Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
School of Medicine South China University of Technology, Guangzhou, China.
Thorac Cancer. 2024 May;15(14):1119-1131. doi: 10.1111/1759-7714.15299. Epub 2024 Apr 1.
Tertiary lymphoid structures (TLSs) affect the prognosis and efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC), but the underlying mechanisms are not well understood.
TLSs were identified and categorized online from the Cancer Digital Slide Archive (CDSA). Overall survival (OS) and disease-free survival (DFS) were analyzed. GSE111414 and GSE136961 datasets were downloaded from the GEO database. GSVA, GO and KEGG were used to explore the signaling pathways. Immune cell infiltration was analyzed by xCell, ssGSEA and MCP-counter. The analysis of WGCNA, Lasso and multivariate cox regression were conducted to develop a gene risk score model based on the SU2C-MARK cohort.
TLS-positive was a protective factor for OS according to multivariate cox regression analysis (p = 0.029). Both the TLS-positive and TLS-mature groups exhibited genes enrichment in immune activation pathways. The TLS-mature group showed more activated dendritic cell infiltration than the TLS-immature group. We screened TLS-related genes using WGCNA. Lasso and multivariate cox regression analysis were used to construct a five-genes (RGS8, RUF4, HLA-DQB2, THEMIS, and TRBV12-5) risk score model, the progression free survival (PFS) and OS of patients in the low-risk group were markedly superior to those in the high-risk group (p < 0.0001; p = 0.0015, respectively). Calibration and ROC curves indicated that the combined model with gene risk score and clinical features could predict the PFS of patients who have received immunotherapy more accurately than a single clinical factor.
Our data suggested a pivotal role of TLSs formation in survival outcome and immunotherapy response of NSCLC patients. Tumors with mature TLS formation showed more activated immune microenvironment. In addition, the model constructed by TLS-related genes could predict the response to immunotherapy and is meaningful for clinical decision-making.
三级淋巴结构 (TLSs) 影响非小细胞肺癌 (NSCLC) 患者的免疫治疗预后和疗效，但潜在机制尚不清楚。
从癌症数字幻灯片档案 (CDSA) 在线识别和分类 TLSs。分析总生存期 (OS) 和无病生存期 (DFS)。从 GEO 数据库下载 GSE111414 和 GSE136961 数据集。使用 GSVA、GO 和 KEGG 探索信号通路。通过 xCell、ssGSEA 和 MCP-counter 分析免疫细胞浸润。基于 SU2C-MARK 队列，通过 WGCNA、Lasso 和多变量 cox 回归分析构建基因风险评分模型。
多变量 cox 回归分析表明，TLS 阳性是 OS 的保护因素 (p=0.029)。TLS 阳性和 TLS 成熟组均表现出免疫激活途径的基因富集。与 TLS 不成熟组相比，TLS 成熟组显示出更多激活的树突状细胞浸润。我们使用 WGCNA 筛选与 TLS 相关的基因。Lasso 和多变量 cox 回归分析用于构建由五个基因 (RGS8、RUF4、HLA-DQB2、THEMIS 和 TRBV12-5) 组成的风险评分模型，低风险组的无进展生存期 (PFS) 和 OS 明显优于高风险组 (p<0.0001；p=0.0015)。校准和 ROC 曲线表明，与单一临床因素相比，基因风险评分和临床特征相结合的模型可以更准确地预测接受免疫治疗的患者的 PFS。
我们的数据表明，TLSs 的形成在 NSCLC 患者的生存结果和免疫治疗反应中起着关键作用。形成成熟 TLS 的肿瘤表现出更活跃的免疫微环境。此外，由 TLS 相关基因构建的模型可以预测免疫治疗的反应，对临床决策具有重要意义。
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