Dataset Information

Chemical genetic discovery of targets and anti-targets for cancer polypharmacology.

ABSTRACT: The complexity of cancer has led to recent interest in polypharmacological approaches for developing kinase-inhibitor drugs; however, optimal kinase-inhibition profiles remain difficult to predict. Using a Ret-kinase-driven Drosophila model of multiple endocrine neoplasia type 2 and kinome-wide drug profiling, here we identify that AD57 rescues oncogenic Ret-induced lethality, whereas related Ret inhibitors imparted reduced efficacy and enhanced toxicity. Drosophila genetics and compound profiling defined three pathways accounting for the mechanistic basis of efficacy and dose-limiting toxicity. Inhibition of Ret plus Raf, Src and S6K was required for optimal animal survival, whereas inhibition of the 'anti-target' Tor led to toxicity owing to release of negative feedback. Rational synthetic tailoring to eliminate Tor binding afforded AD80 and AD81, compounds featuring balanced pathway inhibition, improved efficacy and low toxicity in Drosophila and mammalian multiple endocrine neoplasia type 2 models. Combining kinase-focused chemistry, kinome-wide profiling and Drosophila genetics provides a powerful systems pharmacology approach towards developing compounds with a maximal therapeutic index.

SUBMITTER: Dar AC

PROVIDER: S-EPMC3703503 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Chemical genetic discovery of targets and anti-targets for cancer polypharmacology.

Dar Arvin C AC Das Tirtha K TK Shokat Kevan M KM Cagan Ross L RL

Nature 20120606 7401

The complexity of cancer has led to recent interest in polypharmacological approaches for developing kinase-inhibitor drugs; however, optimal kinase-inhibition profiles remain difficult to predict. Using a Ret-kinase-driven Drosophila model of multiple endocrine neoplasia type 2 and kinome-wide drug profiling, here we identify that AD57 rescues oncogenic Ret-induced lethality, whereas related Ret inhibitors imparted reduced efficacy and enhanced toxicity. Drosophila genetics and compound profili ...[more]

PMID: 22678283

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Project description:Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Dataset Information

Chemical genetic discovery of targets and anti-targets for cancer polypharmacology.

Publications

Chemical genetic discovery of targets and anti-targets for cancer polypharmacology.

Similar Datasets

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