NELLY enables patient-centric drug prioritization through interpretable drug-conditioned gene weighting
TL;DR
A translational framework combining patient-centric benchmarking with a pan-cancer pharmacogenomic atlas of patient-derived organoids and a deep learning model integrating transcriptomic and chemical information to predict drug response and prioritize therapies is introduced, supporting NELLY as a promising framework for translationally relevant and interpretable drug response prediction in precision oncology.
Abstract
Precision oncology seeks to match each tumor with the most effective anti-cancer therapy. Advances in pharmacogenomics and machine learning enabled drug response prediction models with strong performance in cancer cell lines. Nonetheless, patient-centric evaluation of drug prioritization and systematic assessment of model generalization in patient-derived systems across cancer types remain largely absent. Here we introduce a translational framework combining patient-centric benchmarking with a pan-cancer pharmacogenomic atlas of patient-derived organoids, together with NELLY, a deep learning model integrating transcriptomic and chemical information to predict drug response and prioritize therapies. NELLY outperformed existing methods for patient-specific drug prioritization across cancer cell lines and patient-derived organoids, including under out-of-distribution evaluation. Its dynamic weighting mechanism provided patient-specific gene attributions, offering a route to connect predicted drug response to molecular programs associated with drug resistance. Our results support NELLY as a promising framework for translationally relevant and interpretable drug response prediction in precision oncology.