Publication Summary

A multi-targeted probe-based strategy to identify signaling vulnerabilities in cancers.

Suman Rao1,2,3, Guangyan Du2,3, Marc Hafner1,4, Kartik Subramanian1, Peter K. Sorger1, *Nathanael S. Gray2,3

*Correspondence: Nathanael_Gray [at] dfci.harvard.edu
1Laboratory of Systems Pharmacology, Harvard Medical School, Boston MA, 02115, USA
2Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
3Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
Present address: 4Department of Bioinformatics & Computational Biology, Genentech, Inc., South San Francisco, CA 94080
 

Synopsis

 

Key Findings

 

Abstract

Most cancer cells are dependent on a network of deregulated signaling pathways for survival and are insensitive, or rapidly evolve resistance, to selective inhibitors aimed at a single target. For these reasons, drugs that target more than one protein (polypharmacology) can be clinically advantageous. The discovery of useful polypharmacology remains serendipitous and is challenging to characterize and validate. In this paper we developed a non-genetic strategy for the identification of pathways that drive cancer cell proliferation and represent exploitable signaling vulnerabilities. Our approach is based on using a multi-targeted kinase inhibitor, SM1-71, as a tool compound to identify combinations of targets whose simultaneous inhibition elicits a potent cytotoxic effect. As a proof-of-concept, we applied this approach to a KRAS-dependent non-small cell lung cancer (NSCLC) cell line, H23-KRASG12C. Using a combination of phenotypic screens, signaling analyses and kinase inhibitors, we found that dual -inhibition of MEK1/2 and insulin-like growth factor 1 receptor (IGF1R)/insulin receptor (INSR) is critical for blocking proliferation in cells. Our work supports the value of multi-targeted tool compounds with well-validated polypharmacology and target space as tools to discover kinase dependencies in cancer. We propose that the strategy described here is complementary to existing genetic-based approaches, generalizable to other systems, and enabling for future mechanistic and translational studies of polypharmacology in the context of signaling vulnerabilities in cancers.
 

Available data and software

 

Funding Sources

 

Related References