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dc.contributor.author | Ikemura, Shinnosuke | en |
dc.contributor.author | Yasuda, Hiroyuki | en |
dc.contributor.author | Matsumoto, Shingo | en |
dc.contributor.author | Kamada, Mayumi | en |
dc.contributor.author | Hamamoto, Junko | en |
dc.contributor.author | Masuzawa, Keita | en |
dc.contributor.author | Kobayashi, Keigo | en |
dc.contributor.author | Manabe, Tadashi | en |
dc.contributor.author | Arai, Daisuke | en |
dc.contributor.author | Nakachi, Ichiro | en |
dc.contributor.author | Kawada, Ichiro | en |
dc.contributor.author | Ishioka, Kota | en |
dc.contributor.author | Nakamura, Morio | en |
dc.contributor.author | Namkoong, Ho | en |
dc.contributor.author | Naoki, Katsuhiko | en |
dc.contributor.author | Ono, Fumie | en |
dc.contributor.author | Araki, Mitsugu | en |
dc.contributor.author | Kanada, Ryo | en |
dc.contributor.author | Ma, Biao | en |
dc.contributor.author | Hayashi, Yuichiro | en |
dc.contributor.author | Mimaki, Sachiyo | en |
dc.contributor.author | Yoh, Kiyotaka | en |
dc.contributor.author | Kobayashi, Susumu S. | en |
dc.contributor.author | Kohno, Takashi | en |
dc.contributor.author | Okuno, Yasushi | en |
dc.contributor.author | Goto, Koichi | en |
dc.contributor.author | Tsuchihara, Katsuya | en |
dc.contributor.author | Soejima, Kenzo | en |
dc.contributor.alternative | 池村, 辰之介 | ja |
dc.contributor.alternative | 安田, 浩之 | ja |
dc.contributor.alternative | 松本, 慎吾 | ja |
dc.contributor.alternative | 鎌田, 真由美 | ja |
dc.contributor.alternative | 浜本, 純子 | ja |
dc.contributor.alternative | 増澤, 啓太 | ja |
dc.contributor.alternative | 小林, 慧悟 | ja |
dc.contributor.alternative | 眞鍋, 維志 | ja |
dc.contributor.alternative | 荒井, 大輔 | ja |
dc.contributor.alternative | 仲地, 一郎 | ja |
dc.contributor.alternative | 川田, 一郎 | ja |
dc.contributor.alternative | 石岡, 宏太 | ja |
dc.contributor.alternative | 中村, 守男 | ja |
dc.contributor.alternative | 南宮, 湖 | ja |
dc.contributor.alternative | 猶木, 克彦 | ja |
dc.contributor.alternative | 小野, 史恵 | ja |
dc.contributor.alternative | 荒木, 望嗣 | ja |
dc.contributor.alternative | 金田, 亮 | ja |
dc.contributor.alternative | 馬, 彪 | ja |
dc.contributor.alternative | 林, 雄一郎 | ja |
dc.contributor.alternative | 三牧, 幸代 | ja |
dc.contributor.alternative | 葉, 清隆 | ja |
dc.contributor.alternative | 小林, 進 | ja |
dc.contributor.alternative | 河野, 隆志 | ja |
dc.contributor.alternative | 奥野, 恭史 | ja |
dc.contributor.alternative | 後藤, 功一 | ja |
dc.contributor.alternative | 土原, 一哉 | ja |
dc.contributor.alternative | 副島, 研造 | ja |
dc.date.accessioned | 2019-05-13T08:25:38Z | - |
dc.date.available | 2019-05-13T08:25:38Z | - |
dc.date.issued | 2019-05-14 | - |
dc.identifier.issn | 0027-8424 | - |
dc.identifier.uri | http://hdl.handle.net/2433/241348 | - |
dc.description | 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. | ja |
dc.description.abstract | 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. | en |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | National Academy of Sciences | en |
dc.rights | © 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). | en |
dc.subject | rare EGFR mutation | en |
dc.subject | mutation diversity | en |
dc.subject | osimertinib | en |
dc.subject | in silico prediction model | en |
dc.subject | nonsmall cell lung | en |
dc.subject | cancer | en |
dc.title | Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Proceedings of the National Academy of Sciences of the United States of America | en |
dc.identifier.volume | 116 | - |
dc.identifier.issue | 20 | - |
dc.identifier.spage | 10025 | - |
dc.identifier.epage | 10030 | - |
dc.relation.doi | 10.1073/pnas.1819430116 | - |
dc.textversion | publisher | - |
dc.identifier.pmid | 31043566 | - |
dc.relation.url | https://www.kyoto-u.ac.jp/ja/research-news/2019-05-13-0 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 22590870 | - |
datacite.awardNumber | 17K09667 | - |
datacite.awardNumber | 16K21746 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
出現コレクション: | 学術雑誌掲載論文等 |
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