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

Authors :
Verhaak RG
Tamayo P
Yang JY
Hubbard D
Zhang H
Creighton CJ
Fereday S
Lawrence M
Carter SL
Mermel CH
Kostic AD
Etemadmoghadam D
Saksena G
Cibulskis K
Duraisamy S
Levanon K
Sougnez C
Tsherniak A
Gomez S
Onofrio R
Gabriel S
Chin L
Zhang N
Spellman PT
Zhang Y
Akbani R
Hoadley KA
Kahn A
Köbel M
Huntsman D
Soslow RA
Defazio A
Birrer MJ
Gray JW
Weinstein JN
Bowtell DD
Drapkin R
Mesirov JP
Getz G
Levine DA
Meyerson M
Source :
The Journal of clinical investigation [J Clin Invest] 2013 Jan; Vol. 123 (1), pp. 517-25. Date of Electronic Publication: 2012 Dec 21.
Publication Year :
2013

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.

Details

Language :
English
ISSN :
1558-8238
Volume :
123
Issue :
1
Database :
MEDLINE
Journal :
The Journal of clinical investigation
Publication Type :
Academic Journal
Accession number :
23257362
Full Text :
https://doi.org/10.1172/JCI65833