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

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dc.contributor.authorMa, L.
dc.contributor.authorBoucher, J.
dc.contributor.authorPaulsen, J.
dc.contributor.authorMatuszewski, S.
dc.contributor.authorEide, C.
dc.contributor.authorOu, J.
dc.contributor.authorEickelberg, G.
dc.contributor.authorPress, R.
dc.contributor.authorZhu, L.
dc.contributor.authorDruker, B.
dc.contributor.authorBranford, S.
dc.contributor.authorWolfe, S.
dc.contributor.authorJensen, J.
dc.contributor.authorSchiffer, C.
dc.contributor.authorGreen, M.
dc.contributor.authorBolon, D.
dc.date.issued2017
dc.description.abstractDeveloping 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.
dc.description.statementofresponsibilityLeyuan Ma, Jeffrey I. Boucher, Janet Paulsen, Sebastian Matuszewski, Christopher A. Eide, Jianhong Ou, Garrett Eickelberg, Richard D. Press, Lihua Julie Zhu, Brian J. Druker, Susan Branford, Scot A. Wolfe, Jeffrey D. Jensen, Celia A. Schiffer, Michael R. Green and Daniel N. Bolon
dc.identifier.citationProceedings of the National Academy of Sciences of the United States of America, 2017; 114(44):11751-11756
dc.identifier.doi10.1073/pnas.1708268114
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.orcidBranford, S. [0000-0002-1964-3626] [0000-0002-5095-7981]
dc.identifier.urihttp://hdl.handle.net/2440/116910
dc.language.isoen
dc.publisherNational Academy of Sciences of the United States of America
dc.rights© 2017 Published under the PNAS license.
dc.source.urihttps://doi.org/10.1073/pnas.1708268114
dc.subjectBCR-ABL; CRISPR-Cas9–based genome editing; drug resistance; saturated mutagenesis; tyrosine kinase inhibitors
dc.titleCRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy
dc.typeJournal article
pubs.publication-statusPublished

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