An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

Gayathri Thillaiyampalam, Fabio Liberante, Liam Murray, Chris Cardwell, Ken Mills, Shu-Dong Zhang

Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer). We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs. A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.