Gene dependency-informed inference of response to targeted cancer therapies
摘要
Targeted therapies such as small-molecule inhibitors act by blocking proteins essential for cancer cell survival, yet omics-based modeling of drug sensitivity often lacks mechanistic grounding. We present FORGE (Factorization Of Response and Gene Essentiality), a joint matrix factorization framework that co-models drug response and target gene dependency to enable biologically informed stratification of treatment groups. FORGE derives a Benefit Score from basal gene expression to estimate therapeutic potential. In unseen cell lines treated with erlotinib, FORGE achieves high concordance for dependency (0.69) and IC50 (0.62), with benefit score stratification showing increased dependency and decreased IC50 across quartiles. Joint modeling improves predictive performance over single-task approaches and enhances agreement between gene-level effects (p = 0.039). Validation across independent datasets shows that higher benefit scores associate with tumor regression in patient-derived xenografts and with predicted drug susceptibility in the Tahoe-100M dataset. Mechanistic analyses further identify gene programs underlying drug susceptibility.