Abstract A18: Predicting synergistic drug combinations from genomic features and single-agent response profiles

Abstract

Abstract 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 whom to give them to based on the genetic or pathological features of their disease. However, accomplishing this goal has been precluded 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 response profiles of hundreds of single-agent small molecules, might be leveraged to nominate synergistic drug combinations, 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 public cell-line drug-sensitivity data to identify candidate compound-gene pairs. We identified 7 examples in which outlier expression of a druggable candidate protein was associated with lack of single-agent response. Inhibition of 6/7 candidate co-targets resulted in cell-line-specific synergistic cell killing across multiple cell line models, validating the overall approach. For example, consistent with clinical findings, we found that high expression of the MGMT gene, encoding O-6-methylguanine-DNA methyltransferase, was uniquely associated with response to alkylating agents such as temozolomide, and that combination of the MGMT inhibitor O-6-benzylguanine with temozolomide resulted in synergistic killing. 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, Amanda Walker, Cory M. Johannessen. Predicting synergistic drug combinations from genomic features and single-agent response profiles [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A18.