High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS

Review

10.1586/14789450.5.3.445 © 2008 Expert Reviews Ltd ISSN 1478-9450 445www.expert-reviews.com

High-throughput proteomics of breast carcinoma cells: a focus on F TICR-MSExpert Rev. Proteomics 5(3), 445–455 (2008)

Arzu Umar†, Malgorzata Jaremko, Peter C Burgers, Theo M Luider, John A Foekens and Ljiljana Paša-Tolic†Author for correspondenceErasmus MC, University Medical Center Rotterdam, Josephine Nefkens Institute Laboratory of Genomics & Proteomics in Breast Cancer, Department of Medical Oncology,Dr. Molewaterplein 50, Be 430c, PO BOX 2040, 3000 CA, Rotterdam, The NetherlandsTel.: +31 10 7043 [email protected]

Discovery of better biomarkers for diagnosis, prognosis and therapy-response prediction is themost critical task of a scientific quest aimed at developing novel, tailormade therapies forpatients with cancer. Consequently, a proteome-wide analysis, in addition to genomic studies,is an absolute requirement for a complete functional understanding of tumor biology. Ultra-sensitive, high-performance Fourier-transform ion-cyclotron resonance (FTICR) massspectrometry (MS) currently holds an important role in fulfilling the demands of biomarkerdiscovery. In this review, we describe the applicability of FTICR-MS for breast cancerproteomics, particularly for the analysis of complex protein mixtures obtained from a limitednumber of cells typically available from clinical specimens.

KEYWORDS: accurate mass and time tag • biomarker discovery • breast cancer • Fourier transform ion cyclotron resonance • high throughput • laser-capture microdissection • MALDI • nanoscale liquid chromatography • protein array

The search for new biomarkers has been fueledby the introduction of microarray technology.Gene expression profiling significantly contrib-uted to better classification and prognosis ofmany cancer types. However, gene expressioncannot be directly linked to protein expressionor activity, and is therefore insufficient for com-plete functional understanding of tumor bio-logy. Information on proteins (i.e., identity andquantity) is crucial to further expand andimprove the spectrum of biomarkers an, ulti-mately, to translate molecular knowledge intoclinical practice. Comprehensive, high-through-put identification of proteins within a broaddynamic range is a pivotal step in the discoveryof cancer-specific protein biomarkers or, in fact,markers for any disease. Recent advances in pro-teomics and bioinformatics not only enablehigh-throughput identification and classifica-tion of low-abundance proteins, but also pro-vide a basis for functional analyses of biologicalpathways and biomarker discovery.

Basic principles of proteomicsIt is estimated that the human proteome coversover 100,000 different protein species with a dif-ference in abundance levels of up to 10–12 orders

of magnitude (i.e., biological dynamic range)for human tissue and serum, respectively. Thelarge complexity and dynamic range of biologi-cal systems can challenge any analytical tech-nique. Global proteolytic digests are among themost complex mixtures currently being interro-gated, and depending on the specific interest,the need for depth of coverage in liquid chro-matography (LC) mass spectrometry (MS)analyses is virtually open-ended.

The fact that only a limited number of speciescan be resolved and identified by MS at anygiven time has driven improvements in proteinseparations. Conventional approaches include1DE, with separation based on protein mass, or2DE, which adds isoelectric focusing as the firstdimension of separation. A traditional proteo-mics scheme involves excision of a protein ofinterest from the gel, in-gel enzymatic cleavageinto peptide fragments, peptide profiling by MS,peptide sequencing by tandem MS (MS/MS)and protein identification through databasesearches and mass mapping [1].

Separation techniquesGel-based protein separations are well-estab-lished and long-lived techniques, but alsopresent several limitations, such as inability to

k.rowland

Text Box

For reprint orders, please contact: [email protected]

446 Expert Rev. Proteomics 5(3), (2008)

Review Umar, Jaremko, Burgers, Luider, Foekens & Paša-Tolic

analyze certain classes of proteins (acidic, basic, membranousand low abundance), and limited throughput, despite significantautomation efforts. In order to improve the detection limit ofless abundant proteins and to reduce gel-to-gel variability, 2D-DIGE technology was introduced [2]. On the other hand, gel-free separations, which can be directly coupled to the mass spec-trometer, are increasingly winning over conventional separationmethodologies (FIGURE 1). Both, high-performance LC (HPLC)and capillary electrophoresis (CE) have the capacity to effec-tively separate complex mixtures into individual components,

thereby reducing the complexity of a sample under investiga-tion. Molecules can be separated by HPLC using different col-umn materials, such as reversed-phase (RP; hydrophobicity-based separation), ion exchange (charge), size-exclusion (molec-ular weight) or affinity (specific biomolecular interactions).Compared with conventional HPLC, CE requires less time andgenerally has lower sample consumption, resulting in highersensitivity [3]. Transferring isoelectric focusing from the gel tocapillary format contributes to a high-resolution protein separa-tion, and enhances characterization of low-abundance proteins

[4]. One of the key advantages of solution-based separations, such as HPLC and CE,is the ability to couple various columns andmethods, thus facilitating multidimen-sional separations, which are advantageousbecause they increase overall separationpeak capacity (i.e., the number of compo-nents that can potentially be distinguishedin a mixture). In addition, solution-basedseparations contribute to increased pro-teome coverage due to better use of the MSdynamic range and reduced discriminationduring ionization. Multidimensional sepa-rations, however, typically demonstratehigher sample consumption and longeranalysis time in comparison with nanoscale1D separations. This has driven improve-ments in 1D separations and to the devel-opment of nano-LC systems that not onlyprovide high peak capacities (∼1000), butare also capable of attaining both subatto-mole sensitivity and broad dynamic range.These capillary LC systems make use ofhigh pressure (10,000–20,000 psi), verysmall porous particles (≤3 µm) and smallcolumn inner diameters (≤50 µm) toachieve mobile-phase flow rates as low as10 nl/min, which increases sensitivity andis inversely proportional to flow rate [5].

The next step in the proteomics pipelineis MS analysis and protein identificationusing various bioinformatics tools. Themost commonly used ionization tech-niques in conjunction with MS areMALDI and ESI [6,7]. A variety of massanalyzers have been employed in proteom-ics (e.g., different ion traps, TOF analyz-ers, triple-quadrupoles and varioushybrids, such as TOF/TOF, quadrupole-TOF and ion trap coupled with Fourier-transform ion-cyclotron resonance(FTICR) or Orbitrap™, each with theirown specific application niche.

Figure 1. Breast cancer biomarker discovery using FTICR-MS. (A) Fresh-frozen primary tumor tissues are cryosectioned and subjected to laser-capture microdissection to obtain a relatively pure tumor cell population. Cells are lysed and trypsin digested. (B) Subsequently, peptides can be separated on a reversed-phase C18 column by nano-LC prior to FTICR-MS. Eluting features are then compared against an AMT tag database for protein identification. Label-free quantitation of peptides is used for comparative statistical analysis to identify differentially expressed proteins. (C) Alternatively, peptides can be directly spotted on a target plate and analyzed by MALDI-FTICR-MS, after which, accurate mass spectra are compared with retrieved differentially expressed peak masses. These accurate masses are used for targeted MS/MS in order to identify the corresponding differentially expressed proteins. MALDI-FTICR requires the use of tenfold less sample than nano-LC-FTICR. AMT: Accurate mass and time; FTICR: Fourier transform ion cyclotron resonance; MS: Mass spectrometry; MS/MS: Tandem mass spectrometry; nano-LC: Nanoscale liquid chromatography; OR: Objective response; PD: Progressive disease.

Primary tumor 8 µm cryosection

Laser microdissection

Trypsin digestion

Tumor cells in cap

Proteinlysate

Peptides

nano-LC peptide separation

9/10 1/10

Peptides on target plate

ESI-FTICR MS MALDI-FTICR-MSStatistical comparison

OR PD

AMT tag database search Targeted MS/MS

Identification of differentially expressed proteins

Laser

Expert Rev. Proteomics © Future Science Group Ltd (2008)

High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS Review

www.expert-reviews.com 447

Protein quantitationBoth discovery- and hypothesis-driven proteomics research arebased on the assumption that distinct biological states arereflected by differences in protein expression. Therefore, relia-ble detection and quantitation of changes in protein abun-dances are essential for proteomics studies. Various metabolic,chemical or enzymatic stable isotope labeling methods havebeen introduced to aid accurate MS detection of changes inpeptide/protein abundances [8,9]. In metabolic labeling, iso-topes are incorporated during protein synthesis in a cell ororgan culture system [10]. However, in vivo metabolic labeling isnot applicable for frozen material or body fluids. In these cases,in vitro incorporation of isotopes by chemical derivatization,such as ICAT, or iTRAQ™ is a good alternative [11,12].

ICAT reagents label cysteine residues, which decreases sam-ple complexity and enables quantitation of low-abundance pro-teins over an effective dynamic range of 104 [11]. However,information on unlabeled proteins is lost. The iTRAQ proteinquantitation strategy provides quantitative comparison of four(or more) complex protein pools simultaneously, using MS/MScapabilities [12]. In addition, absolute quantitation of specifictarget proteins can be achieved by using synthetic isobaric pep-tide standards. Both the ICAT and iTRAQ chemical labelingreagents are only available commercially.

Yet another global stable isotope-labeling approach is viaenzymatic transfer of O18 from water to the C-terminus of pep-tides. In this approach, proteins are digested in either H2

16O orH2

18O using trypsin; but the labeling is more consistent whenthe trypsin-catalyzed 16O/18O-exchange is performed postdigestion [13]. Quantitative comparison of proteins using16O/18O-labeling can be performed on all types of samples,including clinically relevant material such as tissue extracts andbody fluids; this constitutes a key advantage over metaboliclabeling schemes.

Alternative, but less precise, approaches based upon directcomparison of MS peak intensities have the advantage of beingbroadly applicable regardless of sample type. This so-called label-free scheme greatly benefits from the use of high-performanceMS that offers high resolving power and mass measurement accu-racy. Several algorithms have recently been described that calcu-late the relative abundance of a particular protein, on the basis ofpeptide abundances for that protein in replicate analyses [14–16].Since MS signal intensities can vary significantly, this approach isuseful for investigating large differences in abundances (particu-larly for limited sample amounts), but is less effective for studyingsubtle variations. In addition, absolute quantitation requires goodand reproducible inter-sample peak alignment. This can beachieved by using normalized elution times (NETs).

A precise quantitation of selected target proteins using abso-lute quantitation of abundance (AQUA) by adding isotope-labeled internal reference standards to the sample prior to MShas recently been demonstrated [17]. AQUA peptides are typi-cally used to measure precisely the absolute levels of proteinsand post-translationally modified proteins after proteolysis, using

selected or multiple reaction monitoring (MRM) analysis in a tri-ple-quadrupole mass spectrometer. Because peptides of interestare specifically selected from a complex mixture based on theirmasses and fragmentation patterns, this technology provides highsensitivity and thus offers the possibility to simultaneouslysequence and quantify low-abundance peptides [18].

Breast cancer proteomicsOverview of breast cancer & treatment optionsBreast cancer affects one out of eight women throughout theirlifetime, and is by far the most commonly diagnosed cancer inwomen in the Western world. A wide spectrum of treatmentoptions is available for breast cancer patients, and the choicemainly depends on factors such as steroid hormone receptorand HER2/neu status, age, menopausal status, tumor size,grade and lymph node status [19]. In recent years, systemic treat-ment of patients with primary breast cancer has become com-mon practice, and has significantly decreased recurrence andmortality rates of patients [20]. A few decades ago, however, pri-mary tumors were mostly left untreated after surgery (except forradiotherapy) until recurrence; this was particularly commonpractice for lymph node-negative patients [20,21].

Tamoxifen is an antiestrogenic agent that has been widelyand successfully used for endocrine treatment of breast cancerover the past decades [20]. Tamoxifen targets and inhibits estro-gen receptor-α (ERα), which is expressed in approximately70% of all breast tumors. When diagnosed at an early stage,breast cancer can be effectively treated with adjuvanttamoxifen. Next to endocrine treatment, adjuvant chemo-therapy is often co-administered to improve survival. However,this success rate drops drastically in recurrent disease due totherapy resistance. Approximately 50% of patients with ER-positive breast cancer have no benefit from tamoxifen alreadyfrom the onset of treatment (intrinsic resistance). Therefore,ER status alone is insufficient for adequate therapy-responseprediction. In addition, patients who initially respond well totherapy eventually develop progressive disease due to acquiredresistance [22,23].

First-line chemotherapy is more frequently used for ER-nega-tive tumors, and for ER-positive tumors, for which endocrinetreatment failed or was not an option to begin with. Anthra-cycline-based chemotherapeutic agents, such as 5-fluoro-uracil/adriamycin/cyclophosphamide (FAC) or 5-fluoro-uracil/epirubicin/cyclophosphamide (FEC), have been widelyused [20]. More recently, taxanes have also been introduced.Despite the proven success of chemotherapy in primarytumors, in the recurrent setting, chemotherapy-resistanceeventually occurs in all patients.

The major issue in the treatment of breast cancer is that cur-rently available clinical and biochemical markers insufficientlypredict therapy response. Therefore, identification of new tar-gets that can better predict therapy efficacy, and ultimatelyimprove patient cure, is a major goal of current research efforts.



Discovery and validation of predictive protein markers or pro-tein profiles starts with comparative proteome analyses foridentification of differentially abundant proteins.

Cell lines, tissue heterogeneity & laser-capture microdissection Breast cancer cell lines have been widely used for the identifica-tion of protein biomarkers, mainly in combination with 2DE[24–27], or LC-MS/MS for protein separation and identification[28–30]. However, it is known that the proteomic make-up of acultured cell is rather different from a proteome profile associ-ating with a tumor cell surrounded by its native microenviron-ment. While 2D maps of cancer cell lines are rather similar,they differ substantially from microdissected tumor cells. Evencell lines that were derived from a tumor showed altered proteinexpression profiles when compared with the primary tumor [31].Thus, one should be careful in drawing conclusions from stud-ies performed on cell lines, since the results may not preciselyrepresent the expression profile of the tumor. Furthermore,tumor tissues, and in particular breast cancer tissues, are veryheterogeneous. Solid tumors typically harbor not only tumorcells, but also stromal, inflammatory and capillary cells, whichcomplicate the biological interpretation of the results [32,33]. Inthis context, laser-capture microdissection (LCM) technologyhas emerged as an ideal tool for selectively extracting cells ofinterest from their natural environment (FIGURE 1A) [34].

Proteome analyses on LCM material has shown that micro-dissected tumor cells have a different protein expression patterncompared with microdissected normal or stromal cells [31].LCM facilitates the analysis of relatively pure cell populations,but the procedure is very time-consuming and yields only smallamounts of material [35]. On the other hand, 50,000–100,000cells are typically required for 2DE analysis. The time requiredto obtain such a large number of cells influences the quality ofthe tissue slides and limits our ability to analyze large sets oftumors needed to enable a statistically meaningful number ofanalyses to account for biological variability. Therefore, highlysensitive proteomics tools, which consume very little samplebut provide comprehensive information, are desirable incombination with LCM.

Biomarker discoveryDespite the limited amount of material typically available viaLCM, this approach has been successfully used in both genom-ics and proteomics cancer biomarker discovery studies. Tumorcells collected using LCM on breast cancer tissues have beensubjected to comparative proteomic analyses using 2DE [36,37],as well as LC-MS/MS [38]. These studies resulted in the identifi-cation of proteins involved in breast cancer metastasis [36,38] andprognosis [37], demonstrating that advanced proteomic technol-ogies can significantly contribute to biomarker discovery. Themajor drawback of all these earlier studies is low sensitivity orrather limited proteome coverage. For example, large amountsof starting material were required (varying from 42–700 µg

protein lysate), or less than 100 proteins were identified in thecases where an insufficient amount of starting material wasused. Comprehensive analysis of extremely small tissue samples(e.g., <1 µg) demands the use of a robust proteomics platformthat offers exceptional sensitivity and a dynamic range of detec-tion. Other key components include the ability to provide thethroughput needed to enable a statistically meaningful numberof analyses for a clinical setting, contained within a robust plat-form that utilizes effective quantitative approaches for highaccuracy and reproducibility.

This review describes the key components of a nano-LC-FTICR-MS-based platform, which, by virtue of enhanced sen-sitivity, dynamic range coverage and throughput, can beapplied to generate robust quantitative measurements in sup-port of biomarker discovery (FIGURE 1). In particular, we will dis-cuss the important role of FTICR-MS in the identification andvalidation of protein profiles for predicting therapy resistancein breast cancer patients. The ultimate goal of these efforts is tounravel functional pathways that lead to clinical resistance totherapy by identifying responsible key proteins. Importantly,these pathways may become targets for newly designed drugsand could conceivably lead to individual treatment strategiesfor patients with (advanced) breast cancer.

FTICR-MSAccurate mass & time tag approachThe combination of nano-LC and FTICR-MS facilitates detec-tion of a large number of peptides (>103) with high sensitivity(subattomole range) and dynamic range (106) due to unparal-leled mass-resolving power (>1,000,000) and mass measure-ment accuracy (near or sub-parts per million [ppm]) of anFTICR mass analyzer, and a high peak capacity offered bynano-LC [39]. In an FTICR mass spectrometer, ions are trans-mitted from an external ion accumulation device, such as a lin-ear quadrupole, into a Penning trap located in the center of ahomogeneous magnetic field, where their orbits (ion cyclotronresonance) are excited to larger radii and detected as inducedimage current signal, the so-called time domain. Using a Fou-rier transform, the time-domain signal is then converted to thefrequency domain to reveal the individual ion frequencies,which are then converted to mass-to-charge (m/z) values usinga calibration equation. Since the physical parameter that can bemeasured with the highest accuracy and resolution based onmodern electronics is radio frequency, the FTICR provides themost accurate mass measurements of all mass analyzers [40].

Accurate mass alone is in most cases insufficient for reliablepeptide identification, which typically relies on MS/MS infor-mation. However, using the LC elution time constraint as adatabase search criterion in addition to accurate mass infor-mation significantly increases the confidence in peptide iden-tification. These two features are exploited in the accuratemass and time (AMT) tag approach developed by Smith andcoworkers [41,42]. This approach initially utilizes conventional



LC-MS/MS measurements to establish a reference database ofAMT tags specific for a particular organism or cell/tissue type(e.g., breast cancer tissue extract). Each tag consists of a theo-retical mass calculated from the peptide sequence, an LCNET value, and an indicator of quality. The AMT tag data-base serves as a look-up table for identifying peptides in sub-sequent quantitative LC-MS analyses. Substituting routineLC-MS/MS analyses (shotgun approach) with LC-FTICR-MS analyses (AMT tag approach) significantly increases over-all throughput and sensitivity. Additionally, quantitativeintensity information related to the protein’s abundance canbe discerned from these analyses. Although the AMT tagapproach has commonly been employed using LC-FTICR-MS, other higher mass accuracy instrumentation can be usedsuch as LC-TOF-MS and the recently introduced LTQ-FTand Orbitrap [43] platforms.

Construction of an AMT tag database consequently elimi-nates the need for subsequent MS/MS measurements, therebyreducing sample requirements while increasing analysisthroughput and sensitivity. AMT tag databases have been con-structed for a variety of organisms and tissues, including breastcancer cell lines [44]. While generation of an AMT tag databasefor a specific organism, tissue or cell type typically requires largeamounts of sample, once generated, AMT tags will enable pep-tide identification using only nanograms or even picograms ofprotein, depending on the complexity of the proteome. Theapplicability of the AMT tag approach for global proteomeanalysis was initially demonstrated for the prokayote Deinococcusradiodurans. In particular, it was shown that the AMT tagapproach and high-efficiency, ultra-long and narrow nano-LCcolumns (15 µm inner diameter [i.d.] × 85 cm), coupled withonline micro-solid phase extraction FTICR-MS, facilitate pro-tein identifications from as little as 0.5 pg of microbial proteomeextract and detection of zeptomoles of individual proteins with adynamic range of 106 [45,46].

Furthermore, a fully automated, four-column HPLC systemfor robust, high-throughput LC-MS/MS analyses has recentlybeen introduced [47]. This system performs multiple LC sepa-rations in parallel in such a way that the mass spectrometerusage is maximized, but without any post-column switchingand introduction of dead volumes. The availability of dedi-cated, automated, high-efficiency nano-LC columns in combi-nation with FTICR-MS and the AMT tag approach hasopened possibilities for sensitive, high-throughput studies ofcomplex protein mixtures from small biological samples, suchas LCM-derived tumor cells.

Accurate mass & time tag approach in breast cancer biomarker discoveryRecently, a number of publications have described the use ofnano-LC-FTICR for the analysis of complex mammalian pro-teomes, such as human breast cancer cell lines [48–50]. Whilemost of these studies were not restricted by the amount of sam-ple available for analysis, an eloquent characterization of the

MCF-7 breast cancer cell line proteome was described thatinvolved samples in the order of 5000 cells. A standard auto-mated 150-µm i.d. × 65 cm LC column coupled to an 11.25 TFTICR mass spectrometer enabled detection of approximately5000 unique LC-MS features that represented 525 unique pep-tides and 224 unique proteins. We have taken this approachone step closer to the clinic and have shown the applicabilityand usefulness of nano-LC-FTICR-MS in conjunction withthe AMT tag strategy for the analysis of only approximately3000 laser microdissected breast cancer cells [51]. In our initialstudy, we described the identification of a total of 2282 pep-tides, corresponding to 1003 unique proteins, from as little as300 ng protein digest, using a dedicated 30-µm i.d. × 80 cmnano-LC column coupled to the same 11.25 T FTICR massspectrometer [51]. We are currently using this approach for alarge-scale breast cancer biomarker discovery study (FIGURE 1).Our methodology involves the use of fresh-frozen breast cancertissues, from which 8-µm cryosections are cut and subjected toLCM for the separate isolation of tumor and stromal cells. Pro-tein lysates and corresponding tryptic digests are prepared, andanalyzed by either nano-LC-FTICR-MS or by MALDI-FTICR-MS (discussed in the next paragraph). The nano-LC-FTICR-MS scheme enables protein identification using abreast-specific AMT tag database, as well as label-free quantita-tion using MS-peak intensities, followed by statistical analysisof relative protein abundances (FIGURE 1B). FIGURE 2 depicts thisprocess in more detail. LC-MS features are typically representedin the form of 2D displays, showing mass versus LC elutiontime. As an example, a peptide with a monoisotopic mass of1658.7927 and a NET of 0.41 matches AMT tag 6962371.The corresponding raw mass spectrum in a nonresponding pro-gressive disease breast cancer sample (FIGURE 2B) and responding(objective response [OR]) sample (FIGURE 2C) clearly shows a dif-ference in abundance. The use of dedicated nano-LC-FTICRin combination with the AMT tag approach has enabled theidentification and relative quantitation of approximately 2000proteins from approximately 5000 LCM-derived breast cancercells, which is the most comprehensive proteomic analysis ofLCM-derived breast cancer cells to date.

MALDI-FTICR-MS

In the plethora of available MS approaches for proteomics stud-ies, MALDI-FTICR-MS is a relatively new addition. Couplingof the MALDI source with an FTICR mass spectrometerresults in a platform that provides all the superior qualities of anFTICR, such as sensitivity, high resolution and high mass accu-racy, in combination with straightforward sample preparation,rapid sample throughput, and relatively uncomplicated massspectra that come along with MALDI. Another advantage ofMALDI is that samples can be measured directly before time-consuming separation and can be stored on the target plate forremeasurment. Furthermore, much less sample consumption isrequired. Offline sample separation by nano-LC can be coupled



to MALDI-FTICR by direct fraction spotting on the targetplate. In this way, the time advantage is lost but storage possi-bilities, sensitivity and limited sample consumption remain. Itwas recently shown that peptides of interest can be identifiedfrom a complex sample with high reliability using matrix-pres-potted MALDI target plates that were measured in both

MALDI-TOF/TOF and MALDI-FTICR[52]. Herein, Dekker and coworkers haveperformed peptide profiling on humancerebrospinal fluid using MALDI-FTICR. After data analysis and observ-ance of peak masses of interest, thecerebrospinal fluid sample was separatedby nano-LC, fractions were spotted on aprespotted MALDI target plate andMS/MS was performed by MALDI-TOF/TOF-MS on the peptides of inter-est. Subsequently, the same target platewas re-analyzed by MALDI-FTICR toconfirm the presence of the peptide ofinterest using its accurate mass. Further-more, it was shown that this method isvery suitable in combination with lasermicrodissection [53,54]. In a glioma profil-ing study using approximately 1500microdissected blood vessel cells, thismethod was applied to identify angio-genesis-related proteins in glioma [53]. Inaddition, this method was used in com-bination with LCM to identify discrimi-nating proteins for early-onset pre-eclampsia [54]. In summary, peptide pro-filing by MALDI-FTICR-MS can be rel-atively easily performed on a large sam-ple set with high sample throughputusing only minute amounts of startingmaterial. After data analysis, peptideidentification can than be directlyfocused on peaks of interest and per-formed by using nano-LC-MALDI-TOF/TOF as an example, or a number ofother types of mass spectrometers.

In our search for breast cancer bio-markers, we have performed MALDI-FTICR to profile peptides in addition tonano-LC-FTICR-MS and the AMT tagapproach (FIGURE 1C). After LCM, cell lysisand trypsin digestion, a small part of thesample (corresponding to ∼400 cells)was spotted on the target plate and ana-lyzed by MALDI-FTICR. We have com-pared peptide profiles from stroma andtumor cells and observed significant dif-ferences in expression (FIGURE 3). Mass

spectra from stroma and tumor samples were compared usingin-house built software [55], which uses a Wilcoxon rank sumtest to discriminate peak masses between groups with a prob-ability value (p-value). The frequency distribution of calcu-lated p-values of masses is subsequently presented in a histo-gram. In addition, a randomization of the data is performed

Figure 2. Identification of differentially expressed peptides by nano-LC-FTICR-MS. (A) Partial 2D display of nano-LC-FTICR analysis of a breast cancer sample. For one particular peptide the matching AMT database information is shown in the table (e.g., MW, m/z, intensity and AMT tag). (B) Mass spectrum of the peptide shown in (A) in a progressive disease sample. (C) Mass spectrum of the same peptide in an OR sample. Note the decrease in peak intensity. AMT: Accurate mass and time; m/z: Mass-to-charge ratio; MW: Molecular weight; NET: Normalized elution time; OR: Objective response; PD: Progressive disease; SLiC: Spatially localized confidence score; UMC: Unique mass class.

Inte

nsi

ty

0.0e + 6

4.0e + 6

829.25 831.75m/z

1.3e + 6

2.7e + 6

PD

Inte

nsi

ty

0.0e + 6

4.0e + 6

829.25 831.75m/z

1.3e + 6

2.7e + 6

OR

1641.1

1643.4

1659.1

Mo

no

iso

top

ic m

ass

0.405 0.4150.410

File number2520

m/z830.4036

MW1658.7927

Intensity2550000

Expression ratioNA

UMC index691

AMT: 6962371 (MW Error: -0/78 p.p.m.) (SLiC: 1) (DelSLiC: 1) (N: 18)

NET



OR

OR

PD

PD

Stroma Tumor

1669

1673

1677

m/z

Stroma

Tumor

0.0

0.2

0.4

0.6

0.8

1.0

Pro

bab

ility

(P

)

Frequency

0

400

800

1200

Randomization = 1

00.1

00.1

Fig

ure

3. I

den

tifi

cati

on

of

dif

fere

nti

ally

exp

ress

ed

pep

tid

es b

y M

ALD

I-FT

ICR

-MS.

(A

) Acc

urat

e pe

ak m

asse

s ar

e su

bjec

ted

to a

Wilc

oxon

ran

k te

st. T

he p

roba

bilit

y at

w

hich

a d

iffer

ence

in p

eak

coun

t or

inte

nsity

is o

bser

ved

is

plot

ted

agai

nst

the

freq

uenc

y, w

here

gre

en r

epre

sent

s st

rom

al s

ampl

es a

nd b

lue

repr

esen

ts t

umor

sam

ples

. The

pr

obab

ility

and

fre

quen

cy o

f a

rand

om d

istr

ibut

ion

is

repr

esen

ted

by t

he r

ed li

ne. (

B)

Exam

ple

of d

iffer

enti a

lly

expr

esse

d pe

ak m

asse

s be

twee

n st

rom

a an

d tu

mor

sam

ples

, irr

espe

ctiv

e of

the

rapy

res

pons

e. A

rrow

s po

int

to

diff

eren

tially

exp

ress

ed p

eaks

.FT

ICR:

Fou

rier-

tran

sfor

m io

n-cy

clot

ron

reso

nanc

e; M

S: M

ass

spec

trom

etry

; m/z

: Mas

s-to

-cha

rge

ratio

; OR:

Obj

ectiv

e re

spon

se; P

D: P

rogr

essi

ve d

isea

se.

1668

.858

5616

70.8

6531

1672

.804

9816

74.8

1244

0

Intensity

1668

.855

6416

70.7

9765

1672

.980

65

0246x1

05

1668

.860

1316

70.8

6773

0246x1

05

1668

.862

1416

70.8

9379

1672

.985

83

00.

51.

0x1

06

1668

.857

6016

70.7

9748

1672

.802

3016

74.9

3124

00.

51.

0x1

06

1670

.803

0816

72.8

0508

1674

.779

85

0246x1

05

1670

.801

4716

72.9

9356

1674

.781

52

00.

51.

0x1

06

1668

.868

8316

72.8

3494

1674

.782

18

00.

51.

0

1668

.853

3016

74.7

7616

0246x1

05

1674

.781

39

012x1

05

x106

x106

1



to assess false-discovery rates, and corresponding p-valuesare also plotted in the same histogram, (red line in FIGURE 3A.FIGURE 3A illustrates that most peak masses in tumor (bluebars) and stroma (green bars) samples have a very lowp-value (p < 0.01), meaning that these masses are differen-tially expressed between samples. An example of two differ-entially expressed masses is given in FIGURE 3B. Mass spectrafrom tumor and stromal cells obtained from both respond-ing and nonresponding breast carcinomas are aligned, ofwhich a small detail is shown. The peptide with average m/z1668.860 is clearly present in all stroma samples, but absentin tumor samples, whereas peptide 1674.775 is only presentin tumor samples [UNPUBLISHED DATA]. We are currently using atargeted peptide identification approach on the Orbitrap,which enables targeted MS/MS on peptides of interest onlythrough the use of an inclusion list in addition to accuratemass measurements.

Expert commentary

The field of proteomics started out mainly as a technology-driven research area and is now growing more and more intoa hypothesis-driven research area, in which the aim is notonly the mere identification and cataloging of proteins, butalso ultimately understanding their function within the cellor tissue in the broader context of systems biology. Bio-marker discovery in breast cancer is no exception to thistrend. Our aim is to identify protein markers that can predicttherapy response in breast cancer, but in a larger context, thisknowledge will be translated into the understanding ofunderlying molecular pathways, as well as clinical diagnos-tics. We have experienced tremendous technologicaladvances that have benefited the field of biomarker discoveryin the past few years, in particular, the applicability ofFTICR-MS. Traditionally, FTICR mass spectrometers havenot been easily and widely accessible because of their highprice tag and maintenance costs, and newer LTQ-FT andOrbitrap platforms are becoming more easily available to abroader research community due to their relatively lower costand simple operation. The recently introduced Orbitrapmass spectrometer closely approaches FTICR performance interms of resolution and mass accuracy, and provides the addi-tional advantage of MSn capabilities when compared withearlier custom-built FTICR spectrometers. While the Orbi-trap is physically limited to a lower mass accuracy, produc-tion of FT-MS mass spectrometers with higher magnetic fieldstrength will create new research areas exploiting sub-ppmaccuracies and improved dynamic range. In terms of nano-LC separations, narrow diameter columns that are bettersuited for extensive separation of small sample quantities arebecoming commercially available. In addition, developmentof monolithic columns may have an important impact onbiomarker discovery by allowing rapid and high-resolutionpeptide and protein separations in a nano-LC setting [56].

Despite availability and applicability of advanced proteo-mics technologies for biomarker discovery, validation of candi-date biomarkers remains an obstacle for clinical translation.Four main characteristics must be considered for biomarkervalidation: screening must be performed in a large-scale, high-throughput and quantitative fashion, and crossvalidated inindependent sample sets [57]. Finally, the biomarker must beevaluated in multicentered, prospective clinical trials.

In solid tumors, such as breast cancer, biomarker discovery ismost ideally performed using microdissected tumor tissue,which is the source of molecular changes that occur duringtumorigenesis. However, once validated, it must be transferredto a clinical setting. In order for a biomarker to be successful inthe clinic, it must be easily and quickly measured. While collec-tion of tumor tissues or biopsies is an invasive procedure, serumcollection from patients is routinely performed and not muchof a burden. Therefore, serum is an ideal source for the meas-urement and validation of biomarkers. For breast cancer, noserum biomarkers are currently in clinical use.

At present, the most commonly used technology for valida-tion at the protein level is classical immunohistochemistry,which may also provide pivotal information on protein locali-zation. Tissue microarrays, which are the large-scale, high-throughput format of the same assay, facilitate the simultane-ous analysis of hundreds or even thousands of tissue samples.However, the requirement for good antibodies, the semi-quantitative nature of this technology and its inability todetect subtle protein changes represent significant drawbacksof immunohistochemistry and thus tissue microarrays.

Protein arrays offer large-scale, high-throughput, quantitativeanalysis of proteins. The use of printed lysates, referred to as RPprotein arrays, has been pioneered by the Petricoin group, andhas proved valuable for phosphoproteomics and kinase-signal-ing profiling [58,59]. RP protein arrays can be used to screen forbiomarkers in serum, and may potentially be very useful for thelarge-scale validation of potential biomarkers.

Despite technological advances, dependence on antibodiesfor protein validation remains a limitation. Therefore, quanti-tative, MS-based methods, such as MRM (e.g., in a triple-quadrupole mass spectrometer) in combination with stableisotope-labeled peptides are becoming the new gold standard[60]. In addition, quantification through MRM is faster thanantibody-based assays, and at least as accurate, with the sensi-tivity typically lying in the femtomolar range. Thus, MRMmay be the ultimate approach to quantitatively validatebiomarker proteins in tissue or body fluids in a large-scale,high-throughput fashion.

Five-year view

Future clinical proteomics platforms will not only have sufficientanalytical depth to reliably detect and quantify low-abundanceproteins, but will also have sufficient throughput to supportclinical studies with adequate statistical power. Technological



advancements in proteomics are still evolving and within thenext 5 years will undeniably result in even broader availabilityof high-performance hybrid mass spectrometers, which willenable measurements with higher throughput, improved sensi-tivity and dynamic range. In combination with enhanced bio-informatics algorithms, label-free quantitation will likelybecome more widely used. Robust protein separation willbecome available allowing extensive yet sensitive sample fraction-ation and improved proteome coverage. All these developmentswill assist the accurate identification of protein biomarkers forvarious diseases.

Proteomics technologies have matured in such a way thatbiomarker discovery and validation are achievable goals.However, unraveling functional pathways that underlietumor biology will require more extensive functional analy-sis. Understanding the systems biology of breast cancer andtumorigenesis in general, and finding the appropriate proteintargets for drug development will become the major focus inthe near future.

Financial & competing interests disclosure

Part of this research was performed at the Environmental MolecularScience Laboratory, a US Department of Energy (DOE) national scientificuser facility located on the campus of Pacific Northwest NationalLaboratory (PNNL) in Richland, Washington. During this period, ArzuUmar was financially supported through a personal fellowship from theDutch Cancer Society. Malgorzata Jaremko was supported through a grantfrom the European Science Foundation. Theo Luider was supported by theNetherlands Proteomics Center.

We thank the US DOE Office of Biological and EnvironmentalResearch, and the National Institutes of Health, through the NationalCenter for Research Resources (RR018522) for support of portions of thereviewed research.

The authors have no other relevant affiliations or financial involvementwith any organization or entity with a financial interest in or financialconflict with the subject matter or materials discussed in the manuscriptapart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Key issues

• Currently available clinical and biochemical markers for breast cancer insufficiently predict therapy response in the recurrent setting.

• High-throughput, accurate, quantitative and sensitive technologies are needed for protein biomarker discovery.

• Nano-liquid chromatography-Fourier transform ion cyclotron resonance (FTICR)-mass spectrometry (MS) in combination with the accurate mass and time tag approach provided the most comprehensive proteomics analysis of minute amounts of laser-capture microdissection (LCM) derived breast cancer cells to date.

• MALDI-FTICR-MS and targeted protein identification are valuable tools for the analysis of LCM-derived cells with high sample throughput but limited coverage.

• Large-scale biomarker validation employing protein arrays and targeted tandem MS approaches, together with functional proteomics in the context of systems biology will become the focus of clinical proteomics research over the next 5 years.

References

Papers of special note have been highlighted as:

• of interest

•• of considerable interest

1 Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 422(6928), 198–207 (2003).

• Overview of proteomics technology.

2 Friedman DB, Lilley KS. Optimizing the difference gel electrophoresis (DIGE) technology. Methods Mol. Biol. 428, 93–124 (2007).

3 Issaq HJ, Conrads TP, Janini GM, Veenstra TD. Methods for fractionation, separation and profiling of proteins and peptides. Electrophoresis 23(17), 3048–3061 (2002).

4 Chen J, Lee CS, Shen Y, Smith RD, Baehrecke EH. Integration of capillary isoelectric focusing with capillary reversed-phase liquid chromatography for two-dimensional proteomics separation. Electrophoresis 23(18), 3143–3148 (2002).

5 Shen Y, Smith RD. Advanced nanoscale separations and mass spectrometry for sensitive high-throughput proteomics. Expert Rev. Proteomics 2(3), 431–447 (2005).

•• Provides a detailed overview of nanoscale liquid chromatography and Fourier transform mass spectrometry, and their applicability for the analysis of low-nanogram samples.

6 Karas M, Hillenkamp F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal. Chem. 60(20), 2299–2301 (1988).

7 Fenn JB, Mann M, Meng CK, Wong SF, Whitehouse CM. Electrospray ionization for mass spectrometry of large biomolecules. Science 246(4926), 64–71 (1989).

8 Chen X, Sun L, Yu Y, Xue Y, Yang P. Amino acid-coded tagging approaches in quantitative proteomics. Expert Rev. Proteomics 4(1), 25–37 (2007).

9 Fenselau C. A review of quantitative methods for proteomic studies. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 855(1), 14–20 (2007).

10 Ong SE, Blagoev B, Kratchmarova I et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1(5), 376–386 (2002).

11 Gygi SP, Rist B, Griffin TJ, Eng J, Aebersold R. Proteome analysis of low-abundance proteins using multidimensional chromatography and isotope-coded affinity tags. J. Proteome Res. 1(1), 47–54 (2002).

12 Ross PL, Huang YN, Marchese JN et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 3(12), 1154–1169 (2004).

13 Heller M, Mattou H, Menzel C, Yao X. Trypsin catalyzed 16O-to-18O exchange for comparative proteomics: tandem mass spectrometry comparison using



MALDI-TOF, ESI-QTOF, and ESI-ion trap mass spectrometers. J. Am. Soc. Mass Spectrom. 14(7), 704–718 (2003).

14 Yang F, Jaitly N, Jayachandran H et al. Applying a targeted label-free approach using LC-MS AMT tags to evaluate changes in protein phosphorylation following phosphatase inhibition. J. Proteome Res. 6(11), 4489–4497 (2007).

• Describes the use of label-free quantitation in accurate mass and time (AMT) tag proteomics.

15 Kim YJ, Zhan P, Feild B, Ruben SM, He T. Reproducibility assessment of relative quantitation strategies for LC-MS based proteomics. Anal. Chem. 79(15), 5651–5658 (2007).

16 Andreev VP, Li L, Cao L et al. A new algorithm using cross-assignment for label-free quantitation with LC-LTQ-FT MS. J. Proteome Res. 6(6), 2186–2194 (2007).

17 Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl Acad. Sci. USA 100(12), 6940–6945 (2003).

18 Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5(4), 573–588 (2006).

19 Harris L, Fritsche H, Mennel R et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J. Clin. Oncol. 25(33), 5287–5312 (2007).

• Comprehensive overview of the effects of various therapies on recurrence and survival.

20 EBCTCG. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365(9472), 1687–1717 (2005).

21 EBCTCG. Tamoxifen for early breast cancer: an overview of the randomised trials. Early Breast Cancer Trialists’ Collaborative Group. Lancet 351(9114), 1451–1467 (1998).

22 Clarke R, Liu MC, Bouker KB et al. Antiestrogen resistance in breast cancer and the role of estrogen receptor signaling. Oncogene 22(47), 7316–7339 (2003).

23 Massarweh S, Schiff R. Resistance to endocrine therapy in breast cancer: exploiting estrogen receptor/growth factor signaling crosstalk. Endocr. Relat. Cancer 13(Suppl. 1), S15–S24 (2006).

24 Gehrmann ML, Hathout Y, Fenselau C. Evaluation of metabolic labeling for comparative proteomics in breast cancer cells. J. Proteome Res. 3(5), 1063–1068 (2004).

25 Malorni L, Cacace G, Cuccurullo M et al. Proteomic analysis of MCF-7 breast cancer cell line exposed to mitogenic concentration of 17β-estradiol. Proteomics 6(22), 5973–5982 (2006).

26 Ou K, Kesuma D, Ganesan K et al. Quantitative profiling of drug-associated proteomic alterations by combined 2-nitrobenzenesulfenyl chloride (NBS) isotope labeling and 2DE/MS identification. J. Proteome Res. 5(9), 2194–2206 (2006).

27 Chuthapisith S, Layfield R, Kerr ID, Hughes C, Eremin O. Proteomic profiling of MCF-7 breast cancer cells with chemoresistance to different types of anti-cancer drugs. Int. J. Oncol. 30(6), 1545–1551 (2007).

28 Wu SL, Hancock WS, Goodrich GG, Kunitake ST. An approach to the proteomic analysis of a breast cancer cell line (SKBR-3). Proteomics 3(6), 1037–1046 (2003).

29 Sandhu C, Connor M, Kislinger T, Slingerland J, Emili A. Global protein shotgun expression profiling of proliferating mcf-7 breast cancer cells. J. Proteome Res. 4(3), 674–689 (2005).

30 Sarvaiya HA, Yoon JH, Lazar IM. Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation. Rapid Commun. Mass Spectrom. 20(20), 3039–3055 (2006).

31 Ornstein DK, Gillespie JW, Paweletz CP et al. Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines. Electrophoresis 21(11), 2235–2242 (2000).

•• Demonstrates the importance of laser-capture microdissection (LCM) in cancer research by showing differences in proteome expression between tumor and stromal cells, and between primary and cultured cells.

32 Stingl J, Caldas C. Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis. Nat. Rev. Cancer 7(10), 791–799 (2007).

33 Yang F, Foekens JA, Yu J et al. Laser microdissection and microarray analysis of breast tumors reveal ER-α related genes and pathways. Oncogene 25(9), 1413–1419 (2006).

34 Emmert-Buck MR, Bonner RF, Smith PD et al. Laser capture microdissection. Science 274(5289), 998–1001 (1996).

• First ground-breaking description of LCM technology.

35 Fuller AP, Palmer-Toy D, Erlander MG, Sgroi DC. Laser capture microdissection and advanced molecular analysis of human breast cancer. J. Mammary Gland Biol. Neoplasia 8(3), 335–345 (2003).

36 Hudelist G, Singer CF, Pischinger KI et al. Proteomic analysis in human breast cancer: identification of a characteristic protein expression profile of malignant breast epithelium. Proteomics 6(6), 1989–2002 (2006).

37 Neubauer H, Clare SE, Kurek R et al. Breast cancer proteomics by laser capture microdissection, sample pooling, 54-cm IPG IEF, and differential iodine radioisotope detection. Electrophoresis 27(9), 1840–1852 (2006).

38 Zang L, Palmer Toy D, Hancock WS, Sgroi DC, Karger BL. Proteomic analysis of ductal carcinoma of the breast using laser capture microdissection, LC-MS, and 16O/18O isotopic labeling. J. Proteome Res. 3(3), 604–612 (2004).

39 Qian WJ, Camp DG II, Smith RD. High-throughput proteomics using Fourier transform ion cyclotron resonance mass spectrometry. Expert Rev. Proteomics 1(1), 87–95 (2004).

40 Heeren RM, Kleinnijenhuis AJ, McDonnell LA, Mize TH. A mini-review of mass spectrometry using high-performance FTICR-MS methods. Anal. Bioanal. Chem. 378(4), 1048–1058 (2004).

41 Pasa-Tolic L, Masselon C, Barry RC, Shen Y, Smith RD. Proteomic analyses using an accurate mass and time tag strategy. Biotechniques 37(4), 621–624, 626–633, 636 (2004).

•• Eloquently describes the usefulness of the AMT tag strategy for detailed proteome analysis.

42 Smith RD, Anderson GA, Lipton MS et al. An accurate mass tag strategy for quantitative and high-throughput proteome measurements. Proteomics 2(5), 513–523 (2002).

43 Hu Q, Noll RJ, Li H et al. The Orbitrap: a new mass spectrometer? J. Mass Spectrom. 40(4), 430–443 (2005).

44 Patwardhan AJ, Strittmatter EF, Camp DG II, Smith RD, Pallavicini MG. Quantitative proteome analysis of breast



cancer cell lines using 18O-labeling and an accurate mass and time tag strategy. Proteomics 6(9), 2903–2915 (2006).

45 Shen Y, Tolic N, Masselon C et al. Nanoscale proteomics. Anal. Bioanal. Chem. 378(4), 1037–1045 (2004).

46 Shen Y, Tolic N, Masselon C et al. Ultrasensitive proteomics using high-efficiency on-line micro-SPE-nanoLC-nanoESI MS and MS/MS. Anal. Chem. 76(1), 144–154 (2004).

47 Livesay EA, Tang K, Taylor BK et al. Fully automated four-column capillary LC-MS system for maximizing throughput in proteomic analyses. Anal. Chem. 80(1), 294–302 (2008).

48 Jacobs JM, Mottaz HM, Yu LR et al. Multidimensional proteome analysis of human mammary epithelial cells. J. Proteome Res. 3(1), 68–75 (2004).

49 Liu T, Qian WJ, Chen WN et al. Improved proteome coverage by using high efficiency cysteinyl peptide enrichment: the human mammary epithelial cell proteome. Proteomics 5(5), 1263–1273 (2005).

50 Wang H, Qian WJ, Mottaz HM et al. Development and evaluation of a micro- and nanoscale proteomic sample preparation method. J. Proteome Res. 4(6), 2397–2403 (2005).

51 Umar A, Luider TM, Foekens JA, Pasa-Tolic L. NanoLC-FT-ICR MS improves proteome coverage attainable for approximately 3000 laser-microdissected breast carcinoma cells. Proteomics 7(2), 323–329 (2007).

•• First description of applicability of the AMT tag approach for the analysis of minute amounts of clinical sample with increased sensitivity.

52 Dekker LJ, Burgers PC, Guzel C, Luider TM. FTMS and TOF/TOF mass spectrometry in concert: identifying peptides with high reliability using matrix prespotted MALDI target plates. J. Chromatogr. B. Analyt. Technol. Biomed. Life. Sci., 847(1), 62–64 (2007).

53 Mustafa DA, Burgers PC, Dekker LJ et al. Identification of glioma neovascularization-related proteins by using MALDI-FTMS and nano-LC fractionation to microdissected tumor vessels. Mol. Cell Proteomics 6(7), 1147–1157 (2007).

• Describes a sensitive and high-throughput MALDI-Fourier-transform ion-cyclotron resonance methodology for biomarker screening of microdissected cells and targeted protein identification.

54 de Groot CJ, Guzel C, Steegers-Theunissen RP et al. Specific peptides identified by mass spectrometry in placental tissue from pregnancies complicated by early onset preeclampsia attained by laser capture micodissection. Proteomics Clin. Appl. 1, 325–335 (2007).

55 Titulaer MK, Siccama I, Dekker LJ et al. A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls. BMC Bioinformatics 7, 403 (2006).

56 Rieux L, Lubda D, Niederlander HA, Verpoorte E, Bischoff R. Fast, high-efficiency peptide separations on a 50-microm reversed-phase silica monolith in a nanoLC-MS set-up. J. Chromatogr. A 1120(1–2), 165–172 (2006).

57 Mischak H, Apweiler R, Banks RE et al. Clinical proteomics: a need to define the field and te begin to set adequate standards. Proteomics Clin. Appl. 1, 148–156 (2007).

58 Sheehan KM, Calvert VS, Kay EW et al. Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma. Mol. Cell. Proteomics 4(4), 346–355 (2005).

59 Gulmann C, Sheehan KM, Kay EW, Liotta LA, Petricoin EF III. Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J. Pathol. 208(5), 595–606 (2006).

60 Melanson JE, Chisholm KA, Pinto DM. Targeted comparative proteomics by liquid chromatography/matrix-assisted laser desorption/ionization triple-quadrupole mass spectrometry. Rapid Commun. Mass. Spectrom. 20(5), 904–910 (2006).

Affiliations

• Arzu Umar, PhDResearch scientist, Erasmus MC, University Medical Center Rotterdam, Josephine Nefkens Institute Laboratory of Genomics & Proteomics in Breast Cancer, Department of Medical Oncology, Dr. Molewaterplein 50, Be 430c, PO Box 2040, 3000 CA, Rotterdam, The NetherlandsTel.: +31 107 043 [email protected]

• Malgorzata Jaremko, PhDMount Sinai School of Medicine, New York, [email protected]

• Peter C Burgers, PhDDepartment of Neurology, Laboratory of Clinical & Cancer Proteomics, Erasmus Medical Center Rotterdam, Josephine Nefkens Institute, The NetherlandsTel.: +31 10 704 4522Fax: +31 10 704 [email protected]

• Theo M Luider, PhDDepartment of Neurology, Laboratory of Clinical & Cancer Proteomics, Erasmus Medical Center Rotterdam, Josephine Nefkens Institute, The NetherlandsTel.: +31 10 703 8069Fax: +31 10 704 [email protected]

• John A Foekens, PhDDepartment of Medical Oncology, Laboratory of Genomics & Proteomics in Breast Cancer, Erasmus Medical Center Rotterdam, Josephine Nefkens Institute, The NetherlandsTel.: +31 10 704 4369Fax: +31 10 704 [email protected]

• Ljiljana Paša-Tolic, PhDWilliam R. Wiley Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99352, USATel.: +1 509 371 6585Fax: +1 509 371 [email protected]

High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS

Documents

Transcript of High-throughput proteomics of breast carcinoma cells: a focus on FTICR-MS