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タイトル: | Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations |
著者: | Ikemura, Shinnosuke Yasuda, Hiroyuki Matsumoto, Shingo Kamada, Mayumi Hamamoto, Junko Masuzawa, Keita Kobayashi, Keigo Manabe, Tadashi Arai, Daisuke Nakachi, Ichiro Kawada, Ichiro Ishioka, Kota Nakamura, Morio Namkoong, Ho Naoki, Katsuhiko Ono, Fumie Araki, Mitsugu Kanada, Ryo Ma, Biao Hayashi, Yuichiro Mimaki, Sachiyo Yoh, Kiyotaka Kobayashi, Susumu S. Kohno, Takashi Okuno, Yasushi Goto, Koichi Tsuchihara, Katsuya Soejima, Kenzo |
著者名の別形: | 池村, 辰之介 安田, 浩之 松本, 慎吾 鎌田, 真由美 浜本, 純子 増澤, 啓太 小林, 慧悟 眞鍋, 維志 荒井, 大輔 仲地, 一郎 川田, 一郎 石岡, 宏太 中村, 守男 南宮, 湖 猶木, 克彦 小野, 史恵 荒木, 望嗣 金田, 亮 馬, 彪 林, 雄一郎 三牧, 幸代 葉, 清隆 小林, 進 河野, 隆志 奥野, 恭史 後藤, 功一 土原, 一哉 副島, 研造 |
キーワード: | rare EGFR mutation mutation diversity osimertinib in silico prediction model nonsmall cell lung cancer |
発行日: | 14-May-2019 |
出版者: | National Academy of Sciences |
誌名: | Proceedings of the National Academy of Sciences of the United States of America |
巻: | 116 |
号: | 20 |
開始ページ: | 10025 |
終了ページ: | 10030 |
抄録: | Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of EGFR mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot EGFR mutations (n = 3, 779) revealed that the majority (>90%) of cases with rare EGFR mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI (R2 = 0.72, P = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare EGFR mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer. |
記述: | LC-SCRUM-Japanで構築した日本最大のがん臨床ゲノムデータを活用しスーパーコンピュータで治療薬の効き目を予測 --がんゲノム医療における新たなツールの開発--. 京都大学プレスリリース. 2019-05-13. |
著作権等: | © 2019 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). |
URI: | http://hdl.handle.net/2433/241348 |
DOI(出版社版): | 10.1073/pnas.1819430116 |
PubMed ID: | 31043566 |
関連リンク: | https://www.kyoto-u.ac.jp/ja/research-news/2019-05-13-0 |
出現コレクション: | 学術雑誌掲載論文等 |
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