Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations.
Proc Natl Acad Sci U S A
; 116(20): 10025-10030, 2019 05 14.
Article
em En
| MEDLINE
| ID: mdl-31043566
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Carcinoma Pulmonar de Células não Pequenas
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Genes erbB-1
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Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
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Middle aged
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Japão