Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate.

ABSTRACT: Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen

SUBMITTER: Juul M 

PROVIDER: S-EPMC5440169 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature