Systematic identification of biomarker-driven drug combinations to overcome resistance

The ability to understand and predict variable responses to therapeutic agents may improve outcomes in patients with cancer. We hypothesized that the basal gene-transcription state of cancer cell lines, coupled with cell viability profiles of small molecules, might be leveraged to nominate specific mechanisms of intrinsic resistance and to predict drug combinations that overcome resistance. We analyzed 564,424 sensitivity profiles to identify candidate gene–compound pairs, and validated nine such relationships. We determined the mechanism of a novel relationship, in which expression of the serine hydrolase enzymes monoacylglycerol lipase (MGLL) or carboxylesterase 1 (CES1) confers resistance to the histone lysine demethylase inhibitor GSK-J4 by direct enzymatic modification. Insensitive cell lines could be sensitized to GSK-J4 by inhibition or gene knockout. These analytical and mechanistic studies highlight the potential of integrating gene-expression features with small-molecule response to identify patient populations that are likely to benefit from treatment, to nominate rational candidates for combinations and to provide insights into mechanisms of action.