Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant is highly transmissible and evades pre-established immunity. Messenger RNA (mRNA) vaccination against ancestral strain spike protein can induce intact T-cell immunity against the Omicron variant, but efficacy of booster vaccination in patients with late-stage lung cancer on immune-modulating agents including anti-programmed cell death protein 1(PD-1)/programmed death-ligand 1 (PD-L1) has not yet been elucidated.
Methods We assessed T-cell responses using a modified activation-induced marker assay, coupled with high-dimension flow cytometry analyses. Peripheral blood mononuclear cells (PBMCs) were stimulated with various viral peptides and antigen-specific T-cell responses were evaluated using flow cytometry.
Results Booster vaccines induced CD8+ T-cell response against the ancestral SARS-CoV-2 strain and Omicron variant in both non-cancer subjects and patients with lung cancer, but only a marginal induction was detected for CD4+ T cells. Importantly, antigen-specific T cells from patients with lung cancer showed distinct subpopulation dynamics with varying degrees of differentiation compared with non-cancer subjects, with evidence of dysfunction. Notably, female-biased T-cell responses were observed.
Conclusion We conclude that patients with lung cancer on immunotherapy show a substantial qualitative deviation from non-cancer subjects in their T-cell response to mRNA vaccines, highlighting the need for heightened protective measures for patients with cancer to minimize the risk of breakthrough infection with the Omicron and other future variants.
Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package ‘LRT’, and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps ‘shinyClone’ and ‘shinyClust’ that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.
Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduced MarsGT: Multi-omics Analysis for Rare population inference using Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperformed existing tools in identifying rare cells across 400 simulated and four real human datasets. In mouse retina data, it revealed unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detected an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identified a rare MAIT-like population impacted by a high IFN-I response and revealed the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.
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.
