Blockade of the T cell immunoreceptor with the immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) can enhance innate and adaptive tumor immunity and radiotherapy (RT) can enhance anti-tumor immunity. However, our data suggest that TIGIT-mediated immune suppression may be an impediment to such goals. Herein, we report on the synergistic effects of RT combined with anti-TIGIT therapy and the mechanism of their interaction. Treatment efficacy was assessed by measuring primary and secondary tumor growth, survival, and immune memory capacity. The function of CD103 + dendritic cells (DCs) under the combined treatment was assessed in wild-type and BATF3-deficient (BATF3^−/−) mice. FMS-like tyrosine kinase 3 ligand (Flt3L) was used to confirm the role of CD103 + DCs in RT combined with anti-TIGIT therapy. TIGIT was upregulated in immune cells following RT in both esophageal squamous cell carcinoma patients and mouse models. Administration of the anti-TIGIT antibody enhanced the efficacy of RT through a CD8 + T cell-dependent mechanism. It was observed that RT and the anti-TIGIT antibody synergistically enhanced the accumulation of tumor-infiltrating DCs, which activated CD8 + T cells. The efficacy of the combination therapy was negated in the BATF3^−/− mouse model. CD103 + DCs were required to promote the anti-tumor effects of combination therapy. Additionally, Flt3L therapy enhanced tumor response to RT combined with TIGIT blockade. Our study demonstrated TIGIT blockade can synergistically enhance anti-tumor T cell responses to RT via CD8 + T cells (dependent on CD103 + DCs), suggesting the clinical potential of targeting the TIGIT pathway and expanding CD103 + DCs in RT.

TCF1+CD8+T cells are reported to exhibit stem‐like properties with the ability to self‐renew and differentiate into terminal effector T cells (TCF1‐CD8+T cells) to enhance antitumor response. Previous studies indicated that TCF1+CD8+ tumor‐infiltrating lymphocytes (TILs) are related to response to immunotherapy. However, their role in predicting prognosis for patients with primary small cell carcinoma of the esophagus (PSCCE) remains unclear. In this study, the expression of TCF1+CD8+T was analyzed by multiplex fluorescence immunohistochemistry in tumor tissues of 79 patients with PSCCE. High infiltration of TCF1+CD8+T cells had longer overall survival (OS) than low infiltration (P = .009, hazard ratio [HR] = 0.506). High TCF1+CD8/CD8 ratio (>21%) showed superior OS compared with low ratio (≤21%) (P < .001, HR = 0.394). In the validation set (n = 20), the prognostic value of TCF1+CD8+T cells on OS was also verified. TCF1+CD8+T cells are strong prognostic predictors.

Objectives: This study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD). Methods: One hundred patients with ADPKD were randomly divided into training group (n = 69) and test group (n = 31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD. Results: Fourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group [training group: (AUC) = 0.7542, test group (AUC) = 0.7417]. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets [radiomics model (AUC) = 0.7505, radiomics nomogram model (AUC) = 0.8435, p value = 0.005]. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value. Conclusion: radiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making. Trial Registration: ChiCTR, ChiCTR2100046739. Registered 27 May 2021—retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955. Graphical