Drug mechanism enrichment analysis interprets rank-ordered drug lists to facilitate biological understanding

ABSTRACT

There is a pressing need for improved methods to identify effective therapeutics for disease. Many computational approaches have been developed to predict drugs based on molecular signatures. However, extracting biological or mechanistic insight from these tools remains a challenge because they often produce long, rank-ordered lists of candidate drugs that are difficult to interpret. Here, we developed Drug Mechanism Enrichment Analysis (DMEA), an approach to generate biological insight from rank-ordered drug lists by identifying enriched sets of drugs that share a common mechanism of action. The method is based on Gene Set Enrichment Analysis (GSEA) and derives its power by integrating prior knowledge about the mechanisms of action (MOAs) and/or molecular targets of individual drugs. To test our approach, we first demonstrated that DMEA can successfully identify an enriched drug MOA in simulated data. Next, we validated DMEA using three types of rank-ordered drug lists including gene expression connectivity scores, cell viability connectivity scores, and weighted gene voting molecular classification scores of intrinsic and acquired drug resistance. For all these test cases, DMEA successfully detected the enrichment of the expected MOA and other MOAs of interest. Finally, to demonstrate how DMEA can generate hypotheses regarding other biological processes, we predicted senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells. We then experimentally validated the DMEA-generated hypothesis that EGFR inhibitors are senolytic in primary human mammary epithelial cells. Taken together, our results show that DMEA is a highly versatile bioinformatic tool which can identify enriched drug MOAs in rank-ordered drug lists to facilitate biological understanding. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA.