Abstract 788: Transcriptional landscape of drug response guides the design of potent and synergistic drug combinations
Authors: Hafner Marc , Niepel Mario , Duan Qiaonan , Paull Evan , Stuart Josh , Subramanian Aravind , Ma’ayan Avi , Sorger Peter K. 
Published: July 2016

Transcriptional profiling of drug-treated cells yields high dimensional response signatures that allow drugs to be compared with each other. For example, the Connectivity Map collects signatures that are aggregated across multiple cell types. However, most therapeutic drugs are effective only against a subset of disease genotypes, particularly in the case of anti-cancer drugs. Here we ask how transcriptional signatures vary across cell lines and dose and correlate these signatures to the phenotypic response (growth inhibition). Using these cell line specific signatures, we inferred which signaling pathways are perturbed by specific kinase inhibitors and identified synergistic drug combinations.

We treated 6 breast cancer cell lines with more than 100 targeted inhibitors at 6 doses and measured their transcriptional response at 2 time points. We focused on inhibitors targeting key the PI3K and MAPK signaling pathways, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs); many of them are currently studied in clinical trials. We identified that ∼40% of the perturbations induce a significant difference in their gene expression profile. Clustering revealed the signatures are time point specific. Some clusters contain perturbations from multiple cell lines, like CDK inhibitors that down regulate genes related to the cell cycle in all six lines. In contrast, clusters comprising inhibitors of the PI3K and MAPK pathways are specific to each cell line and pathway. The perturbations induced by RTK and non-RTK inhibitors cluster with either the PI3K or the MAPK inhibitors depending on the cell line. Thus, the transcriptional response allows us to identify differences in pathway usage between cell lines, in particular to which pathway RTKs signal predominantly.

We found that the significance of the transcriptional signature is not necessarily related to growth inhibition. In particular, some inhibitors have little effect on growth, yet induce a significant transcriptional signature. The most striking case is the inhibition of MEK and EGFR in BT20 that induces strong transcriptional and biochemical responses but inhibits growth by only ∼20%. Based on the transcriptional signature we inferred and validated experimentally that FoxO, which is generally regulated by the PI3K pathway, is partially activated by MEK or EGFR inhibition. This suggests that EGFR and PI3K inhibitors act synergistically in BT20, which we validated experimentally both at the level of FoxO activation and growth inhibition. We validated the most promising drug pair by treating xenografts.

We have shown how we can use measurements of expression signatures and cellular phenotypes following single drug perturbations to identify drug combinations that are synergistic in individual cell lines. This approach is a step toward the rational design of co-drugging strategies with differential effect and larger therapeutic windows.

Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Evan Paull, Josh Stuart, Aravind Subramanian, Avi Ma’ayan, Peter K. Sorger. Transcriptional landscape of drug response guides the design of potent and synergistic drug combinations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 788.