Cancer mutational signatures identification in clinical assays using neural embedding-based representations.

ABSTRACT: While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation

SUBMITTER: Yaacov A 

PROVIDER: S-EPMC11228799 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature