Rapid Screen for Tyrosine Kinase Inhibitor Resistance
Mutations and Substrate Specificity
Posted on 2019-08-08 - 15:37
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.
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Taft, Joseph
M.; Georgeon, Sandrine; Allen, Chris; Reckel, Sina; DeSautelle, Joseph; Hantschel, Oliver; et al. (2019). Rapid Screen for Tyrosine Kinase Inhibitor Resistance
Mutations and Substrate Specificity. ACS Publications. Collection. https://doi.org/10.1021/acschembio.9b00283
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AUTHORS (8)
JT
Joseph
M. Taft
SG
Sandrine Georgeon
CA
Chris Allen
SR
Sina Reckel
JD
Joseph DeSautelle
OH
Oliver Hantschel
GG
George Georgiou
BI
Brent L. Iverson