Abstract B20: Transcriptional landscape of drug response guides the design of specific and potent drug combinations
Authors: Hafner Marc , Niepel Mario , Duan Qiaonan , Paull Evan , Stuart Josh , Subramanian Aravind , Ma'ayan Avi , Sorger Peter 
Published: May 2016

Understanding responses to targeted agents is a key step toward the design of new therapeutic strategies that improve clinical cancer care. Here, we profiled the effects of a collection of kinase inhibitors using the L1000 transcriptional assay and combined these results with both phenotypic and biochemical response measurements to gain a more complete understanding of drug response. Using algorithms that reconstruct which signaling pathways are perturbed by specific kinase inhibitors, we identified potentially synergistic drug combinations and validated them experimentally.

We treated six breast cancer cell lines with more than 100 targeted inhibitors at six doses and measured their transcriptional response at two 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 37% of the perturbations induce a significant difference in their gene expression profile based on the characteristic direction of the response. Clustering of signatures revealed they are time point specific: 3 hour signatures differ from the 24 hour ones. 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/AKT 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 allow us to identify differences in pathway usage between cell lines, in particular which RTK signals predominantly to the PI3K or the MAPK pathway.

When we related transcriptional response to the growth inhibition after three days, we found that the strength of the transcriptional signature is not necessarily related to growth inhibition. In particular, we identified cases where inhibitors have little effect on growth, yet induce a significant transcriptional signature. The most striking case is the inhibition of MEK and EGFR in BT-20 that induces strong transcriptional and biochemical responses but only 20-30% of growth inhibition. Based on the transcriptional signature we inferred and validated experimentally that FoxO, which is generally regulated by the PI3K pathway, is partially activated following MEK or EGFR inhibition. This suggests that EGFR inhibitors and PI3K inhibitors act synergistically in BT-20, which we validated experimentally both at the level of FoxO activation and growth inhibition. We are currently verifying the most promising drug pair in 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 potent and specific to 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 Sorger. Transcriptional landscape of drug response guides the design of specific and potent drug combinations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B20.