Domain-adversarial learning predicts clinically actionable drug combination synergy in leukemia patients using bulk transcriptomics data

Abstract

Deep learning has gained popularity in drug combination synergy prediction; however, DL models require large training datasets from cell line pharmacogenomic screens that poorly capture the heterogeneity in transcriptomic features and phenotypic responses seen in patients. To that end, we developed a domain-adversarial neural network (DANN) for personalized drug synergy prediction that accounts for systematic differences between cell line and patient domains. In applications to AML and CLL patient samples, we demonstrate how DANN boosts prediction accuracy under realistic data constraints. The model predictions demonstrated elevated synergy among clinically used combinations, such as venetoclax-based regimens, supporting its ability to identify both pharmaceutically and clinically meaningful combinations. DANN estimates prediction uncertainty and prioritizes high-confidence combination predictions to aid clinical translation. Together, DANN provides a systematic approach to improving accuracy and reliability in cross-domain drug synergy prediction, advancing the development of methods that are aligned with the translational requirements of precision hematology.