CD69+ Vδ1γδ T cells are anti-tumor subpopulations in hepatocellular carcinoma

Characterisation of Cytotoxicity-Related Receptors on γδ T Cells in Chronic Lymphocytic Leukaemia

Chronic lymphocytic leukaemia (CLL) is a haematological malignancy primarily affecting older adults, characterised by the proliferation of functionally impaired B lymphocytes with abnormal expression of CD5, a typical T cell marker. The current study investigates the expression of cytotoxicity-related receptors (CD16, CD56, CD57, CD69) and a checkpoint (LAG-3) on γδ T cells in CLL patients. Sixty-nine treatment-naive CLL patients and fourteen healthy controls were recruited. Flow cytometry analysis revealed that the CLL patients had higher expressions of CD56 and LAG-3 and lower CD16 on their γδ T cells compared to the healthy controls. Subgroup analysis showed that ZAP-70-negative patients exhibited increased CD69, while CD38-negative patients showed higher CD16 expression. Additionally, CD16 expression was inversely correlated with serum LDH levels, a marker of disease progression. Bioinformatic analysis of the LAG-3 ligand mRNA in a CLL dataset indicated higher expression of HLA-DQA2 and HLA-DRB5 in patients with unmutated IGVH. Our findings highlight the altered expression of key cytotoxicity markers on γδ T cells in CLL, suggesting their potential role in disease progression and as a therapeutic target. In particular, the use of anti-LAG-3 antibodies seems promising.

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis

High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules, the interpretability of DL-extracted features can be challenging. To address these challenges, we propose an interpretable cancer prognosis model called Cox-Sage. Specifically, we first propose an algorithm to construct a patient similarity graph from heterogeneous clinical data, and then extract protein-coding genes from the patient's gene expression data to embed them as features into the graph nodes. We utilize multilayer graph convolution to model proportional hazards pattern and introduce a mathematical method to clearly explain the meaning of our model's parameters. Based on this approach, we propose two metrics for measuring gene importance from different perspectives: mean hazard ratio and reciprocal of the mean hazard ratio. These metrics can be used to discover two types of important genes: genes whose low expression levels are associated with high cancer prognosis risk, and genes whose high expression levels are associated with high cancer prognosis risk. We conducted experiments on seven datasets from TCGA, and our model achieved superior prognostic performance compared with some state-of-the-art methods. As a primary research, we performed prognostic biomarker discovery on the LIHC (Liver Hepatocellular Carcinoma) dataset. Our code and dataset can be found at https://github.com/beeeginner/Cox-sage.

Hepatocellular carcinoma cells induce γδ T cells through metabolic reprogramming into tumor-progressive subpopulation

Tumor immune microenvironment (TIME) is a tiny structure that contains multiple immune cell components around tumor cells, which plays an important role in tumorigenesis, and is also the potential core area of activated immunotherapy. How immune cells with tumor-killing capacity in TIME are hijacked by tumor cells during the progression of tumorigenesis and transformed into subpopulations that facilitate cancer advancement is a question that needs to be urgently addressed nowadays. γδ T cells (their T cell receptors are composed of γ and δ chains), a unique T cell subpopulation distinguished from conventional αβ T cells, are involved in a variety of immune response processes through direct tumor-killing effects and/or indirectly influencing the activity of other immune cells. However, the presence of γδ T cells in the tumor microenvironment (TME) has been reported to be associated with poor prognosis in some tumors, suggesting that certain γδ T cell subsets may also have pro-tumorigenic effects. Recent studies have revealed that metabolic pathways such as activation of glycolysis, increase of lipid metabolism, enhancement of mitochondrial biosynthesis, alterations of fatty acid metabolism reshape the local TME, and immune cells trigger metabolic adaptation through metabolic reprogramming to meet their own needs and play the role of anti-tumor or immunosuppression. Combining previous studies and our bioinformatics results, we hypothesize that γδT cells compete for resources with hepatocellular carcinoma (HCC) cells by means of fatty acid metabolic regulation in the TME, which results in the weakening or loss of their ability to recognize and kill HCC cells through genetic and epigenetic alterations, thus allowing γδT cells to be hijacked by HCC cells as a subpopulation that promotes HCC progression.