Drug repositioning for prostate cancer: using a data-driven approach to gain new insights

Prostate cancer (PC) is the most common cancer and the third leading cause of cancer death in men worldwide. Despite its high incidence and mortality, the likelihood of a cure is low for late-stages of PC. There is an unmet need for more effective agents for treating PC. Here, we present a drug repositioning system, GenoPredict, for finding innovative drug candidates for treating PC. GenoPredict leverages upon a large amount of disease genomics data and a large-scale drug treatment knowledge base (TreatKB) that we recently constructed. We first constructed a genetic disease network (GDN) that comprised of 882 nodes and 200,758 edges and applied a network-based ranking algorithm to find diseases from GDN that are genetically related to PC. We developed a drug prioritization algorithm to reposition drugs from PC-related diseases to treat PC. When evaluated in a de-novo prediction setting using 27 FDA- approved PC drugs, GenoPredict found 25 of 27 FDA-approved PC drugs and ranked them highly (recall: 0.925, mean ranking: 27.3%, median ranking: 15.6%). When compared to PREDICT, a comprehensive drug repositioning system, in novel predictions, GenoPredict performed better than PREDICT across two evaluation datasets. GenoPredict achieved a mean average precision (MAP) of 0.447 when evaluated with 172 PC drugs extracted from 172,888 clinical trial reports, representing a 164.5% improvement as compared to a MAP of 0.169 for PREDICT. When evaluated with 72 PC drugs extracted from 43,811 ongoing clinical trial reports, GenoPredict achieved a MAP of 0.278, representing a 231.1% improvement as compared to a MAP of 0.084 for PREDICT. The data is publicly available at: http://nlp.

edu/public/data/PC_GenoPredict and http: //nlp.

edu/public/data/treatKB.