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Life sciences · Journal article
Genome Medicine · September 30, 2026
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Accurate drug response prediction is essential for optimizing cancer therapy, yet genomic heterogeneity drives variable responses even among tumors with identical driver mutations. We developed DrugSAGE, a Graph Neural Network framework that predicts drug response from transcriptomic data by aggregating features from each sample and its most similar counterparts. A customized linear layer incorporating gene-pathway annotations provides biological interpretability. Benchmarking across independent bulk and single-cell datasets showed significant associations with known drug targets and treatment-stratified patient groups. DrugSAGE effectively predicts single-cell drug responses and identifies key genes and pathways, offering a novel, interpretable approach with superior or comparable performance.