Abstract 1083: Synergistic drug combination prediction through drug differential dependency network analysis
Seungchan Kim, Gil Speyer, Harshil Dhruv, Jeffrey Kiefer, Michael E. Berens · Cancer Research · 2017
Abstract In an effort to discover strategies which identify effective drug combinations, we analyzed 39 of the 480 compounds screened in the Cancer Therapeutics Response Portal (CTRP) where combinations of two compounds were tested against 860 cancer cell lines; this enabled a comparison of the drug sensitivity of the combinations versus that of the individual compounds. More than half of the drug combinations (n=21) did not significantly improve the drug sensitivity, compared to individual compounds alone. In fact, some of the combinations showed reduced drug sensitivity. In EDDY-CTRP* analysis, the Cancer Cell Line Encyclopedia (CCLE) RNAseq data and CTRP compound response measurements were analyzed to discover both 1) pathways enriched with differential dependencies between sensitive and non-sensitive cell lines for each compound and 2) the mediators of cell line response to a drug. A mediator is a gene in a pathway that plays a significantly different role between sensitive and non-sensitive conditions. The significance is assessed for either essentiality, measured as a node’s centrality change, or specificity, measured as the difference in condition specific edges. These drug-pathway-mediator connections are predicted to reveal crucial molecular determinants of drug sensitivity that otherwise are hidden in the complexities of the molecular networks of the cell (Speyer et al., PSB 22:497-508, 2017). We further investigated whether mediators identified for single compounds could predict sensitivity to drug combinations. This analysis revealed that if two single compounds share the same specificity mediators, i.e. the genes with the most significant re-wiring of gene dependencies between sensitive and non-sensitive cell lines, combination of these two compounds correlate with improved sensitivity. The converse was also found: compounds that do not share mediators rarely show synergy. Further analysis and empirical testing of predicted combinations promises to prioritize synergistic drug combinations. We believe that this methodology may predict synergistic drug combinations from cancer cell line drug screening data. Supported by NIH U01CA168397. *available at http://biocomputing.tgen.org/software/EDDY/CTRP Citation Format: Seungchan Kim, Gil Speyer, Harshil Dhruv, Jeff Kiefer, Michael Berens. Synergistic drug combination prediction through drug differential dependency network analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1083. doi:10.1158/1538-7445.AM2017-1083