Genome-wide mapping of somatic mutation rates uncovers drivers of cancer.

ABSTRACT: Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these ma

SUBMITTER: Sherman MA 

PROVIDER: S-EPMC9646522 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature