Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain–containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy.
The mixture of cell subpopulations in tumors is considered one of the important characteristics for drug resistance, metastasis, and disease relapse. The presence of diverse immune cells in a tumor microenvironment (TME) may profoundly affect clinical outcomes. One significant challenge in immuno-oncology is identifying the heterogeneity of immune cells in tumors and their differentiation process. Traditional profiling approaches, such as flow cytometry or mass cytometry, rely heavily on pre-existing knowledge and cell-type defining markers. Bulk transcriptional analyses dilute the contribution of a small subset of immune cells in the overall gene expression pattern. To overcome these limitations, a scMulti-omics study can offer detailed identification of diverse immune subsets at a higher resolution and provide an opportunity to understand the contribution of immune cells to tumor progression.
Sex bias exists in the development and progression of non-reproductive organ cancers, but the underlying mechanisms are enigmatic. Studies so far have focused largely on sexual dimorphisms in cancer biology and socioeconomic factors. Here, we establish a role for CD8+ T cell-dependent anti-tumor immunity in mediating sex differences in tumor aggressiveness, which is driven by the gonadal androgen but not sex chromosomes. A male bias exists in the frequency of intratumoral antigen-experienced Tcf7/TCF1+ progenitor exhausted CD8+ T cells that are devoid of effector activity as a consequence of intrinsic androgen receptor (AR) function. Mechanistically, we identify a novel sex-specific regulon in progenitor exhausted CD8+ T cells and a pertinent contribution from AR as a direct transcriptional trans-activator of Tcf7/TCF1. The T cell intrinsic function of AR in promoting CD8+ T cell exhaustion in vivo was established using multiple approaches including loss-of-function studies with CD8-specific Ar knockout mice. Moreover, ablation of the androgen-AR axis rewires the tumor microenvironment to favor effector T cell differentiation and potentiates the efficacy of anti-PD-1 immune checkpoint blockade. Collectively, our findings highlight androgen-mediated promotion of CD8+ T cell dysfunction in cancer and imply broader opportunities for therapeutic development from understanding sex disparities in health and disease.
The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of view, such immune cellular data should be treated as compositional. In compositional data, each element is positive, and all the elements sum to a constant, which can be set to one in general. Standard statistical methods are not directly applicable for the analysis of compositional data because they do not appropriately handle correlations between the compositional elements. In this paper, we review statistical methods for compositional data analysis and illustrate them in the context of immunology. Specifically, we focus on regression analyses using log-ratio transformations and the generalized linear model with Dirichlet distribution, discuss their theoretical foundations, and illustrate their applications with immune cellular fraction data generated from colorectal cancer patients.
Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.
The cancer research field is finally starting to unravel the mystery behind why males have a higher incidence and mortality rate than females for nearly all cancer types of the non-reproductive systems. Here, we explain how sex – specifically sex chromosomes and sex hormones – drive differential adaptive immunity across immune-related disease states including cancer, and why males are consequently more predisposed to tumor development. We highlight emerging data on the roles of cell-intrinsic androgen receptor in driving CD8+ T cell dysfunction or exhaustion in the tumor microenvironment and summarize on-going clinical efforts to determine the impact of androgen blockade on cancer immunotherapy. Finally, we outline a framework of future research in cancer biology and immuno-oncology, underscoring the importance of a holistic research approach to understand the mechanisms of sex dimorphisms in cancer, so sex will be considered as an imperative factor for guiding treatment decisions in the future.