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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Inyoung Sung,
  • Dongmin Bang,
  • Sun Kim,
  • Sangseon Lee

摘要

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.