Explainable deep learning for identifying cancer driver genes based on the Cancer Dependency Map.

ABSTRACT: Detecting cancer driver genes and mutations is critical but challenging in cancer biology, essential for understanding tumor progression and developing targeted therapies. In this study, we leveraged the Cancer Dependency Map (DepMap) to identify potential cancer driver genes and infer multi-driver mutations within tumor samples. We developed xNNDriver, a biologically informed, supervised deep learning model that links the mutation status of a candidate cancer driver gene to genome-wide dependency scores. The model's fitness provides a quantifiable measure of a gene's likelihood to act as a cancer driver. Our model successfully recovers well-established drivers, including NRAS, KRAS, HRAS, and BRAF, and identifies novel candidates such as VDR. The model's interpretabil

SUBMITTER: Yin Q 

PROVIDER: S-EPMC12247957 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature