TCR Coexpression Signature Predicts Immunotherapy Resistance in NSCLC

Abstract

Background: Lung cancer has the highest morbidity and mortality rate among types of malignant tumors, and as such, research into prolonging the survival time of patients is vital. The emergence of immune checkpoint inhibitors (ICIs) has greatly improved the survival of patients with non-small cell lung cancer (NSCLC), however, the lack of effective biomarkers to predict the prognosis of immunotherapy has made it difficult to maximize the benefits. T cell receptor (TCR) is one of the most important components for recognizing tumor cells, and with this study we aim to clarify the relationship between TCR coexpression and the prognosis of NSCLC patients receiving immunotherapy.Methods: Univariate COX regression, logistics regression, and KM survival analysis were used to evaluate the relationship between TCR coexpression and the prognosis of immunotherapy. Additionally, CIBERSORT, Gene Set Enrichment Analysis (GSEA), and single-sample GSEA (ssGSEA) algorithms were used to evaluate the tumor immune microenvironment (TIME) of NSCLC patients.Results: Univariate Cox regression analysis showed that the TCR coexpression signature can be used as a clinical prognostic indicator for NSCLC patients receiving immunotherapy (p = 0.0205). In addition, those in the NSCLC group with a high TCR coexpression signature had significantly improved progression-free survival (PFS) (p = 0.014). In the ICI treatment cohort (GSE35640). In addition, there was a high infiltration of CD8+T cells, activated memory CD4+T cells, and M1 macrophages in the TIME of those with a high TCR coexpression signature. The results of pathway enrichment analysis showed that patients with a high TCR coexpression signature had significantly activated signal pathways such as lymphocyte proliferation and activation, chemokine binding, and inflammatory cytokine production. Also, we found that patients with a high TCR coexpression signature had an elevated T cell inflammation gene expression profile (GEP).Conclusion: We show that the TCR coexpression signature may be useful as a new biomarker for the prognosis of NSCLC patients undergoing immunotherapy, with high signatures indicating better treatment response. Additionally, we found that patients with a high TCR coexpression signature had tumor immune microenvironments with beneficial anti-tumor characteristics.

Effect of bacillus subtilis strain Z15 secondary metabolites on immune function in mice

“…Chitosan-Zn chelate could increase serum complement 3, and complement 4 levels and improve immune function in weaned piglets [ 52 ]. The T cell antigen receptor (TCR) is expressed on T cells and coordinates the adaptive immune response [ 53 ] to peptide antigens presented in the context of major histocompatibility complex (MHC) molecules, which is the first step in T cell activation. In organisms with a high TCR co-expression profile, the activity of signaling pathways such as lymphocyte activation and proliferation is significantly upregulated, which activates and regulates immune cells and mediates the activation, proliferation and differentiation of T and B cells.…”

Effect of Bacillus subtilis strain Z15 secondary metabolites on immune function in mice

“…The T cell antigen receptor (TCR) is expressed on T cells and coordinates the adaptive immune response (43) to peptide antigens presented in the context of major histocompatibility complex (MHC) molecules, which is the rst step in T cell activation. In organisms with a high TCR co-expression pro le, the activity of signaling pathways such as lymphocyte activation and proliferation is signi cantly upregulated, which activates and regulates immune cells and mediates the activation, proliferation and differentiation of T and B cells.…”

Biomarkers for Immune Checkpoint Inhibitor Response in NSCLC: Current Developments and Applicability

“…A TCR co-expression signature has been identified as a valuable predictor of prognosis in NSCLC patients undergoing immunotherapy. Elevated expression levels of this specific gene-set are indicative of more favorable treatment responses [76]. Additionally, a cancerspecific immune score model has been developed to predict ICI response with satisfactory performance, achieving an area under the curve (AUC) of 0.68, indicating its potential for accurately assessing ICI response [77].…”