He Ping, Liu Jie, Xu Qingyuan, Ma Huaijun, Niu Beifang, Huang Gang, Wu Wei
Department of Cardiac Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Department of Thoracic Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Front Oncol. 2023 Feb 24;13:1089179. doi: 10.3389/fonc.2023.1089179. eCollection 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 effificacy of immune checkpoint inhibitors (ICIs) in patients with NSCLC.
Random Forest (RF) classifiers were generated to identify tumor gene mutated features associated with immunotherapy outcomes. Then the best classifier with the highest accuracy served for the development of the predictive model. The correlations of some reported biomarkers with the model were analyzed, such as TMB, PD-(L)1, KEAP1-driven co-mutations, and immune subtypes. The training cohort and validation cohorts performed survival analyses to estimate the predictive efficiency independently.
An 18-gene set was selected using random forest (RF) classififiers. 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 confifirmed 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 infifiltration cells such as CD8 T and macrophage cells, and upregulated immune checkpoint molecules, suggesting potential biological advantages in ICIs therapy.
The predictive model could precisely predict the immunotherapeutic efficacy in NSCLC based on the mutant genes within the model. Furthermore, some immune-related features and cell expression could support robust efficiency.
免疫疗法在非小细胞肺癌（NSCLC）围手术期变得越来越重要。在本研究中，我们旨在建立一种基于突变的模型，以预测免疫检查点抑制剂（ICI）对NSCLC患者的治疗效果。
生成随机森林（RF）分类器，以识别与免疫治疗结果相关的肿瘤基因突变特征。然后，选择准确率最高的最佳分类器来建立预测模型。分析了一些已报道的生物标志物与该模型的相关性，如肿瘤突变负荷（TMB）、程序性死亡受体1（PD-1）及其配体1（PD-L1）、KEAP1驱动的共突变和免疫亚型。训练队列和验证队列分别进行生存分析，以独立评估预测效率。
使用随机森林（RF）分类器选择了一个包含18个基因的基因集。基于候选基因中的突变基因数量建立了一个预测模型，并将患者分为MT组（突变基因≥2）和WT组（突变基因<2）。与WT组（N = 290）相比，MT组（N = 54）的总生存期（OS）更好；MT组的中位OS未达到，而WT组为9个月（P < 0.0001，曲线下面积[AUC]=0.73）。在三个验证队列中证实了该模型具有强大的预测性能，AUC分别为0.70、0.57和0.64（P < 0.05）。MT组的特征是肿瘤新抗原负荷（TNB）高、免疫浸润细胞（如CD8 T细胞和巨噬细胞）增加以及免疫检查点分子上调，提示在ICI治疗中具有潜在的生物学优势。
该预测模型可以基于模型中的突变基因精确预测NSCLC的免疫治疗效果。此外，一些免疫相关特征和细胞表达也支持其强大的预测效率。
