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. 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

Leyuan Ma  1   2 , Jeffrey I Boucher  3 , Janet Paulsen  3 , Sebastian Matuszewski  4   5 , Christopher A Eide  6   7 , Jianhong Ou  1 , Garrett Eickelberg  8 , Richard D Press  8 , Lihua Julie Zhu  1   9 , Brian J Druker  6   7 , Susan Branford  10   11   12   13 , Scot A Wolfe  1   3 , Jeffrey D Jensen  4   5 , Celia A Schiffer  3 , Michael R Green  14   2 , Daniel N Bolon  15
Affiliations

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

Leyuan Ma et al. Proc Natl Acad Sci U S A. .

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.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
An optimized CRISPR-Cas9–based strategy efficiently integrates barcoded BCR-ABL1 libraries into a consistent genomic location. (A) Schematic of the CRISPR-Cas9–based strategy to introduce barcoded libraries of ABL1KD mutations into the genome. (B) HDR efficiency following optimization of the sgRNA and donor template sequences (step 1), homology arm length (step 2), clonal cell line (step 3), and the amount of guide RNA and donor template (step 4). The initial HDR efficiency before optimization is shown. Error bars represent the SD of three independent experiments. (C and D) Correlation of amino acid (C) and codon (D) frequencies of the library of T315X mutants between the donor library and those integrated into the genome. The plots show all 20 amino acids (including wild type) and stop codon (C), and all 64 codons (D).
Fig. 2.
Fig. 2.
Fitness of T315 mutations under various selection pressures. (A) Flowchart showing the experimental pipeline for analysis of mutant enrichment following various growth/inhibitor selection conditions. (B) Enrichment analysis of T315X mutants for growth effect [day 9 (−IL-3, DMSO) versus day 3 (+IL-3)], inhibitor effect [day 9 (−IL-3, imatinib) versus day 9 (−IL-3, DMSO)] and combined effect [day 9 (−IL-3, imatinib) versus day 3 (+IL-3)]. Mutations that are accessible through one, two, or three base changes are indicated. Error bars represent SE from three independent experiments. (C) Correlation of growth effect of all amino acid mutants between two replicates of the T315X library. (D) IC50 values of selected T315X mutants for imatinib.
Fig. 3.
Fig. 3.
Increased adaptation to TKI therapy accessible by single base substitution. (A) Enrichment analysis of T315X mutants for combined effect at increasing concentrations of imatinib (Top) or ponatinib (Bottom). (B) Single base mutational pathway from Thr to Met or Glu. Mutations with increased fitness are marked in green, decreased fitness in red, near neutral mutations in black.
Fig. 4.
Fig. 4.
Evolutionary adaptation of BCR-ABL1 mutations. (A) Growth effect of all possible mutations at each amino acid position in the 311–319 region. Each group comprises 20 dots, representing all possible amino acid variants; the wild-type amino acid was set to 0. (B) Correlation of growth effect of all amino acid mutations between two replicates of the 311–319X library. (C) Correlation of growth effect of all T315X mutants between the T315X library and 311–319X library.
Fig. 5.
Fig. 5.
BIG EMPIRIC accurately predicts clinical prevalence of BCR-ABL1 mutations. (A) Lollipop plot showing the distribution of all clinically identified BCR-ABL mutations from a collation of ∼1,400 imatinib-resistant patient samples. Mutations that are accessible through one, two, or three base changes are indicated. Data were collated from the Branford laboratory (10), COSMIC database, and Oregon Health & Science University. (B) Combined effects of all possible mutations at each amino acid position in the 311–319 region. Each group comprises 20 dots, representing all possible amino acid variants; the wild-type amino acid was set to 0. (C) Ranking of the combined effect scores for amino acid substitutions achievable through a single-base change within the 311–319 region. (D) Clinically identified amino acid mutations in the 311–319 region. The asterisk indicates a previously unreported mutation with confirmed imatinib resistance in the current study. The SE of the proportion of each amino acid change observed in the clinical samples is shown. (E) Correlation between prediction score (based on the observed combined effects and mutational probabilities) and clinical prevalence for four mutations.
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