A systematic and comprehensive analysis of T cell exhaustion related to therapy in lung adenocarcinoma tumor microenvironment

Construction and Validation of a T Cell Exhaustion-Related Prognostic Signature in Cholangiocarcinoma

Objective: T cell exhaustion (TEX) is a critical determinant of immune resistance. This study was performed to investigate the key genes linked to TEX in cholangiocarcinoma (CCA) and construct a TEX-associated gene signature to forecast the prognosis of patients with CCA. Methods: Based on the expression data acquired from the E-MTAB-6389 dataset, the TEX-related modules and module genes were identified using weighted coexpression network analysis (WGCNA). Subsequently, a TEX-related prognostic signature was built by using the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The immune cell infiltration in each CCA sample was evaluated using the single-sample gene set enrichment analysis (ssGSEA) package, followed by single-cell RNA sequencing (scRNA-seq) analysis. Furthermore, the expression of TEX-related genes in the gene signature was experimentally validated in CCA cells by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and western blot analysis. Results: A total of 15 TEX-associated modules and 23 module genes were identified. Then, a four-gene signature related to TEX was established, containing Palladin, Cytoskeletal Associated Protein (PALLD), Member RAS Oncogene Family (RAB31), ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), and WISP1, which could predict prognosis of patients with CCA. Moreover, neutrophils, endothelial cells, B cells, and T cells exhibited significant infiltration in CCA samples, and these four TEX-related genes were both significantly positively correlated with T cells, endothelial cells, and B cells while negatively correlated with neutrophils. Moreover, a total of 13 cell types were annotated after scRNA-seq analysis. Notably, RAB31 was mainly highly expressed in monocytes, macrophages, DC2 (Dendritic Cells 2), and DC3 (Dendritic Cells 3), and PALLD, ADAMTS2, and WISP1 were mainly overexpressed in fibroblasts. Furthermore, experimental validation revealed that the expression levels of PALLD, RAB31, ADAMTS2, and WISP1 were consistent with the trend results of bioinformatics analysis. Conclusion: A prognostic signature was developed by four TEX-related genes, including PALLD, RAB31, ADAMTS2, and WISP1, which might be a powerful predictor for the prognosis of patients with CCA. These TEX-related genes were related to the infiltration of neutrophils, endothelial cells, B cells, and T cells in CCA.

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

CD109 drives pro-tumorigenic cell properties in human non-small cell lung cancer through interaction with desmoglein-2

Cluster of differentiation 109 (CD109) is a glycosylphosphatidylinositol (GPI) anchored cell surface protein, expressed on epithelial and endothelial cells, CD4+ and CD8+ T-cells, and premature lymphocytes. CD109 interacts with different cell surface receptors and thereby modulates intracellular signaling pathways, which ultimately changes cellular functions. One well-studied example is the interaction of CD109 with the TGFβ/TGFβ-receptor complex at the cell surface. CD109 silences intracellular SMAD2/3 signaling and targets TGFβ/TGFβ-receptor to the endosomal/lysosomal compartment. In recent years, CD109 emerged as a tumor marker for different tumor entities and expression of CD109 could be linked to adverse outcome in patients. In this study, we show that silencing of CD109 in human non-small cell lung cancer (NSCLC) cells, returns these cells to an epithelial like growth phenotype. On the transcriptional level, we describe changes in cell-cell contact and epithelial-mesenchymal transition associated gene clusters. At the cell surface, we identify desmoglein-2 (DSG2) as a new interaction partner of CD109 and demonstrate CD109 dependent targeting of DSG2 to the apical cell surface, where it forms desmosomes between apical and basal cell poles. Both, CD109 and DSG2 are genetic risk factors, linked to reduced overall survival in lung adenocarcinoma patients (subtype of NSCLC). In this study, we show the expression of both proteins in the same tumor and suggest a new CD109-DSG2 axis in NSCLC patients that could present a targetable therapeutic option in the future.

Comprehensive analysis of T cell exhaustion related signature for predicting prognosis and immunotherapy response in HNSCC

BACKGROUND

T cell exhaustion (TEX) signifies a condition of T cell disorder which implicate the therapeutic benefits and prognostic significance in patients with cancer. However, its role in the Head and Neck Squamous Carcinoma (HNSCC) remains incompletely understood.

METHODS

The detailed data of HNSCC samples were obtained from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets. We computed the expression scores of four TEX-related pathways and detected gene modules closely linked to these pathways, indicating prognostic significance. Following this, regression analyses were performed to select eight genes for the development of a predictive signature. The predictive capacity of this signature was evaluated. Additionally, we examined the relationships between TEX-related signature risk scores and the effectiveness of immunotherapy as well as drug sensitivity.

RESULTS

A novel prognostic model, comprising eight TEX-related genes, was established for patients with HNSCC. The prognostic value was further confirmed using additional GEO datasets: GSE65858 and GSE27020. This signature enables the stratification of patients into high- and low- risk groups, each showing distinct survival outcomes and responsiveness to immunotherapy. The low-risk group demonstrated improved prognosis and enhanced efficacy of immunotherapy. In addition, AZD6482, TAF1, Ribociclib, LGK974, PF4708671 and other drugs showed increased sensitivity in the high-risk group based on drug sensitivity values, offering tailored therapeutic recommendations for individuals with various risks profiles.

CONCLUSION

In conclusion, we developed a novel T cell exhaustion-associated signature, which holds considerable predictive value for both the prognosis of patients with HNSCC and the effectiveness of tumor immunotherapy.