Attention-Enhanced Deep Generative Modeling Enables Interpretable Prediction of Cancer Drug Sensitivity

Predicting how tumors respond to specific drugs is a fundamental obstacle in precision oncology, largely because of the vast molecular diversity observed across cancers. Deep learning approaches have demonstrated potential for modeling complex relationships between molecular profiles and drug responses, yet their adoption in clinical settings has been hindered by limited transparency and poor generalization across different datasets. In this work, we introduce Residual Attention Variational Autoencoder with Elastic Net (ResAttnVAE-EN), a deep generative model augmented with attention mechanisms that combines somatic genomic alterations (SGAs) and transcriptomic profiles to learn biologically interpretable cellular representations for predicting drug sensitivity. Leveraging large-scale pharmacogenomic datasets from the Genomics of Drug Sensitivity in Cancer (GDSC) database, we show that ResAttnVAE-EN consistently surpasses standard variational autoencoder and regression-based benchmarks across a broad panel of therapeutic compounds. The attention layers highlight genomically coherent pathway-level drivers, and the learned latent spaces encode cellular states that generalize across multiple cancer types for response prediction. Notably, models derived from cell line experiments effectively distinguish survival trajectories and treatment outcomes in independent The Cancer Genome Atlas (TCGA) lung cancer patient cohorts. These findings position attention-augmented deep generative approaches as a reliable and interpretable framework for clinically translatable drug sensitivity modeling.