancer antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), prolactin,

α-fetoprotein (AFP), leptin and migration inhibitory factor (MIF)) cannot be used to

predict early-stage breast cancer [104]. Similarly, none of these 9 candidate markers

(CA15-3 (cancer antigen 15-3), RANTES/CCL5 (regulated on activation, normal T cell

expressed and secreted/chemokine (C–C motif) ligand 5), OPN (osteopontin), PAI-1

(plasminogen activator inhibitor-1), SLPI (secretory leukocyte protease inhibitor),

HSP90A (heat shock protein 90A), IGFBP3 (insulin-like growth factor-binding protein

3), APOC1 (apolipoprotein C-I) and PAPPA (pappalysin-1) or combinations was useful

for screening breast cancer, and only links with clinico-pathological elements correlated

to prognosis were found for the candidates CA15-3, HSP90A and PAI-1 [105].

However, a panel of complex antigens consisting of B11 (LGALS3), B18

(PHB2), B119 (MUC1) and B130 (GK2) along with CA15-3 significantly increased the

sensitivity (87%), specificity (76%), and overall survival (82.7 %) in the diagnosis of

BC at as early as stage T1N0M0, compared with CA15-3 alone [106]. Even though this

panel of complex antigens required to be validated using more BC samples, it may be a

promise instrument to detect early-stage BC. Furthermore, CA15-3 was also included in

the diagnostic panel constituted of 4 protein peaks [m/z 3,972, 6,850 and 8,115 (BC2)

and 8,949 (BC3)] used to distinguish 62 BC patients with invasive ductal carcinoma

from 16 healthy controls (HCs) and 31 patients with benign breast diseases (BBDs)

[107]. Importanly, the resultant 4 peaks panel together with CA15-3 was demonstrated

to have good sensitivity and specificity for the diagnosis of BC. However, further

investigation using a larger sample size should be performed to verify these results.

The potential of (CA15-3) was also explored for the early diagnosis of

metastatic breast cancer [108]. In this fashion, the sensitivity of CA 15-3, CEA and

HER2 was investigated, and it was found that the combination of two tumour markers

enhanced the sensitivity for detection of metastatic breast cancer, and the determination

of all three tumour markers only improved the sensitivity vaguely. These authors

I. Introduction

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24

suggested the combination of CEA and HER2 in tissue HER2+ tumours and the

combination of CA 15-3 and CEA in tissue HER2− tumours. Nevertheless, sizeable

prospective clinical randomised trials are required to explore the clinical benefits of

early detection and treatment of metastatic disease.

The efficacy of other serum biomarkers on early detection of breast cancer were

also considered. For example, after the evaluation of the efficacy of Bc1, Bc2, and Bc3

serum biomarkers on early detection of breast cancer (BC), only Bc2 was statistically

significant in comparison between the malignant disease group, control group and

benign disease group [109].

On other hand, it is well known that breast cancer is a heterogeneous disease in

which cancer cells can express a variety of aberrant proteins (tumor-associated antigens:

TAA) that are capable of eliciting an immune response (antibody production).

Interestingly, this immune response appears months or years before the clinical

diagnosis of the malignancy [110,111]. TAA and their specific antibodies may offer in

vivo amplification of an early carcinogenic signal, thus possibly allowing earlier

detection of cancer than methods used currently.

In particular, serum possesses several circulating antigens and antibodies related

with cancer progression and development [112,113]. The presence of autoantibodies in

serum against several tumor antigens, such as p53, antineural/antinuclear antigens, and

embryonic neural proteins, has been also assessed in breast cancer [114].

Cancer antigens have demonstrated incredible importance in the clinic for

screening and as prognostic indicators [115,116]. Particularly, heat shock proteins

(HSPs), over-expressed in a extensive range of human cancers, caused the stimulation

of the immune system and accordingly in elevated concentration of anti-HSP

autoantibodies, that are associated with tumor metastasis in breast cancer patients.

Consequently, screening these autoantibodies could be of prognostic and diagnostic

values. In this way, L. Shi et al. [117] immobilized seven proteins belonging to the heat

shock protein family (HSPB1, HSPD1, HSP70, HSP90, HSPA5, HSP90B1) and one

oncoprotein, P53, in six different surface chemistries. Two surface chemistries (COOH

and chitosan) were employed to detect antitumor antigen autoantibodies in 26 healthy

donor and 50 breast cancer sera. The detection of a single autoantibody did not allow

significantly discriminating breast cancer sera from healthy sera, whereas combining

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25

seven autoantibodies (autoantibodies against HSPB1, HSPD1, HSP70, HSP90, HSPA5,

HSP90B1, and P53) increased the specificity and sensitivity of the test (with a

specificity of 100 % and a sensitivity of 86 %). In this study, they have demonstrated

that customized protein microarrays could be effective tools for the rapid screening of

thousands of biomarkers in a parallel and high-throughput fashion. The performance of

protein microarray is influenced by many parameters such as spotting buffer, surface

chemistry, and protein concentration. However, larger cohorts of breast cancer patients

and healthy donors are needed to validate its performance.

An immune proteomic approach also suggested that the presence of serum

autoantibodies against alpha 2HS-glycoprotein (AHSG) protein colud be helpful as

serum biomarkers for early-stage breast cancer minimally invasive diagnosis and

screening [118]. However, the AHSG will need to be tested and validated by multiple

independent studies utilizing an adequately sized test and a training set of sera samples

from very-early-stage breast cancer. Moreover, further verification with samples from

patients with ductal carcinomain situ and breast cancer in stages III and IV would aid in

confirming the specificity of AHSG autoantibodies in this subset of patients with breast

cancer. This research provided additional preliminary, but important, data on the

potential advantage for clinical serological screening of autoantibody measurement to

detect small tumors in early stages, because autoantibody biomarkers have also been

identified in breast cancer, the majority of these have only been reported in the late-

stage, but not in the early-stage, breast cancer.

Breast tumors were found to be related with systemic changes in levels of both

serum protein biomarkers (SPB) and tumor associated autoantibodies (TAAb). Meredith

C. Henderson et al. [119] evaluated for the first time the independent and combinatorial

contribution of SPB and TAAb expression data for identifying BC using a retrospective

cohort of prebiopsy serum samples from 18 participants with no evidence of breast

disease (ND), 92 participants diagnosed with Benign Breast Disease (BBD) and 100

participants diagnosed with BC, including DCIS. It is important to mention that when

modeling integrated data from both SPB and TAAb, the clinical sensitivity and

specificity for detection of BC improved to 81.0% and 78.8%, respectively. These data

showed the advantage of the combination of SPB and TAAb data and toughly sustained

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26

the development of other similar combinatorial proteomic approaches for detecting BC

in the future.

A novel concept for multiplexing without mixing named antibody colocalization

microarray (ACM) was introduced by M. Pla-Roca et al. [120]. This technique was

validated by profiling 32 proteins in the serum of (i) 11 controls from age-matched

patients undergoing reduction mammoplasties, (ii) 15 patients with primary breast

cancer overexpressing the estrogen receptor (ER) in the primary tumor (ER+ subtype).

It was found that six proteins (ENG, LEP, OPN, IL-1B, TNF-α, and uPAR) were

associated with the cancer grade of the patient. The candidate biomarkers that were

identified agree with the findings of previous studies which described increased

concentrations of uPAR [121], TNF-RII [122], IL-1B [123], and ENG [124]. However,

all of them need to be veried in follow-up studies with more patients and controls.

Besides, recognizing and characterizing different forms of a protein (isoforms)

are critial to the study of molecular mechanisms and early detection of complex diseases

such as breast cancer. In this way, F. Zhang et al. [125] showed that isoform-specific

peptides could differenciate normal breast from breast cancer, identifying 92.5 % cancer

samples and 72.5 % of normal samples in an independent set of 40 normal samples and

40 breast cancer samples. It showed that alternative splicing isoform makers could act

as independent markers of breast cancer.

In a study developed by D. L. Wang et al. [126], a functional proteomics

technology was used to monitor protease activities and metabolic enzymes

(hexokinases) from resolved serum proteins produced by a modified 2-D gel separation

and subsequent Protein Elution Plate, a method collectively called PEP. For the first

time, substantial differences were found between breast cancer patient serum and

normal serum in both families of enzymes implicated in the cancer development and

metastasis, giving excellent biomarker candidates for breast cancer diagnosis and drug

development.

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10.1. Protein profiling for diagnosis of breast cancer

Protein and peptide profiling was used to find novel biomarkers in biological

fluids (as serum and plasma) of cancer patients [8]. Among the specific proteins or

peptides that are up or down-regulated in serum proteomic profiling in cancer patients

compared with controls, potential biomarkers could be found [10]. Particularly, several

studies focused on protein profiling using the two-laser desorption/ionisation (LDI)

platforms (as matrix-assisted laser desorption/ionisation time-off light (MALDI-TOF)

MS [127] and its variant surfaceenhanced laser desorption/ionisation (SELDI-TOF MS)

were developed to search novel breast cancer biomarkers [128].

On the other hand, recently novel sample preparation techniques based on

nanomaterials have developed, and applied to the separation and enrichment of peptides

and proteins in biological samples [129]. Particularly, magnetic microspheres with the

properties of the easiness to surface modification, high dispersibility and magnetic

responsivity, were considered as a promising material for the convenient and efficient

enrichment of peptides or proteins [130, 131].

Commercial n-alkyl magnetic polymeric beads (1-10 μm diameter) have widely

been used in the enrichment of low-abundance peptides and proteins in biological

samples [132, 133]. However, the commercial magnetic beads have usually showed

poor magnetic response.

C8-functionalized magnetic nanoparticles (about 50 nm diameter) with high

dispersibility, large surface area and excellent magnetic responsibility, were

successfully applied for convenient, fast and efficient enrichment of low-abundance

peptides from tryptic protein digest and human serum, followed by a direct MALDI-

TOF-MS analysis [134]. Furthermore, weak cation exchanges magnetic beads (MB-

WCX-MBs) were used for the effective enrichment of peptides and proteins in

biological samples. Both enrichment methods were applied for the detection of breast

cancer [135, 136].

Using magnetic bead-hydrophobic interaction chromatography C8 and C18

(HIC-C8-MBs and HIC-C18-MBs), and weak cation exchange (WCX) beads for the

enrichment of proteins presented in human serum samples, 14 biomarkers were found,

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28

whose combination detects breast cancer patients from non-cancer controls with a

sensitivity of 89% and specificity of 67% [137]. Of them, five biomarkers were

comparable with previously identified proteins from published data using similar

approaches (peaks at 4283 and 3972 Da [138,139], 3972 Da [140], 6630 and 6629 Da

[141,142] and 6429 Da [143]). In addition, this biomarker panel were able to

discriminate low-risk (tumor grade G1 or tumor grade G2 with a low level of uPA and

PAI-1) and high-risk breast cancer patients (tumor grade G3 or tumor grade G2 with a

high level of uPA and PAI-1) with a high sensitivity (75%) and specificity (100%).

However, further validation of biomarkers could potentially facilitate the early

diagnosis of breast cancer as an aid to imaging diagnostics.

In a similar way, combining the data resulting from two complementary workup

procedures (WCX-MBs and reversed-phase (RP) C18 magnetic beads (MBs)) improved

the classification of breast cancer, and sensitivity and specificity increased up to 84 and

95%, respectively [144]. Although MALDI-TOF peptide and protein profiles can be

used for classification of breast cancer, larger patient sets must be analyzed for

validation and MS/MS be used to identify the discriminating proteins and peptides for

its use in breast cancer screening programs. More recently, WCX-MBs fractionation

provided predictive model for BC versus healthy controls with 79.04% sensitivity and

82.18% specificity. Furthermore, FGA 605-629, ITIH4 347-356 and APOA2 43-52

were found as potential peptide biomarkers [145].

Using WCX fractionation and mass spectrometry protein profiling, C. L.

Washam et al. [146] found that 12-48aa peptide fragment of parathyroid hormone-

related protein PTHrP(12-48) was significantly increased in the plasma of bone

metastasis (BM) patients compared with patients without BM (p<0.0001). Importantly,

the clinical measurement of PTHrP(12-48) in plasma in combination with NTx in serum

improved the detection of breast cancer BM (diagnostic specificity and accuracy

(AUC=0.99). This result could provide novel opportunities for the improved diagnosis

of bone metastasis, however, some limitations of this study are that is retrospective, the

sample size was somewhat small; and it may also suffer from selection bias. Using the

same methodology, Y. Sun et al [147] found that the candidate biomarker positioned at

m/z 6447.9 identified as apolipoprotein C-I (ApoC-I) was significantly decreased in BC

patients, and its expression intensity was weaker in the triple negative breast cancer

(TNBC) and pre-surgery group compared with the NTNBC and post-surgery group.

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29

These results suggest that ApoC-I peptides may be a potential diagnostic biomarker and

therapeutic approach for BC. However, further replicated experiments with many more

samples are necessary to verify this possible protein biomarker.

Many reviews reported the advantages of immobilized metal ion affinity

chromatography (IMAC) on the purification of peptides and proteins [46]. Using this

approach for the serum proteomic analysis of 36 healthy volunteers and 37 breast cancer

patients, three peaks at m/z 698, 720 and 1866 were idetintified and used to construct

the peptidome patterns with 91.78% accuracy [148]. Using and independent group for

the validation, it was found that the peptidome patterns could differentiate the validation

group achieving a sensitivity of 91.89% (34/37) and a specitivity of 91.67% (33/36) (>

CA 15-3, p < 0.05).

Using the IMAC enrichment approach, several serum proteins that differed in

concentration between women with asymptomatic breast cancer and matched healthy

controls were also detected [149]. Particularly, two SELDI-TOF MS peaks with m/z

3323 (doubly charged apolipoprotein C-I) and m/z 8939 (C3a des-arginine

anaphylatoxin (C3adesArg)), and with 2D-nanoLC-MS/MS, afamin, apolipoprotein E

and isoform 1 of inter-alpha trypsin and inhibitor heavy chain H4 (ITIH4) were higher

in pre-diagnostic breast cancer serum. Particularly, C3adesArg and ITIH4 have

previously been related to the presence of symptomatic and/or mammographically

detectable breast cancer. However, the currently identified proteins were high abundant

and they were unlikely to be breast cancer specific. In order to find low abundant and

probably more specific tumor markers, other techniques would be employed to give

insight into ‘the deeper/low abundant proteome’.

Similarly, performing serum fractionation by IMAC30 array, WCX (CM10)

array and strong anion exchange chromatography preceding protein profiling with

SELDI-TOF MS, eight peaks showed statistically significantly different intensities

between incident breast cancer cases and controls (p < 0.05) [150]. Seven of these peaks

were tentatively identified as heterodimer of apolipoprotein A-I and apolipoprotein AII

(m/z 45435), apolipoprotein C-II (m/z 8909), oxidized apolipoprotein C-II (m/z 8925),

apolipoprotein C-III (m/z 8746), fragment of coagulation factor XIIIa (m/z 3,959),

hemoglobin B-chain (m/z 15915), and post-translational modified hemoglobin (m/z

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30

15,346). However, similar to the previous study [66], discriminating proteins were still

high abundant.

In an attemp to combine both purification methods mentioned above, J. Yang et

al. [151] used magnetic bead-based weak cation exchange chromatography (MB-WCX)

and immobilized metal ion affinity chromatography (MB-IMAC-Cu)) to preanalyze 32

patients with early stage (stages I-II) of Invasive Ductal Carcinoma (IDC) and 30

healthy control serum samples. It was found that the serum samples purified in the MB-

WCX group provided a better proteomic pattern than MB-IMAC-Cu. However, both

accurately distinguished patients with early stage IDC from healthy individuals. It was

found that two candidate biomarkers (m/z 4209 and 4264) were upregulated in patients

with IDC by MB-WCX purification, while similar potential biomarkers (m/z: 4263 and

4208) identified by MB-IMAC-Cu purification were also overexpressed in IDC breast

cancer patients. Thus, these two candidate biomarkers will be further identified by

expanding samples from patients with IDC.

In a different approach, 10 normal control and 10 stage IV breast cancer patient

serum samples were analysed by label-free mass spectrometry using a CaptureSelectTM

Transferrin Affinity Matrix [152], identifying 21 potential candidate biomarkers. After

selecting fibronectin and fibrinogen for further analysis in a larger cohort of patient

samples along with CA15-3, it was found that these molecules were significantly altered

when comparing the controls groups to stage IV breast cancer, highlighting the

usefulness of analysing the high-abundant fraction associated proteins.

Other studies to identify serum proteome patterns specific for early stage breast

cancer [153] and invasive breast cancer (IDC) patients [154] using MALDI-TOF mass

spectrometry were also developed. In the latter case, a novel mammary biomarker,

regulator and therapeutic target, Annexin A3, was identified, however large scale

prospective studies together with long term follow-up and detailed molecular analysis

are required to elucidate the role and mechanism(s) by which ANX A3 might impact

breast pathology, diagnostics, and tumourigenesis.

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10.1.1. Protein profiling for diagnosis of breast cancer: post-translational

modifications

To date, most of the serum/plasma proteomic work has been conducted to

analyze total protein level abundance, with only a few studies to analyze post-

translational modifications (PTMs) [155], usually glycosylation [156, 157]. As one of

the most important mechanisms for regulating protein function, PTMs, including

phosphorylation, acetylation, ubiquitination, and methylation, have been identified and

validated as critical for signaling transduction, protein degradation, and transcriptional

regulation [158]. The known importance of PTMs in cellular signaling provided the

impetus for a large-scale survey of PTMs other than glycosylation by immunoaffinity

enrichment of PTM-containing peptides.

10.1.1.1. Glycosylation

Breast carcinomas develop from mammary epithelial cells through genetic

alterations, and interactions with the surrounding stromal tissue are essential for

malignant transformation and for progression of the disease [159]. Glycosylation of cell

surface proteins and lipids is a common post-translational event that regulates the

interaction between epithelial cells and the microenvironment by altering adhesion

properties, cell-cell interaction, and the immune system and by affecting the cells’

migration properties [160].

Several studies, on both breast and other cancer types, have shown that cancer-

related alterations in glycosylation are reflected in serum [161]. Thus, the

characterization of glycan structures is expected to broaden the scope of discovery

studies beyond the protein level, and thus improves the clinical values of existing

biomarkers. For this reason, the development and application of techniques and

methodologies for enriching or fractionating the glycoproteome has become an

emerging field [46].

Experimentally, a main advantage of targeting glycosylation is that

glycopeptides or glycoproteins can be effectively enriched over nonglycosylated

molecules. In proteomics, enrichment targeted to N-glycosylation has typically been

performed using lectin based enrichment [162, 163] or hydrazide chemistry [164].

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In the particular case of breast cancer, multi-lectin affinity chromatography (M-

LAC) was used to isolate the serum glycoproteome. Subsequently, proteins with altered

abundance and glycosylation that could act as biomarkers were identified:

thrombospondin-1 and 5, alpha-1B-glycoprotein, serum amyloid P-component and

tenascin-X. They will be further investigated in future studies to confirm these

glycoprotein biomarker candidates in a significant group of patients for the improved

early detection for breast cancer [165].

S. Selvaraju et al. [166] developed a fully integrated platform for

capturing/fractionating human fucome from disease-free and breast cancer sera using

two lectin columns specific to fucose, namely Aleuria aurantia lectin (AAL) and Lotus

tetragonolobus agglutinin (LTA). After the comparison of the fucosylated proteins in

both groups (disease-free and breast cancer patients), a broad panel of 35 differentially

expressed proteins (DEP) from the combined LTA and AAL captured proteins and a

narrower panel of 8 DEP that were commonly differentially expressed in both LTA and

AAL captured proteins, were obtained. As advantages, the platform allowed the

“cascading” of the serum sample from column-to-column in the liquid phase with no

sample manipulation between the various steps. This guaranteed no sample loss and no

propagation and dilution or experimental biases between the various columns when

comparing the diseased serum fucome to the disease-free fucome by LC-MS/MS.

Lectin affinity chromatography (LAC) in conjunction with 2-dimensional

difference gel electrophoresis (2D-DIGE) and liquid chromatography-tandem mass

spectrometry (LC-MS/MS) were also used to identify serum markers of metastatic

breast cancer [167]. In this case, helix pomatia agglutinin (HPA) was used to isolate

glycoproteins from pooled breast cancer serum samples due to their properties of

binding aberrant glycans associated with metastatic breast cancer. Following proteomic

identification of HPA binding glycoproteins, cadherin-5 (CDH5), pregnancy zone

protein and the polymeric immunoglobulin receptor emerged as potential markers of

metastasis. It was also observed that CDH5 discriminated patients with no sign of

recurrence from those with recurrent breast cancer with 90% specificity. CDH5 showed

to be a potential marker of metastatic breast cancer with both protein levels and HPA

binding contributing to a test that is comparable to CA15.3 in terms of specificity. As

evidenced by the CDH5 data, the glycoproteomic and validatory approach employed

here presented the capacity to identify novel markers of breast cancer metastasis.

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Hydrophilic interaction chromatography (HILIC) separates both neutral and

charged glycans in a single separation, which facilitates total glycan characterization

with single-column chemistry. Separation is based on the hydrophilic potential of the

glycan, which is affected by its size, charge, composition, structure, linkage, and

oligosaccharide branching [168]. HILIC glycan analysis is continuously advancing, and

ultraperformance liquid chromatography (UPLC) has recently been developed for N-

glycan separation of various samples with high efficiency [85]. Improving on HPLC,

UPLC allows for a decrease in the run times and greatly increased resolution, partially

due to sub-2 μm stationary phase technology.

In previous studies using HPLC-HILIC, K. Marino et al. [78] found significant

changes in glycosylation including sialylation, fucosylation, and branching in breast

cancer, especially indicating the presence of metastasis, spread to the lymph nodes and

correlation with tumor circulating cells. More recently, R. Saldova et al. [169] described

the greatly improved separation of human serum N-glycans on UPLC, as compared with

HPLC, where more than 140 N-glycans were assigned using this technique after

profiling serum samples from healthy controls and newly diagnosed breast cancer

patients. Particularly, they also found decreases in high-mannosylated and biantennary

corefucosylated glycans in breast cancer patients compared with controls. They found

that bisected biantennary nonfucosylated glycans were decreased in patients with

progesterone-receptorpositive tumors, and core-fucosylated biantennary bisected

monogalactosylated glycans were decreased in patients with tumor TP53 mutation. In

conclusion, this UPLC-based glycan analysis technique revealed highly significant

differences between healthy women and breast cancer patients. Furthermore, significant

associations with breast carcinoma and systemic features were also described.

Recently, breast cancer (BC) patients were distinguished from cancer-free (NC)

controls by serum immunoglobulin G (IgG) crystallizable fragment (Fc) region N-

glycosylation profiling using matrix-assisted laser desorption/ionization mass

spectrometry (MALDI-MS) [170]. These results suggested that an unknown humoral

factor or soluble mediator affects IgGs from the earliest stage of breast cancer, and also

suggested that IgG Fc region N-glycosylation could play a role in tumor biology.

Although the sample size in the present study was small, the IgG Fc region N-glycan

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multiple logistic regression (MLR) model developed presented the potential to be used

as a diagnostic biomarker and may provide the insights into tumor immunology.

10.1.1.2. Phosphorylation

Although the status of phosphorylation events could provide evidences regarding

disease status, few phosphoproteins have been developed as disease markers.

Furthermore, with several highly abundant proteins representing more than 95% of the

mass in blood, few phosphorylated proteins in plasma/serum can be identified with

detectable and stable concentrations [171].

Recently, using label-free quantitative phosphoproteomics, 144 phosphoproteins

significantly higher in patients diagnosed with breast cancer compared with healthy

controls were indentified in plasma extracellular vesicles (EVs) [172]. Four biomarkers

were initially validated in individual patients using paralleled reaction monitoring

(PRM) for targeted quantitation: cGMP-dependent protein kinase1 (PKG1), ral GTPase-

activating protein subunit alpha-2 (RALGAPA2), tight junction protein 2 (TJP2), and

nuclear transcription factor, X box-binding protein 1 (NFX1). These four

phosphoproteins showed significant phosphorylation up-regulation in patients with

cancer, and have been associated in several breast cancer studies [173]. Although this

study demonstrated that the development of phosphoproteins in plasma EVs as disease

biomarkers could transform cancer screening, it relied on the isolation of a good

quantity of EVs with high reproducibility and the development of phosphoproteins as

biomarkers was also strictly limited by the availability of phosphospecific antibodies.

H. Gu et al. [174] combined immunoaffinity purification and LC-MS/MS

without depletion of abundant proteins for the enrichment and quantitative analysis of

post-translational modifications (PTMs) in serum samples of patients with breast cancer

(BC). It was found that lysine acetylation (AcK) and arginine mono-methylation (Rme)

were more prevalent than other PTMs, finding several AcK and Rme sites with distinct

abundance distribution patterns. Therefore, this approach could be especially useful for

patient profiling and biomarker discovery research.

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10.1.2. Protein profiling studies related to breast cacer subtypes

Studies on breast cancer showed substantial tumor heterogeneity consisting of

different molecular subtypes, each with distinct clinical and biological and

individualities [175]: luminal, HER2-enriched, basal-like and normal breast-like

subtype. The criteria to classify subtypes were recently refined, in that moderate of a

strong expression of PR and Ki-67 level defined the subtypes as: luminal A-ER

positive, HER2 negative, Ki-67 low, and PR high; luminal B (HER2 negative)-ER

positive, HER2 negative, and either Ki-67 high or PR low; luminal B-like (HER2

positive)-ER positive, HER2 overexpressed or amplified, any Ki-67, and any PR; HER2

positive-HER2 over-expressed or amplified, ER and PR absent; and triple negative-ER

and PR absent and HER2 negative [176,177].

Due to its lower costs and easy implementation into standard pathology

workflow, immunohistochemistry (IHC) is the method used to define surrogate protein

biomarkers for the classification of breast cancer [178]. More recently, the molecular

classification by microarray analysis agreed well to IHC classification of different

breast carcinomas [179,180]. Subsequently, IHC and molecular classifications are

concurrently used to define the breast cancer subtypes.

In this way, proteomics could also detect additional proteins or protein profiles

to improve current breast cancer classifications. Besides, proteomics might show

biological insights and recognize protein biomarkers outlining differences in therapy

resistance, prognosis and metastatic spread within a specific subtype. In this section,

recent proteomic studies developed in relation with the molecular classification of

breast cancer will be discussed.

In a proteomic study developed by H. Nakshatri et al. [181] was found that the

plasma proteome of luminal A and HER2+ breast cancer patients did not differ

significantly from healthy individuals, however, in the luminal B subtype, eight proteins

involved in immune response (as α-glycoprotein orosomucoid 1 (ORM-1) and serum

amyloid protein (SAP)) were significantly increased, whereas 12 proteins involved in

free radical scavenging were significantly decreased. Two complement factors

(complement factor H-related 3 (CFHR3) and complement component 4 binding protein

β (C4BPB)) were identified being elevated in TNBC compared with healthy controls,

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36

and a significant number of plasma proteins (20) were downregulated compared with

healthy individuals.

An integrative clustering analysis of breast cancer subtypes from plasma

proteome samples showed that luminal A and luminal B subtypes were clustered

together, as well as the basal-like and HER2+. Furthermore, luminal A and luminal B

were closer to each other than basal-like and HER2+ to each other. The results also

showed that proteomic pathway-assisted clustering of breast cancer subtypes could offer

relationships and biological insight into the intrinsic mechanisms between the diverse

breast cancer subtypes [182].

More rencently, a multipronged quantitative proteomic approach identified 307

differentially regulated subtype specific proteins: luminal A subtype consisted of 24,

luminal B subtype 38, HER2 Enriched subtype 17 and triple negative breast cancer

subtype 10 [183]. These specific proteins were further subjected to bioinformatic tools

which revealed the involvement in platelet degranulation, fibrinolysis, lipid metabolism,

immune response, complement activation, blood coagulation, glycolysis and cancer

signaling pathways in the subtypes of the breast cancer.

SRM assays in a different cohort of samples verified and confirmed that

Biotinidase (BTD) is down-regulated in LA when compared with other subtypes.

Similarly, APOL1, AATM, TTHY, THRB, AACT, and APOD found to be up-regulated

in LB. SRM assays also confirmed that CPN2 showed increased expression in TN

whereas CD5L showed elevated expression in LB and HE subtypes.

In a work to find protein biomarkers for early detection of ER-positive breast

cancer, it was found that the pre-diagnostic level of plasma EGFR was significant

elevated in women who developed breast cancer. However, this biomarker showed

moderate specificity and sensitivity and for early diagnosis [184]. In another study from

the same group [185], an increased level of glycolysis-related proteins in plasma of

breast cancer patients compared with that in controls was detected. Nevertheless, the

cause of these glycolysis proteins as well as their function in ER-positive breast cancer

was not elucidated until the moment.

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Using an antibody microarray, a panel of 23 proteins was screened in human

plasma from patients with an actual diagnosis of breast cancer of different subtypes

(defined by ER and HER2 status) or benign breast disease [186]. In comparison with

women with benign breast disease, four proteins (amphiregulin, RANTES, heparin-

binding EGF, TGFa) were increased in plasma of ER-positive/HER2-negative BC

patients, three proteins (heparin-binding EGF, EGF, RANTES) in ER-negative/HER2-

positive BC patients, two proteins (RANTES and platelet-derived growth factor

(PDGF)) in ER-positive/HER2-positive BC patients, and two proteins (RANTES and

VEGF) in plasma of ER-negative/HER2-negative of BC patients.

In other antibody-based assay, C. I. Li et al. [187] analyzed plasma samples from

healthy controls and TNBC patients identifying 93 differential proteins. 29 proteins

were confirmed by the validation in an independent cohort. Using a strict criteria, five

proteins (DUSP9, EED, EFNA5, ITGB1 and PTPMT1) were found and colud be

exploited as markers for early detection of TNBC.

Lymph node status is a crucial predictor for the overall survival of invasive

breast cancer. However, lymph node involvement is only detected in about half of

HER2-positive patients. Since patients with lymph node involvement has less

favourable prognosis and higher risk of recurrence, it is important to develop plasma

protein biomarkers for distinguishing lymph node metastasis. In this way, L. Chen et al.

[188] applied label-free quantitative proteomic strategy to construct plasma proteomes

of ten patients with small size HER2-positive breast cancer (five patients with lymph

node metastasis (LNM+) versus five patients without lymph node metastasis (LNM-)).

A total of 388 proteins were identified, of which 33 proteins were differentially

expressed (DE), and 24 proteins were over-expressed in LNM+ group. Western blotting

analysis showed that RARB was greatly increased in LNM+ group and FBLN5 was

slightly elevated, indicating the results of label-free quantification is highly consistent

with western blotting. Although statistical analyses suggested that this aproach is low-

cost and high-efficiency in initial screening of plasma biomarkers, the present dataset

only provided a list of differentially expressed (DE) plasma proteins which could be

used for further screening of genuine biomarkers.

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11. Protein biomarkers for breast cancer prognosis

Compared to diagnostic studies, proteomics studies to discover novel markers to

improve breast cancer prognostication are rather limited.

It was found that adjuvant use of bisphosphonate diminished recurrence in breast

cancer patients through suppression of bone resorption. To determine the prognostic

impact of bone resorption markers, M. Imamura et al. [189] investigated serum levels of

the pyridinoline crosslinked carboxyterminal telopeptide of type I collagen (1CTP) and

N-terminal crosslinking telopeptides of type I collagen (NTX). Relapse-free survival of

130 patients whose 1CTP changed from low at baseline to high at 6 months

postoperatively showed RFS almost as poor as that for patients with high 1CTP

throughout. Only 1CTP could be suitable not only for categorizing patients with adverse

prognosis, but also for choosing patients who colud benefit from administration of bone

modifying agents in an adjuvant scenary.

Recently, it was developed an study to identify density-associated proteins to

improve the understanding of mammographic breast density as a risk factor for breast

cancer [190]. Particularly, it was found that ABCC11, TNFRSF10D, F11R and ERRF

were positively associated with area-based breast density (AD), and SHC1, CFLAR,

ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD.

These data provided insights into the aetiology of breast density as a prominent risk

factor for breast cancer. Furthermore, this study showed that stroma-specific and

epithelial-specific proteins could be found in blood as a consequence of tissue leakage,

which would make them key candidates for future individual risk stratification.

However, further validation and follow-up studies of the shortlisted protein candidates

in independent cohorts will be needed to infer their role in breast density and also its

progression in premenopausal and postmenopausal women.

Among the major risk factors of breast cancer, other important role is played by

familial history of BC. Germ-line mutations in BRCA1/2 genes account for most of the

hereditary ovarian and/or breast cancers. Gene expression profiling studies revealed

particular molecular signatures for BRCA1/2-related breast tumors as compared to

sporadic cases, which could facilitate diagnosis and clinical follow-up. Even though, a

clear hallmark of BRCA1/2-positive BC is still needing.

I. Introduction

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39

Tumor-specific changes in the plasma proteome of BC patients and healthy

family members sharing the same BRCA1 gene founder mutation (5083del19) were

investigated by L. Tammè et al. [191]. The proteomic analysis revealed that gelsolin

was down-expressed in plasma samples of patients with hereditary BC, and that its

levels were associated with the BRCA1 mutation status, showung that this relevant

tumor suppressor gene could stimulate BC cell proliferation, migration and invasion,

also thorough the down-regulation of gelsolin.

Women with inherited BRCA1 mutations are more likely to develop breast

cancer (BC); however, not every carrier will progress to BC. The aim of the study

developed by J. Fan et al. [192]. was to identify and characterize circulating peptides

that correlate with BC patients carrying BRCA1 mutations. After the enrichment of

circulating peptides using a nanoporous silica thin films (NanoTraps), peptides

KNG1K438-R457 and C 3fS1304-R1320 were identified as putative peptide candidates

to differentiate BRCA1 mutant BC from sporadic BC and cancer-free BRCA1 mutant

carriers. Therefore, the expression level of both peptides were associated with cancer

status in BRCA1 carriers. This approach could offer an answer to the dilema of who is

going to get cancer. The long-term longitudinal information would also be greatly

beneficial, particularly for cancer-free BRCA1 mutation carriers who maintain their

high-risk status. We intend for this strategy to improve the early examination of cancer

in the BRCA1 carriers based on the suggestions from the blood-based test. However,

future prospective studies are required to validate these findings.

It was described that the inhibitor of apoptosis (IAP) protein Survivin and its

variants (Survivin-ΔEx3 and Survivin-2B) are differentially expressed in breast cancer

tissues. Furhermore, Survivin is released from tumor cells via small membrane-bound

vesicles called exosomes. Thus, S. Khan et al. [193] developed the analysis of exosomal

Survivin, Survivin-ΔEx3 and Survivin-2B in breast cancer patient sera in parallel with

paired breast tumor tissue. After exsosomal investigation, Survivin and Survivin-ΔEx3

were detected in all of the samples examined, however, Survivin-2B was differentially

expressed depending on the disease aggressiveness: expressed mostly in primary tumors

in early stage disease, low or no expression was found in high-grade tumors and absent

in most distant metastasis. Therefore, exosomal Survivin-2B can be further investigated

as an early diagnostic or prognostic marker in breast cancer.

I. Introduction

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40

M. Giussani et al. [194] tested plasma samples from healthy donors and from

patients with malignant or benign breast disease by ELISA for the presence of

collagen11a1 (COL11A1), collagen oligomeric matrix protein (COMP), and collagen

10a1 (COL10A1). Importantly, the combination of COL11A1, COMP, and COL10A1

was identified as potentially informative in discriminating BC patients from those with

benign disease. The three molecules resulted expressed in the stroma of BC tissue

samples, thus circulating COL11A1, COMP, and COL10A1 could be very

advantageous in diagnostic assessment of suspicious breast nodules.

12. Biomarkers for response prediction and treatment monitoring of breast cancer

Though accurate prediction of chemosensitivity in cancer therapy would allow

personalized therapy, thus avoiding toxic side effects and the use of ineffective agents,

protein profiling studies searching for markers for response prediction and treatment

monitoring of breast cancer are scarce.

In recent years, several studies have reported the diagnostic utility of the low

molecular weight fraction of the human serum peptidome in BCa [53, 195]; however, to

the best of our knowledge, only two studies investigated the putative use of peptide

signals as biomarkers to predict tumour outcome following surgery [196, 197]. M. C.

Gast et al. [113] demonstrated a strong association between serum haptoglobin

phenotype and recurrence-free survival in a group of 63 high-risk early BCa patients. A.

Gonçalves et al. [114] investigated post-operative sera of 83 high-risk BCa patients,

identifying a 40-protein signature that correctly predicted the outcome in 83% of the

cases. Major components of this signature include haptoglobin alpha-1, C3a, transferrin

and apo-lipoproteins A-I and C-I. However, both groups were unable to subsequently

validate their results in an independent patient set.

In this way, recently F. Boccardo et al. [198] employed MALDI-TOF MS to

recognise serum peptidome profiles predictive of mortality in 331 patients who

underwent an operation for infiltrating BCa. At a median follow-up time of 25.5 years

(range 1.3 to 26.9 years), 68 of the 102 patients were deceased, and 45 of these deaths

were attributed to BCa-related causes. It was found that four signals were increased in

deceased patients compared with living patients and only one having mass/charge ratio

(m/z) 1046.49 was associated with BCa-specific mortality. This pea kwas identified as

I. Introduction

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41

Angiotensin II (ATII) whose levels were increased in women who exhibited worse

mortality outcomes, reinforcing the evidence that this peptide potentially significantly

affects the natural history of early BCa.

After clustering analysis of protein spectra to identify protein patterns related to

BC and HV groups [199], five protein peaks (m/z 3808, m/z 6624, m/z 8916, m/z 13870,

and m/z 28268) were indentified that together classified BC and HV with a receiver

operating characteristic (ROC) area-under-the-curve value of 0.961. These proteins

were identified as a fragment of apolipoprotein H (ApoH, m/z 3808), ApoCI (m/z

6624), complement C3a (m/z 8916), transthyretin (m/z 13870), and ApoAI (m/z 28268).

Importantly, this panel significantly predicted disease-free survival (P = 0.005), with

and efficacy greater in women with estrogen receptor (ER)-negative tumors (n = 50, P =

0.003) that in ER-positive (n = 131, P = 0.161). Furthermore, in women with ER-

negative tumors, the combined biomarker could be used as an adjunct to other

pathological variables in predicting patient outcome. However, this method needs to be

confirmed in larger patient cohorts, and also to be developed in a new format (for

example, a multiplexed ELISA kit) that could facilitate its further application.

Although the soluble form of transferrin (sTfR) is frequently used to identify

iron deficiency anaemia [200], a few studies have reported its elevation in a variety of

cancers, including breast cancer [201]. In this way, Q. Xu et al. [202] developed and

validated an advanced LC-MS/MS-based targeted proteomics assay coupled with

peptide immunoaffinity enrichment (SISCAPA) for the quantification of low-level sTfR

(100 ng/ml) in breast cancer patients after the onset of chemotherapy. Using this assay,

60 pairs of serum samples pre- and post-chemotherapy and the corresponding control

samples from 60 healthy volunteers were determined and compared. The results

confirmed sTfR suppression during chemotherapy and suggested that sTfR may be a

potential indicator of transfusion requirement. However, further studies using large

sample sizes in preclinical and clinical trials will be required to confirm the value of this

assay.

Using a fully validated liquid chromatography-tandem mass spectrometric

method, I. van den Broek et al. [203] have compared absolute serum concentrations of

eight peptides derived from inter-α-trypsin inhibitor heavy chain-4 (ITIH4) (ITIH4

I. Introduction

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42

[658-687] to [667-687] (ITIH4-30 to -21)) before and after surgical substraction of the

tumor. Intra-individual comparisons of serum obtained before and after surgery showed

significantly decreased serum levels after surgery for seven of the ITIH4-derived

peptides (p < 0.02). The obtained results particularly suggest potential for these ITIH4-

derived peptides in the follow-up of breast cancer after surgery.

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I. Introduction

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64

II. OBJECTIVES

II. Objectives

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65

The main objective of this Doctoral Thesis has been the discovery of serum

biomarkers (minor proteins) associated to the different breast cancer subtypes throught

the use of a novel nanoproteomic approach.

The specific objectives of the Doctoral Thesis are therefore as follows:

1. To find the optimal conditions of pH, temperature, protein/nanoparticle ratio

and incubation time for the formation of the protein corona around AuNPs,

AgNPs, FeNPs and PtNPs after their interaction with human serum.

2. To qualitatively analyze and compare the functionality of the human serum

proteins adsorbed on the surface of three different nanomaterials stabilized

with citrate: 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm

silver nanoparticles (AgNPs) and, 2.40 ± 0.30 nm platinum nanoparticles

(PtNPs).

3. To identify novel serum biomarkers of triple negative breast cancer (TNBC)

throught the qualitative and quantitative analysis of the protein corora

formed arround 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70

nm silver nanoparticles (AgNPs) and 9.30 ± 0.67 nm magnetic nanoparticles

(MNPs).

4. To identify novel serum biomarkers of the different breast cancer subtypes

(luminal A, luminal B HER2 negative, luminal B HER2 positive, HER2

positive and triple negative breast cancer) throught the qualitative and

quantitative analysis of the protein corora formed arround 12.96 ± 0.72 nm

gold nanoparticles (AuNPs).

II. Objectives

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66

III. RESULTS AND DISCUSSION

CHAPTER 1

Proteomic analysis of the bio-corona formed on the surface of

(Au, Ag, Pt)-nanoparticles in human serum

María del Pilar Chantada-Vázquez, Antonio Castro López, Susana B. Bravo, Sergio

Vázquez-Estévez, Benigno Acea-Nebril, Cristina Núñez

Colloids Surf B Biointerfaces 177 (2019) 141-148

DOI: 10.1016/j.colsurfb.2019.01.056

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

67

Proteomic analysis of the bio-corona formed on the surface of

(Au, Ag, Pt)-nanoparticles in human serum

María del Pilar Chantada-Vázquez,a Antonio Castro López,b Susana B. Bravo,c

Sergio Vázquez-Estévez,d Benigno Acea-Nebril,e Cristina Núñeza

a Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de

Saúde (SERGAS), 27002 Lugo, Spain

b Breast Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde

(SERGAS), 27002 Lugo, Spain

d Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario

Universitario de Santiago de Compostela (CHUS), 15706 Santiago de Compostela,

Spain

d Oncology Division, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de

Saúde (SERGAS), 27002 Lugo, Spain

e Department of Surgery, Breast Unit, Complexo Hospitalario Universitario A Coruña

(CHUAC), SERGAS, A Coruña, Spain

Abstract

Adsorption of biomolecules onto nanoparticles surface in biological samples led

to the formation of a bio-corona, it could modify the “identity” of nanoparticles,

contributing to the determination of their toxicity and biocompatibility.

Gel electrophoresis in combination with liquid chromatography-tandem mass

spectrometry (LC-MS/MS) was employed to qualitatively analyze and identify the

human serum proteins adsorbed on the surface of three different nanomaterials

stabilized with citrate: 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm

silver nanoparticles (AgNPs) and, 2.40 ± 0.30 nm platinum nanoparticles (PtNPs). An

exhaustive analysis and classification of all identified proteins related with their

function were also developed.

Keywords: Gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), platinum

nanoparticles (PtNPs), human serum, protein corona, prognostic biomarker.

III. Results and Discussion. CHAPTER 1

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68

1. Introduction

In recent years, noble metal NPs have gained great interest because their diverse

material properties hold promising potential in advancing current diagnostic and

therapeutic technologies. Such properties are fundamentally dependent on their size,

shape, and composition [1].

Particularly, gold nanoparticles (AuNPs) have a wide range of applications in

the biological and medical fields, including thermotherapy, biosensors, and molecular

imaging [2]. Silver nanoparticles (AgNPs) are known to be potent antimicrobial agents

with a broad antimicrobial spectrum and high efficacy against bacteria [3], and they

have been widely used in consumer products [4]. Platinum nanoparticles (PtNPs) have

been already proposed as efficient and selective radical scavengers for therapies of

oxidative stress diseases [5, 6]. However, their clinical potential has been slowed down

by some toxicological concerns. Nevertheless, the use of PtNPs as additives in

consumer products and cosmetics has been already approved [7]. This suggests that

PtNPs will likely register an increase shortly in a wide range of applications, including

healthcare devices, diagnostics, and cosmetics [8]. Hence, the possibility of living

organisms being exposed to Ag/Au/Pt-NPs either directly or indirectly is greatly

increasing, which has resulted in safety concerns [9].

NPs can enter the human body in different ways, among which the main

exposure routes include inhalation, oral administration, intravenous injection, and

dermal exposure [10]. Once NPs enter the body, they contact various biological

molecules such as proteins, lipid, polysaccharides, and nucleic acids [11]. Particularly,

when NPs are exposed to biological fluids (as serum/plasma), proteins and other

biomolecules are easily adsorbed onto the surface to form a protein ‘corona’ around

NPs, which reduces the surface free energy of NPs [12].

Protein corona is divided into “soft corona” and “hard corona”. While soft

corona is formed by lower affinity proteins reversibly bound to NPs (which can be

further exchangeable), hard corona contains higher affinity proteins on the NP surface

that may irreversibly bind to NPs. Protein corona is a dynamic layer in which amount of

protein and the arrangement are changeable according to the conditions of biological

and physicochemical interaction [13].

During the processes, chemical or physical adsorption takes part in the formation

of protein corona. Coordination, hydrogen bonding, van der Waals forces, electrostatic

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

69

and hydrophobic interactions, steric hindrance, etc. play important roles in driving the

binding of proteins to NPs [14, 15].

Furthermore, protein corona patterns mainly depend on the physicochemical

properties of NPs (nanomaterial, size, charge, surface functional groups, shape) and

exposing environments including immersed media components, temperature, pH,

dynamic shear stress, and interaction (or exposing) time [16-19]. Proteins with large

quantities are first bound to NP surface, and then gradually replaced by higher affinity

proteins (Vroman effect) [20].

When the protein is bound to NPs to form protein corona, proteins may

reorganize their structures to adapt to surrounding environments and NPs surface. The

secondary and/or tertiary protein structure is modified, and this event is known as

“conformational changes” [21]. Furthermore, the formation of a corona may eliminate

the physiological functions of proteins, which leads to the loss of original targeting

capabilities [22], induces various cellular responses including inflammatory responses,

increased lysosomal permeability, activated caspase-related pathways, or even apoptosis

[23, 24].

Understanding how the different properties of nanoparticles affect the

composition of protein coronas is therefore important. The serum and plasma protein

corona compositions of many AuNPs with different coatings and different sizes have

been identified [25-27]. However, the high-throughput protein analyses of the AgNPs’

coronas have been limited [26, 28-30]. To better understand the relationship between

corona composition and the nanoparticles’ properties, a recent study was developed.

This analysis showed specific binding patterns for the blood plasma-derived corona

composition of AgNPs and AuNPs with three surface coatings (polyethyleneimine

(BPEI), citrate (CIT), and polyvinylpyrrolidone (PVP)) [31]. To the best of our

knowledge, the analysis of the composition of the serum protein corona formed around

PtNPs was not carried out until the moment.

On the other hand, a prognostic factor is defined as any parameter, evaluated at

diagnosis (or surgery), which is associated with treatment outcome (local control,

disease-free interval, survival) and may predict patient outcome independent of

treatment. Furthermore, prognostic factors (clinical or biological) may be defined in any

disease stage or setting.

Blood-based biomarkers can be useful as pre-treatment prognostic markers, as

they can reflect variations in the tumor microenvironment and host immune response

III. Results and Discussion. CHAPTER 1

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70

and can complement biopsy-based biomarkers that evaluate tumor cells directly [32]. As

whole blood provides a dynamic representation of physiological and pathological state,

serum or plasma represents the most broadly studied biological matrix for cancer

biomarkers. Therefore, analysis of the plasma or serum proteome could be important to

achieve accurate diagnosis or prognosis.

A great number of proteomics-based studies of plasma and serum have reported

differential peptide/protein ion peaks, either as identified proteins or on the basis of

their mass/charge (m/z) values, for different cancer diagnosis or prognosis as, for

example, breast cancer [33, 34], ovarian cancer [35], head and neck cancer [36], bladder

cancer [37], lung cancer [38]; or other diseases as amyotrophic lateral sclerosis [39],

ST-segment elevation myocardial infarction [40], severe sepsis [41].

Taking into consideration all the aforementioned arguments, with our

experimental work of the serum protein corona formed around AuNPs, AgNPs and

PtNPs, we are providing basic information for toxicological and immunological risk

assessment, as well as information about the properties of different nanomaterials for

the development of novel sensors with potential medical applications.

2. Experimental

2.1. Chemicals and reagents

All reagents and solvents used were HPLC-grade or higher. Sodium citrate

tribasic dihydrate, tannic acid, chloroplatinic acid (H2PtCl6), sodium borohydride

(NaBH4), trypsin, trifluoroacetic acid, DL-Dithiothreitol(DTT), Iodoacetamide (IAA),

acrylamide/bis-acrylamide 30% solution (37.5:1), Glycerol 86-88%, Tris-base,

Coomassie Brilliant Blue R250 (CBB), sodium carbonate, and the Sigma Marker wide

range 6.5-200 KDa were purchased from Sigma-Aldrich (St. Louis, MO, USA). Sodium

dodecylsulfate (SDS) and formaldehyde were purchased from Panreac (Barcelona,

Spain). β-mercaptoethanol was purchased from Merck (Hohen-brunn,Germany) and

bromophenol-blue was purchased from Riedel-de Haen (Seelze,Germany). Hydrogen

tetrachloroaurate (III) hydrate (99.9%-Au) (49%Au) at 10%w/v was purchased from

Strem Chemicals (Newburyport, MA, USA). Ammoniumbicarbonate (AMBIC) and

formic acid were purchased from Fluka (Steinheim, Germany).

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

71

2.2. Instrumentation

Microscopic characterizations of AuNPs, AgNPs and PtNPs were performed by

transmission electron microscopy (TEM) using a Jeol JEM 1011 microscope. Samples

for TEM were prepared by pipetting a drop of the colloidal dispersion onto an ultrathin

carbon-coated copper grid and allowing the solvent to evaporate. Power Pac Basic

power supply from Bio-Rad (CA, USA) was used for sodium dodecyl sulfate

polyacrylamide gel electrophoresis (SDS-PAGE) protein separation. Protein

quantification was accomplished by measuring the absorbance at 280 nm with the use of

a Qubit™ 4 Quantitation Starter Kit from Thermo Fisher Scientific. Gel image

acquisition was carried out with a UVP PhotoDoc-ItTM Imaging System from Analytik

Jena.

2.3. Synthesis of inorganic nanoparticles

Gold nanoparticles (AuNPs), silver nanoparticles (AgNPs) and platinum

nanoparticles (PtNPs) were synthesized by the citrate reduction method in aqueous

solution by the method reported by R. López-Cortés et al. [42], V. Puntes et al. [43] and

W. Chen et al. [44], respectively (see Supplemental Material).

2.4. Serum samples

Venous blood sample was obtained from five disease-free individuals with the

use of VACUETTE® Serum Clot Activator Tubes (10 mL). The collected blood samples

were allowed to clot for 15 min, and then centrifuged for 5 min at 4˚C and 1800 × g.

Sera were transferred into clean plastic tubes (1 mL) and immediately frozen at -80˚C at

Research Unit, Hospital Universitario Lucus Augusti (HULA).

2.5. Depletion of multiple high abundant proteins

Serum aliquots (×3) were filtered with Miller-GP® Filter Unit (Millipore) with a

size of 0.22 μm. Each aliquot of human serum (30 µL) was depleted with dithiothreitol

(DTT) according to the protocol described by Warder el al. [45, 46]. Fresh DTT 500

mM (3.3 µL) was mixed with 30 µL of human serum and vortex briefly. Samples were

then incubated until a viscous white precipitate persisted (60 min), followed by

centrifugation at 18840 × g for 20 min. Supernatants were transferred to a clean tube

prior to protein alkylation and nanoparticles (NPs) fractionation.

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2.6. NPs protein alkylation and fractionation

After protein depletion, the reduced SH-groups were alkylated with iodoacetic

acid (IAA) for 45 min at room temperature and protected from light. After protein

reduction and alkylation, 75 µL of AuNPs (10.02 ± 0.91 nm), 75 µL of AgNPs (9.73 ±

1.70 nm) and 75 µL of PtNPs (2.40 ± 0.30 nm) were added to each different serum

aliquots (×3) belonging to the five disease-free individuals (9 aliquots per individual, 3

with each nanoparticle type), followed by the addition of 40 µL of citrate/citric acid

buffer to a final pH of 5.8, as described by López-Cortés et al [42]. Then, all NPs-serum

solutions were incubated at 37 °C with shaking in a thermostatic bath during 30 min.

Pellets were harvested by centrifugation at 18840 × g (AuNPs and AgNPs) and

24610 × g (PtNPs) during 30 min. In all cases, pellets containing proteins bound to

nanoparticles were washed three times with 25 µL citrate/citric acid buffer and

harvested again by centrifugation at 18840 × g (AuNPs and AgNPs) and 24610 × g

(PtNPs) during 30 min to remove unbound proteins.

All pellets were reconstituted in 10 µL of buffer with 0.2 M Tris-HCl, 2 % w/v

SDS and 20 % v/v glycerol. This 10 µL was mixed with 4 µL of SDS-PAGE loading

buffer (10 % w/v SDS, Tris-Base 40 mM, pH 6.8, 50 % v/v glycerol, 0.1 % v/v

bromophenol blue, 10 % v/v β-mercaptoethanol) in a final volume of 20 µL. Then, all

samples were denatured by heating at 100°C for 5 min and loaded into a 10%

acrylamide/bis-acrylamide, stacking gel /12.5% acrylamide/bis-acrylamide running gel,

of 1 mm thickness, and separated at 180 V (constant voltage) for 120 min. After

electrophoresis, the gel was fixed for 30 minutes with 40% (v/v) ethanol and 10% (v/v)

acetic acid and then stained overnight with Colloidal Coomassie Blue [47]. Gels were

rinsed with distilled water and a 0.5 M sodium chloride solution until a clear

background was observed. Gel imaging was carried out with a with a UVP PhotoDoc-

ItTM Imaging System.

2.7. In-gel protein digestion

Protein bands were excised manually and transferred to 2.5-mL Lo-Bind tubes,

and then washed twice with water and with 50% (v/v) acetonitrile/ 25 mM ammonium

bicarbonate (ambic) until the blue color disappeared.

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

73

Prior to trypsin digestion, gel spots were washed with 25 mM ambic and

dehydrated with acetonitrile. Then, 30 μL of trypsin (20 ng·μL-1 in 12.5 mM ambic/2%

(v/v) acetonitrile) was added to the gel spots and incubated for 60 min at 0ºC.

After this time, gel spots were inspected, trypsin solution not absorbed into the

gel was removed, and the gels were covered with 100 μL of 12.5 mM ambic. Samples

were incubated for 12 h at 37°C. Then 50 μL of 5% (v/v) formic acid was added and the

supernatant was transferred to a new Lo-Bind tube and the peptides were further

extracted from the gel twice with 50% (v/v) acetonitrile/0.1% (v/v) trifluoroacetic acid

(TFA) (×3) and acetonitrile (ACN) (x1). Samples were dried-down and stored at -20 °C

[48].

2.8. Protein identification by mass spectrometry (LC-MS/MS) and data analysis

Digested peptides of each sample were separated using Reverse Phase

Chromatography. Gradient was developed using a micro liquid chromatography system

(Eksigent Technologies nanoLC 400, SCIEX) coupled to high-speed Triple TOF 6600

mass spectrometer (SCIEX) with a micro flow source. The analytical column used was

a silica-based reversed phase column YMC-TRIART C18 150 × 0.30 mm, 3 mm

particle size and 120 Å pore size (YMC Technologies, Teknokroma). The trap column

was a YMC-TRIART C18 (YMC Technologies, Teknokroma with a 3 mm particle size

and 120 Å pore size, switched on-line with the analytical column. The loading pump

delivered a solution of 0.1% formic acid in water at 10 µL/min. The micro-pump

provided a flow-rate of 5 µL/min and was operated under gradient elution conditions,

using 0.1% formic acid in water as mobile phase A, and 0.1% formic acid in acetonitrile

as mobile phase B. Peptides were separated using a 25 minutes gradient ranging from

2% to 90% mobile phase B (mobile phase A: 2% acetonitrile, 0.1% formic acid; mobile

phase B: 100% acetonitrile, 0.1% formic acid). Injection volume was 4 µL.

Data acquisition was carried out in a TripleTOF 6600 System (SCIEX, Foster

City, CA) using a Data dependent workflow. Source and interface conditions were as

follows: ionspray voltage floating (ISVF) 5500 V, curtain gas (CUR) 25, collision

energy (CE) 10 and ion source gas 1 (GS1) 25. Instrument was operated with Analyst

TF 1.7.1 software (SCIEX, USA). Switching criteria was set to ions greater than mass

to charge ratio (m/z) 350 and smaller than m/z 1400 with charge state of 2–5, mass

tolerance 250ppm and an abundance threshold of more than 200 counts (cps). Former

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74

target ions were excluded for 15s. Instrument was automatically calibrated every 4

hours using as external calibrant tryptic peptides from PepcalMix (Sciex).

2.9. Data Analysis

After MS/MS analysis, data files were processed using ProteinPilotTM 5.0.1

software from Sciex which uses the algorithm ParagonTM for database search and

ProgroupTM for data grouping. Data were searched using a Human specific Uniprot

database. False discovery rate was performed using a non-lineal fitting method

displaying only those results that reported a 1% Global false discovery rate or better

[49, 50].

3. Results and discussion

3.1. Serum fraction preparation and protein corona purification

Following the synthetic methods described by R. López-Cortés [42], V. Puntes

et al. [43] and W. Chen et al. [44], AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm)

and PtNPs (2.40 ± 0.30 nm) were successfully obtained and characterized (see Fig

1_SM to Fig 3_SM).

High abundance serum proteins were first depleted using DTT and separated

from low abundance serum proteins [42, 43]. Low abundance proteins were further

processed as described in the experimental section. Afterward, AuNPs (10.02 ± 0.91

nm), AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm) were mixed with serum

aliquots (×3) from five different individuals (9 protein samples per individual: 3 treated

with AuNPs, 3 with AgNPs and 3 with PtNPs).

It is well known that some variables influence the protein-capture efficiency,

namely (i) the NPs/protein ratio, (ii) sample pH and (iii) incubation time [42].

Particularly, the pH value is an important parameter because influences the charge state

of proteins, and a maximum binding capacity between NPs and proteins takes place at

the pH near to the protein pI [51, 52]. The optimum conditions found in preliminary

experiments with AuNPs [42], were used in this case to reduce the complexity and large

dynamic range of the human serum. To this aim, the pH was adjusted to 5.8 with

citrate/citric acid buffer and three incubation times (30, 60 and 90 minutes) were tested.

After the incubation, the pellets and the supernatants were separated via centrifugation

and the respective protein content was assessed through the use of 1D gel

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

75

electrophoresis. Similar results to that obtained after 30 minutes were found with

incubation times of 60 and 90 minutes (Fig 1_SM to Fig 3_SM), suggesting that 30

minutes was enough to achieve a good separation efficiency and it was selected as the

optimum incubation time.

To investigate the influence of the amount of each type of NPs during the

separation process, three volumes of each type of nanoparticles were explored: 75 μL,

100 μL and 125 μL, to get the following protein/NPs ratios: 10.7, 8.6 and 6.5. Fig 2 and

Fig 4_SM to Fig 6_SM show the result of this set of experiments, which suggest good

separation efficiency, even for the lowest amounts of each NPs tested (75 μL).

As it is shown in Figure 2, for an incubation time of 30 minutes, differences in

the protein corona formed around the three different NPs were visible after Coomassie

staining. When the gel profiles of each fraction (AuNPs-protein corona, AgNPs-protein

corona, PtNPs-protein corona) were compared, it was easily noted that there is a

different in the intensity of the bands. However, no conclusion can be drawn unless the

proteins were identified. However, the supernatants were very similar in all cases,

independently of the type of nanoparticle employed and the quantity of each one (Fig

7_SM).

After that, gel bands corresponding to the fractions AuNPs-protein corona,

AgNPs-protein corona, PtNPs-protein corona formed after the addition of 75 μL, were

excised and submitted to the sample treatment described in the experimental section.

The resulting pools of peptides were then analyzed by mass spectrometry (LC-MS/MS)

for protein identification.

A similar number of proteins were identified from the protein corona formed

around AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm)

after their incubation with serum aliquots (×3) from five different individuals (9 protein

samples per individual: 3 treated with AuNPs, 3 with AgNPs and 3 with PtNPs) (see

Table 1_SM). Importantly, 215, 215 and 198 were commonly found in all cases in the

surface of AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30

nm) (see Table 2_SM). From them, 170 proteins were commonly detected in the protein

corona of all three different types of NPs (see Fig 1). One of these proteins was serum

albumin, the most abundant protein in the blood. However, 52 different proteins were

found on the three different NPs surface (see Fig 1): 21 different proteins on the 10.02 ±

0.91 nm AuNPs (see Table 1), 17 on the 9.73 ± 1.70 nm AgNPs (see Table 2) and 14

individual proteins on the 2.40 ± 0.30 nm PtNPs (see Table 3).

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76

Then, an analysis of the protein corona formed around the three nanoparticles

(AuNPs, AgNPs and PtNPs) will be carried out in relation to the different functionality

of the proteins identified.

Table 1. Selection of identified single-detected corona proteins bound to the 10.02 ±

0.91 nm AuNPs after 30 min incubation and subsequent washing. The accession

number, gene name, species (Human), molecular weight (kDa) and protein function

were reported.

Protein Name Entry Name Gene Function ATP-binding cassette sub-family B

member 5 Q2M3G0 ABCB5 Transporter activity

Basement membrane-specific

heparan sulfate proteoglycan core

protein

P98160 HSPG2 Structural

Cadherin-5 P33151 CDH5 Controls the cohesion and organization of the

intercellular junctions

Centlein Q9NXG0 CNTLN Structural

Complement component C8 alpha

chain P07357 C8A Structural

Complement factor D P00746 CFD Catalytic activity

DENN domain-containing protein

5B Q6ZUT9 DENND5B Enzyme regulator activity

F-box only protein 3 Q9UK99 FBXO3 Enzyme regulator activity

Filaggrin-2 Q5D862 FLG2 _

Glyceraldehyde-3-phosphate

dehydrogenase P04406 GAPDH Catalytic activity

HAUS augmin-like complex

subunit 8 Q9BT25 HAUS8

Involved in microtubule generation within the

mitotic spindle

Hepatocyte growth factor activator Q04756 HGFAC Enzyme regulator activity (activator)

Immunoglobulin heavy constant

gamma 1 P01857 IGHG1 Immune response

Immunoglobulin heavy variable 4-

39 P01824 IGHV4-39 Immune response

Immunoglobulin lambda-like

polypeptide 5 B9A064 IGLL5 Immune response

Keratin, type I cytoskeletal 15 P19012 KRT15 Structural

Maestro heat-like repeat-containing

protein family member 2A A6NES4 MROH2A _

Protein ENL Q03111 MLLT1 Enzyme regulator activity

Regulator of G-protein signaling 20 O76081 RGS20 Inhibits signal transduction

Testis-specific gene 10 protein Q9BZW7 TSGA10 Plays a role in the sperm tail fibrous sheath, a

major sperm tail structure

Zinc-alpha-2-glycoprotein P25311 AZGP1 Stimulates lipid degradation in adipocytes

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

77

Table 2. Selection of identified single-detected corona proteins bound to the 9.73 ± 1.70

nm AgNPs after 30 min incubation and subsequent washing. The accession number,

gene name, species (Human), molecular weight (kDa) and protein function were

reported.

Protein Name Entry Name Gene Function Angiotensin-converting enzyme P12821 ACE Catalytic activity

ATP-binding cassette sub-family F

member 1 Q8NE71 ABCF1 Transporter activity

Cathelicidin antimicrobial peptide P49913 CAMP Antibacterial activity

CMP-N-acetylneuraminate-poly-

alpha-2,8-sialyltransferase Q92187 ST8SIA4 Catalytic activity

Dedicator of cytokinesis protein 3 Q8IZD9 DOCK3 Enzyme regulator activity

Dopamine beta-hydroxylase P09172 DBH Catalyctic activity

Eukaryotic translation elongation

factor 1 epsilon-1 O43324 EEF1E1

Positive modulator of ATM response to

DNA damage.

Fibrinogen alpha chain P02671 FGA Coagulation, immune response

Immunoglobulin heavy variable 3-

11 P01762 IGHV3-11 Immune response

Immunoglobulin heavy variable 3-

30-3 P0DP02 IGHV3-30-3 Immune response

Immunoglobulin kappa variable

1D-12 P01611 IGKV1D-12 Immune response

Immunoglobulin lambda variable

3-27 P01718 IGLV3-27 Immune response

L-lactate dehydrogenase B chain P07195 LDHB Catalytic activity

Multiple inositol polyphosphate

phosphatase 1 Q9UNW1 MINPP1 Catalytic activity

Neutrophil cytosol factor 4 Q15080 NCF4 Component of the NADPH-oxidase

Pleckstrin homology domain-

containing family G member 6 Q3KR16 PLEKHG6 Enzyme regulator activity

Unconventional myosin-If O00160 MYO1F Catalytic activity (ATPase)

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Table 3. Selection of identified single-detected corona proteins bound to the 2.40 ± 0.30

nm PtNPs after 30 min incubation and subsequent washing. The accession number,

gene name, species (Human), molecular weight (kDa) and protein function were

reported.

Protein Name Entry Name Gene Function Cartilage acidic protein 1 Q9NQ79 CRTAC1 _

DnaJ homolog subfamily C

member 22 Q8N4W6 DNAJC22 Function as a co-chaperone

Extracellular glycoprotein lacritin Q9GZZ8 LACRT Modulates secretion by lacrimal acinar

cells

Immunoglobulin heavy variable 3-

53 P01767 IGHV3-53 Immune response

Immunoglobulin lambda-like

polypeptide 1 P15814 IGLL1 Immune response

Kynurenine-oxoglutarate

transaminase 3 Q6YP21 KYAT3 Catalytic activity

Leucine-rich repeat-containing

protein 9 Q6ZRR7 LRRC9 _

Nebulin-related-anchoring protein Q86VF7 NRAP Implicated in myofibrilar organization

during cardiomyocyte development

Protein argonaute-3 Q9H9G7 GENE:

AGO3 Enzyme regulator activity

Receptor-type tyrosine-protein

phosphatase eta Q12913 PTPRJ Catalytic activity

Serum amyloid A-1 protein P0DJI8 SAA1 Inflammatory response

Serum amyloid P-component P02743 APCS

Can interact with DNA and histones and

may scavenge nuclear material released

from damaged circulating cells

SHC-transforming protein 1 P29353 SHC1

Signaling adapter that couples activated

growth factor receptors to signaling

pathways

Sulfhydryl oxidase 1 O00391 QSOX1 Catalytic activity

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79

Figure 1. Number of identified proteins found in the protein corona of 10.02 ± 0.91 nm

gold nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 2.40 ±

0.30 nm platinum nanoparticles (PtNPs); and Venn diagram showing the common

proteins in the three different nanoparticles surfaces.

Figure 2. 1D-SDS-PAGE of protein coronas formed around 10.02 ± 0.91 nm gold

nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 2.40 ± 0.30

III. Results and Discussion. CHAPTER 1

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80

nm platinum nanoparticles (PtNPs) in human serum (incubation time: 30 minutes;

volumes of each nanoparticles solution: 75 μL, 100 μL and 125 μL, to get the following

protein/NPs ratios: 10.7, 8.6 and 6.5, respectively). On the left, it marks the lane with

Mw protein standards with molecular weights in kDa.

3.2. Proteins implicated in the immune response

A total of 66 proteins implicated in the immune response (55 of them are

immunoglobulins) were identified from the protein corona formed around the AuNPs,

AgNPs, and PtNPs after their incubation with serum of all individuals. From them, 48

common proteins were found on the surface of the three different nanoparticles (see

Table 1_SM). Particularly, two of these common immune-related proteins were

complement C3 (187.1 kDa) and complement factor B (85.5 kDa). The according

complement system is involved in an effective strategy called opsonization for labeling

pathogens for removal by phagocytic cells from the circulation. To minimize

opsonization of AuNP, AgNPs and PtNPs in pharmaceutical applications (since the

drug-loaded NP is cleared by the organism and cannot release its drug at the desired

site) binding of the NP with complement factors should be avoided [49].

An example of a protein implicated in the immune response that was only found

on the surface of AgNPs was fibrinogen alpha chain (94.9 kDa), involved in blood

coagulation but also opsonizes surfaces of foreign bodies for immune cell recognition.

Furthermore, AuNPs nanoparticles exhibited selective adsorption towards

immunoglobulin heavy constant gamma 1 (36.1 kDa) (IGHG1), similar to that reported

previously in by zeolites in plasma [53].

3.3. Proteins with a transport function

A total of 29 proteins with a transporter function (7 of them apolipoproteins)

were identified from the protein corona formed around the AuNPs, AgNPs, and PtNPs.

From them, 25 common proteins were found on the surface of the three different

nanoparticles (see Table 2_SM). Among them, the most interesting protein regarding

toxicity and drug delivery potential is the ApoE. This transport protein is known to

mediate transcytosis across biological barriers, e.g., the blood-brain barrier [54, 55].

AuNPs and AgNPs exhibited selective adsorption towards two proteins that

belong to the ATP-binding cassette (ABC) transporter superfamily of integral

membrane proteins: ATP-binding cassette sub-family B member 5 (ABCB5) (138.6

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

81

kDa) and ATP-binding cassette sub-family F member 1 (ABCF1) (95.9 kDa),

respectively. Both proteins participate in the ATP-dependent transmembrane transport

of structurally diverse molecules ranging from sugars, small ions, and peptides to more

complex organic molecules [56].

3.4. Proteins with a structural functionality

A total of 32 proteins with a structural functionality were identified from the

protein corona formed around the AuNPs, AgNPs, and PtNPs (18 proteins were

commonly found on the surface of the three different nanoparticles) (see Table 2_SM).

AuNPs nanoparticles exhibited selective adsorption towards 7 proteins. Within

them, basement membrane-specific heparan sulfate proteoglycan core protein (also

known as perlecan) fragments showed to be biomarkers of bone stromal lysis [57], renal

dysfunction [58] and also pancreatic cancer secretome [59]; and the complement

component C8 alpha chain was also identified as a potential serum biomarker in

multiple sclerosis [60].

3.5. Proteins implicated in enzymatic processes

A total of 45 proteins with catalytic activity were identified from the protein

corona formed around the AuNPs, AgNPs, and PtNPs (25 commonly found on the

surface of the three different nanoparticles) (see Table 1_SM).

AuNPs nanoparticles exhibited selective adsorption towards two proteins:

complement factor D (27.1 kDa) and glyceraldehyde-3-phosphate dehydrogenase (36.1

kDa). While complement factor D (CFD; also known as adipsin) regulates activation of

the alternative complement pathway, which is implicated in age-related macular

degeneration (AMD) pathogenesis, glyceraldehyde-3-phosphate dehydrogenase

(GAPDH) (a glycolytic enzyme) can interact with proteins participating in DNA repairs,

such as APE1, PARP1, HMGB1, and HMGB2 [61].

AgNPs nanoparticles exhibited a selective adsorption towards seven proteins,

within them, lactate dehydrogenase (LDH), which was an indirect marker of hypoxia,

was a potentially prognostic factor in several malignancies such as in patients with

hepatocellular carcinoma [62] and advanced pancreatic [63], both receiving sorafenib.

In the case of PtNPs nanoparticles, they exhibited selective adsorption towards

three proteins: kynurenine-oxoglutarate transaminase 3 (51.4 kDa), receptor-type

tyrosine-protein phosphatase eta (149.5 kDa) and sulfhydryl oxidase 1 (82.6 kDa).

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82

Particularly, it was found that the expression of sulfhydryl oxidase 1 was associated

with a highly invasive phenotype and correlates with a poor prognosis in Luminal B

breast cancer [64].

A total of 39 proteins that regulates (activate or inhibit) the activity of different

enzymes were identified from the protein corona formed around the AuNPs, AgNPs,

and PtNPs. From them, 28 common proteins were found on the surface of the three

different nanoparticles.

Four (DENN domain-containing protein 5B (145.1 kDa), F-box only protein 3

(54.6 kDa), hepatocyte growth factor activator (70.7 kDa) and protein ENL (62.1 kDa)),

two (dedicator of cytokinesis protein 3 (233.1 kDa) and pleckstrin homology domain-

containing family G member 6 (88.9 kDa)) and one (protein argonaute-3 (97.4 kDa))

enzymatic regulatory proteins were found on the protein-corona of AuNPs (table 1),

AgNPs (table 2) and PtNPs (table 3), respectively.

3.6. Proteins implicated in the inflammatory response

A total of 6 proteins implicated in the inflammatory response were identified

from the protein corona formed around the AuNPs, AgNPs, and PtNPs.

Particularly, PtNPs nanoparticles exhibited selective adsorption towards one

protein: serum amyloid A-1 protein (SAA1) (13.5 kDa), a sensitive acute phase reactant

primarily produced by the liver in response to acute inflammation. It was recently

shown that SAA affects proliferation, migration, and invasion of glioblastoma cell lines,

which suggest its participation in the malignant process. Consistently, levels of SAA

have been used as a non-invasive biomarker for the prognosis of many cancers and,

particularly, serum amyloid A1 was found to be upregulated in human glioblastoma

[65].

3.7. Proteins with antibiotic activity

Dermcidin (11.3 kDa), a protein with antimicrobial activity, was identified from

the protein corona formed around the AuNPs, AgNPs, and PtNPs. Dermcidin (DCD), an

antimicrobial peptide with a broad spectrum of activity against bacteria such as

Propionibacterium acnes, is expressed constitutively in sweat in the absence of

stimulation due to injury or inflammation. It was found that reduced DCD concentration

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

83

in sweat in patients with inflammatory acne may permit proliferation of P. acnes in

pilosebaceous units, resulting in progression of inflammatory acne [66].

However, only AgNPs nanoparticles exhibited selective adsorption towards one

protein with antibacterial activity: cathelicidin antimicrobial peptide (19.3 kDa),

responsible for protecting the urinary tract against invasive bacterial infection [67].

3.8. Proteins implicated in signal transduction

There were identified three proteins implicated in the signal transduction from

the protein corona formed around the three nanosystems. While protein leucine-rich

alpha-2-glycoprotein (38.2 kDa) (implicated in the protein-protein interaction, signal

transduction and cell adhesion and development) was presented in the protein corona of

the three nanoparticles, the protein regulator of G-protein signaling 20 (31.5 kDa) (that

inhibits signal transduction) and SHC-transforming protein 1 (62.8 kDa) were only

found in the surface of AuNPs and PtNPs, respectively.

In general, in relation with the total number of proteins identified from the

protein corona surfaces (see Fig 3), the most abundant groups are constituted by the

proteins implicated in the immune response, followed by proteins with and enzymatic

function, structural, transporter, inflammatory, signal transduction and with

antibiotic/antibacterial properties.

Figure 3. Total number of identified proteins found on the surface of the three different

nanoparticles (color: black) and commonly found on the surface of AuNPs, AgNPs and

PtNPs (color: grey) with a different function.

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84

However, we found exclusive proteins with structural function and implicated in

the signal transduction only in the protein corona of 10.02 ± 0.91 nm AuNPs (see Table

1), with antibiotic/antibacterial properties in 9.73 ± 1.70 nm AgNPs (see Table 2) and

implicated on inflammatory processes only in 2.40 ± 0.30 nm PtNPs (see Table 3).

4. Conclusions

In this study, it was shown that the interaction of AuNPs (10.02 ± 0.91 nm),

AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm) with human serum withstands the

formation of a protein corona enveloping the nanoparticle, in all cases.

The formation of this protein corona depends on the composition of the

nanoparticle (core material) and its size. In that case, it was observed that smaller NPs

(2.40 ± 0.30 nm PtNPs) have lower protein adsorption (198 proteins) than larger NPs

(10.02 ± 0.91 nm AuNPs and 9.73 ± 1.70 nm AgNPs) (215 proteins). A total of 170

proteins with different functionality were detected in the protein corona of all three

different types of NPs. However, 21, 14 and 14 different proteins were found on the

AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm),

respectively.

In general, the function of all identified proteins ranges from proteins implicated

in the immune response, followed by proteins with an enzymatic function, structural,

transporter, inflammatory, signal transduction and with antibiotic/antibacterial

properties, being the majority group the first one with 66 proteins identified implicated

in the immune response. However, if we observed the different proteins on the corona

of the three different NPs, we found exclusive proteins with structural function and

implicated in the signal transduction only in the protein corona of AuNPs (10.02 ± 0.91

nm), with antibiotic/antibacterial properties in AgNPs (9.73 ± 1.70 nm) and implicated

on inflammatory processes only in PtNPs (2.40 ± 0.30 nm).

This has implications for immune safety, biocompatibility, and information for

developing novel nanomaterials with high specificity and selectivity towards proteins

with an important biological function (prognostic and diagnostic protein biomarkers).

However, it is important to mention that the interaction of these nanoparticles with

human serum could drive to the formation of a different protein corona at a pH

dissimilar of 5.8, for example, under physiological conditions.

III. Results and Discussion. CHAPTER 1 _____________________________________________________________________________

85

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CHAPTER 2

Proteomic investigation on bio-corona of Au, Ag and Fe

nanoparticles for the discovery of triple negative breast cancer

serum protein biomarkers

María del Pilar Chantada-Vázquez, Antonio Castro López, María García Vence, Sergio

Vázquez-Estévez, Benigno Acea-Nebril, David G. Calatayud, Teresa Jardiel, Susana B.

Bravo, Cristina Núñez

Journal of Proteomics 212 (2020) 103581

DOI: 10.1016/j.jprot.2019.103581

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93

Proteomic investigation on bio-corona of Au, Ag and Fe

nanoparticles for the discovery of triple negative breast cancer serum

protein biomarkers

María del Pilar Chantada-Vázquez,a Antonio Castro Lopez,b María García Vence,c

Sergio Vázquez-Estévez,d Benigno Acea-Nebril,e David G. Calatayud,f Teresa Jardiel,f

Susana B. Bravo,c Cristina Núneza

a Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de

Saúde (SERGAS), 27002 Lugo, Spain

b Breast Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde

(SERGAS), 27002 Lugo, Spain

c Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario

Universitario de Santiago de Compostela (CHUS), 15706 Santiago de Compostela,

Spain

d Oncology Division, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de

Saúde (SERGAS), 27002 Lugo, Spain

e Department of Surgery, Breast Unit, Complexo Hospitalario Universitario A Coruña

(CHUAC), SERGAS, A Coruña, Spain

f Department of Electroceramics, Instituto de Cerámica y Vidrio-CSIC, Kelsen 5,

Campus de Cantoblanco, 28049 Madrid, Spain

Abstract

Nowadays, there are no targeted therapeutic modalities for triple negative breast

cancer (TNBC). This disease is associated with poor prognosis and worst clinical

outcome because of the aggressive nature of the tumor, delayed diagnosis, and non-

specific symptoms in the early stages. Therefore, identification of novel specific TNBC

serum biomarkers for screening and therapeutic purposes remains an urgent clinical

requirement.

New user-friendly and cheap methods for biomarker identification are needed,

and nanotechnology offers new opportunities. When dispersed in blood, nanoparticles

(NPs) are covered by a protein shell termed “protein corona” (PC). While alterations in

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94

protein patterns are challeging to detect by conventional blood analyses, PC acts as a

“nano-concentrator” of serum proteins with affinity for NPs’ surface. So, the

characterization of PC could allow the detection of otherwise undetectable changes in

protein concentration at an early stage of the disease or after chemotherapy or surgery.

To explore this research idea, serum samples from 8 triple negative breast cancer

(TNBC) patients and 8 patients without malignancy were allowed to interact with gold

nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm)

and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm). Here, in order to identify

biomarker candidates in serum of TNBC patients, these nanomaterials were combined

with electrophoretic separation (SDS-PAGE) to performed qualitative and quantitative

comparisons of the serum proteomes of TNBC patients (n = 8) and healthy controls (n =

8) by liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis. The

results were validated through a sequential window acquisition of all theoretical mass

spectra (SWATH) analysis, performed in total serum samples (patients and controls)

using this approach as a multiple reaction monitoring (MRM) analysis.

Keywords: Triple negative breast cancer (TNBC); Proteomics; Nanoparticles;

Biomarkers; SWATH-MS; Mass spectrometry (MS).

1. Introduction

Breast cancer (BC) is the most frequently diagnosed cancer and the leading

cause of cancer death in women worldwide, accounting for 23% of total new cancer

cases [1].

Mainly, triple negative breast cancer (TNBC) is a heterogeneous disease that is

characterized by a lack of estrogen receptor/progesterone receptor (ER/PgR) expression

and absence of human epidermal growth factor receptor 2 (HER2) overexpression or

amplification. This subgroup accounts for 12-15% of all types of breast cancer and

exhibits a distinct molecular profile, clinical behavior, and response to therapy [2].

Notably, triple negative tumors are usually high grade and exhibit increased

aggressiveness, poor prognosis, and worst clinical outcome [3]. Because hormonal

(tamoxifen) and HER2-directed (trastuzumab) therapies are not effective, TNBC

patients are managed with standard chemotherapy; however, a high rate of local and

systemic relapse is frequently associated with treatment. Unfortunately, no useful

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95

biomarkers neither targeted therapeutic modalities exist for this breast cancer subtype

[4].

It is well known that proteins secreted from tumor tissues have a higher

likelihood of reaching the systemic circulation and may, therefore, serve as potential

biomarkers for early detection [5]. Serum proteomics is a valuable tool that can

facilitate comprehensive and systematic elucidation of the serum proteome under both

healthy and disease conditions as well as identification of serum protein markers used

for disease diagnosis and prognosis, particularly for identifying breast cancer-specific

markers [5].

Current proteomic technologies that promote large-scale sample screening and

facilitate the identification of proteins associated with disease and treatment are

developing rapidly [6]. Mass spectrometry (MS), a powerful proteomics tool, has

evolved to a high-throughput level, allowing rapid and accurate analysis of several

thousand proteins in a single study [7]. Several studies have addressed the possibility of

applying MS proteome analysis to diagnostics of TNBC, revealing protein patterns

specific for patients with TNBC at either early or late clinical stages [8]. The peptide

markers identified with differentiating patterns include glycolytic enzymes (as for

example MDH2, PGK1, TKT, Aldolase1), cytokeratins (CK7, 8, 9, 14, 17, 19), further

structure proteins (vimentin, fibronectin, L-plastin), for NME1-NME2, lactoferrin, and

members of the Annexin family, among others [9].

SWATH-MS is an emerging technique that combines deep proteome coverage

capabilities with quantitative consistency and accuracy [10]. Mainly, SWATH-MS

analysis offers several advantages, including high reproducibility and reliability of

quantitative information, in discovery proteomics [11]. Furthermore, SWATH-MS

methods can be interchanged to MRM approaches focused on the validated biomarkers.

Therefore, SWATH-MS is an important tool not only for the biomarker discovery but

also for the development of preliminary validation studies [12].

However, currently available proteomic tests detect only a tiny fraction of

potential biomarkers due to their deficient concentration in biofluids, in addition to the

‘swamping’ effect, caused by non-specific highly abundant molecules. The issue of

signal-to-noise exceeds the current capability of proteomic analysis and therefore limits

the diagnostic information that can be obtained [13].

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96

To overcome these challenges, several approaches have been developed,

comprising depletion of high-abundance proteins that mask less abundant proteins [14,

15], chromatographic or electrophoretic sample prefractionation, to decrease complexity

before mass spectrometric analyses [16,17], and direct isolation of preferred proteins

[18]. Nevertheless, none of the methods can provide a standard solution to biomarker

discovery or can give a reproducible diagnostic platform for establishing biomarker

guides. In this way, one promising way taken is the use of nanoscale materials [19].

Nanotechnology-based platforms hold great promise in addressing the above

fundamental and technical issues of biomarker discovery to overcome persistent

deficiencies of conventional methods. Currently, it is well known that the surfaces of

nanoparticles (NPs) are rapidly covered by different types of biomolecules when they

contact biological media called protein corona (PC) [20].

The protein composition and content in the corona depend on several

parameters, including: i) physicochemical properties of the NPs (i.e. composition, size,

shape, curvature, surface chemistry and surface charge, hydrophobicity/hydrophilicity)

[21-23]; ii) characteristics of biological media (i.e. protein source, and temperature) [24-

26]); iii) incubation time [27].

Notably, the composition of the protein corona varies among healthy

individuals, as well as among patients with various diseases/medical conditions. Thus,

the same NPs may have different protein coronas in different individuals. These

alterations are often small and challenging to be detected by conventional blood

analyses.

On the other side, the protein corona can act as a “nano-concentrator” [28] of

those serum proteins with affinity for the NP surface. Therefore, characterization of

protein corona could allow detecting minor changes in protein concentration at the very

early stages of disease development or even after chemotherapy or surgery (i.e., when

an alteration in circulating level of proteins could be undetectable by blood tests).

Keeping in mind that each disease is characterized by different plasma/serum

proteomes, inducing the formation of different PCs on the same nanomaterial, M.

Mahmoudi, et al. introduced the novel concept of “personalized protein corona” (PPC)

[29]. More specifically, depending on the type, period and severity of the disease (which

determines the serum/plasma alterations), each patient may have a personalized protein

corona.

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97

In the present study, gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver

nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ±

0.67 nm) were used to pre-concentrate and separate proteins from sera samples of eight

patients with TNBC as well as from eight healthy people. For protein biomarkers

identification and quantification, the proteome map changes between both groups were

detected using a proteomic approach based on electrophoretic separation (SDS-PAGE)

and mass spectrometry (nLC-MS/MS).

2. Experimental

2.1. Chemicals and reagents

All reagents and solvents used were HPLC-grade or higher. Sodium citrate

tribasic dihydrate, tannic acid, silver nitrate, ammonium hydroxide,

iron(III) chloride hexahydrate and iron(II) sulfate heptahydrate, sodium borohydride

(NaBH4), trypsin, trifluoroacetic acid, DL-Dithiothreitol (DTT), Iodoacetamide (IAA),

acrylamide/bis-acrylamide 30% solution (37.5:1), Glycerol 86-88%, Tris-base,

Coomassie Brilliant Blue R250 (CBB), sodium carbonate, and the Sigma Marker wide

range 6.5-200 kDa were purchased from Sigma-Aldrich (St. Louis, MO, USA). Sodium

dodecyl sulfate (SDS) and formaldehyde were purchased from Panreac (Barcelona,

Spain). β-mercaptoethanol was purchased from Merck (Hohen-Brunn, Germany), and

bromophenol-blue was purchased from Riedel-de Haen (Seelze, Germany). Hydrogen

tetrachloroaurate (III) hydrate (99.9%-Au) (49%Au) at 10%w/v was purchased from

Strem Chemicals (Newburyport, MA, USA). Ammonium bicarbonate (ambic) and

formic acid were purchased from Fluka (Steinheim, Germany).

2.2. Instrumentation

Microscopic characterizations of AuNPs, AgNPs, and MNPs were performed by

transmission electron microscopy (TEM) using a Jeol JEM 1011 microscope. Samples

for TEM were prepared by pipetting a drop of the colloidal dispersion onto an ultrathin

carbon-coated copper grid and allowing the solvent to evaporate. AuNPs, AgNPs and

MNPs ζ-potentials were measured at 25°C before and after protein corona formation

using a Malvern Zetasizer Nano ZS instrument. For ζ-potential measurements samples

were diluted in 1 mL milli-Q water and placed in Zetasizer disposable cuvettes. A

minimum of 3 measurements per sample were made.

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Power Pac Basic power supply from Bio-Rad (CA, USA) was used for sodium

dodecyl sulfate- polyacrylamide gel electrophoresis (SDS-PAGE) protein separation.

Protein quantification was accomplished by measuring the absorbance at 280 nm with

the use of a Qubit™ 4 Quantitation Starter Kit from Thermo Fisher Scientific. Gel

image acquisition was carried out with a UVP PhotoDoc-ItTM Imaging System from

Analytik Jena.

2.3. Synthesis of inorganic nanoparticles

2.3.1. Synthesis of citrate-gold nanoparticles (10.02 ± 0.91 nm)

Gold nanoparticles (AuNPs) were synthesized by the citrate reduction method in

aqueous solution [30]. Briefly, 60 ml of sodium citrate tribasic solution (0.075% w/v)

was heated to 100 °C, and then gold was added as 54 μL of 10% w/v of hydrogen

tetrachloroaurate (III) hydrate solution. The reaction mixture was kept under reflux until

a deep red color was detected. The solution of nanoparticles is chilled at room

temperature and stored at 4 ºC for a maximum of one month.

2.3.2. Synthesis of citrate-silver nanoparticles (9.73 ± 1.70 nm)

Silver nanoparticles (AgNPs) were synthesized by the citrate reduction method

in aqueous solution by the method reported by V. Puntes et al. [31]. A 100 mL volume

of an aqueous solution containing sodium citrate (SC) (5 mM) and tannic acid (TA)

(0.025 mM) was prepared and heated with a heating mantle in a three-neck round-

bottomed flask for 15 min under vigorous stirring. A condenser was used to prevent the

evaporation of the solvent. After boiling had commenced, 1 mL of AgNO3 (25 mM)

was injected into this solution. The solution became bright yellow immediately.

Resultant Ag NPs were purified by centrifugation at 18000×g to remove the excess of

TA and further redispersed in Milli-Q-water before sample characterization.

2.3.3. Synthesis of Fe3O4 magnetic nanoparticles (9.30 ± 0.67 nm)

The synthesis of magnetic nanoparticles was performed according to a

previously described procedure [32]. Briefly, 6 mL of concentrated ammonium

hydroxide and 4 mL of water saturated with N2 were mixed in a round-bottom flask

under an inert atmosphere. Subsequently, in another vessel, 1 g of FeCl3.6H2O and

0.25-0.5 g of FeSO4.7H2O were dissolved in 10 mL of water saturated with N2. After

mixing both solutions, the system was kept under constant stirring for 80 min at 80 °C.

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The final reaction product was washed three times with deionized water and

magnetically separated for 24 h at room temperature.

2.4. Sample resources

Venous blood samples were obtained from eight females with triple negative

breast cancer (TNBC) and eight disease-free individuals with the use of VACUETTE®

Serum Clot Activator Tubes (10 mL). The collected blood samples were allowed to clot

for 15 min and then centrifuged for 5 min at 4˚C and 1,800×g. Sera were transferred

into clean plastic tubes (1 mL) and immediately frozen at -80˚C at Research Unit,

Hospital Universitario Lucus Augusti (HULA). Clinical features of TNBC tumors,

including tumor size, histology, receptor status, clinical stage, and nodal status, are

summarized in Table 1.

Table 1. Clinical features of triple negative breast cancer tumors.

Characteristics Number

Patients 8

Age (years) < 40 1

40-70 4

> 70 3

Tumor size (cm) < 2 3

2-5 4

>5 1

Histological types Ductal invasive

carcinoma

8

Receptor status Triple negative 8

Clinical stage I 1

II 4

III 3

Nodal status N0 7

N1 1

2.5. Sample preparation

2.5.1. Optimization of protein corona formation in serum using magnetic nanoparticles

A series of serum sample aliquots were used for checking the effects of sample

pH and temperature on the high-abundance proteins depletion using DTT, MNP/protein

ratios, and pH of the medium on the washing steps.

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- Depletion of multiple high abundant proteins

Human serum aliquots (x8) were filtered with Miller-GP® Filter Unit

(Millipore) with a size of 0.22 μm. Four aliquots of human serum (30 µL) were depleted

with dithiothreitol (DTT) according to the protocol described by Warder el al. [33, 34].

Briefly, fresh DTT 500 mM (3.3 µL) in milli-Q water was mixed with 30 µL of human

serum and vortex quickly. Samples were then incubated at room temperature until a

viscous white precipitate persisted (60 min), followed by centrifugation at 18,840×g for

20 min. Supernatants were transferred to a clean tube before the protein alkylation and

nanoparticles (NPs) fractionation.

To evaluate the effects of sample pH and temperature on the high-abundance

proteins depletion with DTT, four aliquots of human serum (30 µL) were depleted with

dithiothreitol (DTT) following a modification of the protocol previously published by

Arruda et al. [35]. Four aliquots of 30 μL of human serum were mixed fresh DTT 500

mM (3.3 µL) in ambic (12.5 mmol L-1)] and vortexed. Samples were incubated for 60

min at 37ºC, centrifuged at 13000×g for 40 min to separate supernatants to be alkylated

and fractionated with nanoparticles (NPs).

- NPs protein alkylation and fractionation

After protein depletion, the reduced SH-groups were alkylated with iodoacetic

acid (IAA) for 45 min at room temperature and protected from light. Volumes of serum

reduced and alkylated, were diluted to a final volume of 100 μL in Tris-HCl (0.1 mol L-

1, pH 5.5), and mixed with MNPs (5 μg) to obtain the following MNP/protein ratios:

1:1, 1:2, 1:4, 1:10. Then, all NPs-serum solutions were incubated at 25 °C with shaking

(300 rpm) in a thermostatic bath for 30 min and then pellets were harvested by

centrifugation at 20,186 ×g for 30 min. To evaluate the effects of sample pH on the

stabilization of the protein corona in the washing steps, a fraction of pellets were

washed (x3) with 50 µL of Tris-HCl (0.1 mol L-1, pH 5.5) and another fraction with 50

µL of milli-Q water (x3). In both cases, pellets were harvested again by centrifugation

at 20,186×g for 30 min to remove unbound proteins.

2.5.2. Incubation of nanoparticles with serum samples

Serum aliquots (x2) belonging to the eight disease-free individuals and eight

triple negative breast cancer patients were depleted with dithiothreitol (DTT) following

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the method of Warder el al. [33, 34]. After that, the reduced SH-groups were alkylated

with iodoacetic acid (IAA) at room temperature (45 min in the dark).

After protein reduction and alkylation, serum aliquots (x2) belonging to the eight

disease-free individuals and eight triple negative breast cancer patients were incubated

with AuNPs (10.02 ± 0.91 nm) and AgNPs (9.73 ± 1.70 nm) (4 aliquots per individual,

2 with each nanoparticle type) following the method described by C. Núñez et al. [36].

Briefly, 75 μL of AuNPs (10.02 ± 0.91 nm) and 75 μL of AgNPs (9.73 ± 1.70 nm) were

added to each different serum aliquots (×2) belonging to the eight disease-free

individuals and eight triple negative breast cancer patients (4 aliquots per individual, 2

with each nanoparticle type), followed by the addition of 40 μL of citrate/citric acid

buffer to a final pH of 5.8. Then, all NPs-serum solutions were incubated at 37 °C with

shaking in a thermostatic bath for 30 min. Pellets were harvested by centrifugation at

18,840×g for 30 min. In all cases, pellets containing proteins bound to nanoparticles

were washed three times with 25 μL citrate/citric acid buffer and harvested again by

centrifugation at 18,840×g for 30 min to remove unbound proteins.

In the particular case of magnetic nanoparticles, each different reduced and

alkylated serum aliquot (x2) from disease-free individuals (n = 8) and negative breast

cancer patients (n = 8) were incubated (shaking at 300 rpm, 25 °C, 30 min) with 5 µL of

MNPs (9.30 ± 0.67 nm) after the addition of 87 µL of Tris-HCl (0.1 mol L-1, pH 5.5).

After centrifuging (15000×g, 30 min), pellets were separated and washed (x3)

with 50 µL of Tris-HCl (0.1 mol L-1, pH 5.5) and centrifuged again (20,186×g, 30 min).

2.5.3. Gel electrophoresis

After that, pellets were reconstituted in 10 µL of a buffer with 0.2 M Tris-HCl, 2

% w/v SDS and 20 % v/v glycerol. This 10 µL was mixed with 4 µL of SDS-PAGE

loading buffer (10 % w/v SDS, Tris-Base 40 mM, pH 6.8, 50 % v/v glycerol, 0.1 % v/v

bromophenol blue, 10 % v/v β-mercaptoethanol) in a final volume of 20 µL. Then, all

samples were denatured by heating at 100°C for 5 min and loaded into a 10%

acrylamide/bis-acrylamide, stacking gel /12.5% acrylamide/bis-acrylamide running gel,

of 1 mm thickness, and separated at 180 V (constant voltage) for 120 min. After

electrophoresis, the gel was fixed for 30 minutes with 40% (v/v) ethanol and 10% (v/v)

acetic acid and then stained overnight with Colloidal Coomassie Blue [37]. Gels were

rinsed with distilled water and a 0.5 M sodium chloride solution until a clear

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background was observed. Gel imaging was carried out with a UVP PhotoDoc-ItTM

Imaging System.

2.5.4. In-gel protein digestion

Protein bands were excised manually and transferred to 2.5-mL Lo-Bind tubes,

and then washed twice with water and with 50% (v/v) acetonitrile/ 25 mM ammonium

bicarbonate (ambic) until the blue color disappeared.

Before the trypsin digestion, gel spots were washed with 25 mM ambic and

dehydrated with acetonitrile. Then, 30 μL of trypsin (20 ng·μL-1 in 12.5 mM ambic/2%

(v/v) acetonitrile) was added to the gel spots and incubated for 60 min at 0ºC.

After this time, gel spots were inspected, trypsin solution not absorbed into the

gel was removed, and the gels were covered with 100 μL of 12.5 mM ambic. Samples

were incubated for 12 h at 37°C. Then 50 μL of 5% (v/v) formic acid was added, and

the supernatant was transferred to a new Lo-Bind tube and the peptides were further

extracted from the gel twice with 50% (v/v) acetonitrile/0.1% (v/v) trifluoroacetic acid

(TFA) (x3) and acetonitrile (ACN) (x1). Samples were dried-down and stored at -20 °C

[38].

2.6. Protein identification by mass spectrometry (LC-MS/MS) and data analysis

Digested peptides of each sample were separated using Reverse Phase

Chromatography. The gradient was developed using a micro liquid chromatography

system (Eksigent Technologies nanoLC 400, SCIEX) coupled to high-speed Triple TOF

6600 mass spectrometer (SCIEX) with a microflow source. The analytical column used

was a silica-based reversed phase column Chrom XP C18 150 × 0.30 mm, 3 mm

particle size and 120 Å pore size (Eksigen, SCIEX). The trap column was a YMC-

TRIART C18 (YMC Technologies, Teknokroma with a 3 mm particle size and 120 Å

pore size, switched on-line with the analytical column. The loading pump delivered a

solution of 0.1% formic acid in water at 10 µL/min. The micro-pump provided a flow-

rate of 5 µL/min and was operated under gradient elution conditions, using 0.1% formic

acid in water as mobile phase A, and 0.1% formic acid in acetonitrile as mobile phase

B. Peptides were separated using a 25 minutes gradient ranging from 2% to 90% mobile

phase B (mobile phase A: 2% acetonitrile, 0.1% formic acid; mobile phase B: 100%

acetonitrile, 0.1% formic acid). The injection volume was 4 µL.

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Data acquisition was carried out in a TripleTOF 6600 System (SCIEX, Foster

City, CA) using a Data dependent workflow. Source and interface conditions were as

follows: ion spray voltage floating (ISVF) 5500 V, curtain gas (CUR) 25, collision

energy (CE) 10 and ion source gas 1 (GS1) 25. The instrument was operated with

Analyst TF 1.7.1 software (SCIEX, USA). Switching criteria were set to ions greater

than mass to charge ratio (m/z) 350 and smaller than m/z 1400 with a charge state of 2-

5, mass tolerance 250 ppm and an abundance threshold of more than 200 counts (cps).

Former target ions were excluded for 15s. The instrument was automatically calibrated

every 4 hours using as external calibrant tryptic peptides from PepcalMix (Sciex).

After MS/MS analysis, data files were processed using ProteinPilotTM 5.0.1

software from Sciex, which uses the algorithm ParagonTM for database search and

ProgroupTM for data grouping. Data were searched using a Human-specific UniProt

database. False discovery rate was performed using a non-linear fitting method

displaying only those results that reported a 1% Global false discovery rate or better

[39, 40].

2.7. Protein quantification by SWATH (Sequential Window Acquisition of all

Theoretical Mass Spectra)

2.7.1. Creation of the spectral library

To construct the MS/MS spectral libraries, the peptide solutions were analyzed

by a shotgun data-dependent acquisition (DDA) approach by micro-LC-MS/MS. To get

a good representation of the peptides and proteins present in all samples, pooled vials of

samples from each group (control and triple negative breast cancer patients) were

prepared using equal mixtures of the original samples. 4 μL (4 mg) of each pool was

separated into a micro-LC system Ekspert nLC425 (Eksigen. Dublin. CA. USA) using a

column Chrom XP C18 150 × 0.30 mm. 3 mm particle size and 120 Å pore size

(Eksigent, Sciex) at a flow rate of 5 µL/min. Water and ACN, both containing 0.1%

formic acid, were used as solvents A and B, respectively. The gradient run consisted of

5% to 95% B for 30 min, 5 min at 90% B and finally 5 min at 5% B for column

equilibration, for a total run time of 40 min. When the peptides eluted, they were

directly injected into a hybrid quadrupole-TOF mass spectrometer Triple TOF 6600

(Sciex, Redwood City. CA. USA) operated with a data-dependent acquisition system in

positive ion mode. A Micro source (Sciex) was used for the interface between microLC

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and MS, with an application of 2600 V voltage. The acquisition mode consisted of a

250 ms survey MS scan from 400 to 1250 m/z followed by an MS/MS scan from 100 to

1500 m/z (25 ms acquisition time) of the top 65 precursor ions from the survey scan, for

a total cycle time of 2.8 s. The fragmented precursors were then added to a dynamic

exclusion list for 15 s; any singly charged ions were excluded from the MS/MS

analysis.

The peptide and protein identifications were performed using Protein Pilot

software (version 5.0.1. Sciex) with a Data were searched using a Human-specific

UniProt database, specifying iodoacetamide as Cys alkylation. The false discovery rate

(FDR) was set to 1 for both peptides and proteins. The MS/MS spectra of the identified

peptides were then used to generate the spectral library for SWATH peak extraction

using the add-in for PeakView Software (version 2.2. Sciex) MS/MSALL with SWATH

Acquisition MicroApp (version 2.0. Sciex). Peptides with a confidence score above

99% (as obtained from Protein Pilot database search) were included in the spectral

library).

2.7.2. Relative quantification by SWATH acquisition

SWATH-MS (Sequential Window Acquisition of all Theoretical Mass Spectra)

acquisition was performed on a TripleTOF® 6600 LC-MS/MS system (Sciex). Samples

from control and triple negative breast cancer patients were analyzed using data-

independent acquisition (DIA) method (30 total samples). Each sample (4 μL (from a

mg/ml solution) was analyzed using the LC-MS equipment and LC gradient described

above for building the spectral library but instead using the SWATH-MS acquisition

method. The method consisted of repeating a cycle that consisted of the acquisition of

65 TOF MS/MS scans (400 to 1500 m/z, high sensitivity mode, 50 ms acquisition time)

of overlapping sequential precursor isolation windows of variable width (1 m/z overlap)

covering the 400 to 1250 m/z mass range with a previous TOF MS scan (400 to 1500

m/z. 50 ms acquisition time) for each cycle. The total cycle time was 6.3 s. For each

sample set, the width of the 100 variable windows was optimized according to the ion

density found in the DDA runs using a SWATH variable window calculator worksheet

from Sciex.

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2.7.3. Data analysis

The targeted data extraction of the fragment ion chromatogram traces from the

SWATH runs was performed by PeakView (version 2.2) using the SWATH Acquisition

MicroApp (version 2.0). This application processed the data using the spectral library

created from the shotgun data. Up to ten peptides per protein and seven fragments per

peptide were selected, based on signal intensity; any shared and modified peptides were

excluded from the processing. Five-minute windows and 30 ppm widths were used to

extract the ion chromatograms; SWATH quantitation was attempted for all proteins in

the ion library that were identified by ProteinPilot with an FDR below 1%.

The retention times from the peptides that were selected for each protein were

realigned in each run according to the iRT peptides spiked in each sample and eluted

along the whole-time axis. The extracted ion chromatograms were then generated for

each selected fragment ion; the peak areas for the peptides were obtained by summing

the peak areas from the corresponding fragment ions. PeakView computed an FDR and

a score for each assigned peptide according to the chromatographic and spectral

components; only peptides with an FDR below 1% were used for protein quantitation.

Protein quantitation was calculated by adding the peak areas of the corresponding

peptides.

The integrated peak areas (processed, mrkvw files from PeakView) were directly

exported to the MarkerView software (Sciex) for relative quantitative analysis. The

export will generate three files containing quantitative information about individual

ions, the summed intensity of different ions for a particular peptide, and the summed

intensity of different peptides for a specific protein. MarkerView has been used for the

analysis of SWATH-MS data reported in other proteomics studies [41-44] because of its

data-independent method of quantitation. MarkerView uses processing algorithms that

accurately find chromatographic and spectral peaks direct from the raw SWATH data.

Data alignment by MarkerView compensates for minor variations in both mass and

retention time values, ensuring that identical compounds in different samples are

accurately compared to one another.

To control for possible uneven sample loss across the different samples during

the sample preparation process, we performed a global normalization based on the total

sum of all the peak areas extracted from all the peptides and transitions across the

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replicates of each sample. Unsupervised multivariate statistical analysis using principal

component analysis (PCA) was performed to compare the data across the samples. The

average MS peak area of each protein was derived from the biological replicates of the

SWATH-MS of each sample followed by Student’s t-test analysis using the

MarkerView software for comparison among the samples based on the averaged area

sums of all the transitions derived for each protein. The t-test will indicate how well

each variable distinguishes the two groups, reported as a p-value. To set of differentially

expressed proteins (p-value <0.05) with a 1.5 fold in- or decrease was selected.

Functional analysis was performed by FunRich open access software (Functional

Enrichment analysis tool) for functional enrichment and interaction network analysis

(http://funrich.org/index.html).

2.8. TNBC biomarkers validation

A SWATH-MS analysis was performed using the same conditions described in

section 2.7. In the validation phase, total serum samples from control and triple negative

breast cancer patients previously depleted with DTT were used.

To perform the biomarker validation, the SWATH library was performed using

not only serum pools from incubation with the different nanoparticles but also pools of

total serum samples. Therefore, we improve our library and perform better protein

quantification to achieve biomarker validation.

3. Results and discussion

Following the synthetic methods described by R. López-Cortés [30], V. Puntes

et al. [31] and F. Schüth et al. [32], AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm)

and MNPs (9.30 ± 0.67 nm) were successfully obtained, respectively.

The sizes and ζ-potential of AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm)

and MNPs (9.30 ± 0.67 nm) were examined before and after their incubation with two

pools of human blood serum from healthy individuals and triple negative breast cancer

patients, following the conditions described in section section 2.5.2.

TEM and ζ-potential measurements after the incubation of AuNPs (10.02 ± 0.91

nm), AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm) with serum demonstrated

that, in all cases, the size did not change significantly, and the surface charge remained

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negative (see Fig 1_SM to Fig 10_SM). Upon serum incubation, the mean particle

surface charge of the AuNPs (10.02 ± 0.91 nm) increased (became less negative) from -

37.0 mV to -29.7 mV, and the same measurement for the MNPs (9.30 ± 0.67 nm)

increased from -30.5 mV to -29.3 mV. However, upon serum incubation the mean

particle surface charge of the AgNPs (9.73 ± 1.70 nm) decreased (became more

negative) from -27.4 mV to -30.0 mV. These results agree with previous studies

suggesting that negatively charged NPs do not exclusively interact with positively

charged proteins, as electrostatic interactions are not the only driving force behind NP-

corona interactions [45, 46]. Interestingly, for AuNPs (10.02 ± 0.91 nm) and MNPs

(9.30 ± 0.67 nm), where the ζ-potential was shifted toward less negative values, it could

be suggested preferential interaction with positively charged proteins. The presence of

negatively charged proteins can be explained by a sequential model of protein binding,

in which positively charged proteins initially bind the NP, followed by negatively

charged ones [45,46].

3.1. Optimization of parameters for the protein corona formation in serum using

MNPs

As mentioned above, a great number of variables could influence the efficiency

of protein adsorption on the MNPs surface [47]. For this reason, three parameters were

evaluated: (i) the effects of sample pH and temperature on the depletion of high-

abundance proteins presented in serum using DTT, (ii) MNP/protein ratios; (iii) and pH

of the medium on the washing steps. For this study, shaking and incubation temperature

were previously defined as 300 rpm and 25 ºC, respectively.

In order to evaluate the effects of sample pH and temperature on the depletion of

high-abundance proteins, four human serum samples (x2) (30 µL) were depleted with

fresh DTT 500 mM (3.3 µL) in milli-Q H2O for 60 min at room temperature (protocol

of Warder el al. [33, 34]), and four human serum samples (x2) (30 µL) were depleted

with fresh DTT 500 mM (3.3 µL) in ambic (12.5 mmol L-1) for 60 min at 37ºC

(modification of protocol described by Arruda [35]). In both cases, after the incubation

and centrifugation, supernatants were transferred to a clean tube before the protein

alkylation and nanoparticles (NPs) fractionation. Depletion with fresh DTT 500 mM in

milli-Q for 60 min at room temperature showed more reproducible results (see Figure

4_SM).

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Protein concentration is another critical parameter that may affect the capacity

and kinetics of protein adsorption. To investigate the influence of the MNP/protein ratio

on the formation of the protein corona, volumes of serum reduced and alkylated (x2)

were mixed with MNPs (9.30 ± 0.67 nm), at MNP/protein ratios of 1:1, 1:2, 1:4, and

1:10 (see section 2.5.1). Maintaining the amount of adsorbent (i.e., MNPs) constant and

increasing the protein concentration, would be expected to lead to a decrease of

available adsorption sites, reducing the efficiency of protein removal [48]. As a

compromise between MNPs and protein corona formation, the 1:2 ratio (MNP/protein)

was then selected for future experiments (see Figure 5_SM).

The pH value is an essential parameter because it influences the charge state of

proteins, therefore influencing their interaction with MNPs [49]. To evaluate the effects

of sample pH on the stabilization of the protein corona in the washing steps, a fraction

of pellets was washed three times with 50 µL Tris-HCl (0.1 mol L-1, pH 5.5) and

another fraction was washed three times with milli-Q water. The first one was selected

as the preferred method, because the washes with milli-Q water promoted the

destabilization of the protein corona formed around the MNPs, due to the modifications

of the pH (data not shown).

3.2. Serum fraction preparation and protein corona purification (patients vs. controls)

Serum aliquots (x2) belonging to the eight disease-free individuals and serum

samples from eight triple negative breast cancer patients were depleted with

dithiothreitol (DTT) according to the protocol described by Warder el al. [33, 34]. After

that, the reduced SH-groups were alkylated with iodoacetic acid (IAA) for 45 min at

room temperature and protected from light.

After protein reduction and alkylation, serum aliquots (x2) belonging to the eight

disease-free individuals and eight triple negative breast cancer patients were incubated

with AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm) (6

aliquots per individual, 2 with each nanoparticle type) and further processed as

described in section 2.2.5.

Two protein fractions were thus obtained in each case, one in the supernatant

and the second one attached to the surface of each nanoparticles types (protein corona).

Then, protein fractions (AuNPs-protein corona, AgNPs-protein corona, MNPs-protein

corona) were separately loaded onto a 1D-SDS-PAGE. Proteins were separated and,

after staining, gel bands were excised and submitted to the sample incubation described

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in section 2.5.4. The resulting peptides were then analyzed by mass spectrometry (LC-

MS/MS) for protein identification.

Figure 1 shows the 1D gels for the protein corona formed around the three

different NPs (AuNPs-protein corona, AgNPs-protein corona, MNPs-protein corona)

visible after Coomassie staining. As may be seen, it is quickly noted that there is a

difference in the intensity of the bands on the gel profiles for the healthy controls (from

C1 to C8) and the patients (from P1 to P8) for each type of nanoparticle. However, no

conclusion can be drawn unless the proteins are identified.

Figure 1. 1D-SDS-PAGE of protein coronas formed around 10.02 ± 0.91 nm gold

nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 9.30 ± 0.67

nm magnetic nanoparticles (MNPs) after their incubation with serum aliquots (x2)

belonging to the eight disease-free individuals (C1 to C8) and eight triple negative

breast cancer patients (P1 to P8). On the left, it marks the lane with Mw protein

standards.

As Figure 2 shows, 192, 161 and 142 proteins were commonly detected in the

protein corona formed around AuNPs, AgNPs, and MNPs, after their incubation with

serum samples of the eight-triple negative breast cancer patients and eight healthy

controls, respectively.

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Figure 2. Quantitative Venn diagrams showing the number of identified proteins found

in the protein corona of 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm

silver nanoparticles (AgNPs) and 9.30 ± 0.67 nm magnetic nanoparticles (MNPs) after

their incubation with serum from eight triple negative breast cancer patients and eight

healthy controls.

In the case of all serum samples from healthy controls, 285, 292 and 206 were

identified on the surface of AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and

MNPs (9.30 ± 0.67 nm), respectively. From them, 149 proteins were commonly

detected in the protein corona of all three different types of NPs. However, 202 different

proteins were found on the three distinct NPs surface: 71 different proteins on the

AuNPs, 85 on the AgNPs, and 46 individual proteins on the MNPs (see Table 1_SM).

Furthermore, 231, 206 and 203 were found in the surface of AuNPs (10.02 ±

0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm) after their incubation

with all serum aliquots (x2) from eight triple negative breast cancer patients (6 protein

samples per individual: 2 incubated with AuNPs, 2 with AgNPs and 2 with MNPs) (see

Table 2_SM). A total of 138 proteins were commonly found in the protein corona of all

three different types of NPs. However, 142 different proteins were found in the three

different NPs surface: 56 different proteins on the AuNPs, 33 on the AgNPs and 53

individual proteins on the MNPs.

Fractionation of the proteome using AuNPs, AgNPs, and MNPs allows for the

identification of 39 (see Table 2), 45 (see Table 3) and 61 (see Table 4) protein

biomarkers in the pellet of all patient samples, respectively (see Figure 3). Remarkably,

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these proteins were not identified in the healthy control. These potential biomarkers

came from different cellular components; most of them from the nucleus and cytoplasm

(see Figure 4). Protein biomarkers also showed different functionality and are

constituted by proteins implicated in the immune response, followed by proteins with an

enzymatic function, structural, transporter, inflammatory, signal transduction, and with

antibiotic/antibacterial properties.

Importantly, the GRF-type zinc finger domain-containing protein 1 (protein

ZGRF1) was identified in the protein corona of AuNPs, AgNPs and MNPs after their

incubation with the serum samples of all triple negative breast cancer patients. Zinc-

finger proteins (ZNFs) are one of the most abundant groups of proteins and have a wide

range of molecular functions. Given the wide variety of zinc-finger domains, ZNFs can

interact with DNA, RNA, PAR (poly-ADP-ribose) and other proteins. Thus, ZNFs are

involved in the regulation of several cellular processes. ZNFs are implicated in

transcriptional regulation, ubiquitin-mediated protein degradation, signal transduction,

actin targeting, DNA repair, cell migration, and numerous other processes [50].

Notably, overexpression of similar zinc finger proteins has been shown to promote cell

growth and metastasis in laryngeal squamous cell carcinoma, glioma, non-small cell

lung cancer, gastric cancer, oral squamous cell carcinoma, gallbladder cancer, and

breast cancer [51], and also in triple negative breast cancers [52].

Matrix metalloproteinase-9 (MMP9) was identified in the protein corona of

AuNPs and AgNPs after their incubation with the serum samples of all triple negative

breast cancer patients. Members of the matrix metalloproteinase (MMP) family have

been identified as poor prognosis markers for breast cancer patients and as drivers of

many facets of the tumor phenotype in experimental models [53]. Studies of the

pathological processes involved in tumor progression and metastasis revealed matrix

metalloproteinases (MMPs) as prominent molecules engaged in shaping the tumor

microenvironment and driving cancer progression and metastasis [54-56]. Mainly,

MMP9 was investigated as a potential tumor marker in breast cancer [57]. MMP-9 is

one of 70 genes in the Rosetta poor prognosis signature for breast cancer patients [58],

the basis for the clinically implemented Mammaprint prognostic assay (Agendia Inc.,

Irvine, CA). MMP-9 was also highly expressed in node-positive tumors and the

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112

preoperative blood serum of patients, but MMP-9 activity was appreciably inhibited in

blood serum samples collected after surgery.

Lebercilin and Immunoglobulin lambda variable 3-27 (LV327) were identified

in the protein corona of AuNPs and MNPs after their incubation with the serum samples

of all triple negative breast cancer patients. While the protein expression of lebercilin

was observed in several tissue cancers like colorectal cancer, breast cancer, prostate

cancer, lung cancer (The Human Protein Atlas,

https://www.proteinatlas.org/ENSG00000157578-LCA5L/pathology), immunoglobulin

free light chains as LV327 are biomarkers of poor prognosis in basal-like breast cancer

and are potential targets in tumor-associated inflammation [59].

LINE-1 type transposase domain-containing protein 1 (LITD1), structural

maintenance of chromosomes protein 6 (SMC6) and short coiled-coil protein (SCOC)

were identified in the protein corona of AgNPs and MNPs after their incubation with

the serum samples of all triple negative breast cancer patients.

L1TD1 is an RNA-binding protein that involved with self-renewal of

undifferentiated human embryonic stem cells and cancer cell proliferation [60].

The structural maintenance of chromosomes (SMC) proteins are essential for

successful chromosome transmission during replication and segregation of the genome

in all organisms. SMC proteins function together with other proteins in a range of

chromosomal transactions, including chromosome condensation, sister-chromatid

cohesion, recombination, DNA repair, and epigenetic silencing of gene expression.

Notably, the protein expression of SMC6 was observed in different tissues as colorectal

cancer, breast cancer, prostate cancer, lung cancer, and liver cancer (The Human Protein

Atlas, https://www.proteinatlas.org/ENSG00000163029-SMC6/pathology). In humans,

SCOC is required for autophagosome formation during amino acid starvation [61];

however, this relation with cancer is unknown until the moment.

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Figure 3. Quantitative Venn diagrams showing the number of identified protein

biomarkers found on the surface of the three different nanoparticles (color: black) and

commonly found on the surface of AuNPs (10.02 ± 0.91), AgNPs (9.73 ± 1.70 nm) and

MNPs (9.30 ± 0.67 nm) (color: grey).

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Figure 4. Localization of the 39, 45 and 61 protein biomarkers found in the surface of

AuNPs (10.02 ± 0.91), AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm),

respectively, in the different cellular components.

Table 2. Selection of identified single-detected corona proteins bound to the 10.02 ±

0.91 nm AuNPs after 30 min incubation and subsequent washing. The accession

number, gene name, species (Human), molecular weight (kDa) and protein function

were reported.

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Protein Name Entry Name Gene Function Actin, cytoplasmic 2 P63261 ACTG1 Cell mobility

Atypical kinase COQ8B,

mitochondrial Q96D53 COQ8B Involved in the biosynthesis of coenzyme Q

Beta-ureidopropionase Q9UBR1 UPB1 Catalytic activity

Brefeldin A-inhibited guanine

nucleotide-exchange protein 3 Q5TH69 ARFGEF3

Participates in the regulation of systemic

glucose homeostasis

85/88 kDa calcium-independent

phospholipase A2 O60733 PLA2G6 Catalytic activity

Cilia- and flagella-associated

protein 100 Q494V2 CFAP100

Plays a role in ciliary/flagellar motility by

regulating the assembly and the activity of

axonemal inner dynein arm

Coagulation factor XI P03951 F11 Catalytic activity

Contactin-associated protein-like 2 Q9UHC6 CNTNAP2

Plays a role in the formation of functional

distinct domains critical for saltatory

conduction of nerve impulses in myelinated

nerve fibers

DNA topoisomerase 1 P11387 TOP1 Catalyctic activity

Dynein assembly factor 1,

axonemal Q8NEP3 DNAAF1

Plays a role in cytoplasmic preassembly of

dynein arms

E3 ubiquitin-protein ligase SHPRH Q149N8 SHPRH Enzyme involved in DNA repair

Estrogen sulfotransferase P49888 SULT1E1

Catalytic activity. May play a role in the

regulation of estrogen receptor activity by

metabolizing free estradiol

[F-actin]-monooxygenase MICAL3 Q7RTP6 MICAL3 Catalytic activity

Glial fibrillary acidic protein P14136 GFAP Cell-specific marker during the development

of the central nervous system

Histone-lysine N-methyltransferase

2D O14686 KMT2D

Catalytic activity. Acts as a coactivator for

estrogen receptor by being recruited by

ESR1, thereby activating transcription

Immunoglobulin lambda variable

3-27 P01718 IGLV3-27 Immune response

Inositol hexakisphosphate kinase 2 Q9UHH9 IP6K2 Catalytic activity

Inositol 1,4,5-trisphosphate

receptor type 3 Q14573 ITPR3 Receptor for inositol 1,4,5-trisphosphate

Intraflagellar transport protein 74

homolog Q96LB3 IFT74 Transporter activity

Janus kinase and microtubule-

interacting protein 1 Q96N16 JAKMIP1

Plays a role in the microtubule-dependent

transport of the GABA-B receptor

Lebercilin Q86VQ0 LCA5 Transporter activity

MaFF-interacting protein Q8WZ33 MAFIP Inhibits cell growth and colony-forming

efficiency

Matrix metalloproteinase-9 P14780 MMP9 Catalytic activity

Microtubule-associated protein 2 P11137 MAP2 Stabilizes the microtubules against

depolymerization

Mucolipin-3 Q8TDD5 MCOLN3 Pays a role in the regulation of membrane

trafficking events

Multidrug resistance protein 1 P08183 ABCB1 Responsible for decreased drug accumulation

in multidrug-resistant cells

Nebulette O76041 NEBL Plays an important role in the assembly of the

Z-disk

Nucleoporin NUP188 homolog Q5SRE5 NUP188 May function as a component of the nuclear

pore complex (NPC)

PC membrane recruitment protein

2 Q8N7J2 AMER2

Negative regulator of the canonical Wnt

signaling pathway involved in

neuroectodermal patterning

Protein ELYS Q8WYP5 AHCTF1

Required for the assembly of a functional

nuclear pore complex (NPC) on the surface of

chromosomes

Protein phosphatase 1 regulatory

subunit 26 Q5T8A7 PPP1R26

Inhibits phosphatase activity of protein

phosphatase 1 (PP1) complexes. May

positively regulate cell proliferation.

Protein ZGRF1 Q86YA3 ZGRF1 Zinc ion binding that inhibits factors Xa and

XIa of the coagulation cascade

Putative transmembrane protein Q96M19 LINC00477 Product of a dubious CDS prediction. May be

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encoded by LINC00477 a non-coding RNA

Serine protease 33 PRSS33 PRSS33 Catalytic activity

Serine-protein kinase ATM Q13315 ATM

Activates checkpoint signaling upon double

strand breaks (DSBs), apoptosis and

genotoxic stresses

Serum amyloid A-1 protein P0DJI8 SAA1 Inflammatory response

Sodium-dependent noradrenaline

transporter P23975 SLC6A2 Transporter activity

Suppressor of tumorigenicity 7

protein Q9NRC1 ST7 Acts as a tumor suppressor

Vasorin Q6EMK4 VASN May act as an inhibitor of TGF-beta signaling

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Table 3. Selection of identified single-detected corona proteins bound to the 9.73 ± 1.70

nm AgNPs after 30 min incubation and subsequent washing. The accession number,

gene name, species (Human), molecular weight (kDa) and protein function were

reported.

Protein Name Entry Name Gene Function

Amyloid-beta A4 precursor

protein-binding family A member 2 Q99767 APBA2

Function in synaptic vesicle exocytosis

by binding to STXBP1, an essential

component of the synaptic vesicle

exocytotic machinery

Ankyrin repeat domain-containing

protein 20B Q5CZ79 ANKRD20A8P -

Ankyrin repeat and sterile alpha

motif domain-containing protein

1B

Q7Z6G8 ANKS1B Plays a role as a modulator of APP

processing

Bone morphogenetic protein 10 O95393 BMP10

Inhibits endothelial cell migration and

growth. May reduce cell migration and

cell matrix adhesion in breast cancer cell

lines.

Bromodomain adjacent to zinc

finger domain protein 2A Q9UIF9 BAZ2A

Essential component of the NoRC

(nucleolar remodeling complex) complex

that mediates silencing of a fraction of

rDNA

Caspase recruitment domain-

containing protein 11 Q9BXL7 CARD11

Involved in the costimulatory signal

essential for T-cell receptor (TCR)-

mediated T-cell activation. Also activates

the TORC1 signaling pathway

Complement factor H-related

protein 3 Q02985 CFHR3 Involved in complement regulation

Complement factor H-related

protein 4 Q92496 CFHR4 Plays a role in lipid metabolism

Cyclin-dependent kinase 2 P24941 CDK2 Catalytic activity

Dual specificity protein

phosphatase 9 Q99956 DUSP9 Inactivates MAP kinases

Dystonin Q03001 DST

Acts as an integrator of intermediate

filaments, actin and microtubule

cytoskeleton networks

EGF-containing fibulin-like

extracellular matrix protein 1 Q12805 EFEMP1

Binds EGFR, the EGF receptor, inducing

EGFR autophosphorylation and the

activation of downstream signaling

pathways. May play a role in cell

adhesion and migration

GREB1-like protein Q9C091 GREB1L Plays a major role in early metanephros

and genital development

Hemoglobin subunit gamma-2 P69892 HBG2

Gamma chains make up the fetal

hemoglobin F, in combination with alpha

chains

Histone-lysine N-methyltransferase

2A Q03164 KMT2A Catalytic activity

Immunoglobulin heavy variable 3-

23 P01764 IGHV3-23 Immune response

Importin-4 Q8TEX9 IPO4 Functions in nuclear protein import as

nuclear transport receptor

Keratin, type I cytoskeletal 9 P35527 KRT9 Structural

LINE-1 type transposase domain-

containing protein 1 Q5T7N2 L1TD1 Single-stranded RNA binding

Lysosomal-trafficking regulator Q99698 LYST

Required for sorting endosomal resident

proteins into late multivesicular

endosomes

Matrix metalloproteinase-9 P14780 MMP9 Catalytic activity

Microtubule-associated protein 1A P78559 MAP1A Structural protein involved in the

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118

filamentous cross-bridging between

microtubules and other skeletal elements

MORC family CW-type zinc finger

protein 1 Q86VD1 MORC1 Zinc ion binding.

Nuclear distribution protein nudE-

like 1 Q9GZM8 NDEL1

Required for organization of the cellular

microtubule array and microtubule

anchoring at the centrosome

Polymeric immunoglobulin

receptor P01833 PIGR

This receptor binds polymeric IgA and

IgM at the basolateral surface of

epithelial cells

Polypeptide N-

acetylgalactosaminyltransferase 13 Q8IUC8 GALNT13 Catalytic activity

Probable guanine nucleotide

exchange factor MCF2L2 Q86YR7 MCF2L2

Functions as a guanine nucleotide

exchange factor

Pro-epidermal growth factor P01133 EGF

Stimulates the growth of various

epidermal and epithelial tissues in vivo

and in vitro

Properdin P27918 CFP A positive regulator of the alternate

pathway of complement

Protein MMS22-like Q6ZRQ5 MMS22L Maintain genome integrity during DNA

replication

Protein phosphatase PTC7

homolog Q8NI37 PPTC7 Catalytic activity

Protein Shroom3 Q8TF72 SHROOM3

Controls cell shape changes in the

neuroepithelium during neural tube

closure

Protein ZGRF1 Q86YA3 ZGRF1 Zinc ion binding that inhibits factors Xa

and XIa of the coagulation cascade

Putative solute carrier organic

anion transporter family member

1B7

G3V0H7 SLCO1B7 May encode a non-functional truncated

protein

Ras-interacting protein 1 Q5U651 RASIP1

Acts as a critical and vascular-specific

regulator of GTPase signaling, cell

architecture, and adhesion

Ribosomal protein S6 kinase alpha-

1 Q15418 RPS6KA1 Catalytic activity

Short coiled-coil protein Q9UIL1 SCOC Positive regulator of amino acid

starvation-induced autophagy

Structural maintenance of

chromosomes protein 6 Q96SB8 SMC6 Structural

Synaptotagmin-5 O00445 SYT5 May be involved in Ca2+-dependent

exocytosis of secretory vesicles

Tudor domain-containing protein 1 Q9BXT4 TDRD1

Acts via the piRNA metabolic process,

which mediates the repression of

transposable elements during meiosis

Tyrosine--tRNA ligase,

cytoplasmic P54577 YARS Catalytic activity

Vigilin Q00341 HDLBP Protect cells from over-accumulation of

cholesterol

von Willebrand factor P04275 VWF Plays a major role in blood coagulation

Wee1-like protein kinase P30291 WEE1 Acts as a negative regulator of entry into

mitosis

Zinc finger protein 114 Q8NC26 ZNF114 May be involved in transcriptional

regulation.

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Table 4. Selection of identified single-detected corona proteins bound to the 9.30 ± 0.67

nm MNPs after 30 min incubation and subsequent washing. The accession number,

gene name, species (Human), molecular weight (kDa) and protein function were

reported.

Protein Name Entry Name Gene Function ATP-binding cassette sub-family B

member 5 Q2M3G0 ABCB5 Transporter activity

Bile salt-activated lipase P19835 CEL Catalytic activity

Cathelicidin antimicrobial peptide P49913 CAMP Antibacterial activity

Ceramide synthase 4 Q9HA82 CERS4

May be either a bona fide

(dihydro)ceramide synthase or a

modulator of its activity

C-reactive protein P02741 CRP Displays several functions

associated with host defense

C-type lectin domain family 4

member F Q8N1N0 CLEC4F

Receptor with an affinity for

galactose and fucose. Could be

involved in endocytosis

Cystatin-F O76096 CST7

May play a role in immune

regulation through inhibition of a

unique target in the hematopoietic

system

DDB1- and CUL4-associated factor

15 Q66K64 DCAF15

May be involved in ubiquitination

and degradation through a DBB1-

CUL4 E3 protein-ubiquitin ligase

DNA topoisomerase 2-binding

protein 1 Q92547 TOPBP1

Binds double-stranded DNA breaks

and nicks as well as single-stranded

DNA

Dynein heavy chain 10, axonemal Q8IVF4 DNAH10 Presents ATPase activity

Ellis-van Creveld syndrome protein P57679 EVC Involved in endochondral growth

and skeletal development

Fanconi anemia group B protein Q8NB91 FANCB DNA repair protein required for

FANCD2 ubiquitination

F-box only protein 42 Q6P3S6 FBXO42

Specifically recognizes p53/TP53,

promoting its ubiquitination and

degradation

Galectin-3-binding protein Q08380 LGALS3BP Stimulate host defense against

viruses and tumor cells

HEAT repeat-containing protein 4 Q86WZ0 HEATR4 -

Immunoglobulin heavy variable 6-1 A0A0B4J1U7 IGHV6-1 Imuune response

Immunoglobulin kappa variable 2-

24 A0A0C4DH68 IGKV2-24 Immune response

Immunoglobulin lambda variable 3-

27 P01718 IGLV3-27 Immune response

Kallistatin P29622 SERPINA4 Enzyme regulator inhibitor

Keratin, type I cytoskeletal 14 P02533 KRT14 Structural

Keratin, type II cuticular Hb1 Q14533 KRT81 Structural

Lebercilin Q86VQ0 LCA5 Transporter activity

LINE-1 type transposase domain-

containing protein 1 Q5T7N2 L1TD1 Single-stranded RNA binding

Lipopolysaccharide-binding protein P18428 LBP Immune response

LRP chaperone MESD Q14696 MESD

Assisting the folding of beta-

propeller/EGF modules within the

family of low-density lipoprotein

receptors (LDLRs)

MANSC domain-containing protein

1 Q9H8J5 MANSC1 -

Meckelin Q5HYA8 TMEM67 Required for ciliary structure and

function

Mitochondrial 2-

oxoglutarate/malate carrier protein Q02978 SLC25A11 Catalytic and transporter activities

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NAD(P)H dehydrogenase [quinone]

1 P15559 NQO1 Catalytic activity

N-acetyllactosaminide beta-1,3-N-

acetylglucosaminyltransferase 2 Q9NY97 B3GNT2 Catalytic activity

Nck-associated protein 1 Q9Y2A7 NCKAP1

As component of the WAVE1

complex, required for BDNF-

NTRK2 endocytic trafficking and

signaling from early endosomes

Nesprin-1 Q8NF91 SYNE1

Forms a linking network between

organelles and the actin

cytoskeleton to maintain the

subcellular spatial organization

Nuclear receptor coactivator 6 Q14686 NCOA6 Involved in the coactivation of the

NF-kappa-B pathway

PAN2-PAN3 deadenylation

complex subunit PAN3 Q58A45 PAN3

Regulatory subunit of the poly(A)-

nuclease (PAN) deadenylation

complex

Pentatricopeptide repeat-containing

protein 1, mitochondrial O75127 PTCD1

Mitochondrial protein implicated in

negative regulation of leucine

tRNA levels, as well as negative

regulation of mitochondria-encoded

proteins and COX activity

Phosphatidylcholine translocator

ABCB4 P21439 ABCB4

Acts as a positive regulator of

biliary lipid secretion

1-phosphatidylinositol 4,5-

bisphosphate phosphodiesterase

gamma-2

P16885 PLCG2 Catalytic activity

Phospholipase D1 Q13393 PLD1 Catalytic activity

Polypeptide N-

acetylgalactosaminyltransferase 3 Q14435 GALNT3 Catalytic activity

Protein salvador homolog 1 Q9H4B6 SAV1

Regulator of STK3/MST2 and

STK4/MST1 in the Hippo signaling

pathway which plays a pivotal role

in organ size control and tumor

suppression by restricting

proliferation and promoting

apoptosis

Protein S100-A7 P31151 S100A7 -

Protein S100-A8 P05109 S100A8

Plays a prominent role in the

regulation of inflammatory

processes and immune response

Protein S100-A9 P06702 S100A9

Plays a prominent role in the

regulation of inflammatory

processes and immune response

Protein Z-dependent protease

inhibitor Q9UK55

SERPINA1

0 Enzyme regulator activity

Protein ZGRF1 Q86YA3 ZGRF1

Zinc ion binding that inhibits

factors Xa and XIa of the

coagulation cascade

Protocadherin-12 Q9NPG4 PCDH12

Cellular adhesion molecule that

plays an important role as a

regulator of cell migration,

probably via increasing cell-cell

adhesion

Roundabout homolog 4 Q8WZ75 ROBO4

Mediates the inhibition of primary

endothelial cell migration by Slit

proteins

Serum amyloid A-2 protein P0DJI9 SAA2 Inflammatory response

Short coiled-coil protein Q9UIL1 SCOC Positive regulator of amino acid

starvation-induced autophagy

Structural maintenance of

chromosomes protein 6 Q96SB8 SMC6 Structural

Supervillin O95425 SVIL Structural

Syncoilin Q9H7C4 SYNC

Plays a supportive role in the

efficient coupling of mechanical

stress between the myofibril and

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fiber exterior

TBC1 domain family member 9 Q6ZT07 TBC1D9 Acts as a GTPase-activating protein

for Rab family protein(s)

Transportin-1 Q92973 TNPO1 Transporter activity

Tudor domain-containing protein 5 Q8NAT2 TDRD5

Acts via the piRNA metabolic

process, which mediates the

repression of transposable elements

during meiosis

Ubiquitin-like modifier-activating

enzyme 1 P22314 UBA1 Catalytic activity

E3 ubiquitin-protein ligase MSL2 Q9HCI7 MSL2 Component of histone

acetyltransferase complex

Villin-like protein O15195 VILL Tumor suppressor

Vitamin K-dependent protein C P04070 PROC Catalytic activity

Zinc finger RNA-binding protein Q96KR1 ZFR Involved in the nucleocytoplasmic

shuttling of STAU2

Zinc finger protein 99 A8MXY4 ZNF99 May be involved in transcriptional

regulation

3.3. Proteomic alterations in triple negative breast cancer serum revealed by SWATH-

MS analysis

Label-free SWATH experiments were carried out on a Triple TOF 6600 mass

spectrometer (SCIEX). After a comparison between the different groups of samples

(controls and TNBC patients), it was observed a variation in the number of statistically

significant proteins in the protein corona formed around the three different NPs: 10.02

± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs)

and 9.30 ± 0.67 nm magnetic nanoparticles (MNPs) (see Table 5).

After the analysis of the protein corona formed around AuNPs (10.02 ± 0.91

nm), a total of 177 non-redundant proteins were quantified, out of which 48 were found

to be differentially regulated. 14 proteins had elevated expression, while 34 proteins

showed down-regulation (see Table 6).

In the case of the protein corona formed around AgNPs (9.73 ± 1.70 nm), a total

of 176 non-redundant proteins were quantified, out of which 140 were found to be

differentially regulated. 64 proteins had elevated expression, while 76 proteins showed

down-regulation (see Table 7).

In the protein corona formed around the MNPs (9.30 ± 0.67 nm), a total of 176

non-redundant proteins were quantified, out of which 57 were found to be differentially

regulated. 45 proteins had elevated expression, while 12 proteins showed down-

regulation (see Table 8).

A SWATH library was developed. To this aim, the ProteinPilot software (AB

Sciex; version 4.0) was used where the proteins were identified with minimum of 2

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peptides along with a confidence score above 99% and FDR below 1% as threshold

criteria. Therefore, a spectral library containing 180 proteins found in the nanoparticle

surfaces after serum incubation was employed. In this study, it was shown that this

strategy provided a more comprehensive and reproducible coverage of the proteins that

can join the different nanoparticle surfaces.

We fixed the cut off to considerate a deregulated protein at ≥1.5 for up-

regulation and ≤0.67 for down-regulation. Only proteins with a p-value ≤0.05 were

selected.

Table 5. Number of proteins identified by LC-MS/MS by SWATH analysis.

SWATH-MS

Library 180

SAMPLES

TNBC patients vs. controls (AuNPs) 48 (P-value ≤ 0.05)

TNBC patients vs. controls (AgNPs) 140 (P-value ≤ 0.05)

TNBC patients vs. controls (MNPs) 57 (P-value ≤ 0.05)

Table 6. Significant proteins (p-value < 0.05) in comparisons between triple negative

breast cancer and controls after the analysis of the protein corona of AuNPs (10.02 ±

0.91 nm).

Protein UniProt ID p-value Fold Change

TN

BC

PA

TIE

NT

S v

s. C

ON

TR

OL

S

Complement component C6 CO6 0.021032066 3.249007224 ↑ TNBC

Vitamin D-binding protein VTDB 0.000044522 1.663281358 ↑ TNBC

Complement component C9 CO9 0.000172961 1.658309079 ↑ TNBC

Complement C4-A CO4A 0.003482887 1.525866959 ↑ TNBC

Complement C3 CO3 0.000115208 1.451520626 ↑ TNBC

Plasminogen PLMN 0.005392898 1.329388215 ↑ TNBC

Vitronectin VTNC 0.004766057 1.282037959 ↑ TNBC

Apolipoprotein L1 APOL1 0.022535013 1.251913563 ↑ TNBC

Afamin AFAM 0.020811026 1.248070740 ↑ TNBC

Complement factor H CFAH 0.020861177 1.204471284 ↑ TNBC

Complement C5 CO5 0.016073885 1.185058578 ↑ TNBC

Serum albumin ALBU 0.004803944 1.172992995 ↑ TNBC

Kininogen-1 KNG1 0.024754757 1.167133709 ↑ TNBC

Galectin-3-binding protein LG3BP 0.030403083 1.088223023 ↑ TNBC

Glutathione peroxidase 3 GPX3 0.01510168 7.16326536 ↑ CONTROL

Immunoglobulin heavy variable 5-51 HV551 0.02782821 4.90904368 ↑ CONTROL

Immunoglobulin heavy constant mu IGHM 0.04111454 4.42849357 ↑ CONTROL

Apolipoprotein C-I APOC1 0.00460538 4.09989381 ↑ CONTROL

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Immunoglobulin lambda variable 3-9 LV39 0.0394842 3.35634858 ↑ CONTROL

Apolipoprotein D APOD 0.04050732 3.22034685 ↑ CONTROL

Immunoglobulin heavy constant alpha 1 IGHA1 0.00960489 3.19128417 ↑ CONTROL

Immunoglobulin kappa variable 2-29 KV229 0.00334596 2.48673552 ↑ CONTROL

Immunoglobulin heavy constant alpha 2 IGHA2 0.02785381 2.43044159 ↑ CONTROL

Inter-alpha-trypsin inhibitor heavy chain H4 ITIH4 0.02521756 2.41781135 ↑ CONTROL

Immunoglobulin lambda variable 4-69 LV469 0.00833282 2.37374475 ↑ CONTROL

Apolipoprotein(a) OS=Homo sapiens APOA 0.03681681 2.12362227 ↑ CONTROL

Pigment epithelium-derived factor PEDF 0.00858685 1.94114234 ↑ CONTROL

Alpha-2-macroglobulin A2MG 0.00477503 1.9190963 ↑ CONTROL

Hemoglobin subunit beta HBB 0.00500101 1.90068671 ↑ CONTROL

CD5 antigen-like CD5L 0.0397185 1.7759815 ↑ CONTROL

Serum amyloid A-4 protein SAA4 0.04115058 1.77144695 ↑ CONTROL

Tetranectin TETN 0.01611908 1.7097494 ↑ CONTROL

Apolipoprotein A-II APOA2 0.01467561 1.67488772 ↑ CONTROL

Inter-alpha-trypsin inhibitor heavy chain H2 ITIH2 0.00096553 1.61898708 ↑ CONTROL

Carboxypeptidase B2 CBPB2 0.00728968 1.46619249 ↑ CONTROL

Transthyretin TTHY 0.04987991 1.45277849 ↑ CONTROL

Complement C1r subcomponent-like protein C1RL 0.03887632 1.43784936 ↑ CONTROL

Inter-alpha-trypsin inhibitor heavy chain H1 ITIH1 0.00031251 1.41502216 ↑ CONTROL

Serum paraoxonase/arylesterase 1 PON1 0.00142352 1.40629048 ↑ CONTROL

Apolipoprotein F APOF 0.03956903 1.39106396 ↑ CONTROL

Apolipoprotein M APOM 0.00064927 1.36179665 ↑ CONTROL

Apolipoprotein A-I APOA1 0.02341567 1.34721575 ↑ CONTROL

Serotransferrin TRFE 0.0020036 1.31516423 ↑ CONTROL

Selenoprotein P SEPP1 0.00426885 1.30905254 ↑ CONTROL

Mannan-binding lectin serine protease 1 MASP1 0.03643268 1.29206286 ↑ CONTROL

Carboxypeptidase N subunit 2 CPN2 0.03700623 1.28514087 ↑ CONTROL

Protein AMBP AMBP 0.0084045 1.22749917 ↑ CONTROL

Plasma protease C1 inhibitor IC1 0.04106727 1.2095684 ↑ CONTROL

Table 7. Significant proteins (p-value < 0.05) in comparisons between triple negative

breast cancer and controls after the analysis of the protein corona of AgNPs (9.73 ± 1.70

nm).

Protein UniProt

ID p-value Fold Change

TN

BC

PA

TIE

NT

S v

s.

CO

NT

RO

LS

C-reactive protein CRP 0.00550613 4.84567669 ↑ TNBC

Histidine-rich glycoprotein HRG 3.22E-09 3.93436379 ↑ TNBC

Complement component C9 CO9 6.28E-08 3.56825414 ↑ TNBC

Complement C3 CO3 1.95E-08 3.03698879 ↑ TNBC

Complement factor B CFAB 4.13E-09 2.83840576 ↑ TNBC

Immunoglobulin kappa variable 2-24 KV224 0.00067261 2.77717871 ↑ TNBC

Ficolin-3 FCN3 7.79E-08 2.73769215 ↑ TNBC

Immunoglobulin kappa variable 1-13 KV113 0.00613408 2.71041552 ↑ TNBC

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IgGFc-binding protein FCGBP 0.00010163 2.70272053 ↑ TNBC

Phosphatidylinositol-glycan-specific

phospholipase D PE=1 SV=3 PHLD 0.00305474 2.66489178 ↑ TNBC

Coagulation factor XIII B chain F13B 2.66E-07 2.60635841 ↑ TNBC

Fetuin-B FETUB 1.50E-06 2.55082102 ↑ TNBC

Apolipoprotein L1 APOL1 2.53E-10 2.52932257 ↑ TNBC

Serum albumin ALBU 4.46E-09 2.51568102 ↑ TNBC

Insulin-like growth factor-binding protein

complex acid labile subunit ALS 4.46E-09 2.49955167

↑ TNBC

Inter-alpha-trypsin inhibitor heavy chain H3 ITIH3 7.30E-07 2.38250842 ↑ TNBC

Complement factor I CFAI 1.02E-07 2.3647495 ↑ TNBC

Complement factor H CFAH 4.83E-09 2.35326116 ↑ TNBC

Vitronectin VTNC

1.40E-09 2.31946741 ↑ TNBC

C4b-binding protein alpha chain C4BPA

0.00011973 2.28632913 ↑ TNBC

Hyaluronan-binding protein 2 HABP2 3.29E-07 2.28034134 ↑ TNBC

Plasminogen PLMN 7.79E-05 2.23378207 ↑ TNBC

Keratin type II cytoskeletal 1 K2C1 0.00022394 2.20416961 ↑ TNBC

Kininogen-1 KNG1 1.18E-08 2.19276786 ↑ TNBC

Fibronectin FINC 3.36E-08 2.08858855 ↑ TNBC

Complement C5 CO5 2.42E-08 2.01278029 ↑ TNBC

Complement C1q subcomponent subunit C HUMAN 4.55E-05 2.01080564 ↑ TNBC

Apolipoprotein A-IV APOA4 4.61E-06 2.00364799 ↑ TNBC

Keratin type I cytoskeletal 10 K1C10 0.00259975 1.97893282 ↑ TNBC

Tetranectin TETN 4.94E-05 1.95250374 ↑ TNBC

Clusterin CLUS 1.82E-06 1.9173471 ↑ TNBC

Complement component C8 gamma chain CO8G 6.29E-06 1.89453705 ↑ TNBC

Serotransferrin TRFE 4.33E-05 1.8939175 ↑ TNBC

Complement C4-B CO4B 0.03028632 1.89103397 ↑ TNBC

Complement C1q subcomponent subunit B C1QB 0.00018878 1.88786101 ↑ TNBC

Plasma kallikrein KLKB1 2.85E-06 1.88182482 ↑ TNBC

Galectin-3-binding protein LG3BP 1.76E-09 1.85166575 ↑ TNBC

Alpha-2-HS-glycoprotein FETUA 1.70E-06 1.82851516 ↑ TNBC

Complement C1q subcomponent subunit A C1QA 0.00028866 1.80691958 ↑ TNBC

Prothrombin THRB 0.0030656 1.68707742 ↑ TNBC

Retinol-binding protein 4 RET4 5.98E-06 1.65111784 ↑ TNBC

Carboxypeptidase N catalytic chain CBPN 0.00562324 1.62654965 ↑ TNBC

Complement component C8 beta chain CO8B 0.00298689 1.58131021 ↑ TNBC

Sex hormone-binding globulin SHBG 0.04298875 1.5528487 ↑ TNBC

Serum amyloid P-component SAMP 0.00436056 1.55023657 ↑ TNBC

Zinc-alpha-2-glycoprotein ZA2G 0.00133379 1.54906716 ↑ TNBC

Beta-2-glycoprotein 1 APOH 0.00192644 1.53648273 ↑ TNBC

Vitamin K-dependent protein S PROS 1.47E-06 1.52146296 ↑ TNBC

Complement C4-A CO4A 0.00378789 1.50875703 ↑ TNBC

Mannan-binding lectin serine protease 1 MASP1 0.00057947 1.49286664 ↑ TNBC

Complement C2 CO2 1.04E-05 1.48927332 ↑ TNBC

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Coagulation factor X FA10 0.00022691 1.47652463 ↑ TNBC

Complement C1r subcomponent C1R 0.03355753 1.46634915 ↑ TNBC

Alpha-1-acid glycoprotein 2 A1AG2 0.04347799 1.46608046 ↑ TNBC

Antithrombin-III ANT3 0.00109057 1.45858581 ↑ TNBC

Afamin AFAM 0.02956547 1.42380083 ↑ TNBC

Vitamin D-binding protein VTDB 0.00689951 1.40641874 ↑ TNBC

Hemopexin HEMO 0.00014568 1.38633651 ↑ TNBC

Carboxypeptidase N subunit 2 CPN2 0.03697862 1.36334001 ↑ TNBC

Protein AMBP AMBP 0.0013811 1.32566307 ↑ TNBC

Apolipoprotein E APOE 0.00068645 1.28506343 ↑ TNBC

Attractin ATRN 0.0072892 1.27421822 ↑ TNBC

N-acetylmuramoyl-L-alanine amidase PGRP2 0.00138778 1.23991258 ↑ TNBC

Apolipoprotein M APOM 0.01360078 1.23954254 ↑ TNBC

Immunoglobulin heavy variable 3-15 HV315 4.18E-06 75.3967905 ↑ CONTROL

Immunoglobulin heavy constant gamma 1 IGHG1 1.34E-07 42.3987588 ↑ CONTROL

Alpha-1-antichymotrypsin AACT 6.14E-10 40.3625379 ↑ CONTROL

Immunoglobulin heavy variable 3-73 HV373 6.06E-07 37.7464233 ↑ CONTROL

Immunoglobulin heavy constant gamma 3 IGHG3 3.68E-06 36.0478431 ↑ CONTROL

Ceruloplasmin CERU 2.42E-09 33.0108942 ↑ CONTROL

Immunoglobulin heavy constant gamma 2 IGHG2 1.72E-06 32.3391318 ↑ CONTROL

Alpha-1-antitrypsin A1AT 1.14E-10 28.6542019 ↑ CONTROL

Immunoglobulin heavy constant mu IGHM 7.89E-05 26.3705332 ↑ CONTROL

Corticosteroid-binding globulin CBG 1.19E-09 25.9868345 ↑ CONTROL

Immunoglobulin heavy constant gamma 4 IGHG4 0.00011456 21.9488461 ↑ CONTROL

Cholinesterase CHLE 1.48E-08 21.7678062 ↑ CONTROL

Immunoglobulin kappa variable 4-1 KV401 2.21E-07 20.676772 ↑ CONTROL

Immunoglobulin heavy variable 4-59 HV459 4.18E-08 19.791237 ↑ CONTROL

Immunoglobulin heavy variable 3-30-5 HV335 1.61E-07 18.8319962 ↑ CONTROL

Immunoglobulin lambda variable 1-47 LV147 6.19E-05 18.715722 ↑ CONTROL

Immunoglobulin lambda variable 8-61 LV861 2.74E-08 18.5700233 ↑ CONTROL

Immunoglobulin kappa variable 6-21 KV621 0.0062062 16.6110906 ↑ CONTROL

Immunoglobulin heavy variable 3-49 HV349 2.43E-08 16.3703203 ↑ CONTROL

Immunoglobulin kappa constant IGKC 2.49E-06 15.6449675 ↑ CONTROL

Immunoglobulin lambda constant 3 IGLC3 1.61E-05 15.2226153 ↑ CONTROL

MMS19 nucleotide excision repair protein

homolog

MMS19 0.01743218 15.0005868 ↑ CONTROL

Immunoglobulin kappa variable 3-20 KV320 5.98E-09 14.9565997 ↑ CONTROL

Immunoglobulin kappa variable 3-15 KV315 4.41E-09 13.7845573 ↑ CONTROL

Immunoglobulin lambda-like polypeptide 1 IGLL1 9.67E-08 11.9419924 ↑ CONTROL

Immunoglobulin lambda variable 3-9 LV39 4.22E-07 11.8083139 ↑ CONTROL

Immunoglobulin heavy variable 5-51 HV551 4.24E-07 11.4284904 ↑ CONTROL

Immunoglobulin kappa variable 3-11 KV311 5.08E-09 11.3796551 ↑ CONTROL

Immunoglobulin kappa variable 1-33 KV133 7.64E-07 11.2884931 ↑ CONTROL

Immunoglobulin heavy constant alpha 2 IGHA2 6.08E-05 10.9418774 ↑ CONTROL

Immunoglobulin lambda-like polypeptide 5 V=2 IGLL5 9.57E-05 10.7682004 ↑ CONTROL

Angiotensinogen ANGT 3.42E-11 8.97430276 ↑ CONTROL

Pigment epithelium-derived factor PEDF 8.81E-10 8.87937962 ↑ CONTROL

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126

Immunoglobulin lambda variable 7-43 LV743 3.62E-06 8.19552518 ↑ CONTROL

Hemoglobin subunit alpha HBA 0.0003338 7.79643124 ↑ CONTROL

Immunoglobulin heavy constant alpha 1 IGHA1 1.48E-07 7.66824876 ↑ CONTROL

Immunoglobulin lambda variable 3-25 LV325 1.49E-06 7.44370609 ↑ CONTROL

Immunoglobulin heavy variable 4-28 HV428 1.38E-06 7.26533223 ↑ CONTROL

Immunoglobulin kappa variable 1D-12 KVD12 1.17E-09 7.18559345 ↑ CONTROL

Heparin cofactor 2 HEP2 2.44E-07 7.13401112 ↑ CONTROL

Inter-alpha-trypsin inhibitor heavy chain H4 ITIH4 3.63E-08 6.70198308 ↑ CONTROL

Immunoglobulin lambda variable 9-49 LV949 0.00017391 6.43567288 ↑ CONTROL

Alpha-1B-glycoprotein A1BG 1.70E-09 6.00033312 ↑ CONTROL

Apolipoprotein A-I APOA1 1.65E-09 5.16689397 ↑ CONTROL

Immunoglobulin lambda variable 3-19 LV319 1.92E-08 5.05592895 ↑ CONTROL

Immunoglobulin kappa variable 2-29 KV229 2.59E-07 5.02231162 ↑ CONTROL

Immunoglobulin heavy variable 6-1 HV601 2.19E-05 4.85408469 ↑ CONTROL

Immunoglobulin heavy variable 1-46 HV146 9.86E-06 4.84652628 ↑ CONTROL

Immunoglobulin heavy variable 3-23 HV323 1.25E-05 4.75302522 ↑ CONTROL

Cholesteryl ester transfer protein CETP 1.22E-05 4.64058921 ↑ CONTROL

Apolipoprotein A-II APOA2 2.16E-08 4.62885891 ↑ CONTROL

Thyroxine-binding globulin THBG 1.80E-08 4.45994071 ↑ CONTROL

Protein Z-dependent protease inhibitor ZPI 4.44E-05 4.43951224 ↑ CONTROL

Serum amyloid A-4 protein SAA4 1.38E-07 4.39608833 ↑ CONTROL

Immunoglobulin heavy variable 1-69 HV169 0.00058138 4.33088897 ↑ CONTROL

Immunoglobulin kappa variable 1-9 KV109 3.99E-06 4.20727924 ↑ CONTROL

Gelsolin GELS 7.03E-09 4.09546999 ↑ CONTROL

CD44 antigen CD44 0.00233936 4.0837648 ↑ CONTROL

Apolipoprotein C-I APOC1 0.00038806 3.66742486 ↑ CONTROL

Alpha-2-macroglobulin A2MG 3.58E-09 3.65331919 ↑ CONTROL

Apolipoprotein D APOD 3.95E-08 3.53083248 ↑ CONTROL

Kallistatin KAIN 4.78E-10 3.48621242 ↑ CONTROL

Immunoglobulin kappa variable 1-5 KV105 8.42E-05 3.22054945 ↑ CONTROL

Leucine-rich alpha-2-glycoprotein A2GL 1.29E-08 3.00855446 ↑ CONTROL

Hemoglobin subunit beta HBB 8.64E-05 2.890312 ↑ CONTROL

Coiled-coil domain-containing protein 8 CCDC8 6.16E-07 2.88193259 ↑ CONTROL

Immunoglobulin kappa variable 2D-28 KVD28 0.00104876 2.6282284 ↑ CONTROL

Immunoglobulin kappa variable 6D-21 KVD21 0.01695916 2.61227937 ↑ CONTROL

Immunoglobulin lambda variable 4-69 LV469 8.14E-05 2.35130225 ↑ CONTROL

Biotinidase BTD 0.00622085 2.19095666 ↑ CONTROL

Plasma serine protease inhibitor IPSP 3.04E-05 2.1835559 ↑ CONTROL

Carboxypeptidase B2 CBPB2 8.55E-05 1.8115321 ↑ CONTROL

Immunoglobulin heavy variable 2-26 HV226 0.00977698 1.69947422 ↑ CONTROL

Apolipoprotein B-100 APOB 2.35E-05 1.66025805 ↑ CONTROL

Alpha-2-antiplasmin A2AP 0.00014843 1.60147275 ↑ CONTROL

Apolipoprotein C-III APOC3 0.02144075 1.39259865 ↑ CONTROL

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Table 8. Significant proteins (p-value < 0.05) in comparisons between triple negative

breast cancer and controls after the analysis of the protein corona of MNPs (9.30 ± 0.67

nm).

Protein UniProt ID p-value Fold Change

TN

BC

PA

TIE

NT

S v

s. C

ON

TR

OL

C-reactive protein CRP 0.00169149 3.40849571 ↑ TNBC

Lipopolysaccharide-binding protein LBP 0.00837847 2.93851762 ↑ TNBC

Immunoglobulin kappa variable 2-24 KV224 0.00583062 2.27733934 ↑ TNBC

Serum amyloid P-component SAMP 0.00010388 2.20872021 ↑ TNBC

Keratin. type II cytoskeletal 2 epidermal K22E 0.01682722 2.10615702 ↑ TNBC

Complement C4-B CO4B 0.04180946 1.9800469 ↑ TNBC

Complement component C9 CO9 5.75E-06 1.90848624 ↑ TNBC

IgGFc-binding protein FCGBP 0.01481938 1.86546782 ↑ TNBC

Protein Z-dependent protease inhibitor ZPI 0.00252121 1.78800298 ↑ TNBC

Complement C1q subcomponent subunit B C1QB 0.00199072 1.78457812 ↑ TNBC

Phosphatidylinositol-glycan-specific

phospholipase D

PHLD 0.00435099 1.73844701 ↑ TNBC

Prothrombin THRB 0.00750791 1.72802617 ↑ TNBC

Complement C4-A CO4A 0.00510952 1.67059345 ↑ TNBC

Alpha-1-acid glycoprotein 1 A1AG1 0.02552784 1.64048407 ↑ TNBC

Apolipoprotein C-III APOC3 0.03298589 1.61371634 ↑ TNBC

Keratin. type II cytoskeletal 1 K2C1 0.01154011 1.60905471 ↑ TNBC

Sex hormone-binding globulin SHBG 0.02523265 1.59168855 ↑ TNBC

Inter-alpha-trypsin inhibitor heavy chain H3 ITIH3 0.00066933 1.55061894 ↑ TNBC

Complement C3 CO3 6.41E-06 1.53090904 ↑ TNBC

Keratin. type I cytoskeletal 10 K1C10 0.0384444 1.52567319 ↑ TNBC

C4b-binding protein alpha chain C4BPA 0.00027605 1.51193215 ↑ TNBC

Complement factor H CFAH 2.79E-05 1.46546396 ↑ TNBC

Plasma serine protease inhibitor IPSP 0.00973255 1.4623027 ↑ TNBC

Histidine-rich glycoprotein HRG 0.01164428 1.4616738 ↑ TNBC

Complement factor B CFAB 0.00061903 1.45925143 ↑ TNBC

Pregnancy zone protein PZP 0.00155229 1.44791482 ↑ TNBC

Complement C1q subcomponent subunit A C1QA 0.02763702 1.4062295 ↑ TNBC

Coagulation factor V FA5 0.01312931 1.40131004 ↑ TNBC

Complement factor I CFAI 7.52E-05 1.40080904 ↑ TNBC

Complement C2 CO2 0.00061386 1.33762187 ↑ TNBC

Hemopexin HEMO 3.66E-05 1.29733733 ↑ TNBC

Coagulation factor XIII B chain F13B 0.01256843 1.28491873 ↑ TNBC

Complement C1q subcomponent subunit C C1QC 0.03780247 1.26491165 ↑ TNBC

Kininogen-1 KNG1 0.00429848 1.26291248 ↑ TNBC

Beta-2-glycoprotein 1 APOH 0.04746608 1.24963502 ↑ TNBC

Apolipoprotein L1 APOL1 0.00237944 1.23613552 ↑ TNBC

Antithrombin-III ANT3 0.01869897 1.22888805 ↑ TNBC

Protein AMBP AMBP 0.01434176 1.22857819 ↑ TNBC

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128

Vitronectin VTNC 0.01666796 1.21690616 ↑ TNBC

Inter-alpha-trypsin inhibitor heavy chain H1 ITIH1 0.01847152 1.20880937 ↑ TNBC

Insulin-like growth factor-binding protein

complex acid labile subunit

ALS 0.03555036 1.20445695 ↑ TNBC

Hyaluronan-binding protein 2 HABP2 0.01352977 1.18411135 ↑ TNBC

Clusterin CLUS 0.04346683 1.1756767 ↑ TNBC

Alpha-2-HS-glycoprotein FETUA 0.02891723 1.16730605 ↑ TNBC

Galectin-3-binding protein LG3BP 0.00292722 1.116735 ↑ TNBC

Apolipoprotein C-I APOC1 0.02222358 2.50213212 ↑ CONTROL

Immunoglobulin heavy constant mu IGHM 0.00022527 2.032717896 ↑ CONTROL

Immunoglobulin lambda variable 8-61 LV861 0.00063688 2.023228572 ↑ CONTROL

Immunoglobulin lambda variable 3-19 LV319 0.0082843 1.73294072 ↑ CONTROL

CD5 antigen-like CD5L 0.02066785 1.731935965 ↑ CONTROL

Immunoglobulin lambda variable 3-9 LV39 0.02497423 1.576244295 ↑ CONTROL

Apolipoprotein F APOF 0.02131359 1.556435198 ↑ CONTROL

Immunoglobulin kappa variable 6-21 KV621 0.00608326 1.473240834 ↑ CONTROL

Immunoglobulin heavy variable 3-23 HV323 0.02960462 1.38352045 ↑ CONTROL

Immunoglobulin heavy variable 4-28 HV428 0.02327515 1.304707447 ↑ CONTROL

Plasma kallikrein KLKB1 0.04204343 1.23839695 ↑ CONTROL

Apolipoprotein M APOM 0.01063485 1.232467766 ↑ CONTROL

In the analysis of the protein corona formed around the three nanoparticles

(AuNPs, AgNPs, and MNPs), eight common proteins showed to be statistically

significant and appeared quantitatively increased (up-regulated) in triple negative breast

cancer patients versus controls (healthy people) (Figure 5). These proteins are

complement component C9 (CO9), complement C4-A (CO4A), complement C3 (CO3),

vitronectin (VTNC), apolipoprotein L1 (APOL1), complement factor H (CFAH),

kininogen-1 (KNG1), galectin-3-binding protein (LG3BP). However, three common

proteins appeared quantitatively decreased (down-regulated) in triple negative breast

cancer patients versus controls (healthy people) (Figure 6). These proteins are

immunoglobulin heavy constant mu (IGHM), immunoglobulin lambda variable 3-9

(LV39) and apolipoprotein C-I (APOC1).

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129

Figure 5. Quantitative Venn diagrams showing the number of up-regulated proteins

found in the protein corona of 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70

nm silver nanoparticles (AgNPs) and 9.30 ± 0.67 nm magnetic nanoparticles (MNPs)

after their incubation with serum from eight triple negative breast cancer patients and

eight healthy controls.

Figure 6. Quantitative Venn diagrams showing the number of down-regulated proteins

found in the protein corona of 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70

nm silver nanoparticles (AgNPs) and 9.30 ± 0.67 nm magnetic nanoparticles (MNPs)

after their incubation with serum from eight triple negative breast cancer patients and

eight healthy controls.

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130

After the analysis of the protein corona formed around AuNPs (10.02 ± 0.91

nm), AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm), the principal components

analysis (PCA) clearly revealed that the samples of the triple negative breast cancer

patients and healthy people were separated in the PC1 axis, which explains 47.7, 80.9

and 79.6 % of the variance between the samples, respectively (Figure 13_SM to Figure

15_SM).

In all cases (AuNPs, AgNPs, MNPs), the separation between the groups of

samples is visible between the group of healthy people and the group of triple negative

breast cancer.

3.4. TNBC biomarker validation

Mass spectrometry-based validation assays were performed in a different cohort

of total serum patients samples (n=8) and controls (n=8) which were run in triplicate

using a TripleTOF® 6600 LC-MS/MS system (Sciex). A correct protein validation was

performed. To this aim, the library was improved after the addition of DDA data

acquired from total serum pools (controls and patient), obtaining a total of 205

identified proteins. After comparing the results obtained by this analysis, with previous

analysis performed on serum samples after the incubation with the different

nanoparticles, fascinating results were observed.

Graphically, these variations can be observed through charts such as the volcano

plot. Volcano plots (see Figure 7) of the global quantification of proteins between

healthy and triple negative breast cancer patients with A: AuNPs (10.02 ± 0.91 nm), B:

AgNPs (9.73 ± 1.70 nm) and C: MNPs (9.30 ± 0.67 nm) were generated by plotting the

log 2-fold changes for the identified proteins against their corresponding adjusted p-

value.

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131

-8 -6 -4 -2 0 2 4 6 8

0

5

1 0

1 5

p a t ie n ts v s h e a lth y

lo g 2 ( fo ld c h a n g e )

-Lo

g 1

0 (

P v

alu

e)

P = 0 .0 5

C F AH

KN G 1VT N C

APO L 1

C O 3

L G 3 BP

AL S

C O 5

IG H M

L V3 9

APO C 1

Figure 7. Volcano plots of the SWATH analysis of proteins between healthy and triple

negative breast cancer patients with AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm)

and MNPs (9.30 ± 0.67 nm) . X-axis shows log(2)-fold change and Y-axis the statistical

significance through -log(10)-pvalue. The dot lines represent the cut off (p value ≤0.05).

Protein biomarkers are those that were common and statistical significative in all the

nanoparticles.

Validation assays verified and confirmed that transthyretin (TTHY) was

statistically significant and appeared quantitatively decreased (down-regulated) in triple

negative breast cancer patients when compared with controls (healthy people). This

result was observed after the analysis of the protein corona formed around AuNPs

(10.02 ± 0.91 nm). A similar observation was recently reported, where it was found that

transthyretin (TTHY) was predominantly (68.75%) down-regulated (n = 33/48) in the

sera of breast cancer patients [62].

Furthermore, validation assays verified and confirmed that complement

component C8 gamma chain (CO8G), ficolin-3 (FCN3), retinol-binding protein 4

(RET4), fibronectin (FINC), fetuin-B (FETUB) and apolipoprotein A-IV (APOA4),

were up-regulated in triple negative breast cancer when compared with controls.

However, apolipoprotein C-III (APOC3), immunoglobulin kappa variable 2D-28

(KVD28), immunoglobulin kappa variable 1-5 (KV105), immunoglobulin kappa

variable 4-1 (KV401), immunoglobulin kappa variable 1D-12 (KVD12),

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132

immunoglobulin heavy variable 1-46 (HV146), immunoglobulin heavy variable 3-30-5

(HV335), immunoglobulin heavy constant gamma 2 (IGHG2), immunoglobulin heavy

constant gamma 3 (IGHG3), and immunoglobulin heavy constant gamma 4 (IGHG4)

were down-regulated in triple negative breast cancer when compared with controls.

These results were observed after the analysis of the protein corona formed around

AgNPs (9.73 ± 1.70 nm).

Recently, similar studies found that elevated serum levels of retinol binding

protein 4 (RBP4) [63] and fibronectin (FINC) [64] were associated with breast cancer

risk and they could be useful markers for predicting poor prognosis in breast cancer

patients.

In the particular case of apolipoprotein A-IV (APOA4), comparative proteomic

profiling of immunodepleted plasma of healthy and of BC individuals revealed that this

protein was also up-regulated in the plasma of the BC individuals. Furthermore, this

protein was found to be involved in the pathogenesis of cancer and played an important

role in the regulation of metastasis [65].

Validation assays verified and confirmed that pregnancy zone protein (PZP),

coagulation factor V (FA5), protein Z-dependent protease inhibitor (ZPI), alpha-1-acid

glycoprotein 1 (A1AG1) and inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1), were

statistically significant and appeared quantitatively up-regulated in triple negative breast

cancer patients when compared with controls (healthy people). This result was observed

after the analysis of the protein corona formed around MNPs (9.30 ± 0.67 nm).

Mainly, the pregnancy zone protein (PZP) was found to be implicated the

pathogenesis of breast cancer [66].

It is well known that cancer is associated with hypercoagulability, and

circumstantial evidence suggests that tumor-expressed coagulation factors actively

support cancer pathogenesis and progression. Notably, single nucleotide polymorphisms

(SNPs) in F5, encoding coagulation factor V (FA5), have been found associated with

breast cancer. In 2018, M. Tinholt et al. [67] found that FA5 expression was higher in

breast tumors compared to normal tissue. Importantly, FA5 expression was significantly

increased in patients with tumors of aggressive nature (hormone receptor negative-,

triple negative-, HER2 enriched-, and basal-like tumors). These authors also suggested

that tumor-expressed FA5 could be a possible marker of aggressive breast cancer, and it

could emerge as a promising tumor suppressor candidate.

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133

Finally, protein Z-dependent protease inhibitor (ZPI) were identified by

immunochemistry in breast cancer cells, whereas they were absent from normal breast

tissue [68], and alpha-1-acid glycoprotein (AGP) was also found to be a potential

biomarker for breast cancer in 'at risk' individuals, particularly, TNBC patients [69].

3.5. Discussion

In this study, we provided by the first time the application of a combined

proteomic approach as DDA and SWATH-MS to develop the characterization and

quantification of triple negative breast cancer proteins after bio-corona nanoparticle

protein pre-concentration.

Gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs:

9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm) were assessed in

biomarker discovery as a tool for the pre-concentration and separation of proteins from

complex proteomes. To this end, sera from eight healthy individuals were compared

with sera from eight patients diagnosed with triple negative breast cancer. The

application of these nanomaterials, combined with mass spectrometry, has allowed the

identification of seven potential biomarkers for the diagnostic and control of TNBC

progression: GRF-type zinc finger domain-containing protein 1 (protein ZGRF1),

Matrix metalloproteinase-9 (MMP9), Lebercilin and Immunoglobulin lambda variable

3-27 (LV327) and LINE-1 type transposase domain-containing protein 1 (LITD1),

structural maintenance of chromosomes protein 6 (SMC6) and short coiled-coil protein

(SCOC).

After performing over these samples, a SWATH analysis to quantify the protein

changes, the separation between the group of healthy people and the group of triple

negative breast cancer patients was observed. Moreover, a lot of deregulated proteins

among both groups were observed. However, these proteins are not among the altered

proteins find by the qualitative DDA assay. The proteomic methods used in this study

are complementary and allow improving characterization studies. The fact that they are

complementary and not necessarily identify the same proteins is due to the search

methods DDA (qualitative), and IDA (SWATH-quantitative) analysis is different.

So, in the analysis of the protein corona formed around the three nanoparticles

(AuNPs, AgNPs, and MNPs), eight common proteins showed to be statistically

significant and appeared quantitatively increased (up-regulated) in triple negative breast

III. Results and Discussion. CHAPTER 2

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134

cancer patients versus controls (healthy people). These proteins are complement

component C9 (CO9), complement C4-A (CO4A), complement C3 (CO3), vitronectin

(VTNC), apolipoprotein L1 (APOL1), complement factor H (CFAH), kininogen-1

(KNG1), galectin-3-binding protein (LG3BP). And three common proteins appeared

quantitatively decreased (down-regulated) in triple negative breast cancer patients

versus controls (healthy people). These proteins are immunoglobulin heavy constant mu

(IGHM), immunoglobulin lambda variable 3-9 (LV39), and apolipoprotein C-I

(APOC1).

Moreover, a lot of deregulated proteins not common to all samples incubated

with the different nanoparticles were found. Therefore, protein corona formed around

AuNPs in breast cancer patients showed 14 proteins up-regulated and 34 proteins down-

regulated, in comparison with the protein corona formed around AuNPs in healthy

people. In the case of the protein corona formed around AgNPs, 64 proteins were found

to be up-regulated, and 76 proteins down-regulated. Finally, in the protein corona

formed around the MNPs, 45 proteins had elevated expression, while 12 proteins

showed down-regulation. From this point of view, AgNPs seem to be more suitable for

a clinical translational purpose, because the analysis of the protein corona formed

around these systems allows better differentiation between both groups of study: healthy

and diseased individuals.

All these proteins can be considered potential triple negative breast cancer

biomarker candidates; however, these proteins were found when serum samples were

concentrated using a bio-corona nanoparticle. Thus, it was thought that the best

validation could be finding these proteins in total serum samples. To this aim, it was

performed a new SWATH-MS analysis improving the library, and it allowed us to find

several validated proteins.

When it was compared total serum SWATH with concentrated proteins in

AgNPs, we found again complement component C8 (CO8G), ficolin-3 (FCN3), retinol-

binding protein 4 (RET4), fibronectin (FINC), fetuin-B (FETUB) and apolipoprotein

A-IV (APOA4), up-regulated; and immunoglobulin heavy constant gamma 4 (IGHG4),

immunoglobulin kappa variable 2D-28 (KVD28), immunoglobulin kappa variable 1-5

(KV105), apolipoprotein C-III (APOC3), immunoglobulin heavy variable 1-46

(HV146), immunoglobulin heavy constant gamma 2 (IGHG2), immunoglobulin heavy

constant gamma 3 (IGHG3), immunoglobulin kappa variable 4-1 (KV401),

III. Results and Discussion. CHAPTER 2

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135

immunoglobulin kappa variable 1D-12 (KVD12) and immunoglobulin heavy variable

3-30-5 (HV335), down-regulated. It is consistent with data from previous studies, and

these proteins can be considered validated. In the comparison between total serum

SWATH and MNPs only found 5 up-regulated proteins pregnancy zone protein (PZP),

coagulation factor V (FA5), protein Z-dependent protease inhibitor (ZPI), alpha-1-acid

glycoprotein 1 (A1AG1) and inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1). And

finally, in the comparison between total serum SWATH and AuNPs, only one down-

regulated protein was validated: transthyretin (TTHY).

4. Conclusions

This study shows that serum proteomics is a valuable tool that can facilitate

comprehensive and systematic identification of the serum proteome under both healthy

and disease conditions. Thus, serum proteomics could be used for disease diagnosis and

prognosis. In our case, we found several breast cancer-specific markers that can be used

in the diagnosis. However, because we found a lot of differentiated proteins, it is

necessary complementary assays to reduce the number of protein biomarkers.

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CHAPTER 3

Protein Corona Gold Nanoparticles Fingerprinting Reveals a

Profile of Blood Coagulation Proteins in the Serum of HER2-

Overexpressing Breast Cancer Patients

María del Pilar Chantada-Vázquez, Antonio Castro López, María García-Vence, Benigno

Acea-Nebril, Susana B. Bravo, Cristina Núñez

International Journal of Molecular Sciences 21 (2020) 8449

DOI: 10.3390/ijms21228449

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143

Protein corona gold nanoparticles fingerprinting reveals a profile of blood

coagulation proteins in the serum of HER2-overexpressing breast cancer

patients

María del Pilar Chantada-Vázquez,1,2 Antonio Castro López,3 María García-Vence,2 Benigno

Acea-Nebril,4 Susana B. Bravo,2 Cristina Núñez1

1 Research Unit, Lucus Augusti University Hospital (HULA), Servizo Galego de Saúde

(SERGAS), 27002 Lugo, Spain;

2 Proteomic Unit, Health Research Institute of Santiago de Compostela (IDIS), University

Clinical Hospital of Santiago de Compostela (CHUS), 15706 Santiago de Compostela, Spain;

3 Breast Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde

(SERGAS), 27002 Lugo, Spain;

4 Department of Surgery, Breast Unit, Complexo Hospitalario Universitario A Coruña

(CHUAC), Servizo Galego de Saúde (SERGAS), 15006 A Coruña, Spain;

Abstract

Breast cancer (BC) is a molecularly heterogeneous disease that encompasses five

major molecular subtypes (luminal A (LA), luminal B HER2 negative (LB-), luminal B

HER2 positive (LB+), HER2 positive (HER2+), the triple negative breast cancer (TNBC)).

BC treatment mainly depends on the identification of the specific subtype. Despite the correct

identification, therapies could fail in some patients. Thus, further insights into the genetic and

molecular status of the different BC subtypes could be very useful to improve the response

of BC patients to the range of available therapies. In this way, we use gold nanoparticles

(AuNPs, 12.96 ± 0.72 nm) as a scavenging tool in combination with Sequential Window

Acquisition of All Theoretical Mass Spectra (SWATH-MS) to quantitative analyse the serum

proteome alterations in the different breast cancer intrinsic subtypes. The differentially

regulated proteins specific of each subtype were further analysed with the bioinformatic tools

STRING and PANTHER to identify the major molecular function, biological processes,

cellular origin, protein class and biological pathways altered due to the heterogeneity in

III. Results and Discussion. CHAPTER 3

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144

proteome of the different BC subtypes. Importantly, a profile of blood coagulation proteins

was identified in the serum of HER2-overexpressing BC patients.

Keywords: Protein corona (PC); gold nanoparticles (AuNPs); breast cancer (BC);

fingerprinting; SWATH-MS; HER2+.

1. Introduction

Breast cancer (BC) is a heterogeneous disease that presents a wide variety of molecular

and clinical characteristics, as well as variability in clinical progression [1]. For the treatment

choice, patients are classified according to intrinsic biological subtypes within the BC

spectrum, using clinical-pathological criteria, i.e. the recognition of amplification and/or

overexpression of the human epidermal growth factor receptor 2 (HER2) oncogene, the

immunohistochemical classification of the estrogen receptor (ER) and the progesterone

receptor (PR), and Ki-67 labelling index [ 2 ]. This classification allows for a more

personalized approach to medical treatments, with favourable results. However, in spite of

that, almost 10-15% of these patients still experience local or distant recurrences in the first

5 years from diagnosis [3]. Particularly, HER2-positive BC, defined by the overexpression

of HER2 protein, represents 15-20% of BC cases [4 , 5 ] and, is correlated with poor

prognosis, high rates of recurrence and short survival [6].

Classification of BC might be markedly improved if new biomarkers identified with the

use of high-throughput “omics” approaches could support diagnosis based on

histopathological patterns [7-9]. Nanomaterials have been introduced into the field of

proteomics to establish a new and rapidly evolving research area termed nanoproteomics

[10].

It is well known that the dispersion of a nanomaterial in physiological fluid results in the

formation of a protein shell named “protein corona” (PC). PC varies depending on the

characteristics of the biological media, the physical (size, shape, curvature) and chemical

properties (composition, surface charge/chemistry, hydrophobicity/hydrophilicity) of the

nanomaterial, and the incubation time [11]. Disease-associated biomarkers comprise less

than 1% of serum proteins. In this way, through the formation of the PC, nanoparticles could

act as sorbent materials for the enrichment of low-abundance peptides/proteins presented in

serum samples before the biomarker identification by mass spectrometry (MS) analysis [12-

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14]. Importantly, otherwise undetectable changes in protein concentration at an early stage of

the disease (as breast cancer), after any treatment (chemotherapy, immunotherapy) or surgery

could be detected analyzing the PC composition [15]. Thus, characterization of the PC around

NPs offers distinct advantages over sole proteomic approaches and increases the success of

identifying molecular targets [16].

Particularly, AuNPs present some properties to be used as suitable sorbent

nanomaterials: high surface-area-to-volume ratio, colloidal stability, and the capability to

conjugate with biomolecules [17]. Here, the interaction of AuNPs (12.96 ± 0.72 nm) with the

sera of disease-free women (healthy controls, HC) (n = 42) and BC patients (n = 42) allowed

the pre-concentration of the low-abundance proteins thorough the PC formation. Then, an

exhaustive quantitative analysis of the PCs by SWATH-MS was carried out to identify novel

molecular targets associated to the different BC intrinsic subtypes (see Figure 1).

Figure 1. Simplified representation of the experimental procedure. AuNPs (12.96 ± 0.72

nm) were incubated ex vivo with human serum samples obtained from HC (n = 42) and

BC patients (n = 42). Ex vivo corona-coated AuNPs were recovered and purified from

unbound proteins by centrifugation. The formed PCs were quantitatively characterized

and analysed by SWATH-MS and compared between the groups.

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2. Experimental

2.1. Chemicals and reagents

Acrylamide/bis-acrylamide 30% solution (37.5:1), β-mercaptoethanol (molecular

biology grade), Coomassie Brilliant Blue R250 (CBB), DL-dithiothreitol

(HSCH2CH(OH)CH(OH)CH2SH, 99%) (DTT), glycerol (HOCH2CH(OH)CH2OH, 86-

88%), iodoacetamide (IAA, ICH2CONH2, 99%), sodium citrate tribasic dehydrate

(HOC(COONa)(CH2COONa)2·2H2O, 99%), sodium carbonate (Na2CO3, 99%), tris-base

(NH2C(CH2OH)3), trifluoroacetic acid (CF3COOH, 99%), trypsin from bovine pancreas, and

the Sigma Marker wide range 6.5-200 KDa were purchased from Sigma-Aldrich.

Formaldehyde for molecular biology (36.5-38% in H2O) and sodium dodecylsulfate (SDS,

CH3(CH2)11SO4Na) were purchased from Panreac. Bromophenol-blue was purchased from

Riedel-de Haen. Hydrogen tetrachloroaurate (III) hydrate (HAuCl4·xH2O) (99.9%-Au) (49%

Au) at 10% w/v was purchased from Strem Chemicals. Ammoniumbicarbonate (AMBIC,

NH4HCO3, 99.5%) and formic acid (HCOOH, 95%) were purchased from Fluka.

2.2. Biological samples

Blood samples were collected from n = 42 newly diagnosed BC patients with the five

different breast cancer subtypes: n = 11 patients with the luminal A subtype (ER positive,

HER2 negative, Ki-67 low, and PR high), n = 10 patients with the luminal B-HER2 negative

subtype (ER positive, HER2 negative, and either Ki-67 high or PR low), n = 7 patients with

the luminal B-HER2 positive subtype (ER positive, HER2 overexpressed or amplified, any

Ki-67, and any PR), n = 6 patients with the HER2 positive subtype (HER2 over-expressed or

amplified, ER and PR absent), and n = 8 patients with the triple negative subtype (ER and

PR absent and HER2 negative); for ER: estrogen receptor, PR: progesterone receptor, HER2:

human epidermal growth factor receptor 2 (see Table 1_SM) [2].

Blood samples were also collected from n = 42 age-matched and gender-matched healthy

women (controls). In all cases, venous blood samples were collected in VACUETTE® Serum

Clot Activator Tubes (10 mL).

The experiment was conducted in conformity with the declaration of Helsinki and

approved by the Clinical Research Ethics Committees (CEIC) of Galicia (Spain) with

III. Results and Discussion. CHAPTER 3

147

approval number 2017-021. All participants from Lucus Augusti University Hospital (Spain)

gave written informed consent prior to their participation.

2.3. Synthesis of citrate-gold nanoparticles (AuNPs, 12.96 ± 0.72 nm)

Colloidal AuNPs with a size of 12.96 ± 0.72 nm were prepared by chemical reduction

method as per the protocol developed previously [12-14]. In short, sodium citrate tribasic

solution (0.075% w/v) was dissolved in 60 mL warm distilled water under constant magnetic

stirring. To this, 54 μL of 10% w/v of hydrogen tetrachloroaurate (III) hydrate solution was

then added drop-wise and the reaction was heated to 100 °C under constant magnetic stirring.

Reaction was allowed to proceed further the color of the solution changes from yellow to

deep red indicating the reduction of Au3+ to Au0, which spontaneously aggregates to form

colloidal AuNPs. This colloidal dispersion of AuNPs was cooled to room temperature and

preserved at 4 °C for further analysis.

2.4. Incubation of AuNPs with human serum samples: ex vivo protein corona formation

Firstly, collected blood samples were allowed to clot for 15 min. After that, samples

were centrifuged for 5 min at 4˚C and 1,800×g. Resultant serum were transferred to sterile

cryovials, frozen and stored at -80 °C until further use at Research Unit, Lucus Augusti

University Hospital (HULA). The formation of the ex vivo PC was achieved following the

steps showed in Figure 2 [12-14]. After that, unbound serum proteins from the surface of

AuNPs were removed by centrifugation at 18,840×g for 30 min.

2.4. Characterization of colloidal AuNPs

The morphology of the AuNPs was investigated by transmission electron microscopy

(TEM) with a JEM 1011, JEOL instrument. The size and ζ-potentials of colloidal AuNPs

were measured (3 determinations per sample) with a Malvern Zetasizer Nano ZS instrument

at 25°C.

Protein quantification and protein separation by SDS-PAGE were carried out with the

use of a Qubit™ 4 Quantitation Starter Kit (Thermo Fisher Scientific) and a PowerPacTM

Basic Power Supply (Bio-Rad), respectively.

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2.5. Separation and digestion of the proteins presented in the corona-coated AuNPs

Corona proteins associated with AuNPs were separated by sodium dodecyl sulfate-

polyacrylamide gel electrophoresis (SDS-PAGE) and digested following the scheme

represented in Figure 2.

The digestion was stopped with the addition of 50 μL of 5% (v/v) formic acid. After that,

the extraction of the peptides from the gel was carried out with a solution of 50% (v/v)

acetonitrile/0.1% (v/v) trifluoroacetic acid (TFA) (x3) and acetonitrile (ACN) (x1). Samples

were dried-down and stored at -20 °C until their further use [18].

Figure 2. Flowchart depicting the separation and digestion of the corona proteins

associated with AuNPs.

2.6. Protein quantification by SWATH-MS

SWATH/MS experiments were carried out following the instrumental parameters

described elsewhere [13]. Briefly, two biological replicates of LA, LB-, LB+, HER+, TNBC

and HC samples were used to get extensive quantitative data by label-free SWATH-MS

analysis. Peptides of all samples were analysed with a micro-LC system Ekspert nLC425

(Eksigen, Dublin, CA. USA) couplet to a hybrid quadrupole-TOF mass spectrometer Triple

TOF 6600 (Sciex, Redwood City. CA. USA). One of the first steps is the construction of the

MS/MS spectral libraries. For that purpose, peptide solutions were analyzed by a shotgun

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data-dependent acquisition (DDA) approach by micro-LC-MS/MS. For spectral alignment

and peak extraction was employed the Peakview software (version 2.2; AB Sciex) using the

SWATH Acquisition MicroApp (version 2.0). Parameters used were: number of fragments

= 7, number of peptides = 10, peptide confidence = 95%, XIC width = 30 ppm, XIC extraction

window = 5 min. Exportation of the SWATH file to the MarkerView software (version 1.3.1;

AB Sciex) allowed the quantitative analysis of ions, peptides and proteins in the different

samples. As output result, the summed intensity of ions for the peptide, summed intensity of

the peptides for protein and Area under Curve (AUC) of the ions were provided. The test set

(LA, LB-, LB+, HER+, TNBC) was compared with the control (HC) dataset to generate fold

change ratios. For protein quantitation, only peptides with a False Discovery Rate (FDR)

below 1% were considered. Average MS peak area of each protein derived from the analysis

of the biological replicates and Student’s t-test analysis among samples was developed. t-test

indicates the capacity of each variable to distinguish between two groups, and it was reported

as a p-value. The criteria to select differentially expressed proteins was a p-value <0.05 with

a 1.5-fold in- or decrease.

2.7. Protein functional interaction network analysis and protein ontology classification

The informatic tool STRING v.10.0 database (http://string-db.org) was the used to

analyse the the functional interaction networks of the proteins, integrating direct (physical)

and indirect protein-protein interactions (PPI) [19].

Protein ontology classification was performed with the PANTHER classification system

(http:// www.pantherdb.org/). The differentially expressed proteins in the different breast

cancer subtypes were grouped according to their major molecular function, biological

processes, cellular origin, protein class and biological pathways.

3. Results and discussion

3.1. Incubation of AuNPs (12.96 ± 0.72 nm) with human serum samples: ex vivo protein

corona formation and characterization

Human serum samples from n = 42 healthy controls (HC) and n = 42 breast cancer (BC)

patients (n = 42) have been recruited, handled and analysed in the same way as it is further

detailed in Figure 3. The group of BC patients was divided into the following biological

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subtypes: n = 11 patients with the luminal A (LA) subtype, n = 10 patients with the luminal

B HER2 negative (LB-) subtype, n = 7 patients with the luminal B HER2 positive (LB+)

subtype, n = 6 patients with the HER2 positive (HER2+) subtype, and n = 8 patients with the

triple negative breast cancer (TNBC) subtype. Patient clinical characteristics are summarized

in Table 1_SM.

AuNPs with a size of 12.96 ± 0.72 nm were prepared by a chemical reduction method

[12-14]. As Figure 3 shows, proteins presented in serum samples (x2) were chemically

reduced with dithiothreitol (DTT) and alkylated with iodoacetamide (IAA) before their ex

vivo incubation with AuNPs (12.96 ± 0.72 nm) to get the formation of the PCs [12-14].

Figure 3. Flowchart depicting serum samples pretreatment and protein corona formation.

After the ex vivo incubation of AuNPs with human serum samples of HC (n = 42) and

BC patients (n = 42), the resultant protein corona-coated AuNPs were centrifugated and

structurally characterized by dynamic light scattering (DLS) and negative stain transmission

electron microscopy (TEM). DLS measurements showed that the interaction of serum

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proteins with the surface of AuNPs resulted in an increase of the size of the AuNPs, from

12.96 ± 0.72 nm to 17.33 ± 1.55 nm (HC) and 17.13 ± 1.53 nm (BC) (see Table 2_SM).

Probably, the preferential interaction of positively charged proteins with the AuNPs surface

promoted the increase of the mean particle surface charge from -38.3 mV (bare AuNPs) to -

30.5 mV (HC) and -30.3 mV (BC) [20, 21]. TEM imaging revealed a well-dispersed

nanoparticles population corroborating the PC formation around AuNPs.

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Figure 4. Negative stain TEM imaging of bare (A) and protein corona-coated AuNPs,

recovered post-incubation with human serum obtained from HC (B) and BC patients (C). All

scale bars are 50 nm.

3.2. Quantitative analysis of the protein corona-coated AuNPs by SWATH-MS

Corona proteins associated with AuNPs were separated by sodium dodecyl sulfate-

polyacrylamide gel electrophoresis (SDS-PAGE). After a staining step, gels were processed

following the method described in section 4.5. The resulting peptides were then quantitative

analyzed by the emerging proteomic platform for label-free quantification SWATH-MS.

The comparison of the protein patterns of the ex vivo formed PCs allowed the

identification of the differentially expressed proteins between HC and the different BC

subtypes. Results were filtered to present a p-value ≤ 0.05 and interestingly, n = 60 proteins

were found to be differentially expressed, of which n = 42 were up-regulated and n = 18

down-regulated in BC patients for the LA subtype; n = 132 were found to be differentially

expressed (n = 100 up-regulated, n = 32 down-regulated) for the LB- subtype; n = 67 proteins

were found to be differentially expressed (n = 59 up-regulated, n = 8 down-regulated) for the

LB+ subtype; n = 130 proteins were found to be differentially expressed (n = 95 up-regulated,

n = 35 down-regulated) for the HER2+ subtype; and n = 91 proteins were found to be

differentially expressed (n = 87 up-regulated, n = 4 down-regulated) for the TNBC subtype

(see Table 1). Full list of candidate protein biomarkers identified to be up-regulated or down-

regulated in each different BC subtypes in comparison to healthy controls (HC) with the fold-

change values was shown in Table 3_SM.

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Figure 5. Venn diagram showing the number of shared and specific (or unique)

deregulated proteins identified in the PCs formed after the interaction of AuNPs (12.96

± 0.72 nm) with serum samples of the different BC subtypes (LA, LB-, LB+, HER2+,

TNBC).

Table 1. Number of differentially expressed proteins (up-regulated and down-regulated)

(p-value ≤ 0.05) found in the protein patterns of the ex vivo formed coronas after the

analysis by SWATH-MS for the different breast cancer subtypes (LA LB-, LB+, HER2+,

TNBC) in comparison with healthy controls (HC) samples. The number of differentially

expressed proteins (up-regulated and down-regulated) specific to each of the five

subtypes of BC found in the ex vivo formed coronas were also indicated.

SWATH-MS analysis

Comparison Protein number (p-value ≤ 0.05)

Total Up-regulated Down-regulated Specific Up-regulated Down-regulated

Controls vs LA 60 42 18 8 4 4

Controls vs LB- 132 100 32 27 25 2

Controls vs LB+ 67 59 8 2 2 0

Controls vs HER2+ 130 95 35 28 23 5

Controls vs TNBC 91 87 4 10 9 1

Venn diagram of statistically significant (up- and down-) regulated proteins showed that

seven proteins were found to be commonly altered in all BC subtypes (see Figure 5). These

common proteins were apolipoprotein C-III (APOC3), c-reactive protein (CRP), hemoglobin

subunit beta (HBB), immunoglobulin heavy variable 3-49 (IGHV3-49), serum amyloid A-4

protein (SAA4), serum amyloid P-component (APCS) and serotransferrin (TR) (Table 4_SM

and Table 5_SM).

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As it was mentioned above, subtype-specific unique proteins were also identified using

SWATH-MS (see Figure 5 and Table 2). n = 8 proteins were specifically associated in LA

(out of which n = 4 were up-regulated and n = 4 showed down-regulation). For LB-, n = 27

proteins were found to be specific to this subtype (n = 25 with increased expression and n =

2 with decreased expression). In the LB+ subtype, only n = 2 specific proteins were found to

be up-regulated. In the HER2+ subtype, n = 28 specific proteins were found (n = 23 up-

regulated, n = 5 down-regulated). The TNBC subtype comprised of n = 10 specific proteins

(n = 9 up-regulated, n = 1 down-regulated).

Table 2. Differentially expressed proteins (up-regulated and down-regulated) (p-value ≤

0.05) found in the protein patterns of the ex vivo formed coronas after the analysis by

SWATH-MS specific (or unique) for the different breast cancer subtypes (LA, LB-, LB+,

HER2+, TNBC) in comparison with healthy control (HC) samples. The Fold Change ratio

was calculated as the ratio of geometric means of the sample replicates, which corresponds

to calculating the normal arithmetic ratio of log-transformed areas and back-transforming.

Protein Name Gene p-value Fold Change

Complement C1r subcomponent-like protein C1RL 9.79E-05 1.614689351 ↑ Luminal A

Complement factor H-related protein 2 CFHR2 0.003228805 1.764346734 ↑ Luminal A

Complement component C8 beta chain C8B 0.003730112 1.35440489 ↑ Luminal A

Lysosome-associated membrane glycoprotein 2 LAMP2 0.018383379 1.33466653 ↑ Luminal A

Immunoglobulin kappa variable 3-20 IGKV3-20 0.008581213 7.24153822 ↓ Luminal A

Immunoglobulin heavy constant mu IGHM 0.038320909 2.180105502 ↓ Luminal A

Immunoglobulin heavy variable 1-24 IGHV1-24 0.045225189 2.766559939 ↓ Luminal A

Protein Z-dependent protease inhibitor SERPINA10 0.045960336 2.020513374 ↓ Luminal A

Immunoglobulin lambda variable 2-23 IGLV2-23 2.51E-07 2.96506068 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-53 IGHV3-53 6.90E-06 3.231686892 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 4-1 IGKV4-1 7.88E-06 2.271628981 ↑ Luminal B HER2 Neg

Biotinidase BTD 1.42E-05 1.571319968 ↑ Luminal B HER2 Neg

Immunoglobulin heavy constant alpha 1 IGHA1 1.81E-05 2.45029046 ↑ Luminal B HER2 Neg

Serum paraoxonase/lactonase 3 PON3 3.44E-05 1.614088523 ↑ Luminal B HER2 Neg

Immunoglobulin kappa constant IGKC 6.04E-05 2.234283878 ↑ Luminal B HER2 Neg

Phospholipid transfer protein PLTP 0.000127276 1.491697089 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 3-11 IGKV3-11 0.000235484 2.727555354 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-9 IGHV3-9 0.000418498 2.709287474 ↑ Luminal B HER2 Neg

Alpha-mannosidase 2 MAN2A1 0.000577191 2.094925838 ↑ Luminal B HER2 Neg

Immunoglobulin heavy constant gamma 1 IGHG1 0.000838666 1.832731289 ↑ Luminal B HER2 Neg

Apolipoprotein B-100 APOB 0.001926757 1.618943103 ↑ Luminal B HER2 Neg

Immunoglobulin heavy constant alpha 2 IGHA2 0.00207989 2.137794634 ↑ Luminal B HER2 Neg

Basement membrane-specific heparan sulfate

proteoglycan core protein HSPG2 0.002185804 2.617271104 ↑ Luminal B HER2 Neg

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Pregnancy zone protein PZP 0.002663618 4.343426266 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 1-69D IGHV1-69D 0.00307313 2.762019962 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-74 IGHV3-74 0.005843078 1.63300774 ↑ Luminal B HER2 Neg

Immunoglobulin lambda-like polypeptide 5 IGLL5 0.006474796 1.674164832 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 3D-20 IGKV3D-20 0.007387257 1.722873358 ↑ Luminal B HER2 Neg

L-lactate dehydrogenase B chain LDHB 0.011088233 1.464826821 ↑ Luminal B HER2 Neg

Platelet glycoprotein Ib alpha chain GP1BA 0.014355443 1.435497779 ↑ Luminal B HER2 Neg

Apolipoprotein D APOD 0.02689663 1.565894604 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 3-21 IGLV3-21 0.030887039 1.531598796 ↑ Luminal B HER2 Neg

Mediator of RNA polymerase II transcription subunit

23 MED23 0.034189832 1.582094144 ↑ Luminal B HER2 Neg

Platelet factor 4 variant PF4V1 0.013149247 8.695958011 ↓ Luminal B HER2 Neg

Complement C1s subcomponent C1S 0.040782815 1.400513823 ↓ Luminal B HER2 Neg

Plastin-2 LCP1 0.002018957 4.501473239 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 6-1 IGHV6-1 0.020191442 1.539338405 ↑ Luminal B HER2 Pos

Complement C5 C5 2.95E-15 2.104078338 ↑ HER2 Pos

Adiponectin ADIPOQ 8.28E-09 9.579128591 ↑ HER2 Pos

Immunoglobulin heavy variable 3-73 IGHV3-73 1.67E-06 15.87006684 ↑ HER2 Pos

Coagulation factor XII F12 2.50E-06 4.48323521 ↑ HER2 Pos

Plasma kallikrein KLKB1 2.05E-05 2.839283512 ↑ HER2 Pos

Immunoglobulin heavy variable 3-23 IGHV3-23 8.83E-05 2.952483084 ↑ HER2 Pos

Immunoglobulin lambda variable 1-51 IGLV1-51 0.000382645 2.419267666 ↑ HER2 Pos

Immunoglobulin heavy variable 3-64 IGHV3-64 0.000401774 2.217759109 ↑ HER2 Pos

Selenoprotein P SELENOP 0.000539614 3.796988329 ↑ HER2 Pos

Immunoglobulin kappa variable 1D-12 IGKV1D-12 0.00188481 4.471370936 ↑ HER2 Pos

Immunoglobulin lambda variable 5-45 IGLV5-45 0.00772078 2.858034161 ↑ HER2 Pos

Immunoglobulin lambda variable 6-57 IGLV6-57 0.009251968 5.252644969 ↑ HER2 Pos

Keratin type I cytoskeletal 10 KRT10 0.012361191 1.599012598 ↑ HER2 Pos

Immunoglobulin kappa variable 1-27 IGKV1-27 0.014636279 3.663302525 ↑ HER2 Pos

Immunoglobulin kappa variable 1-5 IGKV1-5 0.015089737 3.097611266 ↑ HER2 Pos

EGF-containing fibulin-like extracellular matrix

protein 1 EFEMP1 0.015909032 1.962284999 ↑ HER2 Pos

Immunoglobulin kappa variable 2-24 IGKV2-24 0.019946847 3.792528291 ↑ HER2 Pos

Immunoglobulin heavy constant gamma 2 IGHG2 0.020013155 1.605775681 ↑ HER2 Pos

Adipocyte plasma membrane-associated protein APMAP 0.021204299 23.89017843 ↑ HER2 Pos

Immunoglobulin kappa variable 1D-16 IGKV1D-16 0.023927557 15.36602613 ↑ HER2 Pos

Coagulation factor V F5 0.025887503 3.739380413 ↑ HER2 Pos

Cysteine-rich secretory protein 3 CRISP3 0.034870563 3.338300324 ↑ HER2 Pos

Immunoglobulin heavy variable 3-33 IGHV3-33 0.038394512 8.344444793 ↑ HER2 Pos

N-acetylmuramoyl-L-alanine amidase PGLYRP2 0.000321687 1.799194526 ↓ HER2 Pos

Alpha-1-antitrypsin SERPINA1 0.001111283 4.959761437 ↓ HER2 Pos

Trypsin-1 PRSS1 0.002454431 4.217786063 ↓ HER2 Pos

Apolipoprotein F APOF 0.005336626 7.55893037 ↓ HER2 Pos

Antithrombin-III SERPINC1 0.018612975 1.308597079 ↓ HER2 Pos

Apolipoprotein E APOE 0.005108321 1.297484301 ↑ Triple Negative

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Voltage-dependent L-type calcium channel subunit

alpha-1F CACNA1F 0.010760078 3.330169664 ↑ Triple Negative

Complement C2 C2 0.024115534 1.265184448 ↑ Triple Negative

Keratin. type II cytoskeletal 1 KRT1 0.02492244 1.394646766 ↑ Triple Negative

Immunoglobulin heavy variable 4-30-2 IGHV4-30-2 0.028590349 4.459516925 ↑ Triple Negative

Attractin ATRN 0.033317422 1.248304377 ↑ Triple Negative

Immunoglobulin kappa variable 2D-30 IGKV2D-30 0.035772725 1.512873113 ↑ Triple Negative

Immunoglobulin kappa variable 1-6 IGKV1-6 0.039260496 1.537751007 ↑ Triple Negative

Platelet basic protein PPBP 0.04971791 23.75806076 ↑ Triple Negative

CD5 antigen-like CD5L 0.012543111 1.999836008 ↓ Triple Negative

3.3. Functional pathway and network analysis for subtype specific breast cancer

The differentially regulated proteins specific to each of the five subtypes of BC found in

the ex vivo formed coronas were analysed with the PANTHER [22] tool to identify the major

molecular function (Figure 1_SM), biological processes (Figure 2_SM), cellular origin

(Figure 3_SM), protein class (Figure 5) and biological pathways (Figure 4_SM) altered due

to the heterogeneity in proteome of the different BC subtypes.

Molecular functions of the differentially regulated proteins specific to each of the five

subtypes of BC were found to be associated with binding, catalytic activity, molecular

regulation, and transportation (Figure 1_SM). Furthermore, except for the specific proteins

identified in the LB+ subtype, most profiled proteins were of extracellular origin (Figure

3_SM).

During the past decade, insight had been gained about the role of the immunological

response in the BC disease process [23], and the possible use of immunological parameters

in the prognosis of BC [24]. The PANTHER classification according to their protein class

revealed that most of the differential proteins belong to defense/immunity (Figure 5). In the

present work, from the 75 specific proteins identified for the different BC subtypes (n = 8 in

LA; n = 27 in LB-; n = 2 in LB+; n = 28 in HE; n = 10 in TNBC), 34 proteins were

immunoglobulins (n = 3 in LA; n = 14 in LB-; n = 1 in LB+; n = 13 in HER2+; n = 3 in

TNBC) (see Table 2). Previous works also found that serum immunoglobulin levels were

related to the disease stage and tumor load in BC patients [25].

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Figure 5. Classification according to the protein class of the differentially regulated

proteins specific to each of the five subtypes of BC found in the ex vivo formed coronas

analyzed with the PANTHER database.

Immune cell activation was also shown to be an altered pathway in BC which was

enriched only for the LA subtype in the present study (Figure 4_SM). Immunoglobulin heavy

constant mu (IGHM) was down-regulated for B cell activation in LA, indicating that

antibody-mediated immune response was implicated in this subtype. Probably, the tumor

alters the immune system mechanism to suppress the B cell activation promoting this down-

regulation.

Complement activation is an important factor of innate immunity and a defense system

against infecting pathogens. Furthermore, complement activation also participates in the

adaptive immune response. Particularly in BC, complement activation contributes to cancer

progression [26]. In the present work, a total of 3 complement system components implicated

in the innate immune response were identified in the PC for the different BC subtypes:

complement component C8 beta chain (C8B) for the LA, complement C5 (C5) for the

HER2+, complement C2 (C2) for the TNBC (see Table 2). Previous works also found

deregulation of some of these complement system components in the sera of BC patients [27,

28].

Other deregulated proteins which may play a role in the innate immune system were

complement C1r subcomponent-like protein (C1RL) and complement factor H-related

protein 2 (CFHR2) (both up-regulated) in the LA subtype; complement C1s subcomponent

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(C1S) (down-regulated) in the LB- subtype; plastin-2 (LCP1) (up-regulated) in the LB+

subtype; cysteine-rich secretory protein 3 (CRISP3) (up-regulated) and N-acetylmuramoyl-

L-alanine amidase (PGLYRP2) (down-regulated) in the HER2+ subtype; and keratin, type II

cytoskeletal 1 (KRT1) (up-regulated) in the TNBC subtype (see Table 2). Particularly, LCP1

plays a role in the activation of T-cells and its exosomal release by breast cancer cells was

found to facilitate metastatic bone osteolysis [29].

After the analysis of the protein corona, HER2+ subtype was enriched with coagulation

factor V (F5), coagulation factor XII (F12) and plasma kallikrein (KLKB1) (up-regulated),

and alpha-1-antitrypsin (SERPINA1), trypsin-1 (PRSS1) and antithrombin-III (SERPINC1)

(down-regulated) for blood coagulation pathway (Figure 4_SM, Tables 2 and 3).

Platelets, as small cell fragments, are not only important coagulation-related factors, also

play a vital role in tumor progression [30]. Particularly, platelet factor 4 (PF4) or CXCL4, a

member of CXC chemokine family, acts as an angiogenesis inhibitor which may contribute

to prevent tumor metastasis [31]. In the present work, it was observed that the inflammation

mediated by chemokine and cytokine signaling pathway was enriched specifically for LB-

subtype with the platelet factor 4 variant (PF4V1) being down-regulated (Figure 4_SM).

Other protein of the family of platelets is the platelet basic protein (PPBP) that was up-

regulated in the TNBC subtype (see Table 2).

The gonadotropin-releasing hormone receptor pathway was found to be enriched with

adiponectin (ADIPOQ) (up-regulated) and voltage-dependent L-type calcium channel

subunit alpha-1F (CACNA1F) (up-regulated) in the HER2+ and TNBC subtypes,

respectively (Figure 4_SM).

In the present work, a group of proteins implicated in the combination and transportation

of lipids, apoliporoteins, were also found to be deregulated in the LB- subtype

(apolipoprotein B-100 (APOB), apolipoprotein D (APOD) and phospholipid transfer protein

(PLTP); up-regulated), the HER2+ subtype (apolipoprotein F (APOF); down-regulated) and

TNBC subtype (apolipoprotein E (APOE); up-regulated) (see Table 2).

Other family of potential molecular targets that were found to be deregulated in the

present study are some glycoproteins as: lysosome-associated membrane glycoprotein 2

(LAMP2) (up-regulated) in the LA subtype; platelet glycoprotein Ib alpha chain (GP1BA)

and basement membrane-specific heparan sulfate proteoglycan core protein (HSPG2) (up-

regulated) in LB- subtype; EGF-containing fibulin-like extracellular matrix protein 1

III. Results and Discussion. CHAPTER 3

159

(EFEMP1) and selenoprotein P (SELENOP) (up-regulated) in the HER2+ subtype; and CD5

antigen-like (CD5L) (down-regulated) in the TNBC subtype (see Table 2).

While some proteins with an enzymatic functionality as biotinidase (BTD), serum

paraoxonase/lactonase 3 (PON3), L-lactate dehydrogenase B chain (LDHB) and alpha-

mannosidase 2 (MAN2A1) were found to be the up-regulated in LB- subtype, protein Z-

dependent protease inhibitor (SERPINA10) were down-regulated in the LA subtype (see

Table 2).

Table 3. Candidate deregulated blood coagulation biomarkers in HER2-overexpressing BC

patients found after the proteomic analysis of the ex vivo corona-coated AuNPs. On the

bottom, a cluster of blood coagulation proteins found in the protein-protein interaction

network map of the genes encoded differentially regulated proteins for the HER2-

overexpressing BC patients found after the proteomic analysis of the ex vivo corona-coated

AuNPs.

Protein Name Gene p-value Fold Change Control vs. HER2 Positive

Coagulation factor XII F12 2.50E-06 4.48323521 ↑ HER2 Pos

Plasma kallikrein KLKB1 2.05E-05 2.839283512 ↑ HER2 Pos

Coagulation factor V F5 0.025887503 3.739380413 ↑ HER2 Pos

Alpha-1-antitrypsin SERPINA1 0.001111283 4.959761437 ↓ HER2 Pos

Trypsin-1 PRSS1 0.002454431 4.217786063 ↓ HER2 Pos

Antithrombin-III SERPINC1 0.018612975 1.308597079 ↓ HER2 Pos

3.4. Discussion

Nowadays, the different breast cancer intrinsic subtypes (LA, LB-, LB+, HER2+,

TNBC) guide the therapy selection [2]. In some patients, therapies could fail for different

reasons as cancer recurrence, therapy resistance, and/or metastasis. Thus, therapy response

in breast cancer patients could be improved with the study of the molecular alterations at the

subtype level.

The application of different omics approaches to deep insight the different BC subtypes

allowed the refinement of the complexity of tumor heterogeneity. In this way, a variety of

III. Results and Discussion. CHAPTER 3

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160

quantitative proteomic studies were developed to identify potential signatures for breast

cancer using clinical samples such as saliva, the ductal lavage fluids, nipple aspirate fluids

(NAFs), urine and tissue [32]. Importantly, blood may be a suitable sample source for

studying the proteomic deregulation in the different BC subtypes with minimally invasive

collection procedures. Thus, the analysis of blood-based markers at the subtype level in

biological fluids such as plasma and serum allowed to find different protein markers related

with the tumor microenvironment and the subtype-specific changes. Following this research

line, the proteomic alterations in blood plasma [7] and blood serum [8] of BC subtypes were

explored.

Potential biomarkers are presented in very low concentrations in blood (less than 1% of

blood proteins). Thus, the isolation and enrichment of low-abundance peptides/proteins from

complex mixtures is a mandatory step in the proteomic biomarkers pipeline and nanoparticles

represent an ideal alternative [33].

In the present work, an exhaustive quantitative analysis of the PCs formed around AuNPs

after their incubation in serum samples was developed by SWATH-MS to identify novel

molecular targets associated to the different BC intrinsic subtypes (see Figure 1).

Seven proteins were found to be commonly altered in all BC subtypes, namely APOC3,

CRP, HBB, IGHV3-49, SAA4, APCS and TR. CRP and SAA4 are acute-phase proteins

(APPs), a class of proteins whose serum concentrations increase or decrease in response to

inflammation. Particularly, a significant association of state of inflammation with stage of

BC was previously described [34]. A recent study found that elevated serum levels of CRP

were associated considerably with a high risk of BC and poor outcome, including metastasis

and recurrence [35]. In addition, the concentrations of SAA4 increased gradually with tumor

progression and the severity of BC stages [36]. Thus, CRP and SAA4 may be good candidate

markers for the staging and prognosis of BC. The expression of HBB and TR, members of

the globin family, was also found to be associated with BC cells aggressiveness and poor

prognosis, indicating to HBB and TR as novel biomarker for BC progression [37, 38].

On the other hand, several studies point out that blood coagulation proteins develop an

important role in tumor progression [39]. These works discussed the impact of the activation

of the blood clotting cascade on primary tumor growth [40], on tumor metastasis and cancer-

associated thrombosis [41]; and antitumor therapies that target blood-coagulation-associated

proteins [42].

III. Results and Discussion. CHAPTER 3

161

In the case of BC, different reports support blood coagulation proteins as an important

patient factor that facilitates the metastatic potential [43]. Particularly, metastatic patients

exhibited significantly higher D-dimer values when compared with early breast cancer

patients [44]. Furthermore, high plasma fibrinogen was found to be correlated with poor

response to trastuzumab treatment in HER2 positive BC patients [45] and circulating levels

of factor VIII (FVIII) were significantly associated with axillary lymph node involvement,

number of metastatic nodes, and HER2 status [ 46 ]. These studies support that the

measurement of some coagulation-related biomarkers could provide additional data for the

evaluation of HER2 positive BC patients' prognosis and could be novel molecular targets.

The present quantitative proteomic analysis revealed a profile of blood coagulation

proteins for the HER2+ subtype, namely F5, F12 and KLKB1 (up-regulated), and

SERPINA1, PRSS1 and SERPINC1) (down-regulated). While F5 is expressed in tumors and

indicates favorable outcome in aggressive BC [47], F12 is involved in the pathogenesis of

thrombosis through the induction and amplification of thrombin generation [48].

KLKB1 (up), SERPINA1 (down) and SERPINC1 (down) are serine proteases.

Particularly, SERPINA1 and SERPINC1 are serine proteases inhibitors (serpins) which

belong to the protease inhibitor family. Members of the kallikrein family, as KLKB, were

also found to be deregulated during malignant transformation [ 49 ]. Nevertheless, the

variations in expression (downregulation/up-regulation), activation and secretion are not

substantially to consider them as suitable biomarkers for follow-up disease progression.

SERPINA1 is synthesized and released by tumor cells and plays major roles in physiologic

and pathologic processes such as angiogenesis, tumor invasion and metastasis [50]. It was

found that that high expression of SERPINA1 could be predictive for a better clinical

outcome of ER+ and ER+/HER2+ patients. Thus, SERPINA1 was found to be a direct ER

target gene and a predictor of survival in BC patients [51]. SERPINC1, an antithrombin,

develops an important role as an inhibitor of the coagulation cascade. Furthermore,

SERPINC1 also functions as an anti-angiogenic, anti-inflammatory, anti-viral and anti-

apoptotic protein. The mechanism by which antithrombin controls invasion, tumor migration,

and angiogenesis is by inhibition of enteropeptidase. This inhibition showed to be a double

anti-tumor effect through producing an anti-angiogenic molecule and inhibiting a protease

implicated in metastasis [52].

In the present work, the gonadotropin-releasing hormone receptor pathway was found to

be enriched with adiponectin (ADIPOQ) in the HER2+ subtype. Although different studies

III. Results and Discussion. CHAPTER 3

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162

reported controversial findings in the association between ADIPOQ and BC, a recent meta-

analysis suggests that low serum adiponectin concentration may be associated with an

increased BC risk in premenopausal and postmenopausal women [53]. A negative correlation

has been also demonstrated between ADIPOQ levels and tumor size and grade.

Interestingly, the correlation between ADIPOQ and BC seems to be more prominent in

oestrogen-negative and progesterone-negative BC [54]. Therefore, it seems there may be a

set group of BC patients that are most susceptible to the effects of ADIPOQ and would benefit

most from a potential treatment. ADIPOQ may serve as a biomarker of BC risk and help to

identify subjects at high risk for BC development.

Numerous research articles have accumulated solid evidence that lipoproteins are closely

related to various types of tumorigenesis, as BC [55]. Apolipoproteins in the blood transfer

lipids to cancer cells to provide energy for cancer cell proliferation and invasion.

Apolipoproteins also function as important factors in cellular signal transduction. In the

present work, different apoliporoteins were found to be deregulated in the different BC

subtypes. Particularly, APOB, APOD and APOE in serum were found to function as a risk

factor for BC, being APOD and APOB involved in BC metastasis [56]. Particularly, a recent

study found that apolipoprotein B is a risk factor for development of intraocular metastasis

(IOM) in patients with BC [57].

Within the group of glycoproteins found to be deregulated in the present study, it was

found that LAMP2 overexpression in breast tumors promotes cancer cell survival via

chaperone-mediated autophagy (CMA) [58]. Thus, inhibiting CMA activity in breast tumor

cells (with a chemotherapeutic drug, for example) can be exploited as a potential therapeutic

application in the treatment of BC. HSPG2, also known as perlecan, is a heavily glycosylated

protein component of the extra-cellular matrix (ECM) that plays essential roles in tumor

vascularization, that is closely related to tumor growth and metastasis [59]. Although HSPG2

expression in BC has not been examined in detail, a recent study investigated the expression

of HSPG2 in human TNBC and the ability of anti-HSPG2 antibodies to specifically target

and inhibit tumor growth in a mouse xenograft model [60], showing that HSPG2 is a

promising therapeutic target in TNBC. EFEMP1, also knon as fibulin 3, may have a potential

cancer-promoting function in BC [ 61 ]. EFEMP1 expression decreases during BC

progression, with low EFEMP1 levels correlating with a poorer prognosis. Functionally, high

EFEMP1 levels inhibited TGF-β-induced EMT, migration, invasion, and endothelial

permeability, while loss of EFEMP1 expression/function promoted these TGF-β-mediated

III. Results and Discussion. CHAPTER 3

163

effects. Further, restoring EFEMP1 expression in breast cancer cells inhibited TGF-β

signaling, breast cancer cell EMT, invasion and metastasis in vivo. Although the role of

CD5L in the oncogenesis of BC is not fully understood, a recent work found that CD5L is

upregulated in hepatocellular carcinoma and promotes liver cancer cell proliferation and

antiapoptotic responses [62].

The enzyme LDHB, that was found to be up-regutaled in the LB- subtype, may help

identify breast cancers most likely to respond to neoadjuvant chemotherapy as well as those

with the highest risk of relapse that may benefit from additional adjuvant therapy [63].

All these novel molecular targets found in the serum of BC patients could detect a

missing invasion, they could be performed in ambulatory settings, they could be repeatedly

checked, and they could be applicable for BC diagnosis, the assessment of prognosis and

selection of treatment.

Importantly, further insights exploring the deregulated blood coagulation proteins as

potential effective prognosis biomarkers and targets for novel therapeutic approaches could

have a great impact in the management of HER2-overexpressing BC patients.

4. Conclusions

The quantitative comparison of the ex vivo PCs formed upon incubation of AuNPs with

serum samples obtained from BC patients revealed 75 deregulated subtype-specific unique

proteins (8, 27, 2, 28 and 10 proteins specifically associated to the LA, LB-, LB+, HER2+

and TNBC subtypes). The analysis of the ex vivo PCs formed onto AuNPs reveals a profile

of blood coagulation proteins in the serum of HER2-overexpressing BC patients, that are

implicated in breast tumor progression, including cellular transformation, proliferation,

tumor cell survival, and angiogenesis. Of all BC patients, HER2+ patients have a worse

outcome. Further insights exploring these blood coagulation proteins as potential effective

prognosis biomarkers and targets for novel therapeutic approaches could have a great impact

on the management of HER2-overexpressing BC patients.

III. Results and Discussion. CHAPTER 3

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164

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III. Results and Discussion. CHAPTER 3

_________________________________________________________________________

172

IV. CONCLUSIONS

IV. Conclusions

______________________________________________________________________

173

1.- The interaction of AuNPs (10.02 ± 0.91 nm and 12.96 ± 0.72 nm), AgNPs (9.73 ±

1.70 nm), PtNPs (2.40 ± 0.30 nm) and MNPs (9.30 ± 0.67 nm) with human serum

withstands the formation of a protein corona enveloping the nanoparticle, in all cases.

2.- The formation of this protein corona depends on the composition of the nanoparticle

(core material) and its size (showing that smaller NPs as PtNPs have lower protein

adsorption than larger NPs as AuNPs), the NPs/protein ratio, the sample pH and the

incubation time.

3.- After adjusting the pH value to 5.8 with citrate/citric acid buffer, the incubation time

to 30 min, and the protein/NPs ratio to 10.7, a total of 215, 215 and 198 proteins were

identified in the surface of AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and PtNPs

(2.40 ± 0.30 nm), respectively. From them, 170 proteins were commonly detected in the

protein corona of all three different types of NPs. 52 unique proteins were found on the

three different NPs surface: 21 unique proteins on the 10.02 ± 0.91 nm AuNPs, 17 on the

9.73 ± 1.70 nm AgNPs and 14 individual proteins on the 2.40 ± 0.30 nm PtNPs.

4.- The function of all identified proteins ranges from proteins implicated in the immune

response, followed by proteins with an enzymatic function, structural, transporter,

inflammatory, signal transduction and with antibiotic/antibacterial properties, being the

majority group the first one with 66 proteins identified implicated in the immune

response.

5.- The unique proteins identified on the corona of the three different NPs were proteins

with structural function and implicated in the signal transduction in the case of AuNPs

(10.02 ± 0.91 nm), proteins with antibiotic/antibacterial properties in the surface of

AgNPs (9.73 ± 1.70 nm) and proteins implicated on inflammatory processes in PtNPs

(2.40 ± 0.30 nm).

6.- The optimal conditions found for the formation of the protein corona around MNPs

(9.30 ± 0.67 nm) were the depletion with fresh DTT 500 mM in milli-Q for 60 min at

room temperature, a 1:2 ratio (MNP/protein) and a pH value of 5.5 in the incubation and

the final washing step.

IV. Conclusions

______________________________________________________________________

174

7.- The application of AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) MNPs (9.30 ±

0.67 nm), combined with mass spectrometry, has allowed the identification of seven

potential biomarkers for the diagnostic and control of TNBC progression: GRF-type zinc

finger domain-containing protein 1 (protein ZGRF1), matrix metalloproteinase-9

(MMP9), lebercilin and immunoglobulin lambda variable 3-27 (LV327) and LINE-1 type

transposase domain-containing protein 1 (LITD1), structural maintenance of

chromosomes protein 6 (SMC6) and short coiled-coil protein (SCOC).

8.- AuNPs (12.96 ± 0.72 nm) was an optimal scavenging tool in combination with

Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) to

quantitatively analyze the serum proteome alterations in the different breast cancer

molecular subtypes (luminal A (LA), luminal B HER2 negative (LB-), luminal B HER2

positive (LB+), HER2 positive (HER2+) and triple negative breast cancer (TNBC)).

9.- The quantitative comparison of the ex vivo PCs formed upon incubation of AuNPs

with serum samples obtained from BC patients revealed 75 deregulated subtype-specific

unique proteins (8, 27, 2, 28 and 10 proteins specifically associated to the LA, LB-, LB+,

HER2+ and TNBC subtypes, respectively).

10.- The analysis of the ex vivo PCs formed onto AuNPs revealed a profile of blood

coagulation proteins in the serum of HER2-overexpressing BC patients that are

implicated in breast tumor progression, including cellular transformation, proliferation,

tumor cell survival and angiogenesis.

ANNEX A

Supplemental Material Chapter 1

175

Figure 1_SM. TEM image of AuNPs@citrate in aqueous phase and the characterization data.

AuNPs@citrate (nm) 1 7.94 21 10.35

2 8.37 22 10.38

3 8.38 23 10.41

4 8.60 24 10.44

5 8.78 25 10.56

6 8.78 26 10.65

7 8.87 27 10.65

8 9.17 28 10.68

9 9.19 29 10.80

10 9.41 30 10.81

11 9.49 31 10.83

12 9.49 32 10.85

13 9.55 33 10.86

14 9.62 34 10.86

15 9.82 35 10.88

16 9.84 36 10.95

17 9.89 37 10.96

18 9.89 38 10.99

19 9.99 39 11.48

20 10.03 40 11.50

Count 40

Mean 10.02

Minimum 7.94

Maximum 11.50

Standar Deviation 0.91

176

177

178

179

Figure 2_SM. TEM image of AgNPs@citrate in aqueous phase and the characterization data.

AgNPs@citrate (nm) 1 7.58 21 9.30

2 7.66 22 9.45

3 7.89 23 9.58

4 7.89 24 9.76

5 7.89 25 9.86

6 7.98 26 10.25

7 8.17 27 10.39

8 8.19 28 10.48

9 8.19 29 10.61

10 8.21 30 10.99

11 8.40 31 11.17

12 8.40 32 11.27

13 8.48 33 11.47

14 8.55 34 11.64

15 8.64 35 11.83

16 8.75 36 11.83

17 8.92 37 12.10

18 9.02 38 13.02

19 9.07 39 13.49

20 9.16 40 13.76

Count 40

Mean 9.73

Minimum 7.58

Maximum 13.76

Standar Deviation 1.70

180

181

182

183

Figure 3_SM. TEM image of PtNPs@citrate in aqueous phase and the characterization data.

PtNPs@citrate (nm) 1 2.02 21 2.31

2 2.02 22 2.32

3 2.03 23 2.38

4 2.05 24 2.42

5 2.08 25 2.50

6 2.10 26 2.51

7 2.16 27 2.54

8 2.16 28 2.57

9 2.16 29 2.58

10 2.16 30 2.68

11 2.17 31 2.69

12 2.17 32 2.70

13 2.17 33 2.75

14 2.17 34 2.80

15 2.17 35 2.83

16 2.18 36 2.83

17 2.18 37 2.89

18 2.22 38 2.94

19 2.23 39 2.99

20 2.23 40 3.00

Count 40

Mean 2.40

Minimum 2.02

Maximum 3.00

Standar Deviation 0.30

184

185

186

187

Table 1_SM. Number of the proteins identified from the protein corona formed around AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and

PtNPs (2.40 ± 0.30 nm) after their incubation with human serum from five disease free individuals.

Serum sample Disease-free individual 1 Disease-free individual 2 Disease-free individual 3 Disease-free individual 4 Disease-free individual 5

NPs type Replicates Identified

peptides ±SD Replicates

Indetified

peptides ± SD Replicates

Identified

peptides ± SD Replicates

Identified

peptides ± SD Replicates

Identified

peptides ± SD

AuNPs

1_AuNP1 215

216±1

2_AuNP1 225

225±2

3_AuNP1 216

216±1

4_AuNP1 239

238±2

5_AuNP1 236

237± 2 1_AuNP2 217 2_AuNP2 223 3_AuNP2 215 4_AuNP2 236 5_AuNP2 239

1_AuNP3 218 2_AuNP3 228 3_AuNP3 218 4_AuNP3 240 5_AuNP3 236

AgNPs

1_AgNP1 222

219±3

2_AgNP1 228

234±6

3_AgNP1 219

219±1

4_AgNP1 242

239±3

5_AgNP1 239

238±4 1_AgNP2 220 2_AgNP2 235 3_AgNP2 218 4_AgNP2 239 5_AgNP2 242

1_AgNP3 215 2_AgNP3 240 3_AgNP3 220 4_AgNP3 236 5_AgNP3 233

PtNPs

1_PtNP1 200

200±2

2_PtNP1 215

211±4

3_PtNPt1 199

199±2

4_PtNP1 209

212±3

5_PtNP1 199

199±2 1_PtNP2 203 2_PtNP2 210 3_PtNPt2 198 4_PtNP2 215 5_PtNP2 198

1_PtNP3 199 2_PtNP3 208 3_PtNPt3 202 4_PtNP3 213 5_PtNP3 202

188

Table 2_SM. Analysis of the commonly protein corona formed around AuNPs (10.02 ± 0.91 nm). AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ±

0.30 nm) after their incubation with serum aliquots (x3) from five different individuals (9 protein samples per individual: 3 treated with AuNPs. 3

with AgNPs and 3 with PtNPs). The accession number. gene name. species (Human). molecular weight and protein function were reported. Grey

color: 170 proteins commonly found in the protein corona of AuNPs (10.02 ± 0.91 nm). AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm).

Pink color: 21 identified corona proteins exclusively bound to the AuNPs (10.02 ± 0.91 nm). Yellow color: 17 identified corona proteins

exclusively bound to the AgNPs (9.73 ± 1.70 nm). Blue: 14 identified corona proteins exclusively bound to the PtNPs (2.40 ± 0.30 nm). Orange

color: 19 identified corona proteins commonly bound to the AuNPs (10.02 ± 0.91 nm) and AgNPs (9.73 ± 1.70 nm). Violet color: 5 identified

corona proteins commonly bound to the AuNPs (10.02 ± 0.91 nm) and PtNPs (2.40 ± 0.30 nm). Green color: 9 identified corona proteins

commonly bound to the AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm).

Protein Name UniProt Name Entry Name Gene AuNPs AgNPs PtNPs Mass (kDa) Function Actin. cytoplasmic 2 ACTG_HUMAN P63261 ACTG1 X X X 41.7 Cell mobility

Adipocyte plasma membrane-

associated protein APMAP_HUMAN Q9HDC9 APMAP X X X

46.5

Catalytic activity

Afamin AFAM_HUMAN P43652 AFM X X X 69.1 Transporter activity

Alpha-1-acid glycoprotein 1 A1AG1_HUMAN P02763 ORM1 X X X 23.5 Transporter activity

Alpha-1-acid glycoprotein 2 A1AG2_HUMAN P19652 ORM2 X X X 23.6 Transporter activity

Alpha-1-antichymotrypsin AACT_HUMAN P01011 SERPINA3 X X X 47.6 Protease ihibitor

Alpha-1-antitrypsin A1AT_HUMAN P01009 SERPINA1 X X X 46.7 Protease ihibitor

Alpha-1B-glycoprotein A1BG_HUMAN P04217 A1BG X X X 54.3 Neutrophil and platelet degranulation

Alpha-2-antiplasmin A2AP_HUMAN P08697 SERPINF2 X X X 54.6 Protease inhibitor

Alpha-2-HS-glycoprotein FETUA_HUMAN P02765 AHSG X X X 39.3

Promotes endocitosis. brain

development and the formation of

bone tissue

Alpha-2-macroglobulin A2MG_HUMAN P01023 A2M X X X 163.3 Immune response

Angiotensin-converting enzyme ACE_HUMAN P12821 ACE X 149.7 Catalytic activity

Angiotensinogen ANGT_HUMAN P01019 AGT X X X 53.1 Regulator of blood pressure. body

fluid and electrolyte homeostasis

Antithrombin-III ANT3_HUMAN P01008 SERPINC1 X X X 52.6 Protease inhibitor

189

Apolipoprotein A-I APOA1_HUMAN P02647 APOA1 X X X 30.8 Metabolism

Apolipoprotein A-II APOA2_HUMAN P02652 APOA2 X X X 11.2 Transport

Apolipoprotein A-IV APOA4_HUMAN P06727 APOA4 X X X 45.4 Hepatic transcellukar lipid transport

Apolipoprotein B-100 APOB_HUMAN P04114 APOB X X X 515.6

Recognition signal for the cellular

binding and internalization of LDL

particles by the apoB/E receptor

Apolipoprotein C-II APOC2_HUMAN P02655 APOC2 X X 11.3 Enzyme regulator activity

(lipoprotein lipase activator)

Apolipoprotein C-III APOC3_HUMAN P02656 APOC3 X X X 10.8 Enzyme regulator activity

(lipoprotein lipase inhibitor)

Apolipoprotein C-IV APOC4_HUMAN P55056 APOC4 X X 14.5 Biological regulation

Apolipoprotein D APOD_HUMAN P05090 APOD X X X 21.3 Transporter activity

Apolipoprotein E APOE_HUMAN P02649 APOE X X X 36.1 Transporter activity and cell uptake

Apolipoprotein F APOF_HUMAN Q13790 APOF X X X 35.4 Transporter activity

Apolipoprotein L1 APOL1_HUMAN O14791 APOL1 X X X 43.9 Transporter activity

Apolipoprotein M APOM_HUMAN O95445 APOM X X X 21.2 Transporter activity

Apolipoprotein(a) APOA_HUMAN P08519 LPA X X X 501.3 Catalytic activity

ATP-binding cassette sub-family

B member 5 ABCB5_HUMAN Q2M3G0 ABCB5 X 138.6 Transporter activity

ATP-binding cassette sub-family

F member 1 ABCF1_HUMAN Q8NE71 ABCF1 X

95.9

Transporter activity

Attractin ATRN_HUMAN O75882 ATRN X X X 158.5

Inflammatory response

Basement membrane-specific

heparan sulfate proteoglycan core

protein

PGBM_HUMAN P98160 HSPG2 X 468.8 Structural

Beta-2-glycoprotein 1 APOH_HUMAN P02749 APOH X X X 38.3 Transporter activity

Beta-Ala-His dipeptidase CNDP1_HUMAN Q96KN2 CNDP1 X X X 56.7 Catalytic activity

Biotinidase BTD_HUMAN P43251 BTD X X X 61.1 Catalytic activity

C4b-binding protein alpha chain C4BPA_HUMAN P04003 C4BPA X X X 67.1 Enzyme regulator activity (inhibitor)

C4b-binding protein beta chain C4BPB_HUMAN P20851 C4BPB X X X 28.4 Enzyme regulator activity (inhibitor)

Cadherin-5 CADH5_HUMAN P33151 CDH5 X 87.5

Controls the cohesion and

organization of the intercellular

junctions

Carboxypeptidase B2 CBPB2_HUMAN Q96IY4 CPB2 X X X 48.4 Catalytic activity

Carboxypeptidase N catalytic

chain CBPN_HUMAN P15169 CPN1 X X X 52.3 Catalytic activity

Carboxypeptidase N subunit 2 CPN2_HUMAN P22792 CPN2 X X X 60.6 Enzyme regulator activity

Cartilage acidic protein 1 CRAC1_HUMAN Q9NQ79 CRTAC1 X 71.4 _

190

Cathelicidin antimicrobial

peptide CAMP_HUMAN P49913 CAMP X 19.3 Antibacterial activity

CD5 antigen-like CD5L_HUMAN O43866 CD5L X X X 38.1 Regulator of lipid synthesis

Centlein CNTLN_HUMAN Q9NXG0 CNTLN X 161.6

Structural

Ceruloplasmin CERU_HUMAN P00450 CP X X X 122.2 Catalytic activity

Cholesteryl ester transfer protein CETP_HUMAN P11597 CETP X X X 54.8

Transporter activity

Cholinesterase CHLE_HUMAN P06276 BCHE X X X 68.4 Catalytic activity

Clusterin CLUS_HUMAN P10909 CLU X X X 52.5 Regulation of cell proliferation

CMP-N-acetylneuraminate-poly-

alpha-2.8-sialyltransferase SIA8D_HUMAN Q92187 ST8SIA4 X

41.3

Catalytic activity

Coagulation factor IX FA9_HUMAN P00740 F9 X X 51.8 Catalytic activity

Coagulation factor V FA5_HUMAN P12259 F5 X X 251.7 Enzyme regulator activity

Coagulation factor X FA10_HUMAN P00742 F10 X X X 54.7 Enzyme regulator activity

Coagulation factor XII FA12_HUMAN P00748 F12 X X X 67.8 Catalytic activity

Coagulation factor XIII B chain F13B_HUMAN P05160 F13B X X X 75.5 Enzyme regulator activity

Complement C1q subcomponent

subunit A C1QA_HUMAN P02745 C1QA X X X 26.1 Enzyme regulator activity

Complement C1q subcomponent

subunit B C1QB_HUMAN P02746 C1QB X X X 26.7 Enzyme regulator activity

Complement C1q subcomponent

subunit C C1QC_HUMAN P02747 C1QC X X X 25.8 Enzyme regulator activity

Complement C1r subcomponent C1R_HUMAN P00736 C1R X X X 80.1 Catalytic activity

Complement C1r subcomponent-

like protein C1RL_HUMAN Q9NZP8 C1RL X X X 53.5 Enzyme regulator activity

Complement C1s subcomponent C1S_HUMAN P09871 C1S X X 76.7 Catalytic activity

Complement C2 CO2_HUMAN P06681 C2 X X X 83.3 Catalytic activity

Complement C3 CO3_HUMAN P01024 C3 X X X 187.1 Immune response

Complement C4-A CO4A_HUMAN P0C0L4 C4A X X X 192.8 Inflammatory response

Complement C4-B CO4B_HUMAN P0C0L5 C4B X X X 192.7 Enzyme regulator activity

Complement C5 CO5_HUMAN P01031 C5 X X X 188.3 Enzyme regulator activity

Complement component C6 CO6_HUMAN P13671 C6 X X X 104.8 Structural

Complement component C7 CO7_HUMAN P10643 C7 X X X 93.5 Structural

Complement component C8

alpha chain CO8A_HUMAN P07357 C8A X 65.2 Structural

Complement component C8 beta

chain CO8B_HUMAN P07358 C8B X X X 67.1 Structural

191

Complement component C8

gamma chain CO8G_HUMAN P07360 C8G X X X 22.3 Structural

Complement component C9 CO9_HUMAN P02748 C9 X X 63.2 Structural

Complement factor B CFAB_HUMAN P00751 CFB X X X 85.5 Immune response

Complement factor D CFAD_HUMAN P00746 CFD X 27.1 Catalytic activity

Complement factor H CFAH_HUMAN P08603 CFH X X X 139.1 Enzyme regulator activity

Complement factor H-related

protein 1 FHR1_HUMAN Q03591 CFHR1 X X X 37.6 Plays a role in lipid metabolism

Complement factor H-related

protein 2 FHR2_HUMAN P36980 CFHR2 X X X

30.6

Plays a role in lipid metabolism

Complement factor I CFAI_HUMAN P05156 CFI X X X 65.7 Catalytic activity

Corticosteroid-binding globulin CBG_HUMAN P08185 SERPINA6 X X X 45.1 Transporter activity

Dedicator of cytokinesis protein

3 DOCK3_HUMAN Q8IZD9 DOCK3 X 233.1 Enzyme regulator activity

DENN domain-containing

protein 5B DEN5B_HUMAN Q6ZUT9 DENND5B X 145.1 Enzyme regulator activity

Dermcidin DCD_HUMAN P81605 DCD X X X 11.3

Antimicrobial activity and proteolytic

activity

DnaJ homolog subfamily C

member 22 DJC22_HUMAN Q8N4W6 DNAJC22 X 38.1 Function as a co-chaperone

Dopamine beta-hydroxylase DOPO_HUMAN P09172 DBH X 69.1 Catalyctic activity

Endophilin-A3 SH3G3_HUMAN Q99963 SH3GL3 X X 39.3 Implicated in endocytosis.

Eukaryotic translation elongation

factor 1 epsilon-1 MCA3_HUMAN O43324 EEF1E1 X 19.8

Positive modulator of ATM response

to DNA damage.

Extracellular glycoprotein lacritin LACRT_HUMAN Q9GZZ8 LACRT X 14.2 Modulates secretion by lacrimal

acinar cells

Extracellular matrix protein 1 ECM1_HUMAN Q16610 ECM1 X X X 60.7 Enzyme regulator inhibitor

F-box only protein 3 FBX3_HUMAN Q9UK99 FBXO3 X 54.561 Enzyme regulator activity

Fetuin-B FETUB_HUMAN FETUB FETUB X X X 42.1 Catalytic activity

Fibrinogen alpha chain FIBA_HUMAN P02671 FGA X 94.9 Coagulation. immune response

Fibronectin FINC_HUMAN P02751 FN1 X X X 262.6 Growth. migration and differenciation

Fibulin-1 FBLN1_HUMAN P23142 FBLN1 X X 77.2

Structural

Ficolin-2 FCN2_HUMAN Q15485 FCN2 X X X 34.0 Immunity response

Ficolin-3 FCN3_HUMAN O75636 FCN3 X X X 32.9 Immunity response

Filaggrin-2 FILA2_HUMAN Q5D862 FLG2 X 248.1 _

Galectin-3-binding protein LG3BP_HUMAN Q08380 LGALS3BP X X X 65.3 Stimulate host defense against viruses

and tumor cells

Gelsolin GELS_HUMAN P06396 GSN X X X 85.7 Actin regulation (assembly)

192

Glyceraldehyde-3-phosphate

dehydrogenase G3P_HUMAN P04406 GAPDH X

36.1

Catalytic activity

Haptoglobin HPT_HUMAN P00738 HP X X X 45.2

Prevents loss of iron through the

kidneys. protecting them from

damage by hemoglobin

Haptoglobin-related protein HPTR_HUMAN P00739 HPR X X X 39

Binds hemoglobin as efficiently as

haptoglobin

HAUS augmin-like complex

subunit 8 HAUS8_HUMAN Q9BT25 HAUS8 X

44.9

Involved in microtubule generation

within the mitotic spindle

Hemopexin HEMO_HUMAN P02790 HPX X X X 51.7 Transporter activity

Heparin cofactor 2 HEP2_HUMAN P05546 SERPIND1 X X X 57.1 Enzyme regulator activity (inhibitor)

Hepatocyte growth factor

activator HGFA_HUMAN Q04756 HGFAC X 70.7 Enzyme regulator activity (activator)

Hepatocyte growth factor-like

protein HGFL_HUMAN P26927 MST1 X X 80.3 Catalytic activity

Histidine-rich glycoprotein HRG_HUMAN P04196 HRG X X X 59.6 Inhibits fibrinolysis and reduces

inhibition of coagulation

Hyaluronan-binding protein 2 HABP2_HUMAN Q14520 HABP2 X X X 62.7

Enzyme regulator activity

IgGFc-binding protein FCGBP_HUMAN Q9Y6R7 FCGBP X X X 572.1 Structural

Immunoglobulin alpha-2 heavy

chain IGA2_HUMAN P0DOX2 N/A X X X 48.9 Immune response

Immunoglobulin delta heavy

chain IGD_HUMAN P0DOX3 N/A X X X 56.2 Immune response

Immunoglobulin epsilon heavy

chain IGE_HUMAN P0DOX4 N/A X X X 60.3 Immune response

Immunoglobulin gamma-1 heavy

chain IGG1_HUMAN P0DOX5 N/A X X X 49.3 Immune response

Immunoglobulin heavy constant

alpha 1 IGHA1_HUMAN P01876 IGHA1 X X X 37.7 Immune response

Immunoglobulin heavy constant

alpha 2 IGHA2_HUMAN P01877 IGHA2 X X X 36.6 Immune response

Immunoglobulin heavy constant

gamma 1 IGHG1_HUMAN P01857 IGHG1 X 36.1 Immune response

Immunoglobulin heavy constant

gamma 2 IGHG2_HUMAN P01859 IGHG2 X X X 35.9 Immune response

Immunoglobulin heavy constant

gamma 3 IGHG3_HUMAN P01860 IGHG3 X X X 41.3 Immune response

Immunoglobulin heavy constant

gamma 4 IGHG4_HUMAN P01861 IGHG4 X X X 35.9 Immune response

193

Immunoglobulin heavy constant

mu IGHM_HUMAN P01871 IGHM X X X

49.4

Immune response

Immunoglobulin heavy variable

1-2 HV102_HUMAN P23083 IGHV1-2 X X X 13.1 Immune response

Immunoglobulin heavy variable

1-46 HV146_HUMAN P01743 IGHV1-46 X X 12.9 Immune response

Immunoglobulin heavy variable

1-8 HV108_HUMAN P0DP01 IGHV1-8 X X 12.9 Immune response

Immunoglobulin heavy variable

3-11 HV311_HUMAN P01762 IGHV3-11 X 12.9 Immune response

Immunoglobulin heavy variable

3-23 HV323_HUMAN P01764 IGHV3-23 X X X 12.6 Immune response

Immunoglobulin heavy variable

3-30-3 HVC33_HUMAN P0DP02

IGHV3-30-

3 X 13 Immune response

Immunoglobulin heavy variable

3-30-5 HV335_HUMAN P0DP03

IGHV3-30-

5 X X X 12.9 Immune response

Immunoglobulin heavy variable

3-43D HV43D_HUMAN P0DP04 IGHV3-43D X X 13.1 Immune response

Immunoglobulin heavy variable

3-48 HV348_HUMAN P01763 IGHV3-48 X X 12.8 Immune response

Immunoglobulin heavy variable

3-53 HV353_HUMAN P01767 IGHV3-53 X 12.8 Immune response

Immunoglobulin heavy variable

3-7 HV307_HUMAN P01780 IGHV3-7 X X X 12.9 Immune response

Immunoglobulin heavy variable

3-9 HV309_HUMAN P01782 IGHV3-9 X X X 12.9 Immune response

Immunoglobulin heavy variable

4-38-2 HVD82_HUMAN P0DP08

IGHV4-38-

2 X X X 13.1 Immune response

Immunoglobulin heavy variable

4-39 HV439_HUMAN P01824 IGHV4-39 X 13.9 Immune response

Immunoglobulin J chain IGJ_HUMAN P01591 JCHAIN X X X 18.1 Immune response

Immunoglobulin kappa constant IGKC_HUMAN P01834 IGKC X X X 11.8 Immune response

Immunoglobulin kappa light

chain IGK_HUMAN P0DOX7 N/A X X X 23.4 Immune response

Immunoglobulin kappa variable

1-16 KV116_HUMAN P04430 IGKV1-16 X X X 12.6 Immune response

Immunoglobulin kappa variable

1-17 KV117_HUMAN P01599 IGKV1-17 X X X 12.8 Immune response

Immunoglobulin kappa variable

1-33 KV133_HUMAN P01594 IGKV1-33 X X X 12.8 Immune response

194

Immunoglobulin kappa variable

1D-12 KVD12_HUMAN P01611 IGKV1D-12 X 12.6 Immune response

Immunoglobulin kappa variable

1D-16 KVD16_HUMAN P01601 IGKV1D-16 X X 12.7 Immune response

Immunoglobulin kappa variable

1D-39 KVD39_HUMAN P04432 IGKV1D-39 X X X 12.7 Immune response

Immunoglobulin kappa variable

2-30 KV230_HUMAN P06310 IGKV2-30 X X X 13.2 Immune response

Immunoglobulin kappa variable

2D-28 KVD28_HUMAN P01615 IGKV2D-28 X X X 12.9 Immune response

Immunoglobulin kappa variable

3-11 KV311_HUMAN P04433 IGKV3-11 X X X 12.6 Immune response

Immunoglobulin kappa variable

3-15 KV315_HUMAN P01624 IGKV3-15 X X X 12.5 Immune response

Immunoglobulin kappa variable

3-20 KV320_HUMAN P01619 IGKV3-20 X X X 12.6 Immune response

Immunoglobulin kappa variable

4-1 KV401_HUMAN P06312 IGKV4-1 X X X 13.4 Immune response

Immunoglobulin lambda constant

2 IGLC2_HUMAN P0DOY2 IGLC2 X X X 11.3 Immune response

Immunoglobulin lambda constant

3 IGLC3_HUMAN P0DOY3 IGLC3 X X X 11.3 Immune response

Immunoglobulin lambda variable

1-47 LV147_HUMAN P01700 IGLV1-47 X X X 12.3 Immune response

Immunoglobulin lambda variable

1-51 LV151_HUMAN P01701 IGLV1-51 X X X 12.2 Immune response

Immunoglobulin lambda variable

2-11 LV211_HUMAN P01706 IGLV2-11 X X 12.6 Immune response

Immunoglobulin lambda variable

2-23 LV223_HUMAN P01705 IGLV2-23 X X 11.9 Immune response

Immunoglobulin lambda variable

3-19 LV319_HUMAN P01714 IGLV3-19 X X X 12.1 Immune response

Immunoglobulin lambda variable

3-21 LV321_HUMAN P80748 IGLV3-21 X X X 12.4 Immune response

Immunoglobulin lambda variable

3-25 LV325_HUMAN P01717 IGLV3-25 X X X 12.1 Immune response

Immunoglobulin lambda variable

3-27 LV327_HUMAN P01718 IGLV3-27 X 12.2 Immune response

Immunoglobulin lambda variable

6-57 LV657_HUMAN P01721 IGLV6-57 X X X 12.6 Immune response

195

Immunoglobulin lambda-1 light

chain IGL1_HUMAN P0DOX8 N/A X X X 22.8 Immune response

Immunoglobulin lambda-like

polypeptide 1 IGLL1_HUMAN P15814 IGLL1 X

22.9

Immune response

Immunoglobulin lambda-like

polypeptide 5 IGLL5_HUMAN B9A064 IGLL5 X

23.1

Immune response

Immunoglobulin mu heavy chain IGM_HUMAN P0DOX6 N/A X X X 63.5 Immune response

Insulin-like growth factor-

binding protein complex acid

labile subunit

ALS_HUMAN P35858 IGFALS X X X 66.1

Binds insulin-like growth factors.

increasing their half-life and their

vascular localization

Inter-alpha-trypsin inhibitor

heavy chain H1 ITIH1_HUMAN P19827 ITIH1 X X X

101.4

Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H2 ITIH2_HUMAN P19823 ITIH2 X X X 106.5 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H3 ITIH3_HUMAN Q06033 ITIH3 X X X

99.9

Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H4 ITIH4_HUMAN Q14624 ITIH4 X X X

103.4

Inflammatory response

Interleukin-1 receptor accessory

protein IL1AP_HUMAN Q9NPH3 IL1RAP X X X

65.4

Inflammatory response

Kallistatin KAIN_HUMAN P29622 SERPINA4 X X X 48.5 Enzyme regulator inhibitor

Keratin. type I cytoskeletal 10 K1C10_HUMAN P13645 KRT10 X X X 58.8 Structural

Keratin. type I cytoskeletal 14 K1C14_HUMAN P02533 KRT14 X X X 51.6 Structural

Keratin. type I cytoskeletal 15 K1C15_HUMAN P19012 KRT15 X 49.2 Structural

Keratin. type I cytoskeletal 16 K1C16_HUMAN P08779 KRT16 X X X 51.3 Structural

Keratin. type I cytoskeletal 9 K1C9_HUMAN P35527 KRT9 X X X 62.1 Structural

Keratin. type II cytoskeletal 1 K2C1_HUMAN P04264 KRT1 X X X 66.1 Enzyme regulator activity

Keratin. type II cytoskeletal 2

epidermal K22E_HUMAN P35908 KRT2 X X X 65.4 Structural

Keratin. type II cytoskeletal 5 K2C5_HUMAN P13647 KRT5 X X X 62.4 Structural

Keratin. type II cytoskeletal 6A K2C6A_HUMAN P02538 KRT6A X X 60.1 Enzyme regulator activity (inhibitor)

Keratin. type II cytoskeletal 6C K2C6C_HUMAN P48668 KRT6C X X 60.1 Structural

Kininogen-1 KNG1_HUMAN P01042 KNG1 X X X 71.9 Enzyme regulator activity

Kynurenine-oxoglutarate

transaminase 3 KAT3_HUMAN Q6YP21 KYAT3 X

51.4

Catalytic activity

Leucine-rich alpha-2-

glycoprotein A2GL_HUMAN P02750 LRG1 X X X 38.2

Protein-protein interaction. signal

transduction. and cell adhesion and

development

Leucine-rich repeat-containing LRRC9_HUMAN Q6ZRR7 LRRC9 X 166.9 _

196

protein 9

Lipopolysaccharide-binding

protein LBP_HUMAN P18428 LBP X X X 53.4 Immune response.

L-lactate dehydrogenase A-like

6B LDH6B_HUMAN Q9BYZ2 LDHAL6B X X 41.9 Catalytic activity

L-lactate dehydrogenase B chain LDHB_HUMAN P07195 LDHB X 36.6 Catalytic activity

L-selectin LYAM1_HUMAN P14151 SELL X X 42.2

Mediates in lymphocyte-endothelial

cell interactions

Lumican LUM_HUMAN P51884 LUM X X X 38.4 Collagen binding. extracellular matrix

structural constituent

Maestro heat-like repeat-

containing protein family

member 2A

MRO2A_HUMAN A6NES4 MROH2A X 189.6 _

Mannan-binding lectin serine

protease 1 MASP1_HUMAN P48740 MASP1 X X X

79.2

Enzyme regulator activity

Mannan-binding lectin serine

protease 2 MASP2_HUMAN O00187 MASP2 X X

75.7

Catalytic activity

Mannosyl-oligosaccharide 1.2-

alpha-mannosidase IA MA1A1_HUMAN P33908 MAN1A1 X X

72.9

Catalytic activity

Monocyte differentiation antigen

CD14 CD14_HUMAN P08571 CD14 X X X 40.1 Immune response

Multiple inositol polyphosphate

phosphatase 1 MINP1_HUMAN Q9UNW1 MINPP1 X

55.1

Catalytic activity

N-acetylmuramoyl-L-alanine

amidase PGRP2_HUMAN Q96PD5 PGLYRP2 X X X

62.2

Catalytic activity

Nebulin-related-anchoring

protein NRAP_HUMAN Q86VF7 NRAP X

197.1

Implicated in myofibrilar

organization during cardiomyocyte

development

Neutrophil cytosol factor 4 NCF4_HUMAN Q15080 NCF4 X 39.1

Component of the NADPH-oxidase

Phosphatidylcholine-sterol

acyltransferase LCAT_HUMAN P04180 LCAT X X X 49.1 Catalytic activity

Phosphatidylinositol-glycan-

specific phospholipase D PHLD_HUMAN P80108 GPLD1 X X X

92.3

Catalytic activity

Phospholipid transfer protein PLTP_HUMAN P55058 PLTP X X X 54.8

Transporter activity

Pigment epithelium-derived

factor PEDF_HUMAN P36955 SERPINF1 X X X 46.3

Induces extensive neuronal

differentiation in retinoblastoma cells

Plasma kallikrein KLKB1_HUMAN P03952 KLKB1 X X X 71.4 Catalytic activity

Plasma protease C1 inhibitor IC1_HUMAN P05155 SERPING1 X X X 55.2 Protease.

197

Plasma serine protease inhibitor IPSP_HUMAN P05154 SERPINA5 X X 45.7 Enzyme regulator activuty (inhibitor)

Plasminogen PLMN_HUMAN P00747 PLG X X X 90.6 Catalytic activity

Platelet glycoprotein Ib alpha

chain GP1BA_HUMAN P07359 GP1BA X X 71.1

Participates in the formation of

platelet plugs

Platelet glycoprotein V GPV_HUMAN P40197 GP5 X X 61 Immune response

Pleckstrin homology domain-

containing family G member 6 PKHG6_HUMAN Q3KR16 PLEKHG6 X 88.9 Enzyme regulator activity

Polymeric immunoglobulin

receptor PIGR_HUMAN P01833 PIGR X X X 83.3

This receptor binds polymeric IgA

and IgM at the basolateral surface of

epithelial cells

Pregnancy zone protein PZP_HUMAN P20742 PZP X X X 163.8 Enzyme regulator activity

Prenylcysteine oxidase 1 PCYOX_HUMAN Q9UHG3 PCYOX1 X X X 56.6 Clatytic activity

Protein AMBP AMBP_HUMAN P02760 AMBP X X X 38.9 Enzyme regulator activity (inhibitor)

Protein argonaute-3 AGO3_HUMAN Q9H9G7 GENE:

AGO3 X 97.4 Enzyme regulator activity

Protein ENL ENL_HUMAN Q03111 MLLT1 X 62.1 Enzyme regulator activity

Protein Shroom3 SHRM3_HUMAN Q8TF72 SHROOM3 X X X 216.8

Controls cell shape changes in the

neuroepithelium during neural tube

closure

Protein Z-dependent protease

inhibitor ZPI_HUMAN Q9UK55

SERPINA1

0 X X X 50.7 Enzyme regulator activity

Proteoglycan 4 PRG4_HUMAN Q92954 PRG4 X X 151.1 Plays a role in boundary lubrication

within articulating joints

Prothrombin THRB_HUMAN P00734 F2 X X X 70.1 Catalytic activity

Pulmonary surfactant-associated

protein B PSPB_HUMAN P07988 SFTPB X X X 42.1

Promotes alveolar stability by

lowering the surface tension at the

air-liquid interface in the peripheral

air spaces

Receptor-type tyrosine-protein

phosphatase eta PTPRJ_HUMAN Q12913 PTPRJ X 145.9 Catalytic activity

Regulator of G-protein signaling

20 RGS20_HUMAN O76081 RGS20 X

31.5

Inhibits signal transduction

Retinol-binding protein 4 RET4_HUMAN P02753 RBP4 X X X 23.1 Transporter activity

Secreted phosphoprotein 24 SPP24_HUMAN Q13103 SPP2 X X 24.1

Bind cytokines of the TGF-β

superfamily and also activate

intracellular signaling pathways

Secretogranin-2 SCG2_HUMAN P13521 SCG2 X X 70.9 Involved in the packaging or sorting

of peptide hormones and

198

neuropeptides into secretory vesicles

Selenoprotein P SEPP1_HUMAN P49908 SELENOP X X X 43.2 Transporter activity

Serotransferrin TRFE_HUMAN P02787 TF X X X 77.1 Transporter activity

Serum albumin ALBU_HUMAN P02768 ALB X X X 69.4 Osmotic pressure

Serum amyloid A-1 protein SAA1_HUMAN P0DJI8 SAA1 X 13.5 Inflammatory response

Serum amyloid A-4 protein SAA4_HUMAN P35542 SAA4 X X X 14.7 Inflammatory response

Serum amyloid P-component SAMP_HUMAN P02743 APCS X 25.4

Can interact with DNA and histones

and may scavenge nuclear material

released from damaged circulating

cells

Serum paraoxonase/arylesterase

1 PON1_HUMAN P27169 PON1 X X X 39.7 Catalytic activity

Serum paraoxonase/lactonase 3 PON3_HUMAN Q15166 PON3 X X X 39.6 Catalytic activity

Sex hormone-binding globulin SHBG_HUMAN P04278 SHBG X X X 43.8

Transporter activity. Regulates the

plasma metabolic clearance rate of

steroid hormones

SHC-transforming protein 1 SHC1_HUMAN P29353 SHC1 X 62.8

Signaling adapter that couples

activated growth factor receptors to

signaling pathways

Sulfhydryl oxidase 1 QSOX1_HUMAN O00391 QSOX1 X 82.6 Catalytic activity

Testis-specific gene 10 protein TSG10_HUMAN Q9BZW7 TSGA10 X 81.4

Plays a role in the sperm tail fibrous

sheath. a major sperm tail structure

Tetranectin TETN_HUMAN P05452 CLEC3B X X 22.5 Involved in the packaging of

molecules destined for exocytosis

Thrombospondin-1 TSP1_HUMAN P07996 THBS1 X X X 129.4 Immune response

Thyroxine-binding globulin THBG_HUMAN P05543 SERPINA7 X X X 46.3 Transporter activity

Transferrin receptor protein 1 TFR1_HUMAN P02786 TFRC X X 84.9 Transporter activity

Transthyretin TTHY_HUMAN P02766 TTR X X X 15.8 Transporter activity

Trypsin-1 TRY1_HUMAN P07477 PRSS1 X X X 26.5 Catalytic activity

Trypsin-2 TRY2_HUMAN P07478 PRSS2 X X 26.5 Catalytic activity

Trypsin-3 TRY3_HUMAN P35030 PRSS3 X X X 32.5 Catalytic activity

Unconventional myosin-If MYO1F_HUMAN O00160 MYO1F X 124.8 Catalytic activity (ATPase)

Vacuolar protein sorting-

associated protein 13D VP13D_HUMAN Q5THJ4 VPS13D X X 491.9 Transporter activity

Villin-like protein VILL_HUMAN O15195 VILL X X 95.9 Tumor suppressor

Vitamin D-binding protein VTDB_HUMAN P02774 GC X X X 52.9

Transporter activity

Vitamin K-dependent protein C PROC_HUMAN P04070 PROC X X X 52.1 Catalytic activity

Vitamin K-dependent protein S PROS_HUMAN P07225 PROS1 X X X 75.1 Anticoagulant plasma protein

199

Vitronectin VTNC_HUMAN P04004 VTN X X X 54.3 Proteolysis regulation

von Willebrand factor VWF_HUMAN P04275 VWF X X X 220

Plays a major role in blood

coagulation

Zinc finger protein 618 ZN618_HUMAN Q5T7W0 ZNF618 X X X 104.9

Involved in transcriptional regulation

Zinc-alpha-2-glycoprotein ZA2G_HUMAN P25311 AZGP1 X 34.3 Stimulates lipid degradation in

adipocytes

200

Figure 4_SM. 1D-SDS-PAGE of protein coronas formed around 10.02 ± 0.91 nm gold nanoparticles (AuNPs) in human serum (incubation

times: 30. 60 and 90 minutes; volumes of AuNPs: 75 μL. 100 μL and 125 μL. to get the following protein/NPs ratios: 10.7. 8.6 and 6.5.

respectively). On the left. it marks the lane with Mw protein standards with molecular weights in kDa.

GEL 1

201

Figure 5_SM. 1D-SDS-PAGE of protein coronas formed around 9.73 ± 1.70 nm silver nanoparticles (AgNPs) in human serum (incubation

times: 30. 60 and 90 minutes; volumes of AgNPs: 75 μL. 100 μL and 125 μL. to get the following protein/NPs ratios: 10.7. 8.6 and 6.5.

respectively). On the left. it marks the lane with Mw protein standards with molecular weights in kDa.

202

Figure 6_SM. 1D-SDS-PAGE of protein coronas formed around 2.40 ± 0.30 nm platinum nanoparticles (PtNPs) in human serum (incubation

times: 30. 60 and 90 minutes; volumes of PtNPs: 75 μL. 100 μL and 125 μL. to get the following protein/NPs ratios: 10.7. 8.6 and 6.5.

respectively). On the left. it marks the lane with Mw protein standards with molecular weights in kDa.

203

Figure 7_SM. 1D-SDS-PAGE of fractions of human serum (supernatants) after the separation of the pellet containing the protein coronas

formed around 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 2.40 ± 0.30 nm platinum

nanoparticles (PtNPs) in human serum (incubation time: 30 minutes; volumes of each nanoparticles solution: 75 μL. 100 μL and 125 μL. to get

the following protein/NPs ratios: 10.7. 8.6 and 6.5. respectively). On the left. it marks the lane with Mw protein standards with molecular

weights in kDa.

204

ANNEX B

Supplemental Material Chapter 2

205

Figure 1_SM. TEM image of AuNPs@citrate in aqueous phase and the characterization data.

AuNPs@citrate (nm) 1 7.94 21 10.35

2 8.37 22 10.38

3 8.38 23 10.41

4 8.60 24 10.44

5 8.78 25 10.56

6 8.78 26 10.65

7 8.87 27 10.65

8 9.17 28 10.68

9 9.19 29 10.80

10 9.41 30 10.81

11 9.49 31 10.83

12 9.49 32 10.85

13 9.55 33 10.86

14 9.62 34 10.86

15 9.82 35 10.88

16 9.84 36 10.95

17 9.89 37 10.96

18 9.89 38 10.99

19 9.99 39 11.48

20 10.03 40 11.50

Count 40

Mean 10.02

Minimum 7.94

Maximum 11.50

Standar Deviation 0.91

206

207

208

209

Figure 2_SM. TEM image of AuNPs@PC-controls in aqueous phase and the characterization data.

AuNPs@PC-controls (nm) 1 10.50 16 12.23

2 10.70 17 12.29

3 10.81 18 12.34

4 10.96 19 12.38

5 10.96 20 12.60

6 10.97 21 12.60

7 11.27 22 12.68

8 11.44 23 12.70

9 11.52 24 12.84

10 11.66 25 12.85

11 11.86 26 12.98

12 11.89 27 13.05

13 11.91 28 13.30

14 11.97 29 13.47

15 11.98 30 14.96

Count 30

Mean 12.12

Minimum 10.50

Maximum 14.96

Standar Deviation 0.97

210

211

212

Figure 3_SM. TEM image of AuNPs@PC-TNBC in aqueous phase and the characterization data.

AuNPs@PC-TNBC (nm) 1 9.99 16 12.19

2 10.58 17 12.19

3 10.96 18 12.25

4 10.98 19 12.34

5 11.18 20 12.38

6 11.32 21 12.38

7 11.32 22 12.40

8 11.37 23 12.48

9 11.61 24 12.48

10 11.66 25 12.49

11 11.83 26 12.82

12 11.85 27 12.91

13 11.85 28 13.18

14 12.07 29 13.38

15 12.19 30 13.60

Count 30

Mean 12.01

Minimum 9.99

Maximum 13.60

Standar Deviation 0.82

213

214

215

Figure 4_SM. TEM image of AgNPs@citrate in aqueous phase and the characterization data.

AgNPs@citrate (nm) 1 7.58 21 9.30

2 7.66 22 9.45

3 7.89 23 9.58

4 7.89 24 9.76

5 7.89 25 9.86

6 7.98 26 10.25

7 8.17 27 10.39

8 8.19 28 10.48

9 8.19 29 10.61

10 8.21 30 10.99

11 8.40 31 11.17

12 8.40 32 11.27

13 8.48 33 11.47

14 8.55 34 11.64

15 8.64 35 11.83

16 8.75 36 11.83

17 8.92 37 12.10

18 9.02 38 13.02

19 9.07 39 13.49

20 9.16 40 13.76

Count 40

Mean 9.73

Minimum 7.58

Maximum 13.76

Standar Deviation 1.70

216

217

218

219

Figure 5_SM. TEM image of AgNPs@PC-controls in aqueous phase and the characterization data.

AgNPs@PC-controls (nm) 1 10.16

2 10.35

3 10.58

4 10.82

5 11.67

6 11.68

7 11.73

8 11.78

9 12.02

10 12.03

11 12.03

12 12.09

13 12.12

14 12.45

15 12.54

16 12.70

17 12.97

18 13.71

19 13.77

20 13.80

Count 20

Mean 12.05

Minimum 10.16

Maximum 13.80

Standar Deviation 1.05

220

221

222

Figure 6_SM. TEM image of AgNPs@PC-TNBC in aqueous phase and the characterization data.

AgNPs@PC-TNBC (nm) 1 6.61 15 12.97

2 6.84 16 13.32

3 7.11 17 13.84

4 7.13 18 15.00

5 7.56 19 15.83

6 7.98 20 16.09

7 8.26 21 16.48

8 9.52 22 17.45

9 9.66 23 20.75

10 10.81 24 21.11

11 11.05 25 21.32

12 11.29 26 22.82

13 11.64 27 33.45

14 12.30

Count 27

Mean 13.64

Minimum 6.61

Maximum 33.45

Standar Deviation 6.25

223

224

225

Figure 7_SM. TEM image of MNPs in aqueous phase and the characterization data.

MNPs (nm) 1 8.07 21 9.41

2 8.27 22 9.42

3 8.26 23 9.42

4 8.32 24 9.57

5 8.34 25 9.59

6 8.36 26 9.62

7 8.45 27 9.72

8 8.52 28 9.76

9 8.55 29 9.77

10 8.59 30 9.77

11 8.94 31 9.78

12 8.95 32 9.79

13 8.96 33 9.80

14 9.14 34 9.90

15 9.15 35 9.99

16 9.15 36 9.99

17 9.15 37 10.07

18 9.15 38 10.22

19 9.36 39 10.42

20 9.36 40 10.82

Count 40

Mean 9.30

Minimum 8.07

Maximum 10.82

Standar Deviation 0.67

226

227

228

229

Figure 8_SM. TEM image of MNPs@PC-controls in aqueous phase and the characterization data.

MNPs@PC-controls (nm) 1 8.62 17 12.59

2 8.73 18 12.63

3 9.45 19 12.63

4 9.73 20 12.86

5 9.97 21 12.86

6 10.20 22 12.95

7 10.32 23 12.99

8 10.65 24 13.19

9 10.73 25 13.36

10 10.96 26 13.47

11 11.23 27 13.78

12 11.36 28 13.97

13 11.36 29 14.18

14 11.60 30 14.54

15 11.83 31 14.79

16 12.45 32 14.97

Count 32

Mean 12.03

Minimum 8.62

Maximum 14.97

Standar Deviation 1.75

230

231

232

Figure 9_SM. TEM image of MNPs@PC-TNBC in aqueous phase and the characterization data.

MNPs@PC-TNBC (nm) 1 7.63 16 12.56

2 8.49 17 12.66

3 9.57 18 12.71

4 10.65 19 12.74

5 11.13 20 12.75

6 11.26 21 13.2

7 11.36 22 13.34

8 11.82 23 13.42

9 11.83 24 13.76

10 11.89 25 14

11 11.96 26 14.09

12 11.97 27 14.61

13 12.15 28 14.76

14 12.17 29 15.01

15 12.55 30 15.54

Count 30

Mean 12.39

Minimum 7.63

Maximum 15.54

Standar Deviation 1.78

233

234

235

Figure 10_SM. The effect of PC formation on the ζ-potential of AuNPs, AgNPs and MNPs.

236

237

238

Figure 11_SM. Analysis of the protein corona formed arround MNPs after the depletion of high-abundance proteins presented in human serum

by two different methods. LEFT: depletion with fresh DTT 500 mM (3.3 µL) in ambic (12.5 mmol L-1) for 60 min at 37ºC (modification of

protocol described by Arruda [47]). RIGHT: depletion with fresh DTT 500 mM (3.3 µL) in milli-Q H2O for 60 min at room temperature

(protocol of Warder el al. [45. 46]). On the left. it marks the lane with Mw protein standards.

239

Figure 12_SM. Analysis of the influence of the MNP/protein ratio on the formation of the protein corona. Volumes of serum reducted and

alkylated (x2) were mixed with MNPs (9.30 ± 0.67 nm). at MNP/protein ratios of 1:1. 1:2. 1:4. and 1:10 (see section 2.5.1). On the left. it marks

the lane with Mw protein standards.

240

Table 1_SM. Analysis of the protein corona formed around AuNPs (10.02 ± 0.91 nm). AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm)

after their incubation with serum aliquots (x2) from eight different healthy controls (6 protein samples per individual: 2 treated with AuNPs. 2

with AgNPs and 2 with MNPs). The accession number. gene name. species (Human). molecular weight and protein function were reported. Grey

color: 149 proteins commonly found in the protein corona of AuNPs (10.02 ± 0.91 nm). AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm).

Pink color: 71 identified corona proteins exclusively bound to the AuNPs (10.02 ± 0.91 nm). Yellow color: 85 identified corona proteins

exclusively bound to the AgNPs (9.73 ± 1.70 nm). Blue: 46 identified corona proteins exclusively bound to the MNPs (9.30 ± 0.67 nm). Orange

color: 56 identified corona proteins commonly bound to the AuNPs (10.02 ± 0.91 nm) and AgNPs (9.73 ± 1.70 nm). Violet color: 9 identified

corona proteins commonly bound to the AuNPs (10.02 ± 0.91 nm) and MNPs (9.30 ± 0.67 nm). Green color: 2 identified corona proteins

commonly bound to the AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm).

241

Protein Name UniProt Name Entry Name Gene AuNPs AgNPs MNPs Mass (kDa) Function

Acid-sensing ion channel 2 ASIC2_HUMAN Q16515 ASIC2 X 57.7

Cation channel with high affinity for

sodium. which is gated by

extracellular protons and inhibited by

the diuretic amiloride

Acrosomal protein KIAA1210 K1210_HUMAN Q9ULL0 KIAA1210 X 187.0 -

Adenylate kinase isoenzyme 6 KAD6_HUMAN Q9Y3D8 AK6 X 20.0 Catalytic activity

Adhesion G protein-coupled

receptor L4 AGRL4_HUMAN Q9HBW9 ADGRL4 X 77.8

Endothelial orphan receptor that acts

as a key regulator of angiogenesis

Adipocyte plasma membrane-

associated protein APMAP_HUMAN Q9HDC9 APMAP X 46.5

May play a role in adipocyte

differentiation

Adiponectin ADIPO_HUMAN Q15848 ADIPOQ X 26.4

Important adipokine involved in the

control of fat metabolism and insulin

sensitivity. with direct anti-diabetic.

anti-atherogenic and anti-

inflammatory activities

Afamin AFAM_HUMAN P43652 AFM X X X 69.1 Transporter activity

Alpha-1-acid glycoprotein 1 A1AG1_HUMAN P02763 ORM1 X X X 23.5 Transporter activity

Alpha-1-acid glycoprotein 2 A1AG2_HUMAN P19652 ORM2 X X X 23.6 Transporter activity

Alpha-1-antichymotrypsin AACT_HUMAN P01011 SERPINA3 X X X 47.6 Protease inhibitor

Alpha-1-antitrypsin A1AT_HUMAN P01009 SERPINA1 X X X 46.7 Protease inhibitor

Alpha-1B-glycoprotein A1BG_HUMAN P04217 A1BG X X X 54.3 Neutrophil and platelet degranulation

Alpha-2-antiplasmin A2AP_HUMAN P08697 SERPINF2 X X X 54.6 Protease inhibitor

Alpha-2-HS-glycoprotein FETUA_HUMAN P02765 AHSG X X X 39.3

Promotes endocitosis. brain

development and the formation of

bone tissue

Alpha-2-macroglobulin A2MG_HUMAN P01023 A2M X X X 163.3 Immune response

Angiotensinogen ANGT_HUMAN P01019 AGT X X X 53.1 Regulator of blood pressure. body

fluid and electrolyte homeostasis

Ankyrin repeat domain-

containing protein 33B AN33B_HUMAN A6NCL7

ANKRD33

B X 53.9 -

Ankyrin repeat domain-

containing protein SOWAHC SWAHC_HUMAN Q53LP3 SOWAHC X 55.7 -

Annexin A6 ANXA6_HUMAN P08133 ANXA6 X 75.9 May associate with CD21

Antithrombin-III ANT3_HUMAN P01008 SERPINC1 X X X 52.6 Protease inhibitor

AP-3 complex subunit beta-2 AP3B2_HUMAN Q13367 AP3B2 X X 119.1 Transporter activity

Apolipoprotein A-I APOA1_HUMAN P02647 APOA1 X X X 30.8 Metabolism

Apolipoprotein A-II APOA2_HUMAN P02652 APOA2 X X X 11.2 Transport

Apolipoprotein A-IV APOA4_HUMAN P06727 APOA4 X X X 45.4 Hepatic transcellukar lipid transport

242

Apolipoprotein B-100 APOB_HUMAN P04114 APOB X X X 515.6

Recognition signal for the cellular

binding and internalization of LDL

particles by the apoB/E receptor

Apolipoprotein C-I APOC1_HUMAN P02654 APOC1 X X X 9.3

Inhibitor of lipoprotein binding to the

low density lipoprotein (LDL)

receptor

Apolipoprotein C-II APOC2_HUMAN P02655 APOC2 X X X 11.3 Enzyme regulator activity

(lipoprotein lipase activator)

Apolipoprotein C-III APOC3_HUMAN P02656 APOC3 X X X 10.8 Enzyme regulator activity

(lipoprotein lipase inhibitor)

Apolipoprotein C-IV APOC4_HUMAN P55056 APOC4 X 14.5 Biological regulation

Apolipoprotein D APOD_HUMAN P05090 APOD X X X 21.3 Transporter activity

Apolipoprotein E APOE_HUMAN P02649 APOE X X X 36.1 Transporter activity and cell uptake

Apolipoprotein F APOF_HUMAN Q13790 APOF X X X 35.4 Transporter activity

Apolipoprotein L1 APOL1_HUMAN O14791 APOL1 X X X 43.9 Transporter activity

Apolipoprotein M APOM_HUMAN O95445 APOM X X X 21.2 Transporter activity

Apolipoprotein(a) APOA_HUMAN P08519 LPA X X X 501.3 Catalytic activity

Arf-GAP with SH3 domain.

ANK repeat and PH domain-

containing protein 1

ASAP1_HUMAN Q9ULH1 ASAP1 X 125.5 Plays a role in ciliogenesis

Armadillo repeat-containing X-

linked protein 5 ARMX5_HUMAN Q6P1M9 ARMCX5 X 62.3 -

Arrestin-C ARRC_HUMAN P36575 ARR3 X 42.8 May play a role in an as yet undefined

retina-specific signal transduction

ATP-binding cassette sub-family

B member 5 ABCB5_HUMAN Q2M3G0 ABCB5 X 138.6 Transporter activity

ATP-binding cassette sub-family

C member 8 ABCC8_HUMAN Q09428 ABCC8 X 176.9

Regulator of ATP-sensitive K+

channels and insulin release

ATP-binding cassette sub-family

F member 1 ABCF1_HUMAN Q8NE71 ABCF1 X X X 95.9 Transporter activity

Attractin ATRN_HUMAN O75882 ATRN X X X 158.5

Involved in the initial immune cell

clustering during inflammatory

response and may regulate

chemotactic activity of chemokines

Beta-Ala-His dipeptidase CNDP1_HUMAN Q96KN2 CNDP1 X X 56.7 Catalytic activity

Beta-2-glycoprotein 1 APOH_HUMAN P02749 APOH X X X 38.3 Transporter activity

Beta-2-microglobulin B2MG_HUMAN P61769 B2M X 13.7

Component of the class I major

histocompatibility complex (MHC).

Involved in the presentation of

peptide antigens to the immune

243

system.

Beta-ureidopropionase BUP1_HUMAN Q9UBR1 UPB1 X 43.1 Catalytic activity

Biotinidase BTD_HUMAN P43251 BTD X X 61.1 Catalytic activity

Bone morphogenetic protein 10 BMP10_HUMAN O95393 BMP10 X 48.0

Inhibits endothelial cell migration and

growth. May reduce cell migration

and cell matrix adhesion in breast

cancer cell lines.

Carbonic anhydrase 1 CAH1_HUMAN P00915 CA1 X 28.9 Catalytic activity

Carboxypeptidase B2 CBPB2_HUMAN Q96IY4 CPB2 X X X 48.4 Catalytic activity

Carboxypeptidase N catalytic

chain CBPN_HUMAN P15169 CPN1 X X X 52.3 Catalytic activity

Carboxypeptidase N subunit 2 CPN2_HUMAN P22792 CPN2 X X X 60.6 Enzyme regulator activity

Carboxypeptidase Q CBPQ_HUMAN Q9Y646 CPQ X 51.8 Catalytic activity

Cardiomyopathy-associated

protein 5 CMYA5_HUMAN Q8N3K9 CMYA5 X 449.2

May serve as an anchoring protein

that mediates the subcellular

compartmentation of protein kinase A

(PKA) via binding to PRKAR2A

Cartilage oligomeric matrix

protein COMP_HUMAN P49747 COMP X X 82.9

May play a role in the structural

integrity of cartilage via its

interaction with other extracellular

matrix proteins such as the collagens

and fibronectin

CASP8-associated protein 2 C8AP2_HUMAN Q9UKL3 CASP8AP2 X 222.6

Involved in TNF-alpha-induced

activation of NF-kappa-B via a

TRAF2-dependent pathway

Cathelicidin antimicrobial

peptide CAMP_HUMAN P49913 CAMP X 19.3 Antibacterial activity

C4b-binding protein alpha chain C4BPA_HUMAN P04003 C4BPA X X X 67.1 Enzyme regulator activity (inhibitor)

C4b-binding protein beta chain C4BPB_HUMAN P20851 C4BPB X X X 28.4 Enzyme regulator activity (inhibitor)

CCR4-NOT transcription

complex subunit 4 CNOT4_HUMAN O95628 CNOT4 X 63.5 Catalytic activity

CDK5 regulatory subunit-

associated protein 2 CK5P2_HUMAN Q96SN8 CDK5RAP2 X 215.0

Involved in regulation of mitotic

spindle orientation

CD5 antigen-like CD5L_HUMAN O43866 CD5L X X X 38.1 Regulator of lipid synthesis

CD44 antigen CD44_HUMAN P16070 CD44 X X 81.5

Mediates cell-cell and cell-matrix

interactions through its affinity for

hyaluronic acid (HA)

CD83 antigen CD83_HUMAN Q01151 CD83 X 23.0

May play a significant role in antigen

presentation or the cellular

interactions that follow lymphocyte

244

activation

Centrosome-associated protein

350 CE350_HUMAN Q5VT06 CEP350 X 350.9 Structural

Ceruloplasmin CERU_HUMAN P00450 CP X X X 122.2 Catalytic activity

Chloride intracellular channel

protein 1 CLIC1_HUMAN O00299 CLIC1 X 26.9

Involved in regulation of the cell

cycle

Cholesteryl ester transfer protein CETP_HUMAN P11597 CETP X X 54.8 Transporter activity

Cholinesterase CHLE_HUMAN P06276 BCHE X 68.4 Catalytic activity

Cip1-interacting zinc finger

protein CIZ1_HUMAN Q9ULV3 CIZ1 X 100.0

May regulate the subcellular

localization of CIP/WAF1

Clusterin CLUS_HUMAN P10909 CLU X X X 52.5 Regulation of cell proliferation

Coagulation factor XIII A chain F13A_HUMAN P00488 F13A1 X X 83.3 Catalytic activity

Coagulation factor XIII B chain F13B_HUMAN P05160 F13B X X X 75.5 Enzyme regulator activity

Coagulation factor IX FA9_HUMAN P00740 F9 X X X 51.8 Catalytic activity

Coagulation factor V FA5_HUMAN P12259 F5 X X X 251.7 Enzyme regulator activity

Coagulation factor XII FA12_HUMAN P00748 F12 X X X 67.8 Catalytic activity

Coiled-coil domain-containing

protein 90B. mitochondrial CC90B_HUMAN Q9GZT6 CCDC90B X 29.5 -

Coiled-coil domain-containing

protein 127 CC127_HUMAN Q96BQ5 CCDC127 X 30.8 -

Coiled-coil domain-containing

protein 146 CC146_HUMAN Q8IYE0 CCDC146 X 112.8 -

Collectin-11 COL11_HUMAN Q9BWP8 COLEC11 X 28.7 Plays a role in innate immunity.

apoptosis and embryogenesis

Complement C1q subcomponent

subunit A C1QA_HUMAN P02745 C1QA X X X 26.1 Enzyme regulator activity

Complement C1q subcomponent

subunit B C1QB_HUMAN P02746 C1QB X X X 26.7 Enzyme regulator activity

Complement C1q subcomponent

subunit C C1QC_HUMAN P02747 C1QC X X X 25.8 Enzyme regulator activity

Complement C1r subcomponent C1R_HUMAN P00736 C1R X X X 80.1 Catalytic activity

Complement C1r subcomponent-

like protein C1RL_HUMAN Q9NZP8 C1RL X X X 53.5 Enzyme regulator activity

Complement C1s subcomponent C1S_HUMAN P09871 C1S X X X 76.7 Catalytic activity

Complement C2 CO2_HUMAN P06681 C2 X X X 83.3 Catalytic activity

Complement C3 CO3_HUMAN P01024 C3 X X X 187.1 Immune response

Complement C4-A CO4A_HUMAN P0C0L4 C4A X X X 192.8 Inflammatory response

Complement C4-B CO4B_HUMAN P0C0L5 C4B X X X 192.7 Enzyme regulator activity

Complement C5 CO5_HUMAN P01031 C5 X X X 188.3 Enzyme regulator activity

245

Complement component C6 CO6_HUMAN P13671 C6 X X X 104.8 Structural

Complement component C7 CO7_HUMAN P10643 C7 X X X 93.5 Structural

Complement component C8

alpha chain CO8A_HUMAN P07357 C8A X X X 65.2 Structural

Complement component C8 beta

chain CO8B_HUMAN P07358 C8B X X X 67.1 Structural

Complement component C8

gamma chain CO8G_HUMAN P07360 C8G X X X 22.3 Structural

Complement component C9 CO9_HUMAN P02748 C9 X X X 63.2 Structural

Complement factor B CFAB_HUMAN P00751 CFB X X X 85.5 Immune response

Complement factor D CFAD_HUMAN P00746 CFD X X X 27.1 Catalytic activity

Complement factor H CFAH_HUMAN P08603 CFH X X X 139.1 Enzyme regulator activity

Complement factor H-related

protein 1 FHR1_HUMAN Q03591 CFHR1 X X X 37.6 Plays a role in lipid metabolism

Complement factor H-related

protein 2 FHR2_HUMAN P36980 CFHR2 X X X 30.6 Plays a role in lipid metabolism

Complement factor H-related

protein 3 FHR3_HUMAN Q02985 CFHR3 X 37.3 Involved in complement regulation

Complement factor H-related

protein 4 FHR4_HUMAN Q92496 CFHR4 X 65.3 Plays a role in lipid metabolism

Complement factor I CFAI_HUMAN P05156 CFI X X X 65.7 Catalytic activity

Conserved oligomeric Golgi

complex subunit 1 COG1_HUMAN Q8WTW3 COG1 X 108.9 Required for normal Golgi function

Contactin-1 CNTN1_HUMAN Q12860 CNTN1 X X 113.3 Mediates cell surface interactions

during nervous system development.

Copine-1 CPNE1_HUMAN Q99829 CPNE1 X 59.1

Calcium-dependent phospholipid-

binding protein that plays a role in

calcium-mediated intracellular

processes

Corticosteroid-binding globulin CBG_HUMAN P08185 SERPINA6 X X 45.1 Transporter activity

C-reactive protein CRP_HUMAN P02741 CRP X 25.0 Displays several functions associated

with host defense

Cysteine-rich secretory protein 3 CRIS3_HUMAN P54108 CRISP3 X 27.6 Defense response

Cytoplasmic polyadenylation

element-binding protein 1 CPEB1_HUMAN Q9BZB8 CPEB1 X 62.6

Sequence-specific RNA-binding

protein that regulates mRNA

cytoplasmic polyadenylation and

translation initiation during oocyte

maturation. early development and at

postsynapse sites of neurons

246

Death-associated protein kinase 2 DAPK2_HUMAN Q9UIK4 DAPK2 X 42.9 Catalyctic activity

Disheveled-associated activator

of morphogenesis 2 DAAM2_HUMAN Q86T65 DAAM2 X 123.5

Key regulator of the Wnt signaling

pathway

DNA polymerase eta POLH_HUMAN Q9Y253 POLH X 46.3 Catalyctic activity

Dolichol-phosphate

mannosyltransferase subunit 1 DPM1_HUMAN O60762 DPM1 X 29.6 Catalyctic activity

Dopamine beta-hydroxylase DOPO_HUMAN P09172 DBH X X 69.1 Catalyctic activity

Double-stranded RNA-specific

editase 1 RED1_HUMAN P78563 ADARB1 X 80.8 Catalyctic activity

EF-hand calcium-binding

domain-containing protein 7 EFCB7_HUMAN A8K855 EFCAB7 X 71.9

Component of the EvC complex that

positively regulates ciliary Hedgehog

(Hh) signaling

EGF-containing fibulin-like

extracellular matrix protein 1 FBLN3_HUMAN Q12805 EFEMP1 X 54.6

Binds EGFR. the EGF receptor.

inducing EGFR autophosphorylation

and the activation of downstream

signaling pathways. May play a role

in cell adhesion and migration

Electron transfer flavoprotein

subunit beta ETFB_HUMAN P38117 ETFB X 27.8

Required for normal mitochondrial

fatty acid oxidation and normal amino

acid metabolism

Endophilin-A3 SH3G3_HUMAN Q99963 SH3GL3 X 39.3

Implicated in endocytosis. May

recruit other proteins to membranes

with high curvature

ERC protein 2 ERC2_HUMAN O15083 ERC2 X 110.5

Involved in the organization of the

cytomatrix at the nerve terminals

active zone (CAZ) which regulates

neurotransmitter release

E3 ubiquitin-protein ligase

HUWE1 HUWE1_HUMAN Q7Z6Z7 HUWE1 X 481.9 Catalytic activity

Eukaryotic translation initiation

factor 3 subunit C EIF3C_HUMAN Q99613 EIF3C X 105.3

Component of the eukaryotic

translation initiation factor 3 (eIF-3)

complex. which is required for

several steps in the initiation of

protein synthesis

Extracellular matrix protein 1 ECM1_HUMAN Q16610 ECM1 X X X 60.7 Enzyme regulator inhibitor

Fas-binding factor 1 FBF1_HUMAN Q8TES7 FBF1 X 125.4 Keratin-binding protein required for

epithelial cell polarization

FERM. ARHGEF and pleckstrin

domain-containing protein 1 FARP1_HUMAN Q9Y4F1 FARP1 X 118.6

Functions as guanine nucleotide

exchange factor for RAC1

FERM and PDZ domain- FRPD3_HUMAN Q5JV73 FRMPD3 X 199.2 Neutrophil degranulation

247

containing protein 3

Fetuin-B FETUB_HUMAN FETUB FETUB X X X 42.1 Catalytic activity

Fibronectin FINC_HUMAN P02751 FN1 X X X 262.6 Growth. migration and differenciation

Fibrinogen alpha chain FIBA_HUMAN P02671 FGA X X X 94.9 Coagulation. immune response

Ficolin-2 FCN2_HUMAN Q15485 FCN2 X X X 34.0 Immunity response

Ficolin-3 FCN3_HUMAN O75636 FCN3 X X X 32.9 Immunity response

Fibulin-1 FBLN1_HUMAN P23142 FBLN1 X X 77.2 Structural

FYVE. RhoGEF and PH domain-

containing protein 5 FGD5_HUMAN Q6ZNL6 FGD5 X 159.9

Activates CDC42. a member of the

Ras-like family of Rho- and Rac

proteins

Galectin-3-binding protein LG3BP_HUMAN Q08380 LGALS3BP X X 65.3 Stimulate host defense against viruses

and tumor cells

Gelsolin GELS_HUMAN P06396 GSN X X X 85.7 Actin regulation (assembly)

Glutathione peroxidase 3 GPX3_HUMAN P22352 GPX3 X 25.5 Catalytic activity

Haptoglobin HPT_HUMAN P00738 HP X X X 45.2

Prevents loss of iron through the

kidneys. protecting them from

damage by hemoglobin

Haptoglobin-related protein HPTR_HUMAN P00739 HPR X X X 39 Binds hemoglobin as efficiently as

haptoglobin

HEAT repeat-containing protein

4 HEAT4_HUMAN Q86WZ0 HEATR4 X 117.2 -

Heat shock 70 kDa protein 1-like HS71L_HUMAN P34931 HSPA1L X 70.4

Implicates in the protection of the

proteome from stress. folding and

transport of newly synthesized

polypeptides. activation of proteolysis

of misfolded proteins and the

formation and dissociation of protein

complexes

Hemoglobin subunit alpha HBA_HUMAN P69905 HBA1 X X X 15.3 Transporter activity

Hemoglobin subunit beta HBB_HUMAN P68871 HBB X X X 15.9 Transporter activity

Hemopexin HEMO_HUMAN P02790 HPX X X X 51.7 Transporter activity

Heparan sulfate glucosamine 3-

O-sulfotransferase 5 HS3S5_HUMAN Q8IZT8 HS3ST5 X 40.4 Catalytic activity

Heparin cofactor 2 HEP2_HUMAN P05546 SERPIND1 X X X 57.1 Enzyme regulator activity (inhibitor)

Hepatocyte growth factor

activator HGFA_HUMAN Q04756 HGFAC X 70.7 Enzyme regulator activity (activator)

Hepatocyte growth factor-like

protein HGFL_HUMAN P26927 MST1 X 80.3 Catalytic activity

Histidine-rich glycoprotein HRG_HUMAN P04196 HRG X X X 59.6 Inhibits fibrinolysis and reduces

248

inhibition of coagulation

Homeobox protein GBX-1 GBX1_HUMAN Q14549 GBX1 X 37.6 DNA-binding transcription activator

activity. RNA polymerase II-specific

Hyaluronan-binding protein 2 HABP2_HUMAN Q14520 HABP2 X X X 62.7 Enzyme regulator activity

IgGFc-binding protein FCGBP_HUMAN Q9Y6R7 FCGBP X X X 572.1 Structural

Immortalization up-regulated

protein IMUP_HUMAN Q9GZP8 IMUP X 10.9 -

Immunoglobulin heavy constant

alpha 1 IGHA1_HUMAN P01876 IGHA1 X X X 37.7 Immune response

Immunoglobulin heavy constant

alpha 2 IGHA2_HUMAN P01877 IGHA2 X X X 36.6 Immune response

Immunoglobulin heavy constant

delta IGHD_HUMAN P01880 IGHD X X X 42.3 Immune response

Immunoglobulin heavy constant

gamma 1 IGHG1_HUMAN P01857 IGHG1 X X X 36.1 Immune response

Immunoglobulin heavy constant

gamma 2 IGHG2_HUMAN P01859 IGHG2 X X X 35.9 Immune response

Immunoglobulin heavy constant

gamma 3 IGHG3_HUMAN P01860 IGHG3 X X X 41.3 Immune response

Immunoglobulin heavy constant

gamma 4 IGHG4_HUMAN P01861 IGHG4 X X X 35.9 Immune response

Immunoglobulin heavy constant

mu IGHM_HUMAN P01871 IGHM X X X 49.4 Immune response

Immunoglobulin heavy variable

1-2 HV102_HUMAN P23083 IGHV1-2 X X 13.1 Immune response

Immunoglobulin heavy variable

1-3 HV103_HUMAN A0A0C4DH29 IGHV1-3 X 13.0 Immune response

Immunoglobulin heavy variable

1-8 HV108_HUMAN P0DP01 IGHV1-8 X X 12.9 Immune response

Immunoglobulin heavy variable

1-18 HV118_HUMAN A0A0C4DH31 IGHV1-18 X 12.8 Immune response

Immunoglobulin heavy variable

1-46 HV146_HUMAN P01743 IGHV1-46 X 12.9 Immune response

Immunoglobulin heavy variable

1-69 HV169_HUMAN P01742 IGHV1-69 X X 12.6 Immune response

Immunoglobulin heavy variable

1-69D HV69D_HUMAN A0A0B4J2H0 IGHV1-69D X 12.7 Immune response

Immunoglobulin heavy variable

2-5 HV205_HUMAN P01817 IGHV2-5 X X 13.2 Immune response

249

Immunoglobulin heavy variable

2-26 HV226_HUMAN A0A0B4J1V2 IGHV2-26 X 13.2 Immune response

Immunoglobulin heavy variable

2-70D HV70D_HUMAN A0A0C4DH43 IGHV2-70D X X 13.3 Immune response

Immunoglobulin heavy variable

3-7 HV307_HUMAN P01780 IGHV3-7 X X X 12.9 Immune response

Immunoglobulin heavy variable

3-9 HV309_HUMAN P01782 IGHV3-9 X 12.9 Immune response

Immunoglobulin heavy variable

3-11 HV311_HUMAN P01762 IGHV3-11 X 12.9 Immune response

Immunoglobulin heavy variable

3-15 HV315_HUMAN A0A0B4J1V0 IGHV3-15 X X 12.9 Immune response

Immunoglobulin heavy variable

3-20 HV320_HUMAN A0A0C4DH32 IGHV3-20 X 12.7 Immune response

Immunoglobulin heavy variable

3-21 HV321_HUMAN A0A0B4J1V1 IGHV3-21 X 12.8 Immune response

Immunoglobulin heavy variable

3-23 HV323_HUMAN P01764 IGHV3-23 X 12.6 Immune response

Immunoglobulin heavy variable

3-30-3 HVC33_HUMAN P0DP02

IGHV3-30-

3 X X X 13 Immune response

Immunoglobulin heavy variable

3-30-5 HV335_HUMAN P0DP03

IGHV3-30-

5 X X X 12.9 Immune response

Immunoglobulin heavy variable

3-43D HV43D_HUMAN P0DP04 IGHV3-43D X X 13.1 Immune response

Immunoglobulin heavy variable

3-48 HV348_HUMAN P01763 IGHV3-48 X 12.8 Immune response

Immunoglobulin heavy variable

3-49 HV349_HUMAN A0A0A0MS15 IGHV3-49 X X 13.0 Immune response

Immunoglobulin heavy variable

3-53 HV353_HUMAN P01767 IGHV3-53 X 12.8 Immune response

Immunoglobulin heavy variable

3-64D HV64D_HUMAN A0A0J9YX35 IGHV3-64D X X 12.8 Immune response

Immunoglobulin heavy variable

3-66 HV366_HUMAN A0A0C4DH42 IGHV3-66 X X 12.7 Immune response

Immunoglobulin heavy variable

3-72 HV372_HUMAN A0A0B4J1Y9 IGHV3-72 X X X 13.2 Immune response

Immunoglobulin heavy variable

3-73 HV373_HUMAN A0A0B4J1V6 IGHV3-73 X 12.8 Immune response

Immunoglobulin heavy variable

3-74 HV374_HUMAN A0A0B4J1X5 IGHV3-74 X X X 12.8 Immune response

250

Immunoglobulin heavy variable

4-4 HV404_HUMAN A0A075B6R2 IGHV4-4 X X 12.8 Immune response

Immunoglobulin heavy variable

4-28 HV428_HUMAN A0A0C4DH34 IGHV4-28 X X 13.1 Immune response

Immunoglobulin heavy variable

4-30-2 HV432_HUMAN A0A087WSY4

IGHV4-30-

2 X 13.0 Immune response

Immunoglobulin heavy variable

4-34 HV434_HUMAN P06331 IGHV4-34 X X 13.8 Immune response

Immunoglobulin heavy variable

4-38-2 HVD82_HUMAN P0DP08

IGHV4-38-

2 X X X 13.1 Immune response

Immunoglobulin heavy variable

4-39 HV439_HUMAN P01824 IGHV4-39 X 13.9 Immune response

Immunoglobulin heavy variable

4-59 HV459_HUMAN P01825 IGHV4-59 X 12.9 Immune response

Immunoglobulin heavy variable

5-10-1 HV5X1_HUMAN A0A0J9YXX1

IGHV5-10-

1 X X 12.8 Imuune response

Immunoglobulin heavy variable

5-51 HV551_HUMAN A0A0C4DH38 IGHV5-51 X X X 12.7 Immune response

Immunoglobulin heavy variable

6-1 HV601_HUMAN A0A0B4J1U7 IGHV6-1 X X 13.5 Imuune response

Immunoglobulin J chain IGJ_HUMAN P01591 JCHAIN X X X 18.1 Immune response

Immunoglobulin kappa constant IGKC_HUMAN P01834 IGKC X X X 11.8 Immune response

Immunoglobulin kappa variable

1D-8 KVD08_HUMAN A0A087WSZ0 IGKV1D-8 X 12.8 Immune response

Immunoglobulin kappa variable

1D-12 KVD12_HUMAN P01611 IGKV1D-12 X 12.6 Immune response

Immunoglobulin kappa variable

1-5 KV105_HUMAN P01602 IGKV1-5 X X 12.8 Immune response

Immunoglobulin kappa variable

1-6 KV106_HUMAN A0A0C4DH72 IGKV1-6 X 12.7 Immune response

Immunoglobulin kappa variable

1-8 KV108_HUMAN A0A0C4DH67 IGKV1-8 X X X 12.5 Immune response

Immunoglobulin kappa variable

1-9 KV109_HUMAN A0A0C4DH69 IGKV1-9 X X 12.7 Immune response

Immunoglobulin kappa variable

1-13 KV113_HUMAN P0DP09 IGKV1-13 X X 12.5 Immune response

Immunoglobulin kappa variable

1-16 KV116_HUMAN P04430 IGKV1-16 X 12.6 Immune response

Immunoglobulin kappa variable

1-17 KV117_HUMAN P01599 IGKV1-17 X X 12.8 Immune response

251

Immunoglobulin kappa variable

1-27 KV127_HUMAN A0A075B6S5 IGKV1-27 X X X 12.7 Immune response

Immunoglobulin kappa variable

1-33 KV133_HUMAN P01594 IGKV1-33 X X X 12.8 Immune response

Immunoglobulin kappa variable

1D-16 KVD16_HUMAN P01601 IGKV1D-16 X 12.7 Immune response

Immunoglobulin kappa variable

1D-39 KVD39_HUMAN P04432 IGKV1D-39 X X X 12.7 Immune response

Immunoglobulin kappa variable

2-24 KV224_HUMAN A0A0C4DH68 IGKV2-24 X X 13.1 Immune response

Immunoglobulin kappa variable

2-29 KV229_HUMAN A2NJV5 IGKV2-29 X X 13.1 Immune response

Immunoglobulin kappa variable

2-30 KV230_HUMAN P06310 IGKV2-30 X X X 13.2 Immune response

Immunoglobulin kappa variable

2D-28 KVD28_HUMAN P01615 IGKV2D-28 X X X 12.9 Immune response

Immunoglobulin kappa variable

2D-29 KVD29_HUMAN A0A075B6S2 IGKV2D-29 X 13.1 Immune response

Immunoglobulin kappa variable

3D-15 KVD15_HUMAN A0A087WSY6 IGKV3D-15 X 12.5 Immune response

Immunoglobulin kappa variable

3-11 KV311_HUMAN P04433 IGKV3-11 X X X 12.6 Immune response

Immunoglobulin kappa variable

3-15 KV315_HUMAN P01624 IGKV3-15 X X X 12.5 Immune response

Immunoglobulin kappa variable

3-20 KV320_HUMAN P01619 IGKV3-20 X X X 12.6 Immune response

Immunoglobulin kappa variable

3D-20 KVD20_HUMAN A0A0C4DH25 IGKV3D-20 X X X 12.5 Immune response

Immunoglobulin kappa variable

4-1 KV401_HUMAN P06312 IGKV4-1 X X X 13.4 Immune response

Immunoglobulin kappa variable

6-21 KV621_HUMAN A0A0C4DH24 IGKV6-21 X 12.4 Immune response

Immunoglobulin kappa variable

6D-21 KVD21_HUMAN A0A0A0MT36 IGKV6D-21 X X 12.3 Immune response

Immunoglobulin lambda constant

2 IGLC2_HUMAN P0DOY2 IGLC2 X X X 11.3 Immune response

Immunoglobulin lambda constant

3 IGLC3_HUMAN P0DOY3 IGLC3 X X X 11.3 Immune response

Immunoglobulin lambda constant

7 IGLC7_HUMAN A0M8Q6 IGLC7 X 11.2 Immune response

252

Immunoglobulin lambda-like

polypeptide 1 IGLL1_HUMAN P15814 IGLL1 X X 22.9 Immune response

Immunoglobulin lambda variable

1-36 LV136_HUMAN A0A0B4J1U3 IGLV1-36 X 12.5 Immune response

Immunoglobulin lambda variable

1-40 LV140_HUMAN P01703 IGLV1-40 X 12.3 Immune response

Immunoglobulin lambda variable

1-47 LV147_HUMAN P01700 IGLV1-47 X X X 12.3 Immune response

Immunoglobulin lambda variable

1-51 LV151_HUMAN P01701 IGLV1-51 X X 12.2 Immune response

Immunoglobulin lambda variable

2-8 LV208_HUMAN P01709 IGLV2-8 X 12.4 Immune response

Immunoglobulin lambda variable

2-11 LV211_HUMAN P01706 IGLV2-11 X 12.6 Immune response

Immunoglobulin lambda variable

2-14 LV214_HUMAN P01704 IGLV2-14 X 12.6 Immune response

Immunoglobulin lambda variable

2-18 LV218_HUMAN A0A075B6J9 IGLV2-18 X 12.4 Immune response

Immunoglobulin lambda variable

2-23 LV223_HUMAN P01705 IGLV2-23 X 11.9 Immune response

Immunoglobulin lambda variable

3-9 LV39_HUMAN A0A075B6K5 IGLV3-9 X 12.3 Immune response

Immunoglobulin lambda variable

3-19 LV319_HUMAN P01714 IGLV3-19 X X 12.0 Immune response

Immunoglobulin lambda variable

3-21 LV321_HUMAN P80748 IGLV3-21 X X X 12.4 Immune response

Immunoglobulin lambda variable

3-25 LV325_HUMAN P01717 IGLV3-25 X X 12.0 Immune response

Immunoglobulin lambda variable

4-60 LV460_HUMAN A0A075B6I1 IGLV4-60 X 12.9 Immune response

Immunoglobulin lambda variable

4-69 LV469_HUMAN A0A075B6H9 IGLV4-69 X X 12.8 Immune response

Immunoglobulin lambda variable

5-45 LV545_HUMAN A0A087WSX0 IGLV5-45 X 13.2 Immune response

Immunoglobulin lambda variable

6-57 LV657_HUMAN P01721 IGLV6-57 X 12.6 Immune response

Immunoglobulin lambda variable

7-43 LV743_HUMAN P04211 IGLV7-43 X X 12.4 Immune response

Immunoglobulin lambda variable

7-46 LV746_HUMAN A0A075B6I9 IGLV7-46 X X 12.5 Immune response

253

Immunoglobulin lambda variable

8-61 LV861_HUMAN A0A075B6I0 IGLV8-61 X X 12.8 Immune response

Immunoglobulin lambda variable

9-49 LV949_HUMAN A0A0B4J1Y8 IGLV9-49 X 13.0 Immune response

Immunoglobulin lambda variable

10-54 LVX54_HUMAN A0A075B6I4 IGLV10-54 X 12.4 Immune response

Immunoglobulin lambda-like

polypeptide 5 IGLL5_HUMAN B9A064 IGLL5 X X X 23.1 Immune response

Interleukin-22 IL22_HUMAN Q9GZX6 IL22 X 20.0 Cytokine that contributes to the

inflammatory response in vivo.

Importin-4 IPO4_HUMAN Q8TEX9 IPO4 X 118.7 Functions in nuclear protein import as

nuclear transport receptor

Inositol polyphosphate 1-

phosphatase INPP_HUMAN P49441 INPP1 X 43.9 Catalytic activity

Insulin-like growth factor-

binding protein 3 IBP3_HUMAN P17936 IGFBP3 X 31.7

IGF-binding proteins prolong the

half-life of the IGFs

Insulin-like growth factor-

binding protein complex acid

labile subunit

ALS_HUMAN P35858 IGFALS X X X 66.1

Binds insulin-like growth factors.

increasing their half-life and their

vascular localization

Inter-alpha-trypsin inhibitor

heavy chain H1 ITIH1_HUMAN P19827 ITIH1 X X X 101.4 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H2 ITIH2_HUMAN P19823 ITIH2 X X X 106.5 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H3 ITIH3_HUMAN Q06033 ITIH3 X X X 99.9 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H4 ITIH4_HUMAN Q14624 ITIH4 X X X 103.4 Inflammatory response

Kallistatin KAIN_HUMAN P29622 SERPINA4 X X 48.5 Enzyme regulator inhibitor

Kelch-like protein 38 KLH38_HUMAN Q2WGJ6 KLHL38 X 65.5 -

Keratin. type I cytoskeletal 9 K1C9_HUMAN P35527 KRT9 X X 62.1 Structural

Keratin. type I cytoskeletal 10 K1C10_HUMAN P13645 KRT10 X X X 58.8 Structural

Keratin. type II cytoskeletal 1 K2C1_HUMAN P04264 KRT1 X X X 66.1 Enzyme regulator activity

Keratin. type II cytoskeletal 2

epidermal K22E_HUMAN P35908 KRT2 X X X 65.4 Structural

Keratin. type II cuticular Hb1 KRT81_HUMAN Q14533 KRT81 X 54.9 Structural

Kinesin heavy chain isoform 5A KIF5A_HUMAN Q12840 KIF5A X 117.4

Microtubule-dependent motor

required for slow axonal transport of

neurofilament proteins (NFH. NFM

and NFL)

254

Kininogen-1 KNG1_HUMAN P01042 KNG1 X X X 71.9 Enzyme regulator activity

Lactotransferrin TRFL_HUMAN P02788 LTF X 78.2 Transporter activity

Laminin subunit beta-4 LAMB4_HUMAN A4D0S4 LAMB4 X 193.5

It is thought to mediate the

attachment. migration and

organization of cells into tissues

during embryonic development by

interacting with other extracellular

matrix components

Leucine-rich alpha-2-

glycoprotein A2GL_HUMAN P02750 LRG1 X X 38.2

Protein-protein interaction. signal

transduction. and cell adhesion and

development

Leucine-rich glioma-inactivated

protein 1 LGI1_HUMAN O95970 LGI1 X 63.8

Regulates voltage-gated potassium

channels assembled from KCNA1.

KCNA4 and KCNAB1

Leucine-rich repeat and coiled-

coil domain-containing protein 1 LRCC1_HUMAN Q9C099 LRRCC1 X 119.6

Maintains the structural integrity of

centrosomes during mitosis

LIM domain and actin-binding

protein 1 LIMA1_HUMAN Q9UHB6 LIMA1 X 85.2

Actin-binding protein involved in

actin cytoskeleton regulation and

dynamics

LIM domain only protein 7 LMO7_HUMAN Q8WWI1 LMO7 X 192.7 DNA-binding transcription factor

activity

Lipopolysaccharide-binding

protein LBP_HUMAN P18428 LBP X X 53.4 Immune response.

Little elongation complex subunit

1 ICE1_HUMAN Q9Y2F5 ICE1 X 247.9

Component of the little elongation

complex (LEC). a complex required

to regulate small nuclear RNA

(snRNA) gene transcription by RNA

polymerase II and III

Lumican LUM_HUMAN P51884 LUM X X X 38.4 Collagen binding. extracellular matrix

structural constituent

Lymphatic vessel endothelial

hyaluronic acid receptor 1 LYVE1_HUMAN Q9Y5Y7 LYVE1 X 35.2

Ligand-specific transporter trafficking

between intracellular organelles

(TGN) and the plasma membrane

Lymphocyte cytosolic protein 2 LCP2_HUMAN Q13094 LCP2 X 60.2 Involved in T-cell antigen receptor

mediated signaling

Lysine-specific demethylase 4C KDM4C_HUMAN Q9H3R0 KDM4C X X 119.9 Catalytic activity

Lysosomal-trafficking regulator LYST_HUMAN Q99698 LYST X 429.1

Required for sorting endosomal

resident proteins into late

multivesicular endosomes

Lysosome-associated membrane LAMP2_HUMAN P13473 LAMP2 X 44.9 Plays an important role in chaperone-

255

glycoprotein 2 mediated autophagy

Maestro heat-like repeat-

containing protein family

member 2A

MRO2A_HUMAN A6NES4 MROH2A X 189.6 -

MAGUK p55 subfamily member

3 MPP3_HUMAN Q13368 MPP3 X 66.1 -

Mannan-binding lectin serine

protease 1 MASP1_HUMAN P48740 MASP1 X X X 79.2 Enzyme regulator activity

Mannan-binding lectin serine

protease 2 MASP2_HUMAN O00187 MASP2 X 75.7 Catalytic activity

Mediator of RNA polymerase II

transcription subunit 15 MED15_HUMAN Q96RN5 MED15 X 86.7

Component of the Mediator complex.

a coactivator involved in the

regulated transcription of nearly all

RNA polymerase II-dependent genes

Microtubule-associated protein

1A MAP1A_HUMAN P78559 MAP1A X 305.5

Structural protein involved in the

filamentous cross-bridging between

microtubules and other skeletal

elements

Monocyte differentiation antigen

CD14 CD14_HUMAN P08571 CD14 X X X 40.1 Immune response

MORC family CW-type zinc

finger protein 1 MORC1_HUMAN Q86VD1 MORC1 X X 112.9 Zinc ion binding.

Multiple inositol polyphosphate

phosphatase 1 MINP1_HUMAN Q9UNW1 MINPP1 X X 55.0 Catalytic activity

Myomegalin MYOME_HUMAN Q5VU43 PDE4DIP X 265.1

Functions as an anchor sequestering

components of the cAMP-dependent

pathway to Golgi and/or centrosomes

Myosin-10 MYH10_HUMAN P35580 MYH10 X 228.9

Cellular myosin that appears to play a

role in cytokinesis. cell shape. and

specialized functions such as

secretion and capping

N-acetylmuramoyl-L-alanine

amidase PGRP2_HUMAN Q96PD5 PGLYRP2 X X X 62.2 Catalytic activity

NAD(P)H dehydrogenase

[quinone] 1 NQO1_HUMAN P15559 NQO1 X 30.9 Catalytic activity

Nebulin-related-anchoring

protein NRAP_HUMAN Q86VF7 NRAP X 197.1

May be involved in anchoring the

terminal actin filaments in the

myofibril to the membrane and in

transmitting tension from the

myofibrils to the extracellular matrix

256

Negative elongation factor C/D NELFD_HUMAN Q8IXH7 NELFCD X 66.2

Essential component of the NELF

complex. a complex that negatively

regulates the elongation of

transcription by RNA polymerase II

Nephrocystin-3 NPHP3_HUMAN Q7Z494 NPHP3 X 150.8 Required for normal ciliary

development and function

Nesprin-1 SYNE1_HUMAN Q8NF91 SYNE1 X 1011.1

Forms a linking network between

organelles and the actin cytoskeleton

to maintain the subcellular spatial

organization

Neuroblastoma-amplified

sequence NBAS_HUMAN A2RRP1 NBAS X 268.6

Involved in Golgi-to-endoplasmic

reticulum (ER) retrograde transport

NIPA-like protein 3 NPAL3_HUMAN Q6P499 NIPAL3 X 44.7 Magnesium ion transmembrane

transporter activity

Nuclear distribution protein

nudE-like 1 NDEL1_HUMAN Q9GZM8 NDEL1 X 38.4

Required for organization of the

cellular microtubule array and

microtubule anchoring at the

centrosome

Olfactory receptor 51H1 O51H1_HUMAN Q8NH63 OR51H1 X 33.8 Odorant receptor

Oral-facial-digital syndrome 1

protein OFD1_HUMAN O75665 OFD1 X 116.7

Involved in the biogenesis of the

cilium. a centriole-associated function

Outer dense fiber protein 3 ODF3A_HUMAN Q96PU9 ODF3 X 27.7

Filamentous structures located on the

outside of the axoneme in the

midpiece and principal piece of the

mammalian sperm tail

Patatin-like phospholipase

domain-containing protein 4 PLPL4_HUMAN P41247 PNPLA4 X X 27.9 Catalytic activity

PC membrane recruitment

protein 2 AMER2_HUMAN Q8N7J2 AMER2 X 69.5

Negative regulator of the canonical

Wnt signaling pathway involved in

neuroectodermal patterning

Peptidase inhibitor 16 PI16_HUMAN Q6UXB8 PI16 X 49.5 May inhibit cardiomyocyte growth

Peroxisomal membrane protein 4 PXMP4_HUMAN Q9Y6I8 PXMP4 X 24.3

Hypermethylation-mediated silencing

of PXMP4 may be involved in

prostate carcinogenesis

Peroxisomal NADH

pyrophosphatase NUDT12 NUD12_HUMAN Q9BQG2 NUDT12 X 52.1 Catalytic activity

Phosphatidylcholine-sterol

acyltransferase LCAT_HUMAN P04180 LCAT X X 49.1 Catalytic activity

Phosphatidylinositol-glycan-

specific phospholipase D PHLD_HUMAN P80108 GPLD1 X X X 92.3 Catalytic activity

257

Phospholipid transfer protein PLTP_HUMAN P55058 PLTP X X X 54.8 Transporter activity

Phosphopantothenate-cysteine

ligase PPCS_HUMAN Q9HAB8 PPCS X - Catalytic activity

Pigment epithelium-derived

factor PEDF_HUMAN P36955 SERPINF1 X X 46.3

Induces extensive neuronal

differentiation in retinoblastoma cells

Plasma kallikrein KLKB1_HUMAN P03952 KLKB1 X X X 71.4 Catalytic activity

Plasma protease C1 inhibitor IC1_HUMAN P05155 SERPING1 X X X 55.2 Protease.

Plasma serine protease inhibitor IPSP_HUMAN P05154 SERPINA5 X X X 45.7 Enzyme regulator activuty (inhibitor)

Plasminogen PLMN_HUMAN P00747 PLG X X X 90.6 Catalytic activity

Plastin-2 PLSL_HUMAN P13796 LCP1 X 70.3

Actin-binding protein. Plays a role in

the activation of T-cells in response to

costimulation through TCR/CD3 and

CD2 or CD28

Platelet basic protein CXCL7_HUMAN P02775 PPBP X X X 13.9 Stimulates DNA synthesis. mitosis.

glycolysis

Platelet glycoprotein Ib alpha

chain GP1BA_HUMAN P07359 GP1BA X X 71.1

Participates in the formation of

platelet plugs

Platelet glycoprotein V GPV_HUMAN P40197 GP5 X 60.9 Mediates vWF-dependent platelet

adhesion to blood vessels

Polymeric immunoglobulin

receptor PIGR_HUMAN P01833 PIGR X 83.3

This receptor binds polymeric IgA

and IgM at the basolateral surface of

epithelial cells

Polypeptide N-

acetylgalactosaminyltransferase 1 GALT1_HUMAN Q10472 GALNT1 X X 62.4 Catalytic activity

Potassium voltage-gated channel

subfamily S member 2 KCNS2_HUMAN Q9ULS6 KCNS2 X 54.2

Modulates the delayed rectifier

voltage-gated potassium channel

activation and deactivation rates of

KCNB1 and KCNB2

Pregnancy zone protein PZP_HUMAN P20742 PZP X X X 163.8 Enzyme regulator activity

Pre-mRNA-splicing factor SYF1 SYF1_HUMAN Q9HCS7 XAB2 X 100.0 Involved in pre-mRNA splicing as

component of the spliceosome

Prenylcysteine oxidase 1 PCYOX_HUMAN Q9UHG3 PCYOX1 X 56.6 Catalytic activity

Presenilins-associated rhomboid-

like protein. mitochondrial PARL_HUMAN Q9H300 PARL X 42.2 Catalytic activity

Prickle planar cell polarity

protein 3 PRIC3_HUMAN O43900 PRICKLE3 X 68.6

Involved in the planar cell polarity

(PCP) pathway that is essential for the

polarization of epithelial cells during

morphogenetic processes

Prolyl endopeptidase FAP SEPR_HUMAN Q12884 FAP X 87.7 Catalytic activity

Protein AMBP AMBP_HUMAN P02760 AMBP X X X 38.9 Enzyme regulator activity (inhibitor)

258

Protein arginine N-

methyltransferase 8 ANM8_HUMAN Q9NR22 PRMT8 X 45.3 Catalytic activity

Protein C10 C10_HUMAN Q99622 C12orf57 X 13.2 In brain. may be required for corpus

callusum development

Protein-glutamine gamma-

glutamyltransferase Z TGM7_HUMAN Q96PF1 TGM7 X 79.9 Catalytic activity

Protein LEG1 homolog LEG1H_HUMAN Q6P5S2 LEG1 X 37.9 May be involved in early liver

development

Protein Shroom3 SHRM3_HUMAN Q8TF72 SHROOM3 X X 216.8

Controls cell shape changes in the

neuroepithelium during neural tube

closure

Protein Z-dependent protease

inhibitor ZPI_HUMAN Q9UK55

SERPINA1

0 X X 50.7 Enzyme regulator activity

Proteoglycan 4 PRG4_HUMAN Q92954 PRG4 X X X 151.1 Plays a role in boundary lubrication

within articulating joints

Prothrombin THRB_HUMAN P00734 F2 X X X 70.1 Catalytic activity

Protocadherin-20 PCD20_HUMAN Q8N6Y1 PCDH20 X Potential calcium-dependent cell-

adhesion protein

Protocadherin alpha-11 PCDAB_HUMAN Q9Y5I1 PCDHA11 X 103.3 Potential calcium-dependent cell-

adhesion protein

Putative uncharacterized protein

MYH16 MYH16_HUMAN Q9H6N6 MYH16 X 128.3 -

Ras association domain-

containing protein 6 RASF6_HUMAN Q6ZTQ3 RASSF6 X 43.4

Involved in the induction of

apoptosis. through both caspase-

dependent and caspase-independent

pathways

Replication factor C subunit 1 RFC1_HUMAN P35251 RFC1 X 128.2

Could play a role in DNA

transcription regulation as well as

DNA replication and/or repair

Retinol-binding protein 4 RET4_HUMAN P02753 RBP4 X X X 23.1 Transporter activity

Rho guanine nucleotide exchange

factor 15 ARHGF_HUMAN O94989 ARHGEF15 X 91.9

Regulates vascular smooth muscle

contractility

Secreted phosphoprotein 24 SPP24_HUMAN Q13103 SPP2 X X 24.1

Bind cytokines of the TGF-β

superfamily and also activate

intracellular signaling pathways

Selenoprotein P SEPP1_HUMAN P49908 SELENOP X X X 43.2 Transporter activity

Semaphorin-4G SEM4G_HUMAN Q9NTN9 SEMA4G X 91.5 Cell surface receptor for PLXNB2.

May play a role in axon guidance

Separin ESPL1_HUMAN Q14674 ESPL1 X 233.2 Catalytic activity

Serine/threonine-protein kinase MRCKA_HUMAN Q5VT25 CDC42BPA X 197.3 Catalytic activity

259

MRCK alpha

Serine/threonine-protein kinase

H2 KPSH2_HUMAN Q96QS6 PSKH2 X 43.0 Catalytic activity

Serine/threonine-protein kinase

mTOR MTOR_HUMAN P42345 MTOR X 288.9 Catalytic activity

Serotransferrin TRFE_HUMAN P02787 TF X X X 77.1 Transporter activity

Serum albumin ALBU_HUMAN P02768 ALB X X X 69.4 Osmotic pressure

Serum amyloid A-1 protein SAA1_HUMAN P0DJI8 SAA1 X 13.5 Inflammatory response

Serum amyloid A-2 protein SAA2_HUMAN P0DJI9 SAA2 X X 13.5 Inflammatory response

Serum amyloid A-4 protein SAA4_HUMAN P35542 SAA4 X X X 14.7 Inflammatory response

Serum amyloid P-component SAMP_HUMAN P02743 APCS X X 25.4

Can interact with DNA and histones

and may scavenge nuclear material

released from damaged circulating

cells

Serum paraoxonase/arylesterase

1 PON1_HUMAN P27169 PON1 X X X 39.7 Catalytic activity

Serum paraoxonase/lactonase 3 PON3_HUMAN Q15166 PON3 X X 39.6 Catalytic activity

Sex hormone-binding globulin SHBG_HUMAN P04278 SHBG X X X 43.8

Transporter activity. Regulates the

plasma metabolic clearance rate of

steroid hormones

Sodium channel protein type 5

subunit alpha SCN5A_HUMAN Q14524 SCN5A X 226.9

Mediates the voltage-dependent

sodium ion permeability of excitable

membranes

Sodium-coupled neutral amino

acid transporter 9 S38A9_HUMAN Q8NBW4 SLC38A9 X 63.8

Lysosomal amino acid transporter

involved in the activation of

mTORC1 in response to amino acid

levels

Sodium-dependent phosphate

transporter 1 S20A1_HUMAN Q8WUM9 SLC20A1 X 73.7

Plays a fundamental housekeeping

role in phosphate transport

Spectrin alpha chain. non-

erythrocytic 1 SPTN1_HUMAN Q13813 SPTAN1 X 284.5

Interacts with calmodulin in a

calcium-dependent manner and is

thus candidate for the calcium-

dependent movement of the

cytoskeleton at the membrane

Spermatogenesis-associated

protein 5-like protein 1 SPA5L_HUMAN Q9BVQ7 SPATA5L1 X 80.7 ATPase activity

Striatin-interacting protein 2 STRP2_HUMAN Q9ULQ0 STRIP2 X 95.4

Plays a role in the regulation of cell

morphology and cytoskeletal

organization

Syntaxin-10 STX10_HUMAN O60499 STX10 X X 28.1 SNARE involved in vesicular

260

transport from the late endosomes to

the trans-Golgi network

Taste receptor type 2 member 39 T2R39_HUMAN P59534 TAS2R39 X X 38.6

Receptor that may play a role in the

perception of bitterness and is

gustducin-linked

T-complex-associated testis-

expressed protein 1 TCTE1_HUMAN - - X - -

Telomere length regulation

protein TEL2 homolog TELO2_HUMAN Q9Y4R8 TELO2 X 91.7

Regulator of the DNA damage

response (DDR)

Teneurin-3 TEN3_HUMAN Q9P273 TENM3 X 300.9

Involved in neural development by

regulating the establishment of proper

connectivity within the nervous

system

Tetranectin TETN_HUMAN P05452 CLEC3B X X X 22.5 Involved in the packaging of

molecules destined for exocytosis

Thrombospondin-1 TSP1_HUMAN P07996 THBS1 X X X 129.4 Immune response

Thyroxine-binding globulin THBG_HUMAN P05543 SERPINA7 X X 46.3 Transporter activity

TOM1-like protein 1 TM1L1_HUMAN O75674 TOM1L1 X 52.9 Adapter protein involved in signaling

pathways

Transcobalamin-1 TCO1_HUMAN P20061 TCN1 X 48.2

Binds vitamin B12 with femtomolar

affinity and protects it from the acidic

environment of the stomach

Transcription factor AP-2-epsilon AP2E_HUMAN Q6VUC0 TFAP2E X 46.2

Sequence-specific DNA-binding

protein that interacts with inducible

viral and cellular enhancer elements

to regulate transcription of selected

genes

Transcription factor HES-1 HES1_HUMAN Q14469 HES1 X 29.5

Transcriptional repressor of genes

that require a bHLH protein for their

transcription.

Transferrin receptor protein 1 TFR1_HUMAN P02786 TFRC X X 84.9 Transporter activity

Transient receptor potential

cation channel subfamily V

member 5

TRPV5_HUMAN Q9NQA5 TRPV5 X 82.5

Constitutively active calcium

selective cation channel thought to be

involved in Ca2+ reabsorption in

kidney and intestine

Transmembrane protein 198 TM198_HUMAN Q66K66 TMEM198 X X X 39.4

Promotes low-density lipoprotein

receptor-related protein 6 (LRP6)

phosphorylation

Transthyretin TTHY_HUMAN P02766 TTR X X 15.8 Transporter activity

Tyrosine--tRNA ligase. SYYC_HUMAN P54577 YARS X 59.1 Catalytic activity

261

cytoplasmic

Transmembrane protein 223 TM223_HUMAN A0PJW6 TMEM223 X 22.0 Nervous system development

Ubiquitin carboxyl-terminal

hydrolase 24 UBP24_HUMAN Q9UPU5 USP24 X 294.4 Catalytic activity

E3 ubiquitin-protein ligase

BRE1A BRE1A_HUMAN Q5VTR2 RNF20 X 113.7 Catalytic activity

Uncharacterized protein

C6orf132 CF132_HUMAN Q5T0Z8 C6orf132 X 124.0 -

Uncharacterized protein

C17orf80 CQ080_HUMAN Q9BSJ5 C17orf80 X 67.3 -

Uncharacterized protein

FLJ45252 YJ005_HUMAN Q6ZSR9 N/A X 37.9 -

Vacuolar protein sorting-

associated protein 13D VP13D_HUMAN Q5THJ4 VPS13D X 491.9

Functions in promoting mitochondrial

clearance by mitochondrial autophagy

(mitophagy)

Villin-like protein VILL_HUMAN O15195 VILL X 95.9 Tumor suppressor

Vitamin D-binding protein VTDB_HUMAN P02774 GC X X X 52.9 Transporter activity

Vitamin K-dependent protein C PROC_HUMAN P04070 PROC X X 52.1 Catalytic activity

Vitamin K-dependent protein S PROS_HUMAN P07225 PROS1 X X X 75.1 Anticoagulant plasma protein

Vitamin K-dependent protein Z PROZ_HUMAN P22891 PROZ X 44.7

Appears to assist hemostasis by

binding thrombin and promoting its

association with phospholipid

vesicles

Vitronectin VTNC_HUMAN P04004 VTN X X X 54.3 Proteolysis regulation

Voltage-dependent L-type

calcium channel subunit alpha-1F CAC1F_HUMAN O60840 CACNA1F X 220.7

Mediates the entry of calcium ions

into excitable cells and are also

involved in a variety of calcium-

dependent processes. including

muscle contraction. hormone or

neurotransmitter release. gene

expression. cell motility. cell division

and cell death

von Willebrand factor VWF_HUMAN P04275 VWF X 220 Plays a major role in blood

coagulation

VPS10 domain-containing

receptor SorCS3 SORC3_HUMAN Q9UPU3 SORCS3 X X 135.7 Neuropeptide receptor activity

WD repeat-containing protein 46 WDR46_HUMAN O15213 WDR46 X 68.1 Scaffold component of the nucleolar

structure

Zinc-alpha-2-glycoprotein ZA2G_HUMAN P25311 AZGP1 X 34.2 Stimulates lipid degradation in

adipocytes and causes the extensive

262

fat losses associated with some

advanced cancers

Zinc finger and BTB domain-

containing protein 4 ZBTB4_HUMAN Q9P1Z0 ZBTB4 X 105.1

Transcriptional repressor with

bimodal DNA-binding specificity

Zinc finger CCCH domain-

containing protein 4 ZC3H4_HUMAN Q9UPT8 ZC3H4 X 140.2

DNA-binding transcription factor

activity

Zinc finger protein 350 ZN350_HUMAN Q9GZX5 ZNF350 X 60.0 Transcriptional repressor

Zinc finger protein 570 ZN570_HUMAN Q96NI8 ZNF570 X 62.3 May be involved in transcriptional

regulation

Zinc finger protein 700 ZN700_HUMAN Q9H0M5 ZNF700 X 86.2 May be involved in transcriptional

regulation

263

Table 2_SM. Analysis of the protein corona formed around AuNPs (10.02 ± 0.91 nm). AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm)

after their incubation with serum aliquots (x2) from eight different triple negative breast cancer patients (6 protein samples per individual: 2

treated with AuNPs. 2 with AgNPs and 2 with MNPs). The accession number. gene name. species (Human). molecular weight and protein

function were reported. Grey color: 138 proteins commonly found in the protein corona of AuNPs (10.02 ± 0.91 nm). AgNPs (9.73 ± 1.70 nm)

and MNPs (9.30 ± 0.67 nm). Pink color: 56 identified corona proteins exclusively bound to the AuNPs (10.02 ± 0.91 nm). Yellow color: 33

identified corona proteins exclusively bound to the AgNPs (9.73 ± 1.70 nm). Blue: 53 identified corona proteins exclusively bound to the MNPs

(9.30 ± 0.67 nm). Orange color: 30 identified corona proteins commonly bound to the AuNPs (10.02 ± 0.91 nm) and AgNPs (9.73 ± 1.70 nm).

Violet color: 7 identified corona proteins commonly bound to the AuNPs (10.02 ± 0.91 nm) and MNPs (9.30 ± 0.67 nm). Green color: 5

identified corona proteins commonly bound to the AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm).

264

Protein Name UniProt Name Entry Name Gene AuNPs AgNPs MNPs Mass (kDa) Function Actin. cytoplasmic 2 ACTG_HUMAN P63261 ACTG1 X 41.7 Cell mobility

Afamin AFAM_HUMAN P43652 AFM X X X 69.1 Transporter activity

Alpha-1-acid glycoprotein 1 A1AG1_HUMAN P02763 ORM1 X X 23.5 Transporter activity

Alpha-1-acid glycoprotein 2 A1AG2_HUMAN P19652 ORM2 X X 23.6 Transporter activity

Alpha-1-antichymotrypsin AACT_HUMAN P01011 SERPINA3 X X X 47.6 Protease inhibitor

Alpha-1-antitrypsin A1AT_HUMAN P01009 SERPINA1 X X X 46.7 Protease inhibitor

Alpha-1B-glycoprotein A1BG_HUMAN P04217 A1BG X X X 54.3 Neutrophil and platelet degranulation

Alpha-2-antiplasmin A2AP_HUMAN P08697 SERPINF2 X X X 54.6 Protease inhibitor

Alpha-2-HS-glycoprotein FETUA_HUMAN P02765 AHSG X X X 39.3

Promotes endocitosis. brain

development and the formation of

bone tissue

Alpha-2-macroglobulin A2MG_HUMAN P01023 A2M X X X 163.3 Immune response

Amyloid-beta A4 precursor

protein-binding family A

member 2

APBA2_HUMAN Q99767 APBA2 X 82.5

Function in synaptic vesicle

exocytosis by binding to STXBP1. an

essential component of the synaptic

vesicle exocytotic machinery

Angiotensinogen ANGT_HUMAN P01019 AGT X X X 53.1 Regulator of blood pressure. body

fluid and electrolyte homeostasis

Ankyrin repeat domain-

containing protein 20B AN20B_HUMAN Q5CZ79

ANKRD20

A8P X 93.9 -

Ankyrin repeat and sterile alpha

motif domain-containing protein

1B

ANS1B_HUMAN Q7Z6G8 ANKS1B X 138.1 Plays a role as a modulator of APP

processing

Annexin A6 ANXA6_HUMAN P08133 ANXA6 X 75.9 May associate with CD21

Antithrombin-III ANT3_HUMAN P01008 SERPINC1 X X X 52.6 Protease inhibitor

AP-3 complex subunit beta-2 AP3B2_HUMAN Q13367 AP3B2 X 119.1 Transporter activity

PC membrane recruitment

protein 2 AMER2_HUMAN Q8N7J2 AMER2 X 69.5

Negative regulator of the canonical

Wnt signaling pathway involved in

neuroectodermal patterning

Apolipoprotein A-I APOA1_HUMAN P02647 APOA1 X X X 30.8 Metabolism

Apolipoprotein A-II APOA2_HUMAN P02652 APOA2 X X X 11.2 Transport

Apolipoprotein A-IV APOA4_HUMAN P06727 APOA4 X X X 45.4 Hepatic transcellukar lipid transport

Apolipoprotein B-100 APOB_HUMAN P04114 APOB X X X 515.6

Recognition signal for the cellular

binding and internalization of LDL

particles by the apoB/E receptor

Apolipoprotein C-I APOC1_HUMAN P02654 APOC1 X X 9.3

Inhibitor of lipoprotein binding to the

low density lipoprotein (LDL)

receptor

265

Apolipoprotein C-II APOC2_HUMAN P02655 APOC2 X X X 11.3 Enzyme regulator activity

(lipoprotein lipase activator)

Apolipoprotein C-III APOC3_HUMAN P02656 APOC3 X X X 10.8 Enzyme regulator activity

(lipoprotein lipase inhibitor)

Apolipoprotein C-IV APOC4_HUMAN P55056 APOC4 X 14.5 Biological regulation

Apolipoprotein D APOD_HUMAN P05090 APOD X X X 21.3 Transporter activity

Apolipoprotein E APOE_HUMAN P02649 APOE X X X 36.1 Transporter activity and cell uptake

Apolipoprotein F APOF_HUMAN Q13790 APOF X X X 35.4 Transporter activity

Apolipoprotein L1 APOL1_HUMAN O14791 APOL1 X X X 43.9 Transporter activity

Apolipoprotein M APOM_HUMAN O95445 APOM X X X 21.2 Transporter activity

Apolipoprotein(a) APOA_HUMAN P08519 LPA X X X 501.3 Catalytic activity

ATP-binding cassette sub-family

B member 5 ABCB5_HUMAN Q2M3G0 ABCB5 X X 138.6 Transporter activity

ATP-binding cassette sub-family

F member 1 ABCF1_HUMAN Q8NE71 ABCF1 X X X

95.9

Transporter activity

Attractin ATRN_HUMAN O75882 ATRN X X X 158.5

Involved in the initial immune cell

clustering during inflammatory

response and may regulate

chemotactic activity of chemokines

Atypical kinase COQ8B.

mitochondrial COQ8B_HUMAN Q96D53 COQ8B X 60.1

Involved in the biosynthesis of

coenzyme Q

Beta-2-glycoprotein 1 APOH_HUMAN P02749 APOH X X X 38.3 Transporter activity

Biotinidase BTD_HUMAN P43251 BTD X X 61.1 Catalytic activity

Beta-ureidopropionase BUP1_HUMAN Q9UBR1 UPB1 X 43.1 Catalytic activity

Bile salt-activated lipase CEL_HUMAN P19835 CEL X 79.3 Catalytic activity

Bone morphogenetic protein 10 BMP10_HUMAN O95393 BMP10 X 48.0

Inhibits endothelial cell migration and

growth. May reduce cell migration

and cell matrix adhesion in breast

cancer cell lines.

Brefeldin A-inhibited guanine

nucleotide-exchange protein 3 BIG3_HUMAN Q5TH69 ARFGEF3 X 240.6

Participates in the regulation of

systemic glucose homeostasis

Bromodomain adjacent to zinc

finger domain protein 2A BAZ2A_HUMAN Q9UIF9 BAZ2A X 211.2

Essential component of the NoRC

(nucleolar remodeling complex)

complex that mediates silencing of a

fraction of rDNA

C4b-binding protein alpha chain C4BPA_HUMAN P04003 C4BPA X X X 67.1 Enzyme regulator activity (inhibitor)

C4b-binding protein beta chain C4BPB_HUMAN P20851 C4BPB X X X 28.4 Enzyme regulator activity (inhibitor)

85/88 kDa calcium-independent

phospholipase A2 PLPL9_HUMAN O60733 PLA2G6 X 89.9 Catalytic activity

266

Carboxypeptidase B2 CBPB2_HUMAN Q96IY4 CPB2 X X X 48.4 Catalytic activity

Carboxypeptidase N catalytic

chain CBPN_HUMAN P15169 CPN1 X X X 52.3 Catalytic activity

Carboxypeptidase N subunit 2 CPN2_HUMAN P22792 CPN2 X X X 60.6 Enzyme regulator activity

CASP8-associated protein 2 C8AP2_HUMAN Q9UKL3 CASP8AP2 X 222.6

Involved in TNF-alpha-induced

activation of NF-kappa-B via a

TRAF2-dependent pathway

Caspase recruitment domain-

containing protein 11 CAR11_HUMAN Q9BXL7 CARD11 X 133.3

Involved in the costimulatory signal

essential for T-cell receptor (TCR)-

mediated T-cell activation. Also

activates the TORC1 signaling

pathway

Cathelicidin antimicrobial

peptide CAMP_HUMAN P49913 CAMP X 19.3 Antibacterial activity

CD5 antigen-like CD5L_HUMAN O43866 CD5L X X X 38.1 Regulator of lipid synthesis

CD44 antigen CD44_HUMAN P16070 CD44 X X 81.5

Mediates cell-cell and cell-matrix

interactions through its affinity for

hyaluronic acid (HA)

Centrosome-associated protein

350 CE350_HUMAN Q5VT06 CEP350 X 350.9 Structural

Ceramide synthase 4 CERS4_HUMAN Q9HA82 CERS4 X 46.4

May be either a bona fide

(dihydro)ceramide synthase or a

modulator of its activity

Ceruloplasmin CERU_HUMAN P00450 CP X X X 122.2 Catalytic activity

Cholesteryl ester transfer protein CETP_HUMAN P11597 CETP X 54.8 Transporter activity

Cilia- and flagella-associated

protein 100 CP100_HUMAN Q494V2 CFAP100 X 71.1

Plays a role in ciliary/flagellar

motility by regulating the assembly

and the activity of axonemal inner

dynein arm

Clusterin CLUS_HUMAN P10909 CLU X X X 52.5 Regulation of cell proliferation

Coagulation factor IX FA9_HUMAN P00740 F9 X X X 51.8 Catalytic activity

Coagulation factor V FA5_HUMAN P12259 F5 X X X 251.7 Enzyme regulator activity

Coagulation factor X FA10_HUMAN P00742 F10 X X X 54.7 Enzyme regulator activity

Coagulation factor XI FA11_HUMAN P03951 F11 X 70.1 Catalytic activity

Coagulation factor XII FA12_HUMAN P00748 F12 X X X 67.8 Catalytic activity

Coagulation factor XIII A chain F13A_HUMAN P00488 F13A1 X 83.3 Catalytic activity

Coagulation factor XIII B chain F13B_HUMAN P05160 F13B X X X 75.5 Enzyme regulator activity

Complement C1q subcomponent

subunit A C1QA_HUMAN P02745 C1QA X X X 26.1 Enzyme regulator activity

267

Complement C1q subcomponent

subunit B C1QB_HUMAN P02746 C1QB X X X 26.7 Enzyme regulator activity

Complement C1q subcomponent

subunit C C1QC_HUMAN P02747 C1QC X X X 25.8 Enzyme regulator activity

Complement C1r subcomponent C1R_HUMAN P00736 C1R X X X 80.1 Catalytic activity

Complement C1r subcomponent-

like protein C1RL_HUMAN Q9NZP8 C1RL X X X 53.5 Enzyme regulator activity

Complement C1s subcomponent C1S_HUMAN P09871 C1S X X X 76.7 Catalytic activity

Complement C2 CO2_HUMAN P06681 C2 X X X 83.3 Catalytic activity

Complement C3 CO3_HUMAN P01024 C3 X X X 187.1 Immune response

Complement C4-A CO4A_HUMAN P0C0L4 C4A X X X 192.8 Inflammatory response

Complement C4-B CO4B_HUMAN P0C0L5 C4B X X X 192.7 Enzyme regulator activity

Complement C5 CO5_HUMAN P01031 C5 X X X 188.3 Enzyme regulator activity

Complement component C6 CO6_HUMAN P13671 C6 X X 104.8 Structural

Complement component C7 CO7_HUMAN P10643 C7 X X X 93.5 Structural

Complement component C8

alpha chain CO8A_HUMAN P07357 C8A X X 65.2 Structural

Complement component C8 beta

chain CO8B_HUMAN P07358 C8B X X X 67.1 Structural

Complement component C8

gamma chain CO8G_HUMAN P07360 C8G X X X 22.3 Structural

Complement component C9 CO9_HUMAN P02748 C9 X X X 63.2 Structural

Complement factor B CFAB_HUMAN P00751 CFB X X X 85.5 Immune response

Complement factor D CFAD_HUMAN P00746 CFD X X X 27.1 Catalytic activity

Complement factor H CFAH_HUMAN P08603 CFH X X X 139.1 Enzyme regulator activity

Complement factor H-related

protein 1 FHR1_HUMAN Q03591 CFHR1 X X X 37.6 Plays a role in lipid metabolism

Complement factor H-related

protein 2 FHR2_HUMAN P36980 CFHR2 X X X 30.6 Plays a role in lipid metabolism

Complement factor H-related

protein 3 FHR3_HUMAN Q02985 CFHR3 X X 37.3 Involved in complement regulation

Complement factor H-related

protein 4 FHR4_HUMAN Q92496 CFHR4 X X 65.3 Plays a role in lipid metabolism

Complement factor I CFAI_HUMAN P05156 CFI X X X 65.7 Catalytic activity

Contactin-1 CNTN1_HUMAN Q12860 CNTN1 X 113.3 Mediates cell surface interactions

during nervous system development.

Contactin-associated protein-like

2 CNTP2_HUMAN Q9UHC6 CNTNAP2 X 148.2

Plays a role in the formation of

functional distinct domains critical for

saltatory conduction of nerve

268

impulses in myelinated nerve fibers

Corticosteroid-binding globulin CBG_HUMAN P08185 SERPINA6 X 45.1 Transporter activity

C-reactive protein CRP_HUMAN P02741 CRP X 25.0 Displays several functions associated

with host defense

C-type lectin domain family 4

member F CLC4F_HUMAN Q8N1N0 CLEC4F X 65.5

Receptor with an affinity for

galactose and fucose. Could be

involved in endocytosis

Cyclin-dependent kinase 2 CDK2_HUMAN P24941 CDK2 X 33.9 Catalytic activity

Cystatin-F CYTF_HUMAN O76096 CST7 X 16.4

May play a role in immune regulation

through inhibition of a unique target

in the hematopoietic system

DDB1- and CUL4-associated

factor 15 DCA15_HUMAN Q66K64 DCAF15 X 66.5

May be involved in ubiquitination

and degradation through a DBB1-

CUL4 E3 protein-ubiquitin ligase

DNA topoisomerase 1 TOP1_HUMAN P11387 TOP1 X 90.7 Catalyctic activity

DNA topoisomerase 2-binding

protein 1 TOPB1_HUMAN Q92547 TOPBP1 X 170.7

Binds double-stranded DNA breaks

and nicks as well as single-stranded

DNA

Dopamine beta-hydroxylase DOPO_HUMAN P09172 DBH X X 69.1 Catalyctic activity

Dual specificity protein

phosphatase 9 DUS9_HUMAN Q99956 DUSP9 X 41.9 Inactivates MAP kinases

Dynein assembly factor 1.

axonemal DAAF1_HUMAN Q8NEP3 DNAAF1 X 80.0

Plays a role in cytoplasmic

preassembly of dynein arms

Dynein heavy chain 10.

axonemal DYH10_HUMAN Q8IVF4 DNAH10 X 514.8 Presents ATPase activity

Dystonin DYST_HUMAN Q03001 DST X 860.6

Acts as an integrator of intermediate

filaments. actin and microtubule

cytoskeleton networks

EGF-containing fibulin-like

extracellular matrix protein 1 FBLN3_HUMAN Q12805 EFEMP1 X 54.6

Binds EGFR. the EGF receptor.

inducing EGFR autophosphorylation

and the activation of downstream

signaling pathways. May play a role

in cell adhesion and migration

Ellis-van Creveld syndrome

protein EVC_HUMAN P57679 EVC X 111.9

Involved in endochondral growth and

skeletal development

Estrogen sulfotransferase ST1E1_HUMAN P49888 SULT1E1 X 35.1

Catalytic activity. May play a role in

the regulation of estrogen receptor

activity by metabolizing free estradiol

Extracellular matrix protein 1 ECM1_HUMAN Q16610 ECM1 X X X 60.7 Enzyme regulator inhibitor

[F-actin]-monooxygenase MICA3_HUMAN Q7RTP6 MICAL3 X 224.3 Catalytic activity

269

MICAL3

Fanconi anemia group B protein FANCB_HUMAN Q8NB91 FANCB X 97.7 DNA repair protein required for

FANCD2 ubiquitination

F-box only protein 42 FBX42_HUMAN Q6P3S6 FBXO42 X 77.8

Specifically recognizes p53/TP53.

promoting its ubiquitination and

degradation

Fetuin-B FETUB_HUMAN FETUB FETUB X X X 42.1 Catalytic activity

Fibrinogen alpha chain FIBA_HUMAN P02671 FGA X 94.9 Coagulation. immune response

Fibronectin FINC_HUMAN P02751 FN1 X X X 262.6 Growth. migration and differenciation

Fibulin-1 FBLN1_HUMAN P23142 FBLN1 X X 77.2 Structural

Ficolin-2 FCN2_HUMAN Q15485 FCN2 X X 34.0 Immunity response

Ficolin-3 FCN3_HUMAN O75636 FCN3 X X X 32.9 Immunity response

Galectin-3-binding protein LG3BP_HUMAN Q08380 LGALS3BP X X X 65.3 Stimulate host defense against viruses

and tumor cells

Gelsolin GELS_HUMAN P06396 GSN X X X 85.7 Actin regulation (assembly)

Glial fibrillary acidic protein GFAP_HUMAN P14136 GFAP X 49.9

Cell-specific marker during the

development of the central nervous

system

GREB1-like protein GRB1L_HUMAN Q9C091 GREB1L X 214.3 Plays a major role in early

metanephros and genital development

Haptoglobin HPT_HUMAN P00738 HP X X X 45.2

Prevents loss of iron through the

kidneys. protecting them from

damage by hemoglobin

Haptoglobin-related protein HPTR_HUMAN P00739 HPR X X X 39

Binds hemoglobin as efficiently as

haptoglobin

HEAT repeat-containing protein

4 HEAT4_HUMAN Q86WZ0 HEATR4 X 117.2 -

Hemoglobin subunit alpha HBA_HUMAN P69905 HBA1 X X X 15.3 Transporter activity

Hemoglobin subunit beta HBB_HUMAN P68871 HBB X X X 15.9 Transporter activity

Hemoglobin subunit gamma-2 HBG2_HUMAN P69892 HBG2 X 16.1

Gamma chains make up the fetal

hemoglobin F. in combination with

alpha chains

Hemopexin HEMO_HUMAN P02790 HPX X X X 51.7 Transporter activity

Heparan sulfate glucosamine 3-

O-sulfotransferase 5 HS3S5_HUMAN Q8IZT8 HS3ST5 X 40.4 Catalytic activity

Heparin cofactor 2 HEP2_HUMAN P05546 SERPIND1 X X X 57.1 Enzyme regulator activity (inhibitor)

Hepatocyte growth factor

activator HGFA_HUMAN Q04756 HGFAC X 70.7 Enzyme regulator activity (activator)

Hepatocyte growth factor-like HGFL_HUMAN P26927 MST1 X 80.3 Catalytic activity

270

protein

Histidine-rich glycoprotein HRG_HUMAN P04196 HRG X X X 59.6 Inhibits fibrinolysis and reduces

inhibition of coagulation

Histone-lysine N-

methyltransferase 2A KMT2A_HUMAN Q03164 KMT2A X 431.8 Catalytic activity

Histone-lysine N-

methyltransferase 2D KMT2D_HUMAN O14686 KMT2D X 593.4

Catalytic activity. Acts as a

coactivator for estrogen receptor by

being recruited by ESR1. thereby

activating transcription

Hyaluronan-binding protein 2 HABP2_HUMAN Q14520 HABP2 X X X 62.7 Enzyme regulator activity

IgGFc-binding protein FCGBP_HUMAN Q9Y6R7 FCGBP X X X 572.1 Structural

Immunoglobulin heavy constant

alpha 1 IGHA1_HUMAN P01876 IGHA1 X X X 37.7 Immune response

Immunoglobulin heavy constant

alpha 2 IGHA2_HUMAN P01877 IGHA2 X X 36.6 Immune response

Immunoglobulin heavy constant

delta IGHD_HUMAN P01880 IGHD X X X 42.3 Immune response

Immunoglobulin heavy constant

gamma 1 IGHG1_HUMAN P01857 IGHG1 X X X 36.1 Immune response

Immunoglobulin heavy constant

gamma 2 IGHG2_HUMAN P01859 IGHG2 X X X 35.9 Immune response

Immunoglobulin heavy constant

gamma 3 IGHG3_HUMAN P01860 IGHG3 X X X 41.3 Immune response

Immunoglobulin heavy constant

gamma 4 IGHG4_HUMAN P01861 IGHG4 X X 35.9 Immune response

Immunoglobulin heavy constant

mu IGHM_HUMAN P01871 IGHM X X X 49.4 Immune response

Immunoglobulin heavy variable

1-2 HV102_HUMAN P23083 IGHV1-2 X 13.1 Immune response

Immunoglobulin heavy variable

3-23 HV323_HUMAN P01764 IGHV3-23 X X 12.6 Immune response

Immunoglobulin heavy variable

3-30-3 HVC33_HUMAN P0DP02

IGHV3-30-

3 X X X 13 Immune response

Immunoglobulin heavy variable

3-30-5 HV335_HUMAN P0DP03

IGHV3-30-

5 X X X 12.9 Immune response

Immunoglobulin heavy variable

3-43D HV43D_HUMAN P0DP04 IGHV3-43D X 13.1 Immune response

Immunoglobulin heavy variable

3-7 HV307_HUMAN P01780 IGHV3-7 X X X 12.9 Immune response

271

Immunoglobulin heavy variable

3-72 HV372_HUMAN A0A0B4J1Y9 IGHV3-72 X 13.2 Immune response

Immunoglobulin heavy variable

3-74 HV374_HUMAN A0A0B4J1X5 IGHV3-74 X X X 12.8 Immune response

Immunoglobulin heavy variable

4-38-2 HVD82_HUMAN P0DP08

IGHV4-38-

2 X X X 13.1 Immune response

Immunoglobulin heavy variable

5-10-1 HV5X1_HUMAN A0A0J9YXX1

IGHV5-10-

1 X X 12.8 Imuune response

Immunoglobulin heavy variable

6-1 HV601_HUMAN A0A0B4J1U7 IGHV6-1 X X X 13.5 Imuune response

Immunoglobulin J chain IGJ_HUMAN P01591 JCHAIN X X X 18.1 Immune response

Immunoglobulin kappa constant IGKC_HUMAN P01834 IGKC X X X 11.8 Immune response

Immunoglobulin kappa variable

1-5 KV105_HUMAN P01602 IGKV1-5 X X 12.8 Immune response

Immunoglobulin kappa variable

1-13 KV113_HUMAN P0DP09 IGKV1-13 X 12.5 Immune response

Immunoglobulin kappa variable

1-27 KV127_HUMAN A0A075B6S5 IGKV1-27 X X 12.7 Immune response

Immunoglobulin kappa variable

1-33 KV133_HUMAN P01594 IGKV1-33 X 12.8 Immune response

Immunoglobulin kappa variable

2-24 KV224_HUMAN A0A0C4DH68 IGKV2-24 X X X 13.1 Immune response

Immunoglobulin kappa variable

2-29 KV229_HUMAN A2NJV5 IGKV2-29 X X 13.1 Immune response

Immunoglobulin kappa variable

2-30 KV230_HUMAN P06310 IGKV2-30 X X X 13.2 Immune response

Immunoglobulin kappa variable

2D-28 KVD28_HUMAN P01615 IGKV2D-28 X X X 12.9 Immune response

Immunoglobulin kappa variable

3-11 KV311_HUMAN P04433 IGKV3-11 X X X 12.6 Immune response

Immunoglobulin kappa variable

3-15 KV315_HUMAN P01624 IGKV3-15 X X X 12.5 Immune response

Immunoglobulin kappa variable

3-20 KV320_HUMAN P01619 IGKV3-20 X X X 12.6 Immune response

Immunoglobulin kappa variable

3D-20 KVD20_HUMAN A0A0C4DH25 IGKV3D-20 X X X 12.5 Immune response

Immunoglobulin kappa variable

4-1 KV401_HUMAN P06312 IGKV4-1 X X X 13.4 Immune response

Immunoglobulin lambda constant

2 IGLC2_HUMAN P0DOY2 IGLC2 X X X 11.3 Immune response

272

Immunoglobulin lambda constant

3 IGLC3_HUMAN P0DOY3 IGLC3 X X X 11.3 Immune response

Immunoglobulin lambda variable

3-21 LV321_HUMAN P80748 IGLV3-21 X X X 12.4 Immune response

Immunoglobulin lambda variable

3-27 LV327_HUMAN P01718 IGLV3-27 X X 12.2 Immune response

Immunoglobulin lambda-like

polypeptide 5 IGLL5_HUMAN B9A064 IGLL5 X X X 23.1 Immune response

Importin-4 IPO4_HUMAN Q8TEX9 IPO4 X X 118.7 Functions in nuclear protein import as

nuclear transport receptor

Inositol hexakisphosphate kinase

2 IP6K2_HUMAN Q9UHH9 IP6K2 X 49.2 Catalytic activity

Inositol 1.4.5-trisphosphate

receptor type 3 ITPR3_HUMAN Q14573 ITPR3 X 304.1

Receptor for inositol 1.4.5-

trisphosphate

Inositol polyphosphate 1-

phosphatase INPP_HUMAN P49441 INPP1 X 43.9 Catalytic activity

Insulin-like growth factor-

binding protein 3 IBP3_HUMAN P17936 IGFBP3 X 31.7

IGF-binding proteins prolong the

half-life of the IGFs

Insulin-like growth factor-

binding protein complex acid

labile subunit

ALS_HUMAN P35858 IGFALS X X X 66.1

Binds insulin-like growth factors.

increasing their half-life and their

vascular localization

Inter-alpha-trypsin inhibitor

heavy chain H1 ITIH1_HUMAN P19827 ITIH1 X X X 101.4 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H2 ITIH2_HUMAN P19823 ITIH2 X X X 106.5 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H3 ITIH3_HUMAN Q06033 ITIH3 X X X 99.9 Transporter activity

Inter-alpha-trypsin inhibitor

heavy chain H4 ITIH4_HUMAN Q14624 ITIH4 X X X 103.4 Inflammatory response

Intraflagellar transport protein 74

homolog IFT74_HUMAN Q96LB3 IFT74 X 69.2 Transporter activity

Janus kinase and microtubule-

interacting protein 1 JKIP1_HUMAN Q96N16 JAKMIP1 X 73.2

Plays a role in the microtubule-

dependent transport of the GABA-B

receptor

Kallistatin KAIN_HUMAN P29622 SERPINA4 X X 48.5 Enzyme regulator inhibitor

Keratin. type II cuticular Hb1 KRT81_HUMAN Q14533 KRT81 X 54.9 Structural

Keratin. type I cytoskeletal 10 K1C10_HUMAN P13645 KRT10 X X X 58.8 Structural

Keratin. type I cytoskeletal 14 K1C14_HUMAN P02533 KRT14 X 51.6 Structural

Keratin. type I cytoskeletal 9 K1C9_HUMAN P35527 KRT9 X X X 62.1 Structural

273

Keratin. type II cytoskeletal 1 K2C1_HUMAN P04264 KRT1 X X X 66.1 Enzyme regulator activity

Keratin. type II cytoskeletal 2

epidermal K22E_HUMAN P35908 KRT2 X X X 65.4 Structural

Kininogen-1 KNG1_HUMAN P01042 KNG1 X X X 71.9 Enzyme regulator activity

Lactotransferrin TRFL_HUMAN P02788 LTF X 78.2 Transporter activity

Lebercilin LCA5_HUMAN Q86VQ0 LCA5 X X 80.5 Transporter activity

Leucine-rich alpha-2-

glycoprotein A2GL_HUMAN P02750 LRG1 X X 38.2

Protein-protein interaction. signal

transduction. and cell adhesion and

development

LINE-1 type transposase domain-

containing protein 1 LITD1_HUMAN Q5T7N2 L1TD1 X X 98.8 Single-stranded RNA binding

Lipopolysaccharide-binding

protein LBP_HUMAN P18428 LBP X X X 53.4 Immune response.

LRP chaperone MESD MESD_HUMAN Q14696 MESD X 26.1

Assisting the folding of beta-

propeller/EGF modules within the

family of low-density lipoprotein

receptors (LDLRs)

Lumican LUM_HUMAN P51884 LUM X X X 38.4 Collagen binding. extracellular matrix

structural constituent

Lysine-specific demethylase 4C KDM4C_HUMAN Q9H3R0 KDM4C X X 119.9 Catalytic activity

Lysosomal-trafficking regulator LYST_HUMAN Q99698 LYST X X 429.1

Required for sorting endosomal

resident proteins into late

multivesicular endosomes

MaFF-interacting protein MAFIP_HUMAN Q8WZ33 MAFIP X 13.9 Inhibits cell growth and colony-

forming efficiency

Mannan-binding lectin serine

protease 1 MASP1_HUMAN P48740 MASP1 X X X 79.2 Enzyme regulator activity

Mannan-binding lectin serine

protease 2 MASP2_HUMAN O00187 MASP2 X 75.7 Catalytic activity

MANSC domain-containing

protein 1 MANS1_HUMAN Q9H8J5 MANSC1 X 46.8 -

Matrix metalloproteinase-9 MMP9_HUMAN P14780 MMP9 X X 78.5 Catalytic activity

Meckelin MKS3_HUMAN Q5HYA8 TMEM67 X 111.7 Required for ciliary structure and

function

Microtubule-associated protein

1A MAP1A_HUMAN P78559 MAP1A X X 305.5

Structural protein involved in the

filamentous cross-bridging between

microtubules and other skeletal

elements

Microtubule-associated protein 2 MTAP2_HUMAN P11137 MAP2 X 199.5 Stabilizes the microtubules against

274

depolymerization

Mitochondrial 2-

oxoglutarate/malate carrier

protein

M2OM_HUMAN Q02978 SLC25A11 X 34.1 Catalytic and transporter activities

Monocyte differentiation antigen

CD14 CD14_HUMAN P08571 CD14 X X X 40.1 Immune response

MORC family CW-type zinc

finger protein 1 MORC1_HUMAN Q86VD1 MORC1 X 112.9 Zinc ion binding.

Mucolipin-3 MCLN3_HUMAN Q8TDD5 MCOLN3 X 64.2 Pays a role in the regulation of

membrane trafficking events

Multidrug resistance protein 1 MDR1_HUMAN P08183 ABCB1 X 141.5

Responsible for decreased drug

accumulation in multidrug-resistant

cells

NAD(P)H dehydrogenase

[quinone] 1 NQO1_HUMAN P15559 NQO1 X 30.9 Catalytic activity

N-acetyllactosaminide beta-1.3-

N-acetylglucosaminyltransferase

2

B3GN2_HUMAN Q9NY97 B3GNT2 X 46.0 Catalytic activity

N-acetylmuramoyl-L-alanine

amidase PGRP2_HUMAN Q96PD5 PGLYRP2 X X X 62.2 Catalytic activity

Nck-associated protein 1 NCKP1_HUMAN Q9Y2A7 NCKAP1 X 128.8

As component of the WAVE1

complex. required for BDNF-NTRK2

endocytic trafficking and signaling

from early endosomes

Nebulette NEBL_HUMAN O76041 NEBL X 116.4 Plays an important role in the

assembly of the Z-disk

Nesprin-1 SYNE1_HUMAN Q8NF91 SYNE1 X 1011.1

Forms a linking network between

organelles and the actin cytoskeleton

to maintain the subcellular spatial

organization

Nuclear distribution protein

nudE-like 1 NDEL1_HUMAN Q9GZM8 NDEL1 X X 38.4

Required for organization of the

cellular microtubule array and

microtubule anchoring at the

centrosome

Nuclear receptor coactivator 6 NCOA6_HUMAN Q14686 NCOA6 X 219.1 Involved in the coactivation of the

NF-kappa-B pathway

Nucleoporin NUP188 homolog NU188_HUMAN Q5SRE5 NUP188 X 196.1 May function as a component of the

nuclear pore complex (NPC)

PAN2-PAN3 deadenylation

complex subunit PAN3 PAN3_HUMAN Q58A45 PAN3 X 95.6

Regulatory subunit of the poly(A)-

nuclease (PAN) deadenylation

275

complex

Patatin-like phospholipase

domain-containing protein 4 PLPL4_HUMAN P41247 PNPLA4 X 27.9 Catalytic activity

Pentatricopeptide repeat-

containing protein 1.

mitochondrial

PTCD1_HUMAN O75127 PTCD1 X 78.8

Mitochondrial protein implicated in

negative regulation of leucine tRNA

levels. as well as negative regulation

of mitochondria-encoded proteins and

COX activity

Phosphatidylcholine-sterol

acyltransferase LCAT_HUMAN P04180 LCAT X X 49.1 Catalytic activity

Phosphatidylcholine translocator

ABCB4 MDR3_HUMAN P21439 ABCB4 X 141.5

Acts as a positive regulator of biliary

lipid secretion

1-phosphatidylinositol 4.5-

bisphosphate phosphodiesterase

gamma-2

PLCG2_HUMAN P16885 PLCG2 X 147.8 Catalytic activity

Phosphatidylinositol-glycan-

specific phospholipase D PHLD_HUMAN P80108 GPLD1 X X X

92.3

Catalytic activity

Phospholipase D1 PLD1_HUMAN Q13393 PLD1 X 124.2 Catalytic activity

Phospholipid transfer protein PLTP_HUMAN P55058 PLTP X X X 54.8 Transporter activity

Protocadherin-12 PCD12_HUMAN Q9NPG4 PCDH12 X 128.9

Cellular adhesion molecule that plays

an important role as a regulator of cell

migration. probably via increasing

cell-cell adhesion

Pigment epithelium-derived

factor PEDF_HUMAN P36955 SERPINF1 X 46.3

Induces extensive neuronal

differentiation in retinoblastoma cells

Plasma kallikrein KLKB1_HUMAN P03952 KLKB1 X X X 71.4 Catalytic activity

Plasma protease C1 inhibitor IC1_HUMAN P05155 SERPING1 X X X 55.2 Protease.

Plasma serine protease inhibitor IPSP_HUMAN P05154 SERPINA5 X X X 45.7 Enzyme regulator activuty (inhibitor)

Plasminogen PLMN_HUMAN P00747 PLG X X X 90.6 Catalytic activity

Platelet basic protein CXCL7_HUMAN P02775 PPBP X X 13.9 Stimulates DNA synthesis. mitosis.

glycolysis

Platelet glycoprotein Ib alpha

chain GP1BA_HUMAN P07359 GP1BA X 71.1

Participates in the formation of

platelet plugs

Polymeric immunoglobulin

receptor PIGR_HUMAN P01833 PIGR X X 83.3

This receptor binds polymeric IgA

and IgM at the basolateral surface of

epithelial cells

Polypeptide N-

acetylgalactosaminyltransferase 1 GALT1_HUMAN Q10472 GALNT1 X 62.4 Catalytic activity

Polypeptide N-

acetylgalactosaminyltransferase 3 GALT3_HUMAN Q14435 GALNT3 X 72.6 Catalytic activity

276

Polypeptide N-

acetylgalactosaminyltransferase

13

GLT13_HUMAN Q8IUC8 GALNT13 X 64.0 Catalytic activity

Pregnancy zone protein PZP_HUMAN P20742 PZP X X X 163.8 Enzyme regulator activity

Probable guanine nucleotide

exchange factor MCF2L2 MF2L2_HUMAN Q86YR7 MCF2L2 X 126.9

Functions as a guanine nucleotide

exchange factor

Properdin PROP_HUMAN P27918 CFP X 51.3 A positive regulator of the alternate

pathway of complement

Protein AMBP AMBP_HUMAN P02760 AMBP X X X 38.9 Enzyme regulator activity (inhibitor)

Protein ELYS ELYS_HUMAN Q8WYP5 AHCTF1 X 252.5

Required for the assembly of a

functional nuclear pore complex

(NPC) on the surface of

chromosomes

Pro-epidermal growth factor EGF_HUMAN P01133 EGF X 133.9

Stimulates the growth of various

epidermal and epithelial tissues in

vivo and in vitro

Protein MMS22-like MMS22_HUMAN Q6ZRQ5 MMS22L X 142.3 Maintain genome integrity during

DNA replication

Protein phosphatase PTC7

homolog PPTC7_HUMAN Q8NI37 PPTC7 X 32.6 Catalytic activity

Protein phosphatase 1 regulatory

subunit 26 PPR26_HUMAN Q5T8A7 PPP1R26 X 127.3

Inhibits phosphatase activity of

protein phosphatase 1 (PP1)

complexes. May positively regulate

cell proliferation.

Protein S100-A7 S10A7_HUMAN P31151 S100A7 X 11.5 -

Protein S100-A8 S10A8_HUMAN P05109 S100A8 X 10.8

Plays a prominent role in the

regulation of inflammatory processes

and immune response

Protein S100-A9 S10A9_HUMAN P06702 S100A9 X 13.2

Plays a prominent role in the

regulation of inflammatory processes

and immune response

Protein salvador homolog 1 SAV1_HUMAN Q9H4B6 SAV1 X 44.6

Regulator of STK3/MST2 and

STK4/MST1 in the Hippo signaling

pathway which plays a pivotal role in

organ size control and tumor

suppression by restricting

proliferation and promoting apoptosis

Protein Shroom3 SHRM3_HUMAN Q8TF72 SHROOM3 X X 216.8

Controls cell shape changes in the

neuroepithelium during neural tube

closure

277

Protein Z-dependent protease

inhibitor ZPI_HUMAN Q9UK55

SERPINA1

0 X 50.7 Enzyme regulator activity

Protein ZGRF1 ZGRF1_HUMAN Q86YA3 ZGRF1 X X X 236.6

Zinc ion binding that inhibits factors

Xa and XIa of the coagulation

cascade

Proteoglycan 4 PRG4_HUMAN Q92954 PRG4 X X X 151.1 Plays a role in boundary lubrication

within articulating joints

Prothrombin THRB_HUMAN P00734 F2 X X X 70.1 Catalytic activity

Putative solute carrier organic

anion transporter family member

1B7

SO1B7_HUMAN G3V0H7 SLCO1B7 X 71.2 May encode a non-functional

truncated protein

Prickle planar cell polarity

protein 3 PRIC3_HUMAN O43900 PRICKLE3 X 68.6

Involved in the planar cell polarity

(PCP) pathway that is essential for the

polarization of epithelial cells during

morphogenetic processes

Putative transmembrane protein

encoded by LINC00477 CL067_HUMAN Q96M19 LINC00477 X 18.2

Product of a dubious CDS prediction.

May be a non-coding RNA

Ras-interacting protein 1 RAIN_HUMAN Q5U651 RASIP1 X 103.4

Acts as a critical and vascular-

specific regulator of GTPase

signaling. cell architecture. and

adhesion

Retinol-binding protein 4 RET4_HUMAN P02753 RBP4 X X X 23.1 Transporter activity

Ribosomal protein S6 kinase

alpha-1 KS6A1_HUMAN Q15418 RPS6KA1 X 82.7 Catalytic activity

Roundabout homolog 4 ROBO4_HUMAN Q8WZ75 ROBO4 X 107.4

Mediates the inhibition of primary

endothelial cell migration by Slit

proteins

Secreted phosphoprotein 24 SPP24_HUMAN Q13103 SPP2 X 24.1

Bind cytokines of the TGF-β

superfamily and also activate

intracellular signaling pathways

Selenoprotein P SEPP1_HUMAN P49908 SELENOP X X X 43.2 Transporter activity

Serine protease 33 PRS33_HUMAN PRSS33 PRSS33 X 29.8 Catalytic activity

Serine-protein kinase ATM ATM_HUMAN Q13315 ATM X 350.7

Activates checkpoint signaling upon

double strand breaks (DSBs).

apoptosis and genotoxic stresses

Serotransferrin TRFE_HUMAN P02787 TF X X X 77.1 Transporter activity

Serum albumin ALBU_HUMAN P02768 ALB X X X 69.4 Osmotic pressure

Serum amyloid A-1 protein SAA1_HUMAN P0DJI8 SAA1 X X 13.5 Inflammatory response

Serum amyloid A-2 protein SAA2_HUMAN P0DJI9 SAA2 X X 13.5 Inflammatory response

Serum amyloid A-4 protein SAA4_HUMAN P35542 SAA4 X X X 14.7 Inflammatory response

278

Serum amyloid P-component SAMP_HUMAN P02743 APCS X 25.4

Can interact with DNA and histones

and may scavenge nuclear material

released from damaged circulating

cells

Serum paraoxonase/arylesterase

1 PON1_HUMAN P27169 PON1 X X X 39.7 Catalytic activity

Sex hormone-binding globulin SHBG_HUMAN P04278 SHBG X X X 43.8

Transporter activity. Regulates the

plasma metabolic clearance rate of

steroid hormones

Short coiled-coil protein SCOC_HUMAN Q9UIL1 SCOC X X 18.0 Positive regulator of amino acid

starvation-induced autophagy

Sodium-dependent noradrenaline

transporter SC6A2_HUMAN P23975 SLC6A2 X 69.3 Transporter activity

Structural maintenance of

chromosomes protein 6 SMC6_HUMAN Q96SB8 SMC6 X X 126.3 Structural

Supervillin SVIL_HUMAN O95425 SVIL X 247.7 Structural

Suppressor of tumorigenicity 7

protein ST7_HUMAN Q9NRC1 ST7 X 67.2 Acts as a tumor suppressor

Synaptotagmin-5 SYT5_HUMAN O00445 SYT5 X 42.9 May be involved in Ca2+-dependent

exocytosis of secretory vesicles

Syncoilin SYNCI_HUMAN Q9H7C4 SYNC X 55.3

Plays a supportive role in the efficient

coupling of mechanical stress

between the myofibril and fiber

exterior

TBC1 domain family member 9 TBCD9_HUMAN Q6ZT07 TBC1D9 X 143.2 Acts as a GTPase-activating protein

for Rab family protein(s)

Tetranectin TETN_HUMAN P05452 CLEC3B X X X 22.5 Involved in the packaging of

molecules destined for exocytosis

Thrombospondin-1 TSP1_HUMAN P07996 THBS1 X X X 129.4 Immune response

Thyroxine-binding globulin THBG_HUMAN P05543 SERPINA7 X 46.3 Transporter activity

Transferrin receptor protein 1 TFR1_HUMAN P02786 TFRC X X 84.9 Transporter activity

Transmembrane protein 198 TM198_HUMAN Q66K66 TMEM198 X 39.4

Promotes low-density lipoprotein

receptor-related protein 6 (LRP6)

phosphorylation

Transportin-1 TNPO1_HUMAN Q92973 TNPO1 X 102.3 Transporter activity

Transthyretin TTHY_HUMAN P02766 TTR X X 15.8 Transporter activity

Tudor domain-containing protein

1 TDRD1_HUMAN Q9BXT4 TDRD1 X 132.0

Acts via the piRNA metabolic

process. which mediates the

repression of transposable elements

during meiosis

279

Tudor domain-containing protein

5 TDRD5_HUMAN Q8NAT2 TDRD5 X 109.7

Acts via the piRNA metabolic

process. which mediates the

repression of transposable elements

during meiosis

Tyrosine--tRNA ligase.

cytoplasmic SYYC_HUMAN P54577 YARS X 59.1 Catalytic activity

Ubiquitin-like modifier-

activating enzyme 1 UBA1_HUMAN P22314 UBA1 X 117.8 Catalytic activity

E3 ubiquitin-protein ligase MSL2 MSL2_HUMAN Q9HCI7 MSL2 X 62.5 Component of histone

acetyltransferase complex

E3 ubiquitin-protein ligase

SHPRH SHPRH_HUMAN Q149N8 SHPRH X 193.1 Enzyme involved in DNA repair

Vasorin VASN_HUMAN Q6EMK4 VASN X 71.7 May act as an inhibitor of TGF-beta

signaling

Vigilin VIGLN_HUMAN Q00341 HDLBP X 141.4 Protect cells from over-accumulation

of cholesterol

Villin-like protein VILL_HUMAN O15195 VILL X 95.9 Tumor suppressor

Vitamin D-binding protein VTDB_HUMAN P02774 GC X X X 52.9 Transporter activity

Vitamin K-dependent protein C PROC_HUMAN P04070 PROC X X X 52.1 Catalytic activity

Vitamin K-dependent protein S PROS_HUMAN P07225 PROS1 X X X 75.1 Anticoagulant plasma protein

Vitronectin VTNC_HUMAN P04004 VTN X X X 54.3 Proteolysis regulation

von Willebrand factor VWF_HUMAN P04275 VWF X X 220 Plays a major role in blood

coagulation

VPS10 domain-containing

receptor SorCS3 SORC3_HUMAN Q9UPU3 SORCS3 X 135.7 Neuropeptide receptor activity

Wee1-like protein kinase WEE1_HUMAN P30291 WEE1 X 71.6 Acts as a negative regulator of entry

into mitosis

Zinc finger protein 99 ZNF99_HUMAN A8MXY4 ZNF99 X 100.8 May be involved in transcriptional

regulation

Zinc finger protein 114 ZN114_HUMAN Q8NC26 ZNF114 X 47.7 May be involved in transcriptional

regulation.

Zinc finger RNA-binding protein ZFR_HUMAN Q96KR1 ZFR X 117.0 Involved in the nucleocytoplasmic

shuttling of STAU2

280

Figure 13_SM. Principal Component Analysis of the log2 transformed SWATH Areas for control and triple negative breast cancer samples

(with AuNPs).

281

Figure 14_SM. Principal Component Analysis of the log2 transformed SWATH Areas for control and triple negative breast cancer samples

(with AgNPs).

282

Figure 15_SM. Principal Component Analysis of the log2 transformed SWATH Areas for control and triple negative breast cancer samples

(with MNPs).

ANNEX C

Supplemental Material Chapter 3

283

Figure 1_SM. Classification according to the molecular function of the differentially regulated proteins specific to each of the five subtypes of

BC found in the ex vivo formed coronas analyzed with the PANTHER database.

284

Figure 2_SM. Classification according to the biological process of the differentially regulated proteins specific to each of the five subtypes of

BC found in the ex vivo formed coronas analyzed with the PANTHER database.

285

Figure 3_SM. Classification according to the cellular component of the differentially regulated proteins specific to each of the five subtypes of

BC found in the ex vivo formed coronas analyzed with the PANTHER database.

286

Figure 4_SM. Classification according to the biological pathway of the differentially regulated proteins specific to each of the five subtypes of

BC found in the ex vivo formed coronas analyzed with the PANTHER database.

287

Table 1_SM. Clinical features of breast cancer tumors.

Characteristics Number

Patients

Age (years)

< 40 4

40-59 21

60-80 16

> 80 1

Tumor size (cm) < 2 25

2-5 14

>5 3

Histological types

In situ ductal carcinoma 2

Invasive ductal carcinoma 36

In situ lobular carcinoma 1

Invasive lobular carcinoma 3

Receptor status

Luminal A 11

Luminal B HER2 negative 10

Luminal B HER2 positive 7

HER2 positive 6

Triple negative 8

Clinical stage I 15

II 20

III 7

Nodal status N0 25

N1 17

288

Table 2_SM. Table shows the average mean hydrodynamic diameter (nm) determined by dynamic light scaterring (DLS) of bare and protein

corona-coated AuNPs, recovered post-incubation with human serum obtained from HC and BC patients.

Sample Name Hydrodynamic Diameter (nm)

bare AuNPs

12.6

13.8

12.5

Mean ± s.d = 12.96 ± 0.72

PC-coated AuNPs (HC)

17.8

15.6

18.6

Mean ± s.d = 17.33 ± 1.55

PC-coated AuNPs (BC)

18.9

16.3

16.2

Mean ± s.d = 17.13 ± 1.53

289

Table 3_SM. Differentially expressed proteins (up-regulated and down-regulated) (p-value ≤ 0.05) found in the protein patterns of the ex vivo

formed coronas after the analysis by SWATH-MS for the different breast cancer subtypes (LA, n = 11; LB-, n = 10; LB+, n = 7; HER2+, n = 6;

TNBC, n = 8) in comparison with healthy control (HC) samples. The accession number, species (Human) and fold change values were also

reported. Only proteins with p ≤ 0.05 are shown.

Con

trol vs

. L

um

inal

A

Protein Name Entry Name UniProt Name p-value Fold Change

Dopamine beta-hydroxylase P09172 DOPO_HUMAN 1.18E-11 1.950147057 ↑ Luminal A

Fibrinogen alpha chain P02671 FIBA_HUMAN 3.02E-08 2.045923215 ↑ Luminal A

C4b-binding protein alpha chain P04003 C4BPA_HUMAN 6.57E-07 2.006126542 ↑ Luminal A

Complement component C9 P02748 CO9_HUMAN 1.09E-06 1.627212883 ↑ Luminal A

Plasma protease C1 inhibitor P05155 IC1_HUMAN 2.31E-06 1.569699351 ↑ Luminal A

Cathelicidin antimicrobial peptide P49913 CAMP_HUMAN 8.81E-06 1.588534625 ↑ Luminal A

Ficolin-2 Q15485 FCN2_HUMAN 1.60E-05 1.866106539 ↑ Luminal A

Complement C1r subcomponent-like

protein Q9NZP8 C1RL_HUMAN 9.79E-05 1.614689351 ↑ Luminal A

Plasminogen P00747 PLMN_HUMAN 0.000145031 1.388556273 ↑ Luminal A

Nuclear receptor coactivator 6 Q14686 NCOA6_HUMAN 0.000155599 1.502869606 ↑ Luminal A

IgGFc-binding protein Q9Y6R7 FCGBP_HUMAN 0.000452171 1.343798096 ↑ Luminal A

Beta-2-glycoprotein 1 P02749 APOH_HUMAN 0.000738185 1.48991011 ↑ Luminal A

Ficolin-3 O75636 FCN3_HUMAN 0.000776505 1.41771829 ↑ Luminal A

Apolipoprotein C-III P02656 APOC3_HUMAN 0.000794669 1.702941426 ↑ Luminal A

Coagulation factor X P00742 FA10_HUMAN 0.000869295 1.500969977 ↑ Luminal A

Complement C1r subcomponent P00736 C1R_HUMAN 0.000915575 1.419961586 ↑ Luminal A

Hemopexin P02790 HEMO_HUMAN 0.000948646 1.201990625 ↑ Luminal A

Vitamin K-dependent protein C P04070 PROC_HUMAN 0.001634691 1.251503273 ↑ Luminal A

Haptoglobin P00738 HPT_HUMAN 0.001967281 1.712283929 ↑ Luminal A

C-reactive protein P02741 CRP_HUMAN 0.002021267 3.793350802 ↑ Luminal A

Complement factor B P00751 CFAB_HUMAN 0.003001733 1.404455044 ↑ Luminal A

290

Complement factor H-related protein 2 P36980 FHR2_HUMAN 0.003228805 1.764346734 ↑ Luminal A

Complement component C8 beta chain P07358 CO8B_HUMAN 0.003730112 1.35440489 ↑ Luminal A

Apolipoprotein L1 O14791 APOL1_HUMAN 0.004924638 1.379020279 ↑ Luminal A

Plasma serine protease inhibitor P05154 IPSP_HUMAN 0.005373866 1.461857956 ↑ Luminal A

Inter-alpha-trypsin inhibitor heavy chain

H3 Q06033 ITIH3_HUMAN 0.006148372 1.20110357 ↑ Luminal A

Hyaluronan-binding protein 2 Q14520 HABP2_HUMAN 0.006476169 1.356977288 ↑ Luminal A

Vitronectin P04004 VTNC_HUMAN 0.006605812 1.33651024 ↑ Luminal A

Collectin-11 Q9BWP8 COL11_HUMAN 0.00792774 1.745818384 ↑ Luminal A

Serum amyloid P-component P02743 SAMP_HUMAN 0.007991583 2.368589888 ↑ Luminal A

Histidine-rich glycoprotein P04196 HRG_HUMAN 0.00943722 1.435955402 ↑ Luminal A

Alpha-2-antiplasmin P08697 A2AP_HUMAN 0.010470312 1.30176602 ↑ Luminal A

Coagulation factor IX P00740 FA9_HUMAN 0.011045501 1.456336295 ↑ Luminal A

Peroxiredoxin-2 P32119 PRDX2_HUMAN 0.013776963 1.415849095 ↑ Luminal A

Complement component C8 gamma

chain P07360 CO8G_HUMAN 0.016920167 1.322470801 ↑ Luminal A

Fibronectin P02751 FINC_HUMAN 0.018192086 1.366200008 ↑ Luminal A

Lysosome-associated membrane

glycoprotein 2 P13473 LAMP2_HUMAN 0.018383379 1.33466653 ↑ Luminal A

Vitamin K-dependent protein S P07225 PROS_HUMAN 0.027466364 1.205727193 ↑ Luminal A

Hemoglobin subunit beta P68871 HBB_HUMAN 0.031563001 1.41675834 ↑ Luminal A

Serotransferrin P02787 TRFE_HUMAN 0.032339274 1.298146303 ↑ Luminal A

Serum amyloid A-4 protein P35542 SAA4_HUMAN 0.034951962 1.464799784 ↑ Luminal A

Lipopolysaccharide-binding protein P18428 LBP_HUMAN 0.036796813 1.341308401 ↑ Luminal A

Alpha-2-macroglobulin P01023 A2MG_HUMAN 0.000421478 2.059179008 ↑ Control

Keratin. type I cytoskeletal 14 P02533 K1C14_HUMAN 0.001165874 1.925697694 ↑ Control

Sex hormone-binding globulin P04278 SHBG_HUMAN 0.001691964 1.37783039 ↑ Control

291

Immunoglobulin heavy variable 7-4-1 A0A0J9YVY3 HV741_HUMAN 0.002730217 2.943962624 ↑ Control

Immunoglobulin heavy variable 4-28 A0A0C4DH34 HV428_HUMAN 0.003319191 2.434694876 ↑ Control

Immunoglobulin kappa variable 3-20 P01619 KV320_HUMAN 0.008581213 7.24153822 ↑ Control

Immunoglobulin heavy variable 3-49 A0A0A0MS15 HV349_HUMAN 0.014026719 1.743286666 ↑ Control

Immunoglobulin kappa variable 1-8 A0A0C4DH67 KV108_HUMAN 0.022022978 2.189831126 ↑ Control

Cadherin-5 P33151 CADH5_HUMAN 0.025861321 1.661020794 ↑ Control

Carboxypeptidase N catalytic chain P15169 CBPN_HUMAN 0.02720155 1.197835464 ↑ Control

Beta-Ala-His dipeptidase Q96KN2 CNDP1_HUMAN 0.030943391 1.505285886 ↑ Control

Immunoglobulin heavy variable 3-72 A0A0B4J1Y9 HV372_HUMAN 0.032233962 1.515212348 ↑ Control

Ceruloplasmin P00450 CERU_HUMAN 0.035237156 1.836741181 ↑ Control

Immunoglobulin heavy constant mu P01871 IGHM_HUMAN 0.038320909 2.180105502 ↑ Control

Carboxypeptidase N subunit 2 P22792 CPN2_HUMAN 0.04141732 1.267661976 ↑ Control

Corticosteroid-binding globulin P08185 CBG_HUMAN 0.044573382 2.390576271 ↑ Control

Immunoglobulin heavy variable 1-24 A0A0C4DH33 HV124_HUMAN 0.045225189 2.766559939 ↑ Control

Protein Z-dependent protease inhibitor Q9UK55 ZPI_HUMAN 0.045960336 2.020513374 ↑ Control

Con

trol vs

. L

um

inal

B H

ER

2

Neg

ati

ve

Protein Name Entry Name UniProt Name p-value Fold Change

Retinol-binding protein 4 P02753 RET4_HUMAN 2.36E-11 1.973091607 ↑ Luminal B HER2 Neg

Apolipoprotein L1 O14791 APOL1_HUMAN 2.92E-10 2.185971827 ↑ Luminal B HER2 Neg

Apolipoprotein A-II P02652 APOA2_HUMAN 1.47E-08 1.906377953 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 2-23 P01705 LV223_HUMAN 2.51E-07 2.96506068 ↑ Luminal B HER2 Neg

Angiotensinogen P01019 ANGT_HUMAN 3.00E-07 2.34185762 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 1-47 P01700 LV147_HUMAN 3.57E-07 3.087251144 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 3-10 A0A075B6K4 LV310_HUMAN 5.46E-07 3.944815387 ↑ Luminal B HER2 Neg

Inter-alpha-trypsin inhibitor heavy chain

H1 P19827 ITIH1_HUMAN 1.19E-06 1.589603796 ↑ Luminal B HER2 Neg

Cathelicidin antimicrobial peptide P49913 CAMP_HUMAN 1.22E-06 1.910900014 ↑ Luminal B HER2 Neg

Inter-alpha-trypsin inhibitor heavy chain P19823 ITIH2_HUMAN 1.41E-06 1.580896901 ↑ Luminal B HER2 Neg

292

H2

Apolipoprotein C-II P02655 APOC2_HUMAN 1.43E-06 1.785136638 ↑ Luminal B HER2 Neg

Serum amyloid A-4 protein P35542 SAA4_HUMAN 2.91E-06 2.245805522 ↑ Luminal B HER2 Neg

Alpha-1B-glycoprotein P04217 A1BG_HUMAN 3.09E-06 2.508360284 ↑ Luminal B HER2 Neg

Immunoglobulin lambda constant 7 A0M8Q6 IGLC7_HUMAN 3.52E-06 3.764178124 ↑ Luminal B HER2 Neg

IgGFc-binding protein Q9Y6R7 FCGBP_HUMAN 4.86E-06 1.502574907 ↑ Luminal B HER2 Neg

Complement component C6 P13671 CO6_HUMAN 6.19E-06 3.744383593 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-53 P01767 HV353_HUMAN 6.90E-06 3.231686892 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 4-1 P06312 KV401_HUMAN 7.88E-06 2.271628981 ↑ Luminal B HER2 Neg

Beta-Ala-His dipeptidase Q96KN2 CNDP1_HUMAN 1.16E-05 1.782534521 ↑ Luminal B HER2 Neg

Lumican P51884 LUM_HUMAN 1.24E-05 1.753050901 ↑ Luminal B HER2 Neg

Alpha-2-antiplasmin P08697 A2AP_HUMAN 1.39E-05 1.680683866 ↑ Luminal B HER2 Neg

Biotinidase P43251 BTD_HUMAN 1.42E-05 1.571319968 ↑ Luminal B HER2 Neg

Immunoglobulin heavy constant alpha 1 P01876 IGHA1_HUMAN 1.81E-05 2.45029046 ↑ Luminal B HER2 Neg

Heparin cofactor 2 P05546 HEP2_HUMAN 2.38E-05 1.568815111 ↑ Luminal B HER2 Neg

Apolipoprotein C-IV P55056 APOC4_HUMAN 2.43E-05 2.516727604 ↑ Luminal B HER2 Neg

Serum paraoxonase/lactonase 3 Q15166 PON3_HUMAN 3.44E-05 1.614088523 ↑ Luminal B HER2 Neg

Properdin P27918 PROP_HUMAN 3.87E-05 1.820837643 ↑ Luminal B HER2 Neg

Immunoglobulin kappa constant P01834 IGKC_HUMAN 6.04E-05 2.234283878 ↑ Luminal B HER2 Neg

Phospholipid transfer protein P55058 PLTP_HUMAN 0.000127276 1.491697089 ↑ Luminal B HER2 Neg

Cadherin-5 P33151 CADH5_HUMAN 0.000141516 1.802437488 ↑ Luminal B HER2 Neg

Serum paraoxonase/arylesterase 1 P27169 PON1_HUMAN 0.000147602 1.47911577 ↑ Luminal B HER2 Neg

Alpha-2-HS-glycoprotein P02765 FETUA_HUMAN 0.000221817 1.391954267 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 3-11 P04433 KV311_HUMAN 0.000235484 2.727555354 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-49 A0A0A0MS15 HV349_HUMAN 0.000241713 2.514304893 ↑ Luminal B HER2 Neg

Carboxypeptidase B2 Q96IY4 CBPB2_HUMAN 0.000274145 1.377044298 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 6D-21 A0A0A0MT36 KVD21_HUMAN 0.000275144 2.982480226 ↑ Luminal B HER2 Neg

293

Complement C4-A P0C0L4 CO4A_HUMAN 0.000311066 1.582264414 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-9 P01782 HV309_HUMAN 0.000418498 2.709287474 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 4-31 P0DP07 HV431_HUMAN 0.000447522 4.063279859 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 3-9 A0A075B6K5 LV39_HUMAN 0.000527457 2.179105185 ↑ Luminal B HER2 Neg

Tetranectin P05452 TETN_HUMAN 0.000529868 1.375681621 ↑ Luminal B HER2 Neg

Alpha-mannosidase 2 Q16706 MA2A1_HUMAN 0.000577191 2.094925838 ↑ Luminal B HER2 Neg

Peroxiredoxin-2 P32119 PRDX2_HUMAN 0.000663743 1.658367127 ↑ Luminal B HER2 Neg

Cholinesterase P06276 CHLE_HUMAN 0.000739103 2.443938708 ↑ Luminal B HER2 Neg

Immunoglobulin heavy constant gamma

1 P01857 IGHG1_HUMAN 0.000838666 1.832731289 ↑ Luminal B HER2 Neg

Galectin-3-binding protein Q08380 LG3BP_HUMAN 0.000924525 1.669562645 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 1-40 P01703 LV140_HUMAN 0.00099044 2.058275359 ↑ Luminal B HER2 Neg

Kallistatin P29622 KAIN_HUMAN 0.001372229 1.767108231 ↑ Luminal B HER2 Neg

Alpha-1-acid glycoprotein 1 P02763 A1AG1_HUMAN 0.001636216 2.215227977 ↑ Luminal B HER2 Neg

Apolipoprotein B-100 P04114 APOB_HUMAN 0.001926757 1.618943103 ↑ Luminal B HER2 Neg

Immunoglobulin heavy constant alpha 2 P01877 IGHA2_HUMAN 0.00207989 2.137794634 ↑ Luminal B HER2 Neg

Basement membrane-specific heparan

sulfate proteoglycan core protein P98160 PGBM_HUMAN 0.002185804 2.617271104 ↑ Luminal B HER2 Neg

Cholesteryl ester transfer protein P11597 CETP_HUMAN 0.002360349 1.663599851 ↑ Luminal B HER2 Neg

Phosphatidylcholine-sterol

acyltransferase P04180 LCAT_HUMAN 0.002466447 2.154281745 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 1-16 P04430 KV116_HUMAN 0.002548994 2.530617013 ↑ Luminal B HER2 Neg

Pregnancy zone protein P20742 PZP_HUMAN 0.002663618 4.343426266 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 1-46 P01743 HV146_HUMAN 0.00286485 3.896313023 ↑ Luminal B HER2 Neg

Apolipoprotein A-I P02647 APOA1_HUMAN 0.002969507 1.292738115 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 3-19 P01714 LV319_HUMAN 0.003020851 1.830912469 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 1-69D A0A0B4J2H0 HV69D_HUMAN 0.00307313 2.762019962 ↑ Luminal B HER2 Neg

294

Apolipoprotein C-III P02656 APOC3_HUMAN 0.003268869 1.544477322 ↑ Luminal B HER2 Neg

Coagulation factor X P00742 FA10_HUMAN 0.003928385 1.52489515 ↑ Luminal B HER2 Neg

Pigment epithelium-derived factor P36955 PEDF_HUMAN 0.004477954 1.582067399 ↑ Luminal B HER2 Neg

Carbonic anhydrase 1 P00915 CAH1_HUMAN 0.004778689 4.78458974 ↑ Luminal B HER2 Neg

Immunoglobulin J chain P01591 IGJ_HUMAN 0.005504448 1.421634254 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 3-74 A0A0B4J1X5 HV374_HUMAN 0.005843078 1.63300774 ↑ Luminal B HER2 Neg

C-reactive protein P02741 CRP_HUMAN 0.006150605 2.762940453 ↑ Luminal B HER2 Neg

Inter-alpha-trypsin inhibitor heavy chain

H4 Q14624 ITIH4_HUMAN 0.006196813 1.442742283 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 1-33 P01594 KV133_HUMAN 0.006299022 1.859474241 ↑ Luminal B HER2 Neg

Immunoglobulin lambda-like

polypeptide 5 B9A064 IGLL5_HUMAN 0.006474796 1.674164832 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 3D-20 A0A0C4DH25 KVD20_HUMAN 0.007387257 1.722873358 ↑ Luminal B HER2 Neg

Lipopolysaccharide-binding protein P18428 LBP_HUMAN 0.007650788 1.353311199 ↑ Luminal B HER2 Neg

Hemoglobin subunit delta P02042 HBD_HUMAN 0.007912148 6.029835988 ↑ Luminal B HER2 Neg

Hemoglobin subunit beta P68871 HBB_HUMAN 0.008912028 36.84859718 ↑ Luminal B HER2 Neg

Hemoglobin subunit alpha P69905 HBA_HUMAN 0.009565304 24.71644777 ↑ Luminal B HER2 Neg

L-lactate dehydrogenase B chain P07195 LDHB_HUMAN 0.011088233 1.464826821 ↑ Luminal B HER2 Neg

Thrombospondin-1 P07996 TSP1_HUMAN 0.012439842 2.079938026 ↑ Luminal B HER2 Neg

Serum amyloid P-component P02743 SAMP_HUMAN 0.013063985 1.786687389 ↑ Luminal B HER2 Neg

Platelet glycoprotein Ib alpha chain P07359 GP1BA_HUMAN 0.014355443 1.435497779 ↑ Luminal B HER2 Neg

Carbonic anhydrase 2 P00918 CAH2_HUMAN 0.014428974 1.895853685 ↑ Luminal B HER2 Neg

Apolipoprotein C-I P02654 APOC1_HUMAN 0.016106686 1.456534364 ↑ Luminal B HER2 Neg

Protein AMBP P02760 AMBP_HUMAN 0.018347785 1.296261299 ↑ Luminal B HER2 Neg

Vitamin K-dependent protein S P07225 PROS_HUMAN 0.019317019 1.214924135 ↑ Luminal B HER2 Neg

Immunoglobulin heavy variable 1-2 P23083 HV102_HUMAN 0.020170868 1.407214078 ↑ Luminal B HER2 Neg

Mannose-binding protein C P11226 MBL2_HUMAN 0.021869726 2.267023235 ↑ Luminal B HER2 Neg

295

Immunoglobulin lambda variable 7-46 A0A075B6I9 LV746_HUMAN 0.023386767 1.309979917 ↑ Luminal B HER2 Neg

Coagulation factor IX P00740 FA9_HUMAN 0.025068014 1.295436285 ↑ Luminal B HER2 Neg

Apolipoprotein D P05090 APOD_HUMAN 0.02689663 1.565894604 ↑ Luminal B HER2 Neg

Alpha-1-antichymotrypsin P01011 AACT_HUMAN 0.027689996 1.834328725 ↑ Luminal B HER2 Neg

Leucine-rich alpha-2-glycoprotein P02750 A2GL_HUMAN 0.029045042 1.920903764 ↑ Luminal B HER2 Neg

Haptoglobin-related protein P00739 HPTR_HUMAN 0.029342027 1.312804206 ↑ Luminal B HER2 Neg

Alpha-1-acid glycoprotein 2 P19652 A1AG2_HUMAN 0.029382714 1.963019465 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 3-21 P80748 LV321_HUMAN 0.030887039 1.531598796 ↑ Luminal B HER2 Neg

Collectin-11 Q9BWP8 COL11_HUMAN 0.031474412 1.701990672 ↑ Luminal B HER2 Neg

C4b-binding protein beta chain P20851 C4BPB_HUMAN 0.033906593 1.291393262 ↑ Luminal B HER2 Neg

Mediator of RNA polymerase II

transcription subunit 23 Q9ULK4 MED23_HUMAN 0.034189832 1.582094144 ↑ Luminal B HER2 Neg

Immunoglobulin lambda variable 3-25 P01717 LV325_HUMAN 0.037087746 1.366655688 ↑ Luminal B HER2 Neg

Transferrin receptor protein 1 P02786 TFR1_HUMAN 0.0389771 1.371797758 ↑ Luminal B HER2 Neg

Gelsolin P06396 GELS_HUMAN 0.047520129 1.413658316 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 3D-15 A0A087WSY6 KVD15_HUMAN 0.049277542 1.620870123 ↑ Luminal B HER2 Neg

Complement factor D P00746 CFAD_HUMAN 9.02E-06 5.53018627 ↑ Control

Monocyte differentiation antigen CD14 P08571 CD14_HUMAN 2.51E-05 2.220852523 ↑ Control

Kininogen-1 P01042 KNG1_HUMAN 2.60E-05 2.199628835 ↑ Control

Tenascin-X P22105 TENX_HUMAN 3.94E-05 1.6826234 ↑ Control

Insulin-like growth factor-binding

protein complex acid labile subunit P35858 ALS_HUMAN 6.12E-05 1.780055952 ↑ Control

Complement factor I P05156 CFAI_HUMAN 0.000120005 1.759588079 ↑ Control

Serotransferrin P02787 TRFE_HUMAN 0.000126524 2.02525736 ↑ Control

Serum albumin P02768 ALBU_HUMAN 0.000171148 1.683767104 ↑ Control

Plasminogen P00747 PLMN_HUMAN 0.000277629 1.496330117 ↑ Control

Complement factor B P00751 CFAB_HUMAN 0.000315845 1.866989692 ↑ Control

296

Complement C3 P01024 CO3_HUMAN 0.000648107 1.956090281 ↑ Control

Hyaluronan-binding protein 2 Q14520 HABP2_HUMAN 0.000924595 1.722448064 ↑ Control

Nuclear receptor coactivator 6 Q14686 NCOA6_HUMAN 0.000998012 1.819162172 ↑ Control

Complement factor H P08603 CFAH_HUMAN 0.001926344 1.494359684 ↑ Control

Vitamin D-binding protein P02774 VTDB_HUMAN 0.003402591 1.73672517 ↑ Control

Complement component C8 gamma

chain P07360 CO8G_HUMAN 0.007168406 1.461243032 ↑ Control

Complement factor H-related protein 1 Q03591 FHR1_HUMAN 0.008181812 2.315495477 ↑ Control

Mannan-binding lectin serine protease 2 O00187 MASP2_HUMAN 0.012755497 0.485843644 ↑ Control

Complement factor H-related protein 4 Q92496 FHR4_HUMAN 0.013093998 8.082016269 ↑ Control

Platelet factor 4 variant P10720 PF4V_HUMAN 0.013149247 8.695958011 ↑ Control

Proteoglycan 4 Q92954 PRG4_HUMAN 0.014358816 1.819998433 ↑ Control

Complement C1q subcomponent subunit

A P02745 C1QA_HUMAN 0.020491258 1.620607626 ↑ Control

Complement C1q subcomponent subunit

B P02746 C1QB_HUMAN 0.020779055 1.40753788 ↑ Control

Apolipoprotein(a) P08519 APOA_HUMAN 0.027494746 1.753437328 ↑ Control

Extracellular matrix protein 1 Q16610 ECM1_HUMAN 0.028973967 1.594180697 ↑ Control

Complement C1q subcomponent subunit

C P02747 C1QC_HUMAN 0.029229257 1.350965642 ↑ Control

Apolipoprotein M O95445 APOM_HUMAN 0.033725746 1.267210422 ↑ Control

Coagulation factor XIII B chain P05160 F13B_HUMAN 0.034419397 1.368179222 ↑ Control

Fibrinogen alpha chain P02671 FIBA_HUMAN 0.037000628 1.371569583 ↑ Control

Afamin P43652 AFAM_HUMAN 0.039195834 1.267057624 ↑ Control

Complement C1s subcomponent P09871 C1S_HUMAN 0.040782815 1.400513823 ↑ Control

Fibronectin P02751 FINC_HUMAN 0.046347063 1.434387799 ↑ Control

Co

ntr ol

vs.

Lu

mi

nal

B

HE

R2

Pos

itiv e

Protein Name Entry Name UniProt Name p-value Fold Change

Hemoglobin subunit alpha P69905 HBA_HUMAN 5.21E-08 3.249069603 ↑ Luminal B HER2 Pos

297

Hemoglobin subunit beta P68871 HBB_HUMAN 4.10E-07 3.803126189 ↑ Luminal B HER2 Pos

Pigment epithelium-derived factor P36955 PEDF_HUMAN 5.54E-07 3.00054601 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 3-49 A0A0A0MS15 HV349_HUMAN 1.97E-06 2.863927632 ↑ Luminal B HER2 Pos

Apolipoprotein C-I P02654 APOC1_HUMAN 2.13E-05 2.739130492 ↑ Luminal B HER2 Pos

Hemoglobin subunit delta P02042 HBD_HUMAN 2.67E-05 3.306697207 ↑ Luminal B HER2 Pos

Immunoglobulin kappa variable 6D-21 A0A0A0MT36 KVD21_HUMAN 2.68E-05 3.798498309 ↑ Luminal B HER2 Pos

Apolipoprotein C-II P02655 APOC2_HUMAN 4.39E-05 1.846194109 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 1-47 P01700 LV147_HUMAN 5.77E-05 2.850504061 ↑ Luminal B HER2 Pos

Serum amyloid A-4 protein P35542 SAA4_HUMAN 6.36E-05 2.160815047 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 4-31 P0DP07 HV431_HUMAN 7.03E-05 4.89656649 ↑ Luminal B HER2 Pos

Apolipoprotein L1 O14791 APOL1_HUMAN 8.28E-05 1.900750537 ↑ Luminal B HER2 Pos

C4b-binding protein beta chain P20851 C4BPB_HUMAN 0.00010547 2.155401464 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 4-28 A0A0C4DH34 HV428_HUMAN 0.000139308 2.192608342 ↑ Luminal B HER2 Pos

Serum amyloid P-component P02743 SAMP_HUMAN 0.000181209 2.454265354 ↑ Luminal B HER2 Pos

Immunoglobulin lambda constant 7 A0M8Q6 IGLC7_HUMAN 0.000665214 3.191779846 ↑ Luminal B HER2 Pos

Gelsolin P06396 GELS_HUMAN 0.000974323 1.951968831 ↑ Luminal B HER2 Pos

Apolipoprotein A-IV P06727 APOA4_HUMAN 0.001025945 1.544141914 ↑ Luminal B HER2 Pos

Apolipoprotein A-II P02652 APOA2_HUMAN 0.001171293 1.776849765 ↑ Luminal B HER2 Pos

Plastin-2 P13796 PLSL_HUMAN 0.002018957 4.501473239 ↑ Luminal B HER2 Pos

Kallistatin P29622 KAIN_HUMAN 0.002130037 1.810271897 ↑ Luminal B HER2 Pos

Polymeric immunoglobulin receptor P01833 PIGR_HUMAN 0.002912228 1.841450361 ↑ Luminal B HER2 Pos

Apolipoprotein C-III P02656 APOC3_HUMAN 0.003035205 2.024844997 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 3-10 A0A075B6K4 LV310_HUMAN 0.003668828 3.284821215 ↑ Luminal B HER2 Pos

Alpha-1-acid glycoprotein 1 P02763 A1AG1_HUMAN 0.005475644 2.208206222 ↑ Luminal B HER2 Pos

Galectin-3-binding protein Q08380 LG3BP_HUMAN 0.006686721 1.692847346 ↑ Luminal B HER2 Pos

Complement component C9 P02748 CO9_HUMAN 0.006887579 1.41639914 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 3-19 P01714 LV319_HUMAN 0.007858626 1.985710229 ↑ Luminal B HER2 Pos

298

Immunoglobulin kappa variable 1-13 P0DP09 KV113_HUMAN 0.008612844 2.555407574 ↑ Luminal B HER2 Pos

C-reactive protein P02741 CRP_HUMAN 0.008678988 3.370377857 ↑ Luminal B HER2 Pos

Complement C4-A P0C0L4 CO4A_HUMAN 0.010923185 1.488032623 ↑ Luminal B HER2 Pos

Corticosteroid-binding globulin P08185 CBG_HUMAN 0.011580639 1.994591255 ↑ Luminal B HER2 Pos

Xaa-Pro dipeptidase P12955 PEPD_HUMAN 0.012031361 2.165189136 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 2-11 P01706 LV211_HUMAN 0.01295552 1.570173745 ↑ Luminal B HER2 Pos

Leucine-rich alpha-2-glycoprotein P02750 A2GL_HUMAN 0.013102391 2.443411846 ↑ Luminal B HER2 Pos

Keratin. type II cytoskeletal 2 epidermal P35908 K22E_HUMAN 0.01347707 1.499797459 ↑ Luminal B HER2 Pos

Complement component C6 P13671 CO6_HUMAN 0.013979583 2.568266773 ↑ Luminal B HER2 Pos

Cholinesterase P06276 CHLE_HUMAN 0.015593089 2.140328724 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 4-61 A0A0C4DH41 HV461_HUMAN 0.015895648 2.015912108 ↑ Luminal B HER2 Pos

Transthyretin P02766 TTHY_HUMAN 0.015961518 1.320554649 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 1-40 P01703 LV140_HUMAN 0.016785314 1.847853806 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 3-9 A0A075B6K5 LV39_HUMAN 0.017837178 1.884838259 ↑ Luminal B HER2 Pos

Complement component C8 alpha chain P07357 CO8A_HUMAN 0.01851134 1.790235827 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 2-70D A0A0C4DH43 HV70D_HUMAN 0.019383958 2.403651553 ↑ Luminal B HER2 Pos

Plasma protease C1 inhibitor P05155 IC1_HUMAN 0.0196862 1.276319883 ↑ Luminal B HER2 Pos

Immunoglobulin heavy variable 6-1 A0A0B4J1U7 HV601_HUMAN 0.020191442 1.539338405 ↑ Luminal B HER2 Pos

Alpha-1-antichymotrypsin P01011 AACT_HUMAN 0.026362641 2.102965709 ↑ Luminal B HER2 Pos

Transferrin receptor protein 1 P02786 TFR1_HUMAN 0.02753498 1.498502068 ↑ Luminal B HER2 Pos

Immunoglobulin lambda variable 3-25 P01717 LV325_HUMAN 0.028817061 1.44474454 ↑ Luminal B HER2 Pos

Serum paraoxonase/arylesterase 1 P27169 PON1_HUMAN 0.029928679 1.297670132 ↑ Luminal B HER2 Pos

Immunoglobulin kappa variable 1-33 P01594 KV133_HUMAN 0.030577751 1.791455428 ↑ Luminal B HER2 Pos

Prenylcysteine oxidase 1 Q9UHG3 PCYOX_HUMAN 0.030672346 1.304466122 ↑ Luminal B HER2 Pos

Vitronectin P04004 VTNC_HUMAN 0.035879886 1.313506051 ↑ Luminal B HER2 Pos

Immunoglobulin kappa variable 3D-15 A0A087WSY6 KVD15_HUMAN 0.04021589 1.814796476 ↑ Luminal B HER2 Pos

Mannose-binding protein C P11226 MBL2_HUMAN 0.040341289 2.238378752 ↑ Luminal B HER2 Pos

299

Immunoglobulin lambda variable 7-46 A0A075B6I9 LV746_HUMAN 0.043520066 1.392118872 ↑ Luminal B HER2 Pos

Zinc-alpha-2-glycoprotein P25311 ZA2G_HUMAN 0.04421543 1.324950673 ↑ Luminal B HER2 Pos

Apolipoprotein A-I P02647 APOA1_HUMAN 0.044781205 1.382011988 ↑ Luminal B HER2 Pos

Peroxiredoxin-2 P32119 PRDX2_HUMAN 0.049384389 1.483685429 ↑ Luminal B HER2 Pos

Carboxypeptidase N catalytic chain P15169 CBPN_HUMAN 0.002104892 1.399185246 ↑ Control

Vitamin K-dependent protein C P04070 PROC_HUMAN 0.002774626 1.40587544 ↑ Control

Serotransferrin P02787 TRFE_HUMAN 0.003456816 1.847074609 ↑ Control

Tenascin-X P22105 TENX_HUMAN 0.008151383 1.459417617 ↑ Control

Histidine-rich glycoprotein P04196 HRG_HUMAN 0.026419851 1.704325244 ↑ Control

Coagulation factor XIII B chain P05160 F13B_HUMAN 0.026838663 1.488680992 ↑ Control

Complement C1q subcomponent subunit

A P02745 C1QA_HUMAN 0.038888946 1.704874113 ↑ Control

Complement C1q subcomponent subunit

C P02747 C1QC_HUMAN 0.0443687 1.422681116 ↑ Control

Con

trol vs

. H

ER

2 P

osi

tive

Protein Name Entry Name UniProt Name p-value Fold Change

Complement C5 P01031 CO5_HUMAN 2.95E-15 2.104078338 ↑ HER2 Pos

Clusterin P10909 CLUS_HUMAN 4.06E-14 2.272392895 ↑ HER2 Pos

Immunoglobulin lambda variable 2-11 P01706 LV211_HUMAN 1.43E-11 2.809437426 ↑ HER2 Pos

Vitronectin P04004 VTNC_HUMAN 3.15E-11 2.321296178 ↑ HER2 Pos

Hemoglobin subunit delta P02042 HBD_HUMAN 4.58E-10 5.624944078 ↑ HER2 Pos

Immunoglobulin heavy variable 3-49 A0A0A0MS15 HV349_HUMAN 8.12E-10 4.557168904 ↑ HER2 Pos

Carboxypeptidase N subunit 2 P22792 CPN2_HUMAN 6.65E-09 2.059087817 ↑ HER2 Pos

Adiponectin Q15848 ADIPO_HUMAN 8.28E-09 9.579128591 ↑ HER2 Pos

Afamin P43652 AFAM_HUMAN 4.78E-08 2.45261206 ↑ HER2 Pos

Plasminogen P00747 PLMN_HUMAN 7.26E-08 2.032047844 ↑ HER2 Pos

C4b-binding protein alpha chain P04003 C4BPA_HUMAN 4.82E-07 2.537213683 ↑ HER2 Pos

Keratin. type I cytoskeletal 9 P35527 K1C9_HUMAN 7.19E-07 2.250911692 ↑ HER2 Pos

300

Immunoglobulin heavy variable 3-73 A0A0B4J1V6 HV373_HUMAN 1.67E-06 15.87006684 ↑ HER2 Pos

Vitamin D-binding protein P02774 VTDB_HUMAN 1.96E-06 2.050789715 ↑ HER2 Pos

Coagulation factor XII P00748 FA12_HUMAN 2.50E-06 4.48323521 ↑ HER2 Pos

Complement C3 P01024 CO3_HUMAN 2.61E-06 2.060113843 ↑ HER2 Pos

Immunoglobulin heavy variable 4-61 A0A0C4DH41 HV461_HUMAN 4.13E-06 5.17636397 ↑ HER2 Pos

Ficolin-2 Q15485 FCN2_HUMAN 7.19E-06 2.289548422 ↑ HER2 Pos

Fibrinogen alpha chain P02671 FIBA_HUMAN 1.19E-05 1.946313958 ↑ HER2 Pos

Plasma kallikrein P03952 KLKB1_HUMAN 2.05E-05 2.839283512 ↑ HER2 Pos

Transthyretin P02766 TTHY_HUMAN 2.53E-05 1.710033019 ↑ HER2 Pos

Hemoglobin subunit alpha P69905 HBA_HUMAN 3.18E-05 1.915979149 ↑ HER2 Pos

Serum albumin P02768 ALBU_HUMAN 6.15E-05 1.617918878 ↑ HER2 Pos

Immunoglobulin heavy variable 3-23 P01764 HV323_HUMAN 8.83E-05 2.952483084 ↑ HER2 Pos

Prenylcysteine oxidase 1 Q9UHG3 PCYOX_HUMAN 9.23E-05 2.137601432 ↑ HER2 Pos

Fibronectin P02751 FINC_HUMAN 0.000101285 1.917381182 ↑ HER2 Pos

Coagulation factor IX P00740 FA9_HUMAN 0.00012729 1.600811451 ↑ HER2 Pos

Serum amyloid P-component P02743 SAMP_HUMAN 0.000144697 4.818702495 ↑ HER2 Pos

Complement component C8 alpha chain P07357 CO8A_HUMAN 0.0001513 1.959771044 ↑ HER2 Pos

Complement factor H P08603 CFAH_HUMAN 0.000175819 1.83792215 ↑ HER2 Pos

Complement factor H-related protein 4 Q92496 FHR4_HUMAN 0.000204336 3.107198257 ↑ HER2 Pos

Glutathione peroxidase 3 P22352 GPX3_HUMAN 0.000266714 2.917862479 ↑ HER2 Pos

Complement factor D P00746 CFAD_HUMAN 0.00027027 2.477435187 ↑ HER2 Pos

Immunoglobulin lambda variable 1-51 P01701 LV151_HUMAN 0.000382645 2.419267666 ↑ HER2 Pos

Immunoglobulin heavy variable 3-64 A0A075B6Q5 HV364_HUMAN 0.000401774 2.217759109 ↑ HER2 Pos

Hemoglobin subunit beta P68871 HBB_HUMAN 0.000414991 2.238927783 ↑ HER2 Pos

Proteoglycan 4 Q92954 PRG4_HUMAN 0.000502566 2.732642249 ↑ HER2 Pos

Selenoprotein P P49908 SEPP1_HUMAN 0.000539614 3.796988329 ↑ HER2 Pos

Insulin-like growth factor-binding P35858 ALS_HUMAN 0.000815729 1.776314681 ↑ HER2 Pos

301

protein complex acid labile subunit

Fetuin-B Q9UGM5 FETUB_HUMAN 0.000972914 2.086049105 ↑ HER2 Pos

Immunoglobulin kappa variable 2D-29 A0A075B6S2 KVD29_HUMAN 0.001323181 2.394042999 ↑ HER2 Pos

Complement component C9 P02748 CO9_HUMAN 0.001535735 1.484378394 ↑ HER2 Pos

Immunoglobulin kappa variable 1D-12 P01611 KVD12_HUMAN 0.00188481 4.471370936 ↑ HER2 Pos

Transferrin receptor protein 1 P02786 TFR1_HUMAN 0.002450216 3.02744249 ↑ HER2 Pos

Cholesteryl ester transfer protein P11597 CETP_HUMAN 0.00247772 5.150077322 ↑ HER2 Pos

Keratin. type I cytoskeletal 14 P02533 K1C14_HUMAN 0.003456425 2.475906837 ↑ HER2 Pos

Inter-alpha-trypsin inhibitor heavy chain

H3 Q06033 ITIH3_HUMAN 0.003797768 1.385787386 ↑ HER2 Pos

Carbonic anhydrase 1 P00915 CAH1_HUMAN 0.0039242 2.696352787 ↑ HER2 Pos

C4b-binding protein beta chain P20851 C4BPB_HUMAN 0.004369604 1.498125573 ↑ HER2 Pos

Monocyte differentiation antigen CD14 P08571 CD14_HUMAN 0.004529743 1.520207689 ↑ HER2 Pos

Immunoglobulin kappa variable 1D-8 A0A087WSZ0 KVD08_HUMAN 0.005305701 5.877786114 ↑ HER2 Pos

Immunoglobulin lambda variable 3-9 A0A075B6K5 LV39_HUMAN 0.005513194 6.067712275 ↑ HER2 Pos

Thrombospondin-1 P07996 TSP1_HUMAN 0.006951569 2.329232667 ↑ HER2 Pos

Complement component C8 gamma

chain P07360 CO8G_HUMAN 0.00726575 1.510600297 ↑ HER2 Pos

Immunoglobulin lambda variable 5-45 A0A087WSX0 LV545_HUMAN 0.00772078 2.858034161 ↑ HER2 Pos

Immunoglobulin lambda variable 6-57 P01721 LV657_HUMAN 0.009251968 5.252644969 ↑ HER2 Pos

DDB1- and CUL4-associated factor 12-

like protein 1 Q5VU92 DC121_HUMAN 0.009802853 58.66867232 ↑ HER2 Pos

Sex hormone-binding globulin P04278 SHBG_HUMAN 0.009859708 1.968382289 ↑ HER2 Pos

Xaa-Pro dipeptidase P12955 PEPD_HUMAN 0.010710939 8.299869418 ↑ HER2 Pos

Polymeric immunoglobulin receptor P01833 PIGR_HUMAN 0.010816257 6.100766211 ↑ HER2 Pos

Keratin type I cytoskeletal 10 P13645 K1C10_HUMAN 0.012361191 1.599012598 ↑ HER2 Pos

C-reactive protein P02741 CRP_HUMAN 0.013347928 10.58414211 ↑ HER2 Pos

Extracellular matrix protein 1 Q16610 ECM1_HUMAN 0.014298021 1.703726247 ↑ HER2 Pos

302

Immunoglobulin lambda constant 7 A0M8Q6 IGLC7_HUMAN 0.01446295 9.877133521 ↑ HER2 Pos

Immunoglobulin kappa variable 1-27 A0A075B6S5 KV127_HUMAN 0.014636279 3.663302525 ↑ HER2 Pos

Mannan-binding lectin serine protease 2 O00187 MASP2_HUMAN 0.014654148 2.620003402 ↑ HER2 Pos

Immunoglobulin kappa variable 1-5 P01602 KV105_HUMAN 0.015089737 3.097611266 ↑ HER2 Pos

EGF-containing fibulin-like

extracellular matrix protein 1 Q12805 FBLN3_HUMAN 0.015909032 1.962284999 ↑ HER2 Pos

Immunoglobulin heavy variable 2-70D A0A0C4DH43 HV70D_HUMAN 0.018714547 7.341205699 ↑ HER2 Pos

Immunoglobulin kappa variable 2-24 A0A0C4DH68 KV224_HUMAN 0.019946847 3.792528291 ↑ HER2 Pos

Immunoglobulin heavy constant gamma

2 P01859 IGHG2_HUMAN 0.020013155 1.605775681 ↑ HER2 Pos

Zinc-alpha-2-glycoprotein P25311 ZA2G_HUMAN 0.020217231 1.455814777 ↑ HER2 Pos

Phosphatidylcholine-sterol

acyltransferase P04180 LCAT_HUMAN 0.020296054 2.819290037 ↑ HER2 Pos

Complement C1r subcomponent P00736 C1R_HUMAN 0.020917627 1.550066313 ↑ HER2 Pos

Adipocyte plasma membrane-associated

protein Q9HDC9 APMAP_HUMAN 0.021204299 23.89017843 ↑ HER2 Pos

Galectin-3-binding protein Q08380 LG3BP_HUMAN 0.021587392 1.755243828 ↑ HER2 Pos

Immunoglobulin kappa variable 1D-16 P01601 KVD16_HUMAN 0.023927557 15.36602613 ↑ HER2 Pos

L-selectin P14151 LYAM1_HUMAN 0.024869054 1.980068571 ↑ HER2 Pos

Immunoglobulin lambda variable 3-10 A0A075B6K4 LV310_HUMAN 0.025145594 7.27762833 ↑ HER2 Pos

Coagulation factor V P12259 FA5_HUMAN 0.025887503 3.739380413 ↑ HER2 Pos

Matrix metalloproteinase-9 P14780 MMP9_HUMAN 0.026145337 1.695003211 ↑ HER2 Pos

Immunoglobulin heavy variable 1-8 P0DP01 HV108_HUMAN 0.026443384 3.102831151 ↑ HER2 Pos

Complement component C7 P10643 CO7_HUMAN 0.027419082 2.14923558 ↑ HER2 Pos

von Willebrand factor P04275 VWF_HUMAN 0.02845827 1.636077066 ↑ HER2 Pos

Complement factor H-related protein 1 Q03591 FHR1_HUMAN 0.028584853 1.915607927 ↑ HER2 Pos

Immunoglobulin kappa variable 1-16 P04430 KV116_HUMAN 0.029567938 4.78416667 ↑ HER2 Pos

303

Cysteine-rich secretory protein 3 P54108 CRIS3_HUMAN 0.034870563 3.338300324 ↑ HER2 Pos

Keratin. type II cytoskeletal 2 epidermal P35908 K22E_HUMAN 0.035300084 1.805122667 ↑ HER2 Pos

Immunoglobulin heavy variable 3-33 P01772 HV333_HUMAN 0.038394512 8.344444793 ↑ HER2 Pos

Properdin P27918 PROP_HUMAN 0.038520124 1.768625617 ↑ HER2 Pos

Complement C1q subcomponent subunit

A P02745 C1QA_HUMAN 0.039735285 1.871918365 ↑ HER2 Pos

Pigment epithelium-derived factor P36955 PEDF_HUMAN 0.042658539 1.861759712 ↑ HER2 Pos

Immunoglobulin heavy variable 1-46 P01743 HV146_HUMAN 0.043543848 4.597946701 ↑ HER2 Pos

Immunoglobulin kappa variable 1-8 A0A0C4DH67 KV108_HUMAN 0.044140149 1.81212033 ↑ HER2 Pos

Immunoglobulin heavy variable 3-72 A0A0B4J1Y9 HV372_HUMAN 0.047935423 1.687200865 ↑ HER2 Pos

Ficolin-3 O75636 FCN3_HUMAN 1.60E-07 5.889667169 ↑ Control

Apolipoprotein A-I P02647 APOA1_HUMAN 6.44E-07 3.362021938 ↑ Control

Nuclear receptor coactivator 6 Q14686 NCOA6_HUMAN 8.47E-07 8.441443914 ↑ Control

Heparin cofactor 2 P05546 HEP2_HUMAN 1.00E-05 3.661036082 ↑ Control

Kininogen-1 P01042 KNG1_HUMAN 3.73E-05 3.357349607 ↑ Control

Vitamin K-dependent protein S P07225 PROS_HUMAN 9.45E-05 1.853196173 ↑ Control

Histidine-rich glycoprotein P04196 HRG_HUMAN 0.000112488 5.940948958 ↑ Control

Immunoglobulin J chain P01591 IGJ_HUMAN 0.000238757 2.91931338 ↑ Control

N-acetylmuramoyl-L-alanine amidase Q96PD5 PGRP2_HUMAN 0.000321687 1.799194526 ↑ Control

Angiotensinogen P01019 ANGT_HUMAN 0.00043286 3.016543479 ↑ Control

Inter-alpha-trypsin inhibitor heavy chain

H2 P19823 ITIH2_HUMAN 0.00074408 1.923949583 ↑ Control

Apolipoprotein M O95445 APOM_HUMAN 0.000850537 1.771464702 ↑ Control

Haptoglobin-related protein P00739 HPTR_HUMAN 0.000868695 2.12707613 ↑ Control

Alpha-1-antitrypsin P01009 A1AT_HUMAN 0.001111283 4.959761437 ↑ Control

Inter-alpha-trypsin inhibitor heavy chain

H1 P19827 ITIH1_HUMAN 0.001316207 1.682469513 ↑ Control

304

Beta-2-glycoprotein 1 P02749 APOH_HUMAN 0.001791485 2.272364551 ↑ Control

Alpha-2-HS-glycoprotein P02765 FETUA_HUMAN 0.001836174 1.586208463 ↑ Control

Apolipoprotein C-IV P55056 APOC4_HUMAN 0.001950165 6.335750729 ↑ Control

Apolipoprotein A-II P02652 APOA2_HUMAN 0.002133097 2.248237048 ↑ Control

Trypsin-1 P07477 TRY1_HUMAN 0.002454431 4.217786063 ↑ Control

Apolipoprotein C-III P02656 APOC3_HUMAN 0.002567875 3.440587523 ↑ Control

Plasma protease C1 inhibitor P05155 IC1_HUMAN 0.00372715 1.579476004 ↑ Control

Apolipoprotein F Q13790 APOF_HUMAN 0.005336626 7.55893037 ↑ Control

Carboxypeptidase B2 Q96IY4 CBPB2_HUMAN 0.009929161 1.517845486 ↑ Control

Antithrombin-III P01008 ANT3_HUMAN 0.018612975 1.308597079 ↑ Control

Complement C1q subcomponent subunit

B P02746 C1QB_HUMAN 0.020285082 1.835321411 ↑ Control

Inter-alpha-trypsin inhibitor heavy chain

H4 Q14624 ITIH4_HUMAN 0.02171345 1.978217456 ↑ Control

Alpha-2-macroglobulin P01023 A2MG_HUMAN 0.021759431 1.818611048 ↑ Control

Ceruloplasmin P00450 CERU_HUMAN 0.023271461 3.101927554 ↑ Control

Alpha-1B-glycoprotein P04217 A1BG_HUMAN 0.030481015 3.465086804 ↑ Control

Haptoglobin P00738 HPT_HUMAN 0.032119775 1.834688417 ↑ Control

Serum amyloid A-4 protein P35542 SAA4_HUMAN 0.033032185 2.177960496 ↑ Control

Complement C1q subcomponent subunit

C P02747 C1QC_HUMAN 0.034179123 1.548970133 ↑ Control

Serotransferrin P02787 TRFE_HUMAN 0.034307125 1.559639179 ↑ Control

Apolipoprotein(a) P08519 APOA_HUMAN 0.046687987 2.207374657 ↑ Control

Con

trol

vs. T

rip

le

Neg

ati

ve Protein Name Entry Name UniProt Name p-value Fold Change

Immunoglobulin heavy variable 4-31 P0DP07 HV431_HUMAN 3.40E-06 6.336923351 ↑ Triple Negative

Hemoglobin subunit beta P68871 HBB_HUMAN 4.66E-06 1.856970774 ↑ Triple Negative

Beta-2-glycoprotein 1 P02749 APOH_HUMAN 9.79E-06 1.826994929 ↑ Triple Negative

305

Haptoglobin P00738 HPT_HUMAN 1.31E-05 2.047411407 ↑ Triple Negative

Coagulation factor IX P00740 FA9_HUMAN 1.45E-05 1.878304045 ↑ Triple Negative

Apolipoprotein C-I P02654 APOC1_HUMAN 1.84E-05 2.981683921 ↑ Triple Negative

Complement component C9 P02748 CO9_HUMAN 2.46E-05 2.09824133 ↑ Triple Negative

Afamin P43652 AFAM_HUMAN 2.61E-05 1.741122003 ↑ Triple Negative

Galectin-3-binding protein Q08380 LG3BP_HUMAN 2.64E-05 2.278337206 ↑ Triple Negative

Hemoglobin subunit delta P02042 HBD_HUMAN 2.86E-05 6.391315854 ↑ Triple Negative

C4b-binding protein beta chain P20851 C4BPB_HUMAN 2.98E-05 1.75486955 ↑ Triple Negative

Lipopolysaccharide-binding protein P18428 LBP_HUMAN 4.09E-05 3.179730825 ↑ Triple Negative

Immunoglobulin heavy variable 3-49 A0A0A0MS15 HV349_HUMAN 4.10E-05 5.091742669 ↑ Triple Negative

Apolipoprotein C-IV P55056 APOC4_HUMAN 4.12E-05 2.359368787 ↑ Triple Negative

Polymeric immunoglobulin receptor P01833 PIGR_HUMAN 4.79E-05 2.533797187 ↑ Triple Negative

Immunoglobulin lambda variable 1-47 P01700 LV147_HUMAN 5.70E-05 2.588797941 ↑ Triple Negative

Dopamine beta-hydroxylase P09172 DOPO_HUMAN 0.000148225 3.155747162 ↑ Triple Negative

Plasma protease C1 inhibitor P05155 IC1_HUMAN 0.000185866 1.444277794 ↑ Triple Negative

Serum amyloid P-component P02743 SAMP_HUMAN 0.000323991 2.653614489 ↑ Triple Negative

Zinc-alpha-2-glycoprotein P25311 ZA2G_HUMAN 0.000734893 1.658606922 ↑ Triple Negative

Serum amyloid A-4 protein P35542 SAA4_HUMAN 0.001564521 1.767558789 ↑ Triple Negative

Vitronectin P04004 VTNC_HUMAN 0.001792262 1.453911285 ↑ Triple Negative

Keratin. type I cytoskeletal 9 P35527 K1C9_HUMAN 0.001912466 1.575612234 ↑ Triple Negative

C-reactive protein P02741 CRP_HUMAN 0.002038334 7.916907504 ↑ Triple Negative

Retinol-binding protein 4 P02753 RET4_HUMAN 0.002148466 1.436880465 ↑ Triple Negative

Pigment epithelium-derived factor P36955 PEDF_HUMAN 0.003263088 2.30578555 ↑ Triple Negative

Hemoglobin subunit alpha P69905 HBA_HUMAN 0.003339445 1.715827887 ↑ Triple Negative

von Willebrand factor P04275 VWF_HUMAN 0.003416651 1.583518619 ↑ Triple Negative

Protein AMBP P02760 AMBP_HUMAN 0.004092418 1.786125711 ↑ Triple Negative

Complement factor I P05156 CFAI_HUMAN 0.004267426 1.531161402 ↑ Triple Negative

306

Apolipoprotein E P02649 APOE_HUMAN 0.005108321 1.297484301 ↑ Triple Negative

Peroxiredoxin-2 P32119 PRDX2_HUMAN 0.005462251 1.56831661 ↑ Triple Negative

IgGFc-binding protein Q9Y6R7 FCGBP_HUMAN 0.006429638 1.543604283 ↑ Triple Negative

Transferrin receptor protein 1 P02786 TFR1_HUMAN 0.006576023 2.659048849 ↑ Triple Negative

Complement component C6 P13671 CO6_HUMAN 0.006913895 3.211027998 ↑ Triple Negative

Matrix metalloproteinase-9 P14780 MMP9_HUMAN 0.007583113 2.1547884 ↑ Triple Negative

Immunoglobulin heavy variable 1-8 P0DP01 HV108_HUMAN 0.007621133 2.188587398 ↑ Triple Negative

Gelsolin P06396 GELS_HUMAN 0.008449427 1.600858701 ↑ Triple Negative

Cadherin-5 P33151 CADH5_HUMAN 0.008460741 1.74346858 ↑ Triple Negative

Plasma serine protease inhibitor P05154 IPSP_HUMAN 0.008634823 1.750498274 ↑ Triple Negative

Immunoglobulin heavy variable 1-2 P23083 HV102_HUMAN 0.008919673 1.559197168 ↑ Triple Negative

Fetuin-B Q9UGM5 FETUB_HUMAN 0.009781609 1.67811206 ↑ Triple Negative

Voltage-dependent L-type calcium

channel subunit alpha-1F O60840 CAC1F_HUMAN 0.010760078 3.330169664 ↑ Triple Negative

Properdin P27918 PROP_HUMAN 0.010990918 2.859498176 ↑ Triple Negative

Heparin cofactor 2 P05546 HEP2_HUMAN 0.011416361 1.356715555 ↑ Triple Negative

Immunoglobulin lambda variable 1-40 P01703 LV140_HUMAN 0.011566545 2.233258232 ↑ Triple Negative

Transthyretin P02766 TTHY_HUMAN 0.012616261 1.557894268 ↑ Triple Negative

Apolipoprotein A-IV P06727 APOA4_HUMAN 0.013273745 1.393597405 ↑ Triple Negative

Immunoglobulin kappa variable 2D-29 A0A075B6S2 KVD29_HUMAN 0.014548683 1.976553582 ↑ Triple Negative

Immunoglobulin lambda constant 7 A0M8Q6 IGLC7_HUMAN 0.015204337 2.30606418 ↑ Triple Negative

Hyaluronan-binding protein 2 Q14520 HABP2_HUMAN 0.017778041 1.837364492 ↑ Triple Negative

Complement component C7 P10643 CO7_HUMAN 0.018680554 2.247802867 ↑ Triple Negative

Immunoglobulin lambda variable 3-9 A0A075B6K5 LV39_HUMAN 0.018777301 5.992460294 ↑ Triple Negative

Apolipoprotein C-III P02656 APOC3_HUMAN 0.020727463 1.580436985 ↑ Triple Negative

Complement factor H-related protein 4 Q92496 FHR4_HUMAN 0.021147868 2.333186411 ↑ Triple Negative

Hemopexin P02790 HEMO_HUMAN 0.021507045 1.152784299 ↑ Triple Negative

307

Apolipoprotein C-II P02655 APOC2_HUMAN 0.022665952 1.368769534 ↑ Triple Negative

Cathelicidin antimicrobial peptide P49913 CAMP_HUMAN 0.024037233 2.485659851 ↑ Triple Negative

Keratin. type II cytoskeletal 2 epidermal P35908 K22E_HUMAN 0.024080471 1.556563063 ↑ Triple Negative

Complement C2 P06681 CO2_HUMAN 0.024115534 1.265184448 ↑ Triple Negative

Keratin. type II cytoskeletal 1 P04264 K2C1_HUMAN 0.02492244 1.394646766 ↑ Triple Negative

Xaa-Pro dipeptidase P12955 PEPD_HUMAN 0.025128744 8.197509777 ↑ Triple Negative

Sex hormone-binding globulin P04278 SHBG_HUMAN 0.025579843 2.464769888 ↑ Triple Negative

Tetranectin P05452 TETN_HUMAN 0.026202789 1.352737436 ↑ Triple Negative

Immunoglobulin heavy variable 4-30-2 A0A087WSY4 HV432_HUMAN 0.028590349 4.459516925 ↑ Triple Negative

Keratin type I cytoskeletal 14 P02533 K1C14_HUMAN 0.030094113 1.561799843 ↑ Triple Negative

Immunoglobulin lambda variable 3-10 A0A075B6K4 LV310_HUMAN 0.031988087 3.907325632 ↑ Triple Negative

Lumican P51884 LUM_HUMAN 0.032174228 1.406284475 ↑ Triple Negative

Clusterin P10909 CLUS_HUMAN 0.03243292 1.190567099 ↑ Triple Negative

Histidine-rich glycoprotein P04196 HRG_HUMAN 0.032443596 1.394921042 ↑ Triple Negative

Attractin O75882 ATRN_HUMAN 0.033317422 1.248304377 ↑ Triple Negative

Immunoglobulin kappa variable 2D-30 A0A075B6S6 KVD30_HUMAN 0.035772725 1.512873113 ↑ Triple Negative

Alpha-1-acid glycoprotein 2 P19652 A1AG2_HUMAN 0.035780803 2.040647761 ↑ Triple Negative

Immunoglobulin kappa variable 1-13 P0DP09 KV113_HUMAN 0.035987663 3.049898703 ↑ Triple Negative

Kallistatin P29622 KAIN_HUMAN 0.038988076 1.506271081 ↑ Triple Negative

Immunoglobulin kappa variable 1-6 A0A0C4DH72 KV106_HUMAN 0.039260496 1.537751007 ↑ Triple Negative

Immunoglobulin kappa variable 1D-8 A0A087WSZ0 KVD08_HUMAN 0.040751298 8.357751705 ↑ Triple Negative

Immunoglobulin heavy variable 7-4-1 A0A0J9YVY3 HV741_HUMAN 0.040865385 2.222501572 ↑ Triple Negative

Prenylcysteine oxidase 1 Q9UHG3 PCYOX_HUMAN 0.041567545 1.458990938 ↑ Triple Negative

Glutathione peroxidase 3 P22352 GPX3_HUMAN 0.043164541 4.984894872 ↑ Triple Negative

C4b-binding protein alpha chain P04003 C4BPA_HUMAN 0.044164801 1.446279607 ↑ Triple Negative

DDB1- and CUL4-associated factor 12-

like protein 1 Q5VU92 DC121_HUMAN 0.047731266 29.59481864 ↑ Triple Negative

308

Complement C4-A P0C0L4 CO4A_HUMAN 0.048405342 1.576071914 ↑ Triple Negative

Monocyte differentiation antigen CD14 P08571 CD14_HUMAN 0.04850195 1.325673944 ↑ Triple Negative

Carbonic anhydrase 2 P00918 CAH2_HUMAN 0.048684224 2.001285154 ↑ Triple Negative

Cholesteryl ester transfer protein P11597 CETP_HUMAN 0.049353744 2.914758788 ↑ Triple Negative

Platelet basic protein P02775 CXCL7_HUMAN 0.04971791 23.75806076 ↑ Triple Negative

Serotransferrin P02787 TRFE_HUMAN 0.010845697 1.589906375 ↑ Control

Complement C1q subcomponent subunit

C P02747 C1QC_HUMAN 0.011358029 1.543819449 ↑ Control

CD5 antigen-like O43866 CD5L_HUMAN 0.012543111 1.999836008 ↑ Control

L-selectin P14151 LYAM1_HUMAN 0.013557058 1.42160042 ↑ Control

309

Table 4_SM. Specific or unique differentially expressed proteins (up-regulated and down-regulated) (p-value ≤ 0.05) found in the protein

patterns of the ex vivo formed coronas after the analysis by SWATH-MS for the different breast cancer subtypes (LA, n = 11; LB-, n = 10; LB+,

n = 7; HER2+, n = 6; TNBC, n = 8) in comparison with healthy control (HC) samples.

Luminal A

Entry name Statistically significant

Luminal B-HER negative

Entry name

Statistically significant

Luminal B-HER positive Entry name

Statistically significant

HER2+

Entry name

Statistically significant

TNBC

Entry name

Statistically significant

up-regulated

n = 42

down-

regulated

n = 18

up-regulated

n = 100

down-

regulated

n = 32

up-regulated

n = 59

down-

regulated

n = 8

up-regulated

n = 95

down-

regulated

n = 35

up-regulated

n = 87

down-

regulated

n = 4

uniques = 4 uniques =

4

uniques = 25 uniques = 2 uniques = 2 uniques =

0

uniques = 23 uniques = 5 uniques = 9 uniques = 1

A2AP ↑ A2MG ↓ A1AG1 ↑ IGHA1 ↑ AFAM ↓ A1AG1 ↑ K22E ↑ C1QA ↓ ADIPO ↑ HV461 ↑ A1AT ↓ A1AG2 ↑ HV102 ↑ C1QC ↓

APOC3 ↑ CADH5 ↓ A1AG2 ↑ IGHA2 ↑ ALBU ↓ A2GL ↑ KAIN ↑ C1QC ↓ AFAM ↑ HV70D ↑ A1BG ↓ AFAM ↑ HV108 ↑ CD5L ↓

APOH ↑ CBG ↓ A1BG ↑ IGHG1 ↑ ALS ↓ AACT ↑ KV113 ↑ CBPN ↓ ALBU ↑ IGHG2 ↑ A2MG ↓ AMBP ↑ HV349 ↑ LYAM1 ↓

APOL1 ↑ CBPN ↓ A2AP ↑ IGJ ↑ APOA ↓ APOA1 ↑ KV133 ↑ F13B ↓ ALS ↑ IGLC7 ↑ ANGT ↓ APOA4 ↑ HV431 ↑ TRFE ↓

C1R ↑ CERU ↓ A2GL ↑ IGKC↑ APOM ↓ APOA2 ↑ KVD15 ↑ HRG ↓ APMAP ↑ ITIH3 ↑ ANT3 ↓ APOC1↑ HV432 ↑

C1RL ↑ CNDP1 ↓ AACT ↑ IGLC7 ↑ C1QA ↓ APOA4 ↑ KVD21 ↑ PROC ↓ C1QA ↑ K1C10 ↑ APOA ↓ APOC2 ↑ HV741 ↑

C4BPA ↑ CPN2 ↓ AMBP ↑ IGLL5 ↑ C1QB ↓ APOC1 ↑ LG3BP ↑ TENX ↓ C1R ↑ K1C14 ↑ APOA1 ↓ APOC3 ↑ IC1 ↑

CAMP ↑ HV124 ↓ ANGT ↑ ITIH1 ↑ C1QC↓ APOC2 ↑ LV140 ↑ TRFE ↓ C4BPA ↑ K1C9 ↑ APOA2 ↓ APOC4 ↑ IGLC7 ↑

CFAB ↑ HV349 ↓ APOA1 ↑ ITIH2 ↑ C1S ↓ APOC3 ↑ LV147 ↑ C4BPB ↑ K22E ↑ APOC3 ↓ APOE ↑ IPSP ↑

CO8B ↑ HV372 ↓ APOA2 ↑ ITIH4 ↑ CD14 ↓ APOL1 ↑ LV211 ↑ CAH1 ↑ KLKB1 ↑ APOC4 ↓ APOH ↑ K1C14 ↑

CO8G ↑ HV428 ↓ APOB ↑ KAIN ↑ CFAB ↓ C4BPB ↑ LV310 ↑ CD14 ↑ KV105 ↑ APOF ↓ ATRN ↑ K1C9 ↑

CO9 ↑ HV741 ↓ APOC1 ↑ KV116 ↑ CFAD ↓ CBG ↑ LV319 ↑ CETP ↑ KV108 ↑ APOH ↓ C4BPA ↑ K22E ↑

COL11 ↑ IGHM ↓ APOC2 ↑ KV133 ↑ CFAH ↓ CHLE ↑ LV325 ↑ CFAD ↑ KV116 ↑ APOM ↓ C4BPB ↑ K2C1 ↑

CRP ↑ K1C14 ↓ APOC3 ↑ KV311 ↑ CFAI ↓ CO4A ↑ LV39 ↑ CFAH ↑ KV127 ↑ C1QB ↓ CAC1F ↑ KAIN ↑

DOPO ↑ KV108 ↓ APOC4 ↑ KV401 ↑ CO3 ↓ CO6 ↑ LV746 CLUS ↑ KV224 ↑ C1QC ↓ CADH5 ↑ KV106 ↑

FA10 ↑ KV320 ↓ APOD ↑ KVD15 ↑ CO8G ↓ CO8A ↑ MBL2 ↑ CO3 ↑ KVD08 ↑ CBPB2 ↓ CAH2 ↑ KV113 ↑

FA9 ↑ SHBG ↓ APOL1 ↑ KVD20 ↑ ECM1↓ CO9 ↑ PCYOX ↑ CO5 ↑ KVD12 ↑ CERU ↓ CAMP ↑ KVD08 ↑

FCGBP ↑ ZPI ↓ BTD ↑ KVD21↑ F13B ↓ CRP ↑ PEDF ↑ CO7 ↑ KVD16 ↑ FCN3 ↓ CD14 ↑ KVD29 ↑

FCN2 ↑ C4BPB ↑ LBP ↑ FHR1 ↓ GELS PEPD ↑ CO8A ↑ KVD29 ↑ FETUA ↓ CETP ↑ KVD30 ↑

FCN3 ↑ CADH5 ↑ LCAT ↑ FHR4 ↓ HBA ↑ PIGR ↑ CO8G ↑ LCAT ↑ HEP2 ↓ CFAI ↑ LBP ↑

FHR2 ↑ CAH1 ↑ LDHB ↑ FIBA ↓ HBB ↑ PLSL ↑ CO9 ↑ LG3BP ↑ HPT ↓ CLUS ↑ LG3BP ↑

FIBA ↑ CAH2 ↑ LG3BP ↑ FINC ↓ HBD ↑ PON1 ↑ CPN2 ↑ LV151 ↑ HPTR ↓ CO2 ↑ LUM ↑

FINC ↑ CAMP ↑ LUM ↑ HABP2 ↓ HV349 ↑ PRDX2 ↑ CRIS3 ↑ LV211 ↑ HRG ↓ CO4A ↑ LV140 ↑

310

HABP2 ↑ CBPB2 ↑ LV140 ↑ KNG1↓ HV428 ↑ SAA4 ↑ CRP ↑ LV310 ↑ IC1 ↓ CO6 ↑ LV147 ↑

HBB ↑ CETP ↑ LV147 ↑ MASP2 ↓ HV431 ↑ SAMP ↑ DC121 ↑ LV39 ↑ IGJ ↓ CO7 ↑ LV310 ↑

HEMO ↑ CHLE ↑ LV223 ↑ NCOA6 ↓ HV461 ↑ TFR1 ↑ ECM1 ↑ LV545 ↑ ITIH1 ↓ CO9 ↑ LV39 ↑

HPT ↑ CNDP1↑ LV310 ↑ PF4V ↓ HV601↑ TTHY ↑ FA12 ↑ LV657 ↑ ITIH2 ↓ CRP ↑ MMP9 ↑

HRG ↑ CO4A ↑ LV319 ↑ PLMN ↓ HV70D ↑ VTNC ↑ FA5 ↑ LYAM1 ↑ ITIH4 ↓ CXCL7 ↑ PCYOX ↑

IC1 ↑ CO6 ↑ LV321 ↑ PRG4 ↓ IC1 ↑ ZA2G ↑ FA9 ↑ MASP2 ↑ KNG1 ↓ DC121 ↑ PEDF ↑

IPSP ↑ COL11 ↑ LV325 ↑ TENX ↓ IGLC7 ↑ FBLN3 ↑ MMP9 ↑ NCOA6 ↓ DOPO ↑ PEPD ↑

ITIH3 ↑ CRP ↑ LV39 ↑ TRFE ↓ FCN2 ↑ PCYOX ↑ PGRP2 ↓ FA9 ↑ PIGR ↑

LAMP2 ↑ FA10 ↑ LV746 ↑ VTDB ↓ FETUB ↑ PEDF ↑ PROS ↓ FCGBP ↑ PRDX2 ↑

LBP ↑ FA9 ↑ MA2A1 ↑ FHR1 ↑ PEPD ↑ SAA4 ↓ FETUB ↑ PROP ↑

NCOA6 ↑ FCGBP ↑ MBL2 ↑ FHR4 ↑ PIGR ↑ TRFE ↓ FHR4 ↑ RET4 ↑

PLMN ↑ FETUA ↑ MED23 ↑ FIBA ↑ PLMN ↑ TRY1 ↓ GELS ↑ SAA4 ↑

PRDX2 ↑ GELS ↑ PEDF ↑ FINC ↑ PRG4 ↑ GPX3 ↑ SAMP ↑

PROC ↑ GP1BA ↑ PGBM ↑ GPX3 ↑ PROP ↑ HABP2 ↑ SHBG ↑

PROS ↑ HBA ↑ PLTP ↑ HBA ↑ SAMP ↑ HBA ↑ TETN ↑

SAA4 ↑ HBB ↑ PON1 ↑ HBB ↑ SEPP1 ↑ HBB ↑ TFR1 ↑

SAMP ↑ HBD ↑ PON3 ↑ HBD ↑ SHBG ↑ HBD ↑ TTHY ↑

TRFE ↑ HEP2 ↑ PRDX2 ↑ HV108 ↑ TFR1 ↑ HEMO ↑ VTNC ↑

VTNC ↑ HPTR ↑ PROP ↑ HV146 ↑ TSP1 ↑ HEP2 ↑ VWF ↑

HV102 ↑ PROS ↑ HV323 ↑ TTHY ↑ HPT ↑ ZA2G ↑

HV146 ↑ PZP ↑ HV333 ↑ VTDB ↑ HRG ↑

HV309 ↑ RET4 ↑ HV349 ↑ VTNC ↑

HV349 ↑ SAA4 ↑ HV364 ↑ VWF ↑

HV353 ↑ SAMP ↑ HV372 ↑ ZA2G ↑

HV374 ↑ TETN ↑ HV373 ↑

HV431 ↑ TFR1 ↑

HV69D ↑ TSP1 ↑

311

Table 5_SM. Differentially expressed proteins (up-regulated and down-regulated) (p-value ≤ 0.05) found in the protein patterns of the ex vivo

formed coronas after the analysis by SWATH-MS common and specific for the different breast cancer subtypes (LA, n = 11; LB-, n = 10; LB+, n

= 7; HER2+, n = 6; TNBC, n = 8) in comparison with controls samples. The accession number, gene name and species (Human) were reported.

Protein Name UniProt Name Entry Name Gene Luminal A

Luminal B

HER2

Negative

Luminal B

HER2

Positive

HER2

Positive

Triple

Negative

Serum amyloid P-component

Fold Change SAMP_HUMAN P02743 APCS

X

2.37

X

1.79

X

2.45

X

4.82

X

2.65

C-reactive protein

Fold Change CRP_HUMAN P02741 CRP

X

3.79

X

2.76

X

3.37

X

10.58

X

7.92

Hemoglobin subunit beta

Fold Change HBB_HUMAN P68871 HBB

X

1.42

X

36.85

X

3.80

X

2.39

X

1.85

Serotransferrin TRFE_HUMAN P02787 TF X

1.30

X

2.02

X

1.85

X

1.56

X

1.59

Immunoglobulin heavy

variable 3-49 HV349_HUMAN

A0A0A0MS1

5

IGHV3-

49

X

1.74

X

2.51

X

2.86

X

4.56

X

5.09

Apolipoprotein C-III APOC3_HUMA

N P02656 APOC3

X

1.70

X

1.54

X

2.02

X

X

1.85

Serum amyloid A-4 protein SAA4_HUMAN P35542 SAA4 X

1.46

X

2.24

X

2.16

X

2.39

X

1.85

Complement C1r

subcomponent-like protein C1RL_HUMAN Q9NZP8 C1RL X

Complement component C8

beta chain CO8B_HUMAN P07358 C8B X

Complement factor H-related

protein 2 FHR2_HUMAN P36980 CFHR2 X

312

Lysosome-associated membrane

glycoprotein 2

LAMP2_HUMA

N P13473 LAMP2 X

Immunoglobulin heavy variable

1-24 HV124_HUMAN A0A0C4DH33

IGHV1-

24 X

Immunoglobulin heavy constant

mu IGHM_HUMAN P01871 IGHM X

Immunoglobulin kappa variable

3-20 KV320_HUMAN P01619

IGKV3-

20 X

Protein Z-dependent protease

inhibitor ZPI_HUMAN Q9UK55

SERPINA

10 X

Apolipoprotein B-100 APOB_HUMAN P04114 APOB X

Apolipoprotein D APOD_HUMAN P05090 APOD X

Biotinidase BTD_HUMAN P43251 BTD X

Platelet glycoprotein Ib alpha

chain

GP1BA_HUMA

N P07359 GP1BA X

Immunoglobulin heavy variable

3-9 HV309_HUMAN P01782 IGHV3-9 X

Immunoglobulin heavy variable

3-53 HV353_HUMAN P01767

IGHV3-

53 X

Immunoglobulin heavy variable

3-74 HV374_HUMAN A0A0B4J1X5

IGHV3-

74 X

Immunoglobulin heavy variable

1-69D

HV69D_HUMA

N A0A0B4J2H0

IGHV1-

69D X

Immunoglobulin heavy constant

alpha 1 IGHA1_HUMAN P01876 IGHA1 X

Basement membrane-specific

heparan sulfate proteoglycan

core protein

IGHA2_HUMAN P01877 IGHA2 X

Immunoglobulin heavy constant

gamma 1 IGHG1_HUMAN P01857 IGHG1 X

313

Immunoglobulin kappa constant IGKC_HUMAN P01834 IGKC X

Immunoglobulin lambda-like

polypeptide 5 IGLL5_HUMAN B9A064 IGLL5 X

Immunoglobulin kappa variable

3-11 KV311_HUMAN P04433

IGKV3-

11 X

Immunoglobulin kappa variable

4-1 KV401_HUMAN P06312 IGKV4-1 X

Immunoglobulin kappa variable

3D-20

KVD20_HUMA

N A0A0C4DH25

IGKV3D-

20 X

L-lactate dehydrogenase B

chain LDHB_HUMAN P07195 LDHB X

Immunoglobulin lambda

variable 2-23 LV223_HUMAN P01705

IGLV2-

23 X

Immunoglobulin lambda

variable 3-21 LV321_HUMAN P80748

IGLV3-

21 X

Alpha-mannosidase 2 MA2A1_HUMA

N Q16706 MAN2A1 X

Mediator of RNA polymerase II

transcription subunit 23

MED23_HUMA

N Q9ULK4 MED23 X

Basement membrane-specific

heparan sulfate proteoglycan

core protein

PGBM_HUMAN P98160 HSPG2 X

Phospholipid transfer protein PLTP_HUMAN P55058 PLTP X

Serum paraoxonase/lactonase 3 PON3_HUMAN Q15166 PON3 X

Pregnancy zone protein PZP_HUMAN P20742 PZP X

Complement C1s

subcomponent C1S_HUMAN P09871 C1S X

Platelet factor 4 variant PF4V_HUMAN P10720 PF4V1 X

Immunoglobulin heavy variable

6-1 HV601_HUMAN A0A0B4J1U7 IGHV6-1 X

Plastin-2 PLSL_HUMAN P13796 LCP1 X

Adiponectin ADIPO_HUMAN Q15848 ADIPOQ X

314

Immunoglobulin heavy variable

3-64

APMAP_HUMA

N Q9HDC9 APMAP X

Complement C5 CO5_HUMAN P01031 C5 X

Cysteine-rich secretory protein

3 CRIS3_HUMAN P54108 CRISP3 X

Coagulation factor XII FA12_HUMAN P00748 F12 X

Coagulation factor V FA5_HUMAN P12259 F5 X

EGF-containing fibulin-like

extracellular matrix protein 1 FBLN3_HUMAN Q12805 EFEMP1 X

Immunoglobulin heavy variable

3-23 HV323_HUMAN P01764

IGHV3-

23 X

Immunoglobulin heavy variable

3-33 HV333_HUMAN P01772

IGHV3-

33 X

Immunoglobulin heavy variable

3-64 HV364_HUMAN A0A075B6Q5

IGHV3-

64 X

Immunoglobulin heavy variable

3-73 HV373_HUMAN A0A0B4J1V6

IGHV3-

73 X

Immunoglobulin heavy constant

gamma 2 IGHG2_HUMAN P01859 IGHG2 X

Keratin, type I cytoskeletal 10 K1C10_HUMAN P13645 KRT10 X

Plasma kallikrein KLKB1_HUMA

N P03952 KLKB1 X

Immunoglobulin kappa variable

1-5 KV105_HUMAN P01602 IGKV1-5 X

Immunoglobulin kappa variable

1-27 KV127_HUMAN A0A075B6S5

IGKV1-

27 X

Immunoglobulin kappa variable

2-24 KV224_HUMAN A0A0C4DH68

IGKV2-

24 X

Immunoglobulin kappa variable

1D-12

KVD12_HUMA

N P01611

IGKV1D-

12 X

Immunoglobulin kappa variable

1D-16

KVD16_HUMA

N P01601

IGKV1D-

16 X

315

Immunoglobulin lambda

variable 1-51 LV151_HUMAN P01701

IGLV1-

51 X

Immunoglobulin lambda

variable 5-45 LV545_HUMAN

A0A087WSX

0

IGLV5-

45 X

Immunoglobulin lambda

variable 6-57 LV657_HUMAN P01721

IGLV6-

57 X

Selenoprotein P SEPP1_HUMAN P49908 SELENO

P X

Alpha-1-antitrypsin A1AT_HUMAN P01009 SERPINA

1 X

Antithrombin-III ANT3_HUMAN P01008 SERPINC

1 X

Apolipoprotein F APOF_HUMAN Q13790 APOF X

N-acetylmuramoyl-L-alanine

amidase PGRP2_HUMAN Q96PD5

PGLYRP

2 X

Trypsin-1 TRY1_HUMAN P07477 PRSS1 X

Apolipoprotein E APOE_HUMAN P02649 APOE X Attractin ATRN_HUMAN O75882 ATRN X

Voltage-dependent L-type

calcium channel subunit alpha-

1F

CAC1F_HUMAN O60840 CACNA1

F X

Complement C2 CO2_HUMAN P06681 C2 X

Platelet basic protein CXCL7_HUMA

N P02775 PPBP X

Immunoglobulin heavy variable

4-30-2 HV432_HUMAN

A0A087WSY

4

IGHV4-

30-2 X

Keratin, type II cytoskeletal 1 K2C1_HUMAN P04264 KRT1 X Immunoglobulin kappa variable

1-6 KV106_HUMAN A0A0C4DH72 IGKV1-6 X

Immunoglobulin kappa variable

2D-30

KVD30_HUMA

N A0A075B6S6

IGKV2D-

30 X

CD5 antigen-like CD5L_HUMAN O43866 CD5L X

316

Peroxiredoxin-2 PRDX2_HUMA

N P32119 PRDX2 X X X X

Coagulation factor IX FA9_HUMAN P00740 F9 X X X X

Plasma protease C1 inhibitor IC1_HUMAN P05155 SERPING

1 X X X X

Vitronectin VTNC_HUMAN P04004 VTN X X X X

Histidine-rich glycoprotein HRG_HUMAN P04196 HRG X X X X

Complement component C9 CO9_HUMAN P02748 C9 X X X X

Transferrin receptor protein 1 TFR1_HUMAN P02786 TFRC X X X X

Complement C1q

subcomponent subunit C C1QC_HUMAN P02747 C1QC X X X X

Immunoglobulin lambda

variable 3-10 LV310_HUMAN A0A075B6K4

IGLV3-

10 X X X X

Immunoglobulin lambda

variable 3-9 LV39_HUMAN A0A075B6K5 IGLV3-9 X X X

Galectin-3-binding protein LG3BP_HUMAN Q08380 LGALS3

BP X X X X

Immunoglobulin lambda

constant 7 IGLC7_HUMAN A0M8Q6 IGLC7 X X X X

C4b-binding protein beta chain C4BPB_HUMAN P20851 C4BPB X X X X

Pigment epithelium-derived

factor PEDF_HUMAN P36955

SERPINF

1 X X X X

Hemoglobin subunit delta HBD_HUMAN P02042 HBD X X X X

Hemoglobin subunit alpha HBA_HUMAN P69905 HBA1 X X X X

Apolipoprotein L1 APOL1_HUMAN O14791 APOL1 X X X

Fibronectin FINC_HUMAN P02751 FN1 X X X

Plasminogen PLMN_HUMAN P00747 PLG X X X

Complement component C8

gamma chain CO8G_HUMAN P07360 C8G X X X

Vitamin K-dependent protein S PROS_HUMAN P07225 PROS1 X X X

Fibrinogen alpha chain FIBA_HUMAN P02671 FGA X X X

Nuclear receptor coactivator 6 NCOA6_HUMA Q14686 NCOA6 X X X

317

N

Hyaluronan-binding protein 2 HABP2_HUMA

N Q14520 HABP2 X X X

Cadherin-5 CADH5_HUMA

N P33151 CDH5 X X X

IgGFc-binding protein FCGBP_HUMA

N Q9Y6R7 FCGBP X X X

Lipopolysaccharide-binding

protein LBP_HUMAN P18428 LBP X X X

Cathelicidin antimicrobial

peptide CAMP_HUMAN P49913 CAMP X X X

C4b-binding protein alpha chain C4BPA_HUMAN P04003 C4BPA X X X

Keratin, type I cytoskeletal 14 K1C14_HUMAN P02533 KRT14 X X X

Beta-2-glycoprotein 1 APOH_HUMAN P02749 APOH X X X

Sex hormone-binding globulin SHBG_HUMAN P04278 SHBG X X X

Haptoglobin HPT_HUMAN P00738 HP X X X

Apolipoprotein A-II APOA2_HUMA

N P02652 APOA2 X X X

Apolipoprotein A-I APOA1_HUMA

N P02647 APOA1 X X X

Complement C1q

subcomponent subunit A C1QA_HUMAN P02745 C1QA X X X

Gelsolin GELS_HUMAN P06396 GSN X X X

Complement component C6 CO6_HUMAN P13671 C6 X X X

Immunoglobulin lambda

variable 1-40 LV140_HUMAN P01703

IGLV1-

40 X X X

Complement C4-A CO4A_HUMAN P0C0L4 C4A X X X

Apolipoprotein C-I APOC1_HUMA

N P02654 APOC1 X X X

Immunoglobulin lambda

variable 1-47 LV147_HUMAN P01700

IGLV1-

47 X X X

Apolipoprotein C-II APOC2_HUMA P02655 APOC2 X X X

318

N

Immunoglobulin heavy variable

4-31 HV431_HUMAN P0DP07

IGHV4-

31 X X X

Kallistatin KAIN_HUMAN P29622 SERPINA

4 X X X

Properdin PROP_HUMAN P27918 CFP X X X

Monocyte differentiation

antigen CD14 CD14_HUMAN P08571 CD14 X X X

Cholesteryl ester transfer

protein CETP_HUMAN P11597 CETP X X X

Heparin cofactor 2 HEP2_HUMAN P05546 SERPIND

1 X X X

Afamin AFAM_HUMAN P43652 AFM X X X

Complement factor H-related

protein 4 FHR4_HUMAN Q92496 CFHR4 X X X

Apolipoprotein C-IV APOC4_HUMA

N P55056 APOC4 X X X

Polymeric immunoglobulin

receptor PIGR_HUMAN P01833 PIGR X X X

Prenylcysteine oxidase 1 PCYOX_HUMA

N Q9UHG3 PCYOX1 X X X

Zinc-alpha-2-glycoprotein ZA2G_HUMAN P25311 AZGP1 X X X

Xaa-Pro dipeptidase PEPD_HUMAN P12955 PEPD X X X

Transthyretin TTHY_HUMAN P02766 TTR X X X

Keratin, type II cytoskeletal 2

epidermal K22E_HUMAN P35908 KRT2 X X X

Alpha-2-antiplasmin A2AP_HUMAN P08697 SERPINF

2 X X

Complement factor B CFAB_HUMAN P00751 CFB X X

Coagulation factor X FA10_HUMAN P00742 F10 X X

Collectin-11 COL11_HUMAN Q9BWP8 COLEC1

1 X X

319

Beta-Ala-His dipeptidase CNDP1_HUMA

N Q96KN2 CNDP1 X X

Immunoglobulin heavy variable

4-28 HV428_HUMAN A0A0C4DH34

IGHV4-

28 X X

Corticosteroid-binding globulin CBG_HUMAN P08185 SERPINA

6 X X

Vitamin K-dependent protein C PROC_HUMAN P04070 PROC X X

Carboxypeptidase N catalytic

chain CBPN_HUMAN P15169 CPN1 X X

Immunoglobulin heavy variable

3-72 HV372_HUMAN A0A0B4J1Y9

IGHV3-

72 X X

Complement C1r subcomponent C1R_HUMAN P00736 C1R X X

Carboxypeptidase N subunit CPN2_HUMAN P22792 CPN2 X X

Ceruloplasmin CERU_HUMAN P00450 CP X X

Ficolin-2 FCN2_HUMAN Q15485 FCN2 X X

Alpha-2-macroglobulin A2MG_HUMAN P01023 A2M X X

Inter-alpha-trypsin inhibitor

heavy chain H3 ITIH3_HUMAN Q06033 ITIH3 X X

Immunoglobulin kappa variable

1-8 KV108_HUMAN A0A0C4DH67 IGKV1-8 X X

Ficolin-3 FCN3_HUMAN O75636 FCN3 X X

Immunoglobulin heavy variable

7-4-1 HV741_HUMAN A0A0J9YVY3

IGHV7-4-

1 X X

Plasma serine protease inhibitor IPSP_HUMAN P05154 SERPINA

5 X X

Dopamine beta-hydroxylase DOPO_HUMAN P09172 DBH X X

Hemopexin HEMO_HUMAN P02790 HPX X X

Serum paraoxonase/arylesterase

1 PON1_HUMAN P27169 PON1 X X

Immunoglobulin kappa variable

3D-15

KVD15_HUMA

N

A0A087WSY

6

IGKV3D-

15 X X

Immunoglobulin lambda LV319_HUMAN P01714 IGLV3- X X

320

variable 3-19 19

Mannose-binding protein C MBL2_HUMAN P11226 MBL2 X X

Immunoglobulin kappa variable

1-33 KV133_HUMAN P01594

IGKV1-

33 X X

Cholinesterase CHLE_HUMAN P06276 BCHE X X

Immunoglobulin lambda

variable 7-46 LV746_HUMAN A0A075B6I9

IGLV7-

46 X X

Leucine-rich alpha-2-

glycoprotein A2GL_HUMAN P02750 LRG1 X X

Alpha-1-acid glycoprotein 1 A1AG1_HUMA

N P02763 ORM1 X X

Immunoglobulin lambda

variable 3-25 LV325_HUMAN P01717

IGLV3-

25 X X

Alpha-1-antichymotrypsin AACT_HUMAN P01011 SERPINA

3 X X

Tenascin-X TENX_HUMAN P22105 TNXB X X

Immunoglobulin kappa variable

6D-21

KVD21_HUMA

N

A0A0A0MT3

6

IGKV6D-

21 X X

Coagulation factor XIII B chain F13B_HUMAN P05160 F13B X X

Extracellular matrix protein 1 ECM1_HUMAN Q16610 ECM1 X X

Thrombospondin-1 TSP1_HUMAN P07996 THBS1 X X

Apolipoprotein M APOM_HUMAN O95445 APOM X X

Complement C3 CO3_HUMAN P01024 C3 X X

Haptoglobin-related protein HPTR_HUMAN P00739 HPR X X

Alpha-1B-glycoprotein A1BG_HUMAN P04217 A1BG X X

Apolipoprotein(a) APOA_HUMAN P08519 LPA X X

Inter-alpha-trypsin inhibitor

heavy chain H2 ITIH2_HUMAN P19823 ITIH2 X X

Angiotensinogen ANGT_HUMAN P01019 AGT X X

Phosphatidylcholine-sterol

acyltransferase LCAT_HUMAN P04180 LCAT X X

Serum albumin ALBU_HUMAN P02768 ALB X X

321

Insulin-like growth factor-

binding protein complex acid

labile subunit

ALS_HUMAN P35858 IGFALS X X

Kininogen-1 KNG1_HUMAN P01042 KNG1 X X

Carbonic anhydrase 1 CAH1_HUMAN P00915 CA1 X X

Complement factor H CFAH_HUMAN P08603 CFH X X

Immunoglobulin heavy variable

1-46 HV146_HUMAN P01743

IGHV1-

46 X X

Mannan-binding lectin serine

protease 2

MASP2_HUMA

N O00187 MASP2 X X

Immunoglobulin kappa variable

1-16 KV116_HUMAN P04430

IGKV1-

16 X X

Carboxypeptidase B2 CBPB2_HUMAN Q96IY4 CPB2 X X

Inter-alpha-trypsin inhibitor

heavy chain H1 ITIH1_HUMAN P19827 ITIH1 X X

Vitamin D-binding protein VTDB_HUMAN P02774 GC X X

Complement factor H-related

protein 1 FHR1_HUMAN Q03591 CFHR1 X X

Inter-alpha-trypsin inhibitor

heavy chain H4 ITIH4_HUMAN Q14624 ITIH4 X X

Proteoglycan 4 PRG4_HUMAN Q92954 PRG4 X X

Immunoglobulin J chain IGJ_HUMAN P01591 JCHAIN X X

Complement factor D CFAD_HUMAN P00746 CFD X X

Complement C1q

subcomponent subunit B C1QB_HUMAN P02746 C1QB X X

Alpha-2-HS-glycoprotein FETUA_HUMA

N P02765 AHSG X X

Immunoglobulin heavy variable

1-2 HV102_HUMAN P23083 IGHV1-2 X X

Retinol-binding protein 4 RET4_HUMAN P02753 RBP4 X X

Alpha-1-acid glycoprotein 2 A1AG2_HUMA

N P19652 ORM2 X X

322

Protein AMBP AMBP_HUMAN P02760 AMBP X X

Carbonic anhydrase 2 CAH2_HUMAN P00918 CA2 X X

Lumican LUM_HUMAN P51884 LUM X X Complement factor I CFAI_HUMAN P05156 CFI X X

Tetranectin TETN_HUMAN P05452 CLEC3B X X Complement component C8

alpha chain CO8A_HUMAN P07357 C8A X X

Immunoglobulin heavy variable

4-61 HV461_HUMAN A0A0C4DH41

IGHV4-

61 X X

Immunoglobulin lambda

variable 2-11 LV211_HUMAN P01706

IGLV2-

11 X X

Immunoglobulin heavy variable

2-70D

HV70D_HUMA

N A0A0C4DH43

IGHV2-

70D X X

Apolipoprotein A-IV APOA4_HUMA

N P06727 APOA4 X X

Immunoglobulin kappa variable

1-13 KV113_HUMAN P0DP09

IGKV1-

13 X X

Immunoglobulin kappa variable

1D-8

KVD08_HUMA

N A0A087WSZ0

IGKV1D-

8 X X

Immunoglobulin kappa variable

2D-29

KVD29_HUMA

N A0A075B6S2

IGKV2D-

29 X X

DDB1- and CUL4-associated

factor 12-like protein 1 DC121_HUMAN Q5VU92

DCAF12

L1 X X

Matrix metalloproteinase-9 MMP9_HUMAN P14780 MMP9 X X

Clusterin CLUS_HUMAN P10909 CLU X X

von Willebrand factor VWF_HUMAN P04275 VWF X X

Glutathione peroxidase 3 GPX3_HUMAN P22352 GPX3 X X

Complement component C7 CO7_HUMAN P10643 C7 X X

Keratin, type I cytoskeletal 9 K1C9_HUMAN P35527 KRT9 X X

Immunoglobulin heavy variable

1-8 HV108_HUMAN P0DP01 IGHV1-8 X X

L-selectin LYAM1_HUMA P14151 SELL X X

323

N

Fetuin-B FETUB_HUMA

N Q9UGM5 FETUB X X

324

ANNEX D

Extended abstract

325

Resumen extendido

El cáncer de mama es uno de los tipos de cáncer más común en mujeres y supone

aproximadamente el 14% de las muertes relacionadas con el cáncer en mujeres de todo el

mundo. La detección precoz del cáncer de mama juega un papel determinante en el

pronóstico y la tasa de supervivencia de dicha enfermedad. Hoy en día, la mamografía es

la técnica que se emplea para el diagnóstico precoz del cáncer de mama, sin embargo,

esta técnica presenta una baja sensibilidad (25-59%) para el diagnóstico en las mujeres

jóvenes.

En la práctica clínica habitual del cáncer de mama, las pruebas pronósticas

requieren la obtención de tejido tumoral mediante una biopsia u otros abordajes

quirúrgicos. Con el objetivo de minimizar dichos procedimientos invasivos, los

biomarcadores presentes en el suero/plasma sanguíneo abren una nueva vía para el

diagnóstico precoz del cáncer de mama en mujeres asintomáticas, un pronóstico más

preciso y la predicción de la respuesta al tratamiento.

A pesar de que en las última tres décadas se han evaluado varios biomarcadores

séricos, éstos carecen de la sensibilidad suficiente para detectar el cáncer de mama en una

fase inicial de la enfermedad y ninguno de estos posee la precisión suficiente para predecir

la recurrencia. Por lo tanto, es imprescindible encontrar nuevos biomarcadores

sanguíneos relacionados con el cáncer de mama.

La proteómica es una ciencia que permite identificar y cuantificar las

proteínas/péptidos presentes en una determinada muestra biológica (suero, plasma, orina),

empleando para ello la técnica de espectrometría de masas. Partiendo de que la detección

del cáncer en una fase temprana se basa en la hipótesis de que la enfermedad se desarrolla

al producirse una desviación del estado normal, la comparación de los perfiles

proteómicos de los pacientes con cáncer y los individuos sanos (controles) nos permitirá

localizar distintos biomarcadores. Además, el análisis proteómico es complementario de

los estudios genéticos que se desarrollan para evaluar el pronóstico del cáncer de mama.

Concretamente, centraremos esta introducción en los estudios proteómicos llevados a

cabo en el cáncer de mama para la detección precoz, el pronóstico y la evaluación de la

respuesta al tratamiento en este tipo de pacientes.

326

Existen biomarcadores tumorales circulantes (en la sangre) capaces de detectar la

existencia de malignidad en etapas previas al diagnóstico clínico y actualmente se están

evaluando en ensayos clínicos con ciertos tipos de cáncer; por ejemplo, CA125 en la

detección del cáncer de ovario. Sin embargo, actualmente no hay biomarcadores

circulantes (en la sangre) adecuados para el diagnóstico o la detección del cáncer de

mama. Aunque existen candidatos, como el antígeno carcinoembrionario (CEA), la forma

soluble de la proteína MUC1 (CA15-3, CA27.29), la proteína oncogénica RS / DJ-1, el

factor de crecimiento epidérmico humano receptor-2 (HER2) o los fragmentos circulantes

de citoqueratina (TPA, TPS y CYFRA 21-1), estos presentan algunas limitaciones tanto

en su sensibilidad como en su especificidad para el diagnóstico precoz del cáncer de

mama. Por lo tanto, en la práctica clínica estos biomarcadores se usan principalmente para

predecir la respuesta a la terapia, la monitorización después de la terapia primaria, o como

indicadores pronósticos.

De entre todos, el antígeno carbohidrato 15-3 (CA 15-3) es el marcador sérico más

utilizado en las pacientes con cáncer de mama. CA 15-3 se utiliza para el cribado de rutina

del cáncer de mama, así como para el control y el seguimiento de pacientes [Clin. Chim.

Acta 411 (2010) 1869-1874]. El valor medio de CA 15-3 es de 17 U/mL (rango 3.9-99.5

U/mL) en pacientes con cáncer de mama primario no tratado.

Con el fin de desarrollar estrategias novedosas para la detección ultrasensible de

CA 15-3, se desarrolló un inmunosensor electroquímico nanoestructurado basado en

óxido de grafeno y funcionalizado no covalentemente (GO/Py-COOH), así como

nanotubos de carbono de pared múltiple (MWCNTs). Este método permitió la detección

de CA 15-3 en suero sanguíneo a niveles tan bajos como 0.01 ± 0.07 U/mL. Las ventajas

de este nanosistema es que es altamente selectivo y se puede regenerar para ser utilizado

múltiples veces, lo que ofrece un gran potencial para su uso en el futuro como método de

diagnóstico del cáncer de mama. En esta línea, el ácido perclórico (PCA) se usó para

mejorar la detección de proteínas O-glicosiladas en suero (como CA 27.29 y CA 15-3)

usando un ensayo de lectina enlazada con enzimas sandwich (ELLA). Al analizar

mediante electroforesis en gel de poliacrilamida (SDS-PAGE) las fracciones de

glicoproteína capturadas con la lectina champedak galactose binding (CGB) procedentes

de suero de las pacientes con cáncer de mama en estadio 0 (n = 31), estadio I (n = 48) y

controles (n = 105), se detectó una abundancia alterada inversa sustancial del inhibidor

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de la proteasa plasmática C1 y del proteoglicano 4 en estos dos primeros estadios en

relación con el grupo control. Aunque es necesario la validación del método empleando

poblaciones clínicamente representativas, la ratio de expresión de proteoglicano 4 frente

al inhibidor de la proteasa C1 se puede explotar para la detección temprana del cáncer de

mama usando muestras de suero sanguíneo.

Además, se exploró el potencial de diferentes paneles de biomarcadores que

contienen CA15-3 para el diagnóstico temprano del cáncer de mama. Se descubrió que

un conjunto de diez posibles biomarcadores séricos de cáncer de mama y antígenos de

cáncer (haptoglobina, osteopontina (OPN), CA15-3, antígeno de cáncer 125 (CA-125),

antígeno de cáncer 19-9 (CA19-9), el antígeno carcinoembrionario (CEA), la prolactina,

la α- fetoproteína (AFP), la leptina y el factor inhibidor de la migración (MIF)) no fue útil

para predecir el cáncer de mama en fases tempranas de la enfermedad. Otro estudio

proporciona resultados similares, en el que ninguno de los 9 marcadores candidatos

utilizados (CA15-3, RANTES/CCL5 (regulados por activación de células T normales

expresadas y secretadas/ el ligando 5 de quimiocina (motivo CC)), OPN (osteopontina) ,

PAI-1 (inhibidor del activador del plasminógeno-1), SLPI (inhibidor de la proteasa

leucocitaria secretora), HSP90A (proteína de choque térmico 90A), IGFBP3 (proteína de

unión al factor de crecimiento similar a la insulina 3), APOC1 (apolipoproteína CI) y

PAPPA (pappalysin -1), o sus combinaciones, resultaron útiles para detectar el cáncer de

mama; además se encontraron vínculos entre los candidatos CA15-3, HSP90A y PAI-1

con aspectos clínico-patológicos, que, a su vez, se correlacionaban con el pronóstico.

Sin embargo, un panel de antígenos compuesto por B11 (LGALS3), B18 (PHB2),

B119 (MUC1) y B130 (GK2), junto con CA15-3, mostró un aumento significativo de la

sensibilidad (87%), la especificidad (76%) y en la supervivencia global (OS) (82,7%)

para el diagnóstico del cáncer de mama en fase T1N0M0, en comparación con la utilización

de CA15-3 sólo. Aunque este panel de antígenos requiere de ser validado utilizando más

muestras de cáncer de mama, se presenta como un procedimiento prometedor para

detectar el cáncer de mama en una fase temprana. Además, CA15-3 también se incluyó

en el panel de diagnóstico constituido por los 4 picos de un conjunto de proteínas

[ m/z 3,972, 6,850 y 8,115 (BC2) y 8,949 (BC3)] utilizadas para distinguir a 62 pacientes

con cáncer de mama con carcinoma ductal invasivo de 16 controles sanos (HCs) y 31

pacientes con enfermedades mamarias benignas (BBDs). Se demostró que este panel de

4 picos junto con CA15-3 ofrecía una buena sensibilidad y especificidad para el

diagnóstico de cáncer de mama. Cierto es que, como mencionamos anteriormente, se

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debería realizar una investigación adicional utilizando un tamaño de muestra más grande

para poder verificar estos resultados.

También se exploró el potencial de CA15-3 en relación con el diagnóstico precoz

del cáncer de mama metastásico. Así, se investigó la sensibilidad de CA 15-3, CEA y

HER2, descubriéndose que la combinación de dos de estos marcadores tumorales

mejoraba la sensibilidad para la detección del cáncer de mama metastásico, y,

curiosamente, la determinación conjunta de los tres marcadores tumorales sólo mejoraba

levemente la sensibilidad. Los autores propusieron el uso combinado de CEA y HER2 en

tumores del subtipo HER2+ y la combinación de CA 15-3 y CEA en tumores del subtipo

HER2−. De nuevo, se requieren ensayos clínicos aleatorios prospectivos para explorar

los beneficios clínicos de la detección temprana y el tratamiento de la enfermedad

metastásica.

También se evaluó la eficacia de los biomarcadores séricos Bc1, Bc2 y Bc3 en la

detección temprana del cáncer de mama, siendo sólo la expresión de Bc2 estadísticamente

significativa entre el grupo de enfermedad maligna, el grupo control y el grupo de

enfermedad benigna.

El cáncer de mama es una enfermedad heterogénea en la que las células

cancerígenas pueden expresar una variedad de proteínas aberrantes (los denominados

antígenos asociados a tumores: TAAs) que son capaces de provocar una respuesta inmune

y producir anticuerpos. Curiosamente, esta respuesta inmune aparece meses y hasta años

antes del diagnóstico clínico de la neoplasia maligna. Así, la identificación de los TAAs

y sus anticuerpos específicos podrían ofrecer una amplificación in vivo de una señal

carcinogénica en fases muy tempranas, lo que posiblemente permita una detección mucho

más precoz del cáncer que con los métodos disponibles actualmente.

En particular, en el suero se encuentran varios antígenos y anticuerpos circulantes

que pueden relacionarse con la progresión y el desarrollo del cáncer. Así, se se han

evaluado en cáncer de mama la presencia de autoanticuerpos en suero contra antígenos

tumorales como son p53, antígenos antineurales/antinucleares, o proteínas neurales

embrionarias.

Los antígenos del cáncer han demostrado ser de una importancia extraordinaria

para el cribado, así como ser indicadores pronósticos. En particular, podemos citar a las

proteínas de choque térmico (HSPs), que se encuentran sobreexpresadas en una amplia

gama de cánceres humanos y son capaces de causar la estimulación del sistema inmune

y, en consecuencia, generar una concentración elevada de autoanticuerpos anti-HSP.

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Estos autoanticuerpos se han asociado a metástasis tumorales en pacientes con cáncer de

mama, y, en consecuencia, su detección podría tener utilidad en el pronóstico y

diagnóstico. De esta manera, L. Shi et al. inmovilizaron siete proteínas pertenecientes a

la familia de proteínas de choque térmico (HSPB1, HSPD1, HSP70, HSP90, HSPA5,

HSP90B1) y una oncoproteína, P53, a seis superficies químicas diferentes. Se emplearon

dos superficies químicas (COOH y quitosano) para detectar autoanticuerpos antigénicos

antitumorales en 26 muestras de suero pertenecientes a donantes sanos y 50 muestras a

pacientes con cáncer de mama. La detección de un único autoanticuerpo no permitió

discriminar significativamente los sueros pertenecientes a las pacientes con cáncer de

mama de los sueros de los donantes sanos, pero la combinación de los siete

autoanticuerpos (autoanticuerpos contra HSPB1, HSPD1, HSP70, HSP90, HSPA5,

HSP90B1 y P53) aumentó la especificidad y la sensibilidad de la prueba (con una

especificidad del 100% y una sensibilidad del 86%). Este estudio ha demostrado que los

microarrays de proteínas personalizados pueden ser herramientas efectivas para la

detección rápida de miles de biomarcadores paralelamente ofreciendo un alto

rendimiento. Por supuesto, este rendimiento del microarray de proteínas estaría

influenciado por muchos parámetros, como el tampón de detección que se use, las

características químicas de la superficie utilizada, o la concentración de proteínas, lo que

requiere validar exhaustivamente los procedimientos; además, se necesitarán cohortes

más grandes de pacientes con cáncer de mama y de donantes sanos para validar su utilidad

a gran escala.

El enfoque proporcionado por la inmuno-proteómica también apoya el uso de

autoanticuerpos. Así, la presencia de autoanticuerpos en suero contra la alfa-2-

glicoproteína-HS (AHSG) podría ser útil en el diagnóstico y la detección mínimamente

invasiva del cáncer de mama en una fase temprana. Sin embargo, la determinación de

AHSG deberá ser probada y validada aún por múltiples estudios independientes que

utilicen un tamaño muestral adecuado y un conjunto de muestras de suero de pacientes

con cáncer de mama en una fase muy temprana. Además, la verificación adicional con

muestras de pacientes con carcinoma ductal in situ y cáncer de mama en estadios III y IV

ayudaría a confirmar la especificidad de los autoanticuerpos AHSG en este subconjunto

de pacientes con cáncer de mama. Cierto es que esta investigación ha proporcionado datos

preliminares de interés sobre la ventaja potencial que supone la medición de

autoanticuerpos para detectar tumores pequeños en etapas tempranas, ya que hasta el

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momento estos autoanticuerpos solo se habían empleado en el estudio del cáncer de mama

en etapas más tardías.

Se descubrió que el estado de los tumores de mama podía relacionarse con

cambios sistémicos en los niveles de biomarcadores de proteínas séricas (SPB) y de

autoanticuerpos asociados a tumores (TAAb). Meredith C. Henderson y col. evaluaron

por primera vez la contribución independiente y combinatoria de los datos de expresión

SPB y TAAb para identificar cáncer de mama utilizando una cohorte retrospectiva de

muestras de suero obtenidas en prebiopsia de 18 participantes que no mostraban evidencia

de enfermedad mamaria (ND), de 92 participantes diagnosticadas con enfermedad mama

benigna (BBD) y de 100 participantes diagnosticados con cáncer de mama, incluido

DCIS. Es importante mencionar que al modelar los datos integrados de SPB y TAAb, la

sensibilidad clínica y la especificidad para la detección del cáncer de mama mejoraron a

81.0% y 78.8%, respectivamente. Estos datos mostraron la ventaja que supone combinar

SPB y TAAb y son un argumento claro para defender el futuro desarrollo de otros

enfoques proteómicos combinatorios similares.

M. Pla-Roca et al. introdujeron un nuevo concepto para la detección multiplex

mediante un microarray de colocalización de anticuerpos (ACM). Esta técnica se validó

determinando 32 proteínas en el suero de: (i) 11 individuos control de edad similar que

se sometieron a mamoplastias de reducción, y (ii) 15 pacientes con cáncer de mama

primario que sobreexpresa el receptor de estrógenos (ER) en el tumor primario (subtipo

ER+). Se encontró una asociación entre seis proteínas (ENG, LEP, OPN, IL-1B, TNF- α

y uPAR) y el grado de cáncer de las pacientes. Los biomarcadores candidatos

identificados coinciden con los hallazgos de estudios preliminares, en los que había

mayores concentraciones de uPAR, TNF-RII, IL-1B y ENG. Sin embargo, repetimos,

todos ellos deben verificarse en estudios de seguimiento con más pacientes y controles.

Reconocer y caracterizar las diferentes formas de una proteína (isoformas) es

fundamental para el estudio de los mecanismos moleculares y la detección temprana de

enfermedades complejas como es el cáncer de mama. De esta manera, F. Zhang et al.

mostraron que usando los péptidos específicos de isoformas eran capaces de diferenciar

el cáncer de mama de una muestra sana, siendo capaces de identificar el 92.5% de las

muestras de cáncer y el 72.5% de las muestras normales en un conjunto independiente de

40 muestras normales y 40 muestras de cáncer de mama. Este trabajo demuestra que el

uso de péptidos obtenidos por mecanismos de splicing alternativo puede proporcionar

biomarcadores de interés para el estudio del cáncer de mama.

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En un estudio desarrollado por D.L. Wang et al. se utilizó una tecnología

proteómica funcional para evaluar la actividad de proteasas y enzimas metabólicas

(hexoquinasas) a partir de una mezcla de proteínas séricas que se resolvía mediante una

primera separación en un gel bidimensional (2D) modificado y posterior transferencia a

platos de elución de proteínas (PEP) en los que realizar dicha determinación de la

actividad enzimática. Por primera vez, se encontraron diferencias sustanciales entre el

suero de pacientes con cáncer de mama y el suero normal en estas familias de enzimas,

lo que las señalaba como potenciales implicados en el desarrollo del cáncer y el proceso

de metástasis. Esta plataforma de trabajo proporciona excelentes biomarcadores

candidatos para el diagnóstico de cáncer de mama y para el desarrollo de fármacos.

El cáncer de mama es una enfermedad heterogénea con una amplia variedad de

características moleculares y clínicas, así como una variabilidad en la progresión clínica.

Para la elección del tratamiento adecuado, las pacientes con cáncer de mama se clasifican

en distintos subtipos empleando criterios clínico-patológicos que se basan en los niveles

de expresión del oncogén del receptor 2 del factor de crecimiento epidérmico humano

(HER2), la clasificación inmunohistoquímica del receptor de estrógeno (RE) y el receptor

de progesterona (RP), y el índice Ki-67. La clasificación del cáncer de mama en los

distintos subtipos permite un abordaje más personalizado de los tratamientos médicos,

con resultados favorables. Sin embargo, a pesar de esto, casi el 10-15% de estos pacientes

todavía experimentan recidivas locales o distantes en los primeros 5 años tras el

diagnóstico.

Las nuevas herramientas “ómicas” permiten la identificación de nuevos

biomarcadores que respalden el diagnóstico basado en patrones histopatológicos, lo que

se traduce en una mejora en la clasificación del cáncer de mama.

Con esta finalidad se comenzaron a aplicar los nanomateriales en el campo de la

proteómica, dando lugar a una nueva área de investigación denominada nanoproteómica.

La nanoproteómica se basa en que la dispersión de un nanomaterial en un fluido

fisiológico da como resultado la formación de una capa de proteinas denominada

"corona". Esta corona de proteínas varía en función de las características del medio

biológico, las propiedades físicas (tamaño, forma, curvatura) y químicas (composición,

carga superficial/química, hidrofobicidad/hidrofilicidad) del nanomaterial y el tiempo de

incubación.

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Los biomarcadores asociados a un determinada enfermedad suponen menos del 1%

de las proteínas presentes en el suero sanguíneo. Con la formación de la corona de

proteínas, los nanomateriales actúan como materiales adsorbentes con los que se lleva a

cabo el enriquecimiento de los péptidos/proteínas de baja abundancia presentes en el

suero sanguíneo. El análisis de estas proteínas ancladas a la superficie de los

nanomateriales mediante técnicas de espectrometría de masas permitirá la identificación

de nuevos biomarcadores asociados con una determinada enfermedad, como el cáncer de

mama. Así, mediante este tipo de análisis, se podrán detectar cambios en la concentración

de proteínas en una fase temprana de una enfermedad, tras cualquier tratamiento

(quimioterapia, inmunoterapia) o una intervención quirúrgica. Por lo tanto, la

caracterización de la corona de proteínas que se forma alrededor de los nanomateriales

ofrece distintas ventajas en relación con los análisis proteómicos convencionales, y es

más eficaz a la hora de llevar a cabo la identificación de nuevas dianas moleculares.

El objetivo principal de la presente Tesis Doctoral se centra en descubrir nuevos

biomarcadores proteicos presentes en el suero sanguíneo asociados con los diferentes

subtipos de cáncer de mama empleando una nueva herramienta nanoproteómica. Los

objetivos específicos son:

1. Encontrar las condiciones óptimas de pH, temperatura, relación

[proteína]/[NPs] y tiempo de incubación, para la formación de la corona de proteínas

alrededor de las AuNPs, AgNPs, FeNPs y PtNPs tras su interacción con el suero

sanguíneo.

2. Llevar a cabo un análisis cualitativo y comparar la funcionalidad de las

proteínas presentes en el suero sanguíneo adsorbidas en la superficie de diferentes

nanomateriales funcionalizados con citrato, concretamente AuNPs (10.02 ± 0.91 nm),

AgNPs (9.73 ± 1.70 nm) y PtNPs (2.40 ± 0.30 nm).

3. Identificar nuevos biomarcadores proteicos presentes en el suero

sanguíneo relacionados con el cáncer de mama triple negativo a través del análisis

cualitativo y cuantitativo de la corona de proteínas que se forma alrededor de AuNPs

(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) y MNPs (9.30 ± 0.67 nm).

4. Identificar nuevos biomarcadores proteicos presentes en el suero

sanguíneo relacionados con los distintos subtipos de cáncer de mama (luminal A, luminal

B HER2 negativo, luminal B HER2 positivo, HER2 positivo y triple negativo) a través

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del análisis cualitativo y cuantitativo de la corona de proteínas que se forma alrededor de

AuNPs (12.96 ± 0.72 nm).

Las conclusiones principales de esta Tesis Doctoral son:

1. En todos los casos, tras la interacción de AuNPs (10.02 ± 0.91 nm y 12.96 ±

0.72 nm), AgNPs (9.73 ± 1.70 nm), PtNPs (2.40 ± 0.30 nm) y MNPs (9.30 ± 0.67 nm)

con suero sanguíneo, tiene lugar la formación de una corona de proteínas alrededor de la

superficie de los nanomaterials.

2. La formación de la corona de proteínas depende de la composición del

nanomaterial y su tamaño (observándose que las nanopartículas más pequeñas como las

PtNPs tienen una capacidad de adsorción inferior que las de mayor tamaño como las

AuNPs), la relación [proteína]/[NPs], el pH de la muestra y el tiempo de incubación.

3. Se identificaron un total de 215, 215 y 198 proteínas en la corona de proteínas

de AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) y PtNPs (2.40 ± 0.30 nm),

respectivamente, tras el ajuste de las siguientes condiciones óptimas: valor de pH de 5.8

con un tampon de citrato/ácido cítrico, tiempo de incubación igual a 30 minutos, y una

relación [proteína]/[NPs] de 10,7. Del total de proteínas identificadas, 170 son comunes

en los tres tipos de NPs y 52 son proteínas específicas de cada tipo (21 proteínas de las

AuNPs, 17 proteínas de las AgNPs y 14 proteínas de las PtNPs).

4. Del total de proteínas identificadas, la mayoría (un total de 66) participan en la

respuesta immune. Además, tienen otra funcionalidad como la enzimática, estructural,

transportadora, inflamatoria, transducción de la señal y propiedades

antibióticas/antibacterianas.

5. Las proteínas específicas identificadas en la superficie de las AuNPs tienen una

función estructural y están implicadas en la transdución de señal, son proteínas con

propiedades antibióticas/antibacterianas en el caso de las AgNPs, y son proteínas

implicadas en procesos inflamatorios en el caso de las PtNPs.

6. Las condiciones óptimas para la formación de la corona de proteínas alrededor

de las MNPs (9.30 ± 0.67 nm) son una depleción con DTT fresco en agua milli-Q en una

concentración 500 mM durante 60 minutos a temperatura ambiente, una relación

[proteína]/[MNPs] 1:2 y un valor de pH de 5.5 tanto en el paso de incubación como en el

paso final de lavado.

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7. La combinación de AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) y MNPs

(9.30 ± 0.67 nm) con la espectrometría de masas permitió la identificación de siete

biomarcadores potenciales para el diagnóstico y la evaluación de la progresión del cancer

de mama triple negative: GRF-type zinc finger domain-containing protein 1 (proteína

ZGRF1), metaloproteinasa de matriz 9 (MMP9), lebercilina, immunoglobulina lambda

variable 3-27 (LV327), LINE-1 type transposase domain-containing protein 1 (proteína

LITD1), structural maintenance of chromosomes protein 6 (proteina SMC6) y short

coiled-coil protein (proteína SCOC).

8. La combinación de AuNPs (12.96 ± 0.72 nm) con la técnica de espectrometría

de masas Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS)

permitió analizar cuantitativamente las alteraciones proteómicas presents en el suero

sanguíneo en los diferentes subtipos de cancer de mama (luminal A, luminal B HER2

negativo, luminal B HER2 positivo, HER2 positivo y triple negativo).

9. El análisis cuantitativo de la composición de la corona de proteínas que se forma

alrededor de las AuNPs tras la incubación con las muestras de suero sanguíneo

pertenecientes a pacientes con los distintos subtipos de cancer de mama permitió

identificar 75 potenciales biomarcadores específicos: 8, 27, 2, 28 y 10 proteínas

específicas asociadas a los subtipos LA, LB-, LB+, HER2+ y TNBC, respectivamente.

10. El análisis cuantitativo de la composición de la corona de proteínas que se

forma alrededor de las AuNPs tras la incubación con las muestras de suero sanguíneo

pertenecientes a pacientes con los distintos subtipos de cancer de mama revelaron una

alteración en los niveles de las proteínas de coagulación sanguínea en el caso de los

pacientes en los que hay una sobreexpresion de HER2. Estas proteínas biomarcadoras

están implicadas en la progresión del tumor de mama, incluida la transformación celular,

la proliferación, la supervivencia de las células tumorales y la angiogénesis.

ANNEX E

List of Publications

Contents lists available at ScienceDirect

Colloids and Surfaces B: Biointerfaces

journal homepage: www.elsevier.com/locate/colsurfb

Proteomic analysis of the bio-corona formed on the surface of (Au, Ag, Pt)-nanoparticles in human serum

María del Pilar Chantada-Vázqueza, Antonio Castro Lópezb, Susana B. Bravoc,Sergio Vázquez-Estévezd, Benigno Acea-Nebrile, Cristina Núñeza,⁎

a Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002, Lugo, Spainb Breast Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002, Lugo, Spainc Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), 15706, Santiago de Compostela,SpaindOncology Division, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002, Lugo, Spaine Department of Surgery, Breast Unit, Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spain

A R T I C L E I N F O

Keywords:Gold nanoparticles (AuNPs)Silver nanoparticles (AgNPs)Platinum nanoparticles (PtNPs)Human serumProtein coronaPrognostic biomarker

A B S T R A C T

Adsorption of biomolecules onto nanoparticles surface in biological samples led to the formation of a bio-corona,it could modified the “identity” of nanoparticles, contributing to the determination of their toxicity and bio-compatibility.

Gel electrophoresis in combination with liquid chromatography-tandem mass spectrometry (LC–MS/MS) wasemployed to qualitatively analyze and identify the human serum proteins adsorbed on the surface of threedifferent nanomaterials stabilized with citrate: 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nmsilver nanoparticles (AgNPs) and, 2.40 ± 0.30 nm platinum nanoparticles (PtNPs). An exhaustive analysis andclassification of all identified proteins related with their function were also developed.

1. Introduction

In recent years, noble metal NPs have gained great interest becausetheir diverse material properties hold promising potential in advancingcurrent diagnostic and therapeutic technologies. Such properties arefundamentally dependent on their size, shape, and composition [1].

Particularly, gold nanoparticles (AuNPs) have a wide range of ap-plications in the biological and medical fields, including thermo-therapy, biosensors, and molecular imaging [2]. Silver nanoparticles(AgNPs) are known to be potent antimicrobial agents with a broadantimicrobial spectrum and high efficacy against bacteria [3], and theyhave been widely used in consumer products [4]. Platinum nano-particles (PtNPs) have been already proposed as efficient and selectiveradical scavengers for therapies of oxidative stress diseases [5,6].However, their clinical potential has been slowed down by some tox-icological concerns. Nevertheless, the use of PtNPs as additives inconsumer products and cosmetics has been already approved [7]. Thissuggests that PtNPs will likely register an increase shortly in a widerange of applications, including healthcare devices, diagnostics, andcosmetics [8]. Hence, the possibility of living organisms being exposedto Ag/Au/Pt-NPs either directly or indirectly is greatly increasing,

which has resulted in safety concerns [9].NPs can enter the human body in different ways, among which the

main exposure routes include inhalation, oral administration, in-travenous injection, and dermal exposure [10]. Once NPs enter thebody, they contact various biological molecules such as proteins, lipid,polysaccharides, and nucleic acids [11]. Particularly, when NPs areexposed to biological fluids (as serum/plasma), proteins and otherbiomolecules are easily adsorbed onto the surface to form a protein‘corona’ around NPs, which reduces the surface free energy of NPs [12].

Protein corona is divided into “soft corona” and “hard corona”.While soft corona is formed by lower affinity proteins reversibly boundto NPs (which can be further exchangeable), hard corona containshigher affinity proteins on the NP surface that may irreversibly bind toNPs. Protein corona is a dynamic layer in which amount of protein andthe arrangement are changeable according to the conditions of biolo-gical and physicochemical interaction [13].

During the processes, chemical or physical adsorption takes part inthe formation of protein corona. Coordination, hydrogen bonding, vander Waals forces, electrostatic and hydrophobic interactions, sterichindrance, etc. play important roles in driving the binding of proteins toNPs [14,15].

https://doi.org/10.1016/j.colsurfb.2019.01.056Received 11 September 2018; Received in revised form 15 January 2019; Accepted 26 January 2019

⁎ Corresponding author.E-mail address: cristina.nunez.gonzalez@sergas.es (C. Núñez).

Colloids and Surfaces B: Biointerfaces 177 (2019) 141–148

Available online 29 January 20190927-7765/ © 2019 Elsevier B.V. All rights reserved.

T

Furthermore, protein corona patterns mainly depend on the physi-cochemical properties of NPs (nanomaterial, size, charge, surfacefunctional groups, shape) and exposing environments including im-mersed media components, temperature, pH, dynamic shear stress, andinteraction (or exposing) time [16–19]. Proteins with large quantitiesare first bound to NP surface, and then gradually replaced by higheraffinity proteins (Vroman effect) [20].

When the protein is bound to NPs to form protein corona, proteinsmay reorganize their structures to adapt to surrounding environmentsand NPs surface. The secondary and/or tertiary protein structure ismodified, and this event is known as “conformational changes” [21].Furthermore, the formation of a corona may eliminate the physiologicalfunctions of proteins, which leads to the loss of original targetingcapabilities [22], induces various cellular responses including in-flammatory responses, increased lysosomal permeability, activatedcaspase-related pathways, or even apoptosis [23,24].

Understanding how the different properties of nanoparticles affectthe composition of protein coronas is therefore important. The serumand plasma protein corona compositions of many AuNPs with differentcoatings and different sizes have been identified [25–27]. However, thehigh-throughput protein analyses of the AgNPs’ coronas have beenlimited [26,28–30]. To better understand the relationship betweencorona composition and the nanoparticles’ properties, a recent studywas developed. This analysis showed specific binding patterns for theblood plasma-derived corona composition of AgNPs and AuNPs withthree surface coatings (polyethyleneimine (BPEI), citrate (CIT), andpolyvinylpyrrolidone (PVP)) [31]. To the best of our knowledge, theanalysis of the composition of the serum protein corona formed aroundPtNPs was not carried out until the moment.

On the other hand, a prognostic factor is defined as any parameter,evaluated at diagnosis (or surgery), which is associated with treatmentoutcome (local control, disease-free interval, survival) and may predictpatient outcome independent of treatment. Furthermore, prognosticfactors (clinical or biological) may be defined in any disease stage orsetting.

Blood-based biomarkers can be useful as pre-treatment prognosticmarkers, as they can reflect variations in the tumor microenvironmentand host immune response and can complement biopsy-based bio-markers that evaluate tumor cells directly [32]. As whole blood pro-vides a dynamic representation of physiological and pathological state,serum or plasma represents the most broadly studied biological matrixfor cancer biomarkers. Therefore, analysis of the plasma or serumproteome could be important to achieve accurate diagnosis or prog-nosis.

A great number of proteomics-based studies of plasma and serumhave reported differential peptide/protein ion peaks, either as identi-fied proteins or on the basis of their mass/charge (m/z) values, fordifferent cancer diagnosis or prognosis as, for example, breast cancer[33,34], ovarian cancer [35], head and neck cancer [36], bladdercancer [37], lung cancer [38]; or other diseases as amyotrophic lateralsclerosis [39], ST-segment elevation myocardial infarction [40], severesepsis [41].

Taking into consideration all the aforementioned arguments, withour experimental work of the serum protein corona formed aroundAuNPs, AgNPs and PtNPs, we are providing basic information for tox-icological and immunological risk assessment, as well as informationabout the properties of different nanomaterials for the development ofnovel sensors with potential medical applications.

2. Experimental

2.1. Chemicals and reagents

All reagents and solvents used were HPLC-grade or higher. Sodiumcitrate tribasic dihydrate, tannic acid, chloroplatinic acid (H2PtCl6),sodium borohydride (NaBH4), trypsin, trifluoroacetic acid, DL-

Dithiothreitol(DTT), Iodoacetamide (IAA), acrylamide/bis-acrylamide30% solution (37.5:1), Glycerol 86–88%, Tris-base, Coomassie BrilliantBlue R250 (CBB), sodium carbonate, and the Sigma Marker wide range6.5–200 KDa were purchased from Sigma-Aldrich (St. Louis, MO, USA).Sodium dodecylsulfate (SDS) and formaldehyde were purchased fromPanreac (Barcelona, Spain). β-mercaptoethanol was purchased fromMerck (Hohen-brunn,Germany) and bromophenol-blue was purchasedfrom Riedel-de Haen (Seelze,Germany). Hydrogen tetrachloroaurate(III) hydrate (99.9%-Au) (49%Au) at 10%w/v was purchased fromStrem Chemicals (Newburyport, MA, USA). Ammoniumbicarbonate(AMBIC) and formic acid were purchased from Fluka (Steinheim,Germany).

2.2. Instrumentation

Microscopic characterizations of AuNPs, AgNPs and PtNPs wereperformed by transmission electron microscopy (TEM) using a Jeol JEM1011 microscope. Samples for TEM were prepared by pipetting a dropof the colloidal dispersion onto an ultrathin carbon-coated copper gridand allowing the solvent to evaporate. Power Pac Basic power supplyfrom Bio-Rad (CA, USA) was used for sodium dodecyl sulfate poly-acrylamide gel electrophoresis (SDS-PAGE) protein separation. Proteinquantification was accomplished by measuring the absorbance at280 nm with the use of a Qubit™ 4 Quantitation Starter Kit fromThermo Fisher Scientific. Gel image acquisition was carried out with aUVP PhotoDoc-It™ Imaging System from Analytik Jena.

2.3. Synthesis of inorganic nanoparticles

Gold nanoparticles (AuNPs), silver nanoparticles (AgNPs) and pla-tinum nanoparticles (PtNPs) were synthesized by the citrate reductionmethod in aqueous solution by the method reported by R. López-Cortéset al. [42], V. Puntes et al. [43] and W. Chen et al. [44], respectively(see Supplemental Material).

2.4. Serum samples

Venous blood sample was obtained from five disease-free in-dividuals with the use of VACUETTE® Serum Clot Activator Tubes(10mL). The collected blood samples were allowed to clot for 15min,and then centrifuged for 5min at 4 °C and 1800×g. Sera were trans-ferred into clean plastic tubes (1 mL) and immediately frozen at −80 °Cat Research Unit, Hospital Universitario Lucus Augusti (HULA).

2.5. Depletion of multiple high abundant proteins

Serum aliquots (×3) were filtered with Miller-GP® Filter Unit(Millipore) with a size of 0.22 μm. Each aliquot of human serum (30 μL)was depleted with dithiothreitol (DTT) according to the protocol de-scribed by Warder el al. [45,46]. Fresh DTT 500mM (3.3 μL) was mixedwith 30 μL of human serum and vortex briefly. Samples were then in-cubated until a viscous white precipitate persisted (60min), followedby centrifugation at 18,840×g for 20min. Supernatants were trans-ferred to a clean tube prior to protein alkylation and nanoparticles(NPs) fractionation.

2.6. NPs protein alkylation and fractionation

After protein depletion, the reduced SH-groups were alkylated withiodoacetic acid (IAA) for 45min at room temperature and protectedfrom light. After protein reduction and alkylation, 75 μL of AuNPs(10.02 ± 0.91 nm), 75 μL of AgNPs (9.73 ± 1.70 nm) and 75 μL ofPtNPs (2.40 ± 0.30 nm) were added to each different serum aliquots(×3) belonging to the five disease-free individuals (9 aliquots per in-dividual, 3 with each nanoparticle type), followed by the addition of40 μL of citrate/citric acid buffer to a final pH of 5.8, as described by

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López-Cortés et al [42]. Then, all NPs-serum solutions were incubatedat 37 °C with shaking in a thermostatic bath during 30min.

Pellets were harvested by centrifugation at 18,840×g (AuNPs andAgNPs) and 24,610×g (PtNPs) during 30min. In all cases, pelletscontaining proteins bound to nanoparticles were washed three timeswith 25 μL citrate/citric acid buffer and harvested again by cen-trifugation at 18,840×g (AuNPs and AgNPs) and 24,610×g (PtNPs)during 30min to remove unbound proteins.

All pellets were reconstituted in 10 μL of buffer with 0.2 MTris−HCl, 2% w/v SDS and 20% v/v glycerol. This 10 μL was mixedwith 4 μL of SDS-PAGE loading buffer (10% w/v SDS, Tris-Base 40mM,pH 6.8, 50% v/v glycerol, 0.1% v/v bromophenol blue, 10% v/v β-mercaptoethanol) in a final volume of 20 μL. Then, all samples weredenatured by heating at 100 °C for 5min and loaded into a 10% acry-lamide/bis-acrylamide, stacking gel /12.5% acrylamide/bis-acrylamiderunning gel, of 1 mm thickness, and separated at 180 V (constant vol-tage) for 120min. After electrophoresis, the gel was fixed for 30minwith 40% (v/v) ethanol and 10% (v/v) acetic acid and then stainedovernight with Colloidal Coomassie Blue [47]. Gels were rinsed withdistilled water and a 0.5 M sodium chloride solution until a clearbackground was observed. Gel imaging was carried out with a with aUVP PhotoDoc-It™ Imaging System.

2.7. In-gel protein digestion

Protein bands were excised manually and transferred to 2.5-mL Lo-Bind tubes, and then washed twice with water and with 50% (v/v)acetonitrile/ 25mM ammonium bicarbonate (ambic) until the bluecolor disappeared.

Prior to trypsin digestion, gel spots were washed with 25mM ambicand dehydrated with acetonitrile. Then, 30 μL of trypsin (20 ng μL−1 in12.5 mM ambic/2% (v/v) acetonitrile) was added to the gel spots andincubated for 60min at 0 °C.

After this time, gel spots were inspected, trypsin solution not ab-sorbed into the gel was removed, and the gels were covered with 100 μLof 12.5mM ambic. Samples were incubated for 12 h at 37 °C. Then50 μL of 5% (v/v) formic acid was added and the supernatant wastransferred to a new Lo-Bind tube and the peptides were further ex-tracted from the gel twice with 50% (v/v) acetonitrile/0.1% (v/v) tri-fluoroacetic acid (TFA) (×3) and acetonitrile (ACN) (x1). Samples weredried-down and stored at −20 °C [48].

2.8. Protein identification by mass spectrometry (LC–MS/MS) and dataanalysis

Digested peptides of each sample were separated using ReversePhase Chromatography. Gradient was developed using a micro liquidchromatography system (Eksigent Technologies nanoLC 400, SCIEX)coupled to high speed Triple TOF 6600 mass spectrometer (SCIEX) witha micro flow source. The analytical column used was a silica-basedreversed phase column YMC-TRIART C18 150× 0.30mm, 3mm par-ticle size and 120 Å pore size (YMC Technologies, Teknokroma). Thetrap column was a YMC-TRIART C18 (YMC Technologies, Teknokromawith a 3mm particle size and 120 Å pore size, switched on-line with theanalytical column. The loading pump delivered a solution of 0.1%formic acid in water at 10 μL/min. The micro-pump provided a flow-rate of 5 μL/min and was operated under gradient elution conditions,using 0.1% formic acid in water as mobile phase A, and 0.1% formicacid in acetonitrile as mobile phase B. Peptides were separated using a25min gradient ranging from 2% to 90% mobile phase B (mobile phaseA: 2% acetonitrile, 0.1% formic acid; mobile phase B: 100% acetoni-trile, 0.1% formic acid). Injection volume was 4 μL.

Data acquisition was carried out in a TripleTOF 6600 System(SCIEX, Foster City, CA) using a Data dependent workflow. Source andinterface conditions were as follows: ionspray voltage floating (ISVF)5500 V, curtain gas (CUR) 25, collision energy (CE) 10 and ion source

gas 1 (GS1) 25. Instrument was operated with Analyst TF 1.7.1 software(SCIEX, USA). Switching criteria was set to ions greater than mass tocharge ratio (m/z) 350 and smaller than m/z 1400 with charge state of2–5, mass tolerance 250 ppm and an abundance threshold of more than200 counts (cps). Former target ions were excluded for 15 s. Instrumentwas automatically calibrated every 4 h using as external calibranttryptic peptides from PepcalMix (Sciex).

2.9. Data analysis

After MS/MS analysis, data files were processed usingProteinPilotTM 5.0.1 software from Sciex which uses the algorithmParagonTM for database search and ProgroupTM for data grouping.Data were searched using a Human specific Uniprot database. Falsediscovery rate was performed using a non-lineal fitting method dis-playing only those results that reported a 1% Global false discovery rateor better [49,50].

3. Results and discussion

3.1. Serum fraction preparation and protein corona purification

Following the synthetic methods described by R. López-Cortés [42],V. Puntes et al. [43] and W. Chen et al. [44], AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and PtNPs(2.40 ± 0.30 nm) were successfully obtained and characterized (seeFigs. 1_SM to 3_SM).

High abundance serum proteins were first depleted using DTT andseparated from low abundance serum proteins [42,43]. Low abundanceproteins were further processed as described in the experimental sec-tion. Afterward, AuNPs (10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm)and PtNPs (2.40 ± 0.30 nm) were mixed with serum aliquots (×3)from five different individuals (9 protein samples per individual: 3treated with AuNPs, 3 with AgNPs and 3 with PtNPs).

It is well known that some variables influence the protein-captureefficiency, namely (i) the NPs/protein ratio, (ii) sample pH and (iii)incubation time [42]. Particularly, the pH value is an important para-meter because influences the charge state of proteins, and a maximumbinding capacity between NPs and proteins takes place at the pH near tothe protein pI [51,52]. The optimum conditions found in preliminaryexperiments with AuNPs [42], were used in this case to reduce thecomplexity and large dynamic range of the human serum. To this aim,the pH was adjusted to 5.8 with citrate/citric acid buffer and threeincubation times (30, 60 and 90min) were tested. After the incubation,the pellets and the supernatants were separated via centrifugation andthe respective protein content was assessed through the use of 1D gelelectrophoresis. Similar results to that obtained after 30min were foundwith incubation times of 60 and 90min (Figs. 1_SM to 3_SM), sug-gesting that 30min was enough to achieve a good separation efficiencyand it was selected as the optimum incubation time.

To investigate the influence of the amount of each type of NPsduring the separation process, three volumes of each type of nano-particles were explored: 75 μL, 100 μL and 125 μL, to get the followingprotein/NPs ratios: 10.7, 8.6 and 6.5. Fig. 2 and Fig. 4_SM to Fig. 6_SMshow the result of this set of experiments, which suggest good separa-tion efficiency, even for the lowest amounts of each NPs tested (75 μL).

As it is shown in Fig. 2, for an incubation time of 30min, differencesin the protein corona formed around the three different NPs werevisible after Coomassie staining. When the gel profiles of each fraction(AuNPs-protein corona, AgNPs-protein corona, PtNPs-protein corona)were compared, it was easily noted that there is a different in the in-tensity of the bands. However, no conclusion can be drawn unless theproteins were identified. However, the supernatants were very similarin all cases, independently of the type of nanoparticle employed and thequantity of each one (Fig. 7_SM).

After that, gel bands corresponding to the fractions AuNPs-protein

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corona, AgNPs-protein corona, PtNPs-protein corona formed after theaddition of 75 μL, were excised and submitted to the sample treatmentdescribed in the experimental section. The resulting pools of peptideswere then analyzed by mass spectrometry (LC–MS/MS) for proteinidentification.

A similar number of proteins were identified from the proteincorona formed around AuNPs (10.02 ± 0.91 nm), AgNPs(9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm) after their incubationwith serum aliquots (×3) from five different individuals (9 proteinsamples per individual: 3 treated with AuNPs, 3 with AgNPs and 3 withPtNPs) (see Table 1_SM). Importantly, 215, 215 and 198 were com-monly found in all cases in the surface of AuNPs (10.02 ± 0.91 nm),AgNPs (9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm) (see Table 2_SM). From them, 170 proteins were commonly detected in the proteincorona of all three different types of NPs (see Fig. 1). One of this pro-teins was serum albumin, the most abundant protein in the blood.

However, 52 different proteins were found on the three different NPssurface (see Fig. 1): 21 different proteins on the 10.02 ± 0.91 nmAuNPs (see Table 1), 17 on the 9.73 ± 1.70 nm AgNPs (see Table 2)and 14 individual proteins on the 2.40 ± 0.30 nm PtNPs (see Table 3).

Then, an analysis of the protein corona formed around the threenanoparticles (AuNPs, AgNPs and PtNPs) will be carried out in relationto the different functionality of the proteins identified.

3.2. Proteins implicated in the immnune response

A total of 66 proteins implicated in the immune response (55 ofthem are immunoglobulins) were identified from the protein coronaformed around the AuNPs, AgNPs, and PtNPs after their incubationwith serum of all individuals. From them, 48 common proteins werefound on the surface of the three different nanoparticles (seeTable 1_SM). Particularly, two of these common immune-related

Fig. 1. Number of identified proteins found in the protein corona of 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and2.40 ± 0.30 nm platinum nanoparticles (PtNPs); and Venn diagram showing the common proteins in the three different nanoparticles surfaces.

Fig. 2. 1D-SDS-PAGE of protein coronas formed around10.02 ± 0.91 nm gold nanoparticles (AuNPs),9.73 ± 1.70 nm silver nanoparticles (AgNPs) and2.40 ± 0.30 nm platinum nanoparticles (PtNPs) in humanserum (incubation time: 30min; volumes of each nano-particles solution: 75 μL, 100 μL and 125 μL, to get the fol-lowing protein/NPs ratios: 10.7, 8.6 and 6.5, respectively). Onthe left, it marks the lane with Mw protein standards withmolecular weights in kDa.

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proteins were complement C3 (187.1 kDa) and complement factor B(85.5 kDa). The according complement system is involved in an effec-tive strategy called opsonization for labeling pathogens for removal byphagocytic cells from the circulation. To minimize opsonization ofAuNP, AgNPs and PtNPs in pharmaceutical applications (since thedrug-loaded NP is cleared by the organism and cannot release its drugat the desired site) binding of the NP with complement factors shouldbe avoided [49].

An example of a protein implicated in the immune response thatwas only found on the surface of AgNPs was fibrinogen alpha chain(94.9 kDa), involved in blood coagulation but also opsonizes surfaces offoreign bodies for immune cell recognition. Furthermore, AuNPs na-noparticles exhibited selective adsorption towards immunoglobulinheavy constant gamma 1 (36.1 kDa) (IGHG1), similar to that reportedpreviously in by zeolites in plasma [53].

3.3. Proteins with a transport function

A total of 29 proteins with a transporter function (7 of them apo-lipoproteins) were identified from the protein corona formed aroundthe AuNPs, AgNPs, and PtNPs. From them, 25 common proteins werefound on the surface of the three different nanoparticles (seeTable 2_SM). Among them, the most interesting protein regardingtoxicity and drug delivery potential is the ApoE. This transport proteinis known to mediate transcytosis across biological barriers, e.g., theblood-brain barrier [54,55].

AuNPs and AgNPs exhibited selective adsorption towards two pro-teins that belong to the ATP-binding cassette (ABC) transporter super-family of integral membrane proteins: ATP-binding cassette sub-familyB member 5 (ABCB5) (138.6 kDa) and ATP-binding cassette sub-familyF member 1 (ABCF1) (95.9 kDa), respectively. Both proteins participatein the ATP-dependent transmembrane transport of structurally diverse

Table 1Selection of identified single-detected corona proteins bound to the 10.02 ± 0.91 nm AuNPs after 30min incubation and subsequent washing. The accessionnumber, gene name, species (Human), molecular weight (kDa) and protein function were reported.

Protein Name UniProt Name Entry Name Gene Mass(kDa)

Function

ATP-binding cassette sub-family B member 5 ABCB5_HUMAN Q2M3G0 ABCB5 138.6 Transporter activityBasement membrane-specific heparan sulfate proteoglycan

core proteinPGBM_HUMAN P98160 HSPG2 468.8 Structural

Cadherin-5 CADH5_HUMAN P33151 CDH5 87.5 Controls the cohesion and organization of theintercellular junctions

Centlein CNTLN_HUMAN Q9NXG0 CNTLN 161.6 StructuralComplement component C8 alpha chain CO8A_HUMAN P07357 C8A 65.2 StructuralComplement factor D CFAD_HUMAN P00746 CFD 27.1 Catalytic activityDENN domain-containing protein 5B DEN5B_HUMAN Q6ZUT9 DENND5B 145.1 Enzyme regulator activityF-box only protein 3 FBX3_HUMAN Q9UK99 FBXO3 54.6 Enzyme regulator activityFilaggrin-2 FILA2_HUMAN Q5D862 FLG2 248.1 _Glyceraldehyde-3-phosphate dehydrogenase G3P_HUMAN P04406 GAPDH 36.1 Catalytic activityHAUS augmin-like complex subunit 8 HAUS8_HUMAN Q9BT25 HAUS8 44.9 Involved in microtubule generation within the mitotic

spindleHepatocyte growth factor activator HGFA_HUMAN Q04756 HGFAC 70.7 Enzyme regulator activity (activator)Immunoglobulin heavy constant gamma 1 IGHG1_HUMAN P01857 IGHG1 36.1 Immune responseImmunoglobulin heavy variable 4-39 HV439_HUMAN P01824 IGHV4-39 13.9 Immune responseImmunoglobulin lambda-like polypeptide 5 IGLL5_HUMAN B9A064 IGLL5 23.1 Immune responseKeratin, type I cytoskeletal 15 K1C15_HUMAN P19012 KRT15 49.2 StructuralMaestro heat-like repeat-containing protein family member

2AMRO2A_HUMAN A6NES4 MROH2A 189.6 _

Protein ENL ENL_HUMAN Q03111 MLLT1 62.1 Enzyme regulator activityRegulator of G-protein signaling 20 RGS20_HUMAN O76081 RGS20 31.5 Inhibits signal transductionTestis-specific gene 10 protein TSG10_HUMAN Q9BZW7 TSGA10 81.4 Plays a role in the sperm tail fibrous sheath, a major

sperm tail structureZinc-alpha-2-glycoprotein ZA2G_HUMAN P25311 AZGP1 34.3 Stimulates lipid degradation in adipocytes

Table 2Selection of identified single-detected corona proteins bound to the 9.73 ± 1.70 nm AgNPs after 30min incubation and subsequent washing. The accession number,gene name, species (Human), molecular weight (kDa) and protein function were reported.

Protein Name UniProt Name Entry Name Gene Mass (kDa) Function

Angiotensin-converting enzyme ACE_HUMAN P12821 ACE 149.7 Catalytic activityATP-binding cassette sub-family F member 1 ABCF1_HUMAN Q8NE71 ABCF1 95.9 Transporter activityCathelicidin antimicrobial peptide CAMP_HUMAN P49913 CAMP 19.3 Antibacterial activityCMP-N-acetylneuraminate-poly-alpha-2,8-sialyltransferase SIA8D_HUMAN Q92187 ST8SIA4 41.3 Catalytic activityDedicator of cytokinesis protein 3 DOCK3_HUMAN Q8IZD9 DOCK3 233.1 Enzyme regulator activityDopamine beta-hydroxylase DOPO_HUMAN P09172 DBH 69.1 Catalyctic activityEukaryotic translation elongation factor 1 epsilon-1 MCA3_HUMAN O43324 EEF1E1 19.8 Positive modulator of ATM response to DNA damage.Fibrinogen alpha chain FIBA_HUMAN P02671 FGA 94.9 Coagulation, immune responseImmunoglobulin heavy variable 3-11 HV311_HUMAN P01762 IGHV3-11 12.9 Immune responseImmunoglobulin heavy variable 3-30-3 HVC33_HUMAN P0DP02 IGHV3-30-3 13 Immune responseImmunoglobulin kappa variable 1D-12 KVD12_HUMAN P01611 IGKV1D-12 12.6 Immune responseImmunoglobulin lambda variable 3-27 LV327_HUMAN P01718 IGLV3-27 12.2 Immune responseL-lactate dehydrogenase B chain LDHB_HUMAN P07195 LDHB 36.6 Catalytic activityMultiple inositol polyphosphate phosphatase 1 MINP1_HUMAN Q9UNW1 MINPP1 55.1 Catalytic activityNeutrophil cytosol factor 4 NCF4_HUMAN Q15080 NCF4 39.1 Component of the NADPH-oxidasePleckstrin homology domain-containing family G member 6 PKHG6_HUMAN Q3KR16 PLEKHG6 88.9 Enzyme regulator activityUnconventional myosin-If MYO1F_HUMAN O00160 MYO1F 124.8 Catalytic activity (ATPase)

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molecules ranging from sugars, small ions, and peptides to more com-plex organic molecules [56].

3.4. Proteins with a structural functionality

A total of 32 proteins with a structural functionality were identifiedfrom the protein corona formed around the AuNPs, AgNPs, and PtNPs(18 proteins were commonly found on the surface of the three differentnanoparticles) (see Table 2_SM).

AuNPs nanoparticles exhibited selective adsorption towards 7 pro-teins. Within them, basement membrane-specific heparan sulfate pro-teoglycan core protein (also known as perlecan) fragments showed tobe biomarkers of bone stromal lysis [57], renal dysfunction [58] andalso pancreatic cancer secretome [59]; and the complement componentC8 alpha chain was also identified as a potential serum biomarker inmultiple sclerosis [60].

3.5. Proteins implicated in enzymatic processes

A total of 45 proteins with catalytic activity were identified from theprotein corona formed around the AuNPs, AgNPs, and PtNPs (25commonly found on the surface of the three different nanoparticles)(see Table 1_SM).

AuNPs nanoparticles exhibited selective adsorption towards twoproteins: complement factor D (27.1 kDa) and glyceraldehyde-3-phos-phate dehydrogenase (36.1 kDa). While complement factor D (CFD; alsoknown as adipsin) regulates activation of the alternative complementpathway, which is implicated in age-related macular degeneration(AMD) pathogenesis, glyceraldehyde-3-phosphate dehydrogenase(GAPDH) (a glycolytic enzyme) can interact with proteins participatingin DNA repairs, such as APE1, PARP1, HMGB1, and HMGB2 [61].

AgNPs nanoparticles exhibited a selective adsorption towards sevenproteins, within them, lactate dehydrogenase (LDH), which was anindirect marker of hypoxia, was a potentially prognostic factor in sev-eral malignancies such as in patients with hepatocellular carcinoma[62] and advanced pancreatic [63], both receiving sorafenib.

In the case of PtNPs nanoparticles, they exhibited selective ad-sorption towards three proteins: kynurenine-oxoglutarate transaminase3 (51.4 kDa), receptor-type tyrosine-protein phosphatase eta(149.5 kDa) and sulfhydryl oxidase 1 (82.6 kDa). Particularly, it wasfound that the expression of sulfhydryl oxidase 1 was associated with ahighly invasive phenotype and correlates with a poor prognosis inLuminal B breast cancer [64].

A total of 39 proteins that regulates (activate or inhibit) the activityof different enzymes were identified from the protein corona formedaround the AuNPs, AgNPs, and PtNPs. From them, 28 common proteinswere found on the surface of the three different nanoparticles.

Four (DENN domain-containing protein 5B (145.1 kDa), F-box onlyprotein 3 (54.6 kDa), hepatocyte growth factor activator (70.7 kDa) andprotein ENL (62.1 kDa)), two (dedicator of cytokinesis protein 3(233.1 kDa) and pleckstrin homology domain-containing family Gmember 6 (88.9 kDa)) and one (protein argonaute-3 (97.4 kDa)) enzy-matic regulatory proteins were found on the protein-corona of AuNPs(Table 1), AgNPs (Table 2) and PtNPs (Table 3), respectively.

3.6. Proteins implicated in the inflammatory response

A total of 6 proteins implicated in the inflammatory response wereidentified from the protein corona formed around the AuNPs, AgNPs,and PtNPs.

Particularly, PtNPs nanoparticles exhibited selective adsorption to-wards one protein: serum amyloid A-1 protein (SAA1) (13.5 kDa), asensitive acute phase reactant primarily produced by the liver in re-sponse to acute inflammation. It was recently shown that SAA affectsproliferation, migration, and invasion of glioblastoma cell lines, whichsuggest its participation in the malignant process. Consistently, levels ofTa

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M. del Pilar Chantada-Vázquez et al. Colloids and Surfaces B: Biointerfaces 177 (2019) 141–148

146

SAA have been used as a non-invasive biomarker for the prognosis ofmany cancers and, particularly, serum amyloid A1 was found to beupregulated in human glioblastoma [65].

3.7. Proteins with antibiotic activity

Dermcidin (11.3 kDa), a protein with antimicrobial activity, wasidentified from the protein corona formed around the AuNPs, AgNPs,and PtNPs. Dermcidin (DCD), an antimicrobial peptide with a broadspectrum of activity against bacteria such as Propionibacterium acnes, isexpressed constitutively in sweat in the absence of stimulation due toinjury or inflammation. It was found that reduced DCD concentration insweat in patients with inflammatory acne may permit proliferation of P.acnes in pilosebaceous units, resulting in progression of inflammatoryacne [66].

However, only AgNPs nanoparticles exhibited selective adsorptiontowards one protein with antibacterial activity: cathelicidin anti-microbial peptide (19.3 kDa), responsible for protecting the urinarytract against invasive bacterial infection [67].

3.8. Proteins implicated in signal transduction

There were identified three proteins implicated in the signal trans-duction from the protein corona formed around the three nanosystems.While protein leucine-rich alpha-2-glycoprotein (38.2 kDa) (implicatedin the protein-protein interaction, signal transduction and cell adhesionand development) was presented in the protein corona of the threenanoparticles, the protein regulator of G-protein signaling 20(31.5 kDa) (that inhibits signal transduction) and SHC-transformingprotein 1 (62.8 kDa) were only found in the surface of AuNPs andPtNPs, respectively.

In general, in relation with the total number of proteins identifiedfrom the protein corona surfaces (see Fig. 3), the most abundant groupsare constituted by the proteins implicated in the immune response,followed by proteins with and enzymatic function, structural, trans-porter, inflammatory, signal transduction and with antibiotic/anti-bacterial properties.

However, we found exclusive proteins with structural function andimplicated in the signal transduction only in the protein corona of10.02 ± 0.91 nm AuNPs (see Table 1), with antibiotic/antibacterialproperties in 9.73 ± 1.70 nm AgNPs (see Table 2) and implicated oninflammatory processes only in 2.40 ± 0.30 nm PtNPs (see Table 3).

4. Conclusions

In this study, it was shown that the interaction of AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and PtNPs(2.40 ± 0.30 nm) with human serum withstands the formation of aprotein corona enveloping the nanoparticle, in all cases.

The formation of this protein corona depends on the composition ofthe nanoparticle (core material) and its size. In that case, it was ob-served that smaller NPs (2.40 ± 0.30 nm PtNPs) have lower proteinadsorption (198 proteins) than larger NPs (10.02 ± 0.91 nm AuNPsand 9.73 ± 1.70 nm AgNPs) (215 proteins). A total of 170 proteinswith different functionality were detected in the protein corona of allthree different types of NPs. However, 21, 14 and 14 different proteinswere found on the AuNPs (10.02 ± 0.91 nm), AgNPs(9.73 ± 1.70 nm) and PtNPs (2.40 ± 0.30 nm), respectively.

In general, the function of all identified proteins ranges from pro-teins implicated in the immune response, followed by proteins with anenzymatic function, structural, transporter, inflammatory, signaltransduction and with antibiotic/antibacterial properties, being themajority group the first one with 66 proteins identified implicated inthe immune response. However, if we observed the different proteinson the corona of the three different NPs, we found exclusive proteinswith structural function and implicated in the signal transduction onlyin the protein corona of AuNPs (10.02 ± 0.91 nm), with antibiotic/antibacterial properties in AgNPs (9.73 ± 1.70 nm) and implicated oninflammatory processes only in PtNPs (2.40 ± 0.30 nm).

This has implications for immune safety, biocompatibility and in-formation for developing novel nanomaterials with high specificity andselectivity towards proteins with an important biological function(prognostic and diagnostic protein biomarkers). However, it is im-portant to mention that the interaction of these nanoparticles withhuman serum could drive to the formation of a different protein coronaat a pH dissimilar of 5.8, for example, under physiological conditions.

Conflict of interest

The authors declare that they have no conflict of interest.

Acknowledgments

All authors acknowledge Miguel Servet I Programme (CP16/00139)from the “Instituto de Salud Carlos III” (Plan Estatal de I+D+i 2013-2016 and European Development Regional Fund) of the SpanishMinistry of Economy and Competitiveness. M. P. Chantada-Vázquezand A. Castro López contributed equally to this work.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in theonline version, at doi:https://doi.org/10.1016/j.colsurfb.2019.01.056.

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Contents lists available at ScienceDirect

Journal of Proteomics

journal homepage: www.elsevier.com/locate/jprot

Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for thediscovery of triple negative breast cancer serum protein biomarkersMaría del Pilar Chantada-Vázqueza, Antonio Castro Lópezb, María García Vencec,Sergio Vázquez-Estévezd, Benigno Acea-Nebrile, David G. Calatayudf, Teresa Jardielf,Susana B. Bravoc,⁎, Cristina Núñeza,⁎

a Research Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spainb Breast Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spainc Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), 15706 Santiago de Compostela,Spaind Oncology Division, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo, Spaine Department of Surgery, Breast Unit, Complexo Hospitalario Universitario A Coruña (CHUAC), SERGAS, A Coruña, Spainf Department of Electroceramics, Instituto de Cerámica y Vidrio-CSIC, Kelsen 5, Campus de Cantoblanco, 28049 Madrid, Spain

A R T I C L E I N F O

Keywords:Triple negative breast cancer (TNBC)ProteomicsNanoparticlesBiomarkersSWATH-MSMass spectrometry (MS)

A B S T R A C T

Nowadays, there are no targeted therapeutic modalities for triple negative breast cancer (TNBC). This disease isassociated with poor prognosis and worst clinical outcome because of the aggressive nature of the tumor, de-layed diagnosis, and non-specific symptoms in the early stages. Therefore, identification of novel specific TNBCserum biomarkers for screening and therapeutic purposes remains an urgent clinical requirement.

New user-friendly and cheap methods for biomarker identification are needed, and nanotechnology offersnew opportunities. When dispersed in blood, nanoparticles (NPs) are covered by a protein shell termed “proteincorona” (PC). While alterations in protein patterns are challeging to detect by conventional blood analyses, PCacts as a “nano-concentrator” of serum proteins with affinity for NPs’ surface. So, the characterization of PCcould allow the detection of otherwise undetectable changes in protein concentration at an early stage of thedisease or after chemotherapy or surgery.

To explore this research idea, serum samples from 8 triple negative breast cancer (TNBC) patients and 8patients without malignancy were allowed to interact with gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silvernanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm). Here, in orderto identify biomarker candidates in serum of TNBC patients, these nanomaterials were combined with elec-trophoretic separation (SDS-PAGE) to performed qualitative and quantitative comparisons of the serum pro-teomes of TNBC patients (n = 8) and healthy controls (n = 8) by liquid chromatography tandem-mass spec-trometry (LC-MS/MS) analysis. The results were validated through a sequential window acquisition of alltheoretical mass spectra (SWATH) analysis, performed in total serum samples (patients and controls) using thisapproach as a multiple reaction monitoring (MRM) analysis.Significance: It is well known that several proteins presented in human serum are important biomarkers for thediagnosis or prognosis of different diseases, as triple negative breast cancer (TNBC). Determining how nano-materials as gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) andmagnetic nanoparticles (MNPs: (9.30 ± 0.67 nm) interact with human serum will assist not only in under-standing their effects on the biological system (biocompability and toxicity), but also to obtain information fordeveloping novel nanomaterials with high specificity and selectivity towards proteins with an important bio-logical function (prognostic and diagnostic protein biomarkers).

https://doi.org/10.1016/j.jprot.2019.103581Received 24 June 2019; Received in revised form 14 October 2019; Accepted 7 November 2019

⁎ Corresponding authors.E-mail addresses: susana.belen.bravo.lopez@sergas.es (S.B. Bravo), cristina.nunez.gonzalez@sergas.es (C. Núñez).

Journal of Proteomics 212 (2020) 103581

Available online 12 November 20191874-3919/ © 2019 Published by Elsevier B.V.

T

1. Introduction

Breast cancer (BC) is the most frequently diagnosed cancer and theleading cause of cancer death in women worldwide, accounting for 23%of total new cancer cases [1].

Mainly, triple negative breast cancer (TNBC) is a heterogeneousdisease that is characterized by a lack of estrogen receptor/proges-terone receptor (ER/PgR) expression and absence of human epidermalgrowth factor receptor 2 (HER2) overexpression or amplification. Thissubgroup accounts for 12–15% of all types of breast cancer and exhibitsa distinct molecular profile, clinical behavior, and response to therapy[2]. Notably, triple negative tumors are usually high grade and exhibitincreased aggressiveness, poor prognosis, and worst clinical outcome[3]. Because hormonal (tamoxifen) and HER2-directed (trastuzumab)therapies are not effective, TNBC patients are managed with standardchemotherapy; however, a high rate of local and systemic relapse isfrequently associated with treatment. Unfortunately, no useful bio-markers neither targeted therapeutic modalities exist for this breastcancer subtype [4].

It is well known that proteins secreted from tumor tissues have ahigher likelihood of reaching the systemic circulation and may, there-fore, serve as potential biomarkers for early detection [5]. Serum pro-teomics is a valuable tool that can facilitate comprehensive and sys-tematic elucidation of the serum proteome under both healthy anddisease conditions as well as identification of serum protein markersused for disease diagnosis and prognosis, particularly for identifyingbreast cancer-specific markers [5].

Current proteomic technologies that promote large-scale samplescreening and facilitate the identification of proteins associated withdisease and treatment are developing rapidly [6]. Mass spectrometry(MS), a powerful proteomics tool, has evolved to a high-throughputlevel, allowing rapid and accurate analysis of several thousand proteinsin a single study [7]. Several studies have addressed the possibility ofapplying MS proteome analysis to diagnostics of TNBC, revealing pro-tein patterns specific for patients with TNBC at either early or lateclinical stages [8]. The peptide markers identified with differentiatingpatterns include glycolytic enzymes (as for example MDH2, PGK1, TKT,Aldolase1), cytokeratins (CK7, 8, 9, 14, 17, 19), further structure pro-teins (vimentin, fibronectin, L-plastin), for NME1-NME2, lactoferrin,and members of the Annexin family, among others [9].

SWATH-MS is an emerging technique that combines deep proteomecoverage capabilities with quantitative consistency and accuracy [10].Mainly, SWATH-MS analysis offers several advantages, including highreproducibility and reliability of quantitative information, in discoveryproteomics [11]. Furthermore, SWATH-MS methods can be inter-changed to MRM approaches focused on the validated biomarkers.Therefore, SWATH-MS is an important tool not only for the biomarkerdiscovery but also for the development of preliminary validation studies[12].

However, currently available proteomic tests detect only a tinyfraction of potential biomarkers due to their deficient concentration inbiofluids, in addition to the ‘swamping’ effect, caused by non-specifichighly abundant molecules. The issue of signal-to-noise exceeds thecurrent capability of proteomic analysis and therefore limits the diag-nostic information that can be obtained [13].

To overcome these challenges, several approaches have been de-veloped, comprising depletion of high-abundance proteins that maskless abundant proteins [14,15], chromatographic or electrophoreticsample prefractionation, to decrease complexity before mass spectro-metric analyses [16,17], and direct isolation of preferred proteins [18].Nevertheless, none of the methods can provide a standard solution tobiomarker discovery or can give a reproducible diagnostic platform forestablishing biomarker guides. In this way, one promising way taken isthe use of nanoscale materials [19].

Nanotechnology-based platforms hold great promise in addressingthe above fundamental and technical issues of biomarker discovery to

overcome persistent deficiencies of conventional methods. Currently, itis well known that the surfaces of nanoparticles (NPs) are rapidlycovered by different types of biomolecules when they contact biologicalmedia called protein corona (PC) [20].

The protein composition and content in the corona depend onseveral parameters, including: i) physicochemical properties of the NPs(i.e. composition, size, shape, curvature, surface chemistry and surfacecharge, hydrophobicity/hydrophilicity) [21,22]; ii) characteristics ofbiological media (i.e. protein source, and temperature) [23,24]); iii)incubation time [25].

Notably, the composition of the protein corona varies amonghealthy individuals, as well as among patients with various diseases/medical conditions. Thus, the same NPs may have different proteincoronas in different individuals. These alterations are often small andchallenging to be detected by conventional blood analyses.

On the other side, the protein corona can act as a “nano-con-centrator” [26] of those serum proteins with affinity for the NP surface.Therefore, characterization of protein corona could allow detectingminor changes in protein concentration at the very early stages ofdisease development or even after chemotherapy or surgery (i.e., whenan alteration in circulating level of proteins could be undetectable byblood tests).

Keeping in mind that each disease is characterized by differentplasma/serum proteomes, inducing the formation of different PCs onthe same nanomaterial, M. Mahmoudi, et al. introduced the novelconcept of “personalized protein corona” (PPC) [27]. More specifically,depending on the type, period and severity of the disease (which de-termines the serum/plasma alterations), each patient may have a per-sonalized protein corona.

In the present study, gold nanoparticles (AuNPs: 10.02 ± 0.91 nm),silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nano-particles (MNPs: (9.30 ± 0.67 nm) were used to pre-concentrate andseparate proteins from sera samples of eight patients with TNBC as wellas from eight healthy people. For protein biomarkers identification andquantification, the proteome map changes between both groups weredetected using a proteomic approach based on electrophoretic separa-tion (SDS-PAGE) and mass spectrometry (nLC-MS/MS).

2. Materials and methods

2.1. Chemicals and reagents

All reagents and solvents used were HPLC-grade or higher. Sodiumcitrate tribasic dihydrate, tannic acid, silver nitrate, ammonium hy-droxide,

iron(III) chloride hexahydrate and iron(II) sulfate heptahydrate,sodium borohydride (NaBH4), trypsin, trifluoroacetic acid, DL-Dithiothreitol (DTT), Iodoacetamide (IAA), acrylamide/bis-acrylamide30% solution (37.5:1), Glycerol 86–88%, Tris-base, Coomassie BrilliantBlue R250 (CBB), sodium carbonate, and the Sigma Marker wide range6.5–200 kDa were purchased from Sigma-Aldrich (St. Louis, MO, USA).Sodium dodecyl sulfate (SDS) and formaldehyde were purchased fromPanreac (Barcelona, Spain). β-mercaptoethanol was purchased fromMerck (Hohen-Brunn, Germany), and bromophenol-blue was purchasedfrom Riedel-de Haen (Seelze, Germany). Hydrogen tetrachloroaurate(III) hydrate (99.9%-Au) (49%Au) at 10%w/v was purchased fromStrem Chemicals (Newburyport, MA, USA). Ammonium bicarbonate(ambic) and formic acid were purchased from Fluka (Steinheim,Germany).

2.2. Instrumentation

Microscopic characterizations of AuNPs, AgNPs, and MNPs wereperformed by transmission electron microscopy (TEM) using a Jeol JEM1011 microscope. Samples for TEM were prepared by pipetting a dropof the colloidal dispersion onto an ultrathin carbon-coated copper grid

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and allowing the solvent to evaporate. AuNPs, AgNPs and MNPs ζ-po-tentials were measured at 25°C before and after protein corona for-mation using a Malvern Zetasizer Nano ZS instrument. For ζ-potentialmeasurements samples were diluted in 1 mL milli-Q water and placed inZetasizer disposable cuvettes. A minimum of 3 measurements persample were made.

Power Pac Basic power supply from Bio-Rad (CA, USA) was used forsodium dodecyl sulfate- polyacrylamide gel electrophoresis (SDS-PAGE)protein separation. Protein quantification was accomplished by mea-suring the absorbance at 280 nm with the use of a Qubit™ 4Quantitation Starter Kit from Thermo Fisher Scientific. Gel image ac-quisition was carried out with a UVP PhotoDoc-ItTM Imaging Systemfrom Analytik Jena.

2.3. Synthesis of inorganic nanoparticles

2.3.1. Synthesis of citrate-gold nanoparticles (10.02 ± 0.91 nm)Gold nanoparticles (AuNPs) were synthesized by the citrate reduc-

tion method in aqueous solution [28]. Briefly, 60 ml of sodium citratetribasic solution (0.075% w/v) was heated to 100 °C, and then gold wasadded as 54 μL of 10% w/v of hydrogen tetrachloroaurate (III) hydratesolution. The reaction mixture was kept under reflux until a deep redcolor was detected. The solution of nanoparticles is chilled at roomtemperature and stored at 4 °C for a maximum of one month.

2.3.2. Synthesis of citrate-silver nanoparticles (9.73 ± 1.70 nm)Silver nanoparticles (AgNPs) were synthesized by the citrate re-

duction method in aqueous solution by the method reported by V.Puntes et al. [29]. A 100 mL volume of an aqueous solution containingsodium citrate (SC) (5 mM) and tannic acid (TA) (0.025 mM) was pre-pared and heated with a heating mantle in a three-neck round-bot-tomed flask for 15 min under vigorous stirring. A condenser was used toprevent the evaporation of the solvent. After boiling had commenced,1 mL of AgNO3 (25 mM) was injected into this solution. The solutionbecame bright yellow immediately. Resultant Ag NPs were purified bycentrifugation at 18,000×g to remove the excess of TA and furtherredispersed in Milli-Q-water before sample characterization.

2.3.3. Synthesis of Fe3O4 magnetic nanoparticles (9.30 ± 0.67 nm)The synthesis of magnetic nanoparticles was performed according to

a previously described procedure [30]. Briefly, 6 mL of concentratedammonium hydroxide and 4 mL of water saturated with N2 were mixedin a round-bottom flask under an inert atmosphere. Subsequently, inanother vessel, 1 g of FeCl3.6H2O and 0.25–0.5 g of FeSO4.7H2O weredissolved in 10 mL of water saturated with N2. After mixing both so-lutions, the system was kept under constant stirring for 80 min at 80 °C.The final reaction product was washed three times with deionizedwater and magnetically separated for 24 h at room temperature.

2.4. Sample resources

Venous blood samples were obtained from eight females with triplenegative breast cancer (TNBC) and eight disease-free individuals withthe use of VACUETTE® Serum Clot Activator Tubes (10 mL). The collectedblood samples were allowed to clot for 15 min and then centrifuged for5 min at 4°C and 1,800×g. Sera were transferred into clean plastictubes (1 mL) and immediately frozen at -80°C at Research Unit, HospitalUniversitario Lucus Augusti (HULA). Clinical features of TNBC tumors,including tumor size, histology, receptor status, clinical stage, andnodal status, are summarized in Table 1.

2.5. Sample preparation

2.5.1. Optimization of protein corona formation in serum using magneticnanoparticles

A series of serum sample aliquots were used for checking the effects

of sample pH and temperature on the high-abundance proteins deple-tion using DTT, MNP/protein ratios, and pH of the medium on thewashing steps.

2.5.1.1. Depletion of multiple high abundant proteins. Human serumaliquots (x8) were filtered with Miller-GP® Filter Unit (Millipore)with a size of 0.22 μm. Four aliquots of human serum (30 μL) weredepleted with dithiothreitol (DTT) according to the protocol describedby Warder el al. [31,32]. Briefly, fresh DTT 500 mM (3.3 μL) in milli-Qwater was mixed with 30 μL of human serum and vortex quickly.Samples were then incubated at room temperature until a viscous whiteprecipitate persisted (60 min), followed by centrifugation at 18,840 ×gfor 20 min. Supernatants were transferred to a clean tube before theprotein alkylation and nanoparticles (NPs) fractionation.

To evaluate the effects of sample pH and temperature on the high-abundance proteins depletion with DTT, four aliquots of human serum(30 μL) were depleted with dithiothreitol (DTT) following a modifica-tion of the protocol previously published by Arruda et al. [33]. Fouraliquots of 30 μL of human serum were mixed fresh DTT 500 mM(3.3 μL) in ambic (12.5 mmol L−1)] and vortexed. Samples were in-cubated for 60 min at 37°C, centrifuged at 13,000 ×g for 40 min toseparate supernatants to be alkylated and fractionated with nano-particles (NPs).

2.5.1.2. NPs protein alkylation and fractionation. After proteindepletion, the reduced SH-groups were alkylated with iodoacetic acid(IAA) for 45 min at room temperature and protected from light.Volumes of serum reduced and alkylated, were diluted to a finalvolume of 100 μL in Tris-HCl (0.1 mol L−1, pH 5.5), and mixed withMNPs (5 μg) to obtain the following MNP/protein ratios: 1:1, 1:2, 1:4,1:10. Then, all NPs-serum solutions were incubated at 25 °C withshaking (300 rpm) in a thermostatic bath for 30 min and then pelletswere harvested by centrifugation at 20,186 ×g for 30 min. To evaluatethe effects of sample pH on the stabilization of the protein corona in thewashing steps, a fraction of pellets were washed (x3) with 50 μL of Tris-HCl (0.1 mol L−1, pH 5.5) and another fraction with 50 μL of milli-Qwater (x3). In both cases, pellets were harvested again by centrifugationat 20,186 ×g for 30 min to remove unbound proteins.

2.5.2. Incubation of nanoparticles with serum samplesSerum aliquots (x2) belonging to the eight disease-free individuals

and eight triple negative breast cancer patients were depleted withdithiothreitol (DTT) following the method of Warder el al. [33,34].After that, the reduced SH-groups were alkylated with iodoacetic acid(IAA) at room temperature (45 min in the dark).

After protein reduction and alkylation, serum aliquots (x2) be-longing to the eight disease-free individuals and eight triple negativebreast cancer patients were incubated with AuNPs (10.02 ± 0.91 nm)

Table 1Clinical features of triple negative breast cancer tumors.

Characteristics Number

Patients 8Age (years) < 40 1

40–70 4> 70 3

Tumor size (cm) < 2 32–5 4> 5 1

Histological types Ductal invasive carcinoma 8Receptor status Triple negative 8Clinical stage I 1

II 4III 3

Nodal status N0 7N1 1

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and AgNPs (9.73 ± 1.70 nm) (4 aliquots per individual, 2 with eachnanoparticle type) following the method described by C. Núñez et al.[34]. Briefly, 75 μL of AuNPs (10.02 ± 0.91 nm) and 75 μL of AgNPs(9.73 ± 1.70 nm) were added to each different serum aliquots (×2)belonging to the eight disease-free individuals and eight triple negativebreast cancer patients (4 aliquots per individual, 2 with each nano-particle type), followed by the addition of 40 μL of citrate/citric acidbuffer to a final pH of 5.8. Then, all NPs-serum solutions were in-cubated at 37 °C with shaking in a thermostatic bath for 30 min. Pelletswere harvested by centrifugation at 18,840 ×g for 30 min. In all cases,pellets containing proteins bound to nanoparticles were washed threetimes with 25 μL citrate/citric acid buffer and harvested again by cen-trifugation at 18,840 ×g for 30 min to remove unbound proteins.

In the particular case of magnetic nanoparticles, each different re-duced and alkylated serum aliquots (x2) from disease-free individuals(n = 8) and negative breast cancer patients (n = 8) were incubated(shaking at 300 rpm, 25 °C, 30 min) with 5 μL of MNPs(9.30 ± 0.67 nm) after the addition of 87 μL of Tris-HCl (0.1 mol L−1,pH 5.5).

After centrifuging (15,000 ×g, 30 min), pellets were separated andwashed (x3) with 50 μL of Tris-HCl (0.1 mol L−1, pH 5.5) and cen-trifuged again (20,186×g, 30 min).

2.5.3. Gel electrophoresisAfter that, pellets were reconstituted in 10 μL of a buffer with 0.2 M

Tris-HCl, 2% w/v SDS and 20% v/v glycerol. This 10 μL was mixed with4 μL of SDS-PAGE loading buffer (10% w/v SDS, Tris-Base 40 mM, pH6.8, 50% v/v glycerol, 0.1% v/v bromophenol blue, 10% v/v β-mer-captoethanol) in a final volume of 20 μL. Then, all samples were de-natured by heating at 100°C for 5 min and loaded into a 10% acryla-mide/bis-acrylamide, stacking gel/12.5% acrylamide/bis-acrylamiderunning gel, of 1 mm thickness, and separated at 180 V (constant vol-tage) for 120 min. After electrophoresis, the gel was fixed for 30 min-utes with 40% (v/v) ethanol and 10% (v/v) acetic acid and then stainedovernight with Colloidal Coomassie Blue [35]. Gels were rinsed withdistilled water and a 0.5 M sodium chloride solution until a clearbackground was observed. Gel imaging was carried out with a UVPPhotoDoc-ItTM Imaging System.

2.5.4. In-gel protein digestionProtein bands were excised manually and transferred to 2.5-mL Lo-

Bind tubes, and then washed twice with water and with 50% (v/v)acetonitrile/25 mM ammonium bicarbonate (ambic) until the bluecolor disappeared.

Before the trypsin digestion, gel spots were washed with 25 mMambic and dehydrated with acetonitrile. Then, 30 μL of trypsin(20 ng μL−1 in 12.5 mM ambic/2% (v/v) acetonitrile) was added to thegel spots and incubated for 60 min at 0°C.

After this time, gel spots were inspected, trypsin solution not ab-sorbed into the gel was removed, and the gels were covered with 100 μLof 12.5 mM ambic. Samples were incubated for 12 h at 37°C. Then 50 μLof 5% (v/v) formic acid was added, and the supernatant was transferredto a new Lo-Bind tube and the peptides were further extracted from thegel twice with 50% (v/v) acetonitrile/0.1% (v/v) trifluoroacetic acid(TFA) (x3) and acetonitrile (ACN) (x1). Samples were dried-down andstored at -20 °C [36].

2.6. Protein identification by mass spectrometry (LC-MS/MS) and dataanalysis

Digested peptides of each sample were separated using ReversePhase Chromatography. The gradient was developed using a micro li-quid chromatography system (Eksigent Technologies nanoLC 400,SCIEX) coupled to high-speed Triple TOF 6600 mass spectrometer(SCIEX) with a microflow source. The analytical column used was asilica-based reversed phase column Chrom XP C18 150 × 0.30 mm,

3 mm particle size and 120 Å pore size (Eksigen, SCIEX). The trapcolumn was a YMC-TRIART C18 (YMC Technologies, Teknokroma witha 3 mm particle size and 120 Å pore size, switched on-line with theanalytical column. The loading pump delivered a solution of 0.1%formic acid in water at 10 μL/min. The micro-pump provided a flow-rate of 5 μL/min and was operated under gradient elution conditions,using 0.1% formic acid in water as mobile phase A, and 0.1% formicacid in acetonitrile as mobile phase B. Peptides were separated using a25 minutes gradient ranging from 2% to 90% mobile phase B (mobilephase A: 2% acetonitrile, 0.1% formic acid; mobile phase B: 100%acetonitrile, 0.1% formic acid). The injection volume was 4 μL.

Data acquisition was carried out in a TripleTOF 6600 System(SCIEX, Foster City, CA) using a Data dependent workflow. Source andinterface conditions were as follows: ion spray voltage floating (ISVF)5500 V, curtain gas (CUR) 25, collision energy (CE) 10 and ion sourcegas 1 (GS1) 25. The instrument was operated with Analyst TF 1.7.1software (SCIEX, USA). Switching criteria were set to ions greater thanmass to charge ratio (m/z) 350 and smaller than m/z 1400 with acharge state of 2–5, mass tolerance 250 ppm and an abundancethreshold of more than 200 counts (cps). Former target ions were ex-cluded for 15s. The instrument was automatically calibrated every4 hours using as external calibrant tryptic peptides from PepcalMix(Sciex).

After MS/MS analysis, data files were processed usingProteinPilotTM 5.0.1 software from Sciex, which uses the algorithmParagonTM for database search and ProgroupTM for data grouping. Datawere searched using a Human-specific UniProt database. False dis-covery rate was performed using a non-linear fitting method displayingonly those results that reported a 1% Global false discovery rate orbetter [37,38].

2.7. Protein quantification by SWATH (Sequential Window Acquisition ofall Theoretical Mass Spectra)

2.7.1. Creation of the spectral libraryTo construct the MS/MS spectral libraries, the peptide solutions

were analyzed by a shotgun data-dependent acquisition (DDA) ap-proach by micro-LC-MS/MS. To get a good representation of the pep-tides and proteins present in all samples, pooled vials of samples fromeach group (control and triple negative breast cancer patients) wereprepared using equal mixtures of the original samples. 4 μL (4 mg) ofeach pool was separated into a micro-LC system Ekspert nLC425(Eksigen. Dublin. CA. USA) using a column Chrom XP C18150 × 0.30 mm. 3 mm particle size and 120 Å pore size (Eksigent,Sciex) at a flow rate of 5 μL/min. Water and ACN, both containing 0.1%formic acid, were used as solvents A and B, respectively. The gradientrun consisted of 5–95% B for 30 min, 5 min at 90% B and finally 5 minat 5% B for column equilibration, for a total run time of 40 min. Whenthe peptides eluted, they were directly injected into a hybrid quadru-pole-TOF mass spectrometer Triple TOF 6600 (Sciex, Redwood City.CA. USA) operated with a data-dependent acquisition system in positiveion mode. A Micro source (Sciex) was used for the interface betweenmicroLC and MS, with an application of 2600 V voltage. The acquisitionmode consisted of a 250 ms survey MS scan from 400 to 1250 m/zfollowed by an MS/MS scan from 100 to 1500 m/z (25 ms acquisitiontime) of the top 65 precursor ions from the survey scan, for a total cycletime of 2.8 s. The fragmented precursors were then added to a dynamicexclusion list for 15 s; any singly charged ions were excluded from theMS/MS analysis.

The peptide and protein identifications were performed usingProtein Pilot software (version 5.0.1. Sciex) with a Data were searchedusing a Human-specific UniProt database, specifying iodoacetamide asCys alkylation. The false discovery rate (FDR) was set to 1 for bothpeptides and proteins. The MS/MS spectra of the identified peptideswere then used to generate the spectral library for SWATH peak ex-traction using the add-in for PeakView Software (version 2.2. Sciex)

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MS/MSALL with SWATH Acquisition MicroApp (version 2.0. Sciex).Peptides with a confidence score above 99% (as obtained from ProteinPilot database search) were included in the spectral library).

2.7.2. Relative quantification by SWATH acquisitionSWATH-MS (Sequential Window Acquisition of all Theoretical Mass

Spectra) acquisition was performed on a TripleTOF® 6600 LC-MS/MSsystem (Sciex). Samples from control and triple negative breast cancerpatients were analyzed using data-independent acquisition (DIA)method (30 total samples). Each sample (4 μL (from a mg/ml solution)was analyzed using the LC-MS equipment and LC gradient describedabove for building the spectral library but instead using the SWATH-MSacquisition method. The method consisted of repeating a cycle thatconsisted of the acquisition of 65 TOF MS/MS scans (400–1500 m/z,high sensitivity mode, 50 ms acquisition time) of overlapping sequen-tial precursor isolation windows of variable width (1 m/z overlap)covering the 400–1250 m/z mass range with a previous TOF MS scan(400–1500 m/z. 50 ms acquisition time) for each cycle. The total cycletime was 6.3 s. For each sample set, the width of the 100 variablewindows was optimized according to the ion density found in the DDAruns using a SWATH variable window calculator worksheet from Sciex.

2.7.3. Data analysisThe targeted data extraction of the fragment ion chromatogram

traces from the SWATH runs was performed by PeakView (version 2.2)using the SWATH Acquisition MicroApp (version 2.0). This applicationprocessed the data using the spectral library created from the shotgundata. Up to ten peptides per protein and seven fragments per peptidewere selected, based on signal intensity; any shared and modifiedpeptides were excluded from the processing. Five-minute windows and30 ppm widths were used to extract the ion chromatograms; SWATHquantitation was attempted for all proteins in the ion library that wereidentified by ProteinPilot with an FDR below 1%.

The retention times from the peptides that were selected for eachprotein were realigned in each run according to the iRT peptides spikedin each sample and eluted along the whole-time axis. The extracted ionchromatograms were then generated for each selected fragment ion; thepeak areas for the peptides were obtained by summing the peak areasfrom the corresponding fragment ions. PeakView computed an FDR anda score for each assigned peptide according to the chromatographic andspectral components; only peptides with an FDR below 1% were usedfor protein quantitation. Protein quantitation was calculated by addingthe peak areas of the corresponding peptides.

The integrated peak areas (processed, mrkvw files from PeakView)were directly exported to the MarkerView software (Sciex) for relativequantitative analysis. The export will generate three files containingquantitative information about individual ions, the summed intensity ofdifferent ions for a particular peptide, and the summed intensity ofdifferent peptides for a specific protein. MarkerView has been used forthe analysis of SWATH-MS data reported in other proteomics studies[39,40] because of its data-independent method of quantitation. Mar-kerView uses processing algorithms that accurately find chromato-graphic and spectral peaks direct from the raw SWATH data. Dataalignment by MarkerView compensates for minor variations in bothmass and retention time values, ensuring that identical compounds indifferent samples are accurately compared to one another.

To control for possible uneven sample loss across the differentsamples during the sample preparation process, we performed a globalnormalization based on the total sum of all the peak areas extractedfrom all the peptides and transitions across the replicates of eachsample. Unsupervised multivariate statistical analysis using principalcomponent analysis (PCA) was performed to compare the data acrossthe samples. The average MS peak area of each protein was derivedfrom the biological replicates of the SWATH-MS of each sample fol-lowed by Student’s t-test analysis using the MarkerView software forcomparison among the samples based on the averaged area sums of all

the transitions derived for each protein. The t-test will indicate howwell each variable distinguishes the two groups, reported as a p-value.To set of differentially expressed proteins (p-value < 0.05) with a 1.5fold in- or decrease was selected.

Functional analysis was performed by FunRich open access software(Functional Enrichment analysis tool) for functional enrichment andinteraction network analysis (http://funrich.org/index.html).

2.8. TNBC biomarkers validation

A SWATH-MS analysis was performed using the same conditionsdescribed in Section 2.7. In the validation phase, total serum samplesfrom control and triple negative breast cancer patients previously de-pleted with DTT were used.

To perform the biomarker validation, the SWATH library was per-formed using not only serum pools from incubation with the differentnanoparticles but also pools of total serum samples. Therefore, weimprove our library and perform better protein quantification toachieve biomarker validation.

3. Results

Following the synthetic methods described by R. López-Cortés [30],V. Puntes et al. [31] and F. Schüth et al. [32], AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPs(9.30 ± 0.67 nm) were successfully obtained, respectively.

The sizes and ζ-potential of AuNPs (10.02 ± 0.91 nm), AgNPs(9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm) were examined beforeand after their incubation with two pools of human blood serum fromhealthy individuals and triple negative breast cancer patients, followingthe conditions described in Section 2.5.2.

TEM and ζ-potential measurements after the incubation of AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPs(9.30 ± 0.67 nm) with serum demonstrated that, in all cases, the sizedid not change significantly and the surface charge remained negative(see Figs. S1–S10). Upon serum incubation, the mean particle surfacecharge of the AuNPs (10.02 ± 0.91 nm) increased (became less nega-tive) from -37.0 mV to -29.7 mV, and the same measurement for theMNPs (9.30 ± 0.67 nm) increased from -30.5 mV to -29.3 mV.However, upon serum incubation the mean particle surface charge ofthe AgNPs (9.73 ± 1.70 nm) decreased (became more negative) from-27.4 mV to -30.0 mV. These results are in agreement with previousstudies suggesting that negatively charged NPs do not exclusively in-teract with positively charged proteins, as electrostatic interactions arenot the only driving force behind NP-corona interactions [41,42]. In-terestingly, for AuNPs (10.02 ± 0.91 nm) and MNPs(9.30 ± 0.67 nm), where the ζ-potential was shifted toward less ne-gative values, it could be suggested preferential interaction with posi-tively charged proteins. The presence of negatively charged proteinscan be explained by a sequential model of protein binding, in whichpositively charged proteins initially bind the NP, followed by negativelycharged ones [45,46].

3.1. Optimization of parameters for the protein corona formation in serumusing MNPs

As mentioned above, a great number of variables could influencethe efficiency of protein adsorption on the MNPs surface [43]. For thisreason, three parameters were evaluated: (i) the effects of sample pHand temperature on the depletion of high-abundance proteins presentedin serum using DTT, (ii) MNP/protein ratios; (iii) and pH of the mediumon the washing steps. For this study, shaking and incubation tempera-ture were previously defined as 300 rpm and 25 °C, respectively.

In order to evaluate the effects of sample pH and temperature on thedepletion of high-abundance proteins, four human serum samples (x2)(30 μL) were depleted with fresh DTT 500 mM (3.3 μL) in milli-Q H2O

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for 60 min at room temperature (protocol of Warder el al. [33,34]), andfour human serum samples (x2) (30 μL) were depleted with fresh DTT500 mM (3.3 μL) in ambic (12.5 mmol L−1) for 60 min at 37°C (mod-ification of protocol described by Arruda [35]). In both cases, after theincubation and centrifugation, supernatants were transferred to a cleantube before the protein alkylation and nanoparticles (NPs) fractiona-tion. Depletion with fresh DTT 500 mM in milli-Q for 60 min at roomtemperature showed more reproducible results (see Fig. S4).

Protein concentration is another critical parameter that may affectthe capacity and kinetics of protein adsorption. To investigate the in-fluence of the MNP/protein ratio on the formation of the proteincorona, volumes of serum reduced and alkylated (x2) were mixed withMNPs (9.30 ± 0.67 nm), at MNP/protein ratios of 1:1, 1:2, 1:4, and1:10 (see Section 2.5.1). Maintaining the amount of adsorbent (i.e.,MNPs) constant and increasing the protein concentration, would beexpected to lead to a decrease of available adsorption sites, reducing theefficiency of protein removal [44]. As a compromise between MNPs andprotein corona formation, the 1:2 ratio (MNP/protein) was then se-lected for future experiments (see Fig. S5).

The pH value is an essential parameter because it influences thecharge state of proteins, therefore influencing their interaction withMNPs [45]. To evaluate the effects of sample pH on the stabilization ofthe protein corona in the washing steps, a fraction of pellets were wa-shed three times with 50 μL Tris-HCl (0.1 mol L−1, pH 5.5) and anotherfraction was washed three times with milli-Q water. The first one wasselected as the preferred method, because the washes with milli-Qwater promoted the destabilization of the protein corona formedaround the MNPs, due to the modifications of the pH (data not shown).

3.2. Serum fraction preparation and protein corona purification (patientsvs. controls)

Serum aliquots (x2) belonging to the eight disease-free individualsand serum samples from eight triple negative breast cancer patientswere depleted with dithiothreitol (DTT) according to the protocol de-scribed by Warder el al. [33,34]. After that, the reduced SH-groupswere alkylated with iodoacetic acid (IAA) for 45 min at room tem-perature and protected from light.

After protein reduction and alkylation, serum aliquots (x2) be-longing to the eight disease-free individuals and eight triple negativebreast cancer patients were incubated with AuNPs (10.02 ± 0.91 nm),AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm) (6 aliquots perindividual, 2 with each nanoparticle type) and further processed asdescribed in Section 2.5.2.

Two protein fractions were thus obtained in each case, one in thesupernatant and the second one attached to the surface of each nano-particles types (protein corona). Then, protein fractions (AuNPs-proteincorona, AgNPs-protein corona, MNPs-protein corona) were separatelyloaded onto a 1D-SDS-PAGE. Proteins were separated and, afterstaining, gel bands were excised and submitted to the sample incuba-tion described in Section 2.5.4. The resulting peptides were then ana-lyzed by mass spectrometry (LC-MS/MS) for protein identification.

Fig. 1 shows the 1D gels for the protein corona formed around thethree different NPs (AuNPs-protein corona, AgNPs-protein corona,MNPs-protein corona) visible after Coomassie staining. As may be seen,it is quickly noted that there is a difference in the intensity of the bandson the gel profiles for the healthy controls (from C1 to C8) and thepatients (from P1 to P8) for each type of nanoparticle. However, noconclusion can be drawn unless the proteins are identified.

As Fig. 2 shows, 192, 161 and 142 proteins were commonly de-tected in the protein corona formed around AuNPs, AgNPs, and MNPs,after their incubation with serum samples of the eight triple negativebreast cancer patients and eight healthy controls, respectively.

In the particular case of all serum samples from healthy controls,285, 292 and 206 were identified on the surface of AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPs

(9.30 ± 0.67 nm), respectively. From them, 149 proteins were com-monly detected in the protein corona of all three different types of NPs.However, 202 different proteins were found on the three distinct NPssurface: 71 different proteins on the AuNPs, 85 on the AgNPs, and 46individual proteins on the MNPs (see Table S1).

Furthermore, 231, 206 and 203 were found in the surface of AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPs(9.30 ± 0.67 nm) after their incubation with all serum aliquots (x2)from eight triple negative breast cancer patients (6 protein samples perindividual: 2 incubated with AuNPs, 2 with AgNPs and 2 with MNPs)(see Table S2). A total of 138 proteins were commonly found in theprotein corona of all three different types of NPs. However, 142 dif-ferent proteins were found in the three different NPs surface: 56 dif-ferent proteins on the AuNPs, 33 on the AgNPs and 53 individualproteins on the MNPs.

Fractionation of the proteome using AuNPs, AgNPs, and MNPs al-lows for the identification of 39 (see Table 2), 45 (see Table 3) and 61(see Table 4) protein biomarkers in the pellet of all patient samples,respectively (see Fig. 3). Remarkably, these proteins were not identifiedin the healthy control. These potential biomarkers came from differentcellular components; most of them from the nucleus and cytoplasm (seeFig. 4). Protein biomarkers also showed different functionality and areconstituted by proteins implicated in the immune response, followed byproteins with an enzymatic function, structural, transporter, in-flammatory, signal transduction, and with antibiotic/antibacterialproperties.

Importantly, the GRF-type zinc finger domain-containing protein 1(protein ZGRF1) was identified in the protein corona of AuNPs, AgNPsand MNPs after their incubation with the serum samples of all triplenegative breast cancer patients. Zinc-finger proteins (ZNFs) are one ofthe most abundant groups of proteins and have a wide range of mole-cular functions. Given the wide variety of zinc-finger domains, ZNFscan interact with DNA, RNA, PAR (poly-ADP-ribose) and other proteins.Thus, ZNFs are involved in the regulation of several cellular processes.ZNFs are implicated in transcriptional regulation, ubiquitin-mediatedprotein degradation, signal transduction, actin targeting, DNA repair,cell migration, and numerous other processes [46]. Notably, over-expression of similar zinc finger proteins has been shown to promotecell growth and metastasis in laryngeal squamous cell carcinoma,glioma, non-small cell lung cancer, gastric cancer, oral squamous cellcarcinoma, gallbladder cancer, and breast cancer [47], and also in triplenegative breast cancers [48].

Matrix metalloproteinase-9 (MMP9) was identified in the proteincorona of AuNPs and AgNPs after their incubation with the serumsamples of all triple negative breast cancer patients. Members of thematrix metalloproteinase (MMP) family have been identified as poorprognosis markers for breast cancer patients and as drivers of manyfacets of the tumor phenotype in experimental models [49]. Studies ofthe pathological processes involved in tumor progression and metas-tasis revealed matrix metalloproteinases (MMPs) as prominent mole-cules engaged in shaping the tumor microenvironment and drivingcancer progression and metastasis [50,51]. Mainly, MMP9 was in-vestigated as a potential tumor marker in breast cancer [52]. MMP-9 isone of 70 genes in the Rosetta poor prognosis signature for breastcancer patients [53], the basis for the clinically implemented Mam-maprint prognostic assay (Agendia Inc., Irvine, CA). MMP-9 was alsohighly expressed in node-positive tumors and the preoperative bloodserum of patients, but MMP-9 activity was appreciably inhibited inblood serum samples collected after surgery.

Lebercilin and Immunoglobulin lambda variable 3-27 (LV327) wereidentified in the protein corona of AuNPs and MNPs after their incuba-tion with the serum samples of all triple negative breast cancer patients.While the protein expression of lebercilin was observed in several tissuecancers like colorectal cancer, breast cancer, prostate cancer, lung cancer(The Human Protein Atlas, https://www.proteinatlas.org/ENSG00000157578-LCA5L/pathology), immunoglobulin free light

M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

6

chains as LV327 are biomarkers of poor prognosis in basal-like breastcancer and are potential targets in tumor-associated inflammation [54].

LINE-1 type transposase domain-containing protein 1 (LITD1),structural maintenance of chromosomes protein 6 (SMC6) and shortcoiled-coil protein (SCOC) were identified in the protein corona ofAgNPs and MNPs after their incubation with the serum samples of alltriple negative breast cancer patients.

L1TD1 is an RNA-binding protein that involved with self-renewal ofundifferentiated human embryonic stem cells and cancer cell pro-liferation [55].

The structural maintenance of chromosomes (SMC) proteins areessential for successful chromosome transmission during replicationand segregation of the genome in all organisms. SMC proteins functiontogether with other proteins in a range of chromosomal transactions,including chromosome condensation, sister-chromatid cohesion, re-combination, DNA repair, and epigenetic silencing of gene expression.

Notably, the protein expression of SMC6 was observed in different tis-sues as colorectal cancer, breast cancer, prostate cancer, lung cancer,and liver cancer (The Human Protein Atlas, https://www.proteinatlas.org/ENSG00000163029-SMC6/pathology). In humans, SCOC is re-quired for autophagosome formation during amino acid starvation[56]; however, this relation with cancer is unknown until the moment.

3.3. Proteomic alterations in triple negative breast cancer serum revealed bySWATH-MS analysis

Label-free SWATH experiments were carried out on a Triple TOF6600 mass spectrometer (SCIEX). After a comparison between the dif-ferent groups of samples (controls and TNBC patients), it was observeda variation in the number of statistically significant proteins in theprotein corona formed around the three different NPs:10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm silver

Fig. 1. 1D-SDS-PAGE of protein coronas formed around 10.02 ± 0.91 nm gold nanoparticles (AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and9.30 ± 0.67 nm magnetic nanoparticles (MNPs) after their incubation with serum aliquots (x2) belonging to the eight disease-free individuals (C1–C8) and eighttriple negative breast cancer patients (P1–P8). On the left, it marks the lane with Mw protein standards.

Fig. 2. Quantitative Venn diagrams showing the number of identified proteins found in the protein corona of 10.02 ± 0.91 nm gold nanoparticles (AuNPs),9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 9.30 ± 0.67 nm magnetic nanoparticles (MNPs) after their incubation with serum from eight triple negativebreast cancer patients and eight healthy controls.

M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

7

Table2

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M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

8

Table3

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rted

.

M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

9

Table4

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ctio

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M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

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nanoparticles (AgNPs) and 9.30 ± 0.67 nm magnetic nanoparticles(MNPs) (see Table 5).

After the analysis of the protein corona formed around AuNPs(10.02 ± 0.91 nm), a total of 177 non-redundant proteins werequantified, out of which 48 were found to be differentially regulated. 14proteins had elevated expression, while 34 proteins showed down-regulation (see Table 6).

In the case of the protein corona formed around AgNPs(9.73 ± 1.70 nm), a total of 176 non-redundant proteins were quan-tified, out of which 140 were found to be differentially regulated. 64proteins had elevated expression, while 76 proteins showed down-regulation (see Table 7).

In the protein corona formed around the MNPs (9.30 ± 0.67 nm), atotal of 176 non-redundant proteins were quantified, out of which 57were found to be differentially regulated. 45 proteins had elevatedexpression, while 12 proteins showed down-regulation (see Table 8).

A SWATH library was developed. To this aim, the ProteinPilotsoftware (AB Sciex; version 4.0) was used where the proteins wereidentified with minimum of 2 peptides along with a confidence scoreabove 99% and FDR below 1% as threshold criteria. Therefore, aspectral library containing 180 proteins found in the nanoparticle sur-faces after serum incubation was employed. In this study, it was shownthat this strategy provided a more comprehensive and reproduciblecoverage of the proteins that can join the different nanoparticle sur-faces.

We fixed the cut off to considerate a deregulated protein at ≥1.5 forup-regulation and ≤0.67 for down-regulation. Only proteins with a p-value ≤0.05 were selected.

In the analysis of the protein corona formed around the three na-noparticles (AuNPs, AgNPs, and MNPs), eight common proteins showedto be statistically significant and appeared quantitatively increased (up-regulated) in triple negative breast cancer patients versus controls(healthy people) (Fig. 5). These proteins are complement component C9(CO9), complement C4-A (CO4A), complement C3 (CO3), vitronectin(VTNC), apolipoprotein L1 (APOL1), complement factor H (CFAH),kininogen-1 (KNG1), galectin-3-binding protein (LG3BP). However,three common proteins appeared quantitatively decreased (down-regulated) in triple negative breast cancer patients versus controls(healthy people) (Fig. 6). These proteins are immunoglobulin heavyconstant mu (IGHM), immunoglobulin lambda variable 3-9 (LV39) andapolipoprotein C-I (APOC1).

After the analysis of the protein corona formed around AuNPs(10.02 ± 0.91 nm), AgNPs (9.73 ± 1.70 nm) and MNPsTa

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.Fig. 3. Quantitative Venn diagrams showing the number of identified proteinbiomarkers found on the surface of the three different nanoparticles (color:black) and commonly found on the surface of AuNPs (10.02 ± 0.91), AgNPs(9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm) (color: grey).

M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

11

(9.30 ± 0.67 nm), the principal components analysis (PCA) clearlyrevealed that the samples of the triple negative breast cancer patientsand healthy people were separated in the PC1 axis, which explains 47.7,80.9 and 79.6% of the variance between the samples, respectively (Figs.S13–S15).

In all cases (AuNPs, AgNPs, MNPs), the separation between the

groups of samples is visible between the group of healthy people andthe group of triple negative breast cancer.

3.4. TNBC biomarker validation

Mass spectrometry-based validation assays were performed in adifferent cohort of total serum patients samples (n=8) and controls(n=8) which were run in triplicate using a TripleTOF® 6600 LC-MS/MSsystem (Sciex). A correct protein validation was performed. To this aim,the library was improved after the addition of DDA data acquired fromtotal serum pools (controls and patient), obtaining a total of 205identified proteins. After comparing the results obtained by this ana-lysis, with previous analysis performed on serum samples after the in-cubation with the different nanoparticles, fascinating results were ob-served.

Graphically, these variations can be observed through charts such asthe volcano plot. Volcano plots (see Fig. 7) of the global quantification

Fig. 4. Localization of the 39, 45 and 61 protein biomarkers found in the surface of AuNPs (10.02 ± 0.91), AgNPs (9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm),respectively, in the different cellular components.

Table 5Number of proteins identified by LC-MS/MS by SWATH analysis.

SWATH-MS

Library 180

SamplesTNBC patients vs. controls (AuNPs) 48 (P-value ≤ 0.05)TNBC patients vs. controls (AgNPs) 140 (P-value ≤ 0.05)TNBC patients vs. controls (MNPs) 57 (P-value ≤ 0.05)

M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

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of proteins between healthy and triple negative breast cancer patientswith A: AuNPs (10.02 ± 0.91 nm), B: AgNPs (9.73 ± 1.70 nm) and C:MNPs (9.30 ± 0.67 nm) were generated by plotting the log 2-foldchanges for the identified proteins against their corresponding adjustedp-value.

Validation assays verified and confirmed that transthyretin (TTHY)was statistically significant and appeared quantitatively decreased(down-regulated) in triple negative breast cancer patients when com-pared with controls (healthy people). This result was observed after theanalysis of the protein corona formed around AuNPs(10.02 ± 0.91 nm). A similar observation was recently reported,where it was found that transthyretin (TTHY) was predominantly(68.75%) down-regulated (n = 33/48) in the sera of breast cancerpatients [57].

Furthermore, validation assays verified and confirmed that com-plement component C8 gamma chain (CO8G), ficolin-3 (FCN3), retinol-binding protein 4 (RET4), fibronectin (FINC), fetuin-B (FETUB) andapolipoprotein A-IV (APOA4), were up-regulated in triple negativebreast cancer when compared with controls. However, apolipoprotein

C-III (APOC3), immunoglobulin kappa variable 2D-28 (KVD28), im-munoglobulin kappa variable 1-5 (KV105), immunoglobulin kappavariable 4-1 (KV401), immunoglobulin kappa variable 1D-12 (KVD12),immunoglobulin heavy variable 1-46 (HV146), immunoglobulin heavyvariable 3-30-5 (HV335), immunoglobulin heavy constant gamma 2(IGHG2), immunoglobulin heavy constant gamma 3 (IGHG3), and im-munoglobulin heavy constant gamma 4 (IGHG4) were down-regulatedin triple negative breast cancer when compared with controls. Theseresults were observed after the analysis of the protein corona formedaround AgNPs (9.73 ± 1.70 nm).

Recently, similar studies found that elevated serum levels of retinolbinding protein 4 (RBP4) [58] and fibronectin (FINC) [59] were asso-ciated with breast cancer risk and they could be useful markers forpredicting poor prognosis in breast cancer patients.

In the particular case of apolipoprotein A-IV (APOA4), comparativeproteomic profiling of immunodepleted plasma of healthy and of BCindividuals revealed that this protein was also up-regulated in theplasma of the BC individuals. Furthermore, this protein was found to beinvolved in the pathogenesis of cancer and played an important role in

Table 6Significant proteins (p-value < 0.05) in comparisons between triple negative breast cancer and controls after the analysis of the protein corona of AuNPs(10.02 ± 0.91 nm).

Protein UniProt ID p-value Fold change

TNBC patients vs. controls Complement component C6 CO6 0.021032066 3.249007224 ↑ TNBCVitamin D-binding protein VTDB 0.000044522 1.663281358 ↑ TNBCComplement component C9 CO9 0.000172961 1.658309079 ↑ TNBCComplement C4-A CO4A 0.003482887 1.525866959 ↑ TNBCComplement C3 CO3 0.000115208 1.451520626 ↑ TNBCPlasminogen PLMN 0.005392898 1.329388215 ↑ TNBCVitronectin VTNC 0.004766057 1.282037959 ↑ TNBCApolipoprotein L1 APOL1 0.022535013 1.251913563 ↑ TNBCAfamin AFAM 0.020811026 1.248070740 ↑ TNBCComplement factor H CFAH 0.020861177 1.204471284 ↑ TNBCComplement C5 CO5 0.016073885 1.185058578 ↑ TNBCSerum albumin ALBU 0.004803944 1.172992995 ↑ TNBCKininogen-1 KNG1 0.024754757 1.167133709 ↑ TNBCGalectin-3-binding protein LG3BP 0.030403083 1.088223023 ↑ TNBCGlutathione peroxidase 3 GPX3 0.01510168 7.16326536 ↑ CONTROLImmunoglobulin heavy variable 5-51 HV551 0.02782821 4.90904368 ↑ CONTROLImmunoglobulin heavy constant mu IGHM 0.04111454 4.42849357 ↑ CONTROLApolipoprotein C-I APOC1 0.00460538 4.09989381 ↑ CONTROLImmunoglobulin lambda variable 3-9 LV39 0.0394842 3.35634858 ↑ CONTROLApolipoprotein D APOD 0.04050732 3.22034685 ↑ CONTROLImmunoglobulin heavy constant alpha 1 IGHA1 0.00960489 3.19128417 ↑ CONTROLImmunoglobulin kappa variable 2-29 KV229 0.00334596 2.48673552 ↑ CONTROLImmunoglobulin heavy constant alpha 2 IGHA2 0.02785381 2.43044159 ↑ CONTROLInter-alpha-trypsin inhibitor heavy chain H4 ITIH4 0.02521756 2.41781135 ↑ CONTROLImmunoglobulin lambda variable 4-69 LV469 0.00833282 2.37374475 ↑ CONTROLApolipoprotein(a) OS=Homo sapiens APOA 0.03681681 2.12362227 ↑ CONTROLPigment epithelium-derived factor PEDF 0.00858685 1.94114234 ↑ CONTROLAlpha-2-macroglobulin A2MG 0.00477503 1.9190963 ↑ CONTROLHemoglobin subunit beta HBB 0.00500101 1.90068671 ↑ CONTROLCD5 antigen-like CD5L 0.0397185 1.7759815 ↑ CONTROLSerum amyloid A-4 protein SAA4 0.04115058 1.77144695 ↑ CONTROLTetranectin TETN 0.01611908 1.7097494 ↑ CONTROLApolipoprotein A-II APOA2 0.01467561 1.67488772 ↑ CONTROLInter-alpha-trypsin inhibitor heavy chain H2 ITIH2 0.00096553 1.61898708 ↑ CONTROLCarboxypeptidase B2 CBPB2 0.00728968 1.46619249 ↑ CONTROLTransthyretin TTHY 0.04987991 1.45277849 ↑ CONTROLComplement C1r subcomponent-like protein C1RL 0.03887632 1.43784936 ↑ CONTROLInter-alpha-trypsin inhibitor heavy chain H1 ITIH1 0.00031251 1.41502216 ↑ CONTROLSerum paraoxonase/arylesterase 1 PON1 0.00142352 1.40629048 ↑ CONTROLApolipoprotein F APOF 0.03956903 1.39106396 ↑ CONTROLApolipoprotein M APOM 0.00064927 1.36179665 ↑ CONTROLApolipoprotein A-I APOA1 0.02341567 1.34721575 ↑ CONTROLSerotransferrin TRFE 0.0020036 1.31516423 ↑ CONTROLSelenoprotein P SEPP1 0.00426885 1.30905254 ↑ CONTROLMannan-binding lectin serine protease 1 MASP1 0.03643268 1.29206286 ↑ CONTROLCarboxypeptidase N subunit 2 CPN2 0.03700623 1.28514087 ↑ CONTROLProtein AMBP AMBP 0.0084045 1.22749917 ↑ CONTROLPlasma protease C1 inhibitor IC1 0.04106727 1.2095684 ↑ CONTROL

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Table 7Significant proteins (p-value < 0.05) in comparisons between triple negative breast cancer and controls after the analysis of the protein corona of AgNPs(9.73 ± 1.70 nm).

Protein UniProt ID p-value Fold change

TNBC patients vs.controls

C-reactive protein CRP 0.00550613 4.84567669 ↑ TNBCHistidine-rich glycoprotein HRG 3.22E-09 3.93436379 ↑ TNBCComplement component C9 CO9 6.28E-08 3.56825414 ↑ TNBCComplement C3 CO3 1.95E-08 3.03698879 ↑ TNBCComplement factor B CFAB 4.13E-09 2.83840576 ↑ TNBCImmunoglobulin kappa variable 2-24 KV224 0.00067261 2.77717871 ↑ TNBCFicolin-3 FCN3 7.79E-08 2.73769215 ↑ TNBCImmunoglobulin kappa variable 1-13 KV113 0.00613408 2.71041552 ↑ TNBCIgGFc-binding protein FCGBP 0.00010163 2.70272053 ↑ TNBCPhosphatidylinositol-glycan-specificphospholipase D PE=1 SV=3

PHLD 0.00305474 2.66489178 ↑ TNBC

Coagulation factor XIII B chain F13B 2.66E-07 2.60635841 ↑ TNBCFetuin-B FETUB 1.50E-06 2.55082102 ↑ TNBCApolipoprotein L1 APOL1 2.53E-10 2.52932257 ↑ TNBCSerum albumin ALBU 4.46E-09 2.51568102 ↑ TNBCInsulin-like growth factor-binding proteincomplex acid labile subunit

ALS 4.46E-09 2.49955167 ↑ TNBC

Inter-alpha-trypsin inhibitor heavy chain H3 ITIH3 7.30E-07 2.38250842 ↑ TNBCComplement factor I CFAI 1.02E-07 2.3647495 ↑ TNBCComplement factor H CFAH 4.83E-09 2.35326116 ↑ TNBCVitronectin VTNC 1.40E-09 2.31946741 ↑ TNBCC4b-binding protein alpha chain C4BPA 0.00011973 2.28632913 ↑ TNBCHyaluronan-binding protein 2 HABP2 3.29E-07 2.28034134 ↑ TNBCPlasminogen PLMN 7.79E-05 2.23378207 ↑ TNBCKeratin type II cytoskeletal 1 K2C1 0.00022394 2.20416961 ↑ TNBCKininogen-1 KNG1 1.18E-08 2.19276786 ↑ TNBCFibronectin FINC 3.36E-08 2.08858855 ↑ TNBCComplement C5 CO5 2.42E-08 2.01278029 ↑ TNBCComplement C1q subcomponent subunit C HUMAN 4.55E-05 2.01080564 ↑ TNBCApolipoprotein A-IV APOA4 4.61E-06 2.00364799 ↑ TNBCKeratin type I cytoskeletal 10 K1C10 0.00259975 1.97893282 ↑ TNBCTetranectin TETN 4.94E-05 1.95250374 ↑ TNBCClusterin CLUS 1.82E-06 1.9173471 ↑ TNBCComplement component C8 gamma chain CO8G 6.29E-06 1.89453705 ↑ TNBCSerotransferrin TRFE 4.33E-05 1.8939175 ↑ TNBCComplement C4-B CO4B 0.03028632 1.89103397 ↑ TNBCComplement C1q subcomponent subunit B C1QB 0.00018878 1.88786101 ↑ TNBCPlasma kallikrein KLKB1 2.85E-06 1.88182482 ↑ TNBCGalectin-3-binding protein LG3BP 1.76E-09 1.85166575 ↑ TNBCAlpha-2-HS-glycoprotein FETUA 1.70E-06 1.82851516 ↑ TNBCComplement C1q subcomponent subunit A C1QA 0.00028866 1.80691958 ↑ TNBCProthrombin THRB 0.0030656 1.68707742 ↑ TNBCRetinol-binding protein 4 RET4 5.98E-06 1.65111784 ↑ TNBCCarboxypeptidase N catalytic chain CBPN 0.00562324 1.62654965 ↑ TNBCComplement component C8 beta chain CO8B 0.00298689 1.58131021 ↑ TNBCSex hormone-binding globulin SHBG 0.04298875 1.5528487 ↑ TNBCSerum amyloid P-component SAMP 0.00436056 1.55023657 ↑ TNBCZinc-alpha-2-glycoprotein ZA2G 0.00133379 1.54906716 ↑ TNBCBeta-2-glycoprotein 1 APOH 0.00192644 1.53648273 ↑ TNBCVitamin K-dependent protein S PROS 1.47E-06 1.52146296 ↑ TNBCComplement C4-A CO4A 0.00378789 1.50875703 ↑ TNBCMannan-binding lectin serine protease 1 MASP1 0.00057947 1.49286664 ↑ TNBCComplement C2 CO2 1.04E-05 1.48927332 ↑ TNBCCoagulation factor X FA10 0.00022691 1.47652463 ↑ TNBCComplement C1r subcomponent C1R 0.03355753 1.46634915 ↑ TNBCAlpha-1-acid glycoprotein 2 A1AG2 0.04347799 1.46608046 ↑ TNBCAntithrombin-III ANT3 0.00109057 1.45858581 ↑ TNBCAfamin AFAM 0.02956547 1.42380083 ↑ TNBCVitamin D-binding protein VTDB 0.00689951 1.40641874 ↑ TNBCHemopexin HEMO 0.00014568 1.38633651 ↑ TNBCCarboxypeptidase N subunit 2 CPN2 0.03697862 1.36334001 ↑ TNBCProtein AMBP AMBP 0.0013811 1.32566307 ↑ TNBCApolipoprotein E APOE 0.00068645 1.28506343 ↑ TNBCAttractin ATRN 0.0072892 1.27421822 ↑ TNBCN-acetylmuramoyl-L-alanine amidase PGRP2 0.00138778 1.23991258 ↑ TNBCApolipoprotein M APOM 0.01360078 1.23954254 ↑ TNBCImmunoglobulin heavy variable 3-15 HV315 4.18E-06 75.3967905 ↑ CONTROLImmunoglobulin heavy constant gamma 1 IGHG1 1.34E-07 42.3987588 ↑ CONTROLAlpha-1-antichymotrypsin AACT 6.14E-10 40.3625379 ↑ CONTROLImmunoglobulin heavy variable 3-73 HV373 6.06E-07 37.7464233 ↑ CONTROLImmunoglobulin heavy constant gamma 3 IGHG3 3.68E-06 36.0478431 ↑ CONTROLCeruloplasmin CERU 2.42E-09 33.0108942 ↑ CONTROLImmunoglobulin heavy constant gamma 2 IGHG2 1.72E-06 32.3391318 ↑ CONTROL

(continued on next page)

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Table 7 (continued)

Protein UniProt ID p-value Fold change

Alpha-1-antitrypsin A1AT 1.14E-10 28.6542019 ↑ CONTROLImmunoglobulin heavy constant mu IGHM 7.89E-05 26.3705332 ↑ CONTROLCorticosteroid-binding globulin CBG 1.19E-09 25.9868345 ↑ CONTROLImmunoglobulin heavy constant gamma 4 IGHG4 0.00011456 21.9488461 ↑ CONTROLCholinesterase CHLE 1.48E-08 21.7678062 ↑ CONTROLImmunoglobulin kappa variable 4-1 KV401 2.21E-07 20.676772 ↑ CONTROLImmunoglobulin heavy variable 4-59 HV459 4.18E-08 19.791237 ↑ CONTROLImmunoglobulin heavy variable 3-30-5 HV335 1.61E-07 18.8319962 ↑ CONTROLImmunoglobulin lambda variable 1-47 LV147 6.19E-05 18.715722 ↑ CONTROLImmunoglobulin lambda variable 8-61 LV861 2.74E-08 18.5700233 ↑ CONTROLImmunoglobulin kappa variable 6-21 KV621 0.0062062 16.6110906 ↑ CONTROLImmunoglobulin heavy variable 3-49 HV349 2.43E-08 16.3703203 ↑ CONTROLImmunoglobulin kappa constant IGKC 2.49E-06 15.6449675 ↑ CONTROLImmunoglobulin lambda constant 3 IGLC3 1.61E-05 15.2226153 ↑ CONTROLMMS19 nucleotide excision repair proteinhomolog

MMS19 0.01743218 15.0005868 ↑ CONTROL

Immunoglobulin kappa variable 3-20 KV320 5.98E-09 14.9565997 ↑ CONTROLImmunoglobulin kappa variable 3-15 KV315 4.41E-09 13.7845573 ↑ CONTROLImmunoglobulin lambda-like polypeptide 1 IGLL1 9.67E-08 11.9419924 ↑ CONTROLImmunoglobulin lambda variable 3-9 LV39 4.22E-07 11.8083139 ↑ CONTROLImmunoglobulin heavy variable 5-51 HV551 4.24E-07 11.4284904 ↑ CONTROLImmunoglobulin kappa variable 3-11 KV311 5.08E-09 11.3796551 ↑ CONTROLImmunoglobulin kappa variable 1-33 KV133 7.64E-07 11.2884931 ↑ CONTROLImmunoglobulin heavy constant alpha 2 IGHA2 6.08E-05 10.9418774 ↑ CONTROLImmunoglobulin lambda-like polypeptide 5V=2

IGLL5 9.57E-05 10.7682004 ↑ CONTROL

Angiotensinogen ANGT 3.42E-11 8.97430276 ↑ CONTROLPigment epithelium-derived factor PEDF 8.81E-10 8.87937962 ↑ CONTROLImmunoglobulin lambda variable 7-43 LV743 3.62E-06 8.19552518 ↑ CONTROLHemoglobin subunit alpha HBA 0.0003338 7.79643124 ↑ CONTROLImmunoglobulin heavy constant alpha 1 IGHA1 1.48E-07 7.66824876 ↑ CONTROLImmunoglobulin lambda variable 3-25 LV325 1.49E-06 7.44370609 ↑ CONTROLImmunoglobulin heavy variable 4-28 HV428 1.38E-06 7.26533223 ↑ CONTROLImmunoglobulin kappa variable 1D-12 KVD12 1.17E-09 7.18559345 ↑ CONTROLHeparin cofactor 2 HEP2 2.44E-07 7.13401112 ↑ CONTROLInter-alpha-trypsin inhibitor heavy chain H4 ITIH4 3.63E-08 6.70198308 ↑ CONTROLImmunoglobulin lambda variable 9-49 LV949 0.00017391 6.43567288 ↑ CONTROLAlpha-1B-glycoprotein A1BG 1.70E-09 6.00033312 ↑ CONTROLApolipoprotein A-I APOA1 1.65E-09 5.16689397 ↑ CONTROLImmunoglobulin lambda variable 3-19 LV319 1.92E-08 5.05592895 ↑ CONTROLImmunoglobulin kappa variable 2-29 KV229 2.59E-07 5.02231162 ↑ CONTROLImmunoglobulin heavy variable 6-1 HV601 2.19E-05 4.85408469 ↑ CONTROLImmunoglobulin heavy variable 1-46 HV146 9.86E-06 4.84652628 ↑ CONTROLImmunoglobulin heavy variable 3-23 HV323 1.25E-05 4.75302522 ↑ CONTROLCholesteryl ester transfer protein CETP 1.22E-05 4.64058921 ↑ CONTROLApolipoprotein A-II APOA2 2.16E-08 4.62885891 ↑ CONTROLThyroxine-binding globulin THBG 1.80E-08 4.45994071 ↑ CONTROLProtein Z-dependent protease inhibitor ZPI 4.44E-05 4.43951224 ↑ CONTROLSerum amyloid A-4 protein SAA4 1.38E-07 4.39608833 ↑ CONTROLImmunoglobulin heavy variable 1-69 HV169 0.00058138 4.33088897 ↑ CONTROLImmunoglobulin kappa variable 1-9 KV109 3.99E-06 4.20727924 ↑ CONTROLGelsolin GELS 7.03E-09 4.09546999 ↑ CONTROLCD44 antigen CD44 0.00233936 4.0837648 ↑ CONTROLApolipoprotein C-I APOC1 0.00038806 3.66742486 ↑ CONTROLAlpha-2-macroglobulin A2MG 3.58E-09 3.65331919 ↑ CONTROLApolipoprotein D APOD 3.95E-08 3.53083248 ↑ CONTROLKallistatin KAIN 4.78E-10 3.48621242 ↑ CONTROLImmunoglobulin kappa variable 1-5 KV105 8.42E-05 3.22054945 ↑ CONTROLLeucine-rich alpha-2-glycoprotein A2GL 1.29E-08 3.00855446 ↑ CONTROLHemoglobin subunit beta HBB 8.64E-05 2.890312 ↑ CONTROLCoiled-coil domain-containing protein 8 CCDC8 6.16E-07 2.88193259 ↑ CONTROLImmunoglobulin kappa variable 2D-28 KVD28 0.00104876 2.6282284 ↑ CONTROLImmunoglobulin kappa variable 6D-21 KVD21 0.01695916 2.61227937 ↑ CONTROLImmunoglobulin lambda variable 4-69 LV469 8.14E-05 2.35130225 ↑ CONTROLBiotinidase BTD 0.00622085 2.19095666 ↑ CONTROLPlasma serine protease inhibitor IPSP 3.04E-05 2.1835559 ↑ CONTROLCarboxypeptidase B2 CBPB2 8.55E-05 1.8115321 ↑ CONTROLImmunoglobulin heavy variable 2-26 HV226 0.00977698 1.69947422 ↑ CONTROLApolipoprotein B-100 APOB 2.35E-05 1.66025805 ↑ CONTROLAlpha-2-antiplasmin A2AP 0.00014843 1.60147275 ↑ CONTROLApolipoprotein C-III APOC3 0.02144075 1.39259865 ↑ CONTROL

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the regulation of metastasis [60].Validation assays verified and confirmed that pregnancy zone pro-

tein (PZP), coagulation factor V (FA5), protein Z-dependent proteaseinhibitor (ZPI), alpha-1-acid glycoprotein 1 (A1AG1) and inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1), were statistically significantand appeared quantitatively up-regulated in triple negative breastcancer patients when compared with controls (healthy people). Thisresult was observed after the analysis of the protein corona formedaround MNPs (9.30 ± 0.67 nm).

Mainly, the pregnancy zone protein (PZP) was found to be im-plicated the pathogenesis of breast cancer [61].

It is well known that cancer is associated with hypercoagulability,and circumstantial evidence suggests that tumor-expressed coagulationfactors actively support cancer pathogenesis and progression. Notably,single nucleotide polymorphisms (SNPs) in F5, encoding coagulationfactor V (FA5), have been found associated with breast cancer. In 2018,M. Tinholt et al. [62] found that FA5 expression was higher in breasttumors compared to normal tissue. Importantly, FA5 expression wassignificantly increased in patients with tumors of aggressive nature(hormone receptor negative-, triple negative-, HER2 enriched-, andbasal-like tumors). These authors also suggested that tumor-expressedFA5 could be a possible marker of aggressive breast cancer, and it could

Table 8Significant proteins (p-value < 0.05) in comparisons between triple negative breast cancer and controls after the analysis of the protein corona of MNPs(9.30 ± 0.67 nm).

Protein UniProt ID p-value Fold change

TNBC patients vs. control C-reactive protein CRP 0.00169149 3.40849571 ↑ TNBCLipopolysaccharide-binding protein LBP 0.00837847 2.93851762 ↑ TNBCImmunoglobulin kappa variable 2-24 KV224 0.00583062 2.27733934 ↑ TNBCSerum amyloid P-component SAMP 0.00010388 2.20872021 ↑ TNBCKeratin. type II cytoskeletal 2 epidermal K22E 0.01682722 2.10615702 ↑ TNBCComplement C4-B CO4B 0.04180946 1.9800469 ↑ TNBCComplement component C9 CO9 5.75E-06 1.90848624 ↑ TNBCIgGFc-binding protein FCGBP 0.01481938 1.86546782 ↑ TNBCProtein Z-dependent protease inhibitor ZPI 0.00252121 1.78800298 ↑ TNBCComplement C1q subcomponent subunit B C1QB 0.00199072 1.78457812 ↑ TNBCPhosphatidylinositol-glycan-specific phospholipase D PHLD 0.00435099 1.73844701 ↑ TNBCProthrombin THRB 0.00750791 1.72802617 ↑ TNBCComplement C4-A CO4A 0.00510952 1.67059345 ↑ TNBCAlpha-1-acid glycoprotein 1 A1AG1 0.02552784 1.64048407 ↑ TNBCApolipoprotein C-III APOC3 0.03298589 1.61371634 ↑ TNBCKeratin. type II cytoskeletal 1 K2C1 0.01154011 1.60905471 ↑ TNBCSex hormone-binding globulin SHBG 0.02523265 1.59168855 ↑ TNBCInter-alpha-trypsin inhibitor heavy chain H3 ITIH3 0.00066933 1.55061894 ↑ TNBCComplement C3 CO3 6.41E-06 1.53090904 ↑ TNBCKeratin. type I cytoskeletal 10 K1C10 0.0384444 1.52567319 ↑ TNBCC4b-binding protein alpha chain C4BPA 0.00027605 1.51193215 ↑ TNBCComplement factor H CFAH 2.79E-05 1.46546396 ↑ TNBCPlasma serine protease inhibitor IPSP 0.00973255 1.4623027 ↑ TNBCHistidine-rich glycoprotein HRG 0.01164428 1.4616738 ↑ TNBCComplement factor B CFAB 0.00061903 1.45925143 ↑ TNBCPregnancy zone protein PZP 0.00155229 1.44791482 ↑ TNBCComplement C1q subcomponent subunit A C1QA 0.02763702 1.4062295 ↑ TNBCCoagulation factor V FA5 0.01312931 1.40131004 ↑ TNBCComplement factor I CFAI 7.52E-05 1.40080904 ↑ TNBCComplement C2 CO2 0.00061386 1.33762187 ↑ TNBCHemopexin HEMO 3.66E-05 1.29733733 ↑ TNBCCoagulation factor XIII B chain F13B 0.01256843 1.28491873 ↑ TNBCComplement C1q subcomponent subunit C C1QC 0.03780247 1.26491165 ↑ TNBCKininogen-1 KNG1 0.00429848 1.26291248 ↑ TNBCBeta-2-glycoprotein 1 APOH 0.04746608 1.24963502 ↑ TNBCApolipoprotein L1 APOL1 0.00237944 1.23613552 ↑ TNBCAntithrombin-III ANT3 0.01869897 1.22888805 ↑ TNBCProtein AMBP AMBP 0.01434176 1.22857819 ↑ TNBCVitronectin VTNC 0.01666796 1.21690616 ↑ TNBCInter-alpha-trypsin inhibitor heavy chain H1 ITIH1 0.01847152 1.20880937 ↑ TNBCInsulin-like growth factor-binding protein complex acid labile subunit ALS 0.03555036 1.20445695 ↑ TNBCHyaluronan-binding protein 2 HABP2 0.01352977 1.18411135 ↑ TNBCClusterin CLUS 0.04346683 1.1756767 ↑ TNBCAlpha-2-HS-glycoprotein FETUA 0.02891723 1.16730605 ↑ TNBCGalectin-3-binding protein LG3BP 0.00292722 1.116735 ↑ TNBCApolipoprotein C-I APOC1 0.02222358 2.50213212 ↑ CONTROLImmunoglobulin heavy constant mu IGHM 0.00022527 2.032717896 ↑ CONTROLImmunoglobulin lambda variable 8-61 LV861 0.00063688 2.023228572 ↑ CONTROLImmunoglobulin lambda variable 3-19 LV319 0.0082843 1.73294072 ↑ CONTROLCD5 antigen-like CD5L 0.02066785 1.731935965 ↑ CONTROLImmunoglobulin lambda variable 3-9 LV39 0.02497423 1.576244295 ↑ CONTROLApolipoprotein F APOF 0.02131359 1.556435198 ↑ CONTROLImmunoglobulin kappa variable 6-21 KV621 0.00608326 1.473240834 ↑ CONTROLImmunoglobulin heavy variable 3-23 HV323 0.02960462 1.38352045 ↑ CONTROLImmunoglobulin heavy variable 4-28 HV428 0.02327515 1.304707447 ↑ CONTROLPlasma kallikrein KLKB1 0.04204343 1.23839695 ↑ CONTROLApolipoprotein M APOM 0.01063485 1.232467766 ↑ CONTROL

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emerge as a promising tumor suppressor candidate.Finally, protein Z-dependent protease inhibitor (ZPI) were identified

by immunochemistry in breast cancer cells, whereas they were absentfrom normal breast tissue [63], and alpha-1-acid glycoprotein (AGP)was also found to be a potential biomarker for breast cancer in 'at risk'individuals, particularly, TNBC patients [64].

4. Discussion

In this study, we provided by the first time the application of acombined proteomic approach as DDA and SWATH-MS to develop thecharacterization and quantification of triple negative breast cancerproteins after bio-corona nanoparticle protein pre-concentration.

Gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles(AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs:(9.30 ± 0.67 nm) were assessed in biomarker discovery as a tool forthe pre-concentration and separation of proteins from complex pro-teomes. To this end, sera from eight healthy individuals were comparedwith sera from eight patients diagnosed with triple negative breastcancer. The application of these nanomaterials, combined with massspectrometry, has allowed the identification of seven potential bio-markers for the diagnostic and control of TNBC progression: GRF-typezinc finger domain-containing protein 1 (protein ZGRF1), Matrix me-talloproteinase-9 (MMP9), Lebercilin and Immunoglobulin lambdavariable 3-27 (LV327) and LINE-1 type transposase domain-containingprotein 1 (LITD1), structural maintenance of chromosomes protein 6(SMC6) and short coiled-coil protein (SCOC).

After performing over these samples, a SWATH analysis to quantifythe protein changes, the separation between the group of healthypeople and the group of triple negative breast cancer patients was ob-served. Moreover, a lot of deregulated proteins among both groupswere observed. However, these proteins are not among the alteredproteins find by the qualitative DDA assay. The proteomic methodsused in this study are complementary and allow improving character-ization studies. The fact that they are complementary and not ne-cessarily identify the same proteins is due to the search methods DDA(qualitative), and IDA (SWATH-quantitative) analysis is different.

So, in the analysis of the protein corona formed around the threenanoparticles (AuNPs, AgNPs, and MNPs), eight common proteinsshowed to be statistically significant and appeared quantitatively in-creased (up-regulated) in triple negative breast cancer patients versuscontrols (healthy people). These proteins are complement componentC9 (CO9), complement C4-A (CO4A), complement C3 (CO3), vi-tronectin (VTNC), apolipoprotein L1 (APOL1), complement factor H(CFAH), kininogen-1 (KNG1), galectin-3-binding protein (LG3BP). Andthree common proteins appeared quantitatively decreased (down-regulated) in triple negative breast cancer patients versus controls(healthy people). These proteins are immunoglobulin heavy constantmu (IGHM), immunoglobulin lambda variable 3-9 (LV39), and apoli-poprotein C-I (APOC1).

Moreover, a lot of deregulated proteins not common to all samplesincubated with the different nanoparticles were found. Therefore,protein corona formed around AuNPs in breast cancer patients showed14 proteins up-regulated and 34 proteins down-regulated, in compar-ison with the protein corona formed around AuNPs in healthy people.In the case of the protein corona formed around AgNPs, 64 proteinswere found to be up-regulated, and 76 proteins down-regulated.Finally, in the protein corona formed around the MNPs, 45 proteins hadelevated expression, while 12 proteins showed down-regulation. Fromthis point of view, AgNPs seem to be more suitable for a clinicaltranslational purpose, because the analysis of the protein coronaformed around these systems allows better differentiation between bothgroups of study: healthy and diseased individuals.

All these proteins can be considered potential triple negative breastcancer biomarker candidates; however, these proteins were found whenserum samples were concentrated using a bio-corona nanoparticle.

Fig. 5. Quantitative Venn diagrams showing the number of up-regulated pro-teins found in the protein corona of 10.02 ± 0.91 nm gold nanoparticles(AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 9.30 ± 0.67 nmmagnetic nanoparticles (MNPs) after their incubation with serum from eighttriple negative breast cancer patients and eight healthy controls.

Fig. 6. Quantitative Venn diagrams showing the number of down-regulatedproteins found in the protein corona of 10.02 ± 0.91 nm gold nanoparticles(AuNPs), 9.73 ± 1.70 nm silver nanoparticles (AgNPs) and 9.30 ± 0.67 nmmagnetic nanoparticles (MNPs) after their incubation with serum from eighttriple negative breast cancer patients and eight healthy controls.

Fig. 7. Volcano plots of the SWATH analysis of proteins between healthy andtriple negative breast cancer patients with AuNPs (10.02 ± 0.91 nm), AgNPs(9.73 ± 1.70 nm) and MNPs (9.30 ± 0.67 nm). X-axis shows log(2)-foldchange and Y-axis the statistical significance through -log(10)-pvalue. The dotlines represent the cut off (p value ≤0.05). Proteins biomarkers are those thatwere common and statistical significative in all the nanoparticles.

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Thus, it was thought that the best validation could be finding theseproteins in total serum samples. To this aim, it was performed a newSWATH-MS analysis improving the library, and it allowed us to findseveral validated proteins.

When it was compared total serum SWATH with concentratedproteins in AgNPs, we found again complement component C8 (CO8G),ficolin-3 (FCN3), retinol-binding protein 4 (RET4), fibronectin (FINC),fetuin-B (FETUB) and apolipoprotein A-IV (APOA4), up-regulated; andimmunoglobulin heavy constant gamma 4 (IGHG4), immunoglobulinkappa variable 2D-28 (KVD28), immunoglobulin kappa variable 1-5(KV105), apolipoprotein C-III (APOC3), immunoglobulin heavy vari-able 1-46 (HV146), immunoglobulin heavy constant gamma 2(IGHG2), immunoglobulin heavy constant gamma 3 (IGHG3), im-munoglobulin kappa variable 4-1 (KV401), immunoglobulin kappavariable 1D-12 (KVD12) and immunoglobulin heavy variable 3-30-5(HV335), down-regulated. It is consistent with data from previousstudies, and these proteins can be considered validated. In the com-parison between total serum SWATH and MNPs only found 5 up-regulated proteins pregnancy zone protein (PZP), coagulation factor V(FA5), protein Z-dependent protease inhibitor (ZPI), alpha-1-acid gly-coprotein 1 (A1AG1) and inter-alpha-trypsin inhibitor heavy chain H1(ITIH1). And finally, in the comparison between total serum SWATHand AuNPs, only one down-regulated protein was validated: trans-thyretin (TTHY).

This study shows that serum proteomics is a valuable tool that canfacilitate comprehensive and systematic identification of the serumproteome under both healthy and disease conditions. Thus, serumproteomics could be used for disease diagnosis and prognosis. In ourcase, we found several breast cancer-specific markers that can be usedin the diagnosis. However, due to we found a lot of differentiatedproteins, it is necessary complementary assays to reduce the number ofprotein biomarkers.

Declaration of competing interest

The authors declare that they have no conflict of interest.

Acknowledgment

All authors acknowledge Miguel Servet I Programme (CP16/00139)from the “Instituto de Salud Carlos III” (Plan Estatal de I+D+i 2013-2016 and European Development Regional Fund) of the SpanishMinistry of Science, Innovation and Universities. A. Castro López andM.P. Chantada-Vázquez contributed equally to this work.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jprot.2019.103581.

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M. del Pilar Chantada-Vázquez, et al. Journal of Proteomics 212 (2020) 103581

19

International Journal of

Molecular Sciences

Article

Protein Corona Gold Nanoparticles FingerprintingReveals a Profile of Blood Coagulation Proteins in theSerum of HER2-Overexpressing BreastCancer Patients

María del Pilar Chantada-Vázquez 1,2, Antonio Castro López 3, María García-Vence 2,Benigno Acea-Nebril 4 , Susana B. Bravo 2,* and Cristina Núñez 1,*

1 Research Unit, Lucus Augusti University Hospital (HULA), Servizo Galego de Saúde (SERGAS),27002 Lugo, Spain; mariadelpilarchantadavazquez@gmail.com

2 Proteomic Unit, Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital ofSantiago de Compostela (CHUS), 15706 Santiago de Compostela, Spain; mariagarve@outlook.es

3 Breast Unit, Hospital Universitario Lucus Augusti (HULA), Servizo Galego de Saúde (SERGAS), 27002 Lugo,Spain; Antonio.Castro.Lopez@sergas.es

4 Department of Surgery, Breast Unit, Complexo Hospitalario Universitario A Coruña (CHUAC),Servizo Galego de Saúde (SERGAS), 15006 A Coruña, Spain; Benigno.Acea.Nebril@sergas.es

* Correspondence: sbbravo@gmail.com (S.B.B.); cristina.nunez.gonzalez@sergas.es (C.N.)

Received: 15 October 2020; Accepted: 9 November 2020; Published: 10 November 2020�����������������

Abstract: Breast cancer (BC) is a molecularly heterogeneous disease that encompasses five majormolecular subtypes (luminal A (LA), luminal B HER2 negative (LB-), luminal B HER2 positive (LB+),HER2 positive (HER2+) and triple negative breast cancer (TNBC)). BC treatment mainly dependson the identification of the specific subtype. Despite the correct identification, therapies could failin some patients. Thus, further insights into the genetic and molecular status of the different BCsubtypes could be very useful to improve the response of BC patients to the range of availabletherapies. In this way, we used gold nanoparticles (AuNPs, 12.96 ± 0.72 nm) as a scavenging toolin combination with Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS)to quantitatively analyze the serum proteome alterations in the different breast cancer intrinsicsubtypes. The differentially regulated proteins specific of each subtype were further analyzed withthe bioinformatic tools STRING and PANTHER to identify the major molecular function, biologicalprocesses, cellular origin, protein class and biological pathways altered due to the heterogeneityin proteome of the different BC subtypes. Importantly, a profile of blood coagulation proteins wasidentified in the serum of HER2-overexpressing BC patients.

Keywords: protein corona (PC); gold nanoparticles (AuNPs); breast cancer (BC); fingerprinting;SWATH-MS; HER2+

1. Introduction

Breast cancer (BC) is a heterogeneous disease that presents a wide variety of molecular and clinicalcharacteristics, as well as variability in clinical progression [1]. For the treatment choice, patients areclassified according to intrinsic biological subtypes within the BC spectrum, using clinical-pathologicalcriteria, i.e., the recognition of amplification and/or overexpression of the human epidermal growthfactor receptor 2 (HER2) oncogene, the immunohistochemical classification of the estrogen receptor(ER) and the progesterone receptor (PR) and Ki-67 labeling index [2]. This classification allows fora more personalized approach to medical treatments, with favorable results. However, despite thatalmost 10–15% of these patients still experience local or distant recurrences in the first five years

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from diagnosis [3]. Particularly, HER2-positive BC, defined by the overexpression of HER2 protein,represents 15–20% of BC cases [4,5] and is correlated with poor prognosis, high rates of recurrence andshort survival [6].

Classification of BC might be markedly improved if new biomarkers identified with the use ofhigh-throughput “omics” approaches could support diagnosis based on histopathological patterns [7–9].Nanomaterials have been introduced into the field of proteomics to establish a new and rapidly evolvingresearch area termed nanoproteomics [10].

It is well known that the dispersion of a nanomaterial in physiological fluid results in the formationof a protein shell named “protein corona” (PC). PC varies depending on the characteristics of thebiological media, the physical (size, shape, curvature) and chemical properties (composition, surfacecharge/chemistry and hydrophobicity/hydrophilicity) of the nanomaterial and the incubation time [11].Disease-associated biomarkers comprise less than 1% of serum proteins. In this way, through theformation of the PC, nanoparticles could act as sorbent materials for the enrichment of low-abundancepeptides/proteins presented in serum samples before the biomarker identification by mass spectrometry(MS) analysis [12–14]. Importantly, otherwise undetectable changes in protein concentration at anearly stage of the disease (as breast cancer), after any treatment (chemotherapy, immunotherapy) orsurgery could be detected analyzing the PC composition [15]. Thus, characterization of the PC aroundNPs offers distinct advantages over sole proteomic approaches and increases the success of identifyingmolecular targets [16].

Particularly, AuNPs present some properties to be used as suiTable Sorbent nanomaterials: highsurface-area-to-volume ratio, colloidal stability and the ability to conjugate with biomolecules [17].Here, the interaction of AuNPs (12.96 ± 0.72 nm) with the sera of disease-free women (healthy controls,HC) (n = 42) and BC patients (n = 42) allowed the pre-concentration of the low-abundance proteinsthorough the PC formation. Then, an exhaustive quantitative analysis of the PCs by SWATH-MSwas carried out to identify novel molecular targets associated to the different BC intrinsic subtypes(see Figure 1).

Figure 1. Simplified representation of the experimental procedure. AuNPs (12.96 ± 0.72 nm) wereincubated ex vivo with human serum samples obtained from HC (n = 42) and BC patients (n = 42).Ex vivo corona-coated AuNPs were recovered and purified from unbound proteins by centrifugation.The formed PCs were quantitatively characterized, analyzed by SWATH-MS and compared betweenthe groups.

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2. Results

2.1. Incubation of AuNPs (12.96 ± 0.72 nm) with Human Serum Samples: Ex Vivo Protein Corona Formationand Characterization

Human serum samples from n = 42 healthy controls (HC) and n = 42 breast cancer (BC) patients(n = 42) were recruited, handled and analyzed in the same way as further detailed in Figure 2. The groupof BC patients was divided into the following biological subtypes: n = 11 patients with the luminal A(LA) subtype, n = 10 patients with the luminal B HER2 negative (LB-) subtype, n = 7 patients with theluminal B HER2 positive (LB+) subtype, n = 6 patients with the HER2 positive (HER2+) subtype andn = 8 patients with the triple negative breast cancer (TNBC) subtype. Patient clinical characteristics aresummarized in Table S1.

Figure 2. Flowchart depicting serum samples pretreatment and protein corona formation.

AuNPs with a size of 12.96 ± 0.72 nm were prepared by a chemical reduction method [12–14].As Figure 2 shows, proteins presented in serum samples (×2) were chemically reduced withdithiothreitol (DTT) and alkylated with iodoacetamide (IAA) before their ex vivo incubation withAuNPs (12.96 ± 0.72 nm) to get the formation of the PCs [12–14].

After the ex vivo incubation of AuNPs with human serum samples of HC (n = 42) and BC patients(n = 42), the resultant protein corona-coated AuNPs were centrifugated and structurally characterizedby dynamic light scattering (DLS) and negative stain transmission electron microscopy (TEM). DLSmeasurements showed that the interaction of serum proteins with the surface of AuNPs resulted in anincrease of the size of the AuNPs, from 12.96 ± 0.72 to 17.33 ± 1.55 (HC) and 17.13 ± 1.53 nm (BC) (seeTable S2). Probably, the preferential interaction of positively charged proteins with the AuNPs surfacepromoted the increase of the mean particle surface charge from −38.3 (bare AuNPs) to −30.5 (HC) and−30.3 mV (BC) [18,19]. TEM imaging revealed a well-dispersed nanoparticles population corroboratingthe PC formation around AuNPs (see Figure 3).

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Figure 3. Negative stain TEM imaging of bare (A) and protein corona-coated AuNPs, recoveredpost-incubation with human serum obtained from HC (B) and BC patients (C). All scale bars are 50 nm.

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2.2. Quantitative Analysis of the Protein Corona-Coated AuNPs by SWATH-MS

Corona proteins associated with AuNPs were separated by sodium dodecyl sulfate–polyacrylamidegel electrophoresis (SDS-PAGE). After a staining step, gels were processed following the methoddescribed in Section 4.5. The resulting peptides were then quantitatively analyzed by the emergingproteomic platform for label-free quantification SWATH-MS.

The comparison of the protein patterns of the ex vivo formed PCs allowed the identification of thedifferentially expressed proteins between HC and the different BC subtypes. The results were filteredto present a p-value ≤ 0.05, and, interestingly, n = 60 proteins were found to be differentially expressed,of which n = 42 were upregulated and n = 18 downregulated in BC patients for the LA subtype; n = 132were found to be differentially expressed (n = 100 upregulated and n = 32 downregulated) for theLB- subtype; n = 67 proteins were found to be differentially expressed (n = 59 upregulated and n = 8downregulated) for the LB+ subtype; n = 130 proteins were found to be differentially expressed (n = 95upregulated and n = 35 downregulated) for the HER2+ subtype; and n = 91 proteins were found tobe differentially expressed (n = 87 upregulated and n = 4 downregulated) for the TNBC subtype (seeTable 1). The full list of candidate protein biomarkers identified to be upregulated or downregulatedin each different BC subtypes in comparison to healthy controls (HC) with the fold-change values isshown in Table S3.

Table 1. Number of differentially expressed proteins (up- and downregulated) (p-value ≤ 0.05) foundin the protein patterns of the ex vivo formed coronas after the analysis by SWATH-MS for the differentbreast cancer subtypes (LA LB-, LB+, HER2+ and TNBC) in comparison with healthy controls (HC)samples. The number of differentially expressed proteins (up- and downregulated) specific to each ofthe five subtypes of BC found in the ex vivo formed coronas is also indicated.

SWATH-MS Analysis

ComparisonProtein Number (p-Value ≤ 0.05)

Total Upregulated Downregulated Specific Upregulated Downregulated

Controls vs. LA 60 42 18 8 4 4

Controls vs. LB- 132 100 32 27 25 2

Controls vs. LB+ 67 59 8 2 2 0

Controls vs. HER2+ 130 95 35 28 23 5

Controls vs. TNBC 91 87 4 10 9 1

The Venn diagram of statistically significant (up- and down)regulated proteins shows that sevenproteins were found to be commonly altered in all BC subtypes (see Figure 4): apolipoprotein C-III(APOC3), c-reactive protein (CRP), hemoglobin subunit beta (HBB), immunoglobulin heavy variable3–49 (IGHV3–49), serum amyloid A-4 protein (SAA4), serum amyloid P-component (APCS) andserotransferrin (TF) (Tables S4 and S5).

As mentioned above, subtype-specific unique proteins were also identified using SWATH-MS (seeFigure 4 and Table 2). Overall, n = 8 proteins were specifically associated in LA (of which n = 4 wereupregulated and n = 4 showed downregulation). For LB-, n = 27 proteins were found to be specificto this subtype (n = 25 with increased expression and n = 2 with decreased expression). In the LB+

subtype, only n = 2 specific proteins were found to be upregulated. In the HER2+ subtype, n = 28specific proteins were found (n = 23 upregulated and n = 5 downregulated). The TNBC subtypecomprised of n = 10 specific proteins (n = 9 upregulated and n = 1 downregulated).

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Figure 4. Venn diagram showing the number of shared and specific (or unique) deregulated proteinsidentified in the PCs formed after the interaction of AuNPs (12.96 ± 0.72 nm) with serum samples ofthe different BC subtypes (LA, LB-, LB+, HER2+ and TNBC).

Table 2. Differentially expressed proteins (up- and downregulated) (p-value ≤ 0.05) found in theprotein patterns of the ex vivo formed coronas after the analysis by SWATH-MS specific (or unique) forthe different breast cancer subtypes (LA, LB-, LB+, HER2+ and TNBC) in comparison with healthycontrol (HC) samples. The fold change ratio was calculated as the ratio of geometric means of thesample replicates, which corresponds to calculating the normal arithmetic ratio of log-transformedareas and back-transforming.

Protein Name Gene p-Value Fold ChangeComplement C1r

subcomponent-like protein C1RL 0.0000979 1.614689351 ↑ Luminal A

Complement factor H-related protein 2 CFHR2 0.003228805 1.764346734 ↑ Luminal AComplement component C8 beta chain C8B 0.003730112 1.35440489 ↑ Luminal A

Lysosome-associated membraneglycoprotein 2 LAMP2 0.018383379 1.33466653 ↑ Luminal A

Immunoglobulin kappa variable 3–20 IGKV3–20 0.008581213 7.24153822 ↓ Luminal AImmunoglobulin heavy constant mu IGHM 0.038320909 2.180105502 ↓ Luminal AImmunoglobulin heavy variable 1–24 IGHV1–24 0.045225189 2.766559939 ↓ Luminal A

Protein Z-dependent protease inhibitor SERPINA10 0.045960336 2.020513374 ↓ Luminal AImmunoglobulin lambda variable 2–23 IGLV2–23 0.000000251 2.96506068 ↑ Luminal B HER2 NegImmunoglobulin heavy variable 3–53 IGHV3–53 0.00000690 3.231686892 ↑ Luminal B HER2 NegImmunoglobulin kappa variable 4–1 IGKV4–1 0.00000788 2.271628981 ↑ Luminal B HER2 Neg

Biotinidase BTD 0.0000142 1.571319968 ↑ Luminal B HER2 NegImmunoglobulin heavy constant alpha 1 IGHA1 0.0000181 2.45029046 ↑ Luminal B HER2 Neg

Serum paraoxonase/lactonase 3 PON3 0.0000344 1.614088523 ↑ Luminal B HER2 NegImmunoglobulin kappa constant IGKC 0.0000604 2.234283878 ↑ Luminal B HER2 Neg

Phospholipid transfer protein PLTP 0.000127276 1.491697089 ↑ Luminal B HER2 NegImmunoglobulin kappa variable 3–11 IGKV3–11 0.000235484 2.727555354 ↑ Luminal B HER2 NegImmunoglobulin heavy variable 3–9 IGHV3–9 0.000418498 2.709287474 ↑ Luminal B HER2 Neg

Alpha-mannosidase 2 MAN2A1 0.000577191 2.094925838 ↑ Luminal B HER2 NegImmunoglobulin heavy constant gamma 1 IGHG1 0.000838666 1.832731289 ↑ Luminal B HER2 Neg

Apolipoprotein B-100 APOB 0.001926757 1.618943103 ↑ Luminal B HER2 NegImmunoglobulin heavy constant alpha 2 IGHA2 0.00207989 2.137794634 ↑ Luminal B HER2 Neg

Basement membrane-specific heparansulfate proteoglycan core protein HSPG2 0.002185804 2.617271104 ↑ Luminal B HER2 Neg

Pregnancy zone protein PZP 0.002663618 4.343426266 ↑ Luminal B HER2 NegImmunoglobulin heavy variable 1–69D IGHV1–69D 0.00307313 2.762019962 ↑ Luminal B HER2 NegImmunoglobulin heavy variable 3–74 IGHV3–74 0.005843078 1.63300774 ↑ Luminal B HER2 Neg

Immunoglobulin lambda-likepolypeptide 5 IGLL5 0.006474796 1.674164832 ↑ Luminal B HER2 Neg

Immunoglobulin kappa variable 3D-20 IGKV3D-20 0.007387257 1.722873358 ↑ Luminal B HER2 NegL-lactate dehydrogenase B chain LDHB 0.011088233 1.464826821 ↑ Luminal B HER2 Neg

Platelet glycoprotein Ib alpha chain GP1BA 0.014355443 1.435497779 ↑ Luminal B HER2 NegApolipoprotein D APOD 0.02689663 1.565894604 ↑ Luminal B HER2 Neg

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Table 2. Cont.

Protein Name Gene p-Value Fold ChangeImmunoglobulin lambda variable 3–21 IGLV3–21 0.030887039 1.531598796 ↑ Luminal B HER2 Neg

Mediator of RNA polymerase IItranscription subunit 23 MED23 0.034189832 1.582094144 ↑ Luminal B HER2 Neg

Platelet factor 4 variant PF4V1 0.013149247 8.695958011 ↓ Luminal B HER2 NegComplement C1s subcomponent C1S 0.040782815 1.400513823 ↓ Luminal B HER2 Neg

Plastin-2 LCP1 0.002018957 4.501473239 ↑ Luminal B HER2 PosImmunoglobulin heavy variable 6–1 IGHV6–1 0.020191442 1.539338405 ↑ Luminal B HER2 Pos

Complement C5 C5 0.000000000000002952.104078338 ↑ HER2 PosAdiponectin ADIPOQ 0.00000000828 9.579128591 ↑ HER2 Pos

Immunoglobulin heavy variable 3–73 IGHV3–73 0.00000167 15.87006684 ↑ HER2 PosCoagulation factor XII F12 0.00000250 4.48323521 ↑ HER2 Pos

Plasma kallikrein KLKB1 0.0000205 2.839283512 ↑ HER2 PosImmunoglobulin heavy variable 3–23 IGHV3–23 0.0000883 2.952483084 ↑ HER2 Pos

Immunoglobulin lambda variable 1–51 IGLV1–51 0.000382645 2.419267666 ↑ HER2 PosImmunoglobulin heavy variable 3–64 IGHV3–64 0.000401774 2.217759109 ↑ HER2 Pos

Selenoprotein P SELENOP 0.000539614 3.796988329 ↑ HER2 PosImmunoglobulin kappa variable 1D-12 IGKV1D-12 0.00188481 4.471370936 ↑ HER2 PosImmunoglobulin lambda variable 5–45 IGLV5–45 0.00772078 2.858034161 ↑ HER2 PosImmunoglobulin lambda variable 6–57 IGLV6–57 0.009251968 5.252644969 ↑ HER2 Pos

Keratin type I cytoskeletal 10 KRT10 0.012361191 1.599012598 ↑ HER2 PosImmunoglobulin kappa variable 1–27 IGKV1–27 0.014636279 3.663302525 ↑ HER2 PosImmunoglobulin kappa variable 1–5 IGKV1–5 0.015089737 3.097611266 ↑ HER2 Pos

EGF-containing fibulin-like extracellularmatrix protein 1 EFEMP1 0.015909032 1.962284999 ↑ HER2 Pos

Immunoglobulin kappa variable 2–24 IGKV2–24 0.019946847 3.792528291 ↑ HER2 PosImmunoglobulin heavy constant gamma 2 IGHG2 0.020013155 1.605775681 ↑ HER2 Pos

Adipocyte plasmamembrane-associated protein APMAP 0.021204299 23.89017843 ↑ HER2 Pos

Immunoglobulin kappa variable 1D-16 IGKV1D-16 0.023927557 15.36602613 ↑ HER2 PosCoagulation factor V F5 0.025887503 3.739380413 ↑ HER2 Pos

Cysteine-rich secretory protein 3 CRISP3 0.034870563 3.338300324 ↑ HER2 PosImmunoglobulin heavy variable 3–33 IGHV3–33 0.038394512 8.344444793 ↑ HER2 PosN-acetylmuramoyl-L-alanine amidase PGLYRP2 0.000321687 1.799194526 ↓ HER2 Pos

Alpha-1-antitrypsin SERPINA1 0.001111283 4.959761437 ↓ HER2 PosTrypsin-1 PRSS1 0.002454431 4.217786063 ↓ HER2 Pos

Apolipoprotein F APOF 0.005336626 7.55893037 ↓ HER2 PosAntithrombin-III SERPINC1 0.018612975 1.308597079 ↓ HER2 PosApolipoprotein E APOE 0.005108321 1.297484301 ↑ Triple Negative

Voltage-dependent L-type calcium channelsubunit alpha-1F CACNA1F 0.010760078 3.330169664 ↑ Triple Negative

Complement C2 C2 0.024115534 1.265184448 ↑ Triple NegativeKeratin. type II cytoskeletal 1 KRT1 0.02492244 1.394646766 ↑ Triple Negative

Immunoglobulin heavy variable 4–30-2 IGHV4–30-2 0.028590349 4.459516925 ↑ Triple NegativeAttractin ATRN 0.033317422 1.248304377 ↑ Triple Negative

Immunoglobulin kappa variable 2D-30 IGKV2D-30 0.035772725 1.512873113 ↑ Triple NegativeImmunoglobulin kappa variable 1–6 IGKV1–6 0.039260496 1.537751007 ↑ Triple Negative

Platelet basic protein PPBP 0.04971791 23.75806076 ↑ Triple NegativeCD5 antigen-like CD5L 0.012543111 1.999836008 ↓ Triple Negative

2.3. Functional Pathway and Network Analysis for Subtype Specific Breast Cancer

The differentially regulated proteins specific to each of the five subtypes of BC found in theex vivo formed coronas were analyzed with the PANTHER [20] tool to identify the major molecularfunction (Figure S1), biological processes (Figure S2), cellular origin (Figure S3), protein class (Figure 5)and biological pathways (Figure S4) altered due to the heterogeneity in proteome of the differentBC subtypes.

Molecular functions of the differentially regulated proteins specific to each of the five subtypes ofBC were found to be associated with binding, catalytic activity, molecular regulation and transportation(Figure S1). Furthermore, with the exception of the specific proteins identified in the LB+ subtype,the majority of profiled proteins were of extracellular origin (Figure S3).

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Figure 5. Classification according to the protein class of the differentially regulated proteinsspecific to each of the five subtypes of BC found in the ex vivo formed coronas analyzed withthe PANTHER database.

During the past decade, insight has been gained about the role of the immunological responsein the BC disease process [21] and the possible use of immunological parameters in the prognosisof BC [22]. The PANTHER classification according to their protein class revealed that most of thedifferential proteins belong to defense/immunity (Figure 5). In the present work, from the 75 specificproteins identified for the different BC subtypes (n = 8 in LA; n = 27 in LB-; n = 2 in LB+; n = 28in HE; and n = 10 in TNBC), 34 proteins were immunoglobulins (n = 3 in LA; n = 14 in LB-; n = 1in LB+; n = 13 in HER2+; and n = 3 in TNBC) (see Table 2). Previous works also found that serumimmunoglobulin levels were related to the disease stage and tumor load in BC patients [23].

Immune cell activation was also shown to be an altered pathway in BC which was enriched onlyfor the LA subtype in the present study (Figure S4). Immunoglobulin heavy constant mu (IGHM) wasdownregulated for B cell activation in LA, indicating that antibody-mediated immune response wasimplicated in this subtype. Probably, the tumor alters the immune system mechanism to suppress theB cell activation promoting this downregulation.

Complement activation is an important factor of innate immunity and a defense system againstinfecting pathogens. Furthermore, complement activation also participates in the adaptive immuneresponse. Particularly in BC, complement activation contributes to cancer progression [24]. In thepresent work, three complement system components implicated in the innate immune response wereidentified in the PC for the different BC subtypes: complement component C8 beta chain (C8B) for LA,complement C5 (C5) for HER2+ and complement C2 (C2) for TNBC (see Table 2). Previous studies alsofound deregulation of some of these complement system components in the sera of BC patients [25,26].

Other deregulated proteins which may play a role in the innate immune system were complementC1r subcomponent-like protein (C1RL) and complement factor H-related protein 2 (CFHR2) (bothupregulated) in the LA subtype; complement C1s subcomponent (C1S) (downregulated) in the LB-subtype; plastin-2 (LCP1) (upregulated) in the LB+ subtype; cysteine-rich secretory protein 3 (CRISP3)(upregulated) and N-acetylmuramoyl-L-alanine amidase (PGLYRP2) (downregulated) in the HER2+

subtype; and keratin, type II cytoskeletal 1 (KRT1) (upregulated) in the TNBC subtype (see Table 2).Particularly, LCP1 plays a role in the activation of T-cells and its exosomal release by breast cancer cellswas found to facilitate metastatic bone osteolysis [27].

After the analysis of the protein corona, HER2+ subtype was enriched with coagulation factorV (F5), coagulation factor XII (F12) and plasma kallikrein (KLKB1) (upregulated), while alpha-1

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-antitrypsin (SERPINA1), trypsin-1 (PRSS1) and antithrombin-III (SERPINC1) (downregulated) forblood coagulation pathway (see Tables 2 and 3, and Figure S4).

Table 3. Candidate deregulated blood coagulation biomarkers in HER2-overexpressing BC patientsfound after the proteomic analysis of the ex vivo corona-coated AuNPs. On the bottom, a cluster ofblood coagulation proteins found in the protein–protein interaction network map of the genes encodeddifferentially regulated proteins for the HER2-overexpressing BC patients found after the proteomicanalysis of the ex vivo corona-coated AuNPs.

Protein Name Gene p-Value Fold Change Control vs. HER2 PositiveCoagulation factor XII F12 0.00000250 4.48323521 ↑ HER2 Pos

Plasma kallikrein KLKB1 0.0000205 2.839283512 ↑ HER2 PosCoagulation factor V F5 0.025887503 3.739380413 ↑ HER2 PosAlpha-1-antitrypsin SERPINA1 0.001111283 4.959761437 ↓ HER2 Pos

Trypsin-1 PRSS1 0.002454431 4.217786063 ↓ HER2 PosAntithrombin-III SERPINC1 0.018612975 1.308597079 ↓ HER2 Pos

Platelets, as small cell fragments, are not only important coagulation-related factors, also playa vital role in tumor progression [28]. Particularly, platelet factor 4 (PF4) or CXCL4, a member ofCXC chemokine family, acts as an angiogenesis inhibitor which may contribute to prevent tumormetastasis [29]. In the present work, it was observed that the inflammation mediated by chemokine andcytokine signaling pathway was enriched specifically for LB- subtype with the platelet factor 4 variant(PF4V1) being downregulated (Figure S4). Another protein of the family of platelets, the platelet basicprotein (PPBP), was upregulated in the TNBC subtype (see Table 2).

The gonadotropin-releasing hormone receptor pathway was found to be enriched with adiponectin(ADIPOQ) (upregulated) and voltage-dependent L-type calcium channel subunit alpha-1F (CACNA1F)(upregulated) in the HER2+ and TNBC subtypes, respectively (Figure S4).

In the present work, a group of proteins implicated in the combination and transportationof lipids, apolipoproteins, were also found to be deregulated in the LB- (apolipoprotein B-100(APOB), apolipoprotein D (APOD) and phospholipid transfer protein (PLTP); upregulated), HER2+

(apolipoprotein F (APOF); downregulated) and TNBC subtypes (apolipoprotein E (APOE); upregulated)(see Table 2).

Another family of potential molecular targets that was found to be deregulated in the present studyincludes some glycoproteins: lysosome-associated membrane glycoprotein 2 (LAMP2) (upregulated)in the LA subtype; platelet glycoprotein Ib alpha chain (GP1BA) and basement membrane-specificheparan sulfate proteoglycan core protein (HSPG2) (upregulated) in the LB- subtype; EGF-containingfibulin-like extracellular matrix protein 1 (EFEMP1) and selenoprotein P (SELENOP) (upregulated) inthe HER2+ subtype; and CD5 antigen-like (CD5L) (downregulated) in the TNBC subtype (see Table 2).

While some proteins with an enzymatic functionality such as biotinidase (BTD), serumparaoxonase/lactonase 3 (PON3), L-lactate dehydrogenase B chain (LDHB) and alpha-mannosidase 2(MAN2A1) were found to be the upregulated in LB- subtype, protein Z-dependent protease inhibitor(SERPINA10) were downregulated in the LA subtype (see Table 2).

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3. Discussion

Nowadays, the different breast cancer intrinsic subtypes (LA, LB-, LB+, HER2+ and TNBC) guidethe therapy selection [2]. In some patients, therapies could fail for different reasons such as cancerrecurrence, therapy resistance and/or metastasis. Thus, therapy response in breast cancer patientscould be improved with the study of the molecular alterations at the subtype level.

The application of different omics approaches to deep insight the different BC subtypes allowedthe refinement of the complexity of tumor heterogeneity. In this way, a variety of quantitative proteomicstudies were developed to identify potential signatures for breast cancer using clinical samples such assaliva, the ductal lavage fluids, nipple aspirate fluids (NAFs), urine and tissue [30]. Importantly, bloodmay be a suiTable Sample source for studying the proteomic deregulation in the different BC subtypeswith minimally invasive collection procedures. Thus, the analysis of blood-based markers at thesubtype level in biological fluids such as plasma and serum allowed finding different protein markersrelated with the tumor microenvironment and the subtype-specific changes. Following this researchline, the proteomic alterations in blood plasma [7] and blood serum [8] of BC subtypes were explored.

Potential biomarkers are presented in very low concentrations in blood (less than 1% of bloodproteins). Thus, the isolation and enrichment of low-abundance peptides/proteins from complexmixtures is a mandatory step in the proteomic biomarkers pipeline, and nanoparticles represent anideal alternative [31].

In the present work, an exhaustive quantitative analysis of the PCs formed around AuNPs aftertheir incubation in serum samples was developed by SWATH-MS to identify novel molecular targetsassociated to the different BC intrinsic subtypes (see Figure 1).

Seven proteins were found to be commonly altered in all BC subtypes, namely APOC3, CRP,HBB, IGHV3–49, SAA4, APCS and TR. CRP and SAA4 are acute-phase proteins (APPs), a class ofproteins whose serum concentrations increase or decrease in response to inflammation. Particularly,a significant association of state of inflammation with stage of BC was previously described [32].A recent study found that elevated serum levels of CRP were associated considerably with a high riskof BC and poor outcome, including metastasis and recurrence [33]. In addition, the concentrations ofSAA4 increased gradually with tumor progression and the severity of BC stages [34]. Thus, CRP andSAA4 may be good candidate markers for the staging and prognosis of BC. The expression of HBB andTR, members of the globin family was also found to be associated with BC cells aggressiveness andpoor prognosis, indicating HBB and TR as novel biomarker for BC progression [35,36].

On the other hand, several studies point out that blood coagulation proteins develop an importantrole in tumor progression [37]. These works discussed the impact of the activation of the blood clottingcascade on primary tumor growth [38], tumor metastasis and cancer-associated thrombosis [39] andantitumor therapies that target blood-coagulation-associated proteins [40].

In the particular case of BC, different reports support blood coagulation proteins as an importantpatient factor that facilitates the metastatic potential [41]. Particularly, metastatic patients exhibitedsignificantly higher D-dimer values when compared with early breast cancer patients [42]. Furthermore,high plasma fibrinogen was found to be correlated with poor response to trastuzumab treatment inHER2 positive BC patients [43] and circulating levels of factor VIII (FVIII) were significantly associatedwith axillary lymph node involvement, number of metastatic nodes and HER2 status [44]. These studiessupport that the measurement of some coagulation-related biomarkers could provide additional datafor the evaluation of HER2 positive BC patients’ prognosis and could be novel molecular targets.

The present quantitative proteomic analysis revealed a profile of blood coagulation proteins forthe HER2+ subtype, namely F5, F12 and KLKB1 (upregulated), and SERPINA1, PRSS1 and SERPINC1)(downregulated). While F5 is expressed in tumors and indicates favorable outcome in aggressiveBC [45], F12 is involved in the pathogenesis of thrombosis through the induction and amplification ofthrombin generation [46].

KLKB1 (up), SERPINA1 (down) and SERPINC1 (down) are serine proteases. Particularly,SERPINA1 and SERPINC1 are serine proteases inhibitors (serpins) which belong to the protease inhibitor

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family. Members of the kallikrein family, such as KLKB, were also found to be deregulated duringmalignant transformation [47]. Nevertheless, the variations in expression (downregulation/upregulation),activation and secretion are not substantial enough to consider them as suitable biomarkers for follow-updisease progression. SERPINA1 is synthesized and released by tumor cells and plays major rolesin physiologic and pathologic processes such as angiogenesis, tumor invasion and metastasis [48].It was found that that high expression of SERPINA1 could be predictive for a better clinical outcomeof ER+ and ER+/HER2+ patients. Thus, SERPINA1 was found to be a direct ER target gene and apredictor of survival in BC patients [49]. SERPINC1, an antithrombin, develops an important role asan inhibitor of the coagulation cascade. Furthermore, SERPINC1 also functions as an anti-angiogenic,anti-inflammatory, anti-viral and anti-apoptotic protein. The mechanism by which antithrombincontrols invasion, tumor migration and angiogenesis is by inhibition of enteropeptidase. This inhibitionshowed to be a double anti-tumor effect through producing an anti-angiogenic molecule and inhibitinga protease implicated in metastasis [50].

In the present work, the gonadotropin-releasing hormone receptor pathway was found to beenriched with adiponectin (ADIPOQ) in the HER2+ subtype. Although different studies reportedcontroversial findings in the association between ADIPOQ and BC, a recent meta-analysis suggests thatlow serum adiponectin concentration may be associated with an increased BC risk in premenopausaland postmenopausal women [51]. A negative correlation has been also demonstrated between ADIPOQlevels and tumor size and grade. Interestingly, the correlation between ADIPOQ and BC seems to bemore prominent in estrogen-negative and progesterone-negative BC [52]. Therefore, it seems theremay be a set group of BC patients that are most susceptible to the effects of ADIPOQ and would benefitmost from a potential treatment. ADIPOQ may serve as a biomarker of BC risk and help to identifysubjects at high risk for BC development.

Numerous research articles have accumulated solid evidence that lipoproteins are closely relatedto various types of tumorigenesis, as BC [53]. Apolipoproteins in the blood transfer lipids to cancercells to provide energy for cancer cell proliferation and invasion. Apolipoproteins also function asimportant factors in cellular signal transduction. In the present work, different apolipoproteins werefound to be deregulated in the different BC subtypes. Particularly, APOB, APOD and APOE in serumwere found to function as a risk factor for BC, being APOD and APOB involved in BC metastasis [54].Particularly, a recent study found that apolipoprotein B is a risk factor for development of intraocularmetastasis (IOM) in patients with BC [55].

Within the group of glycoproteins found to be deregulated in the present study, it was foundthat LAMP2 overexpression in breast tumors promotes cancer cell survival via chaperone-mediatedautophagy (CMA) [56]. Thus, inhibiting CMA activity in breast tumor cells (with a chemotherapeuticdrug, for example) can be exploited as a potential therapeutic application in the treatment of BC.HSPG2, also known as perlecan, is a heavily glycosylated protein component of the extra-cellularmatrix (ECM) that plays essential roles in tumor vascularization, that is closely related to tumorgrowth and metastasis [57]. Although HSPG2 expression in BC has not been examined in detail,a recent study investigated the expression of HSPG2 in human TNBC and the ability of anti-HSPG2antibodies to specifically target and inhibit tumor growth in a mouse xenograft model [58], showingthat HSPG2 is a promising therapeutic target in TNBC. EFEMP1, also known as fibulin 3, may have apotential cancer-promoting function in BC [59]. EFEMP1 expression decreases during BC progression,with low EFEMP1 levels correlating with a poorer prognosis. Functionally, high EFEMP1 levelsinhibited TGF-β-induced EMT, migration, invasion and endothelial permeability, while loss of EFEMP1expression/function promoted these TGF-β-mediated effects. Further, restoring EFEMP1 expression inbreast cancer cells inhibited TGF-β signaling, breast cancer cell EMT, invasion and metastasis in vivo.Although the role of CD5L in the oncogenesis of BC is not fully understood, a recent work found thatCD5L is upregulated in hepatocellular carcinoma and promotes liver cancer cell proliferation andantiapoptotic responses [60].

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The enzyme LDHB, which was found to be upregulated in the LB- subtype, may help identifybreast cancers most likely to respond to neoadjuvant chemotherapy as well as those with the highestrisk of relapse that may benefit from additional adjuvant therapy [61].

All these novel molecular targets found in the serum of BC patients could detect a missing invasion,be performed in ambulatory settings, be repeatedly checked, and be applicable for BC diagnosis,the assessment of prognosis and selection of treatment.

Importantly, further insights exploring the deregulated blood coagulation proteins as potentialeffective prognosis biomarkers and targets for novel therapeutic approaches could have a great impactin the management of HER2-overexpressing BC patients.

4. Materials and Methods

4.1. Chemicals

Acrylamide/bis-acrylamide 30% solution (37.5:1), β-mercaptoethanol (molecular biology grade),Coomassie Brilliant Blue R250 (CBB), DL-dithiothreitol (HSCH2CH(OH)CH(OH)CH2SH, 99%) (DTT),glycerol (HOCH2CH(OH)CH2OH, 86–88%), iodoacetamide (IAA, ICH2CONH2, 99%), sodium citratetribasic dehydrate (HOC(COONa)(CH2COONa)2·2H2O, 99%), sodium carbonate (Na2CO3, 99%),tris-base (NH2C(CH2OH)3), trifluoroacetic acid (CF3COOH, 99%), trypsin from bovine pancreas andthe Sigma Marker wide range 6.5–200 kDa were purchased from Sigma-Aldrich. Formaldehydefor molecular biology (36.5–38% in H2O) and sodium dodecyl sulfate (SDS, CH3(CH2)11SO4Na)were purchased from Panreac. Bromophenol-blue was purchased from Riedel-de Haen. Hydrogentetrachloroaurate (III) hydrate (HAuCl4·xH2O) (99.9%-Au) (49% Au) at 10% w/v was purchased fromStrem Chemicals. Ammoniumbicarbonate (AMBIC, NH4HCO3, 99.5%) and formic acid (HCOOH,95%) were purchased from Fluka.

4.2. Biological Samples

Blood samples were collected from n = 42 newly diagnosed BC patients with the five differentbreast cancer subtypes: n = 11 patients with the luminal A subtype (ER positive, HER2 negative, Ki-67low, and PR high), n = 10 patients with the luminal B-HER2 negative subtype (ER positive, HER2negative, and either Ki-67 high or PR low), n = 7 patients with the luminal B-HER2 positive subtype(ER positive, HER2 overexpressed or amplified, any Ki-67 and any PR), n = 6 patients with the HER2positive subtype (HER2 over-expressed or amplified, ER and PR absent) and n = 8 patients with thetriple negative subtype (ER and PR absent and HER2 negative).

Blood samples were also collected from n = 42 age-matched and gender-matched healthy women(controls). In all cases, venous blood samples were collected in VACUETTE® Serum Clot ActivatorTubes (10 mL).

The experiment was conducted in conformity with the declaration of Helsinki and approved bythe Clinical Research Ethics Committees (CEIC) of Galicia (Spain) with approval number 2017-021.All participants from Lucus Augusti University Hospital (Spain) gave written informed consent priorto their participation.

4.3. Synthesis of Citrate-Gold Nanoparticles (AuNPs, 12.96 ± 0.72 nm)

Colloidal AuNPs with a size of 12.96 ± 0.72 nm were prepared by chemical reduction methodas per the protocol developed previously [12–14]. In short, sodium citrate tribasic solution (0.075%w/v) was dissolved in 60 mL warm distilled water under constant magnetic stirring. To this, 54 µLof 10% w/v of hydrogen tetrachloroaurate (III) hydrate solution was then added drop-wise and thereaction was heated to 100 ◦C under constant magnetic stirring. Reaction was allowed to proceedfurther the color of the solution changes from yellow to deep red indicating the reduction of Au3+ toAu0, which spontaneously aggregates to form colloidal AuNPs. This colloidal dispersion of AuNPswas cooled to room temperature and preserved at 4 ◦C for further analysis.

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4.4. Incubation of AuNPs with Human Serum Samples: Ex Vivo Protein Corona Formation

Firstly, collected blood samples were allowed to clot for 15 min. After that, samples werecentrifuged for 5 min at 4 ◦C and 1800× g. Resultant serum were transferred to sterile cryovials, frozenand stored at −80 ◦C until further use at Research Unit, Lucus Augusti University Hospital (HULA).The formation of the ex vivo PC was achieved following the steps shown in Figure 2 [12–14]. Afterthat, unbound serum proteins from the surface of AuNPs were removed by centrifugation at 18,840× gfor 30 min.

4.5. Characterization of Colloidal AuNPs

The morphology of the AuNPs was investigated by transmission electron microscopy (TEM)with a JEM 1011, JEOL instrument. The size and ζ-potentials of colloidal AuNPs were measured(3 determinations per sample) with a Malvern Zetasizer Nano ZS instrument at 25 ◦C.

Protein quantification and protein separation by SDS-PAGE were carried out with the use of aQubit™ 4 Quantitation Starter Kit (Thermo Fisher Scientific) and a PowerPacTM Basic Power Supply(Bio-Rad), respectively.

4.6. Separation and Digestion of the Proteins Presented in the Corona-Coated AuNPs

Corona proteins associated with AuNPs were separated by sodium dodecyl sulfate–polyacrylamidegel electrophoresis (SDS-PAGE) and digested following the scheme represented in Figure 6.

Figure 6. Flowchart depicting the separation and digestion of the corona proteins associatedwith AuNPs.

The digestion was stopped with the addition of 50 µL of 5% (v/v) formic acid. After that,the extraction of the peptides from the gel was carried out with a solution of 50% (v/v) acetonitrile/0.1%(v/v) trifluoroacetic acid (TFA) (×3) and acetonitrile (ACN) (×1). Samples were dried and stored at−20 ◦C until their further use [62].

4.7. Protein Quantification by SWATH-MS

SWATH/MS experiments were carried out following the instrumental parameters describedelsewhere [13]. Briefly, two biological replicates of LA, LB-, LB+, HER+, TNBC and HC samples wereused to get extensive quantitative data by label-free SWATH-MS analysis. Peptides of all samples were

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analyzed with a micro-LC system Ekspert nLC425 (Eksigen, Dublin, CA. USA) couplet to a hybridquadrupole-TOF mass spectrometer Triple TOF 6600 (Sciex, Redwood City. CA. USA). One of the firststeps is the construction of the MS/MS spectral libraries. For that purpose, peptide solutions wereanalyzed by a shotgun data-dependent acquisition (DDA) approach by micro-LC-MS/MS. For spectralalignment and peak extraction was employed the Peakview software (version 2.2; AB Sciex) usingthe SWATH Acquisition MicroApp (version 2.0). Parameters used were: number of fragments = 7,number of peptides = 10, peptide confidence = 95%, XIC width = 30 ppm and XIC extraction window= 5 min. Exportation of the SWATH file to the MarkerView software (version 1.3.1; AB Sciex) allowedthe quantitative analysis of ions, peptides and proteins in the different samples. As output result,the summed intensity of ions for the peptide, summed intensity of the peptides for protein and Areaunder Curve (AUC) of the ions were provided. The test set (LA, LB-, LB+, HER+ and TNBC) wascompared with the control (HC) dataset to generate fold change ratios. For protein quantitation,only peptides with a False Discovery Rate (FDR) below 1% were considered. Average MS peak area ofeach protein derived from the analysis of the biological replicates and Student’s t-test analysis amongsamples was developed. t-test indicates the capacity of each variable to distinguish between twogroups, and it was reported as a p-value. The criterion to select differentially expressed proteins was ap-value <0.05 with a 1.5-fold in- or decrease.

4.8. Protein Functional Interaction Network Analysis and Protein Ontology Classification

The informatic tool STRING v.10.0 database (http://string-db.org) was the used to analyze thefunctional interaction networks of the proteins, integrating direct (physical) and indirect protein–proteininteractions (PPI) [63].

Protein ontology classification was performed with the PANTHER classification system (http://www.pantherdb.org/). The differentially expressed proteins in the different breast cancer subtypeswere grouped according to their major molecular function, biological processes, cellular origin, proteinclass and biological pathways.

5. Conclusions

The quantitative comparison of the ex vivo PCs formed upon incubation of AuNPs with serumsamples obtained from BC patients revealed 75 deregulated subtype-specific unique proteins (8, 27, 2,28 and 10 proteins specifically associated to the LA, LB-, LB+, HER2+ and TNBC subtypes, respectively).The analysis of the ex vivo PCs formed onto AuNPs revealed a profile of blood coagulation proteinsin the serum of HER2-overexpressing BC patients that are implicated in breast tumor progression,including cellular transformation, proliferation, tumor cell survival and angiogenesis. Of all BC patients,HER2+ patients have the worst outcome. Further insights exploring these blood coagulation proteinsas potential effective prognosis biomarkers and targets for novel therapeutic approaches could have agreat impact on the management of HER2-overexpressing BC patients.

Supplementary Materials: The following are available online at http://www.mdpi.com/1422-0067/21/22/8449/s1.Figure S1. Classification according to the molecular function of the differentially regulated proteins specific toeach of the five subtypes of BC found in the ex vivo formed coronas analyzed with the PANTHER database,Figure S2. Classification according to the biological process of the differentially regulated proteins specific toeach of the five subtypes of BC found in the ex vivo formed coronas analyzed with the PANTHER database,Figure S3. Classification according to the cellular component of the differentially regulated proteins specific toeach of the five subtypes of BC found in the ex vivo formed coronas analyzed with the PANTHER database,Figure S4. Classification according to the biological pathway of the differentially regulated proteins specific toeach of the five subtypes of BC found in the ex vivo formed coronas analyzed with the PANTHER database,Table S1. Clinical features of breast cancer tumors, Table S2. The average mean hydrodynamic diameter (nm)values of bare and protein corona-coated AuNPs, recovered post-incubation with human serum obtained fromHC and BC patients, Table S3. Differentially expressed proteins (up- and downregulated) (p-value ≤ 0.05) foundin the protein patterns of the ex vivo formed coronas after the analysis by SWATH-MS for the different breastcancer subtypes (LA, n = 11; LB-, n = 10; LB+, n = 7; HER2+, n = 6; TNBC, n = 8) in comparison with healthycontrol (HC) samples. The accession number, species (Human) and fold change values are also reported, Table S4.Differentially expressed proteins (up- and downregulated) (p-value ≤ 0.05) found in the protein patterns of the

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ex vivo formed coronas after the analysis by SWATH-MS for the different breast cancer subtypes (LA, n = 11;LB-, n = 10; LB+, n = 7; HER2+, n = 6; TNBC, n = 8) in comparison with healthy control (HC) samples, Table S5.Differentially expressed proteins (up- and downregulated) (p-value ≤ 0.05) found in the protein patterns of theex vivo formed coronas after the analysis by SWATH-MS common and specific for the different breast cancersubtypes (LA, n = 11; LB-, n = 10; LB+, n = 7; HER2+, n = 6; TNBC, n = 8) in comparison with healthy control(HC) samples. The accession number, gene name and species (Human) are reported.

Author Contributions: Conceptualization, C.N.; methodology, M.d.P.C.-V., A.C.L. and C.N.; software, M.d.P.C.-V.,A.C.L., S.B.B. and C.N.; formal analysis, M.d.P.C.-V., A.C.L., S.B.B. and C.N.; investigation, M.d.P.C.-V., A.C.L. andC.N.; resources, S.B.B., M.G.-V., B.A.-N. and C.N.; writing—original draft preparation, C.N.; writing—review andediting, C.N.; visualization, C.N.; supervision, C.N.; project administration, C.N.; and funding acquisition, C.N.All authors have read and agreed to the published version of the manuscript.

Funding: This work was supported by the grant from Instituto de Salud Carlos III (ISCIII) and European RegionalDevelopment Fund (FEDER) (CP16/00139).

Acknowledgments: We would like to acknowledge the essential contribution of the “Unidade de MicroscopíaElectrónica e Confocal” at “Campus Terra, Universidade de Santiago de Compostela, Spain” for the transmission electronmicroscopy (TEM) and ζ-potentials measurements. M.P. Chantada-Vázquez and A. Castro López contributedequally to this work.

Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations

APPs Acute-phase proteinsAuNPs Gold nanoparticlesBC Breast cancerDTT DithiothreitolER Estrogen receptorHC Healthy controlsHER2 Human epidermal growth factor receptor 2IAA Iodoacetic acidLA Luminal ALB Luminal BMS Mass spectrometryPC Protein coronaPR Progesterone receptorSDS-PAGE Sodium dodecyl sulfate–polyacrylamide gel electrophoresisSWATH-MS Sequential Window Acquisition of All Theoretical Mass SpectraTEM Transmission electron microscopyTNBC Triple negative breast cancerTF Serotransferrin

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