Analysis of Chemotherapeutic Response in Ovarian Cancers Using Publicly Available High-Throughput Data

Published OnlineFirst May 21, 2014; DOI: 10.1158/0008-5472.CAN-14-0186

Cancer Therapeutics, Targets, and Chemical Biology Research

Jesus Gonzalez Bosquet1, Douglas C. Marchion2, HyeSook Chon3, Johnathan M. Lancaster3, and Stephen Chanock4

Abstract A third of patients with epithelial ovarian cancer (OVCA) will not respond to standard treatment. The determination of a robust signature that predicts chemoresponse could lead to the identification of molecular markers for response as well as possible clinical implementation in the future to identify patients at risk of failing therapy. This pilot study was designed to identify biologic processes affecting candidate pathways associated with chemoresponse and to create a robust gene signature for follow-up studies. After identifying common pathways associated with chemoresponse in serous OVCA in three independent gene-expression experiments, we assessed the biologic processes associated with them using The Cancer Genome Atlas (TCGA) dataset for serous OVCA. We identified differential copy-number alterations (CNA), mutations, DNA methylation, and miRNA expression between patients that responded to standard treatment and those who did not or recurred prematurely. We correlated these significant parameters with gene expression to create a signature of 422 genes associated with chemoresponse. A consensus clustering of this signature identified two differentiated clusters with unique molecular patterns: cluster 1 was significant for cellular signaling and immune response (mainly cell-mediated); and cluster 2 was significant for pathways involving DNA-damage repair and replication, cell cycle, and apoptosis. Validation through consensus clustering was performed in five independent OVCA gene-expression experiments. Genes were located in the same cluster with consistent agreement in all five studies (k coefficient 0.6 in 4). Integrating high-throughput biologic data have created a robust molecular signature that predicts chemoresponse in OVCA. Cancer Res; 74(14); 3902–12. 2014 AACR.

Introduction chemoresponse, including drug efflux, increased cellular glu- Epithelial ovarian cancer (OVCA) has the highest mortality tathione levels, increased DNA repair, and drug tolerance, but fi – rate of all gynecologic cancers (1), mainly because more than the exact mechanisms are not fully de ned (5 7), and there are 70% of patients present with advanced stage and will have no valid clinical biomarkers or molecular signatures that disseminated intraperitoneal disease at diagnosis (2). But also effectively predict response to chemotherapy (8, 9). Under- fi because between 20% and 30% will not respond to the initial standing the underlying processes could lead to the identi - treatment consisting of a combination of cytoreductive surgery cation of prognostic signatures, which in turn, could be used to and a platinum-based chemotherapy (3). Even in some patients stratify patients who are likely to develop resistance to stan- fi with a complete initial response to chemotherapy, the disease dard chemotherapy and, thus, could bene t from alternative will recur and eventually develop resistance to multiple drugs strategies (9). fi (4). Several mechanisms have been described to contribute to In previous studies, we have identi ed a series of molecular signaling pathways associated with OVCA response to chemotherapy in vitro as well as in clinical settings (10–13). We Authors' Affiliations: 1Division of Gynecologic Oncology, Department of selected pathways associated with chemoresponse to design a Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, Iowa pilot study aimed to identify biologic processes that influence 2 City, Iowa; Department of Women's Oncology, Experimental Therapeutics expression. For this purpose, we used publicly available data Program, Department of Oncologic Sciences; 3Department of Women's Oncology, Gynecologic Oncology, Department of Oncologic Sciences, H. from The Cancer Genome Atlas (TCGA) ovarian cancer data- Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; and sets, gene expression from ovarian cancer samples available at 4Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland the Gene Expression Omnibus (GEO) repository, and OVCA data from our laboratory previously published. All biologic Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). data from these diverse sources also included information about response to chemotherapy. The objectives of this study Corresponding Author: Jesus Gonzalez Bosquet, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Iowa were to identify biologic processes affecting gene expression of Hospitals & Clinics, 200 Hawkins Drive, Iowa City, IA 52242; E-mail: candidate pathways and create a molecular signature associ- [email protected] ated with chemoresponse. Also, we aimed to validate this doi: 10.1158/0008-5472.CAN-14-0186 molecular signature across independent gene-expression 2014 American Association for Cancer Research. microarray experiments.

3902 Cancer Res; 74(14) July 15, 2014

Molecular Analysis of Chemoresponse in Ovarian Cancer

Materials and Methods identify regions with altered copy number in each chromo- Sources of data some (21). The copy number at a particular genomic location Only serous OVCA specimens were used for these compar- was computed on the basis of the segmentation mean log ratio isons. The study included the following sets: data. We found regions with frequent CNA among all samples by performing genomic identification of significant targets in 1. OVCA cultured cell lines A2008, A2780CP, A2780S, C13, cancer (GISTIC) analysis (22). The significance of CNA at a IGROV1, OV2008, OVCAR5, and T8 (12). Cells were particular genomic location is determined on the basis of false subjected to sequential treatment with increasing doses discovery rate (FDR), as previously described (23). To deter- of cisplatin. Both cisplatin resistance and genome-wide mine the performance of our strategy, we initially proceed with expression changes were measured serially at baseline the analysis of the whole sample set, and then repeated the and after three and six cisplatin treatment/expansion methodology with only samples from patients that demon- cycles. Gene expression using Affymetrix Human U133 strated CR (n ¼ 294) and IR (n ¼ 158). CNA in both scenarios Plus 2.0 arrays (Affymetrix) was uploaded at the GEO, was consistent. accession number GSE23553. Mutation analysis. Somatic mutation detection, calling, 2. GEO clinical datasets: 127 serous OVCA with annotation, and validation have been extensively discussed chemoresponse information from the datasets GSE23554 previously (23). Somatic mutation information resulting from (12) and GSE3149 (14) with Affymetrix Human Genome Illumina Genome Analyzer DNA Sequencing GAIIx platform U133 Plus 2.0 and U133A arrays; 240 serous OVCA with (Illumina Inc.) was downloaded and formatted for analysis. chemoresponse information from the dataset GSE9891 Mutation information was downloaded as level 3, or validated (15) with Affymetrix Human Genome U133 Plus 2.0 arrays; somatic mutations. Somatic mutation information was avail- 50 serous OVCA with chemoresponse information from able from 137 samples from patients with CR and 55 with IR. the dataset GSE28739 (16) with Agilent-012097 Human 1A For those patients, there were 6,716 unique genes presenting arrays (V2; Agilent Technologies); 110 serous OVCA with some type of validated somatic mutation. These included: chemoresponse information from the dataset GSE17260 frame shift insertions and deletions, in-frame insertions or (17) with Agilent-014850 Whole Human Genome 4 44 K deletions, missense, nonsense, and nonstop mutations, silence, arrays; 185 serous OVCA from the dataset GSE26712 (18) splice site, and translation start site mutations. All independent with Affymetrix human U133A arrays. significant mutated genes were correlated with gene expres- 3. TCGA (www.cancergenome.nih.gov): More than 20 sion of the candidate pathways genes to determine whether different cancer types are included in this initiative, mutated genes were associated or influenced expression of including more than 560 serous OVCAs, the most common those pathways. To correlate mutated genes with gene expres- histologic subtype of OVCA. TCGA comprehensive sion, we used the Spearman rank correlation test, as both genomic information includes copy-number variation, variables are not completely independent one from another. SNPs, miRNA expression, gene expression (mRNA), and We assessed statistical significance of the correlation by com- DNA methylation as well as clinical and outcome puting q value for FDR (qFDR) and P value, corrected for information. Data from TCGA were downloaded, multiple analyses (24). normalized, formatted, and organized for the integration Gene expression and correlation with CNA. Raw gene- and analysis with other biologic datasets in accordance expression data were downloaded from the TCGA Data Portal with the precepts of the TCGA data sharing agreements. (level 1), extracted, loaded, and normalized with the analytical software, BRB-ArrayTools. In total, 594 microarrays samples All data collection and processing, including the consenting analyzed with Affymetrix HT Human Genome U133 Array: 584 process, were performed after approval by a local Institutional coming from cancer tissue and 10 coming from ovarian normal Review Board and in accord with the TCGA Human Subjects samples. DNA sequences were aligned to NCBI Build 36 of the Protection and Data Access Policies, adopted by the National human genome. There were 452 arrays with clinical informa- Cancer Institute (NCI) and the National Human Genome tion about chemoresponse. During the circular binary seg- Research Institute. mentation analysis of the CNA, a gene-centric table is created, Clinical outcomes which contains a value for each gene covered in the genomic Complete response (CR) was defined as complete disap- array. This value is assigned on the basis of the segmentation pearance of all disease up to 6 months after treatment. In mean log ratios. The gene-centric table is required for the patients with incomplete response (IR) the disease either not correlation analysis between copy number and gene expres- responded or progressed during treatment (refractory) or sion. Positive correlation between gene expression and CNA recurred within 6 months of treatment completion (resistant; (increased CNA/increased gene expression, and decreased refs. 3, 19, 20). CNA/decreased gene expression) was performed using the Spearman rank correlation test, as the expression between Data analysis genes is not completely independent one from another. Sta- Copy-number alteration. Samples from Agilent Human tistical significance was assessed with qFDR and P value, and Genome CGH Microarray 244A (Agilent Technologies) were corrected for multiple analyses (24). processed and DNA sequences were aligned to NCBI Build 36 of To construct a gene signature profile that would classify the human genome. Circular binary segmentation was used to patients between complete responders and incomplete

www.aacrjournals.org Cancer Res; 74(14) July 15, 2014 3903

Gonzalez Bosquet et al.

responders, we used the Class Prediction Tool of BRB-Array- throughput promoter analysis. Differential gene expression Tools. Genes differentially expressed between both classes at between CR and IR was performed on those genes within the significance level P < 0.001 were included in the predictor and CCP set found to have TF-binding sites by TRANSFAC database. evaluated with several methods. To assess how accurately the Differences of gene expression between the classes (CR vs. IR) at groups are predicted by this multivariate class predictor, a the univariate significance level of P < 0.01 were considered cross-validated misclassification rate is computed, usually in significant, as 1,700 genes were introduced in the analysis. the form of the leave-one-out cross-validation method. For Nonnegative matrix factorization consensus clustering of TCGA gene-expression analysis, 40 samples in each group (CR the final model. Nonnegative matrix factorization (NMF) is vs. IR) had 90% power of detect the differentially expressed gene, an unsupervised learning algorithm that has been shown to with a type 1 error of 0.001. A similar experiment, using the same identify molecular patterns when applied to gene-expression software (BRB ArrayTools), same statistics, same outcomes data. NMF detects context-dependent patterns of gene expres- definitions (CR vs. IR), and list of genes from the gene signature sion in complex biologic systems (15, 23). This method com- identified in the testing set (TCGA), was designed to validate the putes multiple k-factor factorization decompositions of the results of the signature profile in independent available data- expression matrix and evaluates the stability of the solutions bases: GEO# GSE9891 (15), GSE28739 (16), and GSE23554 (12). using a cophenetic coefficient. The final subclasses of genes Methylation analysis and correlation with gene expres- were defined on the basis of the most stable k-factor decom- sion. DNA methylation data with b values, methylated (M), position and visual inspection of gene-by-gene correlation and unmethylated (U) intensities were downloaded from the matrices (for details of the method; refs. 15, 23). TCGA Data Portal (level 2), extracted, loaded, and normalized. In total, 574 arrays samples of Illumina Infinium Human DNA Software Methylation 27 (Illumina Inc.): 572 cancer, 2 normal. There The majority of analyses were performed using R statistical were 453 unique DNA-methylation arrays from serous OVCA package for statistical computing and graphics (www.r-proj- with clinical information about chemoresponse. Differential ect.org) as background, using Bioconductor packages as open DNA methylation of gene promoters was computed on the source software for bioinformatics (bioconductor.org). Anal- basis of b values. b values for each sample and locus were ysis of comparative genomic hybridization (CGH) to assess calculated as [M/(MþU); ref. 23]. Differences of gene b values CNA and analysis of gene expression were performed using between the classes (CR vs. IR) at the univariate significance Biometric Research Branch (BRB) ArrayTools, an integrated level of P < 0.001 were considered significant. Rank-based package for the visualization and statistical analysis that uses Spearman correlation was used to allow for nonlinear relation- Excel (Microsoft) as front end, and with tools developed in the ships between DNA methylation and gene expression, along R statistical system. BRB ArrayTools were developed by Dr. with P values. To control the FDR, we used the q value for Richard Simon (Biometric Research Branch, Division of Cancer statistical significance (qFDR; ref. 24) and Bonferroni correc- Treatment and Diagnosis, National Cancer Institute, Rockville, tion for multiple comparisons. MD) and the BRB ArrayTools development team. miRNA expression analysis and its correlation with gene MultiExperiment Viewer was used to implement the NMF expression. Raw miRNA expression data were downloaded consensus clustering and is part of the TM4 suite of tools from the TCGA Data Portal (level 1), extracted, loaded, and (http://www.tm4.org/) developed in Java, an open-source, and normalized with the analytical software, BRB-ArrayTools. In freely available collection of tools of use to a wide range of total, 595 microarrays samples of Agilent Human miRNA laboratories conducting microarray experiments. Microarray Rel12.0 (Agilent Technologies Inc.): 585 cancer, Pathway enrichment analysis. To identify overrepresent- 10 normal. There were 455 unique miRNA expression arrays ed and significant pathways among the selected list of genes, from serous OVCA with clinical information about chemor- we used MetaCore (GeneGo, Inc.), an integrated and curated esponse (23). Differences of miRNA expression between the "knowledge-based" platform for pathway analysis. The P value classes (CR vs. IR) at the univariate significance level of P < 0.05 of significant associated pathways represents the probability were considered significant, as there were 619 unique miRNA that a particular gene of an experiment is placed into a pathway tested. Rank-based Spearman correlation was used to allow for by chance, considering the numbers of genes in the experi- nonlinear relationships between miRNA expression and gene ment, and total genes across all pathways. expression, along with P values. To control the FDR, we used the q value for statistical significance (qFDR; ref. 24) and Bonferroni correction for multiple comparisons. Results Transcription factor–binding sites and their association Selection of candidate pathways associated with with gene expression. To identify transcription factor (TF) chemoresponse and their binding sites within the CNA-correlated pathway Our analysis focused on gene-expression data from different (CCP) gene subset, we used publicly available search tools, The sources to identify genes and pathways involved in OVCA Transcription Factor Database (TRANSFAC; ref. 25). TRANSFAC chemoresistance. Sources included OVCA cultured cell lines is a knowledge-base containing published data on eukaryotic that underwent progressive higher doses of chemotherapy and TFs, their experimentally proven binding sites, and regulated tested with a chemosensitivity analysis measured by IC50. Then genes (26) that uses a range of tools and algorithms to search gene expression of 48 samples was compared before and after DNA sequences for predicted TF-binding sites through high- the treatment (12). Also, we included clinical samples gathered

3904 Cancer Res; 74(14) July 15, 2014 Cancer Research

Molecular Analysis of Chemoresponse in Ovarian Cancer

from our own institution, 127 samples, available at the GEO ciated with chemoresponse in OVCA were used as candidates repository with accession number GSE23554 (10, 12, 13), OVCA pathways: the O-glycan biosynthesis pathway (P ¼ 7 10 3), samples from 240 patients from the GEO database GSE9891 described in our previous in vitro studies of chemosensitivity (15), and data from TCGA, with 465 OVCA samples (23). Only (10), the transport RAB1A regulation pathway (P ¼ 5 10 3) serous OVCA specimens with information of response to that controls vesicle traffic within the cell (27) and members of chemotherapy were used for comparison. Chemoresponse, as the RAS oncogene family (11, 28), MAPK14 and PDGFD (29) part defined in methods, was a significant independent survival of the MAPK signaling pathway (P ¼ 10 4), involved in the factor in TCGA dataset survival analysis (Cox proportional initiation of a G2 delay after UV radiation (30) and also HR), even after control for age, stage, and optimal surgery (P < identified in in vitro studies (11). Only these candidate path- 10 15; Supplementary Fig. S1). We determined genes that were ways, previously associated with chemoresponse, were used in differentially expressed between patients with CR and patients the rest of the study. with IR in the three clinical databases; furthermore, we evaluated genes differentially expressed in the OVCA cell lines Identification of elements influencing expression of based on in vitro chemosensitivity analysis. A series of genes candidate pathways genes were common between these comparison analyses: TIMP3, TCGA datasets for genomic copy-number alterations (CNA), OLFML3, C10orf26, COPZ2, PDGFD, OMD, PKD2, SNRPA, mutation and methylation analysis, and miRNA expression COL8A1, GCNT1, CDK5RAP3, PRPF40A, RAB35, MAPK14, PARN, were used to identify the elements that could potentially NCRNA00184, ERCC5, C13orf33, LHFP, KIAA1033, GJB1, SVEP1, influence the expression of our candidate pathways. Main TPM1, and IMPACT (Supplementary Fig. S2). We introduced clinical and biologic data from TCGA patients included in these common differentially expressed genes in the pathway our study are summarized in Table 1. The TRANSFAC data- enrichment analysis by GeneGO (MetaCore), and only those base (25, 31, 32) was used to predict TF-binding sites in our significant pathways that were previously described as asso- analysis.

Table 1. Clinical and biologic data from TCGA patients included in the study

CR IR Clinical information of patients with gene expression Number of patients 294 158 Age (average) 58.7 59.6 N.S. Grades N.S. Grade 1 4 1 Grade 2 36 18 Grade 3 247 135 Stages P ¼ 0.01 Stage I 10 3 Stage II 19 1 Stage III 226 123 Stage IV 39 29 Surgical outcome P < 0.001 Optimal (<1-cm residual) 185 89 Suboptimal (>1-cm residual) 51 57 Chemotherapy N.S. Contained platinum 282 147 Not contained platinum 1 3 Contained taxane 159 77 All 283 150 Biologic information (number of samples) CGH 283 152 Mutation analysis 137 55 Gene-expression arrays 294 158 DNA methylation arrays 287 153 miRNA expression arrays 295 160

NOTE: Clinical variables were associated to gene-expression analysis primarily through the ID number of each patient. The same ID number was used to correlate gene-expression data with other biologic information: gene mutation, CNA, DNA methylation, and miRNA expression. Abbreviation: N.S., nonsigniﬁcant.

www.aacrjournals.org Cancer Res; 74(14) July 15, 2014 3905

Gonzalez Bosquet et al.

Table 2. CNA affecting candidate pathway genes

Size/arm Number Chromosome Arm Start Size (%) Cytoband of genes Copy-number gains Focal CNA gains 1 q 143809389 13468627 10.98 1q21.1–q23.1 338 1 q 157287924 3811143 3.11 1q23.1–q23.3 89 2 q 154605621 1322 0.00 2q24.1 1 6 p 31563829 1041471 1.72 6p21.33–p21.32 77 6 p 9797279 7685593 12.70 6p24.3–p22.3 36 7 q 128194352 30626911 30.66 7q32.1–q36.3 265 11 q 76074403 2562103 3.16 11q13.5–q14.1 24 19 p 9457512 10236513 35.92 19p13.2–p13.11 323 19 q 32545047 13265170 37.37 19q12–q13.2 219 20 p 12719 4947364 18.26 20p13 93 Regional CNA gains 3 q 133198618 66130974 61.06 3q22.1–q29 386 8 p 41244139 105020707 232.35 8p11.21–q24.3 492 12 p 33854 32885882 92.90 12p13.33–p11.21 314 20 q 29297270 33122264 94.91 20q11.21–q13.33 411 Copy-number losses Focal CNA losses 2 q 154605621 1322 0.00 2q24.1 1 5 q 51632554 47776009 35.84 5q11.2–q21.1 225 12 p 738240 4983 0.01 12p13.33 1 15 q 18362555 26590605 32.04 15q11.1–q21.1 311 Regional CNA losses 4 q 72720228 118433385 84.47 4q13.3–q35.2 479 6 q 90029071 80724035 73.05 6q15–q27 388 8 p 151472 37851200 83.74 8p23.3–p12 249 13 q 18194544 95928796 97.89 13q11–q34 382 16 q 54405667 34284904 67.49 16q12.2–q24.3 354 17 p 1909371 34801218 156.76 17p13.3–q21.2 614 18 q 33659862 42456164 70.88 18q12.2–q23 151 22 q 21579649 27991419 73.28 22q11.22–q13.33 402

NOTE: Only those genomic regions with gene copy-number gain/loss that include genes of the candidate pathways were included.

CNA analysis and correlation with gene expression unknown mechanisms of chemoresponse in OVCA. Details of Of the 452 patients with clinical information about chemor- regional and focal CNA somatic gains and losses affecting esponse (294 CR and 158 IR) and gene-expression data from genes of the candidate pathways are summarized in Table 2. specimens collected at diagnosis, 435 underwent CGH to Of the genes included in these candidate regions with CNA determine CNA. Supplementary Figure S3 and Supplementary (6,622 genes) only 2,364 genes showed statistically significant Table S1 summarize whole genome significant somatic gains positive correlation with gene expression, determined by and losses determined by the GISTIC analysis (22). CNA could Spearman rank correlation (Supplementary Table S2). Positive be divided into regional alterations, when gain or loss of statistical correlation was defined as CNA gain with increased genetic material affects more than 50% of the chromosomal gene expression, or CNA loss with decreased gene expression, arm and focal alterations when gains/losses are smaller (23). In with a P value of <10 4 to account for multiple comparisons a genome-wide assessment, there were four significant region- (23). These 2,364 genes with CNA and correlated gene expres- al alterations with gains, 3q22.1-q29, 8p11.21-q24.3, 12p13.33- sion, including 68 genes from the three candidate pathways, p11.21, and 20q11.21-q13.33, and eight with losses, 4q13.3- may influence or be associated with chemoresponse through q35.2, 6q15-q27, 8p23.3-p12, 13q11-q34, 16q12.2-q24.3, our candidate pathways, and will be used for the remainder of 17p13.3-q21.2, 18q12.2-q23, and 22q11.22-q13.33. Apart from our analyses and referred as the CCP gene subset. the candidate pathways genes, these altered regions contained Differential gene expression at the univariate significance other genes that may be important or associated with level P < 0.001 was used to create a 69 gene-expression

3906 Cancer Res; 74(14) July 15, 2014 Cancer Research

Molecular Analysis of Chemoresponse in Ovarian Cancer

signature predictive of CR versus IR in the CCP subset of genes miRNA expression analysis and its correlation with gene (Supplementary Fig. S4A and Supplementary Table S3). Vali- expression dation of the gene signature was performed in previously Gene expression is also regulated by miRNAs. As previously, published gene-expression studies, including one from our differentially miRNA expression was performed in 455 patients own institution (12, 15, 16). For this validation, we used the with available high-throughput data and information about same study design used in the testing set (TCGA), a retrospec- chemoresponse. A heatmap of the 38 miRNA differentially tive design of microarray gene expression with additional expressed between the CR and IR groups is represented in information about chemoresponse, the same software used Supplementary Fig. S4D. A list of these miRNA could be in the testing set (BRB ArrayTools), same statistics (t test at reviewed at Supplementary Table S5. Possible interactions significance level P < 0.001), same outcomes definitions (CR vs. between differentially expressed miRNA and their possible IR), and the same list of genes from the gene signature in the influence in the expression of our candidate pathways were testing set. The gene signature profile was validated in all explored with a corrected correlation between miRNA expres- independent databases with a P value of <0.001 for GEO# sion and gene expression of the CCP subset (P value and qFDR GSE9891 (15), a P value of 0.01 for GSE28739 (16), and a P 10 6). Differentially expressed miRNAs and their correlated value of 0.02 for GSE23554 (Supplementary Fig. S5; ref. 12). genes were also included to the model or signature.

Somatic mutations and their correlation with gene TF-binding sites and their association with gene expression expression Somatic mutations in genes have been shown to influence Because gene expression is regulated at the transcriptional gene-expression patterns, of both individual genes and distinct level, we examined web-based tools that use algorithms to pathways (33–35). Only validated mutations were used for search DNA sequences for predicted TF-binding sites through these series of analyses (23). Mutations of 6,716 genes were high-throughput promoter analysis. TRANSFAC was used to available for 192 patient samples with information about predict TF-binding sites in genes of the candidate pathways chemoresponse at the TCGA data site. We performed a logistic associated with OVCA chemoresponse (31). Of the 2,364 genes of regression analysis to determine which mutations were asso- the CCP subset, 1,772 genes were identified to have TF-binding ciated with chemoresponse (CR vs. IR). KRT72, MYO5C, ODZ1, sites in their promoter area; 59 of these genes had a differential SMARCA4, and TP53 were statistically associated with the gene expression between CR and IR (Supplementary Fig. S6A outcome. SMARCA4 had mutations in 0.7% of CR samples and and Supplementary Table S6). Only these 59 genes harboring TF- in 7.3% of IR samples (P ¼ 0.04); and TP53 had mutations in binding sites that presented both CNAs and differentially gene 88.3% of CR samples and in 72.7% of IR samples (P ¼ 0.01). expression were also introduced in the final model. Spearman rank correlation, with correction for multiple comparisons, was used to assess the correlation between signifi- External validation of the final model with NMF cant mutated genes and gene expression of the CCP gene consensus clustering subset (P value and qFDR 10 4; Supplementary Fig. S4B). The combined data from all significant and correlated genes Significant mutated genes as well as their 22 correlated genes included 422 unique different genes introduced in the final with significant change in gene expression were included in the model (Supplementary Table S7). A cluster analysis of these model and added to the significant gene signature identified final models was performed with the NMF consensus cluster- from the CCP gene subset. ing, a type of unsupervised learning algorithm that has been shown to identify molecular patterns when applied to gene- Methylation analysis and its correlation with gene expression data (15). This analysis yielded two clusters with expression differentiated gene patterns (Fig. 1A). Attempts to create Epigenetic gene regulation also may affect expression of models with more than two clusters resulted in lower cophe- candidate pathways by inactivating gene function (36). As we netic correlation coefficients (Fig. 1B), and less harmonic did with mutations, an analysis of DNA methylation status was consensus matrices (Fig. 1C). performed in 440 patients with high-throughput data and Validation of this molecular pattern with two defined clus- information about chemoresponse available. Sixty-nine genes ters observed in the final signature model of TCGA data was were differentially methylated between the CR and IR groups performed in five publicly available independent OVCA gene- (Supplementary Fig. S4C). A list of these genes could be expression datasets with the same analytical tool, NMF con- reviewed at Supplementary Table S4. To explore possible sensus clustering. All these independent gene experiments also interactions between differentially methylated genes and their showed two differentiated clusters with the highest cophenetic possible influence in the expression of our candidate pathways, correlation coefficients and the most harmonious consensus we performed a correlation, with correction for multiple matrices (12, 14, 15, 17, 18). Comparison of these six sets of two comparisons, between these differentially methylated genes clusters (TCGA and five validation sets) showed an unprece- and the expression of the CCP gene subset (P value and qFDR dented external validation of molecular signatures associated 10 6). Differentially methylated genes and their correlated with chemoresponse in OVCA (Fig. 2). expressed genes were also added to significant gene signature We also determined whether the individual genes were placed and mutations found previously to improve the molecular by the NMF consensus clustering within the same cluster in all model. databases used for validation. The level of agreement measured

www.aacrjournals.org Cancer Res; 74(14) July 15, 2014 3907

Gonzalez Bosquet et al.

A B 1.0 0.95 0.89 0.89 0.84 0.73 0.68 0.63 0.57 0.52 0.47

C Rank = 2 Rank = 3 Rank = 4 Rank = 5 Rank = 6 Rank = 7 Rank = 8

Figure 1. Consensus matrices of the final signature model. A, consensus matrix of the final model, including 422 genes, suggesting an optimal and more robust result, with limited overlap between clusters, for clustering with k-factor ¼ 2. B, cophenetic correlation coefficients representation, with an optimal result for k ¼ 2. C, other consensus matrices for k ¼ 3 to 6 showing less harmonious models.

with k coefficient was considered "good or substantial" for three Pathway enrichment analysis of them (0.61–0.80; ref. 12, 14, 17), "almost perfect" for another To identify which pathways and biologic processes were one (0.81–1; ref. 15), and "moderate" in only one of them (0.41– overrepresented in both gene clusters identified within the 0.60; Fig. 2; Supplementary Fig. S7; ref. 18). final gene model, further analysis was conducted with

A GSE9891 (Tothill et al.; ref. 15) k: 0.86

Figure 2. Validation of the NMF consensus clustering in k B GSE3149 (Bild et al.; ref. 14) : 0.75 independent publicly available databases. In all of them, we used 422 genes including the final model. All of them also showed robust clustering with k ¼ 2and very limited overlap between C GSE26712 (Bonome et al.; ref. 18) k: 0.45 clusters. Other consensus matrices for k ¼ 3 to 6 were less harmonious (not shown). All cophenetic correlation coefficients D GSE23554 (Marchion et al.; ref. 12) k: 0.66 had optimal results for k ¼ 2 (not shown). The level of agreement (k coefficient) for placement of an individual gene in the same cluster than in TCGA analysis is also shown for each dataset. E GSE17260 (Yoshihara et al.; ref. 17) k: 0.60 A, GSE9891 database (15). B, GSE3149 database (14). C, GSE26712 database (18). D, GSE23554 database (12). E, GSE17260 database (17).

3908 Cancer Res; 74(14) July 15, 2014 Cancer Research

Molecular Analysis of Chemoresponse in Ovarian Cancer

we may, in the future, identify patients at risk for standard Cellular signaling Cell cycle chemotherapy resistance, thus eligible for novel strategies. Immune response DNA-damage repair/replication Also, knowledge of mechanisms involved in chemoresponse may help design new strategies as molecular-targeted therapy is gaining traction (39). Our study determined chemoresponse as the most significant independent clinical factor for survival (P < 10 15) in the TCGA database, agreeing with daily clinical practice and published clinical trials (20). Despite increasing knowledge about mechanisms of chemoresponse in tumor cells there are no valid clinical biomarkers or molecular signatures that could effectively predict response to chemotherapy (8, 9). Initial analysis of gene profiling aiming to identify functional processes associated with chemoresponse in OVCA had showed little overlap between studies looking for expression signatures or pathways associated with response to therapy (40). Previously, several groups have used integration of "omics" data in OVCA to predict other clinical outcomes (41–43). By integrating the comprehensive characterization of TCGA data, namely, CNA, Figure 3. The gene-expression correlation matrix of the 422 and gene mutations, DNA methylation, miRNA expression, and TF- association with pathway analysis. Pathway analysis of both clusters binding sites location, into an analytical framework for gene showed an overrepresentation of cellular signaling and immune response expression, we have created a robust molecular signature that pathways in cluster 1 (blue), and DNA repair/replication within the context of cell-cycle pathways in cluster 2 (yellow). predicts chemoresponse in OVCA. This model, with two clusters involving previous known mechanisms of chemoresponse (9), was the most robust in the TCGA database; but what is even MetaCore and clusterProfiler (37), from the R statistical more important and unprecedented in our study is the external package, which mines the KEGG database (Kyoto Encyclo- validation of the TCGA model with other five independent pedia of Genes and Genomes, www.genome.jp/kegg). The gene-expression studies of OVCA (12, 14, 15, 17, 18). These correlation matrix of each cluster denotes overrepresented findings demonstrate consistency of this signature across predominant pathways (Fig. 3). Cluster 1 showed a signif- diverse studies and platforms that we believe is due to the icant representation of cellular signaling and immune selection of microarray experiments with the same tumor type response (mainly cell-mediated) pathways, but also several of OVCA (serous), statistical design of analyses, and adequately types of metabolic pathways (Fig. 4; Supplementary Table powered. Furthermore, both clusters in the model included the S8). Three fourths (75%) of all signaling and metabolic same individual genes with substantial agreement in all but pathways genes in cluster 1 were overexpressed in the IR one of the five independent gene-expression sets, in which the tumors with respect to the CR samples, with the general agreement was moderate (Fig. 2). We think that adding ele- perception that IR tumors were engaged in higher metabolic ments to the model that did not result exclusively from the rates through external and internal stimuli. Cluster 2 was differential gene expression of microarrays (CNA, mutations, significant for pathways involving DNA-damage repair and methylation, miRNA, and TF-binding sites) added stability to replication as well as cell cycle and apoptosis, all of them the molecular signature and provided enough range to over- with strong influence by mutated TP53. Cluster 2 also come validation difficulties observed by gene-expression presented significant pathways related to cancer and cyto- experiments alone due to tumor heterogeneity (8, 9). skeleton configuration and structure (Fig. 4; Supplementary Most genes included in our molecular signature for chemor- Table S9). Two thirds of all cell cycle and DNA repair genes esponse are drawn from cellular functions previously associ- presented elevated expression in CR tumors when compared ated with response to chemotherapy (9). These biologic with IR tumors, probably driven by mutated key elements of processes include cell signaling pathways, immune response these pathways, like TP53, and increased expression of TFs, pathways, and several types of metabolic pathways that are like ATF6B, CRTC1, E2F1, SIN3B,andNFIX. involved in DNA-damage repair and replication, cell cycle and apoptosis, all of them also have been associated with cancer transformation and proliferation (44). In ovarian cancers, Discussion signaling transduction cascades from the pathways PI3K/ Patients suffering from platinum-refractory or -resistant AKT/mTOR and Ras/Raf/MEK/MAPK/ERK (with representa- ovarian cancer have a median overall survival of around 12 tion in our molecular model, Supplementary Table S7) may to 13 months (3). Furthermore, these patients become resistant result in diverse effects, including cell proliferation, invasion, to multiple drugs early on the course of treatment of their angiogenesis, apoptosis evasion, and response to chemother- disease and, thus, it is challenging to establish efficacious apy (44, 45). The MAPK signaling pathway is also connected to treatment strategies (38). The objective of our study was to the Ras pathway (which includes PAK4), that also regulates cell identify biologic processes associated with chemoresponse so morphology, cytoskeletal organization, and cell proliferation

www.aacrjournals.org Cancer Res; 74(14) July 15, 2014 3909

Gonzalez Bosquet et al.

Cluster 2 Scale

Cluster 1

Figure 4. The gene-expression profile of genes included in clusters 1 and 2 and their association with significant pathways identified with enrichment pathway analyses. Cluster 1, significantly associated with cell signaling (blue), immune response (yellow), and a variety of metabolic pathways (purple). Cluster 2, significantly associated with DNA repair/replication and cell cycle (green), pathways in cancer (red), and cell adhesion/ cytoskeleton pathways (maroon).

Cell signaling pathway Immune response pathway Metabolism pathway DNA repair/replication and cell cycle Pathways in cancer Cell adhesion/regulation of cytoskeleton

and migration; PAK4 can also function as an antiapoptotic various substrates, including TFs and chromatin constituents. protein (46). PAK proteins are critical effectors that link Rho NCAPG, a component of the condensin complex that is GTPases to cytoskeleton reorganization and nuclear signaling. required for both interphase and mitotic condensation, is Both PAK4 and RHOT1 are included in cluster 2. The Ras gene present in cluster 2 of the chemoresponse model. Animal family (which RASA1 is part) encodes membrane-associated, models with condensin mutations, DNA damage induced by guanine nucleotide–binding proteins that are involved also in UV radiation is not repaired, and cells arrested by hydroxyurea the control of cellular proliferation and differentiation, and do not recover (49). In our gene signature, it is notable that a set have a weak intrinsic GTPase activity, effectors of Ras onco- of genes map to the DNA repair pathway (mainly through gene action (47). MAPKs may also have a role in early gene homologous recombination), like RAD52, and elements of the expression by modifying the chromatin environment of target PARP family, like PARP12. PARPs inhibitors have been proven genes (48), an action regulated through phosphorylation of to be efﬁcacious in the treatment of OVCA in carriers of BRCA1

3910 Cancer Res; 74(14) July 15, 2014 Cancer Research

Molecular Analysis of Chemoresponse in Ovarian Cancer

or BRCA2 mutations (50). Also notable are the results of to OVCA that would receive initial platinum-based treatment, bevacizumab, a humanized antibody against VEGF, in the and less suited for other scenarios with different initial ther- adjuvant treatment of OVCA (51). PDGFB, a component of the apeutic strategies. Similarly, it would be interesting to inves- VEGF signaling pathway, is present in cluster 1, with other tigate whether the signature is valid in other solid tumors numerous components of cell signaling pathways. With all treated with comparable platinum-based combinations. these interconnections between signaling pathways, DNA- In summary, integration of diverse biologic data into gene damage repair, and cell cycle, alternative strategies to standard expression may strengthen gene signature models for predic- therapies may have to involve a combination of cross-specific tion of OVCA chemoresponse. Robust validation over five drugs to avoid by-pass of the blocked path (52). independent publicly available gene-expression experiments The strength of our study is that our discovery set has been supports these findings. Nonetheless, this model has to be based on a large genomic dataset, which has high benchmarks validated in vitro or vivo models before can be tested clinically. for quality control and processing (TCGA). The large sample It would be very interesting to apply this molecular signature size confers adequate power to detect important patterns model to other solid tumors that usually receive platinum- while at the same time, it overcomes possible bias introduced based therapy. by outliers; moreover, it permits better selection of the histologic type (serous) and outcome of interest (chemoresponse) to Disclosure of Potential Conflicts of Interest improve homogeneity. We believe that those are major factors No potential conflicts of interest were disclosed. influencing in the important external validation of the molecular signature in five independent gene-expression experi- Authors' Contributions ments, which is unprecedented in microarray analysis (40). Conception and design: J. Gonzalez Bosquet, H. Chon, J.M. Lancaster, Other major factor influencing a significant validation is the S. Chanock Development of methodology: J. Gonzalez Bosquet, J.M. Lancaster integration of diverse biologic data, other than gene expres- Acquisition of data (provided animals, acquired and managed patients, sion, in the final model. For the validation process, we used two provided facilities, etc.): J. Gonzalez Bosquet, S. Chanock Analysis and interpretation of data (e.g., statistical analysis, biostatistics, OVCA datasets that were used initially to identify candidate computational analysis): J. Gonzalez Bosquet, S. Chanock pathways (12, 15). These datasets had the closest clinical Writing, review, and/or revision of the manuscript: J. Gonzalez Bosquet, information, including chemoresponse, and study design to D.C. Marchion, H. Chon, J.M. Lancaster, S. Chanock fi Administrative, technical, or material support (i.e., reporting or orga- TCGA. To avoid data over tting in the validation process, nizing data, constructing databases): J. Gonzalez Bosquet, J.M. Lancaster though, we added three independent gene-expression experi- Study supervision: J. Gonzalez Bosquet, S. Chanock ments not used before, despite presenting minor differences in study design and platform content (14, 17, 18). Acknowledgments A major limitation of this study is its retrospective nature. The authors thank "TCGA Research Network" for generating, curating, and Although the outcome of interest (chemoresponse) was col- providing high quality biologic and clinical data. lected, we had to reformat it to fit our definition of CR and IR. Only patients with information about the outcome variable Grant Support were finally introduced in the analysis. There was also exten- The research was supported in part by the U.S. Army Medical Research and fi Materiel Command, in support of the proposal, "National Functional Genomics sive information about the speci cs of patient treatment after Center" (award number: W81XWH-08-2-0101) at the H. Lee Moffitt Cancer diagnosis, including types of drugs received. Nearly all patients Center and Research Institute, University of South Florida (Tampa, FL); and by the "St. Louis Ovarian Cancer Awareness Research Grant" from the 2012–2013 received standard treatment, with more than 99% of them Foundation for Women's Cancer grants. getting a platinum-based chemotherapy (Table 1). Initial The costs of publication of this article were defrayed in part by the payment treatment with platinum may select for some of molecular of page charges. This article must therefore be hereby marked advertisement in patterns observed in our signature, like cell cycle or DNA repair accordance with 18 U.S.C. Section 1734 solely to indicate this fact. pathways, due to the DNA adducts induced by platinum (53). Received January 23, 2014; revised March 28, 2014; accepted April 21, 2014; Consequently, our molecular model would be most applicable published OnlineFirst May 21, 2014.

References 1. ACS. Cancer facts and ﬁgures 2012. Atlanta: American Cancer Soci- is associated with marked increase of glutathione synthesis. Proc Natl ety; 2012. Acad Sci U S A 1992;89:3070–4. 2. Barnholtz-Sloan JS, Schwartz AG, Qureshi F, Jacques S, Malone J, 6. Johnson SW, Laub PB, Beesley JS, Ozols RF, Hamilton TC. Munkarah AR. Ovarian cancer: changes in patterns at diagnosis and Increased platinum-DNA damage tolerance is associated with cis- relative survival over the last three decades. Am J Obstet Gynecol platin resistance and cross-resistance to various chemotherapeutic 2003;189:1120–7. agents in unrelated human ovarian cancer cell lines. Cancer Res 3. Friedlander ML, Stockler MR, Butow P, King MT, McAlpine J, Tinker A, 1997;57:850–6. et al. Clinical trials of palliative chemotherapy in platinum-resistant or 7. Johnson SW, Swiggard PA, Handel LM, Brennan JM, Godwin AK, -refractory ovarian cancer: time to think differently? J Clin Oncol 2013; Ozols RF, et al. Relationship between platinum-DNA adduct formation 31:2362. and removal and cisplatin cytotoxicity in cisplatin-sensitive and -resis- 4. Baker VV. Salvage therapy for recurrent epithelial ovarian cancer. tant human ovarian cancer cells. Cancer Res 1994;54:5911–6. Hematol Oncol Clin North Am 2003;17:977–88. 8. Colombo PE, Fabbro M, Theillet C, Bibeau F, Rouanet P, Ray-Coquard 5. Godwin AK, Meister A, O'Dwyer PJ, Huang CS, Hamilton TC, Ander- I. Sensitivity and resistance to treatment in the primary management of son ME. High resistance to cisplatin in human ovarian cancer cell lines epithelial ovarian cancer. Crit Rev Oncol Hematol 2013;89:207–16.

www.aacrjournals.org Cancer Res; 74(14) July 15, 2014 3911

Gonzalez Bosquet et al.

9. Hiss D. Optimizing molecular-targeted therapies in ovarian cancer: the 30. Bulavin DV, Higashimoto Y, Popoff IJ, Gaarde WA, Basrur V, Potapova renewed surge of interest in ovarian cancer biomarkers and cell O, et al. Initiation of a G2/M checkpoint after ultraviolet radiation signaling pathways. J Oncol 2012;2012:737981. requires p38 kinase. Nature 2001;411:102–7. 10. Zgheib NB, Xiong Y, Marchion DC, Bicaku E, Chon HS, Stickles XB, 31. Wingender E. The TRANSFAC project as an example of framework et al. The O-glycan pathway is associated with in vitro sensitivity to technology that supports the analysis of genomic regulation. Brief gemcitabine and overall survival from ovarian cancer. Int J Oncol Bioinform 2008;9:326–32. 2012;41:179–88. 32. Wingender E, Chen X, Fricke E, Geffers R, Hehl R, Liebich I, et al. The 11. Bicaku E, Xiong Y, Marchion DC, Chon HS, Stickles XB, Chen N, et al. TRANSFAC system on gene expression regulation. Nucleic Acids Res In vitro analysis of ovarian cancer response to cisplatin, carboplatin, 2001;29:281–3. and paclitaxel identifies common pathways that are also associated 33. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational with overall patient survival. Br J Cancer 2012;106:1967–75. landscape and significance across 12 major cancer types. Nature 12. Marchion DC, Cottrill HM, Xiong Y, Chen N, Bicaku E, Fulp WJ, et al. 2013;502:333–9. BAD phosphorylation determines ovarian cancer chemosensitivity and 34. Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, patient survival. Clin Cancer Res 2011;17:6356–66. et al. Integrative genome analyses identify key somatic driver muta- 13. Bansal N, Marchion DC, Bicaku E, Xiong Y, Chen N, Stickles XB, et al. tions of small-cell lung cancer. Nat Genet 2012;44:1104–10. BCL2 antagonist of cell death kinases, phosphatases, and ovarian 35. Stephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge cancer sensitivity to cisplatin. J Gynecol Oncol 2012;23:35–42. DC, et al. The landscape of cancer genes and mutational processes in 14. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, et al. Oncogenic breast cancer. Nature 2012;486:400–4. pathway signatures in human cancers as a guide to targeted therapies. 36. Jones PA, Laird PW. Cancer epigenetics comes of age. Nat Genet Nature 2006;439:353–7. 1999;21:163–7. 15. Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, et al. Novel 37. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for molecular subtypes of serous and endometrioid ovarian cancer linked comparing biological themes among gene clusters. OMICS 2012;16: to clinical outcome. Clin Cancer Res 2008;14:5198–208. 284–7. 16. Trinh XB, Tjalma WA, Dirix LY, Vermeulen PB, Peeters DJ, Bachvarov 38. Spriggs DR. Drug development for chronic cancers: time to think D, et al. Microarray-based oncogenic pathway profiling in advanced differently? J Clin Oncol 2012;30:3779–80. serous papillary ovarian carcinoma. PLoS ONE 2011;6:e22469. 39. Dutta S, Wang FQ, Phalen A, Fishman DA. Biomarkers for ovarian 17. Yoshihara K, Tajima A, Yahata T, Kodama S, Fujiwara H, Suzuki M, cancer detection and therapy. Cancer Biol Ther 2010;9:668–77. et al. Gene expression profile for predicting survival in advanced-stage 40. Helleman J, Smid M, Jansen MP, van der Burg ME, Berns EM. Pathway serous ovarian cancer across two independent datasets. PLoS ONE analysis of gene lists associated with platinum-based chemotherapy 2010;5:e9615. resistance in ovarian cancer: the big picture. Gynecol Oncol 2010; 18. Bonome T, Levine DA, Shih J, Randonovich M, Pise-Masison CA, 117:170–6. Bogomolniy F, et al. A gene signature predicting for survival in sub- 41. Gevaert O, Villalobos V, Sikic BI, Plevritis SK. Identification of ovarian optimally debulked patients with ovarian cancer. Cancer Res 2008; cancer driver genes by using module network integration of multi- 68:5478–86. omics data. Interface Focus 2013;3:20130013. 19. Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, 42. Verhaak RG, Tamayo P, Yang JY, Hubbard D, Zhang H, Creighton CJ, Rubinstein L, et al. New guidelines to evaluate the response to treat- et al. Prognostically relevant gene signatures of high-grade serous ment in solid tumors. European Organization for Research and Treat- ovarian carcinoma. J Clin Invest 2013;123:517–25. ment of Cancer, National Cancer Institute of the United States, National 43. Zhang W, Liu Y, Sun N, Wang D, Boyd-Kirkup J, Dou X, et al. Integrating Cancer Institute of Canada. J Natl Cancer Inst 2000;92:205–16. genomic, epigenomic, and transcriptomic features reveals modular 20. Friedlander M, Butow P, Stockler M, Gainford C, Martyn J, Oza A, et al. signatures underlying poor prognosis in ovarian cancer. Cell Rep Symptom control in patients with recurrent ovarian cancer: measuring 2013;4:542–53. the benefit of palliative chemotherapy in women with platinum refractory/ 44. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. resistant ovarian cancer. Int J Gynecol Cancer 2009;19 Suppl 2:S44–8. Cell 2011;144:646–74. 21. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary 45. Wang D, Lippard SJ. Cellular processing of platinum anticancer drugs. segmentation for the analysis of array-based DNA copy number data. Nat Rev Drug Discov 2005;4:307–20. Biostatistics 2004;5:557–72. 46. Qu J, Li X, Novitch BG, Zheng Y, Kohn M, Xie JM, et al. PAK4 kinase is 22. Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, essential for embryonic viability and for proper neuronal development. et al. Assessing the significance of chromosomal aberrations in can- Mol Cell Biol 2003;23:7122–33. cer: methodology and application to glioma. Proc Natl Acad Sci U S A 47. Trahey M, Wong G, Halenbeck R, Rubinfeld B, Martin GA, Ladner M, 2007;104:20007–12. et al. Molecular cloning of two types of GAP complementary DNA from 23. Cancer Genome Atlas Research N. Integrated genomic analyses of human placenta. Science 1988;242:1697–700. ovarian carcinoma. Nature 2011;474:609–15. 48. Yang SH, Sharrocks AD, Whitmarsh AJ. Transcriptional regulation by 24. Storey JD, Tibshirani R. Statistical significance for genomewide stud- the MAP kinase signaling cascades. Gene 2003;320:3–21. ies. Proc Natl Acad Sci U S A 2003;100:9440–5. 49. Aono N, Sutani T, Tomonaga T, Mochida S, Yanagida M. Cnd2 has 25. Wingender E, Chen X, Hehl R, Karas H, Liebich I, Matys V, et al. dual roles in mitotic condensation and interphase. Nature 2002;417: TRANSFAC: an integrated system for gene expression regulation. 197–202. Nucleic Acids Res 2000;28:316–9. 50. Audeh MW, Carmichael J, Penson RT, Friedlander M, Powell B, Bell- 26. Maurano MT, Wang H, Kutyavin T, Stamatoyannopoulos JA. Wide- McGuinn KM, et al. Oral poly(ADP-ribose) polymerase inhibitor ola- spread site-dependent buffering of human regulatory polymorphism. parib in patients with BRCA1 or BRCA2 mutations and recurrent PLoS Genet 2012;8:e1002599. ovarian cancer: a proof-of-concept trial. Lancet 2010;376:245–51. 27. Moyer BD, Allan BB, Balch WE. Rab1 interaction with a GM130 effector 51. Burger RA, Brady MF, Bookman MA, Fleming GF, Monk BJ, Huang H, complex regulates COPII vesicle cis–Golgi tethering. Traffic 2001;2: et al. Incorporation of bevacizumab in the primary treatment of ovarian 268–76. cancer. N Engl J Med 2011;365:2473–83. 28. Zahraoui A, Touchot N, Chardin P, Tavitian A. The human Rab genes 52. Tortora G, Ciardiello F, Gasparini G. Combined targeting of EGFR- encodea familyofGTP-bindingproteinsrelated to yeast YPT1and SEC4 dependent and VEGF-dependent pathways: rationale, preclinical products involved in secretion. J Biol Chem 1989;264:12394–401. studies and clinical applications. Nat Clin Pract Oncol 2008;5:521–30. 29. Ben-Hamo R, Efroni S. Biomarker robustness reveals the PDGF 53. Pestell KE, Hobbs SM, Titley JC, Kelland LR, Walton MI. Effect of p53 network as driving disease outcome in ovarian cancer patients in status on sensitivity to platinum complexes in a human ovarian cancer multiple studies. BMC Syst Biol 2012;6:3. cell line. Mol Pharmacol 2000;57:503–11.

3912 Cancer Res; 74(14) July 15, 2014 Cancer Research

Analysis of Chemotherapeutic Response in Ovarian Cancers Using Publicly Available High-Throughput Data

Jesus Gonzalez Bosquet, Douglas C. Marchion, HyeSook Chon, et al.

Cancer Res 2014;74:3902-3912. Published OnlineFirst May 21, 2014.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-14-0186

Supplementary Access the most recent supplemental material at: Material http://cancerres.aacrjournals.org/content/suppl/2014/07/21/0008-5472.CAN-14-0186.DC1

Cited articles This article cites 52 articles, 15 of which you can access for free at: http://cancerres.aacrjournals.org/content/74/14/3902.full#ref-list-1

Citing articles This article has been cited by 5 HighWire-hosted articles. Access the articles at: http://cancerres.aacrjournals.org/content/74/14/3902.full#related-urls

E-mail alerts Sign up to receive free email-alerts related to this article or journal.

Reprints and To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at Subscriptions [email protected].

Permissions To request permission to re-use all or part of this article, use this link http://cancerres.aacrjournals.org/content/74/14/3902. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.