Leveraging cancer mutation data to inform the pathogenicity classification of germline missense variants.

ABSTRACT: Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed. We extracted putative cancer driver missense mutations from the Cancer Hotspots database and annotated them as germline variants, including presence/absence and classification in ClinVar. We trained two supervised learning models (log

SUBMITTER: Haque B 

PROVIDER: S-EPMC11737861 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature