Abstract 954: Predicting synergistic drug combinations and resistance mechanisms from genomic features and single-agent response profiles
Authors: Rees Matthew G. , Brenan Lisa , Duggan Patrick , Johannessen Cory M. 
Published: August 2018

Drug combinations promise to improve clinical responses and/or forestall drug resistance. To capitalize on this promise, we need to know which drugs to combine, and which patients to give them to based on the genetic or pathological features of their disease. However, progress towards this goal has been hindered by the infeasibility of performing comprehensive drug-combination studies across thousands of cellular contexts.

We hypothesized that the basal gene-transcription state of cancer cell lines, in concert with the cell-viability profiles of single-agent small molecules, might be leveraged to nominate specific synergistic drug combinations and identify mechanisms of drug resistance, eliminating the need to test all possible drug/drug combinations across cellular models. Specifically, we predicted that inhibiting the protein product of transcripts associated with drug resistance to a given small molecule might induce drug synergy.

To test this notion, we analyzed nearly 400,000 drug-sensitivity profiles in &gt;800 cancer cell lines to identify candidate compound-gene pairs. We identified over 100 examples where outlier expression of a single transcript was correlated with resistance to a small molecule. Of these gene/drug pairs, 9 genes represented imminently druggable targets, including established clinically-relevant relationships between the alkylating agent temozolomide and MGMT expression, and between a subset of chemotherapeutics including paclitaxel and the efflux pump ABCB1.

Inhibition of candidate “co-targets”, which included 3 previously characterized relationships and 6 novel relationships, resulted in cell-line-specific synergistic cell killing across multiple cell-line models. For validated compound-gene pairs, exogenous expression of the “co-target” was sufficient to confer resistance. For example, we found that high expression of MGLL, encoding monoglyceride lipase, was uniquely associated with lack of response to the histone lysine demethylase inhibitor GSK-J4. Endogenous or exogenous MGLL expression conferred resistance to GSK-J4, while MGLL-proficient cell lines could be sensitized to GSK-J4 up to 50-fold by co-treatment with an irreversible MGLL inhibitor.

These initial studies highlight the potential of integrating basal gene expression features with small-molecule response to nominate rational candidates for drug combinations. As public repositories of single agent response data from diverse cellular contexts continue to expand, so too will our repertoire of therapeutic combinations. Moreover, this approach permits the parallel identification of genomic features that indicate which patient populations are most likely to benefit from such combinations.

Citation Format: Matthew G. Rees, Lisa Brenan, Patrick Duggan, Cory M. Johannessen. Predicting synergistic drug combinations and resistance mechanisms from genomic features and single-agent response profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 954.