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.
Ma L, Boucher JI, Paulsen J, Matuszewski S, Eide CA, Ou J, Eickelberg G, Press RD, Zhu LJ, Druker BJ, Branford S, Wolfe SA, Jensen JD, Schiffer CA, Green MR, Bolon DN. (2017). CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy. Proceedings of the National Academy of Sciences of the United States of America, 114(44)