Cancer growth and progression are associated with immune suppression. Cancer cells have the ability to activate different immune checkpoint pathways that harbor immunosuppressive functions. Monoclonal antibodies that target immune checkpoints provided an immense breakthrough in cancer therapeutics. Among the immune checkpoint inhibitors, PD-1/PD-L1 and CTLA-4 inhibitors showed promising therapeutic outcomes, and some have been approved for certain cancer treatments, while others are under clinical trials. Recent reports have shown that patients with various malignancies benefit from immune checkpoint inhibitor treatment. However, mainstream initiation of immune checkpoint therapy to treat cancers is obstructed by the low response rate and immune-related adverse events in some cancer patients. This has given rise to the need for developing sets of biomarkers that predict the response to immune checkpoint blockade and immune-related adverse events. In this review, we discuss different predictive biomarkers for anti-PD-1/PD-L1 and anti-CTLA-4 inhibitors, including immune cells, PD-L1 overexpression, neoantigens, and genetic and epigenetic signatures. Potential approaches for further developing highly reliable predictive biomarkers should facilitate patient selection for and decision-making related to immune checkpoint inhibitor-based therapies.
Coronavirus disease 2019 (COVID‐19) is caused by SARS‐CoV‐2, a novel coronavirus strain. Some studies suggest that COVID‐19 could be an immune‐related disease, and failure of effective immune responses in initial stages of viral infection could contribute to systemic inflammation and tissue damage, leading to worse disease outcomes. T cells can act as a double‐edge sword with both pro‐ and anti‐roles in the progression of COVID‐19. Thus, better understanding of their roles in immune responses to SARS‐CoV‐2 infection is crucial. T cells primarily react to the spike protein on the coronavirus to initiate antiviral immunity; however, T‐cell responses can be suboptimal, impaired or excessive in severe COVID‐19 patients. This review focuses on the multifaceted roles of T cells in COVID‐19 pathogenesis and rationalizes their significance in eliciting appropriate antiviral immune responses in COVID‐19 patients and unexposed individuals. In addition, we summarize the potential therapeutic approaches related to T cells to treat COVID‐19 patients. These include adoptive T‐cell therapies, vaccines activating T‐cell responses, recombinant cytokines, Th1 activators and Th17 blockers, and potential utilization of immune checkpoint inhibitors alone or in combination with anti‐inflammatory drugs to improve antiviral T‐cell responses against SARS‐CoV‐2.
Regulatory T cells (Tregs) play essential roles in immune homeostasis; however, their role in tumor microenvironment (TME) is not completely evident. Several studies reported that infiltration of Tregs into various tumor tissues promotes tumor progression by limiting antitumor immunity and supporting tumor immune evasion. Furthermore, in TME, Tregs include heterogeneous subsets of cells expressing different immunosuppressive molecules favoring tumor progression. For an effective cancer therapy, it is critical to understand the Treg heterogeneity and biology in the TME. Recent studies have shown that immune checkpoint molecules promote cancer progression through various antitumor inhibitory mechanisms. Recent advances in cancer immunotherapy have shown the promising potentials of immune checkpoint inhibitors (ICIs) in inducing antitumor immune responses and clinical benefits in patients with cancer at late stages. Most studies revealed the effect of ICIs on T effector cells, and little is known about their effect on Tregs. In this review, we highlight the effects of the ICIs, including anti-CTLA-4, anti-PD-1/PD-L1, anti-LAG-3, anti-TIM-3, and anti-TIGIT, on tumor-infiltrating and peripheral Tregs to elicit effector T-cell functions against tumors. Additionally, we discuss how ICIs may target Tregs for cancer immunotherapy.
Demethylation of CpG motifs in the Foxp3 intronic element, conserved noncoding sequence 2 (CNS2), is indispensable for the stable expression of Foxp3 in regulatory T cells (Tregs). In this study, we found that vitamin C induces CNS2 demethylation in Tregs in a ten-eleven-translocation 2 (Tet2)-dependent manner. The CpG motifs of CNS2 in Tregs generated in vitro by TGF-β (iTregs), which were methylated originally, became demethylated after vitamin C treatment. The conversion of 5-methylcytosin into 5-hydroxymethylcytosin was more efficient, and the methyl group from the CpG motifs of Foxp3 CNS2 was erased rapidly in iTregs treated with vitamin C. The effect of vitamin C disappeared in Tet2−/− iTregs. Furthermore, CNS2 in peripheral Tregs in vivo, which were demethylated originally, became methylated after treatment with a sodium-dependent vitamin C transporter inhibitor, sulfinpyrazone. Finally, CNS2 demethylation in thymic Tregs was also impaired in Tet2−/− mice, but not in wild type mice, when they were treated with sulfinpyrazone. Collectively, vitamin C was required for the CNS2 demethylation mediated by Tet proteins, which was essential for Foxp3 expression. Our findings indicate that environmental factors, such as nutrients, could bring about changes in immune homeostasis through epigenetic mechanisms.
BackgroundHigh expression of immune checkpoints in tumor microenvironment plays significant roles in inhibiting anti-tumor immunity, which is associated with poor prognosis and cancer progression. Major epigenetic modifications in both DNA and histone could be involved in upregulation of immune checkpoints in cancer.MethodsExpressions of different immune checkpoint genes and PD-L1 were assessed using qRT-PCR, and the underlying epigenetic modifications including CpG methylation and repressive histone abundance were determined using bisulfite sequencing, and histone 3 lysine 9 trimethylation (H3K9me3) and histone 3 lysine 27 trimethylation (H3K27me3) chromatin immunoprecipitation assays (ChIP), respectively.ResultsWe first assessed the expression level of six immune checkpoints/ligands and found that PD-1, CTLA-4, TIM-3, and LAG-3 were significantly upregulated in breast tumor tissues (TT), compared with breast normal tissues (NT). We investigated the epigenetic modifications beyond this upregulation in immune checkpoint genes. Interestingly, we found that CpG islands in the promoter regions of PD-1, CTLA-4, and TIM-3 were significantly hypomethylated in tumor compared with normal tissues. Additionally, CpG islands of PD-L1 promoter were completely demethylated (100%), LAG-3 were highly hypomethylated (80–90%), and TIGIT were poorly hypomethylated (20–30%), in both NT and TT. These demethylation findings are in accordance with the relative expression data that, out of all these genes, PD-L1 was highly expressed and completely demethylated and TIGIT was poorly expressed and hypermethylated in both NT and TT. Moreover, bindings of H3K9me3 and H3K27me3 were found to be reduced in the promoter loci of PD-1, CTLA-4, TIM-3, and LAG-3 in tumor tissues.ConclusionOur data demonstrate that both DNA and histone modifications are involved in upregulation of PD-1, CTLA-4, TIM-3, and LAG-3 in breast tumor tissue and these epigenetic modifications could be useful as diagnostic/prognostic biomarkers and/or therapeutic targets in breast cancer.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0512-1) contains supplementary material, which is available to authorized users.
Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually labeled training data. We present a framework that learns the classifier online with automatically labeled data for the specific case of detecting moving objects from video. Motion information is used to automatically label training examples collected directly from the live detection task video. An online learner based on the Winnow algorithm incrementally trains a taskspecific classifier with these examples. Since learning occurs online and without manual help, it can continue in parallel with detection and adapt the classifier over time. The framework is demonstrated on a person detection task for an office corridor scene. In this task, we use background subtraction to automatically label training examples. After the initial manual effort of implementing the labeling method, the framework runs by itself on the scene video stream to gradually train an accurate detector.
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