Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors.

The ABL kinase inhibitor imatinib has been used as front-line therapy for Philadelphia-positive chronic myeloid leukemia. However, a significant proportion of imatinib-treated patients relapse due to occurrence of mutations in the ABL kinase domain. Although inhibitor sensitivity for a set of mutations was reported, the role of less frequent ABL kinase mutations in drug sensitivity/resistance is not known. Moreover, recent reports indicate distinct resistance profiles for second-generation ABL inhibitors. We thus employed a computational approach to predict drug sensitivity of 234 point mutations that were reported in chronic myeloid leukemia patients. Initial validation analysis of our approach using a panel of previously studied frequent mutations indicated that the computational data generated in this study correlated well with the published experimental/clinical data. In addition, we present drug sensitivity profiles for remaining point mutations by computational docking analysis using imatinib as well as next generation ABL inhibitors nilotinib, dasatinib, bosutinib, axitinib, and ponatinib. Our results indicate distinct drug sensitivity profiles for ABL mutants toward kinase inhibitors. In addition, drug sensitivity profiles of a set of compound mutations in ABL kinase were also presented in this study. Thus, our large scale computational study provides comprehensive sensitivity/resistance profiles of ABL mutations toward specific kinase inhibitors.

Citation

Swapna Kamasani, Sravani Akula, Sree Kanth Sivan, Vijjulatha Manga, Justus Duyster, Dashavantha Reddy Vudem, Rama Krishna Kancha. Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine. 2017 May;39(5):1010428317701643

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PMID: 28475010

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