Drug Response Prediction: Shifting from Cell Lines to Patients through Pathway-Structured Interaction Learning

Drug response models can achieve strong global correlations while remaining less accurate at ranking patients for the same drug. This discrepancy matters when transferring knowledge from cell-line screens to patient cohorts with lim- ited response labels. We propose EviDR, an evidence-guided framework that combines large-scale PRISM supervision with limited BeatAML ex vivo response labels. EviDR maps both domains into a shared Reactome pathway space and in- tegrates molecular drug representations with cross-fitted drug–pathway Evidence derived from source response associations. Reliability-weighted Evidence rele- vance guides a drug-conditioned pathway gate, while signed Evidence interacts with sample pathway states to construct patient-specific representations. Under patient-held-out five-fold evaluation, EviDR achieves the highest mean Macro Spearman of 0.329 among the evaluated methods, compared with 0.314 for Trans- DRP and 0.313 for MAC-CDR. It also achieves 1.790× enrichment of experimen- tally sensitive samples relative to group-specific random selection. Comparisons with random patient–drug pair splitting highlight the importance of evaluating un- seen patients, while pathway analyses identify recurring source associations and show prediction sensitivity to Evidence identity and direction. These findings sup- port pathway-structured supervised transfer for within-drug response ranking and provide an explicit connection between source-derived associations and patient- specific pathway representations.