Computational Analysis of Epidermal Growth Factor Receptor Mutations Predicts Differential Drug Sensitivity Profiles toward Kinase Inhibitors.

INTRODUCTION: A significant proportion of patients with lung cancer carry mutations in the EGFR kinase domain. The presence of a deletion mutation in exon 19 or L858R point mutation in the EGFR kinase domain has been shown to cause enhanced efficacy of inhibitor treatment in patients with NSCLC. Several less frequent (uncommon) mutations in the EGFR kinase domain with potential implications in treatment response have also been reported. The role of a limited number of uncommon mutations in drug sensitivity was experimentally verified. However, a huge number of these mutations remain uncharacterized for inhibitor sensitivity or resistance. METHODS: A large-scale computational analysis of clinically reported 298 point mutants of EGFR kinase domain has been performed, and drug sensitivity profiles for each mutant toward seven kinase inhibitors has been determined by molecular docking. In addition, the relative inhibitor binding affinity toward each drug as compared with that of adenosine triphosphate was calculated for each mutant. RESULTS: The inhibitor sensitivity profiles predicted in this study for a set of previously characterized mutants correlated well with the published clinical, experimental, and computational data. Both the single and compound mutations displayed differential inhibitor sensitivity toward first- and next-generation kinase inhibitors. CONCLUSIONS: The present study provides predicted drug sensitivity profiles for a large panel of uncommon EGFR mutations toward multiple inhibitors, which may help clinicians in deciding mutant-specific treatment strategies.

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.