Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations
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 mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lun...
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| Published in: | Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 20; p. 10025 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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14.05.2019
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| ISSN: | 1091-6490, 1091-6490 |
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| 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
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
mutations (
= 3,779) revealed that the majority (>90%) of cases with rare
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 (
= 0.72,
= 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
mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer. |
|---|---|
| AbstractList | 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.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. 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 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 mutations ( = 3,779) revealed that the majority (>90%) of cases with rare 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 ( = 0.72, = 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 mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer. |
| Author | Kawada, Ichiro Masuzawa, Keita Ono, Fumie Nakachi, Ichiro Ishioka, Kota Ma, Biao Kobayashi, Keigo Mimaki, Sachiyo Manabe, Tadashi Tsuchihara, Katsuya Araki, Mitsugu Naoki, Katsuhiko Kamada, Mayumi Matsumoto, Shingo Arai, Daisuke Yoh, Kiyotaka Yasuda, Hiroyuki Hamamoto, Junko Hayashi, Yuichiro Kohno, Takashi Kobayashi, Susumu S Soejima, Kenzo Ikemura, Shinnosuke Kanada, Ryo Nakamura, Morio Goto, Koichi Namkoong, Ho Okuno, Yasushi |
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Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan – sequence: 10 givenname: Ichiro surname: Nakachi fullname: Nakachi, Ichiro organization: Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan – sequence: 11 givenname: Ichiro surname: Kawada fullname: Kawada, Ichiro organization: Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan – sequence: 12 givenname: Kota surname: Ishioka fullname: Ishioka, Kota organization: Tokyo Saiseikai Central Hospital, Minato-ku, 108-0073 Tokyo, Japan – sequence: 13 givenname: Morio surname: Nakamura fullname: Nakamura, Morio organization: Tokyo Saiseikai Central Hospital, Minato-ku, 108-0073 Tokyo, Japan – sequence: 14 givenname: Ho surname: Namkoong fullname: Namkoong, Ho organization: Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan – sequence: 15 givenname: Katsuhiko surname: Naoki fullname: Naoki, Katsuhiko organization: Keio Cancer Center, Keio University School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan – sequence: 16 givenname: Fumie surname: Ono fullname: Ono, Fumie organization: Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan – sequence: 17 givenname: Mitsugu surname: Araki fullname: Araki, Mitsugu organization: Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan – sequence: 18 givenname: Ryo surname: Kanada fullname: Kanada, Ryo organization: Compass to Healthy Life Research Complex Program, RIKEN, Kobe, 650-0047 Hyogo, Japan – sequence: 19 givenname: Biao surname: Ma fullname: Ma, Biao organization: Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe, Foundation for Biomedical Research and Innovation, Kobe, 650-0047 Hyogo, Japan – sequence: 20 givenname: Yuichiro surname: Hayashi fullname: Hayashi, Yuichiro organization: Department of Pathology, Keio University School of Medicine, 160-8582 Tokyo, Japan – sequence: 21 givenname: Sachiyo surname: Mimaki fullname: Mimaki, Sachiyo organization: Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577 Chiba, Japan – sequence: 22 givenname: Kiyotaka surname: Yoh fullname: Yoh, Kiyotaka organization: Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577 Chiba, Japan – sequence: 23 givenname: Susumu S surname: Kobayashi fullname: Kobayashi, Susumu S organization: Division of Hematology/Oncology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215 – sequence: 24 givenname: Takashi surname: Kohno fullname: Kohno, Takashi organization: Division of Genome Biology, National Cancer Center Research Institute, 104-0045 Tokyo, Japan – sequence: 25 givenname: Yasushi surname: Okuno fullname: Okuno, Yasushi organization: Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan – sequence: 26 givenname: Koichi surname: Goto fullname: Goto, Koichi organization: Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577 Chiba, Japan – sequence: 27 givenname: Katsuya surname: Tsuchihara fullname: Tsuchihara, Katsuya email: hiroyukiyasuda@a8.keio.jp, ktsuchih@east.ncc.go.jp organization: Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577 Chiba, Japan; hiroyukiyasuda@a8.keio.jp ktsuchih@east.ncc.go.jp – sequence: 28 givenname: Kenzo surname: Soejima fullname: Soejima, Kenzo organization: Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan |
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| Keywords | rare EGFR mutation mutation diversity in silico prediction model nonsmall cell lung cancer osimertinib |
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| SubjectTerms | Acrylamides - therapeutic use Adenocarcinoma - drug therapy Aniline Compounds - therapeutic use Carcinoma, Non-Small-Cell Lung - genetics Genes, erbB-1 Humans Lung Neoplasms - drug therapy Lung Neoplasms - genetics Middle Aged Molecular Dynamics Simulation Mutation Pharmacogenomic Testing Prospective Studies Protein-Tyrosine Kinases - antagonists & inhibitors |
| Title | Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations |
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