Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements in large-scale in vitro drug screening assays have generated extensive drug testing and genomic data, providing valuable resources to explore the relationship between genomic features and drug responses. In this study, we developed a deep neural network model, DrugS (Drug Response prediction Utilizing Genomic features Screening), utilizing gene expression and drug testing data from human-derived cancer cell lines to predict cellular responses to drugs. Leveraging gene expression and mutation data, we elucidated potential molecular mechanisms underlying SN-38 resistance. Additionally, we used DrugS to evaluate the effects of drugs on cancer cell proliferation in patient-derived xenograft models. In in vitro combination drug experiments, DrugS revealed that CDK inhibitors, mTOR inhibitors, and apoptosis inhibitors effectively reverse Ibrutinib resistance, providing new therapeutic strategies to overcome drug resistance. Furthermore, we assessed the applicability of the DrugS model in drug screening and patient prognosis evaluation using drug information and gene expression data from The Cancer Genome Atlas. In summary, our study offers a novel approach for drug response prediction and mechanism research in cancer therapy from a genomic perspective and demonstrates the potential applications of the DrugS model in personalized therapy and resistance mechanism elucidation.