Prevalence, Morbidity, and Mortality of Men With Sex Chromosome Aneuploidy in the Million Veteran Program Cohort

IMPORTANCE: The reported phenotypes of men with 47,XXY and 47,XYY syndromes include tall stature, multisystem comorbidities, and poor health-related quality of life (HRQOL). However, knowledge about these sex chromosome aneuploidy (SCA) conditions has been derived from studies in the less than 15% of patients who are clinically diagnosed and also lack diversity in age and genetic ancestry. OBJECTIVES: To determine the prevalence of clinically diagnosed and undiagnosed X or Y chromosome aneuploidy among men enrolled in the Million Veteran Program (MVP); to describe military service metrics of men with SCAs; and to compare morbidity and mortality outcomes between men with SCA with and without a clinical diagnosis vs matched controls. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used a case-control recruitment design to select biological males enrolled in the MVP biobank in the US Veterans Administration health care system from 2011 to 2022. Cases were participants with 47,XXY syndrome or 47,XYY syndrome, matched 1:5 with controls based on sex, age, and genetic ancestry. Data were analyzed from January 2022 to December 2023. EXPOSURE: Genomic identification of an additional X or Y chromosome. MAIN OUTCOMES AND MEASURES: Outcomes of interest included prevalence of men with SCAs from genomic analysis; clinical SCA diagnosis; Charlson Comorbidity Index; rates of outpatient, inpatient, and emergency encounters per year; self-reported health outcomes; and standardized mortality ratio. RESULTS: Of 595 612 genotyped males in the MVP, 862 had an additional X chromosome (47,XXY) and 747 had an extra Y chromosome (47,XYY), with the highest prevalence among men with East Asian (47,XXY: 10 of 7313 participants; 47,XYY: 14 of 7313 participants) and European (47,XXY: 725 of 427 143 participants; 47,XYY: 625 of 427 143 participants) ancestry. Mean (SD) age at assessment was 61 (12) years, at which point 636 veterans (74.X%) with 47,XXY and 745 veterans (99%) with 47,XYY remained undiagnosed. Individuals with 47,XXY and 47,XYY had similar military service history, all-cause standardized mortality ratio, and age of death compared with matched controls. Individuals with SCA, compared with controls, had higher Charlson Comorbidity Index scores (47,XXY: mean [SD], 4.30 [2.72] vs controls: mean [SD], 3.90 [2.47]; 47,XYY: mean [SD], 4.45 [2.90] vs controls: mean [SD], 3.82 [2.50]) and health care utilization (eg, median [IQR] outpatient encounters per year: 47,XXY, 22.6 [11.8-37.8] vs controls, 16.8 [9.4-28]; 47,XYY: 21.4 [12.4-33.8] vs controls: 17.0 [9.4-28.2]), while several measures of HRQOL were lower (eg, mean [SD] self-reported physical function: 47,XXY: 34.2 [12] vs control mean [SD] 37.8 [12.8]; 47,XYY: 36.3 [11.6] vs control 37.9 [12.8]). Men with a clinical diagnosis of 47,XXY, compared with individuals without a clinical diagnosis, had higher health care utilization (eg, median [IQR] encounters per year: 26.6 [14.9-43.2] vs 22.2 [11.3-36.0]) but lower Charlson Comorbidity Index scores (mean [SD]: 3.7 [2.7] vs 4.5 [4.1]). CONCLUSION AND RELEVANCE: In this case-control study of men with 47,XXY and 47,XYY syndromes, prevalence of SCA was comparable with estimates in the general population. While these men had successfully served in the military, they had higher morbidity and reported poorer HRQOL with aging. Longer longitudinal follow-up of this sample will be informative for clinical and patient-reported outcomes, the role of ancestry, and mortality statistics

The GPCR-gαs-PKA Signaling Axis Promotes T Cell Dysfunction and Cancer Immunotherapy Failure

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, β1AR and β2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs–DREADD to activate CD8-restricted Gαs signaling and show that a Gαs–PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs–GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies

Domain-swapped T cell receptors improve the safety of TCR gene therapy

T cells engineered to express a tumor-specific αβ T cell receptor (TCR) mediate anti-tumor immunity. However, mispairing of the therapeutic αβ chains with endogenous αβ chains reduces therapeutic TCR surface expression and generates self-reactive TCRs. We report a general strategy to prevent TCR mispairing: swapping constant domains between the α and β chains of a therapeutic TCR. When paired, domain-swapped (ds)TCRs assemble with CD3, express on the cell surface, and mediate antigen-specific T cell responses. By contrast, dsTCR chains mispaired with endogenous chains cannot properly assemble with CD3 or signal, preventing autoimmunity. We validate this approach in cell-based assays and in a mouse model of TCR gene transfer-induced graft-versus-host disease. We also validate a related approach whereby replacement of αβ TCR domains with corresponding γδ TCR domains yields a functional TCR that does not mispair. This work enables the design of safer TCR gene therapies for cancer immunotherapy

Osteoclast Activated FoxP3+ CD8+ T-Cells Suppress Bone Resorption in vitro

BACKGROUND: Osteoclasts are the body's sole bone resorbing cells. Cytokines produced by pro-inflammatory effector T-cells (T(EFF)) increase bone resorption by osteoclasts. Prolonged exposure to the T(EFF) produced cytokines leads to bone erosion diseases such as osteoporosis and rheumatoid arthritis. The crosstalk between T-cells and osteoclasts has been termed osteoimmunology. We have previously shown that under non-inflammatory conditions, murine osteoclasts can recruit naïve CD8 T-cells and activate these T-cells to induce CD25 and FoxP3 (Tc(REG)). The activation of CD8 T-cells by osteoclasts also induced the cytokines IL-2, IL-6, IL-10 and IFN-γ. Individually, these cytokines can activate or suppress osteoclast resorption. PRINCIPAL FINDINGS: To determine the net effect of Tc(REG) on osteoclast activity we used a number of in vitro assays. We found that Tc(REG) can potently and directly suppress bone resorption by osteoclasts. Tc(REG) could suppress osteoclast differentiation and resorption by mature osteoclasts, but did not affect their survival. Additionally, we showed that Tc(REG) suppress cytoskeletal reorganization in mature osteoclasts. Whereas induction of Tc(REG) by osteoclasts is antigen-dependent, suppression of osteoclasts by Tc(REG) does not require antigen or re-stimulation. We demonstrated that antibody blockade of IL-6, IL-10 or IFN-γ relieved suppression. The suppression did not require direct contact between the Tc(REG) and osteoclasts. SIGNIFICANCE: We have determined that osteoclast-induced Tc(REG) can suppress osteoclast activity, forming a negative feedback system. As the CD8 T-cells are activated in the absence of inflammatory signals, these observations suggest that this regulatory loop may play a role in regulating skeletal homeostasis. Our results provide the first documentation of suppression of osteoclast activity by CD8 regulatory T-cells and thus, extend the purview of osteoimmunology

Using Germline Variation to Study Inter-Individual Variability in Cancer Risk and Host Anti-Tumor Immune Response

Cancer is a complex disease driven by genetic variation(1–3). Two main types of genetic variation include germline variation, which is inherited, and somatic variation, which is acquired through environmental exposures and endogenous processes, such as DNA replication. Both types of genetic variation have been critical for precision medicine, tailored to each patient’s individual cancer. Traditionally, germline variation has been used for risk stratification while somatic variation can be used for treatment selection. For patient risk stratification, several genome-wide association studies (GWASs) have been conducted to identify underlying genetic determinants that can predict an individual’s cancer risk. In the GWAS catalog to date, 128,550 associations and over 4,000 publications have been reported(4). Despite the wealth of information provided by GWAS studies, biological insights from these studies are limited and there is still a poor understanding of how inherited variation contributes to cancer behavior. One major limitation of GWAS studies is the lack of clinical information to understand association. Most large databases only contain basic information, such as demographics and cancer status. However, germline variation can underlie important patterns of tumor behaviors, such as the immune microenvironment, cancer driver mutation frequency and response to therapy. A better understanding of these germline-somatic interactions can help to improve precision medicine efforts. Another major limitation of GWAS studies is lack of diverse genetic cohorts. Genetic cohorts are dominated by individuals of European ancestry and few genetic cohorts of underrepresented populations, such as African-American and Hispanic individuals are exist. In this dissertation, I address these limitations through characterization of germline determinants underlying the tumor immune microenvironment and diverse ancestral populations.First, in Chapter 1, I identified and characterized germline variants underlying the tumor immune microenvironment in one of the largest adult cancer cohorts, the Cancer Genome Atlas (TCGA). This analysis was motivated by the fact that few biomarkers for immunotherapy exist and a better understanding of germline determinants underlying tumor-immune interactions are needed, especially considering germline variants are present in immune cells along with cancer cells. I identified tumor immune microenvironment SNPs (TIME-SNPs) underlying 157 SNP-heritable immune phenotype components (IP-components) through our own TCGA analysis. Combining these TIME-SNPs with ones collected from literature, we then evaluated cell-type effects of these variants. Finally, we used various bioinformatic pipelines and databases to determine which TIME-SNPs were implicated in cancer risk, survival and ICB response. We validated one of the genes implicated by our TIME-SNPs as a potential novel immunotherapeutic target.Next, in Chapter 2, I explored TIME-SNPs in a pediatric cancer cohort collected from multiple databases. Pediatric patients have fewer environmental exposures compared to adults and have traditionally not been good candidates for immunotherapy. Building on analysis from Chapter 1, I processed genotypes from multiple pediatric cancer cohorts and specifically explored TIME-SNPs related to antigen presentation and macrophage infiltration as these were prominent IP components explored in Chapter 1. Also, pediatric cancer patients with mismatch repair (MMR) deficiencies could be potential good candidates for immunotherapy. Thus, we explored germline determinants underlying MMR and their associations with the TIME.In Chapter 3, I analyzed one of the largest and most diverse genetic databases, the Million Veteran Program, for ancestry-specific associations in testosterone and prostate cancer. Specifically, analyses conducted were 1) a multi-ancestral analysis of total testosterone levels, 2) discovery and evaluation of an African-ancestry specific polygenic risk score and 3) evaluation of a polygenic hazard score for prostate cancer in a multi-ancestry cohort. Through these analyses, I found that genetic associations can differ based on ancestral background and identified novel ancestry-specific associations that improved prostate cancer prediction.With the culmination of these chapters, I demonstrate that inherited variation underlying the tumor immune microenvironment and diverse populations can improve our understanding of cancer and improve precision medicine efforts