Targeting more precisely: Improving sensitivity to EGFR inhibitors in NSCLC
| العنوان: | Targeting more precisely: Improving sensitivity to EGFR inhibitors in NSCLC |
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| المؤلفون: | Arjan Gower, Edward B. Garon |
| المصدر: | Nature |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Lung Neoplasms, biology, business.industry, General Medicine, Article, respiratory tract diseases, ErbB Receptors, Targeted therapies, Egfr mutation, Carcinoma, Non-Small-Cell Lung, Mutation, Cancer research, biology.protein, Medicine, Humans, Non small cell, Epidermal growth factor receptor, business, Protein Kinase Inhibitors, Non-small-cell lung cancer, EGFR inhibitors |
| الوصف: | 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 mutations4–6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown1,3,7–10. 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. Structural classification of mutations in the epidermal growth factor receptor causing non-small cell lung cancer is a better predictor of patient outcomes following drug treatment than traditional exon-based classification. |
| تدمد: | 2666-6340 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6cfcb72bd1a33c72e3ba4014c9c4703Test https://pubmed.ncbi.nlm.nih.gov/34526717Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e6cfcb72bd1a33c72e3ba4014c9c4703 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 26666340 |
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