Interpretable prediction of drug-cell line response by triple matrix factorization

• Published in: Quantitative biology 2021-12, Vol.9 (4), p.426-439
• Main Authors: Yan, Xiao-Ying, Zhang, Shao-Wu, Yiu, Siu-Ming, Shi, Jian-Yu
• Format: Article
• Language: English
• Subjects: Cell lines; Computer applications; Drug dosages; drug resistance; drug response; drug sensitivity; Gene expression; Precision medicine; triple matrix factorization
• ISSN: 2095-4689; 2095-4697
• DOI: 10.15302/J-QB-021-0259

Background: One of the challenges in personalized medicine is to determine specific drugs and their dosages for patient individuals who are undergoing a common disease. The technique of cell lines provides a safe approach to capture the drug responses of patient individuals when given specific drugs with varied dosages. However, it is still costly to determine drug responses in cells w.r.t dosages by biological assays. Computational methods provide a promising screening to infer possible drug responses in the cells of patient individuals on a large scale. Nevertheless, existing computational approaches are insufficient to interpret the underlying reason for drug responses. Methods: In this work, we propose an interpretable model for analyzing and predicting drug responses across cell lines. The proposed model bridges drug features ( e.g., chemical structure fingerprints), cell features ( e.g., gene expression profiles), and drug responses across cells (measured by IC50) by a triple matrix factorization (TMF), such that the underlying reason for drug responses in specific cells is possibly interpreted. Results: The comparison with state-of-the-art computational approaches demonstrates the superiority of our TMF. More importantly, a case study of drug responses in lung-related cell lines shows its interpretable ability to find out highly occurring drug substructures, crucial mutated genes, as well as significant pairs between substructures and mutated genes in terms of drug sensitivity and resistance. Conclusion: TMF is an effective and interpretable approach for predicting cell lines responses to drugs, and can dig out crucial pairs of chemical substructures and genes, which uncovers the underlying reason for drug responses in specific cells.