CXCR6 expressing T cells: Functions and role in the control of tumors.

CXCR6 is a receptor for the chemokine CXCL16, which exists as a membrane or soluble form. CXCR6 is a marker for resident memory T (TRM) cells that plays a role in immunosurveillance through their interaction with epithelial cells. The interaction of CXCR6 with CXCL16 expressed at the membrane of certain subpopulations of intratumor dendritic cells (DC) called DC3, ideally positions these CXCR6+ T cells to receive a proliferation signal from IL-15 also presented by DC3. Mice deficient in cxcr6 or blocking the interaction of CXCR6 with its ligand, experience a poorer control of tumor proliferation by CD8+ T cells, but also by NKT cells especially in the liver. Intranasal vaccination induces CXCL16 production in the lungs and is associated with infiltration by TRM expressing CXCR6, which are then required for the efficacy of anti-tumor vaccination. Therapeutically, the addition of CXCR6 to specific CAR-T cells enhances their intratumoral accumulation and prolongs survival in animal models of pancreatic, ovarian and lung cancer. Finally, CXCR6 is part of immunological signatures that predict response to immunotherapy based on anti-PD-(L)1 in various cancers. In contrast, a protumoral role of CXCR6+T cells has also been reported mainly in Non-alcoholic steatohepatitis (NASH) due to a non-antigen specific mechanism. The targeting and amplification of antigen-specific TRM expressing CXCR6 and its potential use as a biomarker of response to immunotherapy opens new perspectives in cancer treatment.

Plasma CD27, a Surrogate of the Intratumoral CD27-CD70 Interaction, Correlates with Immunotherapy Resistance in Renal Cell Carcinoma.

PURPOSE: CD70 is a costimulatory molecule known to activate CD27-expressing T cells. CD27-CD70 interaction leads to the release of soluble CD27 (sCD27). Clear-cell renal cell carcinoma (ccRCC) expresses the highest levels of CD70 among all solid tumors; however, the clinical consequences of CD70 expression remain unclear. EXPERIMENTAL DESIGN: Tumor tissue from 25 patients with ccRCC was assessed for the expression of CD27 and CD70 in situ using multiplex immunofluorescence. CD27+ T-cell phenotypes in tumors were analyzed by flow cytometry and their gene expression profile were analyzed by single-cell RNA sequencing then confirmed with public data. Baseline sCD27 was measured in 81 patients with renal cell carcinoma (RCC) treated with immunotherapy (35 for training cohort and 46 for validation cohort). RESULTS: In the tumor microenvironment, CD27+ T cells interacted with CD70-expressing tumor cells. Compared with CD27- T cells, CD27+ T cells exhibited an apoptotic and dysfunctional signature. In patients with RCC, the intratumoral CD27-CD70 interaction was significantly correlated with the plasma sCD27 concentration. High sCD27 levels predicted poor overall survival in patients with RCC treated with anti-programmed cell death protein 1 in both the training and validation cohorts but not in patients treated with antiangiogenic therapy. CONCLUSIONS: In conclusion, we demonstrated that sCD27, a surrogate marker of T-cell dysfunction, is a predictive biomarker of resistance to immunotherapy in RCC. Given the frequent expression of CD70 and CD27 in solid tumors, our findings may be extended to other tumors.

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the "digital grade" cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.