Background/Objectives:Non-small cell lung cancer (NSCLC) patients without gene driver mutations receive anti-PD1 treatments either as monotherapy or in combination with chemotherapy based on PD-L1 expression in tumor tissue. Anti-PD1 antibodies target various immune system components, perturbing the balance between immune cells and soluble factors. In this study, we identified the immune signatures of NSCLC patients associated with different clinical outcomes through network analysis.Methods: Twenty-seven metastatic NSCLC patients were assessed at baseline for the levels of circulating CD137+T cells (total, CD4+, and CD8+) via cytofluorimetry, along with 14 soluble checkpoints and 20 cytokines through Luminex analysis. Hierarchical clustering and connectivity heatmaps were executed, analyzing the response to therapy (R vs. NR), performance status (PS = 0 vs. PS > 0), and overall survival (OS < 3 months vs. OS > 3 months).Results:The clustering of immune checkpoints revealed three groups with a significant differential proportion of six checkpoints between patients with PS = 0 and PS > 0 (p< 0.0001). Furthermore, significant pairwise correlations among immune factors evaluated in R were compared to the lack of significant correlations among the same immune factors in NR patients and vice versa. These comparisons were conducted for patients with PS = 0 vs. PS > 0 and OS < 3 months vs. OS > 3 months. The results indicated that NR with PS > 0 and OS ≤ 3 months exhibited an inflammatory-specific signature compared to the contrasting clinical conditions characterized by a checkpoint molecule-based network (p< 0.05).Conclusions:Identifying various connectivity immune profiles linked to response to therapy, PS, and survival in NSCLC patients represents significant findings that can optimize therapeutic choices.
背景/目的：对于无基因驱动突变的非小细胞肺癌（NSCLC）患者，根据肿瘤组织中PD-L1的表达情况，可接受抗PD1单药治疗或联合化疗。抗PD1抗体作用于免疫系统的多个组分，会扰乱免疫细胞与可溶性因子之间的平衡。本研究通过网络分析，识别了与不同临床结局相关的NSCLC患者免疫特征。方法：在基线时，通过流式细胞术检测27例转移性NSCLC患者循环CD137+T细胞（总CD137+T细胞、CD4+及CD8+亚群）水平，并通过Luminex技术分析14种可溶性检查点分子和20种细胞因子。采用层次聚类和连接性热图分析，评估治疗反应（应答者[R] vs. 无应答者[NR]）、体能状态（PS = 0 vs. PS > 0）及总生存期（OS < 3个月 vs. OS > 3个月）。结果：免疫检查点聚类分析显示，根据PS = 0与PS > 0患者之间六种检查点分子比例的显著差异（p < 0.0001），可将患者分为三组。进一步分析发现，在应答者中评估的免疫因子之间存在显著的成对相关性，而无应答者中相同免疫因子之间则缺乏显著相关性，反之亦然。这一比较同样适用于PS = 0与PS > 0患者，以及OS < 3个月与OS > 3个月患者。结果表明，与以检查点分子网络为特征的对立临床状况相比，PS > 0且OS ≤ 3个月的无应答者表现出炎症特异性特征（p < 0.05）。结论：识别与NSCLC患者治疗反应、体能状态及生存期相关的多种连接性免疫特征，是优化治疗选择的重要发现。
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