论文
论文标题：Structure-based classification predicts drug response in EGFR-mutant NSCLC
作者：Robichaux, Jacqulyne P., Le, Xiuning, Vijayan, R. S. K., Hicks, J. Kevin, Heeke, Simon, Elamin, Yasir Y., Lin, Heather Y., Udagawa, Hibiki, Skoulidis, Ferdinandos, Tran, Hai, Varghese, Susan, He, Junqin, Zhang, Fahao, Nilsson, Monique B., Hu, Lemei, Poteete, Alissa, Rinsurongkawong, Waree, Zhang, Xiaoshan, Ren, Chenghui, Liu, Xiaoke, Hong, Lingzhi, Zhang, Jianjun, Diao, Lixia, Madison, Russell, Schrock, Alexa B., Saam, Jennifer, Raymond, Victoria, Fang, Bingliang, Wang, Jing, Ha, Min Jin, Cross, Jason B., Gray, Jhanelle E., Heymach, John V.
期刊：Nature
发表时间：2021/09/15
数字识别码：10.1038/s41586-021-03898-1
摘要：Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18–21 and are established driver mutations in non-small cell lung cancer (NSCLC)1,2,3. Targeted therapies are approved for patients with ‘classical’ mutations and a small number of other mutations4,5,6. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown1,3,7,8,9,10. 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.
近期，美国德州大学MD安德森癌症中心的John V. Heymach团队发现，按照结构和功能对表皮生长因子受体（EGFR）突变进行分类，可预测EGFR突变型NSCLC的药物反应，为患者匹配合适的靶向药物提供了一个更精准的框架。相关研究结果于2021年9月15日发表在Nature期刊上，题为“Structure-based classification predicts drug response in EGFR-mutant NSCLC”。
针对这一问题，Heymach团队分析了来自五个不同据库的11619名EGFR突变NSCLC患者的突变情况，其中，67.1%的患者有经典的EGFR突变，30.8%有非典型的EGFR突变，2.2%两者都有。同时分析这些患者的治疗失败的时间（time to treatment failure, TTF），发现无论使用何中类型的TKI，非典型突变患者的TTF明显短于经典突变患者。
