Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistanceResearch in context
Koutaroh Okada,
Mitsugu Araki,
Takuya Sakashita,
Biao Ma,
Ryo Kanada,
Noriko Yanagitani,
Atsushi Horiike,
Sumie Koike,
Tomoko Oh-hara,
Kana Watanabe,
Keiichi Tamai,
Makoto Maemondo,
Makoto Nishio,
Takeshi Ishikawa,
Yasushi Okuno,
Naoya Fujita,
Ryohei Katayama
Affiliations
Koutaroh Okada
Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
Mitsugu Araki
RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
Takuya Sakashita
Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
Biao Ma
Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe, Foundation for Biomedical Research and Innovation (FBRI), 6-3-5, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
Ryo Kanada
RIKEN Compass to Healthy Life Research Complex Program, 6-3-5, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
Noriko Yanagitani
Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
Atsushi Horiike
Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
Sumie Koike
Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
Tomoko Oh-hara
Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
Kana Watanabe
Department of Respiratory Medicine, Miyagi Cancer Center, Miyagi, Japan
Keiichi Tamai
Division of Cancer Stem Cell, Miyagi Cancer Center Research Institute, Miyagi, Japan
Makoto Maemondo
Department of Respiratory Medicine, Miyagi Cancer Center, Miyagi, Japan
Makoto Nishio
Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
Takeshi Ishikawa
Department of Molecular Microbiology and Immunology, Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
Yasushi Okuno
RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
Naoya Fujita
Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
Ryohei Katayama
Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Corresponding author.
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 IC50 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. Keywords: ALK-rearranged lung cancer, Resistant mutation, Computational simulation, MP-CAFEE, Compound mutation, Quantum chemistry
