Development and validation of a mutation-based model to predict immunotherapeutic efficacy in NSCLC - He, Ping; Liu, Jie; Xu, Qingyuan; Ma, Huaijun; Niu, Beifang; Huang, Gang; Wu, Wei (Frontiers in Oncology, 2023)
Immunotherapy has become increasingly important in the perioperative period of non-small-cell lung cancer (NSCLC). In this study, we intended to develop a mutation-based model to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) in patients with NSCLC. An 18-gene set was selected using random forest (RF) classifiers. A predictive model was developed based on the number of mutant genes among the candidate genes, and patients were divided into the MT group (mutant gene ≥ 2) and WT group (mutant gene < 2). The MT group (N = 54) had better overall survival (OS) compared to the WT group (N = 290); the median OS was not reached vs. nine months (P < 0.0001, AUC = 0.73). The robust predictive performance was confirmed in three validation cohorts, with an AUC of 0.70, 0.57, and 0.64 (P < 0.05). The MT group was characterized by high tumor neoantigen burden (TNB), increased immune infiltration cells such as CD8 T and macrophage cells, and upregulated immune checkpoint molecules, suggesting potential biological advantages in ICIs therapy.