Deep graph embedding for prioritizing synergistic anticancer drug combinations

Computational and Structural Biotechnology Journal

Shujun Huang¹  Zhenyuan Fu²  Zexuan Sun³  Peiran Jiang⁴  Pingzhao Hu⁴  Ted M. Lakowski⁵ 

[1] Department of Bioinformatics &Systems Biology, Huazhong University of Science and Technology, Wuhan 430074, China;College of Pharmacy, University of Manitoba, Winnipeg, Manitoba R3E 0T5, Canada;Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba R3E 0J9, Canada;Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;

关键词: Synergistic drug combination;    Cancer;    Cell line;    Graph convolutional network;    Heterogenous network;   

DOI  :  

来源: DOAJ

【 摘 要 】

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein–protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.

【 授权许可】

Unknown