Gynecologic Oncology 132 (2014) 334–342

Contents lists available at ScienceDirect

Gynecologic Oncology journal homepage: www.elsevier.com/locate/ygyno

POSTN/TGFBI-associated stromal signature predicts poor prognosis in serous epithelial ovarian cancer Beth Y. Karlan a,⁎, Judy Dering b, Christine Walsh a, Sandra Orsulic a, Jenny Lester a, Lee A. Anderson b, Charles L. Ginther b, Marlena Fejzo b, Dennis Slamon b a b

Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA Division of Hematology/Oncology and Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

H I G H L I G H T S • Two distinct molecular subgroups of high grade serous ovarian cancer were identified. • Samples clustering with POSTN/TGFBI have significantly poorer overall survival than those clustering with ESR1/WT1 (30 mos vs 49 mos). • Specific differentially expressed genes between these two subgroups provide potential therapeutic targets.

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Article history: Received 26 September 2013 Accepted 16 December 2013 Available online 22 December 2013 Keywords: Molecular profiling Ovarian cancer Tumor microenvironment

a b s t r a c t Objective. To identify molecular prognosticators and therapeutic targets for high-grade serous epithelial ovarian cancers (EOCs) using genetic analyses driven by biologic features of EOC pathogenesis. Methods. Ovarian tissue samples (n = 172; 122 serous EOCs, 30 other EOCs, 20 normal/benign) collected prospectively from sequential patients undergoing gynecologic surgery were analyzed using RNA expression microarrays. Samples were classified based on expression of genes with potential relevance in ovarian cancer. Gene sets were defined using Rosetta Similarity Search Tool (ROAST) and analysis of variance (ANOVA). Gene copy number variations were identified by array comparative genomic hybridization. Results. No distinct subgroups of EOC could be identified by unsupervised clustering, however, analyses based on genes correlated with periostin (POSTN) and estrogen receptor-alpha (ESR1) yielded distinct subgroups. When 95 high-grade serous EOCs were grouped by genes based on ANOVA comparing ESR1/WT1 and POSTN/ TGFBI samples, overall survival (OS) was significantly shorter for 43 patients with tumors expressing genes associated with POSTN/TGFBI compared to 52 patients with tumors expressing genes associated with ESR1/WT1 (median 30 versus 49 months, respectively; P = 0.022). Several targets with therapeutic potential were identified within each subgroup. BRCA germline mutations were more frequent in the ESR1/WT1 subgroup. Proliferation-associated genes and TP53 status (mutated or wild-type) did not correlate with survival. Findings were validated using independent ovarian cancer datasets. Conclusions. Two distinct molecular subgroups of high-grade serous EOCs based on POSTN/TGFBI and ESR1/ WT1 expressions were identified with significantly different OS. Specific differentially expressed genes between these subgroups provide potential prognostic and therapeutic targets. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Ovarian cancer continues to present a significant clinical challenge. Each year over 225,900 women are newly diagnosed and 140,200 women succumb to the disease worldwide [1]. In the United States,

⁎ Corresponding author at: Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Room 290W, Los Angeles, CA 90048, USA. Fax: +1 310 423 9753. E-mail address: [email protected] (B.Y. Karlan). 0090-8258/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ygyno.2013.12.021

21,990 new diagnoses and 15,460 deaths from this disease were reported in 2011. Currently, no effective screening modalities exist [2], resulting in most women presenting with late-stage metastatic disease. Importantly, 5-year survival rates have remained near 30% for more than two decades [3]. Aggressive surgical cytoreduction and platinumtaxane chemotherapy have improved survival but cures remain infrequent. Newer combinations of chemotherapy or dosing schedules, intraperitoneal approaches, and biologics have not had significant impact on overall survival (OS) [4–6]. Predictive markers to individualize therapeutic approaches and new tumor-specific targets to improve OS are needed.

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The Cancer Genome Atlas Research Network (TCGA) performed a comprehensive genomic classification of serous epithelial ovarian cancers (EOCs) [7]. Serous tumors were examined because they are the most common subtype of ovarian carcinoma and usually present with disseminated disease. TCGA demonstrated that high-grade serous tumors have great genomic instability with few recurrent alterations. Attempts to identify subtypes based on computational approaches to gene expression or patterns of epigenetic alterations revealed multiple groupings but no unifying classification or molecular prognostic indicators. It is apparent that a “one-size-fits-all” approach to serous EOC is not likely to be successful. The overall goal of this study was to identify molecular subgroups that might predict clinical outcomes in serous EOC patients. A second objective was to identify potential therapeutic targets for serous EOC based on improved understanding of the molecular diversity of this disease. 2. Patients and methods 2.1. Patients and samples Ovarian tissues and matched peripheral blood samples were prospectively obtained from sequential patients undergoing planned gynecologic surgery at Cedars-Sinai Medical Center between 1989 and 2005. All patients underwent surgery and received adjuvant chemotherapy with a contemporaneous standard-of-care regimen. All patients signed an Institutional Review Board (IRB)-approved consent for biobanking, clinical data extraction, and molecular analysis. Ovarian tissue specimens (N 1 cm3) were collected during surgery, snap frozen within 30 min, and stored at − 80 °C until RNA and DNA extraction. Clinical data were abstracted from medical records and tumor registry. All tissue pathologies were reviewed by a gynecologic pathologist to confirm histologic diagnosis and assure N 70% tumor cell nuclei and b20% necrosis. 2.2. Expression microarrays Total RNA was isolated using RNeasy (Qiagen Inc., Valencia, CA), quantitated using a Nanodrop Spectrophotometer (Agilent Technologies, Santa Clara, CA), and purified on RNeasy Mini columns (Qiagen Inc.). Total RNA (750 ng) with RNA Integrity Number (RIN) N7.5 was labeled with cyanine 5-CTP or cyanine 3-CTP using the Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies), and hybridized to Agilent Human 1A V2 expression arrays. The reference mix for these experiments was an equal amount of RNA extracted from 106 ovarian samples of various histologies, including papillary serous (n = 67), endothelial (n = 5), mucinous (n = 3), clear cell (n = 3), malignant mixed Müllerian tumor (n = 5), goblet cell (n = 1), squamous (n = 1), transitional cell (n = 1), benign (n = 4), normal (n = 7), and unknown (n = 7). Eighty-nine of the samples in the study were included in the reference mix (Supplemental Table S1). Mean RIN was 9.12. Slides were scanned using the Agilent 2565BA Scanner and data were exported by the Agilent Feature Extraction Software (version 7.5.1) into Rosetta Resolver (Rosetta Inpharmatics LLC, Cambridge, MA) for clustering and statistical analyses. Log ratios of the signals obtained from individual tumors to signals from the reference mix were used for expression analyses. For unsupervised clustering analysis, the sequence and experimental clustering algorithm used was agglomerative, the heuristic criterion was average link, and the metric type was cosine correlation. Data threshold requirements depended on the number of samples included in the cluster. 2.3. Supervised classification and statistical analyses Serous EOCs were classified into prognostic categories based on gene expression profiling conducted in three steps: (1) selection of discriminating candidate genes of known biologic or prognostic relevance for serous ovarian cancer, (2) determination of an optimal set of reporter

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genes, and (3) clustering of samples based on differentially expressed genes (DEGs) determined by ANOVA. Genes correlated with estrogen receptor (ESR1) or periostin (POSTN) were identified using Rosetta Similarity Search Tool (ROAST). There was a correlation P-value computed for each comparison based on the ROAST metric and the number of valid data points used in the similarity calculation. To expand the set of DEGs we compared ESR1 and POSTN sample sets using the analysis of variance (ANOVA) tool in Resolver set to identify DEGs with a false discovery rate at a P ≤ 0.01. Errorweighted one-way ANOVA using Benjamini and Hochberg false discovery rate (FDR) for multiple test correction was used to determine statistically significant differences at the gene probe level between the two groups. Diagnostic and/or prognostic prediction was determined based on expression of the optimal set of reporter genes by clustering all primary high-grade samples across the DEGs. For interpretation of biologic and therapeutic relevance of the DEGs we utilized DAVID bioinformatics tools [8,9]. Statistica version 10 (Statsoft, Tulsa, OK) was used to generate Kaplan–Meier survival curves. The Log Rank test was used to compute the P-value for survival comparison with two groups and the P-value from the Chi-square test was used for comparisons of multiple groups. The dataset and clinical data have been provided to the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) under identifier GSE51088. Univariate survival analysis was performed using log rank statistic. The online Kaplan–Meier plotter tool [10] was used to generate survival curves for TCGA and Tothill validation datasets (settings shown in Supplemental Materials).

2.4. Genetic analyses and identification of therapeutic targets Array comparative genomic hybridization (aCGH), TP53 mutation analysis, and BRCA genotyping were performed using standard protocols (see Supplemental Materials). Ingenuity Pathway Analysis was used to identify FDA-approved therapies that target genes identified in the molecular analyses. 3. Results 3.1. Patients and samples A total of 172 ovarian tissue samples were collected for the study, including 20 normal or benign ovarian tissues and 152 EOCs. Nonepithelial ovarian tumors were excluded from this analysis. Malignant tissues included 122 serous EOCs (Table 1; Supplemental Table S1), of which 108 were primary tumors and 14 were recurrent tumors. Ninety-five primary serous EOCs were high-grade (grade 2 or 3). Survival information was available for 121 of 122 patients with serous EOC. Of surviving patients with serous EOC, 76% had follow-up data for N 72 months (median 101 months, range 49–230 months). Median survival for patients who died was 36 months (range 1–159 months). 3.2. Unsupervised clustering analysis Unsupervised clustering of all 172 samples representing malignant (n = 152), normal (n = 15), and benign (n = 5) tissues was performed across 7214 genes that met the statistical cutoff (Supplemental Fig. S1). The unsupervised analysis clearly identified the normal and benign samples, but no distinct subgroups of the malignant samples could be defined. To further investigate the signature of normal samples, unsupervised clustering was performed on only normal samples across all sequences with a 2-fold change in ≥ 10 of 14 experiments (n = 1460) (Supplemental Fig. S2). Using these criteria, 409 upregulated genes (Supplemental Table S2) and 1051 downregulated genes were identified (Supplemental Table S3).

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Table 1 Demographics and disease characteristics at baseline. Characteristic Age group, n (%) b60 years ≥60 years Unknown

All patients (N = 172)

Patients with papillary serous tumors (N = 122)

91 (53) 77 (45) 4 (2)

62 (51) 59 (48) 1 (1)

Race, n (%) White/Caucasian Black/African American Asian Pacific Islander Hispanic Unknown

145 (84) 13 (8) 4 (2) 4 (2) 6 (4)

103 (84) 7 (6) 4 (3) 2 (2) 6 (5)

Disease stage, n (%) I II III IV Normal or benign Unknown

22 (13) 9 (5) 103 (60) 17 (10) 20 (11) 1 (b1)

8 (7) 3 (3) 93 (76) 17 (14) 0 1 (1)

Patient status, n (%) Dead Alive Benign Unknown

112 (65) 39 (22) 20 (12) 1 (b1)

96 (79) 25 (21) 0 1 (1)

Tumor histology type, n (%) Papillary serous 114 (66) Borderline serous 8 (5) Endometrioid 8 (5) Clear cell 3 (2) Mucinous 5 (3) Borderline mucinous 3 (2) MMMT 7 (4) Transitional cell 2 (1) Adenocarcinoma 2 (1) Benign 5 (3) Normal 15 (8)

114 (94) 8 (6) 0 0 0 0 0 0 0 0 0

Grade, n (%) 0/1 2 3 Normal/benign Unknown

10 (8) 9 (7) 100 (82) 0 3 (3)

16 (10) 14 (8) 119 (70) 20 (11) 3 (2)

3.3. Supervised clustering analyses based on POSTN- and ESR1-correlated gene sets yield prognostic subgroups Genes associated with ovarian biology or oncogenesis were interrogated, including steroid hormone receptors, genes associated with proliferation, differentiation, tumor microenvironment, immune functions, and cell adhesion. Two genes, POSTN and ESR1, were selected for further analysis because of their wide range of expression among the ovarian cancer samples and their significant roles in ovarian cancer biology. POSTN encodes a transforming growth factor-β (TGF-β)-inducible protein [11], which is a ligand for αvβ3 and αvβ5 integrins [12]. POSTN is associated with multiple oncogenesis-related functions, including essential roles in metastasis [13–16], and angiogenesis [15]. We have shown that POSTN exhibits 5-fold increased expression in tumorderived epithelial cells compared with normal ovarian epithelial cells [17]. POSTN expression clustered closely with genes related to cell adhesion, proliferation, angiogenesis, and epithelial–mesenchymal transition in unsupervised clustering analysis of ovarian carcinoma cells, and in TCGA analyses [7], providing a rationale for further exploration of this gene cluster. Sex steroid hormone receptors are known to be central to normal ovarian function and lifetime ovulatory cycles have been linked with ovarian carcinoma. The estrogen receptor gene (ESR1) is expressed

in both normal and malignant ovarian tissues and plays a role in cell proliferation [18]. Recent data link estrogen receptor expression with improved disease-specific ovarian cancer survival [19], and postmenopausal estrogen use was linked to elevated ovarian cancer risk [20]. We investigated genes correlated with POSTN and ESR1 in all 122 patients with serous EOC and the relationship between expression and OS in all 121 patients with survival data available. ROAST analysis identified 200 genes (Supplemental Table S4) highly correlated with POSTN expression (r range = 0.701–0.919; P b 0.0002) (Fig. 1A). POSTN-correlated genes included genes related to TGFβ signaling (TGFBI, TGFB1, TGFB3), integrin signaling (ITGA5, ITGB1), and tumorigenesis (SULF1, PIM1, PLAU). Because of its strong correlation to POSTN as well as its biological function in the TGFβ signaling pathway, TGFBI was selected as an additional marker to define the POSTN group. Median OS of patients with high expression of POSTN/TGFBI-correlated genes was significantly shorter than patients with low expression of POSTN/TGFBIcorrelated genes (31.5 versus 52 months, respectively; P = 0.009) (Fig. 1B). Correlation among the top 200 ROAST-identified ESR1correlated genes (Fig. 1C) was statistically significant (r range = 0.398 to 0.790; P b 0.019) but lower than for POSTN/TGFBI-correlated genes. ESR1-correlated genes (Supplemental Table S5) included urogenital system development genes (WT1), androgen receptor, estrogen metabolic pathway genes (UGT2B11, UGT2B7), sex determination (SOX3), and secretoglobin genes (SCGB1D1, SCGB1D2), and genes for CA125 (MUC16) and its receptor mesothelin (MSLN). For the ESR1-correlated group, WT1 was selected as a representative of pathways involved in urogenital system development since many of the ESR1-correlated genes were associated with this pathway (Supplemental Table S5). Median OS of patients with higher levels of ESR1/WT1-correlated genes was significantly longer than for patients with lower levels of ESR1/WT1-correlated genes (57 versus 31.5 months, respectively; P = 0.026) (Fig. 1D). Notably, the low-grade serous EOCs clustered together across the ESR1/WT1-correlated genes and none expressed the POSTN/TGFBI-correlated gene set. The difference in OS based on overexpression of POSTN/TFGBI- and ESR1/WT1-correlated genes was validated in the Tothill and TCGA datasets (Fig. 2 and Supplemental Methods). Introducing additional gene probes that represent other dominant pathways in each group did not significantly improve the separation of patients based on OS (not shown). 3.4. Subgroups of high grade serous EOCs correlate with overall survival The high-grade serous EOCs (n = 95) were further analyzed to identify prognostic subgroups with actionable translational endpoints. ANOVA was used to further investigate gene expression related to POSTN/TGFBI and ESR1/WT1 gene expression. Samples with upregulated POSTN or TGFBI expression when compared to the reference mix (log(ratio) N 0 and P ≤ 0.01) were classified as POSTN/TGFBI (n = 37). Of the remaining samples, those with upregulated ESR1 or WT1 were classified as ESR1/WT1 (n = 39). The remaining samples that were downregulated for all for genes (log(ratio) b 0 and P N 0.01) were classified as ‘Other’ and excluded from the ANOVA. Of 860 identified DEGs at P ≤ 0.01, 679 were expressed at higher levels in the POSTN/TGFBI group (Supplemental Table S6) and 181 were expressed at higher levels in the ESR1/WT1 group (Supplemental Table S7). The biological relevance of the gene sets was examined using DAVID [8,9]. Many POSTN/ TGFBI-correlated genes were related to extracellular, cell surface, and stromal functions (e.g., angiogenesis, wound healing, inflammation, and cell migration) whereas several ESR1/WT1-correlated genes were involved in nuclear processes (e.g., DNA repair, replication, and cell cycle). Based on this functional classification, POSTN/TGFBI-correlated genes are expected to be primarily associated with stromal cells while ESR1/WT1-correlated genes are expected to be expressed in malignant Müllerian epithelial cells. However, the amount of stroma in

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A) Top 200 POSTN/TGFBI-Correlated Genes

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B) Overall Survival Based on POSTN/TGFBI-Correlated Genes

-0.50000 0 0.50000 Log(Ratio)

Median Months (95% CI)

Cumulative Proportion Surviving

POSTN/TGFBIUp (n=39) POSTN/TGFBI Down (n=82) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

(25-38) (42-62)

P= 0.009

12

24

36

48

60

72

84

96

108 120

Months

Pts at risk: 39 82

31.5 52

34 76

27 68

17 57

13 46

8 34

7 25

5 20

4 15

3 10

2 9

D) Overall Survival Based on

C) Top 200 ESR1/WT1-Correlated Genes

ESR1/WT1-Correlated Genes Median Months (95% CI)

-0.50000 0 0.50000 Log(Ratio)

Cumulative Proportion Surviving

ESR1/WT1 Up (n=72) ESR1/WT1 Down (n=49)

56.6 31.5

(46-67) (25-38)

P= 0.026 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0

12

24

36

48

60

72

84

96

108 120

Months

Pts at risk: 72

67

62

51

41

31

22

17

13

8

6

49

43

33

23

18

11

10

8

6

5

5

Fig. 1. Gene expression profiles and overall survival in serous EOC subgroups. A) Serous EOC samples (n = 122) across POSTN/TGFBI-correlated genes with B) overall survival based on expression of POSTN/TGFBI-correlated genes in 121 samples and C) serous EOC samples (n = 122) across ESR1/WT1-correlated genes (8 borderline samples are indicated by a yellow box) with D) overall survival based on expression of ESR1/WT1-correlated genes in 121 samples. Survival time for patients with recurrence (n = 14) is based on time from original surgery.

representative samples from all subgroups was evaluated by independent pathology review and found to be similar, indicating that the differential gene expression between the POSTN/TGFBI tumors and ESR1/WT1 tumors is not a mere reflection of the ratio between malignant cells and stroma. Hierarchical clustering of all 95 high-grade EOC samples across the DEG set identified two main subgroups (Fig. 3A). These subgroups were strongly associated with POSTN/TGFBI and ESR1/WT1 classification, as expected (Pearson chi-square = 45.21, P b 0.000001) (Fig. 3C). Median OS for patients in the ESR1/WT1 and POSTN/TGFBI clusters was 49 and 30 months, respectively (P = 0.022) (Fig. 3B). These results were similar to OS analysis based on POSTN/TGFBI and ESR1/WT1 group assignment, where OS was significantly worse in the POSTN/ TGFBI subgroup (median 31.5 months) compared to those patients

with the ESR1/WT1 subgroup (median 50.9 months; P = 0.031) (Supplemental Fig. S3). 3.5. BRCA mutation status predicted improved survival but TP53 status did not BRCA and TP53 were analyzed for mutation status in tumor samples, correlation to cluster subgroups, and OS because of their well established relevance to ovarian cancer. BRCA mutation status was analyzed in germline DNA for 56 patients and 13 additional tumor samples were screened for Ashkenazi Jewish founder mutations. Of 22 primary serous EOC samples with mutated BRCA genes (13 BRCA1, 8 BRCA2, 1 both), 4 were from POSTN/TGFBI-expressing tumors, 15 were from ESR1/WT1-expressing tumors, and 3 were classified as ‘Other’

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A) Overall Survival Based on POSTN/TGFBI Gene Expression (Validation Data Sets) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

TCGA Data

POSTN/TGFBI Up POSTN/TGFBI Down HR = 1.92 (95% CI = 1.29-2.84) P = 0.001

0

20

Pts at risk: 69 41 193 142

40

60

80

100

120

5 11

1 5

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

POSTN/TGFBI Up POSTN/TGFBI Down HR = 1.43 (95% CI = 1.12-1.81) P = 0.0036

0

20

Pts at risk: 205 118 347 226

Months 16 53

Cumulative Proportion Surviving

Cumulative Proportion Surviving

Tothill Data

0 3

40

60

80

100

120

5 23

1 6

0 0

Months 51 131

20 55

B) Overall Survival Based on ESR1/WT1 Gene Expression (Validation Data Sets) TCGA Data

ESR1/WT1 Up ESR1/WT1 Down HR = 0.47 (95% CI = 0.32-0.70) P = 0.00013

0

Pts at risk: 178 84

20

40

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Months 133 50

55 14

14 2

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Cumulative Proportion Surviving

Cumulative Proportion Surviving

Tothill Data 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

ESR1/WT1 Up ESR1/WT1 Down HR = 0.76 (95% CI = 0.59-1.0) P = 0.046

0

Pts at risk: 415 137

20 265 79

40 149 33

60 Months 62 13

80

100

120

22 6

5 2

0 0

Fig. 2. Overall survival of patients with serous EOC subgroups in validation datasets. A) Overall survival of patients with tumors expressing high (red) and low (black) levels of POSTN/TGFBI genes and B) ESR1/WT1 genes from the Tothill (n = 262) dataset (left panels) and TCGA (n = 552) (right panels) are shown.

(Fig. 4A, Supplemental Table S8). At more than 2 years after diagnosis, there were no deaths in patients with mutated BRCA genes whereas ~ 35% of patients with wild-type BRCA or unknown status had died of disease (Fig. 4B). Median OS of 22 patients with mutated BRCA was significantly longer than 73 patients with wild-type or unknown BRCA status (65 versus 32 months; P = 0.001). Sample size was insufficient to perform a survival analysis based on BRCA1 vs BRCA2 mutation. Exons 2–11 of TP53 were screened for mutations. Seventy-three of 102 (71%) primary serous EOC samples analyzed had mutant TP53. All 8 low grade samples had wild-type TP53 (Fig. 4A). No difference in TP53 status was observed between ESR1/WT1 and POSTN/TGFBI subgroups (Fig. 4A). No difference in OS was observed between primary high-grade patients with wild-type (n = 19 [21%]) and mutant TP53 (n = 71 [79%]), although there was a trend for patients with wild-type TP53 to have worse outcome in this population (Fig. 4C). 3.6. Proliferation signature does not predict survival To identify a proliferation signature in the serous EOC samples, we analyzed expression of cell-cycle genes that were significantly downregulated in the normal samples (Supplemental Table S3). Genes associated with cell cycle were determined based on the information in the Gene Ontology Annotation (UniProt-GOA) Database for the ID GO: 0007049 (www.ebi.ac.uk/GOA/) filtering for taxonomy =‘Human.’ Samples were grouped into high, moderate, and low proliferation subsets. Notably, there was no survival difference between the proliferation subsets (Supplemental Fig. S4). We posit that this unexpected observation may be due in part to a better response to cytotoxic chemotherapy in the highly proliferating tumors. Furthermore, the proliferation signature was not associated with sample classification based on ESR1/WT1 and POSTN/TGFBI gene expression. However, expression of other genes that are known to correlate with proliferation (AURKA, CCNE1), or anti-correlate with proliferation (PTEN),

was found to be overexpressed (AURKA, CCNE1) or underexpressed (PTEN) in the proliferation signature. In addition, CGH analysis revealed copy number changes of these genes as well as others previously identified in ovarian cancer [7] (Supplemental Tables S9 and S10). AURKA or CCNE1 amplification or PTEN loss was associated with the proliferation signature in all samples profiled by aCGH as well as the subset of highgrade primary EOC (Supplemental Tables S11 and S12). 3.7. Genes with therapeutic potential ESR1/WT1- and POSTN/TGFBI-correlated genes were examined for potential therapeutic targets (Table 2). Several ESR1/WT1-correlated genes are targets for chemotherapies that demonstrate activity against ovarian cancer, most notably cisplatin. Other genes are targets for agents not traditionally used to treat this disease, although these genes could provide some guidance for future clinical trials. Similarly, several POSTN/TGFBI-correlated genes are targets of drugs with significant activity in the treatment of ovarian cancer, including taxanes, vinca alkaloids, and epothilones. These data indicate that the effectiveness of combination therapy may be optimized by adjusting the ratio of platinums and taxanes based on the tumor's expression profile, as well as provide insights for the design of future clinical trials. 4. Discussion Gene expression analyses and molecular profiling are being used to predict clinical outcome and direct therapeutic decisions for an increasing number of solid malignancies. Targeted treatment approaches based on an individual tumor's molecular characteristics are now used to treat over half of non-small cell lung cancers and have resulted in improved survival for thousands of patients [21]. Individualized approaches based on molecular characteristics have changed how we manage treatments and counsel patients with breast cancer, colon cancer, melanoma,

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A) Supervised Clustering of High-Grade

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B) Overall Survival Based on Cluster

Serous EOC Median Months (95%CI)

-0.50000 0 0.50000 Log(Ratio)

49 l 30

Cumulative Proportion Surviving

ESR/WT1 (n=52) POSTN/TGFBI (n=43)

(39-59) (20-40)

P= 0.02 2 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

72

84

Months Pts at risk: 52 48 44 36 28 19 11 43 37 28 16 14 9 8

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96 108 120

7 4

5 4

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C) Distribution of Samples Across ESR1/WT1 and POSTN/TGFBI Clusters ESR1/WT1

POSTN/ TGFBI

Other

Total

ESR1/WT1

35

5

12

52

POSTN/TGFBI

4

32

7

43

Total

39

37

19

95

Cluster

Pearson chi-square = 45.21; P < 0.000001 Fig. 3. Molecular subgroups based on POSTN/TGFBI- and ESR/WT1-expressing serous EOCs. A) Hierarchical cluster for 95 primary serous EOCs defines two main groups: ESR/WT1 (blue box) and POSTN/TGFBI (red box). B) Overall survival for 95 patients with primary serous EOCs on cluster membership is shown. C) Distribution of samples classified based on ESR/WT1 and POSTN/TGFBI expression across two main clusters.

and other cancers [22]. ASCO and NCCN guidelines include the use of commercially available gene expression assays to better inform chemotherapy choices for women with breast cancer, thereby reducing patients' side effects and improving resource utilization [23,24]. Use of companion diagnostics is now recommended by the FDA for new targeted agents to enhance the likelihood of patient benefit while reducing toxicity and unnecessary costs [25]. Despite these advances, ovarian cancer patients have yet to benefit from these targeted approaches. To date, no targeted agents have received FDA approval for treatment of this disease, and molecular profiling as well as other diagnostic assays have failed to effectively direct treatment choices and improve clinical outcomes. For the majority of ovarian cancer patients, therapeutic decisions are based on clinical parameters such as tumor stage and grade, and nearly all patients receive platinum/taxane-based chemotherapy regimens. Molecular profiling of ovarian cancers has demonstrated extensive complexity, heterogeneity, and genomic instability. TCGA ovarian data provide a comprehensive genomic analysis and important insights that can be used as a benchmark [7]. Other investigators have reported signatures that help to predict optimal or suboptimal cytoreduction [26], response to chemotherapy [27–31], and prognosticators for OS [32–38]. However, it has been difficult to validate many of these signatures across studies [39] and the clinical utility of these results has been limited. To date, BRCA mutation status or “BRCA-ness” may be the best molecular characteristic to help select chemotherapy, stratify for clinical trials, and provide prognostic information for women with ovarian cancer [40,41].

In this study, unsupervised hierarchical clustering of samples failed to distinguish distinct molecular subgroups of serous EOCs. Rather than using a computational biology approach similar to the TCGA and other analyses, we elected to characterize and cluster gene expression patterns based on processes that are thought to be biologically relevant in ovarian oncogenesis. In earlier studies, we found POSTN to be differentially expressed at high levels in ovarian cancers [42]. POSTN has recently been described as an essential factor in recruitment of cancer stem cells and the establishment of metastatic colonization [16]. Biological processes driven by genes significantly co-expressed with POSTN include cell migration and cell–matrix adhesion, angiogenesis and vascular development, and inflammatory and immune responses. We have previously demonstrated that targeting POSTN with neutralizing antibody inhibits ovarian tumor growth and metastasis in animal models and thus POSTN may be a novel therapeutic target [43]. Interestingly, the greatest fold-change of any gene in the TCGA analysis was the 17.73-fold change seen in POSTN [44]. Notably, in our analysis POSTN was closely associated with TGFBI overexpression and they predicted poor prognosis and overall survival, suggesting a possible role for the tumor microenvironment in underlying ovarian cancer biology. We also focused on genes with a similar pattern of expression as ESR1, which is known to play critical roles in ovarian function. Recent studies have bolstered the importance of hormonal mechanisms in the etiology of ovarian cancer. A strong temporal association between menopausal hormone use and ovarian cancer incidence suggests a role of hormone replacement therapy in the development of ovarian cancer [20]. In addition, estrogen receptor expression has been found to be a

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A) BRCA and TP53 Status -0.50000 0 0.50000 Log(Ratio)

TGFBI POSTN ESR1 WT1 Subgroup Tumor Type TP53 BRCA

Molecular Subgroup: Tumor Type: TP53 Status: BRCA Status:

ESR1/WT1 (n=48) High-Grade (n=95) TP53 WT (n=29) BRCA WT (n=28)

POSTN/TGFBI (n=40) Low-Grade (n=12) TP53 Mut(n=73) BRCA Mut (n=22)

Other (n=20) Unknown (n=1) TP53 Unk (n=6) BRCA Unk (n=58)

C) Overall Survival of Patients With

B) Overall Survival of Patients With

Wild-Type and Mutated TP53 Primary High-Grade Serous EOC

Wild-Type and Mutated BRCA Primary High-Grade Serous EOC Median Months (95%CI)

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

32 65l

P = 0.001

12 24

36

48

60

72

Months Pts at risk: 73 63 50 33 27 16 12 22 22 22 19 15 12 7

84

8 7

TP53 WT (n=19) TP53 Mut(n=71)

(24-40) (55-76)

96 108 120

5 6

3 6

3 5

Cumulative Proportion Surviving

Cumulative Proportion Surviving

BRCA WT/Unk (n=73) BRCAMut(n=22)

Median Months (95%CI)

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0

33 l46 l

(21-44) (29-62)

P = 0.158

12 24

36

48

60

72

84

96 108 120

Months

Pts at risk: 71 65 55 42 35 19 15 13 9 6

24 15 3 3

13 10 2 1

8 1

7 1

Fig. 4. BRCA and TP53 mutation status and survival. A) Gene expression profiles for POSTN, TGFBI, ESR1, and WT1 are indicated in the top 4 rows of boxes. The bottom 4 rows indicate ESR1/ WT1 and POSTN status, tumor grade, TP53 mutation status, and BRCA mutation status. Overall survival of high-grade primary serous EOC patients by B) BRCA (n = 95) and C) TP53 mutation status (n = 90, 5 patients not tested) survival curves are shown. WT, wild-type; Mut, mutant; Unk, unknown; EOC, epithelial ovarian cancer.

prognostic biomarker for high-grade serous ovarian cancers and has been associated with improved disease-specific survival [19]. In our clustering analysis and others, ESR1 overexpression was closely associated with WT1, a tumor antigen overexpressed by most serous EOCs [45]. The biologic processes associated with ESR1 upregulation include DNA replication and damage checkpoints, RNA modification, cell division, and sterol biosynthesis. Clinical trials using tamoxifen or letrozole have demonstrated response in unselected recurrent ovarian cancers that may be explained, at least in part, by targeting estrogen receptorexpressing tumors [46]. In contrast to unsupervised clustering analyses, our approach yielded two distinct molecular subgroups of high-grade serous EOC tumors: a subgroup that expressed POSTN/TGFBI-correlated genes and a subgroup that expressed ESR1/WT1-correlated genes. Patients in the ESR1/WT1correlated subgroup had significantly better OS of 56.6 months compared with the POSTN/TGFBI subgroup OS of 31.5 months (P = 0.031). The biological pathways associated with these two EOC subgroups are distinct and point to potentially different therapeutic targets. The ESR1/WT1 subgroup differentially expressed gene targets for platinumbased therapy (including HSP90AA1) whereas the POSTN/TGFBI subgroup expressed targets for taxane-based therapy (including tubulins). BRCA mutations were more frequent in the ESR1/WT1 subgroup and

these cancers have increased sensitivity to platinums and possibly decreased sensitivity to paclitaxel, potentially providing enhanced opportunities for PARP inhibitor therapies. Newer therapeutic approaches with immunotherapies or anti-angiogenesis agents may also be better selected using these expression profiles. Identification of genes correlated with increased expression of POSTN/TGFBI and ESR1/WT1 provided promising therapeutic targets. Several drugs that target these genes are currently FDA-approved or in clinical trials for other malignancies. Incorporating these agents into ovarian cancer clinical trials may provide new avenues of therapy for serous EOCs and development of a gene expression assay distinguishing these two groups may allow us to individualize therapies for POSTN/TGFBI and ESR1/WT1 molecular ovarian cancer subgroups. Our analysis differed from that of TCGA (see Supplemental Methods). TCGA identified four high-grade serous EOC subgroups: differentiated, proliferative, immunoreactive, and mesenchymal. There is strong overlap between our POSTN/TGFBI gene set and the TCGA's poor prognosis groups, mesenchymal and immunoreactive. Similar to TCGA analysis, proliferation signature based on cell-cycle genes did not predict OS in our patients; however, many of TCGA's proliferation subtype markers are not typically classified as cell-cycle or DNA-repair genes. Another distinction between the two studies is that TCGA had

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Table 2 ESR1- and POSTN-associated genes that are drug targets. Gene

Drug

Indication

ESR1-associated genes AMY2A Amylase, alpha 2A (pancreatic) AMY2B Amylase, alpha 2B (pancreatic)

Ascorbic acid (vitamin C)

ESR1 HSP90AA1

Estrogen receptor 1 Heat shock protein 90 kDa alpha, class A, member 1 Nuclear receptor subfamily 3, group C, member 2

Arzoxifene, fulvestrant, tamoxifen 17-DMAG, cisplatin

Clinical trials for the treatment of multiple myeloma, desmoplastic small round cell tumor, non-Hodgkin disease, prostate, Wilm's tumor, hematological malignancies Ovarian and breast cancer Ovarian cancer and other malignancies

Phosphoribosyl pyrophosphate amidotransferase Toll-like receptor 3

6-mercaptopurine, thioguanine Ampligen

Polycystic ovary syndrome, hypertension, depressive disorder, hidradenitis suppurativa, premenstrual dysphoric disorder, acne vulgaris, metabolic syndrome X, infection, heart disease, diabetes, chronic renal failure, hirsutism, polymenorrhea, cirrhosis, metabolic disorder, precocious puberty, ascites Leukemia, lymphoma, glioblastoma Clinical trials for ovarian cancer

Rivastigmine Icatibant Gemtuzumab ozogamicin Collagenase clostridium histolyticum

Alzheimer's disease and Parkinson's disease Edema, coronary artery disease Various types of leukemia Dupuytren contracture

Sunitinib, pazopanib Sargramostim

Ovarian cancer and other malignancies Ovarian cancer and other malignancies

Vemurafenib

Clinical trials for treatment of melanoma

Nitroglycerin Tocilizumab Recombinant human interleukin-7 CNTO 95, EMD121974 Dasatinib, imatinib, pazopanib, sunitinib, sorafenib

Clinical trial for treatment of lung cancer Arthritis Clinical trials in hematologic malignancies Clinical trials for the treatment of hematological and solid tumors Ovarian cancer and other malignancies

Cyclosporin A, tacrolimus Enzastaurin Thalidomide HeFi-1 monoclonal antibody

Ovarian cancer and other malignancies Ovarian cancer and other malignancies Ovarian cancer and other malignancies Clinical trial in hematologic malignancies

Belimumab

Systemic lupus erythematosus, rheumatoid arthritis

ABT-751, docetaxel, EC145, epothilone B, eribulin, ixabepilone, paclitaxel, vinblastine, vincristine, vinorelbine

Ovarian cancer and other malignancies

NR3C2

PPAT TLR3

Entrez gene name

POSTN-associated genes BCHE Butyrylcholinesterase BDKRB2 Bradykinin receptor B2 CD33 CD33 molecule COL10A1 Collagens COL11A1 COL12A1 COL1A1 COL1A2 COL3A1 COL4A2 COL5A1 COL5A2 COL6A2 COL8A2 CSF1R Colony stimulating factor 1 receptor CSF2RA Colony stimulating factor 2 receptor, alpha, low-affinity FGR Gardner-Rasheed feline sarcoma viral oncogene homolog GUCY1A3 Guanylate cyclase 1, soluble, alpha 3 IL6 Interleukin 6 IL7R Interleukin 7 receptor ITGAV Integrin, alpha V (CD51) PDGFRB

TUBB2A

Platelet-derived growth factor receptor, beta polypeptide Protein phosphatase 3, catalytic subunit, gamma isozyme Protein kinase C, beta Tumor necrosis factor Tumor necrosis factor receptor superfamily, member 8 Tumor necrosis factor (ligand) superfamily, member 13b Tubulin, beta 2A

TUBB3 TUBB6 VDR

Tubulin, beta 3 Tubulin, beta 6 Vitamin D (1,25-dihydroxyvitamin D3) receptor

PPP3CC PRKCB TNF TNFRSF8 TNFSF13B

Drospirenone, eplerenone, spironolactone

Cabazitaxel 1-Alpha,25-dihydroxy vitamin D3

relatively short clinical follow-up for most of their patients: 37% of surviving patients had b12 months and only 16% had N 60 months followup. By contrast, N 90% of our cohort had follow-up data for N60 months. Key strengths of this study were the high quality of the tissue samples, the biologically based analysis of the expression data, and long duration of follow-up (up to 20 years). The detailed and long-term clinical follow-up allowed us to evaluate potential effects of molecular events on clinical outcomes. The prognostic implications of the ESR1/ WT1 and POSTN/TGFBI subgroups of serous EOC were confirmed with two independent cohorts (TCGA and Tothill data). Additional analyses to identify prognostic signatures and therapeutic targets are needed, but our findings provide potential directions for new therapies to personalize treatment and reduce mortality from PSOC. The “one-size-

Clinical trials for prostate cancer In clinical trials for treatment of prostate cancer Ovarian cancer and other malignancies

fits-all” approach to treatment of high grade serous EOC will hopefully come to an end. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ygyno.2013.12.021. Conflict of interest statement The authors have no conflicts of interest to disclose.

Funding This study was funded by the American Cancer Society Clinical Research Professorship (SIOP-06-258-01-COUN), Milkin Family

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