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A Collagen-Remodeling Gene Signature Regulated by TGF-B Signaling Is Associated with Metastasis and Poor Survival in Serous Ovarian Cancer

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A Collagen-Remodeling Gene Signature Regulated by TGF-B Signaling Is Associated with Metastasis and Poor Survival in Serous Ovarian Cancer Published OnlineFirst November 11, 2013; DOI: 10.1158/1078-0432.CCR-13-1256 Clinical Cancer Imaging, Diagnosis, Prognosis Research A Collagen-Remodeling Gene Signature Regulated by TGF-b Signaling Is Associated with Metastasis and Poor Survival in Serous Ovarian Cancer Dong-Joo Cheon1, Yunguang Tong2, Myung-Shin Sim7, Judy Dering6, Dror Berel3, Xiaojiang Cui1, Jenny Lester1, Jessica A. Beach1,5, Mourad Tighiouart3, Ann E. Walts4, Beth Y. Karlan1,6, and Sandra Orsulic1,6 Abstract Purpose: To elucidate molecular pathways contributing to metastatic cancer progression and poor clinical outcome in serous ovarian cancer. Experimental Design: Poor survival signatures from three different serous ovarian cancer datasets were compared and a common set of genes was identified. The predictive value of this gene signature was validated in independent datasets. The expression of the signature genes was evaluated in primary, metastatic, and/or recurrent cancers using quantitative PCR and in situ hybridization. Alterations in gene expression by TGF-b1 and functional consequences of loss of COL11A1 were evaluated using pharmacologic and knockdown approaches, respectively. Results: We identified and validated a 10-gene signature (AEBP1, COL11A1, COL5A1, COL6A2, LOX, POSTN, SNAI2, THBS2, TIMP3, and VCAN) that is associated with poor overall survival (OS) in patients with high-grade serous ovarian cancer. The signature genes encode extracellular matrix proteins involved in collagen remodeling. Expression of the signature genes is regulated by TGF-b1 signaling and is enriched in metastases in comparison with primary ovarian tumors. We demonstrate that levels of COL11A1, one of the signature genes, continuously increase during ovarian cancer disease progression, with the highest expression in recurrent metastases. Knockdown of COL11A1 decreases in vitro cell migration, invasion, and tumor progression in mice. Conclusion: Our findings suggest that collagen-remodeling genes regulated by TGF-b1 signaling promote metastasis and contribute to poor OS in patients with serous ovarian cancer. Our 10-gene signature has both predictive value and biologic relevance and thus may be useful as a therapeutic target. Clin Cancer Res; 20(3); 1–13. Ó2013 AACR. Introduction Despite similarities in initial presentation, patients with Ovarian cancer is the fifth leading cause of cancer-related high-grade serous ovarian cancer display a broad range of death among women and the most lethal gynecologic survival endpoints. Some patients develop a chronic-type cancer in the United States with survival rates for advanced disease and can be maintained on chemotherapy for more stage disease that have not changed in several decades (1, 2). than 5 years, whereas others are either intrinsically che- moresistant or rapidly become chemoresistant following an initial period of chemosensitivity. Once chemoresistance Authors' Affiliations: 1Women's Cancer Program, 2Department of Med- develops in these patients, their response rate to other icine, 3Biostatistics and Bioinformatics Research Center, Samuel Oschin second-line agents is very low. A reliable method that 4 Comprehensive Cancer Institute; Department of Pathology and Labora- identifies poor outcome patients early in the course of their tory Medicine; 5Graduate Program in Biomedical Sciences and Transla- tional Medicine, Cedars-Sinai Medical Center; 6Department of Obstetrics disease would facilitate timely inclusion into clinical trials and Gynecology, David Geffen School of Medicine, University of California or personalized treatment strategies. The Oncotype DX and at Los Angeles, Los Angeles; and 7John Wayne Cancer Institute, Saint John's Hospital, Santa Monica, California MammaPrint assays for breast cancer are successful exam- ples of this approach and have become the standard of care Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). for individualized treatment decision making in breast cancer (3, 4). Currently, there is no fully validated and Corresponding Author: Sandra Orsulic, Women's Cancer Program, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Suite 290W, Los clinically applied test to guide treatment decisions in ovar- Angeles, CA 90048. Phone: 310-423-9546; Fax: 310-423-9537; E-mail: ian cancer. [email protected] Several research groups have used expression profile data doi: 10.1158/1078-0432.CCR-13-1256 to develop signatures that predict clinical outcomes in Ó2013 American Association for Cancer Research. ovarian cancer. Although each signature has an associated www.aacrjournals.org OF1 Downloaded from clincancerres.aacrjournals.org on October 5, 2021. © 2013 American Association for Cancer Research. Published OnlineFirst November 11, 2013; DOI: 10.1158/1078-0432.CCR-13-1256 Cheon et al. Expression data and clinical data for the Karlan dataset have Translational Relevance been deposited in GEO (GSE51088). Ovarian cancer is the most lethal gynecologic cancer. For the TCGA dataset, 403 samples of high-grade serous Despite clinical and histopathologic similarities at diag- ovarian cancer that were profiled on the Affymetrix U133A nosis, survival outcomes differ due to the development platform were preprocessed with dChip (version 12/05/ of chemoresistant tumors. Early identification of these 2011) software (7) as described in the manual. The genes tumors could help direct the use of tailored or experi- were filtered using 2 criteria: (i) the signal intensity of each mental therapies. In this study, we report a 10-gene gene should be 1.00 < SD/mean < 10.00; and (ii) the P signature that is associated with poor overall survival in call %, the percentage of gene identified as present (P) in the patients with serous ovarian cancer. The 10 validated whole array should be 20%. The 1,458 genes that signature genes are enriched in metastatic serous carci- remained after filtration were used for unsupervised clus- nomas and are primarily involved in collagen remodel- tering. The Pearson correlation was used to calculate the ing. The signature genes are regulated by TGF-b1 signal- similarity between genes and samples. The samples were ing, suggesting that TGF-b1 inhibitors might be an clustered into four major groups. Survival analyses were effective adjunct in treating these patients and may play performed using the dChip survival analysis module (7). In a role in the biology of tumor metastasis. Our poor the group that was associated with the worst survival out- outcome signature provides a novel therapeutic strategy come in comparison with all other groups (P ¼ 0.01869), a that targets collagen-remodeling genes as a means to cluster of 86 genes was identified (Supplementary Table improve survival in women with serous ovarian cancer. S1A). For the analysis of the GSE26712 dataset, the probe level raw expression data in the ovarian cancer samples (n ¼ 185) were analyzed using GeneSpring GX 11.5 (Agilent Technol- predictive ability, the gene signatures described to date ogies). The Robust Multichip Averaging (RMA) algorithm exhibit little overlap and lack apparent biologic relevance was applied to the Affymetrix human U133A microarray to poor outcome. The mechanisms by which individual data. Background correction, normalization, and summa- genes or a group of genes contribute to poor clinical rization were performed and a baseline transformation to outcome are also not well understood. Consequently, the median of all samples was applied. Samples were there is not only a critical need for diagnostic classifiers grouped into 2 distinct survival groups: short survivors that can assess the risk of poor survival in patients with [overall survival (OS) < 2.0 years; n ¼ 52] and long survivors serous ovarian cancer, but also for a better understanding (OS > 4.0 years; n ¼ 67). A gene list was made by selecting of the molecular mechanisms that are involved in tumor genes with a fold change 1.5 between short survivors and progression and can be used to develop new treatment long survivors and an independent t test was applied to strategies. compare short and long survivors. The Benjamini–Hoch- berg multiple comparison adjustment was applied and the Materials and Methods corrected P value < 0.05 was considered statistically signif- Patients and samples icant. The 68 genes that satisfy these criteria are listed in Snap-frozen and paraffin-embedded patient samples and Supplementary Table S1B. paired clinical information were retrieved from the Depart- In the Karlan dataset, periostin (POSTN) was selected as ment of Pathology and Laboratory Medicine and from the a gene of interest because our previous studies demon- Women’s Cancer Program Biorepository at Cedars-Sinai strated that POSTN expression is significantly increased in Medical Center. All patients signed the consent form for ovarian tumors in comparison with normal ovarian epi- biobanking, clinical data extraction, and molecular analysis thelia (8) and that POSTN is required for ovarian cancer and received adjuvant chemotherapy. This study was progression (9, 10). The Rosetta Similarity Search Tool approved by the Institutional Review Board at Cedars-Sinai (ROAST) was used in 122 patients with serous ovarian Medical Center. cancer to identify a cluster of 188 genes (Supplementary Table S1C) that were highly correlated (r range, 0.701– Microarray data analyses for the identification of a 0.919; P < 0.0002) with POSTN expression and associated poor outcome gene signature with poor survival (11). To
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