Yu Hai-Hong, Zeng Jun-Quan, Yuan Jin-Hua, Liu Bin
College of Traditional Chinese Medicine and Pharmacy, Jinggangshan University, P. R. China.
Jiangxi Province Key Laboratory of Organ Development and Epigenetics, Clinical Medical Research Center, Affiliated Hospital of Jinggangshan University, Medical Department of Jinggangshan University, P. R. China.
J Int Med Res. 2025 Sep;53(9):3000605251371278. doi: 10.1177/03000605251371278. Epub 2025 Sep 2.
ObjectiveThe predictive accuracy of the efficacy of immunotherapy remains poor. Therefore, we aimed to develop a predictive model based on gene mutations to assess the immunotherapeutic efficacy in non-small-cell lung cancer.MethodsThree hundred and thirty-five non-small-cell lung cancer patients treated with immune checkpoint inhibitors were included in our study. The least absolute shrinkage and selection operator Cox regression model, multivariable analysis, and Kaplan-Meier test were used in this study.ResultsWe constructed a predictive model based on a 42-gene signature. Patients were classified into low-risk and high-risk groups based on risk scores generated from this model. Compared with patients in the high-risk group, those in the low-risk group showed better survival (median survival time: 36.0 vs. 6.0 months, <0.0001, unadjusted hazard ratio: 0.32, 95% confidence interval, 0.24-0.42). The results were confirmed in an external validation cohort. Moreover, patients with high tumor mutation burden in the high-risk group could not benefit from immune checkpoint inhibitors.ConclusionsA predictive model for evaluating the efficacy of immunotherapy was developed and validated. The model is based on multiple genetic information and has clinical translational value.
目的
免疫疗法疗效的预测准确性仍然较差。因此，我们旨在开发一种基于基因突变的预测模型，以评估非小细胞肺癌的免疫治疗疗效。
方法
我们的研究纳入了335例接受免疫检查点抑制剂治疗的非小细胞肺癌患者。本研究使用了最小绝对收缩和选择算子Cox回归模型、多变量分析和Kaplan-Meier检验。
结果
我们构建了一个基于42个基因特征的预测模型。根据该模型生成的风险评分，将患者分为低风险组和高风险组。与高风险组患者相比，低风险组患者的生存期更长（中位生存时间：36.0个月对6.0个月，<0.0001，未调整风险比：0.32，95%置信区间，0.24 - 0.42）。该结果在外部验证队列中得到证实。此外，高风险组中肿瘤突变负荷高的患者无法从免疫检查点抑制剂中获益。
结论
开发并验证了一种评估免疫治疗疗效的预测模型。该模型基于多种遗传信息，具有临床转化价值。
