Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors

Prediction of patient-level drug response is critical for precision oncology but remains limited by the scarcity of clinical data. While machine learning models trained on cell lines offer a scalable alternative, biological differences introduce domain shifts that hinder direct translation to patient tumors. Here, we present THERAPI (Tumor Heterogeneity-aware Embedding for Response Adaptation and Patient Inference), a deep learning framework designed to bridge this gap. First, THERAPI aligns patient tumors to cell lines through attention-based aggregation guided by tissue context, modeling each tumor as a linear combination of cell lines. Second, THERAPI transfers gene- and cell-level knowledge from pre-trained perturbation and rank embeddings to train drug response predictors. THERAPI outperforms 11 baselines on TCGA dataset, generalizes to external breast and colorectal cancer cohorts, and supports interpretable gene/pathway-level analysis. These results highlight the value of integrating tumor-biology context and perturbation-aware modeling for generalizable and interpretable drug response prediction towards precision oncology.