期刊
NATURE
卷 597, 期 7878, 页码 732-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41586-021-03898-1
关键词
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资金
- University of Texas MD Anderson Lung Cancer Moon Shots Program
- MD Anderson Cancer Center Support Grant [P30 CA016672]
- Research Animal Support Facility (RASF)
- NIH [R01CA247975]
- CPRIT-IIRA [RP200150]
- NIH/NCI [R01CA234183, R01CA190628]
- Lung SPORE [P50 CA070907-20]
- David Bruton Jr Endowment
- Rexanna Foundation for Fighting Lung Cancer [1U54CA224065-01]
- Spectrum Pharmaceuticals
- Hallman Fund
- Stading Fund for EGFR inhibitor resistance
- Gil and Dody Weaver Foundation
- Richardson Fund for EGFR mutant lung cancer research
- ASCO [CDA-57112]
The study characterized the mutational landscape in 16,715 patients with EGFR-mutant NSCLC and established the structure-function relationship of EGFR mutations on drug sensitivity. EGFR mutations can be separated into four distinct subgroups based on sensitivity and structural changes, predicting patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. This structure-based approach delineates functional groups of EGFR mutations that can effectively guide treatment choices and suggest potential improvement in prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.
Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18-21 and are established driver mutations in non-small cell lung cancer (NSCLC)(1-3). Targeted therapies are approved for patients with 'classical' mutations and a small number of other mutations(4-6). However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown(1,3,7-10). Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure-function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure-function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.
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