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

Leyuan 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, Daniel N. Bolon

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)11751-11756
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number44
StatePublished - Oct 31 2017


  • CRISPR-Cas9–based genome editing
  • Drug resistance
  • Saturated mutagenesis
  • Tyrosine kinase inhibitors

ASJC Scopus subject areas

  • General


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