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

Bibliographic Information

Other Title

• Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare <i>EGFR</i> mutations

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.

Journal

• Proceedings of the National Academy of Sciences of the United States of America

  Proceedings of the National Academy of Sciences of the United States of America 116 (20), 10025-10030, 2019-05-14

  National Academy of Sciences

Keywords

• rare EGFR mutation
• mutation diversity
• osimertinib
• in silico prediction model
• nonsmall cell lung
• cancer

Details

• CRID: 1050283688374284928
• NII Article ID: 120006799239
• ISSN: 00278424
• ISSN: 10916490
• DOI: 10.1073/pnas.1819430116
• HANDLE: 2433/241348
• Web Site: http://www.pnas.org/syndication/doi/10.1073/pnas.1819430116
• Web Site: https://pnas.org/doi/pdf/10.1073/pnas.1819430116
• Text Lang: en
• Article Type: journal article
• Data Source:
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  • CiNii Articles
  • KAKEN