Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift

ArXi:2603.16185v1 Announce Type: cross Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and patient tumors. Rather than aiming to improve absolute in vitro prediction accuracy, this work examines whether explicitly separating representation learning from task supervision enables sample-efficient adaptation of drug-response models to patient data under strong biological domain shift.