Stem-like CD8+ T cells sustain the antigen-specific CD8+ T cell response during chronic antigen exposure. However, the signals that control the maintenance and differentiation of these cells are largely unknown. Here, we demonstrated that TGF-β was essential for the optimal maintenance of these cells and inhibited their differentiation into migratory effectors during chronic viral infection. Mechanistically, stem-like CD8+ T cells carried a unique expression pattern of α4 integrins (i.e., α4β1hi and α4β7lo) controlled by TGF-β. In the absence of TGF-β signaling, greatly enhanced expression of migration-related markers, including altered expression of α4 integrins, led to enhanced egress of stem-like CD8+ T cells into circulation accompanied by further differentiation into transitional states. Blocking α4 integrin significantly promoted their lymphoid tissue retention and therefore partially rescued the defective maintenance of Tcf-1+ subset in the absence of TGF-β signaling. Thus, TGF-β promotes the maintenance and inhibits the further differentiation of stem-like T cells at least partially via enforcing their lymphoid tissue residency.

TGF-β signaling is necessary for CD8+ T cell differentiation into tissue resident memory T cells (TRM). Although higher frequency of CD8+ TRM cells in the tumor microenvironment is associated with better prognosis, TGF-β−blockade typically improves rather than worsens outcomes. Here we show that in a mouse melanoma model, in the tumor-draining lymph nodes (TDLN) rather than in the tumors themselves, stem-like CD8+ T cells differentiate into TRMs in a TGF-β and tumor antigen dependent manner. Following vaccination against a melanoma-specific epitope, most tumour-specific CD8+ T cells are maintained in a stem-like state, but a proportion of cells lost TRM status and differentiate into CX3CR1+ effector CD8+ T cells in the TDLN, which are subsequently migrating into the tumours. Disruption of TGF-β signaling changes the dynamics of these developmental processes, with the net result of improving effector CD8+ T cell migration into the tumours. In summary, TDLN stem-like T cells transiently switch from a TGF-β-dependent TRM differentiation program to an anti-tumor migratory effector development upon vaccination, which transition can be facilitated by targeted TGF-β blockade.

Background: To build an automatic pathological diagnosis model to assess the lymph node metastasis status of head and neck squamous cell carcinoma (HNSCC) based on deep learning algorithms. Study Design: A retrospective study. Methods: A diagnostic model integrating two-step deep learning networks was trained to analyze the metastasis status in 85 images of HNSCC lymph nodes. The diagnostic model was tested in a test set of 21 images with metastasis and 29 images without metastasis. All images were scanned from HNSCC lymph node sections stained with hematoxylin-eosin (HE).Results: In the test set, the overall accuracy, sensitivity, and specificity of the diagnostic model reached 86%, 100%, and 75.9%, respectively.Conclusions: Our two-step diagnostic model can be used to automatically assess the status of HNSCC lymph node metastasis with high sensitivity.

BackgroundObservational studies have found associations between smoking, alcohol, radiation, body mass index (BMI), periodontitis, and the hazard of benign salivary gland neoplasms (BSGNs). Nevertheless, the etiology of BSGNs remains unclear. This study aims to assess the causal association between these modifiable factors and the BSGNs.MethodsGenetic instruments associated with exposures at the genome‐wide significance level were selected from corresponding genome‐wide association studies. The summary statistics for BSGNs were obtained from the FinnGen consortium (2445 cases and 340,054 controls). The inverse variance‐weighted method was used as the primary analysis, and several sensitivity analyses were performed to test the reliability.ResultsGenetically predicted higher lifetime smoking index (odds ratio [OR] = 2.10, p = 0.012) and BMI (OR = 1.58, p = 2.29 × 10−5) were associated with elevated risk of BSGNs, whereas other exposures do not. Sensitivity analyses showed consistency. The causal effect of the lifetime smoking index became more significant after adjusting for BMI (OR = 2.89, p = 0.005) and alcohol consumption (OR = 2.49, p = 0.002). A slight negative association emerged for alcohol consumption with adjustment for cigarettes per day (OR = 0.53, p = 0.034) but disappeared when adjusting for cigarettes per day and BMI.ConclusionThis study supports the independent causal role of lifetime smoking index and BMI in BSGNs risk.