Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations

Shinnosuke Ikemura, Hiroyuki Yasuda, Shingo Matsumoto, Mayumi Kamada, Junko Hamamoto, Keita Masuzawa, Keigo Kobayashi, Tadashi Manabe, Daisuke Arai, Ichiro Nakachi, Ichiro Kawada, Kota Ishioka, Morio Nakamura, Ho Namkoong, Katsuhiko Naoki, Fumie Ono, Mitsugu Araki, Ryo Kanada, Biao Ma, Yuichiro HayashiSachiyo Mimaki, Kiyotaka Yoh, Susumu S. Kobayashi, Takashi Kohno, Yasushi Okuno, Koichi Goto, Katsuya Tsuchihara, Kenzo Soejima

Research output: Contribution to journalArticle

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 (R 2 = 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.

Original languageEnglish
Pages (from-to)10025-10030
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number20
DOIs
Publication statusPublished - 2019 May 14

Fingerprint

Molecular Dynamics Simulation
Lung Neoplasms
Mutation
Pharmaceutical Preparations
Protein-Tyrosine Kinases
Carcinoma
Lung
Precision Medicine
Computer Simulation
Exons
Neoplasms
Therapeutics

Keywords

  • In silico prediction model
  • Mutation diversity
  • Nonsmall cell lung cancer
  • Osimertinib
  • Rare EGFR mutation

ASJC Scopus subject areas

  • General

Cite this

Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations. / 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.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 116, No. 20, 14.05.2019, p. 10025-10030.

Research output: Contribution to journalArticle

Ikemura, S, Yasuda, H, Matsumoto, S, Kamada, M, Hamamoto, J, Masuzawa, K, Kobayashi, K, Manabe, T, Arai, D, Nakachi, I, Kawada, I, Ishioka, K, Nakamura, M, Namkoong, H, Naoki, K, Ono, F, Araki, M, Kanada, R, Ma, B, Hayashi, Y, Mimaki, S, Yoh, K, Kobayashi, SS, Kohno, T, Okuno, Y, Goto, K, Tsuchihara, K & Soejima, K 2019, 'Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations', Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 20, pp. 10025-10030. https://doi.org/10.1073/pnas.1819430116
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. / Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations. In: Proceedings of the National Academy of Sciences of the United States of America. 2019 ; Vol. 116, No. 20. pp. 10025-10030.
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AU - Ikemura, Shinnosuke

AU - Yasuda, Hiroyuki

AU - Matsumoto, Shingo

AU - Kamada, Mayumi

AU - Hamamoto, Junko

AU - Masuzawa, Keita

AU - Kobayashi, Keigo

AU - Manabe, Tadashi

AU - Arai, Daisuke

AU - Nakachi, Ichiro

AU - Kawada, Ichiro

AU - Ishioka, Kota

AU - Nakamura, Morio

AU - Namkoong, Ho

AU - Naoki, Katsuhiko

AU - Ono, Fumie

AU - Araki, Mitsugu

AU - Kanada, Ryo

AU - Ma, Biao

AU - Hayashi, Yuichiro

AU - Mimaki, Sachiyo

AU - Yoh, Kiyotaka

AU - Kobayashi, Susumu S.

AU - Kohno, Takashi

AU - Okuno, Yasushi

AU - Goto, Koichi

AU - Tsuchihara, Katsuya

AU - Soejima, Kenzo

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