A central question for the cancer research community is how to use genomic and proteomic data to guide cancer care. We investigated two potential clinical applications of The Cancer Genome Atlas (TCGA) data: predicting patient prognosis and identifying clinically actionable alterations. For four TCGA cohorts, we systematically evaluated the power of molecular data with or without clinical variables in predicting patient survival; and to facilitate further community efforts, we established an open-access model evaluation platform. We demonstrate that incorporating molecular data with clinical variables can improve predictive power. Interestingly, one model derived from ovarian cancer can predict outcomes in kidney cancer, suggesting the potential utility of cross-tumor analysis. Across 12 cancer types, we identified 10,281 somatic alterations in clinically relevant genes in 2,928 out of 3,277 patients (89.4%), revealing recurrent and potentially targetable alterations not revealed in single-tumor datasets. Our study represents the first systematic and comprehensive analysis evaluating the prognostic power of different types of molecular data across multiple cancer types. In addition, the clinically actionable alterations we identified may inform novel clinical trial design and treatment choice.
Citation Format: Han Liang, TCGA Pan-Cancer Clinical/Predictor Group. Dissect the clinical utility of TCGA genomic data across tumor types. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4168. doi:10.1158/1538-7445.AM2014-4168