Identification of a risk score model based on tertiary lymphoid structure‐related genes for predicting immunotherapy efficacy in non‐small cell lung cancer

Abstract Background 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. Methods 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. Results 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. Conclusions 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.