Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistance
Year: 2019
Type: article
Abstract: Background Alectinib has shown a greater efficacy to ALK -rearranged non-small-cell lung cancers in first-line setting; however, most patients relapse due to acquired resistance, such as secondary mutations in ALK including I1171N and G1202R. Although ceritinib or lorlatinib was shown to be effective to these resistant mutants, further resistance often emerges due to ALK-compound mutations in relapse patients following the use of ceritinib or lorlatinib. However, the drug for overcoming resistance has not been established yet. Methods We established lorlatinib-resistant cells harboring ALK-I1171N or -G1202R compound mutations by performing ENU mutagenesis screening or using an in vivo mouse model. We performed drug screening to overcome the lorlatinib-resistant ALK-compound mutations. To evaluate these resistances in silico , we developed a modified computational molecular dynamic simulation (MP-CAFEE). Findings We identified 14 lorlatinib-resistant ALK-compound mutants, including several mutants that were recently discovered in lorlatinib-resistant patients. Some of these compound mutants were found to be sensitive to early generation ALK-TKIs and several BCR-ABL inhibitors. Using our original computational simulation, we succeeded in demonstrating a clear linear correlation between binding free energy and in vitro experimental IC 50 value of several ALK-TKIs to single- or compound-mutated EML4-ALK expressing Ba/F3 cells and in recapitulating the tendency of the binding affinity reduction by double mutations found in this study. Computational simulation revealed that ALK-L1256F single mutant conferred resistance to lorlatinib but increased the sensitivity to alectinib. Interpretation We discovered lorlatinib-resistant multiple ALK-compound mutations and an L1256F single mutation as well as the potential therapeutic strategies for these ALK mutations. Our original computational simulation to calculate the binding affinity may be applicable for predicting resistant mutations and for overcoming drug resistance in silico. Fund This work was mainly supported by MEXT/JSPS KAKENHI Grants and AMED Grants.
Source: EBioMedicine
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,500
Funders: Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, Takeda Pharmaceutical Company, Japan Agency for Medical Research and Development, Vehicle Racing Commemorative Foundation, Taiho Pharmaceutical, Ministry of Education, Culture, Sports, Science and Technology, Fujifilm Corporation, Ministry of Health, Labour and Welfare, Astellas Pharma, Nippon Foundation, Ono Pharmaceutical, Chugai Pharmaceutical, MSD K.K., Loxo Oncology, Japan Society for the Promotion of Science, RIKEN
Awards: Development of comprehensive calculation method of HLA-peptide affinity and application to elucidation of the pathogenesis of Behcet's disease, G1705227743, G2461657676, Drug sensitivity prediction for anaplastic lymphoma kinase mutants using molecular dynamics simulation and its informatics analysis, G292258429, G3080054588, Susceptibility HLA alleles and the biological contribution of HLA for the pathogenesis of Behcet's disease or Scleroderma in East Asia, G365416419, G4880067104, G4942754464, がんリン酸化シグナル伝達因子の活性定量に基づく新次元薬効予測診断システムの開発, G5488337805, G6018864816, G6259499347, G6452549759, Molecular Recognition Mechanism of RNA aptamer, Molecular analysis of acquired resistance to targeted therapies and search for overcoming drugs, Drug resistance mechanisms and therapeutic strategies in rare driver oncogene positive cancers, G7882366961, Advanced Animal Model Support
OpenAlex ID: W2908885437