DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.Author summary: As is known, the phenotype of cancer is closely related to gene expression in the human body. In the era of precision medicine, an increasing amount of anti-cancer drug response data is urgently needed for individualized therapy. Measurements with wet-experiments are time-consuming and costly, and computational methods are necessary. Existing methods mainly focused predominantly on linear or nonlinear relationships between cells and drugs for prediction. However, it has been shown that both linear and nonlinear relationships could contribute to more precise response prediction. Here, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to better use both linear and nonlinear relationships and the experimental results show that it outperforms state-of-the-art algorithms. Our results suggest that drugs showing similar response levels tend to target similar signaling pathways while cell lines sharing similar response patterns tend to come from the same tissue subtype. The analysis of experimental results may benefit medical decision-making.