Gene dependency-informed inference of response to targeted cancer therapies

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.