IP-score correlated to endogenous tumour antigen peptide processing: A candidate clinical response score algorithm of immune checkpoint inhibitors therapy in multiple cohorts - Wang, Yutao; Yan, Kexin; Guo, Ye; Lu, Yi; Su, Hao; Li, Hongjun (Frontiers in Immunology, 2023)
Abstract Endogenous tumor antigen peptide processing played a crucial role in anti-tumor immunity in the tumor microenvironment. It has been reported that a high level of Endogenous tumor antigen peptide processing could improve the carcinoma patients’ prognosis. However, there is insufficient evidence to demonstrate its effect on the clinical response to immune checkpoint inhibitor therapy. Our previous study identified a co-expression network in urothelial carcinoma associated with endogenous tumor antigen peptide processing and CD8+ T lymphocyte infiltration, mainly containing PSMB8, PSMB9, PSMB10, PSME1, PSME2, and IRF1. To conduct a more in-depth study of the effects of the above genes on immunotherapy, we used these genes to construct a gene set related to tumor endogenous antigen peptide treatment based on the GSVA method. We named this scoring method IP score (IPs). Immediately afterward, we used the TCGA pan-cancer cohorts to conduct a comprehensive analysis of 6 genes in the IPs and the analysis results showed that these six genes were related to the proportion of CD8+ T lymphocytes in a variety of solid tumors. As a prognostic protective factor for solid tumors, patients had better prognosis outcomes in the group with high expression levels of the above genes. We included multiple ICI treatment cohorts to analyze the differential expression of 6 genes in the immune checkpoint inhibitor treatment response and disease progression groups. The results showed that the expression levels of the above 6 genes were relatively high in the effective group after treatment with PD-1 or CTLA4 inhibitors, and the expression of genes in the signature was significantly downregulated in the ICI-insensitive group. This indicates that the 6 genes are related to the clinical response to ICI treatment. Finally, we used the GSVA method to enrich the above signatures, and the results showed that PDCD1, CTAL4, CD274 and LAG3 were significantly higher expressed in the IPs high-expression group therefore, based on the processing of endogenous antigenic peptides in tumors, a predictive score of clinical response to immune checkpoint inhibitor therapy composed of 6 genes(PSMB8/PSMB9/PSMB10/PSME1/PSME2/IRF1) was constructed,