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PMID: 23257362 已发表 · ppublish 英语

Prognostically relevant gene signatures of high-grade serous ovarian carcinoma.

The Journal of clinical investigation ·第 123 卷 ·第 1 期 ·2013-03-11

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

摘要

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.

文献信息
期刊
The Journal of clinical investigation
期刊简称
J Clin Invest
发表日期
2013-03-11
收录日期
2013-01-02
更新日期
2016-10-19
语言
英语
国家/地区
United States
NLM ID
7802877
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