Truncation- and motif-based pan-cancer analysis reveals tumor-suppressing kinases.

ABSTRACT: A major challenge in cancer genomics is identifying "driver" mutations from the many neutral "passenger" mutations within a given tumor. To identify driver mutations that would otherwise be lost within mutational noise, we filtered genomic data by motifs that are critical for kinase activity. In the first step of our screen, we used data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas to identify kinases with truncation mutations occurring within or before the kinase domain. The top 30 tumor-suppressing kinases were aligned, and hotspots for loss-of-function (LOF) mutations were identified on the basis of amino acid conservation and mutational frequency. The functional consequences of new LOF mutations were biochemically validated, and the top 15 hotspot LOF residues wer

SUBMITTER: Hudson AM 

PROVIDER: S-EPMC7984728 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature