Proteomic Profiling and Interactome Analysisof ER-Positive/HER2/neu Negative Invasive

Ductal Carcinoma of the Breast:Towards Proteomics Biomarkers

Arvind M. Korwar,1 Hemangi S. Bhonsle,2 Vikram S. Ghole,1 Kachru R. Gawai,1

Chaitanyananda B. Koppikar,3 and Mahesh J. Kulkarni2

Abstract

Breast cancer, especially ER positive/HER2/neu negative IDC, is the predominant subtype of invasive ductalcarcinoma. Although proteomic approaches have been used towards biomarker discovery in clinical breastcancer, ER positive/HER2/neu negative IDC is the least studied subtype. To discover biomarkers, as well as tounderstand the molecular events associated with disease progression of estrogen receptor positive/HER2/neunegative subtype of invasive ductal carcinoma, differential protein expression profiling was performed by usingLC-MSE (MS at elevated energy). A total of 118 proteins were identified, of which 26 were differentiallyexpressed. These identified proteins were functionally classified and their interactions and coexpression wereanalyzed by using bioinformatic tools PANTHER (Protein Analysis THrough Evolutionary Relationships) andSTRING (Search Tool for the Retrieval of Interacting Genes). These proteins were found to be upregulated andwere involved in cytoskeletal organization, calcium binding, and stress response. Interactions of annexin A5,actin, S100 A10, glyceraldehyde 3 phosphate dehydrogenase, superoxide dismutase 1, apolipoprotein, fibrino-gen, and heat shock proteins were prominent. Differential expression of these proteins was validated by two-dimensional gel electrophoresis and Western blot analysis. The cluster of these proteins may serve as a signatureprofile for estrogen receptor positive/ HER2/neu negative subtype.

Introduction

Cancer is a disease with dynamic changes in theproteome resulting from cumulative alteration in gene

expression, mutations, and post-translational modifications,which leads to deregulation of intracellular signaling path-ways allowing cancer cells to evade signals that are involvedin normal functioning of cell (Hanahan et al., 2000). Amongstdifferent cancers, breast cancer is one of the leading causes ofdeath in women worldwide, with 458,400 estimated deaths in2008 accounting for 23% of the total cancer deaths ( Jemalet al., 2011). Breast cancer is a heterogeneous disease charac-terized by a wide spectrum of histological, pathological,clinical, and molecular signatures. Invasive ductal carcinoma(IDC) is one of the major breast cancer characterized by ex-pression of hormone and growth factor receptors, such asestrogen receptor (ER), progesterone receptor (PR), and hu-man epidermal growth receptor (HER2/neu). Based on the

expression of these receptors, IDC has been classified into fivetypes viz. luminal A (ER positive/ HER2/neu negative), lu-minal B (ER-positive/HER2/neu positive), HER2 type, triplenegative/ basal like (negative for HER2, ER and PR), andnormal like, of which luminal A and B types accounts for60% of the tumors (Sorlie et al., 2001; van’t Veer et al., 2002).Further, luminal A subtype is categorized into four groups: a](HER2/neu negative, ER positive, PR positive), b] (HER2/neunegative, ER negative, PR positive), c] (HER2/neu negative,ER positive, PR negative), and d] (HER2/neu negative, ERnegative, PR negative) (Perou et al., 2000; Sorlie et al., 2001).

Proteomics-based studies enable comparative and quanti-tative investigation of cellular protein expression, which helpsin discovery of biomarkers, drug targets, and understandingsignaling pathways that govern tumorigenesis (Simpsonet al., 2001). In view of this, many comparative proteomicstudies in breast cancer have been reported (Zhang et al., 2005;Rezaul et al., 2010). Although HER2/neu positive tumors are

1Department of Chemistry, University of Pune, Ganeshkhind, Pune, India.2Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India.3Jehangir Hospital and Medical Centre, Pune, India.

OMICS A Journal of Integrative BiologyVolume 17, Number 1, 2013ª Mary Ann Liebert, Inc.DOI: 10.1089/omi.2012.0054

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more aggressive phenotype, they account for only 25% to 30%of all IDCs (Slamon et al., 1989), whereas the ER positive/HER2/neu negative subgroup numerically comprises abouttwo-thirds of all breast cancers and in fact is the dominantsubtype. Very few proteomics studies have been carried out inER positive/HER2/neu negative IDC breast clinical tissues tillnow. Only one such study has been reported, where upre-gulation of peptidyl prolyl cis-trans isomerase B, GDP inhib-itor, and tropomyosin alpha 4 were observed consistently(Weitzel et al., 2010). Therefore, it is important to study thissubtype of IDC, which may be useful in better cancer man-agement. Here we have studied the proteomic profile of ERpositive/HER2/neu negative IDCs. Further interaction andcoexpression of proteins were analyzed by using bioinfor-matic tools. In addition, the expressions of key differentiallyexpressed proteins were validated by two-dimensional elec-trophoresis and Western blot analysis.

Materials and Methods

Chemicals

All chemicals were procured from Sigma-Aldrich (St.Louis, MO, USA), unless mentioned. Glu-fibrinopeptide andpolyethylene glycol were procured from Waters (WatersCorporation, Milford, USA). All the primary antibodies werepurchased from Abcam (Abcam, UK).

Clinical breast tumor tissue

A total of six malignant breast tissue biopsy specimens andcorresponding adjacent normal breast tissue from the samepatients without any signs of malignant transformation wereobtained from Jehangir Hospital, Pune, India. Collectedsamples were immediately snap-frozen in liquid nitrogen andwere stored at - 80�C until further use. All these cases werediagnosed between the year 2006 and 2008; and had giventheir informed consent in accordance to the guidelines of thehospital/institutional scientific and ethical committee ap-proval prior to inclusion into the study. The percentage ofepithelial cells in tumor and normal tissue with more than90% were subjected to proteomic analysis. Clinical informa-tion about the samples is listed in Table 1 and histology mi-crophotographs are shown in Figure 1.

Sample preparation

For analysis of LC-MSE (MS at elevated energy), pooledsamples of six IDC clinical tissues and a pooled six adjacentcontrol tissue samples were analyzed in three technical repli-cates. For two-dimensional electrophoresis (2DE) analysis andWestern blot analysis, six individual IDC clinical samples and apooled adjacent control tissue samples were independently an-alyzed. The frozen clinical IDC breast tissue samples werethoroughly washed with phosphate buffered saline (PBS),

Table 1. Clinical Information about IDC Samples

Hormone receptor status

Sl. No HER2/neu status Estrogen Progesterone TNM status Tumor grade

1 Negative Positive Positive T4N1Mx II, Score 62 Negative Positive Positive T3N3Mx II, Score 63 Negative Positive Positive T1N1Mx II, Score 64 Negative Positive Positive NA* II, Score 75 Negative Positive Positive T1N0Mx II, Score 66 Negative Positive Positive NA* II, Score 6

NA*, not available.

FIG. 1. Hematoxylin and eosin stained histological 40 · microphotographs showing lymph node metastasis and tumornecrosis.

28 KORWAR ET AL.

followed by homogenization by using hand-held tissue tearor(BioSpec Pvt. Ltd., USA) in protein extraction buffer containing8 M urea, 2 M thiourea, 4% CHAPS, 70 mM DTT, 1% N-Deconoyl N-methylglucamide and 50lL of mammalian proteaseinhibitor cocktail. Protein extract was centrifuged at 50,000 g for1 h at 20�C. Protein concentration was determined by using aquick start Bradford protein assay (Bio- Rad, Hercules, CA, USA).

In-solution tryptic digestion for LC-MSE analysis

Extracted protein sample was precipitated by TCA/acetone. 10 lg of complex protein mixture was washed andsolubilized in 50 mM ammonium bicarbonate buffer con-taining 0.1% RapiGest (Waters Corporation, MA, USA). Pro-tein solution was subjected to reduction by using 100 mMDTT at 60�C for 15 min, followed by alkylation by using200 mM iodoacetamide in dark at room temperature for30 min. The proteins mixture was digested overnight withproteomic grade trypsin at 37�C.

LC-MSE analysis

Prior to LC-MSE acquisition, for proteome quantificationpredigested internal standard (yeast alcohol dehydrogenase,Waters Corporation) was spiked with 50 fmol per 2 lL ofprotein digest. The 2 lL of protein digest with final concen-tration of 100 ng/lL was analyzed by LC-MSE workflow byusing nanoACQUITY (UPLC) online coupled to Q-TOF massspectrometer, Synapt HDMS system (Waters Corporation)equipped with a nanolockspray ion source with flow rate of300 nL/min (external lockmass standard: Glu-fibrinopeptide(GFP) (m/z 785.8426)). Sampling of the lock spray channelwas performed every 30 s, and the system was tuned for aminimum resolution of 10000 and calibrated using GFP in-fusion. Both tumor and corresponding control digests wereacquired in three technical replicates. Peptide samples wereinjected online onto a 5 lm Symmetry C18 trapping column(180 lm · 2 cm length) at a flow rate of 15 lL/min. Peptideswere separated by in-line gradient elution onto BEH (BridgedEthyl Hybrid) 130 C18 1.7 lM · 75 lM · 150 mm nanoAC-QUITY analytical column, at a flow rate of 300 nL/min usinga linear gradient from 5% to 40% B over 35 min (A. 0.1% for-mic acid in water, B. 0.1% formic acid in acetonitrile). Ac-quisition was performed in positive V mode in a mass range of50–1990 m/z with a scan time of 1 s with alternating low(5 eV) and high (20 to 40 eV) collision energy. A capillaryvoltage of 3.2 kV, source temperature of 80�C, and conevoltage of 32 V were maintained during the analyses.

Data processing and database searching

LC-MSE data were processed by using ProteinLynx GlobalServer (PLGS version 2.4. Waters Corporation) to generatecharge state reduced and deisotoped precursor mass lists aswell as associated product ion mass lists for subsequent pro-tein identification and quantification. The processed datawere allowed to search against reviewed human subset ofUniProt database.

Prior to searching, the internal standard yeast alcohol de-hydrogenase sequence (UniProt) was added to the humandatabase. Default search parameters were used, including the‘‘automatic’’ setting for mass accuracy, a minimum of 1peptide match per protein, a minimum of 3 consecutive

product ion matches per protein, a minimum of 7 totalproduct ion matches per protein, and 2 missed trypticcleavage site were allowed. A fixed modification of carba-midomethylation of cysteine residues and variable modifi-cations as oxidation of methionine residue were used duringsearch. False positive rate (FPR) for protein identification wasset to 4%, and the absolute protein quantification function-ality was enabled using yeast alcohol dehydrogenase as aninternal standard.

Protein–protein interaction and coexpressionanalysis of IDC proteome

IDC proteome identified by LC-MSE were uploaded ontoSTRING (Search Tool for the Retrieval of Interacting Genes)database search (Szklarczyk et al., 2011). STRING databaseversion 9.0 (http://www.string-db.org) covers 5,214,234proteins from more than 1100 completely sequenced organ-isms. It provides comprehensive protein–protein interac-tion and coexpression analysis with a confidence scoreusing both experimental evidence and predicted interactioninformation.

Functional analysis of IDC proteome

IDC proteome identified by LC-MSE were analyzed byPANTHER (Protein Analysis THrough Evolutionary Relation-ships; http://www.pantherdb.org) (Mi et al., 2010). This soft-ware allows predicting protein functions using experimentalevidence and evolutionary relationships. The classified proteinswere then categorized and visualized by a biological process,molecular function, protein class, and pathway ontology terms.

2DE and image analysis

For 2DE analysis, 11 cm nonlinear 4–7pH gradient IPGstrips (Bio-Rad Laboratories, Richmond, CA, USA) wererehydrated with protein (270 lg), solubilized in rehydrationbuffer 8 M urea, 2 M thiourea, 30 mM Tris-HCl, 4% CHAPS,70 mM DTT, 0.11% N-deconoyl N-methylglucamide, 0.1%3-(decyldimethylammonio) propane sulfonate, and 0.1% ofampholytes (GE Healthcare Bio-Sciences, Sweden). First di-mension isoelectric focusing was performed using ProteanIEF cell (Bio-Rad Laboratories) followed by reduction withDTT (2%) and alkylation with iodoacetamide (2.5%) pre-pared in 6 M urea, 2% SDS, 0.375 M Tris-HCl (pH 8.8), 20%glycerol. Second dimension separation on to 12.5% SDSPAGE was performed using either SE 600 Ruby (GEHealthcare Bio-Sciences, Sweden) or Protean II xi Cell (Bio-Rad Laboratories), and resolved proteins were visualized bystaining with CBB R250. Gel images were acquired by usingGS800 calibrated densitometer (Bio-Rad Laboratories) andimage analysis was performed by using PDQuest Advancedsoftware (Bio-Rad Laboratories).

In-gel tryptic digestion

Excised protein gel spots were destained in solution con-taining 50 mM ammonium bicarbonate/50% acetonitrile. Gelspots were dehydrated in 100% acetonitrile followed by dry-ing in a vacuum concentrator. Gel spots were reduced for onehour in 10 mM DTT at 45�C and subsequently alkylated with55 mM iodacetamide in the dark at room temperature. Gelspots were again dehydrated, dried, and rehydrated in

PROTEOMICS BIOMARKERS OF IDC 29

proteomic grade trypsin solution at 20 lg/mL enzyme con-centrations. After about 20 h of incubation at 37�C, the pep-tides were extracted with 5% formic acid in 50% acetonitrile.Supernatants of protein digest were dried in vacuum centri-fuge at room temperature. Evaporated digest samples werestored at - 80�C until further analysis.

MALDI MS/MS analysis

MALDI MS/MS analysis was performed on the SY-NAPT HDMS system (Waters Corporation). Peptide di-gests were reconstituted in 5% ACN containing 0.1% TFAand premixed with equal volume of 10% CHCA matrixand applied on to 96-well MALDI plate. All samples wereacquired on 200 Hz solid state UV laser in V mode byMassLynx 4.1. The quadrupole profile was set to 500 m/zfor 5% of the scan time and then ramped to 1500 m/z forremaining period of the scan. The instrument mass cali-bration was performed by using polyethylene glycol.Proteins were identified by MALDI survey method, in-volving peptide mass fingerprinting (PMF) and MS/MS.In the MS survey method, spectra were recorded in themass range of 800–4,000 m/z for 60 s. MS/MS analysiswas performed in a data-dependent manner for the top 7peptides with higher relative intensity for 30 s each andproduct ion mass range was set to 100 to 1,500 m/z. Forprotein identification, data were processed by PLGS soft-ware, searched against a UniProt human database withmass tolerance set to 100 ppm, carbamidomethylation as

fixed modification and methionine oxidation as variablemodification. Further, amino acid sequences correspond-ing to tryptic peptide masses were identified and sub-jected to BLAST homology searches to rule out alternativeprotein identifications.

Western blot analysis

The total protein extract (10 lg) from both normal and IDCsamples were resolved onto 12.5% SDS-PAGE gels by usingMini PROTEANTM Tetra cell (Bio-Rad, Hercules, CA, USA).The proteins were then transferred on to PVDF membranesand blocked for 1 h with 5% membrane blocking agent (GEHealthcare UK Limited) prepared in TBS, and then incubatedwith antibodies either 1:3000 annexin A5 (ab14196, Abcam),1:500 HSP70 (ab31010, Abcam), 1:5000 GAPDH (ab8245,Abcam), 1:3000 alpha-1-antitrypsin (ab7633, Abcam), 1:4000fibrinogen (ab6666, Abcam), 1:4000 apolipoprotein A1(ab7613, Abcam), 1:5000 SOD2 (ab16956, Abcam), 1:5000 S100A10 (ab52272, Abcam), or 1:7000 beta-actin (A1978, Sigma-Aldrich), followed by 1:5000 of the appropriate biotin conju-gated secondary antibody and 1:2000 streptavidin conjugatedHRP. Protein bands were visualized using SIGMAFASTTM

DAB peroxidase substrate. Blot images were acquired by us-ing GS800 calibrated densitometer (Bio-Rad) and images wereanalyzed by using Quantity One software (Bio-Rad). In ad-dition, Western blots were stained with coomassie stain toassess protein load in Western blot analysis as described byWelinder et al. (2011).

Table 2. LC-MSEMethod Identification of Proteins Differential Expression in IDC Compared to Control Tissue

Sl. N Accession numbera Protein name FEb MWc (Da) PId Plgse score SCf

1 P60709 Actin cytoplasmic 1 3.24 41709 5.14 9550 70.132 P08758 Annexin A5 2.86 35914 4.73 1987 35.633 P01009 Alpha 1 antitrypsin 2.79 46707 5.24 2091 30.384 P68363 Tubulin alpha 1B chain 2.68 50119 4.76 1252 19.515 P04792 Heat shock protein beta 1 2.32 22768 5.96 3264 57.566 P21695 Glycerol 3 phosphate dehydrogenase 2.29 37543 5.76 714 35.247 P02675 Fibrinogen beta chain 2.19 55892 8.25 4540 45.218 O14558 Heat shock protein beta 6 2.15 17124 5.95 722 35.639 P00915 Carbonic anhydrase 1 2.14 28852 6.66 1210 27.20

10 Q6NZI2 Polymerase I and transcript release factor 1.96 43449 5.34 1839 27.4411 P12110 Collagen alpha 2 VI chain 1.92 108511 5.79 960 14.6212 P04075 Fructose bisphosphate aldolase A 1.91 39395 8.06 167 25.0013 P15090 Fatty acid binding protein adipocyte 1.90 14709 7.04 4375 31.8214 P68104 Elongation factor 1 alpha 1 1.89 50109 9.34 186 4.3315 P07585 Decorin 1.86 39721 8.68 1096 29.2516 Q9NZN4 EH domain containing protein 2 1.79 61122 6.00 501 23.5717 Q96Q06-2 Isoform 2 of Perilipin 4 1.77 140588 9.17 561 30.0818 P30043 Flavin reductase 1.74 22105 7.49 816 31.5519 P30041 Peroxiredoxin 6 1.69 25019 5.96 860 29.9120 O60814 Histone H2B type 1 K 1.69 13881 10.7 6150 41.2721 P02679 Fibrinogen gamma chain 1.66 51478 5.24 3826 38.1922 Q06830 Peroxiredoxin 1 1.64 22096 8.24 1870 40.2023 P61981 14-3-3 protein gamma 1.58 28284 4.61 508 8.9124 P02647 Apolipoprotein A I 1.57 30758 5.43 510 36.7025 P04083 Annexin A1 1.56 38689 6.64 2955 39.3126 P62937 Peptidyl prolyl cis trans isomerase A 1.53 18000 7.85 3639 60.61

Serial numbers (Sl. N) from 1–26 proteins were differentially expressed and are arranged from highest fold-expression to lowest. aAccessionnumber of protein is from UniProt database; bFE, fold expression calculated as ratio of tumor to control samples; cMW, molecular weight ofprotein in daltons; dPI, protein isoelectric point; ePlgs score, PLGS score (software generated score that defines the confidence of proteinidentification); fSC, sequence coverage (identified peptides sequence coverage with protein’s full sequence in database).

30 KORWAR ET AL.

FIG. 2. Prediction of (a) protein interactions and (b) coexpression by STRING database search, respectively.

PROTEOMICS BIOMARKERS OF IDC 31

FIG. 3. PANTHER classification of IDC proteins based on (a) biological process, (b) molecular functions, (c) protein class,and (d) molecular pathways.

32 KORWAR ET AL.

FIG. 3. (Continued).

PROTEOMICS BIOMARKERS OF IDC 33

Statistical analysis

LC-MSE data were analyzed according to Cheng et al.(2009). Scatter plot comparison of log2 transformed peptidepeak intensities obtained from technical replicates of rep-resentative four proteins produced a correlation coefficientof 0.981 (Supplementary Fig. S1: Supplementary data areavailable online at www.liebertpub.com/omi). A Studentst-test was performed for the proteins that showed foldchange of 1.5 and above. The fold change was consideredsignificant if p value is less than 0.05 by Students t-test. For2DE analysis, the density of each protein was expressed asmean – SD. The statistical significance was establishedby Student’s t-test. Differences were considered significantif p < 0.05.

Results

Protein identification by LC-MSE method

A total of 118 protein hits were identified from a pooledsample of six IDC tissues by LC-MSE method and details arelisted in Table 2 and Supplementary Table 1. Of these, 26proteins (22%) were overexpressed in tumor in comparisonwith the pooled control tissue samples; details of over-expressed proteins are listed in Table 2. Average fold changeof overexpressed protein was 2.01 with expression range be-

tween 1.5 to 3.24. Annotated MS/MS spectra of two peptidesfor each of the differentially expressed proteins, as well asunique peptide sequence have been provided in Supple-mentary Files 1 and 2.

Protein–protein interaction and coexpression study

The IDC proteome interaction was generated as evi-dence-based prediction (Fig. 2a). The global STRING gen-erated protein network showed high connectivity amongstress-related, cytoskeletal, and glycolysis pathway proteins.The prominent interaction network was found to be withinproteins including SOD1, SOD2, HSP70, thioredoxin, per-oxiredooxin1, triosphosphate isomerase, GAPDH, annexins,beta-actin, and apolipoprotein A1. Further, IDC interactomewas analyzed for coexpression mapping (Fig. 2b) whereS100 A10 coexpress with annexins. The significant coex-pression was found between aldolase and GAPDH, as wellas SOD1 and peroxiredoxin 1. Additionally, actin was alsofound to be coexpressed with tubulin, cofilin, aldolase andGAPDH.

Functional analysis of IDC proteome

The majority IDC proteomes were related to ontology ofcytoskeletal, stress response, and binding. In biological

FIG. 4. (a) Localization of the 30 selected spots on the 2DE image. Spot numbers correspond to serial numbers in Table 3. (b)Cropped images from 2DE gels demonstrating the differential expression of selected proteins listed in Table 3. Circles indicatethe spots of interest. Differential expression of proteins was found to be statistically significant with p value less than 0.05.

34 KORWAR ET AL.

processes, the proteins related to metabolism, stress response,signal transduction, and developmental process were signif-icantly enriched (Fig. 3a). In molecular function, the proteinsbelonging to structural molecule activity, protein binding,oxidoreductase activity, receptor activity, and hydrolase ac-tivity were selectively enhanced emphasizing on the biologi-cal process they are involved (Fig. 3b). In the protein class,cargo protein, cytoskeletal protein, signaling molecule, re-ceptor, transferase, structural protein, and isomerase classeswere enriched (Fig. 3c). In the pathway category, cytoskeletal

regulation, glycolysis, integrin signaling pathways were en-riched (Fig. 3d).

Validation experiments

Protein identification by 2DE method. In the IDC pro-teome, a total of 30 proteins were identified, of which 25proteins were found to be differentially regulated across allsix samples (Fig. 4a and 4b). Average fold changes of upre-gulated proteins was found to be within the range of 1.5 to3.31. Amongst differentially expressed proteins, 24 proteins

FIG. 4. (Continued).

PROTEOMICS BIOMARKERS OF IDC 35

were upregulated and one protein was downregulated, thedetails are listed in Table 3. Only five proteins includingalpha-1-antitrypsin, actin, fibrinogen, annexin A5, and apoli-poprotein A1 were found to be differentially expressed inboth LC-MSE and 2DE analysis. Some of these proteins werefurther validated by Western blotting.

Western blot analysis. Western blot analysis was per-formed using antibodies against alpha1 antitrypsin, annexinA5, HSP70, apolipoprotein, fibrinogen, GAPDH, SOD2, beta-actin, and S100 A10. The differential expression of theseproteins was found to be prominent across all tumor samplesin comparison with the pooled control sample. Upregulationof these proteins in tumor was statistically significant with pvalue less than 0.01 as shown in Figure 5a and 5b.

Discussion

Breast cancers are characterized based on protein expres-sion profile, such as ER, PR hormone receptors, and HER2/neu oncogenes. Such characterization will aid in effectivetherapy (e.g., hormone therapy and Herceptin therapy).However, the lone protein expression status of ER or HER2/neu does not represent the ER dependent or HER2/neu de-pendent signaling pathways. Moreover, different subtypes ofIDC differ in their morphology, clinical course, and responseto therapy. Therefore, the objective of the present study was toidentify differentially expressed proteins associated with ERpositive and HER2/neu negative subtype that may serve asuseful information in understanding the molecular mecha-nisms of carcinogenesis and progression. Widely used gel-

Table 3. 2DE Method Information of Protein Identification and Differential Expression

in IDC in Comparison with Pooled Control Samples

SlNo Protein ACCN

Ratio(T/C)

Mw(kDa)/PI

PLGSScore

SequenceCoverage

1 HSP70 P08107 3.31 70.0/5.3 7.8 33%2 HSP70 P08107 3.31 70.0/5.2 7.8 33%3 Serotransferrin P02787 - 77.0/6.7 9.6 37%4 Serotransferrin P02787 - 77.0/6.7 9.6 35%5 Serum albumin P02768 - 69.3/5.9 8.1 14%6 Serum albumin P02768 - 69.3/5.9 9.4 29%7 Serum albumin P02768 - 69.3/5.9 8.1 22%8 Vitamin D binding protein P02774 1.5 52.9/5.2 5.7 11%9 Vitamin D binding protein P02774 1.5 52.9/5.2 6.8 13%

10 Alpha 1 antitrypsin P01009 1.99 46.7/5.2 8.4 32%11 Alpha 1 antitrypsin P01009 1.97 46.7/5.2 8.5 41%12 Thioredoxin isoform2 Q96J42-2 1.5 38.0/4.6 6.4 10%13 Haptoglobin P00738 1.51 45.1/5.1 3.0 36%14 Actin P60709 2.33 41.7/5.1 8.7 22%15 Actin P60709 2.33 41.7/5.1 8.7 19%16 Fibrinogen gamma chain P02679-2 1.5 49.4/5.6 6.2 24%17 Fibrinogen gamma chain P02679-2 1.5 49.4/5.6 7.1 33%18 45 kDa Ca binding protein Q9BRK5 1.5 41.7/4.6 5.8 16%19 Mg transporter NIPA3 Q6NVV3 1.78 44.6/5.6 6.9 14%20 ADP ribosyl hydrolase Q8NDY3 0.53 40.0/5.6 6.6 15%21 Annexin A5 P08758 2.77 35.9/4.7 9.4 50%22 Isoform 3 of tropomyosin beta chain P07951-3 2.21 28.6/4.4 6.6 50%23 Isoform 3 of tropomyosin beta chain P07951-3 1.5 28.6/4.4 2.3 35%24 Putative selection and upkeep of intraepithelial T cells

protein1A8MVG2 1.5 25.3/5.5 7.3 9%

25 Complement C1q tumor necrosis factor related protein 6 Q9BXI9 1.56 28.6/5.6 6.3 13%26 Transcription elongation factor A Q5H9L2 1.59 23.2/4.5 6.2 13%27 Apolipoprotein A I P02647 1.5 30.7/5.4 8.9 54%28 Glycodelin isoform 2 P09466-2 1.57 18.2/4.9 6.3 55%29 Transthyretin P02766 1.54 15.8/5.4 7.4 9%30 Galectin P09382 1.5 14.7/5.1 6.7 56%

Serial numbers in table corresponds to spot numbers in Figure 4a.

FIG. 5. (a) Western blot analysis of proteins (a. Alpha-1 antitrypsin, b. Annexin5, c. Apolipoprotein A1, d. Fibrinogen, e.GAPDH, f. SOD2, g. HSP70, h. beta-actin, and i. S100 A10) in clinical IDC breast tissue samples and adjacent control samples.Immunoblots images were acquired using GS800 calibrated densitometer and relative average expression levels of proteins werecalculated (in error bar graph, 1 represents control and 2 represents breast tumor samples) with Quantity-One software (BioRad)and p values were less than 0.001. M, molecular weight marker (high range rainbow, product code RPN 756E, GE Healthcare LifeSciences). PC, pooled control breast tissue samples. T (T1, T2, T3, T4, T5, and T6), clinical IDC breast tissue samples. (b)Representative CBB stained blot (alpha 1 antitrypsin) shows the use of coomassie staining after the immunodetection of proteinson PVDF membranes to assess total protein load and the blotting efficiency. PC, pooled control breast tissue samples.

‰

36 KORWAR ET AL.

PROTEOMICS BIOMARKERS OF IDC 37

based approaches have limitations such as being laboriousand time consuming; several other liquid chromatography(LC) based quantification methods have been developed(Gygi et al., 1999; Ong et al., 2002; Ow et al., 2008). Theevolved LC-MSE is a parallel, unbiased, and continuous pro-cess where all peptide precursors are simultaneously sepa-rated and fragmented, regardless of intensity enablingidentification of low abundant peptides that provide en-hanced peptide coverage for identification. LC-MSE techniqueuses an internal spiked standard with alternating cycles ofscans at low and elevated collision energy to obtain bothprotein quantification and identification data set in a singleLC-MS run (Silva et al., 2006).

LC-MSE based quantification was performed to obtaincomparative proteomic data set of six patients with ER posi-tive, HER2/neu negative subtype IDC, and a correspondingpooled control sample. This study identified a total of a 118proteins, of which 26 were differentially expressed. Out ofthese 26 proteins identified by LC-MSE analysis, only fiveproteins including alpha-1-antitrypsin, actin, fibrinogen, an-nexin A5, and apolipoprotein A1 were also found to be dif-ferentially expressed in 2DE analysis with a similar trend offold change as that of LC-MSE (Table 3). Validation experi-ments employing Western blot analysis also confirmed theexpression of proteins including beta-actin, alpha-1 anti-trypsin, annexin A5, HSP70, apolipoprotein A1, fibrinogen,GAPDH, SOD2, and S100 A10. This proteomic study indicatesupregulation of specific proteins involved in cytoskeletal or-ganization, metabolism, and stress.

Cytoskeleton organization proteins

The actin and associated protein of cytoskeleton organiza-tion is critical for cell motility and migration. In this study, theIDC proteome consists of about 15% cytoskeletal-associatedproteins, including beta actin, collagen alpha 1, collagen alpha2, desmin, fibrinogen alpha, fibrinogen beta, fibrinogengamma, gelsolin, lumican, mimecan, myosin, decorin, pro-largin, profilin, transgelin, tubulin beta2A, tubulin beta, andvimentin. Beta actin is associated with cell migration in breastcancer (Vandermoere et al., 2007). Similarly, an abundance offibrinogen and transgelin is associated with structural integ-rity and stabilization of actin cytoskeleton, respectively(Costantini et al., 1991; Gimona et al., 2003). Further, trans-gelin promotes migration and invasion of cancer stem cells(Lee et al., 2010). Many of these cytoskeletal proteins havebeen reported in IDC. However, the implication of cytoskel-etal proteins in carcinogenesis is obvious in general, but theirexpression specificity for ER positive and HER2/neu negativeIDC is not well defined in particular. In addition, actin bind-ing protein tropomyosin mutations and their expressions areassociated with neoplastic phenotype (Marleen et al., 2008),and actin-based cell migration is a result of complex interac-tion network of proteins in the tumor microenvironment(Marleen et al., 2008), which also supports the STRING inter-action network and coexpression as shown in Figure 2a and2b. Hydrolysis of extracellular matrix facilitates cell migrationand evidently the hydrolase activity is significantly enriched inmolecular function (Fig. 3b). The migratory mode from epi-thelial to mesenchymal transition is considerably dependenton cytoskeletal reorganization, and such reorganizations maybe facilitated by coexpression (Fig. 2b) and expression levels of

actin interacting proteins including cofilin, gelsolin, profiling,and transgelin. Many of these proteins are reported to be de-regulated in different cancers (Marleen et al., 2008), indicatingtheir potential role in regulation of actin-cytoskeleton.

Proteins involved in stress response

In this study, upregulation of HSP27, HSP20, HSP70 cog-nate, heat shock factor binding protein 1, calreticulin, proteindisulfide isomerase, superoxide dismutase Cu Zn, superoxidedismutase Mn mitochondrial, catalase, thioredoxin, peroxir-edoxin 1, and peroxiredoxin 6 was observed in tumor tissues.Heat shock proteins (HSPs) during stress prevent aggregationand promote refolding of damaged proteins. HSPs are over-expressed in many of human cancers and are implicated intumor cell proliferation, differentiation, invasion, and recog-nition by the immune system (Ciocca et al., 2005). In ER-positive breast cancers, ER-regulated HSP27 is implicated inco-localization with ER in the cell nucleus (Ciocca et al., 1990)and is associated with both good and poor prognosis, as wellas been proposed to be a marker of estrogenic endometrialresponse (Ciocca et al., 1993). Further, protein disulfideisomerase (PDI) is overexpressed on the cancer cell surfacedue to oxidative stress (Uehara et al., 2000). Apart from ca-talysis of isomerase activity, PDI is a molecular chaperoneassisting in protein folding (Puig et al., 1994; Quan et al.,1995). Generation of reactive oxygen species (ROS) has beenimplicated in the etiology of many human diseases, includingcancer. Estrogen-induced ROS are associated with transduc-ing signals through redox sensitive transcription factors in-cluding Trx-1 and NF-kB, which have been implicated in cellmigration and invasion of the breast cancer (Okoh et al., 2010).Many proteins are expressed as a cellular defense mechanismagainst agents that induce oxidative stress, including super-oxide dismutase (SOD). SOD Mn gene polymorphism is as-sociated with increased breast cancer risk and survival(Mitrunen et al., 2001), and such biologically active SODmimicking compounds would be valuable in understandingsignal mechanisms implicated in the progression of disease( Jenney et al., 1999). The conversion of H2O2 into water andoxygen is catalyzed by catalase and peroxiredoxins, the latterdominates catalysis in cytosol (Finkel et al., 1998).

Calcium binding proteins

Oxidative stress response triggers the activation of differentintracellular pathways, resulting significant changes in cal-cium ions, pH homeostasis, and production of lipid secondmessengers (Finkel et al., 1998). These changes are sensed byannexins, which interact with specific lipid and protein moi-eties at the plasma membrane, thus contributing to stress re-sponse via regulation of various signaling pathways(Monastyrskaya et al., 2009). In this study, annexin A1 andannexin A5 were upregulated and these proteins have beenimplicated in signal transduction pathways associated withinflammation, cell differentiation, and cell proliferation ofboth glandular epithelium and squamous epithelium (Limet al., 2007). Annexin A1 is functionally involved in basal-likebreast cancer metastasis by regulating TGF-b signaling andactin cytoskeletal reorganization (de Graauw et al., 2010).Annexin A5 is involved in the development of skeletal mus-cles (Arcuri et al., 2002), and proteomic studies with clinical

38 KORWAR ET AL.

breast cancer tissues have already reported the over-expression of annexin A1 and annexin A5 (Hondermarcket al., 2001; Weitzel et al., 2010). However, other reports in-dicate the distinctive loss of annexin A1 in breast cancer (Caoet al., 2008; Shen et al., 2005). Similar to annexins, S100 pro-teins are calcium binding proteins associated with intracel-lular and extracellular functions in diseases. These S100proteins interact with many other proteins, including kinasesand actin etc., and are involved in signaling to regulate cellproliferation and differentiation (Miwa et al., 2000). Coex-pressed annexin A2 and S100 A10 form a heterotetramericcomplex and interaction of cathepsin B with such complex onthe surface of tumor cells could facilitate tumor invasion andmetastasis (Mai et al., 2000). Further, the annexin A2 and S100A10 complex is directly involved in reduction reactions and isa substrate for thioredoxin (Kwon et al., 2005). Interaction ofS100 A10 and 14-3-3 proteins with BAD could attenuate thepro-apoptotic signals (Hsu et al., 1997), and annexin A1through interaction with IKK (IkB kinase enzyme complex) isassociated with the activation of transcription factor NF-kB-mediated metastasis in breast cancer (Bist et al., 2011). In theabsence of HER2/neu, we speculate the interaction of S100A10 and RAGE may play a key in tumorigenesis via ROSgeneration and NF-kB activation (Korwar et al., 2012).

Metabolic process

Elevated glycolysis is a characteristic phenomenon of tumorsfor energy requirement as survival, growth, and invasion isconcerned. A significant percentage of proteins associated withmetabolic processes were enriched in PANTHER classificationby their biological process as shown in Figure 3a. Glycolyticproteins including ENOA, ALDOA, GAPDH, TPIS, and PGK1were found to be upregulated in IDC tumors as reported inearlier studies (Hondermarck et al., 2001; Weitzel et al., 2010).Further, LDHB is overexpressed to produce NAD+ , which isessential for glycolysis by converting pyruvate to lactate. Theseupregulated metabolic proteins have been related to additionalfunctions in tumour, such as GAPDH, which is associated withoxidative stress, tubulin binding, mRNA stability, vesicular se-cretory transport, and DNA repair (Durrieu et al., 1987; Siroveret al., 2005; Zhou et al., 2008).

In conclusion, proteomic profiling of IDC resulted in theidentification of 118 proteins, of which 26 proteins were dif-ferentially expressed. STRING interaction analysis revealedthe prominent interaction of annexins with HSP27, beta-actin,S100 A10, and apolipoprotein A1. These proteins, along withS100 A10, may serve as a signature for IDC with ER positive/HER2/neu negative subtype.

Acknowledgment

The authors are grateful to Dr. Rama Sivaram for helping inprocuring clinical breast cancer samples from Jehangir Hos-pital, Pune. This work was supported by CSIR network pro-ject NWP0004. We would also like to thank Dr. SurekhaZingde, ACTREC, for improving the quality of the manu-script, Dr. Uttara Joshi, Dr. N S. Jalnapurkar for helping inhistological analysis, and Dr. Jomon Joseph, NCCS, for hisgenerous donation of anti-actin antibody. AMK would like tothank UGC, New Delhi for research scholarship, YashwantKumar for helping in acquiring LC-MSE data, and Dr.Bhushan B. Dholakia for critical reading of manuscript.

Author Disclosure Statement

The authors have no conflict of interest to state.

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Address correspondence to:Dr. Mahesh J. Kulkarni

Proteomics FacilityDivision of Biochemical Sciences

CSIR-National Chemical LaboratoryPune-411008

India

E-mail: mj.kulkarni@ncl.res.in

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