Assessing the clinical utility of cancer genomic and proteomic data across tumor types.

ABSTRACT: Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC4102885 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature