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Proc Natl Acad Sci U S A. 2017 Oct 31;114(44):11751-11756. doi: 10.1073/pnas.1708268114. Epub 2017 Oct 16.

CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy.

Author information

1
Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605.
2
Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, MA 01605.
3
Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605.
4
School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
5
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
6
Knight Cancer Institute, Division of Hematology and Medical Oncology, Oregon Health & Science University, Portland, OR 97239.
7
Howard Hughes Medical Institute, Oregon Health & Science University, Portland, OR 97239.
8
Knight Cancer Institute, Department of Pathology, Oregon Health & Science University, Portland, OR 97239.
9
Programs in Molecular Medicine and Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605.
10
Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia.
11
School of Pharmacy and Medical Science, University of South Australia, Adelaide, SA 5000, Australia.
12
School of Medicine, University of Adelaide, Adelaide, SA 5005.
13
School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia.
14
Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605; Michael.Green@umassmed.edu Dan.Bolon@umassmed.edu.
15
Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605; Michael.Green@umassmed.edu Dan.Bolon@umassmed.edu.

Abstract

Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics.

KEYWORDS:

BCR-ABL; CRISPR-Cas9–based genome editing; drug resistance; saturated mutagenesis; tyrosine kinase inhibitors

PMID:
29078326
PMCID:
PMC5676903
DOI:
10.1073/pnas.1708268114
[Indexed for MEDLINE]
Free PMC Article

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