AI-Driven Variant Annotation for Precision Oncology in Breast Cancer.

ABSTRACT: Interpreting the functional impact of genomic variants remains a major challenge in precision oncology, particularly in breast cancer, where many variants of unknown significance lack clear therapeutic guidance. Current annotation strategies focus on frequent driver mutations, leaving rare or understudied variants unclassified and clinically uninformative. Here, we present an Artificial Intelligence/Machine Learning (AI/ML)-driven framework that systematically identifies variants associated with key breast cancer phenotypes, including ESR1 and EZH2 activity, by integrating genomic, transcriptomic, structural, and drug response data. Using CCLE/DepMap and TCGA datasets, we analyzed > 12,000 variants across breast cancer genomes, identifying structurally clustered mutations that share functi

SUBMITTER: Shukla K 

PROVIDER: S-EPMC12547363 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature