drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

• URL: http://arxiv.org/abs/2405.08979v2
• Date: Wed, 27 Aug 2025 20:36:18 GMT
• Title: drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network
• Authors: Yoshitaka Inoue, Hunmin Lee, Tianfan Fu, Augustin Luna,
• Abstract summary: drGT is a graph deep learning model that predicts sensitivity and aids in biomarker identification using attention coefficients (ACs)<n>The model is trained and evaluated using major benchmark datasets: Sanger GDSC, NCI60, and Broad CTRP, which cover a wide range of drugs and cancer cell lines.
• Score: 8.664433151000255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A challenge in drug response prediction is result interpretation compared to established knowledge. drGT is a graph deep learning model that predicts sensitivity and aids in biomarker identification using attention coefficients (ACs). drGT leverages a heterogeneous graph composed of relationships drawn from drugs, genes, and cell line responses. The model is trained and evaluated using major benchmark datasets: Sanger GDSC, NCI60, and Broad CTRP, which cover a wide range of drugs and cancer cell lines. drGT demonstrates AUROC of up to 94.5% under random splitting, 84.4% for unseen drugs, and 70.6% for unseen cell lines, comparable to existing benchmark methods while also providing interpretability. Regarding interpretability, we review drug-gene co-occurrences by text-mining PubMed abstracts for high-coefficient genes mentioning particular drugs. Across 976 drugs from NCI60 with known drug-target interactions (DTIs), model predictions utilized both known DTIs (36.9%) as well as additional predictive associations, many supported by literature. In addition, we compare the drug-gene associations identified by drGT with those from an established DTI prediction model and find that 63.67% are supported by either PubMed literature or predictions from the DTI model. Further, we describe the utilization of ACs to identify affected biological processes by each drug via enrichment analyses, thereby enhancing biological interpretability. Code is available at https://github.com/sciluna/drGT.

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