Prognostically relevant gene signatures of high-grade serous ovarian carcinoma

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Abstract

Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named “Classification of Ovarian Cancer” (CLOVAR). We validated CLOVAR on an independent dataset consisting of 879 HGS-OvCa expression profiles. The worst outcome group, accounting for 23% of all cases, was associated with a median survival of 23 months and a platinum resistance rate of 63%, versus a median survival of 46 months and platinum resistance rate of 23% in other cases. Associating the outcome prediction model with BRCA1/BRCA2 mutation status, residual disease after surgery, and disease stage further optimized outcome classification. Ovarian cancer is a disease in urgent need of more effective therapies. The spectrum of outcomes observed here and their association with CLOVAR signatures suggests variations in underlying tumor biology. Prospective validation of the CLOVAR model in the context of additional prognostic variables may provide a rationale for optimal combination of patient and treatment regimens.

Authors

Roel G.W. Verhaak, Pablo Tamayo, Ji-Yeon Yang, Diana Hubbard, Hailei Zhang, Chad J. Creighton, Sian Fereday, Michael Lawrence, Scott L. Carter, Craig H. Mermel, Aleksandar D. Kostic, Dariush Etemadmoghadam, Gordon Saksena, Kristian Cibulskis, Sekhar Duraisamy, Keren Levanon, Carrie Sougnez, Aviad Tsherniak, Sebastian Gomez, Robert Onofrio, Stacey Gabriel, Lynda Chin, Nianxiang Zhang, Paul T. Spellman, Yiqun Zhang, Rehan Akbani, Katherine A. Hoadley, Ari Kahn, Martin Köbel, David Huntsman, Robert A. Soslow, Anna Defazio, Michael J. Birrer, Joe W. Gray, John N. Weinstein, David D. Bowtell, Ronny Drapkin, Jill P. Mesirov, Gad Getz, Douglas A. Levine, Matthew Meyerson