Tissue-resident memory T cells (T_RM) are a specialized T cell population residing in peripheral tissues. The presence and potential impact of T_RM in the tumor immune microenvironment (TIME) remain to be elucidated. Here, we systematically investigated the relationship between T_RM and melanoma TIME based on multiple clinical single-cell RNA-seq datasets and developed signatures indicative of T_RM infiltration. T_RM infiltration is associated with longer overall survival and abundance of T cells, NK cells, M1 macrophages, and memory B cells in the TIME. A 22-gene T_RM–derived risk score was further developed to effectively classify patients into low- and high-risk categories, distinguishing overall survival and immune activation, particularly in T cell-mediated responses. Altogether, our analysis suggests that T_RM abundance is associated with melanoma TIME activation and patient survival, and the T_RM-based machine learning model can potentially predict prognosis in melanoma patients.

Gliomas make up ~80% of malignant brain tumors in adults and are responsible for the majority of deaths from primary brain tumors. The glioma tumor microenvironment (TME) is a dynamic, heterogeneous mixture of extracellular matrix and malignant and non-malignant cells. Several ongoing clinical trials are evaluating the efficacy of therapies that target non-malignant cells, particularly immune cells. Consequently, a better understanding of the TME in glioma is pertinent. We utilized several gene expression datasets to evaluate the relationship between immune-related genes (IRGs) and patient prognosis. We generated microglia signatures using single-cell RNAseq data from human and mouse glioma cells to infer microglia abundance. Lastly, we built a LASSO Cox regression model that predicts patient survival. We found that 428 IRGs were negatively associated with survival in glioma patients. Overall survival was significantly lower in those with a high level of microglia infiltration. In addition, we also found that microglia abundance was significantly associated with several common genomic aberrations, including IDH2 and TP53 mutations. Furthermore, we found that patients with high risk scores had significantly worse overall survival than those with low risk scores in several independent datasets. Altogether, we characterized immune features predictive of overall survival in glioma and found that microglia abundance is negatively associated with survival. We developed a 23-gene risk score that can significantly stratify patients into low- and high-risk categories.

Beclin 2 plays a critical role in metabolic regulation and obesity, but its functions in innate immune signaling and cancer development remain largely unknown. Here, we identified Beclin 2 as a critical negative regulator of inflammation and lymphoma development. Mice with homozygous ablation of BCL2-interacting protein 2 (Becn2) developed splenomegaly and lymphadenopathy and markedly increased ERK1/2 and NF-κB signaling for proinflammatory cytokine production. Beclin 2 targeted the key signaling kinases MEKK3 and TAK1 for degradation through an ATG9A-dependent, but ATG16L/Beclin 1/LC3–independent, autophagic pathway. Mechanistically, Beclin 2 recruited MEKK3 or TAK1 through ATG9A to form a complex (Beclin 2-ATG9A-MEKK3) on ATG9A⁺ vesicles upon ULK1 activation. Beclin 2 further interacted with STX5 and STX6 to promote the fusion of MEKK3- or TAK1-associated ATG9A⁺ vesicles to phagophores for subsequent degradation. Importantly, Becn2-deficient mice had a markedly increased incidence of lymphoma development, with persistent STAT3 activation. Myeloid-specific ablation of MEKK3 (Map3k3) completely rescued the phenotypes (splenomegaly, higher amounts of proinflammatory cytokines, and cancer incidence) of Becn2-deficient mice. Hence, our findings have identified an important role of Beclin 2 in the negative regulation of innate immune signaling and tumor development through an ATG9A-dependent, but ATG16L/Beclin 1/LC3–independent, autophagic pathway, thus providing a potential target for the treatment of inflammatory diseases and cancer.

Peptide-based cancer vaccines have been shown to boost immune systems to kill tumor cells in cancer patients. However, designing an effective T cell epitope peptide-based cancer vaccine still remains a challenge and is a major hurdle for the application of cancer vaccines. In this study, we constructed for the first time a library of peptide-based cancer vaccines and their clinical attributes, named CancerVaccine (https://peptidecancervaccine.weebly.com/). To investigate the association factors that influence the effectiveness of cancer vaccines, these peptide-based cancer vaccines were classified into high (HCR) and low (LCR) clinical responses based on their clinical efficacy. Our study highlights that modified peptides derived from artificially modified proteins are suitable as cancer vaccines, especially for melanoma. It may be possible to advance cancer vaccines by screening for HLA class II affinity peptides may be an effective therapeutic strategy. In addition, the treatment regimen has the potential to influence the clinical response of a cancer vaccine, and Montanide ISA-51 might be an effective adjuvant. Finally, we constructed a high sensitivity and specificity machine learning model to assist in designing peptide-based cancer vaccines capable of providing high clinical responses. Together, our findings illustrate that a high clinical response following peptide-based cancer vaccination is correlated with the right type of peptide, the appropriate adjuvant, and a matched HLA allele, as well as an appropriate treatment regimen. This study would allow for enhanced development of cancer vaccines.

Tissue-resident memory T cells (T_RM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of T_RM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique T_RM signatures, through which we inferred the abundance of T_RM in NSCLC. We discovered that T_RM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, T_RM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A T_RM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher T_RM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the T_RM signature revealed low T_RM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between T_RM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

T cells play a dual role in various physiopathological states, capable of eliminating tumors and infected cells, while also playing a pathogenic role when activated by autoantigens, causing self-tissue damage. The regulation of T cell-peptide/major histocompatibility complex (TCR-pMHC) recognition is crucial for maintaining disease balance and treating cancer, infections, and autoimmune diseases. Despite efforts, predictive models of TCR-pMHC specificity are still in the early stages. Inspired by advances in protein structure prediction via deep neural networks, we evaluated AlphaFold 3 (AF3)-based AI computation as a method to predict TCR epitope specificity. We demonstrate that AlphaFold can model TCR-pMHC interactions, distinguishing valid epitopes from invalid ones with increasing accuracy. Immunogenic epitopes can be identified for vaccine development through in silico high-throughput processes. Additionally, higher-affinity and specific T cells can be designed to enhance therapy efficacy and safety. An accurate TCR-pMHC prediction model is expected to greatly benefit T-cell-mediated immunotherapy and aid drug design. Overall, precise prediction of T-cell immunogenicity holds significant therapeutic potential, allowing the identification of peptide epitopes linked to tumors, infections, and autoimmune diseases. Although there is much work to be done before these predictions achieve widespread practical use, we are optimistic that deep learning-based structural modeling is a promising pathway for the generalizable prediction of TCR-pMHC interactions.