Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways - Zhang, Heming; Chen, Yixin; Li, Fuhai (Frontiers in Bioinformatics, 2021)
Thanks to the availability of multi-omics data of individual cancer patients, precision medicine or personalized medicine are becoming a promising treatment for individual cancer patients. However, the association patterns, i.e., mechanism of response (MoR), between large-scale multi-omics features and drug response are complex and heterogeneous, and remain unclear. Although there are existing computational models for predicting drug response using the high dimensional multi-omics features, it remains challenging to uncover the complex molecular mechanism of drug responses. To reduce the number of predictors/features and make the model more interpretable, in this study, 46 signaling pathways were used to build a deep learning model constrained by signaling pathways, consDeepSignaling, for anti-drug response prediction. Multi-omics data, like gene expression and copy number variation, of individual genes can be integrated naturally in this model. The signaling pathway constrained deep learning model was evaluated using the multi-omics data of ~1000 cancer cell lines in the Broad Institute Cancer Cell Line Encyclopedia (CCLE) database and the corresponding drug-cancer cell line response dataset in the Genomics of Drug Sensitivity in Cancer (GDSC) database. The evaluation results showed that the proposed model outperformed the existing deep neural network models. Also, the model interpretation analysis indicated the distinctive patterns of importance of signaling pathways in anti-cancer drug response prediction.