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 mutati...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOS Press
2017-04-01
|
| Series: | Tumor Biology |
| Online Access: | https://doi.org/10.1177/1010428317701643 |
| _version_ | 1818573602293809152 |
|---|---|
| author | Swapna Kamasani Sravani Akula Sree Kanth Sivan Vijjulatha Manga Justus Duyster Dashavantha Reddy Vudem Rama Krishna Kancha |
| author_facet | Swapna Kamasani Sravani Akula Sree Kanth Sivan Vijjulatha Manga Justus Duyster Dashavantha Reddy Vudem Rama Krishna Kancha |
| author_sort | Swapna Kamasani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-15T00:13:32Z |
| format | Article |
| id | doaj.art-a0b3c16b4df74c0693558d42d7c40d45 |
| institution | Directory Open Access Journal |
| issn | 1423-0380 |
| language | English |
| last_indexed | 2024-12-15T00:13:32Z |
| publishDate | 2017-04-01 |
| publisher | IOS Press |
| record_format | Article |
| series | Tumor Biology |
| spelling | doaj.art-a0b3c16b4df74c0693558d42d7c40d452022-12-21T22:42:30ZengIOS PressTumor Biology1423-03802017-04-013910.1177/1010428317701643Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitorsSwapna Kamasani0Sravani Akula1Sree Kanth Sivan2Vijjulatha Manga3Justus Duyster4Dashavantha Reddy Vudem5Rama Krishna Kancha6Molecular Medicine and Therapeutics Laboratory, Centre for Plant Molecular Biology (CPMB), Osmania University, Hyderabad, IndiaMolecular Medicine and Therapeutics Laboratory, Centre for Plant Molecular Biology (CPMB), Osmania University, Hyderabad, IndiaMolecular Modeling and Medicinal Chemistry Group, Department of Chemistry, Osmania University, Hyderabad, IndiaMolecular Modeling and Medicinal Chemistry Group, Department of Chemistry, Osmania University, Hyderabad, IndiaDepartment of Internal Medicine I, University Medical Center Freiburg, Freiburg, GermanyMolecular Biology Laboratory, Centre for Plant Molecular Biology (CPMB), Osmania University, Hyderabad, IndiaMolecular Medicine and Therapeutics Laboratory, Centre for Plant Molecular Biology (CPMB), Osmania University, Hyderabad, IndiaThe 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.https://doi.org/10.1177/1010428317701643 |
| spellingShingle | 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 Tumor Biology |
| title | Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors |
| title_full | Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors |
| title_fullStr | Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors |
| title_full_unstemmed | Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors |
| title_short | Computational analysis of ABL kinase mutations allows predicting drug sensitivity against selective kinase inhibitors |
| title_sort | computational analysis of abl kinase mutations allows predicting drug sensitivity against selective kinase inhibitors |
| url | https://doi.org/10.1177/1010428317701643 |
| work_keys_str_mv | AT swapnakamasani computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors AT sravaniakula computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors AT sreekanthsivan computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors AT vijjulathamanga computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors AT justusduyster computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors AT dashavanthareddyvudem computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors AT ramakrishnakancha computationalanalysisofablkinasemutationsallowspredictingdrugsensitivityagainstselectivekinaseinhibitors |