Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data.

ABSTRACT:

Motivation

Several tools exist to identify cancer driver genes based on somatic mutation data. However, these tools do not account for subclasses of cancer genes: oncogenes, which undergo gain-of-function events, and tumor suppressor genes (TSGs) which undergo loss-of-function. A method which accounts for these subclasses could improve performance while also suggesting a mechanism of action for new putative cancer genes.

Results

We develop a panel of five complementary statistical tests and assess their performance against a curated set of 99 HiConf cancer genes using a pan-cancer dataset of 1.7 million mutations. We identify patient bias as a novel signal for cancer gene discovery, and use it to significantly improve detection of oncogenes over existing methods (AUROC = 0.8

SUBMITTER: Kumar RD 

PROVIDER: S-EPMC4757952 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature