Drug repurposing for cancer treatment through global propagation with a greedy algorithm in a multilayer network
Xi Cheng, Wensi Zhao, Mengdi Zhu, Bo Wang, Xuege Wang, Xiaoyun Yang, Yuqi Huang, Minjia Tan, Jing Li · Cancer Biology and Medicine · 2021
Objective: Drug repurposing, the application of existing therapeutics to new indications, holds promise in achieving rapid clinicaleffects at a much lower cost than that of de novo drug development. The aim of our study was to perform a more comprehensive drugrepurposing prediction of diseases, particularly cancers. Methods: Here, by targeting 4,096 human diseases, including 384 cancers, we propose a greedy computational model based on aheterogeneous multilayer network for the repurposing of 1,419 existing drugs in DrugBank. We performed additional experimentalvalidation for the dominant repurposed drugs in cancer. Results: The overall performance of the model was well supported by cross-validation and literature mining. Focusing on thetop-ranked repurposed drugs in cancers, we verified the anticancer effects of 5 repurposed drugs widely used clinically in drugsensitivity experiments. Because of the distinctive antitumor effects of nifedipine (an antihypertensive agent) and nortriptyline (anantidepressant drug) in prostate cancer, we further explored their underlying mechanisms by using quantitative proteomics. Ouranalysis revealed that both nifedipine and nortriptyline affected the cancer-related pathways of DNA replication, the cell cycle, andRNA transport. Moreover, in vivo experiments demonstrated that nifedipine and nortriptyline significantly inhibited the growth ofprostate tumors in a xenograft model. Conclusions: Our predicted results, which have been released in a public database named The Predictive Database for DrugRepurposing (PAD), provide an informative resource for discovering and ranking drugs that may potentially be repurposed forcancer treatment and determining new therapeutic effects of existing drugs.