CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response

Karol Szlachta, Cem Kuscu, Turan Tufan, Sara J. Adair, Stephen Shang, Alex D. Michaels, Matthew G. Mullen, Natasha Lopes Fischer, Jiekun Yang, Limin Liu, Prasad Trivedi, Edward B. Stelow, P. Todd Stukenberg, J. Thomas Parsons, Todd W. Bauer and Mazhar Adli ()
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Karol Szlachta: University of Virginia School of Medicine
Cem Kuscu: University of Virginia School of Medicine
Turan Tufan: University of Virginia School of Medicine
Sara J. Adair: University of Virginia School of Medicine
Stephen Shang: University of Virginia School of Medicine
Alex D. Michaels: University of Virginia School of Medicine
Matthew G. Mullen: University of Virginia School of Medicine
Natasha Lopes Fischer: University of Virginia School of Medicine
Jiekun Yang: University of Virginia School of Medicine
Limin Liu: University of Virginia School of Medicine
Prasad Trivedi: University of Virginia School of Medicine
Edward B. Stelow: University of Virginia School of Medicine, Charlottesville
P. Todd Stukenberg: University of Virginia School of Medicine
J. Thomas Parsons: University of Virginia School of Medicine
Todd W. Bauer: University of Virginia School of Medicine
Mazhar Adli: University of Virginia School of Medicine

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract Predicting the response and identifying additional targets that will improve the efficacy of chemotherapy is a major goal in cancer research. Through large-scale in vivo and in vitro CRISPR knockout screens in pancreatic ductal adenocarcinoma cells, we identified genes whose genetic deletion or pharmacologic inhibition synergistically increase the cytotoxicity of MEK signaling inhibitors. Furthermore, we show that CRISPR viability scores combined with basal gene expression levels could model global cellular responses to the drug treatment. We develop drug response evaluation by in vivo CRISPR screening (DREBIC) method and validated its efficacy using large-scale experimental data from independent experiments. Comparative analyses demonstrate that DREBIC predicts drug response in cancer cells from a wide range of tissues with high accuracy and identifies therapeutic vulnerabilities of cancer-causing mutations to MEK inhibitors in various cancer types.

Date: 2018
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DOI: 10.1038/s41467-018-06676-2

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