A chemical-genetic interaction map of small molecules using high‐throughput imaging in cancer cells

• Verfasst von: Breinig, Marco [VerfasserIn]
• Verfasst von: Huber, Wolfgang [VerfasserIn]
• Verfasst von: Boutros, Michael [VerfasserIn]
• Titel: A chemical-genetic interaction map of small molecules using high‐throughput imaging in cancer cells
• Verf.angabe: Marco Breinig, Felix A. Klein, Wolfgang Huber & Michael Boutros
• E-Jahr: 2015
• Jahr: 1 December 2015
• Umfang: 16 S.
• Fussnoten: Gesehen am 28.11.2018
• Titel Quelle: Enthalten in: Molecular systems biology
• Ort Quelle: [London] : Nature Publishing Group UK, 2005
• Jahr Quelle: 2015
• Band/Heft Quelle: 11(2015), 12, Artikel-ID 846
• ISSN Quelle: 1744-4292
• DOI: doi:10.15252/msb.20156400
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: compound mode of action
• Sach-SW: drug synergism
• Sach-SW: high‐content imaging
• Sach-SW: isogenic cell lines
• Sach-SW: systems pharmacology
• K10plus-PPN: 1584609397

Abstract

Small molecules often affect multiple targets, elicit off‐target effects, and induce genotype‐specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high‐content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical-genetic interaction analysis, we observed phenotypic gene-drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off‐target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti‐alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off‐target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs. This study defines a quantitative map of phenotypic pharmacogenetic interactions in human cancer cells using high‐content imaging screens in a panel of isogenic cell lines. The resource is used to predict effective drug combinations, compound mode‐of‐action and off‐target effects. We developed a robust and scalable approach to integrate multiparametric phenotypic profiling and quantitative pharmacogenetic interaction mapping in human cancer cells.We used high‐content screening and automated image analysis to measure genotype‐specific effects of 1,280 drugs on complex phenotypes in a panel of 12 isogenic cancer cell lines, resulting in more than 14,000,000 measurements.We observed a total of 2,359 significant chemical-genetic interactions, only 16 of which affected cell number. Our approach provided increased coverage for gene-drug interaction mapping as compared to strategies that solely rely on cell growth as a phenotypic readout.We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), comprising information about over 300,000 drug-gene-phenotype interactions.The PGPC can be explored to predict compound mode of action and off‐target effects, pathway crosstalk and effective drug combinations.