REVIEW

Proteomics in pancreatic cancer research

Daniela Cecconi1, Marta Palmieri2 and Massimo Donadelli2

1 Department of Biotechnology, Proteomics and Mass Spectrometry laboratory, University of Verona, Verona, Italy2 Department of Life and Reproduction Sciences, Section of Biochemistry, University of Verona, Verona, Italy

Received: June 30, 2010

Revised: August 12, 2010

Accepted: August 25, 2010

In this review, we give an overview of the actual role of proteomic technologies in the study of

pancreatic cancers (PCs). We describe PC proteomics on the basis of sample origins, i.e.

tissues, body fluids, and PC cell lines. As regards PC tissues, we report the identification of a

number of candidate biomarkers of precursor lesions that may allow early diagnosis of this

neoplasia. Moreover, we describe cytoskeletal and hypoxia-regulated proteins that confirm the

involvement of cytoskeleton modifications and metabolism adaptations in carcinogenesis. We

also discuss the most important biomarkers identified by proteomic analysis involved in local

invasion and distant metastasis, and in the cross-talk between pancreatic tumor and the

surrounding stroma. Furthermore, we report novel candidate biomarkers identified in serum,

plasma, and pancreatic juice of cancer patients compared with cancer-free controls. Proteo-

mic alterations in PC cell line models as compared to normal controls and studies on cell

lines treated with drugs or new agents to understand their mechanism of pharmacological

action or the onset of drug resistance are also presented. Finally, we discuss the recent

improvements obtained in classical 2-DE and high-throughput proteomic strategies able to

allow the overcoming of relevant proteomic drawbacks.

Keywords:

Biomarker / Biomedicine / Pancreatic cancer

1 Introduction

Pancreatic cancer (PC) is an aggressive disease and is the

fifth most frequent cause of cancer-related mortality in

Western society [1]. The exocrine and endocrine cells of

the pancreas can generate completely different types of

tumors. About 95% of exocrine PCs are pancreatic ductal

adenocarcinomas (PDAC), while the remaining percentage

includes other types of tumors (for example adenosquamous

carcinomas and hepatoid carcinomas). PDAC is character-

ized by a unique molecular fingerprint constituted by

alterations of the K-ras, p53, p16INK4a, and DPC4/Smad4genes. Pancreatic endocrine tumors (PET) make up about

1% of the total PCs and are yet poorly characterized. Up to

now the most frequently found genetic event is the inacti-

vation of MEN1 gene, while mutations of genes typically

involved in PDAC are uncommon.

The extremely poor prognosis of the majority of PCs is

mainly due to the delayed diagnosis, early metastasis, and

resistance to almost all classes of cytotoxic drugs. For these

reasons, it is extremely important to find new prognostic,

diagnostic, and therapeutical biomarkers.

Proteomic tools have been extensively used in the last

decade for unravelling the early mechanisms of cancer

onset, studying chemoresistance, as well as, for discovering

biomarkers toward preventive, predictive, and personalized

medicine [2]. In this review, we give an overview of the

actual role of proteomics in the study of PCs (Fig. 1).

Furthermore, we discuss novel potential biomarkers

Abbreviations: ABPP, activity-based proteomic profiling; COF1,

cofilin-1; EGFR, epidermal growth factor receptor; IHC, immu-

nohistochemistry; LAC, lectin affinity chromatography; LCM,

laser capture microdissection; LNM, lymphnode metastasis; PC,

pancreatic cancer; PGK, phosphoglycerate kinase 1; PDAC,

pancreatic ductal adenocarcinoma; PET, pancreatic endocrine

tumor; PanIN, pancreatic intraepithelial neoplasia; RBBP9,

retinoblastoma-binding protein 9; SNCG, synuclein-g; SOD2,

superoxide dismutase 2; tPA, tissue-type plasminogen activator;

TSG, tumor suppressor gene; WB, Western blot

Correspondence: Dr. Daniela Cecconi, Department of Biotech-

nology, Proteomics and Mass Spectrometry laboratory, Univer-

sity of Verona, Strada le Grazie 15, 37134 Verona, Italy

E-mail: daniela.cecconi@univr.it

Fax: 139-045-802-7929

& 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

816 Proteomics 2011, 11, 816–828DOI 10.1002/pmic.201000401

predictive of clinical outcome in PC patients subdivided for

source of origin (Table 1) identified in the last five years by

proteomic analyses.

2 PC tissue proteomics

2.1 Identification of candidate biomarkers in PC

precursor lesions

Kelly et al. used a PDAC mouse model to screen for peptides

that specifically bind to cell surface antigens on PDAC cells.

They demonstrated by proteomics analysis that plectin-1

was upregulated in pancreatic intraepithelial neoplasia

(PanIN) lesions and in PDAC [3]. Plectin-1 is a protein

that links intermediate filaments to microtubules and

microfilaments, and also anchors the cytoskeleton, the

plasma, and nuclear membranes [4]. By mean of intravital

confocal microscopy and magnetic resonance imaging,

plectin-1 targeted peptides, conjugated to magneto-

fluorescent nanoparticles, enabled detection of small PDAC

and precursor lesions in engineered mouse models [3].

Scarlett et al. used a Protein-Chip technology, coupled with

SELDI-TOF/MS, to identify a panel of tissue biomarkers

that enable the discrimination between early- and late-stage

PDAC, benign pancreatic disease, and adjacent pancreatic

tissue [5]. Moreover, Pan et al. used quantitative proteomics

to identify deregulated proteins in PanIN-3 lesions of

PDAC, and identified over 200 modulated proteins, involved

in cell motility, inflammatory response, blood-clotting

cascade, cell cycle, and protein degradation. They validated,

by immunohistochemistry (IHC), laminin b-1, galectin-1,

and actinin-4 as proteins overexpressed in the ductal

epithelial cells of PanIN-2 and -3 lesions, which are potential

biomarkers for advanced PanIN and PDAC precursor

lesions [6].

2.2 Identification of candidate biomarkers related to

cytoskeleton

A well-known observation in cancer biology is that trans-

formed cells display a disturbed cytoskeleton. Ni et al.compared the proteome of PC with normal pancreas

and evidenced downregulation of gelsolin [7], an actin-

binding protein crucial for maintaining cytoskeletal

integrity [8]. High activity of the ubiquitin-proteasome

pathway in patient samples and in BxPC-3 PC cells was

detected and the inhibition of the 26S proteasome restored

gelsolin levels in BxPC-3 cells. Interestingly, in a later

step of the transformation process, gelsolin expression

increases again. This result is consistent with an earlier

study of Thomson et al. [9], which reported increased

gelsolin expression in lymphnode-positive cancers and

suggested that the expression of the putative gelsolin-

specific ubiquitin ligase active in the earlier phases of the

transformation process is lost at later stages. Thus, re-

emergence of gelsolin expression would be associated with

the most advanced aggressive stages of the disease. Corre-

lation studies between proteasome-mediated gelsolin

degradation and PC progression could enclose important

clinical significance in its diagnosis, treatment, and prog-

nosis. In a more recent study, Qi et al. performed a

2-DE/MS analysis of PDAC and noncancerous pancreas

tissues. Among the proteins overexpressed in PDAC

tissues, several are cytoskeletal-associated proteins, such as

plastin-1, ankyrin-2, microtubule-actin cross-linking

proteins, synaptopodin, actins, tropomyosin b-chain, and

the microtubule-associated protein, which suggests that

common cytoskeletal modifications can accompany the

development of PDAC [10]. An interesting point that

emerges from a study performed by Tomaino et al. is that

PDAC is characterized by an antibody response to the

cytoskeletal proteins cofilin-1 (COF1) and keratin-type I

cytoskeletal 10 (K1C10) [11]. COF1 is required for tumor

cell directionality in response to chemotactic or growth-

factor stimulation [12], while K1C10 is involved in the

formation of the intermediate filament cytoskeleton of

epithelial cells [13]. Antibodies to cytoskeletal proteins

may reflect the immune system’s attempt to target mole-

cules, such as COF1 and K1C10, involved in tumor metas-

tasis. In this respect, it could be interesting to find out

whether the antibodies to cytoskeletal proteins found in

PDAC patients could inhibit metastatization or tumor cell

proliferation.

Figure 1. Diagram showing the workflow to identify the PC

molecular markers.

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2.3 Identification of candidate biomarkers involved

in oxidative stress and hypoxia

Growing evidence indicates that cancer cells exhibit an

intrinsic oxidative stress higher than normal cells and that

this event favors tumor progression [14]. Hyperactivity or

conditions that interrupt ordered electron transport from

NADH, or hydroquinone, to molecular oxygen and other

acceptors at the external cell surface may result in the

generation of superoxide. A novel hydroquinone NADH

oxidase with protein disulphide-thiol interchange activity

(designated ENOX2 or tNOX) was found on the surface of

cancer cells, and is shed into the sera of cancer patients, as

well as, into the media of cultured cancer cells (but not in

normal human tissues, cells or sera). Tang et al. identified

an alternative splicing as the basis for specific localization of

tNOX to the cancer cell surface [15]. Recently, the same

authors performed a 2-DE with pan-tNOX (ENOX2)

recombinant antibody detection and revealed a family of

more than 20 ENOX2 isoforms that characterize cancers,

Table 1. Novel potential biomarkers, identified through proteomic techniques, useful to predict clinical outcome in patients affected byhuman pancreatic cancers

Potential biomarkers for pancreatic cancer Expression Pancreaticcancer type

Proteomic tools Ref.

Tumour tissue biomarkers

Plectin-1 m PanIN Phage display, SDS-PAGE and LTQ-IT MS/MS

[3]

Laminin b-1, galectin-1, actinin-4 m PanIN SELDI-TOF/MS [6]Gelsolin k PDAC Antibody array [7]Plastin-1, ankyrin-2, microtubule-actin crosslinking proteins,

synaptopodin, actins, tropomyosin b-chain, microtubule-associated proteins

m PDAC 2-DE and MALDI-TOF/TOF-MS [10]

Cofilin-1, keratin-type I cytoskeletal 10 autoantibodies m PDAC Antibody array [11]Hydroquinone NADH oxidase, tNOX m PDAC 2-DE with recombinant antibody [16]Glucose-regulated protein 78, macrophage migration

inhibitory factor, annexin A5m PDAC 2-DE and MALDI-TOF/TOF-MS [22]

a-Enolase, GAPDH, triosophosphate isomerase m PDAC 2-DE/MS/MS [24]Calgranulin A, calgranulin B m PDAC LCM, 2-DE and MALDI-TOF-MS [25]Synuclein-g m PDAC SDS-PAGE and LTQ-IT MS/MS [29]Radixin, moesin, c14orf166 m PDAC7LNM DIGE and MALDI-TOF/TOF-MS [31]

Serum or plasma biomarkers

Phosphoglycerate kinase 1 m PDAC 2-DE and MALDI-TOF/TOF-MS [35]Phosphoglycerate kinase-1 and histone H4 autoantibodies m PDAC protein microarray [36]Fibrinogen-g m PDAC 2-DE, MALDI-TOF-MS and LCQ MS/

MS[37]

Cyclin I, Rab GDP dissociation inhibitor b (GDI2), a-1antitrypsin precursor, haptoglobin, and serotransferrin

m PDAC 2-DE and MALDI-TOF/MS [38]

C14orf166 m PDAC SELDI-TOF/MS [39]Platelet factor 4 k PDAC MALDI-TOF/MS [40]Sialylated plasma protease C1 inhibitor, N83 glycosylation

of the a1-antitrypsink PDAC LAC, SDS-PAGE, 2-DE, MALDI-TOF-

MS, MALDI-QIT-MS, and LC-ESI-TOF-MS

[41]

Prolyl 4-hydroxylation of a-fibrinogen m PDAC LAC, 2DICAL, QTOF-MS, LTQ-Orbitrap MS

[43]

Apolipoprotein A1, transthyretin, apolipoprotein E m PDAC 2-DE, MALDI-TOF-MS, and QSTAR-MS/MS

[44]

LCN2, REG1A, REG3, TIMP1, IGFBP4 m PDAC IPAS, LTQ-FT MS/MS [46]

Pancreatic juice biomarkers

Hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein I

m PDAC SELDI-TOF/MS [48]

Insulin-like growth factor binding protein-2 m PDAC ICAT, LCQ-IT-MS [50]Hemoglobin, fibrinogen, trypsin I, trypsin II,

chymotrypsinogen b, Ig-a1 chain c region, Ig-m chain cregion, ribonuclease, serum albumin

m PDAC ICAT, LCQ-IT-MS [51]

Lithostathine I a k PDAC 2-DE, MALDI-TOF-MS [52]Matrix metalloproteinase-9, oncogene DJ1, a-1B-

glycoprotein precursorm PDAC DIGE, MALDI-TOF-MS and MS/MS [53]

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including PC, of different tissue origins. Interestingly,

ENOX2 proteins are shed into the blood and can also be

found in urine [16]. The tNOX isoform technology is

currently under development as a clinical aid to identify

unknown or uncertain primary cancers, evaluation of

metastatic spread in post surgery patients, monitoring

remission following cessation of therapy, and for early

diagnosis in at-risk populations.

One explanation for the lack of highly effective agents to

treat advanced PDAC is the observation from surgical

specimens that PDAC typically contains large areas of

hypoxia, and this involves not just the hypo-perfused core of

large solid tumor masses, but also a ring of hypoxic tissue at

the expanding cancer/normal tissue interface [17]. Hypoxic

cancer cells activate the survival protein, hypoxia-inducing

factor-1 alpha (HIF-1a), as a means of adapting to growth in

conditions of low oxygen tension [18, 19]. It has been

previously reported that reactive oxygen species tend to

further stabilize HIF-1a levels [20], thus stimulating the

transcription of a variety of cell survival genes including

angiogenic growth factors, glycolysis-related proteins,

hematopoietic, and metabolic survival factors [21]. Cui et al.identified by proteomic tools hypoxia-regulated proteins

overexpressed in microdissected PC nests. IHC confirmed

that PCs had significantly higher expression levels of

glucose-regulated protein 78, macrophage migration inhi-

bitory factor and annexin A5 than normal pancreas tissues,

suggesting these hypoxia-regulated proteins as promising

targets for PC diagnosis and therapy [22].

In contrast to normal cells, cancer cells show increased

rates of glucose uptake and glycolysis activity to meet energy

demands [23]. Mikuriya et al. detected 11 proteins whose

expression was increased in human PDAC tissues. Interest-

ingly, four of them, a-enolase, two spots of GAPDH and

triosephosphate isomerase were enzymes involved in the

glycolytic pathway. Since HIF-1a has been shown to up-

regulate glucose transporters and glycolytic enzymes

(including a-enolase and GAPDH), authors suggested that

induction of these enzymes, and increased activity of glyco-

lysis, might be caused by tumor-associated hypoxia [24].

2.4 Identification of candidate biomarkers in PC

microenvironment and metastasis

PC shows a characteristically intense desmoplastic stroma,

which is composed of different host cells, including fibro-

blasts, small endothelial-lined vessels, and inflammatory

cells. The cross-talk between tumor cells and the

surrounding stroma is a key regulator of cancer growth and

progression. While PC cells have been demonstrated to both

modify and be influenced by their surrounding micro-

environment, much remains to be elucidated about the

molecular interactions between this cancer type and its

microenvironment. Recently, Sheikh et al. employed laser

capture microdissection (LCM) and 2-DE to analyze proteins

in the stromal cells surrounding malignant pancreatic

ductal cells and identified S100A8 (calgranulin A) and

S100A9 (calgranulin B) as upregulated proteins [25]. The

cells strongly positive for S100A8 or S100A9 were identified

as monocytes/immature macrophages, while strongly posi-

tive neutrophils were also observed in blood vessels around

the tumor. S100A8 and S100A9 expression and secretion in

the PC microenvironment are likely to contribute to the host

inflammatory response to the tumor [26, 27].

Aggressive tumor extension into the perineural space is a

common mode of tumor spread in PC. Hibi et al. previously

reported a novel perineural invasion model and distinguished

high and low perineural invasion groups in PC cell lines [28].

In a recent study, the same authors performed proteomic

analyses to compare these two groups [29] and identified,

among the overexpressed proteins, synuclein-g (SNCG).

Multivariate analyses revealed SNCG overexpression as the

only independent predictor of diminished overall survival and

the strongest negative indicator of disease-free survival. In

mouse perineural invasion and orthotopic transplantation

models, SNCG suppression by shRNA reduced the incidence

of perineural invasion and liver/lymphnode metastasis

(LNM) [29]. Zhu et al. suggest that SNCG overexpression may

lead to a more malignant phenotype by promoting nerve

growth factor – tyrosine kinase receptor A signalling and

altering the PC cell architecture, growth, and motility [30].

Several authors have detected SNCG in serum and urine

samples of patients with malignant tumors, promoting

SNCG as an indicator for diagnosis of PC.

Since PC can rapidly develop LNM, it has been proposed

that LNM-associated proteins might play an important role

in PC carcinogenesis. Cui et al. observed by DIGE/MS

analysis different protein profiles in PC nests with and

without LNM. They identified modulated proteins and, by

Western blot (WB) and IHC, confirmed radixin, moesin,

and c14orf166 as upregulated in LNM PC [31]. Kobayashi

et al. suggested that the moesin-expressing cancer cells

degrade the extracellular matrix and are involved in the

pathological processes of tumor metastasis [32]. Accumu-

lating evidences indicate that a possible role of radixin for

metastasis might depend on its regulatory effect on tumor

cell migration [33]. In the same study, Cui et al. confirmed

that c14orf166 is upregulated in PC tissues and associated

with LNM [31]. Recently, c14orf166 was identified as a novel

transcription modulator [34], suggesting that it might

participate in the transcriptional activation of metastasis-

associated genes.

3 PC body fluid proteomics

3.1 PC biomarkers in serum or plasma

To detect human malignancy, blood is the most ideal

biological specimen because of its availability for repeated

collection and reproducible quantification. The standard

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serum marker for PC, the carbohydrate antigen 19-9 (CA

19-9), is widely used, but it is of limited value as a screening

marker being confined to monitoring responses to therapy.

Approximately 10–15% of individuals, indeed, do not secrete

CA 19-9 because of their Lewis antigen status, and false-

positive results are common among patients with benign

pancreatic obiliary disorders. For these reasons, the search

for other blood biomarkers able to uncover PC at the earliest

stage, but also for prognosis, evaluation of the response to

therapy and follow-up is auspicated.

Phosphoglycerate kinase (PGK) 1, a secretable glycolytic

enzyme involved in angiogenesis, was found to be over-

expressed in serum samples of PDAC patients, as compared

to controls, by 2-DE/MS [35]. In line with this observation,

Patwa et al. [36] showed that PGK1 and histone H4 elicited a

significant differential humoral response in cancer sera

compared with sera from normal, chronic pancreatitis, and

diabetes patients. Moreover, 2-DE/MS analysis successfully

identified 154 potential serum markers for PC [37]. Of these,

fibrinogen-g was confirmed to be overexpressed in PC sera

by enzymatic analysis and IHC. However, no correlation

was seen between fibrinogen-g expression and tumor

pathology or survival, although fibrinogen-g deposition

might be related to tumor cell invasion and metastasis. Sun

et al. [38] compared serum proteomes of PC patients with

that of healthy volunteers, gastric cancer, and other

pancreatic disease patients. Ten proteins were found

modulated in PC including cyclin I, Rab GDP dissociation

inhibitor b (GDI2), a-1 antitrypsin precursor, haptoglobin,

and serotransferrin. The upregulation of cyclin I and GDI2

was confirmed by WB in serum and pancreatic juice

samples, and by IHC in additional pancreatic carcinomas.

Another potential biomarker of PDAC is the centrosome

regulatory protein c14orf166. Using a Protein-Chip

approach, Guo et al. [39] found that c14orf166 was elevated

in the serum of PC patients. Moreover, by MALDI-TOF/MS

serum peptidome profiling, Fiedler et al. [40] identified

platelet factor 4 (PF4) as a potential PC biomarker. Although

PF4 was significantly decreased in patients with PC, its

decrease is not a PC-specific effect, as it is also present in

prostate cancer patients.

Aberrant sialylation of proteins in cancer cells is well

known and is thought to be associated with invasiveness and

metastatic potential. Sialic acids, indeed, are regulators of

cellular and molecular interactions by either masking

recognition sites or serving as recognition determinants.

Zhao et al. [41] set up a strategy to quantify changes in

glycoproteins, extent of glycosylation and carbohydrate

structure that correlate with cancer. Approximately 130

sialylated glycoproteins were identified by lectin affinity

chromatography (LAC) and LC/MS in PC serum. Among

them, sialylated plasma protease C1 inhibitor and the N83

glycosylation of the a1-antitrypsin were downregulated

in PC serum. Recently, the same authors developed a

strategy for analysis of N-glycans and glycosylation sites

in serum based on a double LAC [42] and identified 45

oligosaccharides altered in PC serum, increased branching

of N-linked oligosaccharides, increased fucosylation and

sialylation.

Using the two-dimensional image-converted analysis of

liquid chromatography and mass spectrometry (2DICAL)

and a ‘‘glicocapturing’’ through concanavalin A-agarose,

Ono et al. [43] identified a novel prolyl-hydroxylation of

a-fibrinogen by comparing plasma of PC patients and

controls. ELISA revealed the plasma upregulation of prolyl-

4-hydroxylated a-fibrinogen in PC, as well as in other cancer

and chronic pancreatitis.

An important aspect of PC is to rule out at diagnosis

diseases such as chronic pancreatitis and biliary tract stones.

Up to 10% of patients undergoing resection for suspected

pancreatic malignancy will actually have benign disease

following histopathological assessment. Indeed, raised

levels of CA19-9 are also observed in obstructive jaundice.

Comparing patients with PC, chronic pancreatitis or biliary

duct obstruction, Yan et al. [44] found that the levels of

apolipoprotein A1, transthyretin, and apolipoprotein E, as

detected by 2-DE of depleted plasma, were associated with

PC. However, when the effect of bile duct obstruction was

considered, only transthyretin levels were independently

associated with cancer. The lack of sensitivity of this

approach does not exclude that ‘pancreas-specific’ biomar-

kers will be less sensitive to the potential confounding

effects of bile duct obstruction.

In a study performed by Ingvarsson et al. [45], a recom-

binant scFv antibody microarray specific for immunor-

egulatory proteins enabled the discrimination between PDAC

patients and controls, based on a signature of 19 nonre-

dundant serum proteins. This signature only had eotaxin,

IL-5, and IL-13 in common with 14 biomarkers found in

gastrointestinal cancer arisen from Helicobacter pylori, indi-

cating that it was not related to general inflammation.

Genomic analyses of human and mouse cancers have

revealed significant concordance in chromosomal aberra-

tions and expression profiles. These observations opened the

way to generate genetically engineered mice harboring

signature genetic mutations of PDAC, i.e. activated Kras and

Ink4a/Arf deficiency [46]. A quantitative proteomic analysis

of plasmas from engineered mice, at early and advanced

stages of tumor development, allowed the identification of

1442 proteins that were distributed across seven orders of

magnitude of abundance. A panel of five proteins (LCN2,

REG1A, REG3, TIMP1, and IGFBP4) was tested in a blin-

ded study in 26 humans, and discriminated PC cases from

controls in blood specimens obtained between 7 and 13

months prior to the development of symptoms and clinical

diagnosis of PC.

3.2 PC biomarkers in pancreatic juice

Pancreatic juice was extensively studied in the late 1970s

and 1980s, primarily by 2-DE analyses, which led to the

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discovery of several pancreatic enzymes [47]. A direct

analysis of pancreatic ductal fluid using SELDI-TOF/MS led

to the identification of hepatocarcinoma-intestine-pancreas/

pancreatitis-associated protein I as a potential serum

biomarker for PDAC [48]. The levels of this protein were

found to be o1000-fold higher in pancreatic fluid compared

with serum, suggesting that ‘‘proximal’’ fluids may repre-

sent not only a simpler, but also a richer source of cancer

biomarkers. More recently, the same group employed 1-DE

and LC-MS/MS analyses of pancreatic juice [49]. A total of

170 proteins were identified, including several proteins that

were previously found to be overexpressed in PC at the

mRNA level. This study offers the most comprehensive

analysis, although nonquantitative, of the protein constitu-

ents in pancreatic juice.

A first quantitative study of the pancreatic juice proteome

was performed to compare PC juice and controls by using

the ICAT approach [50]. Thirty proteins were identified with

at least twofold modulation. About half of these proteins

were not previously associated with PC and they may

represent novel candidate biomarkers. One of them, the

insulin-like growth factor-binding protein-2 was further

validated by WB to be overexpressed both in pancreatic juice

and tissue from PDAC, but not from chronic pancreatitis

patients. In another study performed by the same group

[51], 9 proteins (hemoglobin, fibrinogen, trypsin I, trypsin

II, chymotrypsinogen b, Ig-a1 chain c region, Ig-m chain c

region, ribonuclease, and human serum albumin) were

found upregulated both in PC juice and pancreatitis juice

and should be excluded in future biomarker study. Inter-

estingly, downregulated proteins in PC did not overlap with

those from pancreatitis.

The presence or absence of obstruction of the main

pancreatic ducts is a factor that may determine an altered

composition of the pancreatic juice. Zhou et al. [52] have

compared the 2-DE profiles of pancreatic juice from a

patient with pancreatic body cancer and a patient with

benign pancreatic disease and demonstrated that the protein

modulations were primarily caused by the blockade of juice

secretion from the pancreas. However, the analysis of

pancreatic juice of patients without obstruction of the main

pancreatic duct showed that five proteins were under-

expressed in PC patients. One of these proteins matched

with the proteins identified earlier by the same authors and

represented an isomeric form of lithostathine I a.

More recently, Tian et al. [53] have characterized the

pancreatic juice proteome in PC patients compared with

cancer-free controls using DIGE/MS. A total of 24 differ-

entially expressed proteins were identified, and the levels of

three upregulated proteins (matrix metalloproteinase-9

(MMP-9), oncogene DJ1 (DJ-1), and a-1B-glycoprotein

precursor) were further confirmed in pancreatic juice,

tissue, and serum samples using WB, IHC, or ELISA.

Overexpression of MMP-9 and DJ-1 in PC tissues had been

already shown in previous studies.

4 PC cell line proteomics

4.1 Identification of candidate biomarkers in PC cell

lines

Analysis of cell lines represents a good strategy to identify

molecular targets that may find applications in vivo after

appropriate validation. Table 2 reports potential additional

therapeutical targets (identified by proteomic analysis of

indicated PC cell lines) suggesting new treatment options in

PCs.

The biomarkers research can involve the handling of

expression of genes already known to be involved in

Table 2. Novel potential therapeutic molecular targets suggesting new treatment options in pancreatic cancers

PC cell line Potential therapeutic molecular targets Ref. New potential treatment options

MIA-PaCa2 Manganese superoxide dismutase(SOD2)

[54] 2-Methoxyestradiol (2ME2)

Capan-1 p16(INK4a) [55] 5-Fluoro-20-deoxycytidineBxPC-3 Methyl-CpG-binding domain protein 1

(MBD1)[56] RNA silencing

Panc-1 tPA receptors (annexin A2, enolase,cytokeratins 8 and 18)

[57] 2,7-Bis-(4-amidino-benzylidene)-cycloheptan-1-onedihydrochloride (tPA-stop)

KLM1 HSP27 [63] Second-generation antisense oligonucleotide OGX-427P196 EGFR [64] Cetuximab, sorafenib, panitumumab, erlotinib, gefitinibColo357 PGHS-2 [66] Celecoxib, etodolacColo357 NAMPT [66] FK866T3M4, CM, BON,

and QGP-1HDAC [69–70] Trichostatin A (TSA), vorinostat

CM, BON, andQGP-1

DNA methyltransferase [71] 5-Aza-20-deoxycytidine (DAC)

Panc-1, Panc-430and XPA3

HSP90 [72] IPI-504

MIA-PaCa2 Transketolase [73] Oxythiamine

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tumorigenesis, such as tumor suppressor gene (TSG) or

oncoproteins. For example, Hurt et al. [54] restored the

expression of the TSG manganese superoxide dismutase

(SOD2) in MIA-PaCa2 cells, and by RPPA and protein/DNA

array detected a decrease in the VEGFR2 phosphorylation

and identified transcription factors (such as Bm-3, C/EBP,

and NPAS2) with altered binding activity. The authors

demonstrated that growth of PC cells harboring low levels of

SOD2 may be efficiently affected by a pro-oxidant treatment,

such as the clinically relevant drug 2-methoxyestradiol

(2ME2). Similarly, Andre et al. [55] analyzed p16(INK4a)-

transfected Capan-1 cells by cDNA microarray, chromato-

graphic glycan profiling, and 2-DE. They found that induced

expression of the TSG p16(INK4a) restores the susceptibility

of tumor cells to anoikis by a mechanism involving the

down-regulation of b1,4-galactosyltransferases-I/V and

the upregulation of b1,4-galactosyltransferase-IV, as well as

the decrease in sialylation of O- and N-glycans. These data

suggest that a demethylating agent (such as 5-fluoro-

20-deoxycytidine) able to restore the expression of the

generally hypermethylated p16(INK4a) gene could represent

a novel potential treatment option. Liu et al. [56] focused

their attention on methyl-CpG-binding domain protein 1

(MBD1), which is instead overexpressed in pancreatic

carcinomas. They established an MBD1-knock-down of

BxPC-3 cells, and by 2-DE/MS experiment identified

modulated proteins associated with MBD1 function, some

of which, vimentin and eIF3, are already prognostic

biomarkers for malignant tumors.

Among tumor markers, an important role is played by

surface and secreted proteins. Since tissue-type plasmino-

gen activator (tPA) (a serine protease) is overexpressed in

95% of PDAC. Roda et al. [57] analyzed Panc-1 cells to

identify putative tPA receptors to associate with a more

invasive behavior of the tumor. Using protein affinity

capture with tPA-Sepharose followed by 2-DE/MS, the

authors identified 31 proteins (including annexin A2,

enolase, cytokeratins 8 and 18), which may represent

receptors involved in the activation of tPA. Altogether, these

data demonstrate that the blockade of the endogenous

proteolytic activity of tPA by a specific inhibitor such as tPA-

STOP could represent a new potential therapeutic strategy

for PC.

The surface peptides of the same cell line have been

analyzed by LC-MS/MS identifying 131 peptides that could

interact with MHC class I [58]. To analyze secreted proteins

of the Panc-1 cells, Gronborg et al. used a MS shotgun

approach, based on SILAC [59]. They reported the over-

expression of some proteins (CD9, perlecan, SDF4, apoE,

and fibronectin receptor), not previously described to be

upregulated in PC, which could represent potential

biomarkers. Okuyama et al. [60] demonstrated that the

media from PSN-1 and MIA-PaCa2 cells enhance the

haptoglobin production in Hep3B hepatoma cells. More-

over, they reported high level of fucosylated haptoglobin in

the sera of PC patients. Also Song et al. [61] used a similar

approach to understand the mechanism by which the

secreted proteins of Panc-1 cells alter the function of rat

pancreatic insulinoma-1 b-cells INS-1. By using a DIGE

approach, they found HSP60, peripherin, Prp19, HMOX1

and GRP78, as modulated proteins in insulinoma cells.

These proteins might be helpful to elucidate the mechanism

of PC associated diabetes and might be of potential clinical

utility in the prediction of PC-associated diabetes. Lee et al.[62] analyzed, instead, by SDS-PAGE and MS, the protein

profile of exosomes secreted by the NIT-1 mouse insuli-

noma cells, identifying for the first time in secreted vesicles

some proteins related to RNA/translation and ubiquitin/

degradation.

Even through the proteomic analysis of cell lines, it is

possible to identify therapeutic biomarkers. For example,

Mori-Iwamoto et al. [63] analyzed by a 2-DE/MS approach,

two human PDAC cell lines, KLM1 (gemcitabine-sensitive)

and KLM1-R (gemcitabine-resistant), and found seven

proteins (HSP27, peroxiredoxin 2, endoplasmic reticulum

protein ERp29 precursor, 6-phosphogluconolactonase, trio-

sphospate isomerase, a enolase, and nucleophosmine) that

could play a role in determining the sensitivity of PC to

gemcitabine. Moreover, they restored the sensitivity to

gemcitabine in KLM1-R cells by HSP27 knocking down,

suggesting that this protein could represent a potential

therapeutic target, as well as a useful biomarker for

predicting the response of patients to treatment with

gemcitabine. Harsha et al. [64] used the SILAC approach to

analyze tyrosine kinase pathways of P196 cell line, revealing

an aberrant activation of the epidermal growth factor

receptor (EGFR) pathway and confirming the activated

EGFR as a target for therapy. Thalappilly et al. [65] analyzed

signal transduction networks in different PDAC cell lines

(BxPC-3, Panc-1, Capan-1, Capan-2, and Mia-PaCa2). They

used a microarray-based analysis to show that 13 multiple

SH3-domain containing scaffold-proteins are expressed in

PDAC cells. By a yeast two-hybrid approach, they identified

proteins, interacting with those adaptor proteins, involved in

cell proliferation and survival (CIZ1, BIRC6, retinoblastoma-

binding protein 6 (RBBP6)), signaLling (LTBP4, Notch2,

TOM1L1, STK24), and membrane dynamics (PLSCR1,

DDEF2, VCP). The authors suggested that interactions

mediated by multi-SH3 domain-containing proteins could

lead to the formation of dynamic protein complexes that

function in PC cell signalling. The identification of these

protein complexes is of paramount importance in deci-

phering PC biology and designing novel therapeutic

approaches. Bauer et al. [66] investigated by 2-DE/MS the

effect of the interleukin-1 (a cytokine involved in the devel-

opment of chemoresistance in PC) on Colo357 cell line and

reported nicotinamide phosphoribosyltransferase (NAMPT)

and prostaglandin H2 synthase 2 (PGHS-2, Cox-2) as crucial

increased proteins. These findings suggested that inhibitors

of nicotinamide phosphoribosyltransferase (i.e. FK866),

and prostaglandin H2 synthase 2 (i.e. celecoxib, etodolac)

could restore sensitivity of PC to chemotherapy. Finally,

822 D. Cecconi et al. Proteomics 2011, 11, 816–828

& 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

Suss et al. [67] analyzed by 2-DE/MS the changes in protein

expression of INS-1 insulinoma b-cells following glucose or

cAMP stimulation, and identified Mrna-binding factors

(such as several hnRNPs) as a major class of rapidly regu-

lated proteins.

4.2 Study of new potential therapeutic strategies by

using PC cell lines

One of the most interesting applications of proteomic

analysis is the study of cell lines treated with drugs or new

agents to understand the mechanism of pharmacological

action, possible side effects, or the onset of drug resistance.

Gemcitabine is the most active agent against PDAC;

however, even with this drug the response rate is scarce. For

this reason, many efforts have been made to identify new

therapeutic strategies.

Our research group is active in this field since 2003 [68]

and, during the last years, is focused on the study of

trichostatin A (TSA, a histone deacetylase inhibitor). We

performed a proteomic analysis of T3M4 cells after a

combined treatment with TSA and gemcitabine and iden-

tified 56 proteins involved in the synergistic induction of cell

growth inhibition and apoptosis. Cytokeratin 8, 14-3-3

protein s, and stathmin, generally controlled by p53 (a gene

inactivated in T3M4 cells) were included among the

modulated proteins, suggesting that the combined treat-

ment is able to compensate p53 loss of function [69]. TSA

was also tested on three different PET cells (CM, BON, and

QGP-1) showing a strong inhibition of cell growth by

arresting the cell cycle in G2/M phase and inducing apop-

tosis. 2-DE/MS analysis revealed that the most important

modulated proteins belong to cell proliferation, cell cycle,

and apoptosis (peroxiredoxins 1 and 2, diablo protein, and

HSP27), while some others to other processes, for example,

regulation of gene expression (nucleophosmin, oncoprotein

dek) and signal transduction (calcium-calmodulin) [70].

More recently, we have shown by a proteomic approach that

the combined treatment of PET cells with TSA and 5-aza-

20-deoxycytidine (a DNA methyltransferase inhibitor) deter-

mines a strong synergistic inhibition of proliferation mainly

due to apoptosis [71]. The modulation of specific proteins

(proteasome-, mithocondrion-, caspase-, p53-, and Ras-rela-

ted proteins) correlated with the synergistic antiproliferative

effect of the drugs. Altogether, our data indicate that

histone-deacetylase inhibitors can be placed among the

potentially most powerful drugs for the treatment of

pancreatic tumors.

Worth noting is the study of Song et al. [72] on the

inhibition of HSP90. These authors showed that IPI-504

(a HSP90 inhibitor) exerts antiproliferative effects in

Panc-1, Panc-430, and XPA3 cells. Moreover, by using

iTRAQ technique, they identified more than 60 regulated

proteins belonging to signal transduction (such as EGFR

pathway substrate 8), immune response (such as MHC

class IB), cell growth and maintenance, transport, and

metabolism.

Among the classes of potential new therapeutic agents,

an important role is played by drug targeting metabolic

enzymes that have a crucial role in controlling the growth of

cancer cells. Zhang et al [73] have recently investigated the

effect of the oxythiamine (a transketolase inhibitor) on MIA-

PaCa2 cells. They reported that oxythiamine inhibited cell

growth through suppression of the cell cycle and modula-

tion of MAPK pathways. In addition, they demonstrated by

Pro-Q Diamond phosphoprotein staining and 2-DE/MS that

oxythiamine suppressed the expression of key phosphopro-

teins (such as peroxiredoxin-4 and peroxiredoxin-6, HSP90,

HSP27, nucleophosmin, nuclear autoantigenic sperm

protein, 14-3-3 protein e, PSMA5). These findings demon-

strate that understanding the principles that govern cancer

responses to small molecule inhibitors and to drugs

targeting specific signalling pathways remains a major

challenge in the development of chemotherapeutic agents.

5 Complexity of proteomics andadvances in biomarker research

The evaluation of proteomic studies on PC for biomarker

research is particularly challenging due to the range of

different sample sources that have been used (i.e. tissue,

body fluids, and cell lines), which all exhibit a specific

proteome.

As concern tissue samples, it is noteworthy that,

although some studies used tissue homogenates, only few

applied LCM to compare duct epithelium with PC. Shekouh

et al. demonstrated the differences between the proteomes

of ductal epithelium enriched by LCM and undissected

pancreatic tissue, which can easily generate highly

misleading results [74]. However, tumor progression also

depends on the surrounding tissue and the tumor–host

interface. Thus, although a pre-fractionation of tissue by

LCM may help to identify proteins differentially expressed

in PDAC compared to nonneoplastic duct epithelium, an

investigation of a tissue homogenate may help to find

important proteins involved in the tumor–host interaction.

With regard to nonneoplastic tissue, samples were obtained

from healthy individuals or region distal from the PC. The

latter usually show secondary chronic pancreatitis, with

various degrees of parenchymal atrophy and inflammation,

adding particular diversity to the proteome.

Furthermore, different MS-based high-throughput

methods were applied to investigate the pancreatic

proteome and identify modulated proteins. However, with

SELDI-technology only m/z ratios were provided, which

complicate the comparison of proteomic studies. Often pre-

fractionation was applied adding further diversity. These

examples, further discussed in [75], show the complexity of

proteomic research and the difficulties in comparing the

different studies.

Proteomics 2011, 11, 816–828 823

& 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

However, in the last years, some important proteomic

advances have been developed allowing the overcoming of

relevant drawbacks. For example, the Liquid Tissues

workflow is a feasible approach for unbiased biomarker

discovery in limited archival material, particularly applicable

to extract proteins from FFPE-tissues and to analyze

precursor lesions of cancer. Cheung et al. utilized Liquid

Tissues and MS to carry out a proteomic analysis of an

intraductal papillary mucinous tumor of the pancreas. They

identified 25 proteins that have been previously reported as

upregulated in PC, thus confirming the high quality of this

protein extraction method [76].

Moreover, an innovative biochemical platform, the activ-

ity-based proteomic profiling (ABPP), has been developed.

This technique simultaneously determines the functional

state of multiple enzymatic activities directly in complex

samples, such as tumor specimens. Bifunctional active site-

directed probes allow derivatization of the tumor-associated

enzymes: one end of the probe contains a reactive group and

the other end carries a biotin moiety to allow avidin

enrichment and molecular identification by MS [77] (Fig. 2).

ABPP probes selectively label active enzymes, but not their

inactive counterparts, allowing characterization of changes

in enzyme activities that occur without corresponding

alterations in transcript or protein abundance. Shields et al.performed ABPP on primary human PDAC and found an

elevated activity of RBBP9. Whereas RBBP9 is expressed in

normal and malignant tissues at similar levels, its serine

hydrolase elevated activity in tumor cells promotes ancho-

rage-independent growth in vitro, as well as pancreatic

carcinogenesis in vivo [78].

Concerning the most recent improvements of the clas-

sical 2-DE approach applied to PC, it is worth to mention the

optimization of protocol for the DIGE analysis of micro-

dissected cells stemming from pancreatic carcinoma

precursor lesions [79], and the optimization of proteasome

preparation of PC cell lines for the analysis by native-PAGE,

2-D native/SDS-PAGE, and 3-D native/IEF/SDS-PAGE [80].

Grote et al. [81] validated the use of RPPA for the analysis of

CA19-9 levels in normal, chronic pancreatitis, and PDAC

serum/plasma samples. In addition, it has been developed a

single-step detergent-based procedure for isolation of native

proteins from PC tissues compatible with antibody micro-

arrays [82].

Also the high-throughput MS-based shotgun proteomic

strategies have been recently improved and applied to PC

research. Indeed, it has been developed a protocol for

determination of protein synthesis and turnover, based on

low enrichment of 15N amino acids in cells (mSILAC) [83],

which was applied for the secretome analysis of MIA-PaCa2

cells after oxythiamine chloride treatment [84]. Moreover, an

improvement in the number of proteins identified and

A ti it b dActivity-basedchemical probechemical probe

reactive groupfluorophore

Proteome Probe-labeled proteome

SDS-gelanalysis

inte

nsit

y

AffinityPurification

m/z

Analysis of labeled enzymesMass Spectrometry Identification

Figure 2. ABPP. ABPP probes label active, but

not inactive enzymes in complex proteomes.

Labeled enzymes can be detected by in-gel

fluorescence scanning and identified by affi-

nity purification and MS analysis.

824 D. Cecconi et al. Proteomics 2011, 11, 816–828

& 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

quantified in PC serum has been obtained by lipid removal,

immunodepletion, and the use of sodium bicarbonate

buffer during iTRAQ labelling [85]. New software and tools

for data analysis were also reported. Ono et al. [86] analyzed

the poorly motile Capan-1 and highly motile BxPC3 PC

cells, and developed an integrated platform, two-dimen-

sional image-converted analysis of liquid chromatography

and mass spectrometry, consisting of machinery and soft-

ware modules for the data analysis of label-free LC-MS.

Taylor et al. [87], instead, reported a novel approach to

simultaneously identifying and quantifying secreted

proteins by Rin-m5F insulinoma cells using hybrid ion trap-

FT-ICR MS. Improvements have been obtained also for

classification of PCs by MS: for example, exogenous reporter

peptides have been used for spiking serum specimens,

improving the standardization and disease classification

accuracy [88]. Finally, Ge et al. [89] proposed a novel meth-

odological framework, based on machine learning method,

which was applied to MS proteomic data generated from

premalignant PC. Considering all these recent advances in

classical and high-throughput proteomic technologies, one

can imagine that proteomics will shortly find a practical

application in clinical management of PC.

This work was supported by Associazione Italiana Ricerca sulCancro (AIRC), Italy, Bando 2008 and Joint Project ofUniversity of Verona, Italy, Bando 2008, and Italian Ministriesof University-Research and Health.

The authors declare no conflict of interest.

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