T-cell exhaustion was originally identified during chronic infection in mice, and was subsequently observed in humans with cancer. The exhausted T cells in the tumor microenvironment show overexpressed inhibitory receptors, decreased effector cytokine production and cytolytic activity, leading to the failure of cancer elimination. Restoring exhausted T cells represents an inspiring strategy for cancer treatment, which has yielded promising results and become a significant breakthrough in the cancer immunotherapy. In this review, we overview the updated understanding on the exhausted T cells in cancer and their potential regulatory mechanisms and discuss current therapeutic interventions targeting exhausted T cells in clinical trials.

Combining whole exome sequencing, transcriptome profiling, and T cell repertoire analysis, we investigate the spatial features of surgically-removed biopsies from multiple loci in tumor masses of 15 patients with non-small cell lung cancer (NSCLC). This revealed that the immune microenvironment has high spatial heterogeneity such that intratumoral regional variation is as large as inter-personal variation. While the local total mutational burden (TMB) is associated with local T-cell clonal expansion, local anti-tumor cytotoxicity does not directly correlate with neoantigen abundance. Together, these findings caution against that immunological signatures can be predicted solely from TMB or microenvironmental analysis from a single locus biopsy.

High tumor mutational burden (TMB), which represents genomic instability, has the potential to induce neoantigen production and further immunogenicity improvement.¹ Recent studies have confirmed that TMB measured by whole-exome sequencing (WES) or a next-generation sequencing (NGS) cancer gene panel (CGP) can serve as a candidate biomarker of clinical outcome from immune checkpoint blockades (ICBs)^2,3 in melanoma,^4,5 lung cancer,^6-9 and urothelial carcinoma.¹⁰ However, a considerable proportion of patients with advanced cancer could not provide sufficient tumor tissue for molecular testing.^8,11 Therefore, whether TMB can be measured using circulating tumor DNA (ctDNA) (namely, blood TMB [bTMB]) as a noninvasive approach to guide ICB therapies has attracted widespread attention from practitioners. A previous study¹² found that blood-derived variants of unknown significance were correlated with ICB responses. More recently, Gandara et al¹³ reported that bTMB is associated with progression-free survival (PFS) benefit from atezolizumab over docetaxel in non–small cell lung cancers (NSCLCs). Further evaluation of bTMB will be performed in patients undergoing first-line treatment in a prospective, phase 3 randomized clinical trial (A Study of Atezolizumab as First-line Monotherapy for Advanced or Metastatic Non–Small Cell Lung Cancer [B-F1RST]).¹⁴ However, the reliability of ctDNA detection is still under debate^15,16; thus, more supported evidence of bTMB is needed to promote its clinical value on guiding ICB delivery. In this study, we aimed to explore the optimal gene panel size and algorithm to design a CGP for TMB estimation, evaluate the panel reliability, and further validate the feasibility of bTMB as a clinically actionable biomarker for immunotherapy.

Macrophages phagocytize pathogens to initiate innate immunity and products from the tumor microenvironment (TME) to mediate tumor immunity. The loss of tumor-associated macrophage (TAM)-mediated immune responses results in immune suppression. To reverse this immune disorder, the regulatory mechanism of TAMs in the TME needs to be clarified. Immune molecules (cytokines and chemokines) from TAMs and the TME have been widely accepted as mutual mediators of signal transduction in the past few decades. Recently, researchers have tried to seek the intrinsic mechanism of TAM phenotypic and functional changes through metabolic connections. Numerous metabolites derived from the TME have been identified that induce the cell-cell crosstalk with TAMs. The bulk tumor cells, immune cells, and stromal cells produce metabolites in the TME that are involved in the metabolic regulation of TAMs. Meanwhile, some products from TAMs regulate the biological functions of the tumor as well. Here, we review the recent reports demonstrating the metabolic regulation between TME and TAMs.

Introduction: A recently emerging respiratory disease named coronavirus disease 2019 (COVID-19) has quickly spread across the world. This disease is initiated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and uncontrolled cytokine storm, but it remains unknown as to whether a robust antibody response is related to clinical deterioration and poor outcome in COVID-19 patients. Methods: Anti-SARS-CoV-2 IgG and IgM antibodies were determined by chemiluminescence analysis (CLIA) in COVID-19 patients at a single center in Wuhan. Median IgG and IgM levels in acute and convalescent-phase sera (within 35 days) for all included patients were calculated and compared between severe and non-severe patients. Immune response phenotyping based on the late IgG levels and neutrophil-to-lymphocyte ratio (NLR) was characterized to stratified patients into different disease severities and outcomes. Results: A total of 222 patients were included in this study. IgG was first detected on day 4 of illness, and its peak levels occurred in the fourth week. Severe cases were more frequently found in patients with high IgG levels, compared to those with low IgG levels (51.8 vs. 32.3%; p = 0.008). Severity rates for patients with NLR^hiIgG^hi, NLR^hiIgG^lo, NLR^loIgG^hi, and NLR^loIgG^lo phenotype were 72.3, 48.5, 33.3, and 15.6%, respectively (p < 0.0001). Furthermore, severe patients with NLR^hiIgG^hi, NLR^hiIgG^lo had higher inflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLR^loIgG^lo phenotype (p < 0.05). Recovery rates for severe patients with NLR^hiIgG^hi, NLR^hiIgG^lo, NLR^loIgG^hi, and NLR^loIgG^lo phenotype were 58.8% (20/34), 68.8% (11/16), 80.0% (4/5), and 100% (12/12), respectively (p = 0.0592). Dead cases only occurred in NLR^hiIgG^hi and NLR^hiIgG^lo phenotypes. Conclusions: COVID-19 severity is associated with increased IgG response, and an immune response phenotyping based on the late IgG response and NLR could act as a simple complementary tool to discriminate between severe and non-severe COVID-19 patients, and further predict their clinical outcome.

Immune checkpoint blockade (ICB) therapies have achieved remarkable clinical responses in patients with many different types of cancer; however, most patients who receive ICB monotherapy fail to achieve long-term responses, and some tumors become immunotherapy-resistant and even hyperprogressive. Type I interferons (IFNs) have been demonstrated to inhibit tumor growth directly and indirectly by acting upon tumor and immune cells, respectively. Furthermore, accumulating evidence indicates that endo- and exogenously enhancing type I IFNs have a synergistic effect on anti-tumor immunity. Therefore, clinical trials studying new treatment strategies that combine type I IFN inducers with ICB are currently in progress. Here, we review the cellular sources of type I IFNs and their roles in the immune regulation of the tumor microenvironment. In addition, we highlight immunotherapies based on type I IFNs and combination therapy between type I IFN inducers and ICBs.