Yuta Doi1, Hiroaki Tagaya1, Ayaka Noge1, Kentaro Semba2,3. 1. Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo, 162-8480, Japan. 2. Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo, 162-8480, Japan. ksemba@waseda.jp. 3. Translational Research Center, Fukushima Medical University, Hikarigaoka, Fukushima, 960-1295, Japan. ksemba@waseda.jp.
Abstract
BACKGROUND: Chromosomal aberrations involving the anaplastic lymphoma kinase (ALK) gene have been observed in approximately 4% of patients with non-small cell lung cancer (NSCLC). Although these patients clinically benefit from treatment with various ALK tyrosine kinase inhibitors (ALK-TKIs), none of these can inhibit the development of resistance mutations. Considering inevitable drug resistance and the variety of available ALK-TKIs, it is necessary to predict the pattern of drug-resistance mutations to determine the optimal treatment strategy. OBJECTIVE: We aimed to establish a polymerase chain reaction (PCR)-based system to predict the development of resistance mutations against ALK-TKIs and identify therapeutic strategies using the upcoming ALK-TKIs repotrectinib (TPX-0005) and ensartinib (X-396) following recurrence on first-line alectinib treatment for ALK-positive NSCLC. METHODS: An error-prone PCR-based method for predicting drug resistance mutations was established and the half-maximal inhibitory concentration (IC50) values of the predicted ALK mutations were evaluated in a Ba/F3 cell-based assay. RESULTS: We predicted several resistance mutations against repotrectinib and ensartinib, and demonstrated that the next-generation ALK-TKI TPX-0131, was active against repotrectinib-resistant mutations and that the FLT3 inhibitor gilteritinib was active against ensartinib-resistant mutations. CONCLUSIONS: We developed a PCR-based system for predicting drug resistance mutations. When this system was applied to repotrectinib and ensartinib, the results suggested that these drugs can be used for the second-line treatment of ALK-positive NSCLC. Predicting resistance mutations against TKIs will provide useful information to aid in the development of effective therapeutic strategies.
BACKGROUND: Chromosomal aberrations involving the anaplastic lymphoma kinase (ALK) gene have been observed in approximately 4% of patients with non-small cell lung cancer (NSCLC). Although these patients clinically benefit from treatment with various ALK tyrosine kinase inhibitors (ALK-TKIs), none of these can inhibit the development of resistance mutations. Considering inevitable drug resistance and the variety of available ALK-TKIs, it is necessary to predict the pattern of drug-resistance mutations to determine the optimal treatment strategy. OBJECTIVE: We aimed to establish a polymerase chain reaction (PCR)-based system to predict the development of resistance mutations against ALK-TKIs and identify therapeutic strategies using the upcoming ALK-TKIs repotrectinib (TPX-0005) and ensartinib (X-396) following recurrence on first-line alectinib treatment for ALK-positive NSCLC. METHODS: An error-prone PCR-based method for predicting drug resistance mutations was established and the half-maximal inhibitory concentration (IC50) values of the predicted ALK mutations were evaluated in a Ba/F3 cell-based assay. RESULTS: We predicted several resistance mutations against repotrectinib and ensartinib, and demonstrated that the next-generation ALK-TKI TPX-0131, was active against repotrectinib-resistant mutations and that the FLT3 inhibitor gilteritinib was active against ensartinib-resistant mutations. CONCLUSIONS: We developed a PCR-based system for predicting drug resistance mutations. When this system was applied to repotrectinib and ensartinib, the results suggested that these drugs can be used for the second-line treatment of ALK-positive NSCLC. Predicting resistance mutations against TKIs will provide useful information to aid in the development of effective therapeutic strategies.
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