Research Gene Expression: Protein Interaction Systems ......Jun 04, 2010 · Sharmila A. Bapat 1, Anagha Krishnan2, Avinash D. Ghanate2, Anjali P. Kusumbe1, and Rajkumar S. Kalra

Published OnlineFirst on June 8, 2010 as 10.1158/0008-5472.CAN-10-0447Published OnlineFirst June 8, 2010; DOI: 10.1158/0008-5472.CAN-10-0447

Integrated Systems and Technologies
Cancer
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Gene Expression: Protein Interaction Systems NetworkModeling Identifies Transformation-Associated Moleculesand Pathways in Ovarian Cancer

Sharmila A. Bapat1, Anagha Krishnan2, Avinash D. Ghanate2, Anjali P. Kusumbe1, and Rajkumar S. Kalra1

Abstract

Authors' Aand 2InstituIndia

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Multiple, dissimilar genetic defects in cancers of the same origin contribute to heterogeneity in tumor phe-notypes and therapeutic responses of patients, yet the associated molecular mechanisms remain elusive. Here,we show at the systems level that serous ovarian carcinoma is marked by the activation of interconnectedmodules associated with a specific gene set that was derived from three independent tumor-specific geneexpression data sets. Network prediction algorithms combined with preestablished protein interaction net-works and known functionalities affirmed the importance of genes associated with ovarian cancer as predic-tive biomarkers, besides “discovering” novel ones purely on the basis of interconnectivity, whose preciseinvolvement remains to be investigated. Copy number alterations and aberrant epigenetic regulation wereidentified and validated as significant influences on gene expression. More importantly, three functional mo-dules centering on c-Myc activation, altered retinoblastoma signaling, and p53/cell cycle/DNA damage repairpathways have been identified for their involvement in transformation-associated events. Further studies willassign significance to and aid the design of a panel of specific markers predictive of individual- and tumor-specific pathways. In the parlance of this emerging field, such networks of gene-hub interactions may definepersonalized therapeutic decisions. Cancer Res; 70(12); 4809–19. ©2010 AACR.

Introduction

Therapeutic decisions in oncology are based on correlationsbetween tumor characteristics and possibility of disease re-lapse (1). Limitations of such approaches, however, are nowleading to the development of therapies considering indivi-dual-specific genetic defects. The resolution of breast cancerinto four molecular gene expression–based classes representsa successful outcome of such approaches. Besides suggestingdistinct cell origins, these classes correlate well with histologicgrading and clinical characteristics (2). Over the last decade,ovarian cancer is realized to represent a group of histologicallydistinct diseases that correlate with different origins (3) and,hence, require appropriation with individual “molecular signa-tures” (4). Gene expression profiling–based identification ofprognostic and/or predictive biomarkers toward improvingdisease and therapy risk assessment along with a mechanistic

ffiliations: 1National Centre for Cell Science, NCCS Complexte of Bioinformatics & Biotechnology, Pune University, Pune,

plementary data for this article are available at Cancernline (http://cancerres.aacrjournals.org/).

rray data have been deposited with the Gene Expressionccession no. GSE18054.

ding Author: Sharmila A. Bapat, National Centre for Cell Sci-S Complex, Pune 411007, India. Phone: 91-020-25708074;0-25692259; E-mail: [email protected].

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understanding of gene interactions in pathways, networks,and/or complexes is desirable to unravel the biological beha-vior of tumors. Unfortunately, most studies are restricted bylimited sample size and a minimal commonality of genesbetween different analyses. We present here an alternativeanalysis of gene expression data to extract a “signature” ofcommonly modulated genes derived from three independentdata sets of serous ovarian carcinoma. Further, resolution ofexpression-based interaction networks of these genes andknown protein-protein interactions (PPI) confirmed existingmarkers with predictive/prognostic value and “discovered”others. More importantly, the predicted network-based inter-actions and known functionality of the identified gene set pro-vide novel insights toward a mechanistic understanding ofcellular transformation processes.

Materials and Methods

CellsWe have earlier reported the establishment of a compre-

hensive in vitro panel of 19 isogenic cell lines from a patientwith grade IV serous adenocarcinoma (5). NontumorigenicA4 and transformed A4 (A4-T) cultures were cultured asdescribed earlier (5).

RNA extraction and A4 microarray hybridizationTotal RNA was isolated from cells using the Qiagen RNeasy

minikit (Qiagen, Inc.) according to the manufacturer's proto-col. Microarray hybridization was carried out at Agilent

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Genotypic Technologies. Briefly, samples were labeled usingthe Agilent Low Input RNA amplification kit and were hy-bridized to Agilent Human Whole Genome 4 × 44 k Array(AMADID: 14850) using the Agilent In situ Hybridiztion kit.Dye swapping was done for one of the replicates to minimizethe noise introduced due to dye intensity. Data normaliza-tion was done in GeneSpring-GX using the recommendedPer Spot and Per Chip: intensity-dependent (Lowess) norma-lization. After normalization, log ratios of each gene from thethree replicates were averaged, and Student's t test was per-formed. Genes with a P value of <0.05 were extracted for fur-ther studies. Gene expression data were further collapsed soas to retain only a single probe per gene having significantP value. Genes with a (log2)fold change of >1 (transformedA4T versus nontransformed A4) were considered upregu-lated, whereas those with a (log2)fold change of <(−1) wereconsidered downregulated. The remaining genes were cate-gorized as stable genes.

Database description and data analysesWe compiled gene expression data from the following

databases:

1. TCGA, The Cancer Genome Atlas (6)2. IST, In Silico Transcriptomics (7).

TCGA is a comprehensive collaborative project that helpsthe science community to better understand the genomicchanges associated with various cancers and is publicly acces-sible. TCGA stores only high-quality and completely anno-tated data. The main advantage of using the TCGA geneexpression data set is its multidimensional data, i.e., it holdsgene expressions for each single sample derived from differentarray origin, which is normalized, annotated, and validated forthe expression variation relevance with the type of tissue rath-er than with type of array generation. Thus, the analysis ofdata from different array origins increases its robustness.We extracted and analyzed level 2 ovarian serous cystadeno-

carcinoma gene expression data from two platforms, Affymetrix(169 tumor and 10 normal samples) and Agilent (216 tumorand 6 normal samples; February 2009 to June 2009). Level 2data are obtained by processing raw expression data usingdifferent normalization algorithms, which involves equal-ization transformation (Quantile normalization), probelevel, and gene level normalization specific for differentplatforms (e.g., Affymetrix-Robust Multiarray Averagemethod, Agilent-Lowess Normalization). This processinghelps in enabling a comparative analysis of data obtainedfrom different experiments. These data are available inlog2-transformed state. Differential gene expression (foldchange) is calculated by comparing the expression valuefor each probe with average expression value in normal cellsof respective array platforms. The genes thus identifiedwere further classified in terms of upregulated and down-regulated genes as described above.The IST database archives normalized, quality checked,

and annotated data, which reduces array generation–basedvariations in expression levels, while retaining different tissuetype–based dependencies of differential gene expression. The

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IST data have been developed from a collection of publiclyavailable gene expression data of over 9,000 human samplesfrom >150 normal and diseased states including 124 to 141ovarian adenocarcinomas. This data mining helps in thecomprehensive analysis of the functional, clinical, and thera-peutic roles of genes in different tissue and tumor types. Thenormalization methods included probe-level preprocessing(which removes ambiguous probes), equalization transfor-mation (Quantile normalization), and array generation–based gene centering (which avoids bias introduced due todifferent array platform generation). These steps produceddata comparable across the major array platforms by redu-cing the array generation–based variations in expressionlevels while retaining the different tissue type–based depen-dencies of the differential gene expression.To evaluate the differential expression pattern of genes ob-

tained from the A4 cell system (GSE18054), the correspondinggene expression profile across different tissue samples for tu-mor state and normal cell state, as well as correlation plotswere retrieved from the IST database. Depending on the ex-pression state of these genes (average fold change value of >2across two data sets: tumor and normal) in the IST database,these genes are classified in terms of upregulated and down-regulated genes.A comparative analysis of gene expression obtained from

multiple sources helps in reducing the dependency of expres-sion variation originating from the noise of array expressionanalysis and improves robustness of the data. Overlaps of up-regulated and downregulated genes extracted from the threedatabases, i.e., A4, IST, and TCGA gene expression data, pro-vided us with a small pool of 30 commonly modulated genes,which had a consistent pattern of expression in all the datasets studied (Fig. 1). This was termed by us as the SeOvCagene signature. The analytic pipeline of analyses to derivethis signature is represented in Fig. 1, and gene lists of eachdata set are compiled in Supplementary Table S1.To further validate the significance of the common 30 genes

in SeOvCa, the comparison of gene expression dynamics withclinical data is required. We retrieved raw expression data forthe Gene Expression Omnibus entry GSE3149 that contains atotal of 153 ovarian tumor samples in four tumor stages (8).The raw data for tumor samples were initially grouped basedon the tumor stages and were normalized separately by RobustMultiarray Average normalization tools in the Expression Pro-filer available at the EBI server Probe. Annotations were re-trieved from Gene Expression Omnibus, and each probe wasmapped to an Entrez Gene Symbol by querying the accompa-nied public identifier in the UniGene database. Becausecorresponding expression profiles for normal tissue type werenot available in the data set, relative gene expression level interms of fold change in gene expression could not be deter-mined. Hence, an analysis of comparative gene expression vari-ation across different tumor stages was carried out in whichgene expression values were normalized by array mean normal-ization. Further, the expression data for SeOvCa genes was ana-lyzed as relative gene expression between the tumor grades thatwere assessed for a positive (increasing) gradient across the fourgrades for upregulated genes and a negative (decreasing)

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Systems Network Analyses of Serous Ovarian Cancer


gradient for downregulated genes. Genes with average foldchange difference of 0.8 to 1.3 across stage I to IV samples wereconsidered as stably expressed genes.Algorithm for the reconstruction of Accurate Cellular Net-

works (ARACNe) was used to predict the interaction networkof SeOvCa genes and their potential interactors toward thedelineation of complex regulatory networks (9). This utilitywas accessed through the open source platform, “geWork-bench” version 1.5.1. Level 2 TCGA data from both Affymetrixand Agilent platforms were uploaded and normalized usingthe “array-centered normalization option” available on ge-Workbench. Each of the 30 SeOvCa genes were consideredas “nodes,” whereas interacting partners (not a signaturegene) were called “interactors.”ARACNe is purely based on the expression data with no in-

fluence of gene ontology or other factors. The relationships arecalculated usingmutual information (MI), which is ameasure ofthe statistical dependency between two variables, implying cor-relation between the two. Genes that are indirectly correlatedthrough an intermediary interactor are also represented inthese networks. False-positive interactions are removed aftercomparing MIs using the data processing inequality that effec-tively infers the most likely path (direct interaction) by elimina-ting indirect interactors by comparing their respective MIs. In

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the present study, the MI was fixed at 0.20, and the data pro-cessing inequality was set at 0.10. The interaction networkwas converted fromprobe based to gene based using the appro-priate microarray platform annotations. The new network wasrepresented in “Cytoscape” using the “Force directed Layout.”Protein Interaction Network Analysis (PINA) was used to

predict PPIs. PINA represents an integrated platform forprotein interaction network construction, filtering, analyses,visualization, and management that integrates PPI datafrom public curated databases that were mined to generatethe PPI networks (10).

Fluorescence in situ hybridizationFluorescence in situ hybridization (FISH) analysis was car-

ried out by a commercial cyotogenetic laboratory (SahyadriMedical Genetics and Tissue Engineering Facility). Commer-cially available probes specific for the 6q21, 8q24, and 20q11locus were used for the analyses. Five hundred cells were ob-served for every chromosomal locus.

ChIP-on-chipChromatin immunoprecipitation (ChIP) combined with

microarray analysis was performed as described earlier (11) us-ing the Agilent Human Promoter CoC 244 k (AMADID:19469)

Figure 1. Derivation of the SeOvCa gene signature and their expression profiles in the three data sets.

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oligoarrays. The genes listed were screened for enrichment witha cutoff of 2-fold change in normalized log ratio. For furtheranalysis, only those genes that had probe enrichment in atleast two replicates of each experiment were retained. Fromthis primary list of genes for each type of histone modification,a set of common genes having at least two different types ofhistonemarks was identified. ChIPs and PCRs were performedas per standard protocols using anti-K4, anti-K9, and anti-K27antibodies from Millipore.

Methyl DNA immunoprecipitationGenome-wide promoter methylation analysis by methyl

DNA immunoprecipitation was performed as described ear-lier (12) using the above oligoarrays. An enrichment criterionfor probes that have at least 2-fold changes in normalizedmethylation intensity levels compared with the control sam-ple was considered.

ImmunoblottingImmunobloting was performed as described (11). Specifi-

cation of antibodies used is available on request.Pathway analyses were carried out using the gene expres-

sion analysis tool of Protein Analysis through EvolutionaryRelationships (13).

Statistical analysisAll experiments were carried out in triplicate; data are ex-

pressed as mean ± SEM of at least three independent experi-ments. The significance of difference in the mean values wasdetermined using two-tailed Student's t test; P < 0.05 wasconsidered significant.

Results

Derivation and validation of a serous ovarianadenocarcinoma gene signatureGenes likely to be important in serous ovarian carcinoma

were identified using three differential gene expression datasets: (a) A4 in vitro ovarian carcinoma progression cell modelestablished earlier from a patient presenting with advancedgrade IV serous adenocarcinoma (5, 11, 14–16) that presentsa pliable cell system for validating top-down data-driven ana-lyses, (b) TCGA gene expression database for ovarian serouscystadenocarcinomas (TCGA), and (c) individual gene pro-filing in the IST serous adenocarcinoma database. Overlapsof differentially expressed genes from these data sets (Supple-mentary Table S1) led to the derivation of a prioritized list of30 commonly regulated genes termed the SeOvCa signature asthese genes expressed a consistent pattern (Fig. 1).SeOvCa also validated in an independent expression data

set (Gene Expression Omnibus accession no. GSE3149; ref. 8)associated with different grades of ovarian tumors. Thisidentified similar expression gradients of 11 upregulatedand 5 downregulated SeOvCa genes with increasing tumorstage, expression levels of eight other genes that were stableirrespective of tumor stage and could be either predictiveof early alterations during transformation, or outliers



(Supplementary Fig. S1); probes for the remaining six genescould not be identified.The significance of SeOvCa genes was determined by

curating published data from research literature (untilNovember 30, 2009). This revealed the association of11 SeOvCa genes with ovarian cancer (MAL, MCM2,MMP9, RRM2, SOX17, SYNCRIP, DAB2, FBN1, HNMT,KLF2, and SMARCA2), of 12 genes with cellular transforma-tion events in other cancers (ATAD2, BCAT1, CDCA4, EXO1,LAMA5, MEST, SLC39A4, EFEMP1, LHFP, SGK1, PAPSS2, andPTGIS), and of 7 genes with no preidentified associationwith cancer (discovery group: TM7SF2, TNNT1, DIXDC1,GNB5, LRRC17, PROS1, and RNASE4).

System network prediction of SeOvCa interactions fromgene expression dataAssociations between SeOvCa genes were identified using a

previously described algorithm (ARACNe; ref. 9) to the TCGAmicroarray data sets. ARACNe identifies statistically signifi-cant gene-gene coregulation byMI, an information-theoreticalmeasure of relatedness that integrates data processing in-equality to eliminate indirect relationships. The final recon-structed network effectively removes bias from partiallyknown functional similarities between genes to reveal rela-tionships with the highest probability of direct interactions.This allows the independent prediction of regulatory phenom-ena within a defined context even for genes without preestab-lished functionalities. Each SeOvCa gene was considered as anindependent node to identify interactions at three levels:

1. Node-node interactions. Strikingly, these were restric-ted to downregulated nodes (Fig. 2A), indicating repres-sion to be more coregulated than activation.

2. Node-linker-node interactions involving 23 nodes and99 connecting linker genes that generate a network(Supplementary Table S2; Fig. 2B). Of these, DAB2and HNMT interact exclusively with downregulatednodes, whereas the SOX17 upregulated hub distanceditself from the main network and interacted with twodownregulated nodes. Node-linker networks can identi-fy coregulatory modules; an example is the FBN1-LHFPnode-node interaction supported by a large number oflinkers, which is thus predicted as being strongly core-gulated than with either PAPSS2 or LRRC17 (Fig. 2A).

3. Node-interactor networks that encompass all possibleinteractions in the gene expression data. The resultingnetwork has three types of nodes (Supplementary DataSet S1; Fig. 2C): (a) isolated nodes (MAL, GNB5, andTM7SF2) that do not associate with any gene in the da-ta set, (b) stand-alone nodes (SLC39A4, MEST, LAMA5,and MMP9) that although distanced from the main net-work, maintain their influence on specific genes to formisolated or “stand-alone” hubs, and (c) social nodes,which are mostly SeOvCa genes that interact with othernodes, linkers, and exclusive interacting genes (interac-tors) to generate a complex network in which upregu-lated and downregulated nodes segregate distinctly.Edge analysis within this network revealed mutual

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exclusivity with positively correlating node-partnergene interactions being more frequent than negativecorrelations. A few specific negative correlations iden-tified at the interface between 20 upregulated anddownregulated SeOvCa nodes, and 13 linker genes cre-ate an interface network, within which linkers correlatepositively with downregulated and negatively with up-regulated nodes (Supplementary Fig. S2). Such interfacenetworks may represent a central regulatory module,targeting which could be critical for effective therapy,as subtle perturbations within such inclusive hubsmay have a pronounced effect on network stability.

Copy number change prediction and identificationChromosomal distribution of all SeOvCa genes and interac-

tors considering localization to the same cytoband led to the

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identification of region-specific enriched/repressed hubs(Supplementary Table S3). To further increase predictionstringencies, only those hubs were considered wherein at leasthalf the interactors were located in the same cytoband as thenode and/or the interactors generated secondary networkswithin the same location (Supplementary Data Set S2; Fig. 3A).The 6q12-q21 associated gene cluster involves SYNCRIP with

19 of its 23 interactors around two loci at 6q12-q16.1 (node+17interactors) and 6q21 (4 interactors). The secondary networkat 6q12-16.1 contained 14 additional region-specific genes,whereas that at 6q21 had 12 region-specific genes. The8q22.1-q24.3 associated gene cluster involves two loci: (a)ATAD2 with 16 of its 33 interactors (8q22.1-24.13) and (b)the entire SLC39A4 hub (8q24.3). Although the secondary net-work generated by ATAD2 did not include region-specificgenes, the SLC39A4 secondary network enlisted 51 additional

Figure 2. ARACNe generated SeOvCa interaction networks for (A) node-node, (B) node-linker-node, and (C) node-interacting gene interactions. Red andgreen circles, upregulated and downregulated nodes, respectively; dark gray nodes, interacting partners predicted by ARACNe. Red and green edges,positively and negatively correlating interacting partners; light blue edges, interacting partners that maintain a stable expression.

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genes of which 31 were cluster specific. The 20q11.2-q13.33associated gene cluster involves two upregulated nodes, i.e.,matrix metalloproteinase 9 (MMP9) that does not generate aregion-specific network and LAMA5 with its primary interac-tors ARFGAP1 and SS18L1 that generates a secondary networkof 41 interactors of which 18 are region specific. Overall, theinvolvement of primary and secondary node componentsranged from 17.5% to 41.94% of total genes in the specific chro-mosomal regions (Fig. 3B).We validated the suggested amplifications in the A4 cell

model using FISH (Fig. 3C). All three amplifications seem tobe early events during transformation; however, regulatoryme-chanisms seemed to be in place in untransformed cells becausethe node gene expression was significantly lower than in trans-formed cells. 6q, 8q, and 20q copy number changes are reportedin ovarian cancer (17, 18); 6q21-25 amplification in cisplatin-resistant ovarian cancer (19) suggests SYNCRIP to be a usefulpredictive biomarker. LAMA5 has been identified as a predictorof amplification and biomarker for cervical cancer (20). Muta-tions, single nucleotide polymorphisms, copy number changes,and rearrangements in 8q24.3 that affect Wnt signaling and



target Myc are frequent in several cancers (21–23). RAD21,NUDCD1, DCC1, YWHAZ, LY6K, HSF1, and BOP1 identifiedin the 8q24.3 cluster in our study are reportedly associatedwith transformation and tumor progression events (24–26).

Epigenetic regulation of SeOvCa genesGlobal hypomethylation and specific promoter hyper-

methylation in tumor-suppressor genes mediate aberrant ex-pression in human malignancies (27). Gene silencing is oftensupplemented by repressive histone modifications includingthe methylation of lysine-9 on histone 3 (K9) or the methyla-tion of lysine-27 on histone 3(K27), whereas activation is sup-ported by the methylation of lysine-4 on histone 3 (K4; ref. 28).Bivalent and trivalent combinations of histone modificationsare also reported in cancer (29). In an ongoing study, we haveprofiled the epigenetic status of genes in the A4 cell model on agenome-wide scale through Me-DIP for DNA methylation andChIP-on-chip for histone methylation (K4, K9, and K27)from which we extracted data relating to SeOvCa genesMAL, MEST, PTGIS, PAPSS2, EFEMP1, and FBN1 becausethese are reported to be epigenetically regulated in human

Figure 3. Prediction and validation of copy number changes. A, secondary ARACNe networks at 6q12-6q16.1, 6q21, 8q24.3, and 20q11.2-13.33. Redcircles, nodes; the 6q21 hub is a subnetwork of SYNCRIP at 6q12-6q16.1. B, gene amplification frequencies at each region. C, representative FISHanalyses indicating 6q21(rhodamine) and 8q24(FITC; top), and 20q11 (rhodamine) amplification.

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malignancies (18, 30–36). The transformed state was strik-ingly associated with hypomethylated promoters of the upre-gulated genes (MAL andMEST); this was further supported bytwo activation K4 marks upstream of the MAL transcriptionstart site (TSS) and an enriched K4-K9 bivalentmark upstreamof the MEST-TSS (Fig. 4A). EFEMP1 promoter methylationmay be an early event in A4 transformation; contrarily, PTGIS,PAPSS2, and FBN1 seemed to be demethylated (data notshown). PAPSS2 and PTGIS harbored bivalent repressivemarks;EFEMP1 had two trivalent repressive marks; whereas FBN1 hadmonovalent K27 and trivalent repressive marks in their promo-ters; all of which validated through specific ChIP-PCRs (Fig. 4B).Retrieval of data from the TCGA DNAmethylation database

for these six genes supported MEST promoter hypomethyla-tion (Fig. 4C). Promoter hypomethylation of MAL is reportedto be a promising predictive marker associated with short pa-tient survival and aggressive ovarian cancer (29). Our data iden-tify a role for K4 methylation in additionally regulating MALexpression. Loss of imprinting and promotor switching in>MEST is linked with its reduced expression. Together, de-methylation and K4 modification seem likely mechanisms forthe increased expression of MAL and MEST. Aberrant promot-er methylation of EFEMP1 in sporadic breast cancer, FBN1 in



prostate, and PTGIS in colorectal and lung cancers are alsoknown. The present study is the first report of probable involve-ment of epigenetic regulation of these genes in ovarian cancer.

SeOvCa protein interactionsWe scanned the Human Protein Atlas (HPR, 38) for SeOvCa

protein expression and identified positive correlation in ovar-ian cancer of some upregulated (ATAD2,MCM2, SYNCRIP, andTNNT1), downregulated (FBN1, PROS1, PTGIS, and RNASE4),and marginal correlations (MEST, DAB2, and SGK1), whereasMMP9 was an outlier (Supplementary Table S4). Literaturesearch additionally supported the involvement ofMAL,HNMT,and SMARCA2 proteins in ovarian cancer (28). However, mostSeOvCa proteins remain to be evaluated in HPR, and theirassociation with ovarian cancer was established. Furtherexploration of known PINA networks led to the delineationof three types of PPIs between SeOvCa proteins:

1. Node-node PPIs between MCM2 and GNB5 (Fig. 5A).2. Node-linker-node PPIs: 12 nodes and 15 connecting lin-

kers generate a network (Supplementary Table S5; Fig. 5B)analogous to the ARACNe-generated interface network.

3. Node-interactor networks (Supplementary Table S6;Fig. 5C) involve all possible interactions within SeOvCa

Figure 4. Epigenetic regulation. A, schematic of enriched Histone-DNA methylation probes with respect to transcription start site (TSS) of each gene. A1marks the amplicon analyzed by ChIP-PCR; A2 is another enriched amplicon. B, ChIP-PCR detection of H3K4, H3K9, or H3K27 enrichment.C, methylation status of the six genes in TCGA methylation database (75 tumor and 10 normal samples;*, P < 0.05).

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to identify (a) independent proteins with no known in-teractions (MAL, SLC39A4, EFEMP1, HNMT, LHFP,LRRC17, PTGIS, and RNASE4), (b) stand-alone proteinnetwork hubs (BCAT1, CDCA4, MEST, SOX17, TM7SF2,KLF2, PROS1, and PAPSS2) that maintain their influ-ence on specific genes to form isolated hubs, and (c)social nodes that are SeOvCa proteins that have exclu-sive PPIs with other nodes or indirect ones through lin-kers to generate a complex network.

Integration of interaction networks suggests c-Myctransformation supported by altered p53 andretinoblastoma signalingDifferences between PINA-generated networks and the

contemporary ones in ARACNe exist due to comparisons atdifferent levels of gene regulation, i.e., experimentally estab-



lished PPIs versus gene expression profile–based interac-tions. Thus, molecules common to both networks (sixnodes and nine interactors) would be strongly associatedwith ovarian cancer (Fig. 6A). The MCM2 hub (CDC7,MCM3, MCM4, and MCM7) involved early in DNA replica-tion, cell cycle progression, and p53 inactivation interacts withthe RRM2 hub to link DNA mismatch repair and replication(37, 38). Significantly, Cdc7 kinase is a predictive marker inovarian cancer (39), maintains cell viability during replicationstress, and is required for loading the MCM2-MCM7 complexonto chromatin. FBN1(DCN) and DAB2(TGFBR2) togetherwith another SeOvCa gene SGK1 are linked to transforminggrowth factor β signaling and are critical in controlling its ap-optotic effects, microsatellite instability, and DNA mismatchrepair (40). All these effects are highly probable in the currentsituation wherein we identified extended networks and

Figure 5. SeOvCa PPI networks generated in PINA for (A) node-node, (B) node-linker-node, and (C) node-interacting protein interactions.

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functional pathways of each hub (Supplementary Figs. S3 andS4). The information along with published SeOvCa associa-tions (Supplementary Table S7) led us to derive a regulatorynetwork involving altered retinoblastoma (Rb) signaling, c-Myc activation, and p53/cell cycle/DNA damage repair path-ways (Fig. 6B).Rb pathway alterations primarily involve inactivation of

its function in senescence that depends on the transientrecruitment of SMARCA2 into RB/HDAC1 megacomplexes.SMARCA2 downregulation abrogates G1-S growth arrestthrough the modulation of E2F, Cdk4/6-cyclin D, Cdk2-cyclinE, and Cip/Kip/Ink4a, which are downstream effectors of Rb.Such evasion of stasis/senescence barriers may be consid-ered as a first step toward immortalization, a recognizedhallmark of cancer (41). ATAD2 is a physiologic target ofpRB/E2F and functions as a coactivator for the transcriptionfactors ERα, AR, and c-myc by recruiting cAMP response el-ement binding protein (CREB) to target E2F1, Cyclin D1, c-MYC, and BIRC5 (survivin) through a positive feedback loop.SGK1 (AR target) downregulation suggests the irrelevance ofAR signaling in ovarian cancer. 8q chromosomal region am-plifications of ATAD2 and c-Myc (Fig. 3C), together withBCAT1 (c-myc target) upregulation, contribute to tumor de-velopment and progression. SYNCRIP associates with insu-



lin-like growth factor II mRNA binding protein 1 (IGF2BP1)to limit the transfer translation–coupled decay of c-myc RNAthat enhances its stability. The phosphoinositide 3-kinase–mediated interaction of c-myc with the prereplicative mini-chromosome maintenance complex MCM2-MCM7 leads toits localization to early sites of DNA synthesis and replicationinitiation. MYCN activation by E2F1 enhances the transcrip-tion of MCM2-MCM7 members and downregulates p27 thattogether with BMI1 further mediates stem cell self-renewal.Myc deregulation generates DNA damage, replication

stress, and genomic instability through the inactivation ofthe p53-mediated DNA damage response involving ATM-ATR-CHK1-CHK2 checkpoints. CDCA4, an E2F1 target, regu-lates E2F and p53 transcription, cellular proliferation, andcell fate determination. ATR phosphorylates MCM2, resultingin the aberrant loading of the MCM complex onto chromatinand cell cycle progression. EXO1 mediates DNA mismatch re-pair, suppresses replication fork instability, and enhancesresistance to DNA-damaging agents. RRM2, another down-stream target of the p53-ATM-ATR-CHK1 axis, mediatesDNA repair and cooperates with MCM2 toward cell prolifera-tion. RRM2 also enhances invasion through NF-κB–dependentMMP9 activation, and angiogenesis through decreasedthrombspondin-1 and increased vascular endothelial growth

Figure 6. Derivation of functional modules. A, overlapping nodes and interactors in the ARACNe and PINA networks. B, schematic representation of thethree functional modules derived to be significant in serous ovarian carcinoma. C, Western blots validating some of the proteins and pathwaysimplicated in the predicted functional modules (Un, untransformed; T, transformed A4 cells).

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factor production. Derepression of CXCR4 through KLF2downregulation further supports migrating cancer cells. Lossof LRRC17 leads to enhanced interactions between RANKLand its ligand NF-κB, whereas EFEMP1 downregulation sig-nifies the loss of antiangiogenesis activity in transformed cells.LAMA5-associated 20q13.3 amplifications may involve theupregulation of CAS and ZNF217 (a putative oncogene), andcorrelate with Cyclin D1, Rb, and p53 alterations (42). A partof our predicted model (p53-Rb inactivation) in mouse OSEhas been shown to lead to the formation of neoplasms com-parable with high-grade human serous ovarian carcinomas(43). Finally, because model systems are essential to validateany set of interactions and regulations, we used the A4 cellsystem that has wild-type p53 (Supplementary Fig. S5) to iden-tify some of the partners implicated in predicted pathway dys-regulation (Fig. 6C).

Discussion

Tumor screening necessitates the identification of a basic,minimum number of markers representative of disease het-erogeneity. Gene expression analysis in the maximum numberof samples across different public databases specific for a can-cer type are important in specific, sensitive detection throughremoval of background noise. Thus, the derivation of an unbi-ased, prioritized list of serous ovarian cancer–specific genesthat validates in multiple databases holds the promise of im-proved data robustness. SeOvCa genes include some earlieridentified biomarkers; this conformance of a data-drivenapproach correlating genome-wide gene expression analyses,predicted interactive networks, and transformation-associated molecular events is very encouraging.A challenge inherent to such analysis is elucidating the

biological connection between signatorial components andphenotypic effects. Toward this end, system network–basedprediction of gene-protein interactions provides a globalunderstanding of the significance of SeOvCa gene hubs intransformation, without imposing a reductionism approach.Further screening of hub interactions at the cellular andtumor levels will affirm their diagnostic applicability. Somepromising genes include the upregulated RRM2-EXO1-MCM2 cluster (DNA licensing, mismatch repair, and aneu-ploidy), DAB2 (DOC2, differentially expressed in ovariancancer), KLF2 (cell cycle inhibitor), and components of in-terface networks, etc. Some genes earlier known to be in-volved in cancers other than ovarian cancer implies acommonality in cell transformation mechanisms. Anotherset of “discovery” genes identified without any knowntransformation-associated functionalities include TNNT1,TM2SF2, RNASE4, PROS1, LRRC17, GNB5, and DIXDC1,whose precise involvement remains to be investigated.



Biological complexity of tumors arises from the fact thatcellular transformation arises from a myriad of interactionsthat are difficult to predict with reductionism. The develop-ment of cancer systems biology–based networks that pre-dict dynamic interactions within—between cells and thetumor niche—represents a fresh insight into serous ovariancarcinoma that ultimately will lead to an increased under-standing of regulatory networks in tumors. Further valida-tion of predicted outcomes based on copy numberchanges and epigenetic regulation presents new viewpointsand assigns prediction credibility in coordination of theoryand experiments.Although correlation between transcript and protein levels

is generally poor, the present study generated several tangi-ble leads in ovarian cancer including the identification ofthree functional modules, centering on c-Myc activationlinked to altered Rb signaling and p53/cell cycle/DNA dam-age repair; together, these define distinct pathways. Dysregu-lated Rb signaling leads to the bypass of cell crisis, andevasion of p53-mediated apoptosis during DNA damageand repair supports cell cycle progression. Together with al-tered c-Myc regulation, these events outline the progressiontoward malignancy. These mechanisms are determined as afunction of a set of SeOvCa genes that may be applied forscreening serous ovarian tumors in the future. Such expres-sion tools additionally include hub-derived genes that delin-eate the specificity of involved pathways across tumors. Suchapproaches open up newer opportunities in the design andapplication of multiple gene profiling as a guide in the deci-sion making and transitioning to a more personalized ovar-ian cancer treatment.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

We thank Dr. G.C. Mishra, Director, National Centre for Cell Science (Pune,India) for the encouragement and support, the publicly available TCGA andIST databases applied in this study, and Avinash Mali and Prasad Chaskarfor the technical assistance.

Grant Support

Department of Biotechnology, Government of India, New Delhi, grant no.BT/PR11465/Med/30/145/2008 (S.A. Bapat). A.P. Kusumbe and R.S. Kalrareceive a research fellowship from the Council of Scientific and IndustrialResearch, New Delhi.

The costs of publication of this article were defrayed in part by the paymentof page charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 02/05/2010; revised 03/24/2010; accepted 04/15/2010; publishedOnlineFirst 06/08/2010.

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Cancer Res; 70(12) June 15, 2010 4819



Published OnlineFirst June 8, 2010.Cancer Res Sharmila A. Bapat, Anagha Krishnan, Avinash D. Ghanate, et al. Pathways in Ovarian CancerModeling Identifies Transformation-Associated Molecules and Gene Expression: Protein Interaction Systems Network

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Research Gene Expression: Protein Interaction Systems ......Jun 04, 2010 · Sharmila A. Bapat 1, Anagha Krishnan2, Avinash D. Ghanate2, Anjali P. Kusumbe1, and Rajkumar S. Kalra