Xie Xiaohong, Li Lifeng, Xie Liang, Liu Zhentian, Zhang Guoliang, Gao Xuan, Peng Wenying, Deng Haiyi, Yang Yilin, Yang Meiling, Chang Lianpeng, Yi Xin, Xia Xuefeng, He Zhiyi, Zhou Chengzhi
Pulmonary and Critical Care Medicine, Guangzhou Institute of Respiratory Health, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
Geneplus-Beijing, Beijing 102206, China.
iScience. 2023 Apr 8;26(6):106584. doi: 10.1016/j.isci.2023.106584. eCollection 2023 Jun 16.
EGFR-TKIs were used in NSCLC patients with actionable EGFR mutations and prolong prognosis. However, most patients treated with EGFR-TKIs developed resistance within around one year. This suggests that residual EGFR-TKIs resistant cells may eventually lead to relapse. Predicting resistance risk in patients will facilitate individualized management. Herein, we built an EGFR-TKIs resistance prediction (R-index) model and validate in cell line, mice, and cohort. We found significantly higher R-index value in resistant cell lines, mice models and relapsed patients. Patients with an elevated R-index had significantly shorter relapse time. We also found that the glycolysis pathway and the KRAS upregulation pathway were related to EGFR-TKIs resistance. MDSC is a significant immunosuppression factor in the resistant microenvironment. Our model provides an executable method for assessing patient resistance status based on transcriptional reprogramming and may contribute to the clinical translation of patient individual management and the study of unclear resistance mechanisms.
表皮生长因子受体酪氨酸激酶抑制剂（EGFR-TKIs）用于治疗具有可操作的EGFR突变的非小细胞肺癌（NSCLC）患者，可延长预后。然而，大多数接受EGFR-TKIs治疗的患者在大约一年内就产生了耐药性。这表明残留的EGFR-TKIs耐药细胞最终可能导致复发。预测患者的耐药风险将有助于个体化管理。在此，我们构建了一个EGFR-TKIs耐药预测（R指数）模型，并在细胞系、小鼠和队列中进行了验证。我们发现在耐药细胞系、小鼠模型和复发患者中R指数值显著更高。R指数升高的患者复发时间明显更短。我们还发现糖酵解途径和KRAS上调途径与EGFR-TKIs耐药有关。髓系来源的抑制细胞（MDSC）是耐药微环境中的一个重要免疫抑制因子。我们的模型提供了一种基于转录重编程评估患者耐药状态的可执行方法，可能有助于患者个体化管理的临床转化以及不明耐药机制的研究。
