2025 Volume 25 Pages 36-52
Accurate prediction of drug response in cancer treatment remains a critical challenge due to the complex biological interactions underlying tumor sensitivity and resistance. In this work, we introduce OT-GNN, a novel graph neural network framework that leverages optimal transport theory to integrate prior drug-target interaction knowledge with gene expression profiles for interpretable and robust drug response prediction. By embedding an optimal transport-based alignment mechanism into the GNN architecture, OT-GNN dynamically reweights gene importance tailored to each drug–cell line pair, enhancing both predictive accuracy and biological interpretability. We evaluate OT-GNN on a processed NCI-60 dataset under zero-shot learning settings, demonstrating superior performance compared to traditional machine learning models, recent deep learning methods, and standard GNN variants without our proposed alignment. OT-GNN achieves state-of-the-art ROC-AUC and PR-AUC scores, with improved stability across multiple runs, highlighting its potential as a reliable tool for precision oncology applications. Our approach bridges the gap between data-driven modeling and biological prior knowledge, providing a pathway toward more transparent and effective drug response prediction.
