CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy
Authors
Ma, LeyuanBoucher, Jeffrey I.
Paulsen, Janet L.
Matuszewski, Sebastian
Eide, Christopher A.
Ou, Jianhong
Eickelberg, Garrett
Press, Richard D.
Zhu, Lihua (Julie)
Druker, Brian J.
Branford, Susan
Wolfe, Scot A.
Jensen, Jeffrey D.
Schiffer, Celia A.
Green, Michael R.
Bolon, Daniel N.
UMass Chan Affiliations
Schiffer LabDepartment of Biochemistry and Molecular Pharmacology
Department of Molecular, Cell and Cancer Biology
Document Type
Journal ArticlePublication Date
2017-10-31Keywords
BCR-ABLCRISPR-Cas9–based genome editing
drug resistance
saturated mutagenesis
tyrosine kinase inhibitors
Biochemistry
Genetics and Genomics
Medicinal Chemistry and Pharmaceutics
Medicinal-Pharmaceutical Chemistry
Molecular Biology
Metadata
Show full item recordAbstract
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.Source
Proc Natl Acad Sci U S A. 2017 Oct 31;114(44):11751-11756. doi: 10.1073/pnas.1708268114. Epub 2017 Oct 16. Link to article on publisher's site
DOI
10.1073/pnas.1708268114Permanent Link to this Item
http://hdl.handle.net/20.500.14038/48874PubMed ID
29078326Related Resources
ae974a485f413a2113503eed53cd6c53
10.1073/pnas.1708268114