Pro-tumor γδ T Cells in Human Cancer: Polarization, Mechanisms of Action, and Implications for Therapy.

The tumor immune microenvironment contributes to tumor initiation, progression and response to therapy. Among the immune cell subsets that play a role in the tumor microenvironment, innate-like T cells that express T cell receptors composed of γ and δ chains (γδ T cells) are of particular interest. Indeed, γδ T cells contribute to the immune response against many cancers, notably through their powerful effector functions that lead to the elimination of tumor cells and the recruitment of other immune cells. However, their presence in the tumor microenvironment has been associated with poor prognosis in various solid cancers (breast, colon and pancreatic cancer), suggesting that γδ T cells also display pro-tumor activities. In this review, we outline the current evidences of γδ T cell pro-tumor functions in human cancer. We also discuss the factors that favor γδ T cell polarization toward a pro-tumoral phenotype, the characteristics and functions of such cells, and the impact of pro-tumor subsets on γδ T cell-based therapies

Clinicopathological Correlates of γδ T Cell Infiltration in Triple-Negative Breast Cancer

International audienceThe prognostic impact of the different tumor-infiltrating lymphocyte (TIL) subpopulations in solid cancers is still debated. Here, we investigated the clinicopathological correlates and prognostic impact of TILs, particularly of γδ T cells, in 162 patients with triple-negative breast cancer (TNBC). A high γδ T cell density (>6.625 γδ T cells/mm2) was associated with younger age (p = 0.008), higher tumor histological grade (p = 0.002), adjuvant chemotherapy (p = 0.010), BRCA1 promoter methylation (p = 0.010), TIL density (p < 0.001), and PD-L1 (p < 0.001) and PD-1 expression (p = 0.040). In multivariate analyses, γδ T cell infiltration (cutoff = 6.625 γδ T cells/mm2) was an independent prognostic factor (5-year relapse-free survival: 63.3% vs. 89.8%, p = 0.027; 5-year overall survival: 73.8% vs. 89.9%, p = 0.031, for low vs. high infiltration). This prognostic impact varied according to the tumor PIK3CA mutational status. High γδ T cell infiltration was associated with better survival in patients with PIK3CA wild-type tumors, but the difference was not significant in the subgroup with PIK3CA-mutated tumors. Altogether, these data suggest that high γδ T cell infiltrate is correlated with immune infiltration and might represent a candidate prognostic tool in patients with TNBC

Diversity of Tumor-Infiltrating, γδ T-Cell Abundance in Solid Cancers

International audienceγδ T-cells contribute to the immune response against many tumor types through their direct cytolytic functions and their capacity to recruit and regulate the biological functions of other immune cells. As potent effectors of the anti-tumor immune response, they are considered an attractive therapeutic target for immunotherapies, but their presence and abundance in the tumor microenvironment are not routinely assessed in patients with cancer. Here, we validated an antibody for immunohistochemistry analysis that specifically detects all γδ T-cell subpopulations in healthy tissues and in the microenvironment of different cancer types. Tissue microarray analysis of breast, colon, ovarian, and pancreatic tumors showed that γδ T-cell density varies among cancer types. Moreover, the abundance of γδ tumor-infiltrating lymphocytes was variably associated with the outcome depending on the cancer type, suggesting that γδ T-cell recruitment is influenced by the context. These findings also suggest that γδ T-cell detection and analysis might represent a new and interesting diagnostic or prognostic marker

Identification of a regulatory Vδ1 gamma delta T cell subpopulation expressing CD73 in human breast cancer.

γδ T cells contribute to the immune response against many cancers, notably through their powerful effector functions that lead to the elimination of tumor cells and the recruitment of other immune cells. However, their presence in the tumor microenvironment has been associated with poor prognosis in breast, colon, and pancreatic cancer, suggesting that γδ T cells may also display pro-tumor activities. Here, we identified in blood from healthy donors a subpopulation of Vδ1T cells that represents around 20% of the whole Vδ1 population, expresses CD73, and displays immunosuppressive phenotype and functions (i.e., production of immunosuppressive molecules, such as IL-10, adenosine, and the chemotactic factor IL-8, and inhibition of αβ T cell proliferation). We then found that in human breast tumors, γδ T cells were present particularly in late stage breast cancer samples, and that ∼20% of tumor-infiltrating γδ T cells expressed CD73. Taken together, these results suggest that regulatory γδ T cells are present in the breast cancer microenvironment and may display immunosuppressive functions through the production of immunosuppressive molecules, such as IL-10, IL-8, and adenosine, thus promoting tumor growth

Intrinsic factors and CD1d1 but not CD1d2 expression levels control invariant natural killer T cell subset differentiation

Invariant natural killer T (NKT) cell subsets are defined based on their cytokine-production profiles and transcription factors. Their distribution is different in C57BL/6 (B6) and BALB/c mice, with a bias for NKT1 and NKT2/NKT17 subsets, respectively. Here, we show that the non-classical class I-like major histocompatibility complex CD1 molecules CD1d2, expressed in BALB/c and not in B6 mice, could not account for this difference. We find however that NKT cell subset distribution is intrinsic to bone marrow derived NKT cells, regardless of syngeneic CD1d-ligand recognition, and that multiple intrinsic factors are likely involved. Finally, we find that CD1d expression levels in combination with T cell antigen receptor signal strength could also influence NKT cell distribution and function. Overall, this study indicates that CD1d-mediated TCR signals and other intrinsic signals integrate to influence strain-specific NKT cell differentiation programs and subset distributions