We present a rapid and high-throughput yeast and flow cytometry based method for predicting kinase inhibitor resistance mutations and determining kinase peptide substrate specificity. Despite the widespread success of targeted kinase inhibitors as cancer therapeutics， resistance mutations arising within the kinase domain of an oncogenic target present a major impediment to sustained treatment efficacy. Our method， which is based on the previously reported YESS system， recapitulated all validated BCR-ABL1 mutations leading to clinical resistance to the second-generation inhibitor dasatinib， in addition to identifying numerous other mutations which have been previously observed in patients， but not yet ... More
We present a rapid and high-throughput yeast and flow cytometry based method for predicting kinase inhibitor resistance mutations and determining kinase peptide substrate specificity. Despite the widespread success of targeted kinase inhibitors as cancer therapeutics， resistance mutations arising within the kinase domain of an oncogenic target present a major impediment to sustained treatment efficacy. Our method， which is based on the previously reported YESS system， recapitulated all validated BCR-ABL1 mutations leading to clinical resistance to the second-generation inhibitor dasatinib， in addition to identifying numerous other mutations which have been previously observed in patients， but not yet validated as drivers of resistance. Further， we were able to demonstrate that the newer inhibitor ponatinib is effective against the majority of known single resistance mutations， but ineffective at inhibiting many compound mutants. These results are consistent with preliminary clinical and in vitro reports， indicating that mutations providing resistance to ponatinib are significantly less common; therefore， predicting ponatinib will be less susceptible to clinical resistance relative to dasatinib. Using the same yeast-based method， but with random substrate libraries， we were able to identify consensus peptide substrate preferences for the SRC and LYN kinases. ABL1 lacked an obvious consensus sequence， so a machine learning algorithm utilizing amino acid covariances was developed which accurately predicts ABL1 kinase peptide substrates.
