A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

Publication date: Available online 2 April 2018
Source:Cancer Cell
Author(s): Ashton C. Berger, Anil Korkut, Rupa S. Kanchi, Apurva M. Hegde, Walter Lenoir, Wenbin Liu, Yuexin Liu, Huihui Fan, Hui Shen, Visweswaran Ravikumar, Arvind Rao, Andre Schultz, Xubin Li, Pavel Sumazin, Cecilia Williams, Pieter Mestdagh, Preethi H. Gunaratne, Christina Yau, Reanne Bowlby, A. Gordon Robertson, Daniel G. Tiezzi, Chen Wang, Andrew D. Cherniack, Andrew K. Godwin, Nicole M. Kuderer, Janet S. Rader, Rosemary E. Zuna, Anil K. Sood, Alexander J. Lazar, Akinyemi I. Ojesina, Clement Adebamowo, Sally N. Adebamowo, Keith A. Baggerly, Ting-Wen Chen, Hua-Sheng Chiu, Steve Lefever, Liang Liu, Karen MacKenzie, Sandra Orsulic, Jason Roszik, Carl Simon Shelley, Qianqian Song, Christopher P. Vellano, Nicolas Wentzensen, John N. Weinstein, Gordon B. Mills, Douglas A. Levine, Rehan Akbani
We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.

Teaser

By performing molecular analyses of 2,579 TCGA gynecological (OV, UCEC, CESC, and UCS) and breast tumors, Berger et al. identify five prognostic subtypes using 16 key molecular features and propose a decision tree based on six clinically assessable features that classifies patients into the subtypes.


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