A predictive model for evaluating the efficacy of immunotherapy in non–small-cell lung cancer patients: A real-world study

Objective The 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. Methods Three 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. Results We 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, p < 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. Conclusions A 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.