Dataset Information

How to Better Understand the Influence of Host Genetics on Developing an Effective Immune Response to Thoracic Cancers.

ABSTRACT: Thoracic cancers pose a significant global health burden. Immune checkpoint blockade therapies have improved treatment outcomes, but durable responses remain limited. Understanding how the host immune system interacts with a developing tumor is essential for the rational development of improved treatments for thoracic malignancies. Recent technical advances have improved our understanding of the mutational burden of cancer cells and changes in cancer-specific gene expression, providing a detailed understanding of the complex biology underpinning tumor-host interactions. While there has been much focus on the genetic alterations associated with cancer cells and how they may impact treatment outcomes, how host genetics affects cancer development is also critical and will greatly determine treatment response. Genome-wide association studies (GWAS) have identified genetic variants associated with cancer predisposition. This approach has successfully identified host genetic risk factors associated with common thoracic cancers like lung cancer, but is less effective for rare cancers like malignant mesothelioma. To assess how host genetics impacts rare thoracic cancers, we used the Collaborative Cross (CC); a powerful murine genetic resource designed to maximize genetic diversity and rapidly identify genes associated with any biological trait. We are using the CC in conjunction with our asbestos-induced MexTAg mouse model, to identify host genes associated with mesothelioma development. Once genes that moderate tumor development and progression are known, human homologues can be identified and human datasets interrogated to validate their association with disease outcome. Furthermore, our CC-MexTAg animal model enables in-depth study of the tumor microenvironment, allowing the correlation of immune cell infiltration and gene expression signatures with disease development. This strategy provides a detailed picture of the underlying biological pathways associated with mesothelioma susceptibility and progression; knowledge that is crucial for the rational development of new diagnostic and therapeutic strategies. Here we discuss the influence of host genetics on developing an effective immune response to thoracic cancers. We highlight current knowledge gaps, and with a focus on mesothelioma, describe the development and application of the CC-MexTAg to overcome limitations and illustrate how the knowledge gained from this unique study will inform the rational design of future treatments of mesothelioma.

SUBMITTER: Behrouzfar K

PROVIDER: S-EPMC8256168 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundDepression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women.MethodsWe performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways.ResultsHigher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence.ConclusionsHigher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.

Project description:BackgroundHuman genetics and host-associated microbial communities have been associated independently with a wide range of chronic diseases. One of the strongest associations in each case is inflammatory bowel disease (IBD), but disease risk cannot be explained fully by either factor individually. Recent findings point to interactions between host genetics and microbial exposures as important contributors to disease risk in IBD. These include evidence of the partial heritability of the gut microbiota and the conferral of gut mucosal inflammation by microbiome transplant even when the dysbiosis was initially genetically derived. Although there have been several tests for association of individual genetic loci with bacterial taxa, there has been no direct comparison of complex genome-microbiome associations in large cohorts of patients with an immunity-related disease.MethodsWe obtained 16S ribosomal RNA (rRNA) gene sequences from intestinal biopsies as well as host genotype via Immunochip in three independent cohorts totaling 474 individuals. We tested for correlation between relative abundance of bacterial taxa and number of minor alleles at known IBD risk loci, including fine mapping of multiple risk alleles in the Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) gene exon. We identified host polymorphisms whose associations with bacterial taxa were conserved across two or more cohorts, and we tested related genes for enrichment of host functional pathways.ResultsWe identified and confirmed in two cohorts a significant association between NOD2 risk allele count and increased relative abundance of Enterobacteriaceae, with directionality of the effect conserved in the third cohort. Forty-eight additional IBD-related SNPs have directionality of their associations with bacterial taxa significantly conserved across two or three cohorts, implicating genes enriched for regulation of innate immune response, the JAK-STAT cascade, and other immunity-related pathways.ConclusionsThese results suggest complex interactions between genetically altered host functional pathways and the structure of the microbiome. Our findings demonstrate the ability to uncover novel associations from paired genome-microbiome data, and they suggest a complex link between host genetics and microbial dysbiosis in subjects with IBD across independent cohorts.

Project description:The outbreak of coronavirus disease 2019 (COVID-19) was caused by the newly emerged corona virus (2019-nCoV alias SARS-CoV-2) that resembles the severe acute respiratory syndrome virus (SARS-CoV). SARS-CoV-2, which was first identified in Wuhan (China) has spread globally, resulting in a high mortality worldwide reaching ~4 million deaths to date. As of first week of July 2021, ~181 million cases of COVID-19 have been reported. SARS-CoV-2 infection is mediated by the binding of virus spike protein to Angiotensin Converting Enzyme 2 (ACE2). ACE2 is expressed on many human tissues; however, the major entry point is probably pneumocytes, which are responsible for synthesis of alveolar surfactant in lungs. Viral infection of pneumocytes impairs immune responses and leads to, apart from severe hypoxia resulting from gas exchange, diseases with serious complications. During viral infection, gene products (e.g. ACE2) that mediate viral entry, antigen presentation, and cellular immunity are of crucial importance. Human leukocyte antigens (HLA) I and II present antigens to the CD8+ and CD4+ T lymphocytes, which are crucial for immune defence against pathogens including viruses. HLA gene variants affect the recognition and presentation of viral antigenic peptides to T-cells, and cytokine secretion. Additionally, endoplasmic reticulum aminopeptidases (ERAP) trim antigenic precursor peptides to fit into the binding groove of MHC class I molecules. Polymorphisms in ERAP genes leading to aberrations in ERAP's can alter antigen presentation by HLA class I molecules resulting in aberrant T-cell responses, which may affect susceptibility to infection and/or activation of immune response. Polymorphisms from these genes are associated, in global genetic association studies, with various phenotype traits/disorders many of which are related to the pathogenesis and progression of COVID-19; polymorphisms from various genes are annotated in genotype-tissue expression data as regulating the expression of ACE2, HLA's and ERAP's. We review such polymorphisms and illustrate variations in their allele frequencies in global populations. These reported findings highlight the roles of genetic modulators (e.g. genotype changes in ACE2, HLA's and ERAP's leading to aberrations in the expressed gene products or genotype changes at other genes regulating the expression levels of these genes) in the pathogenesis of viral infection.

Dataset Information

How to Better Understand the Influence of Host Genetics on Developing an Effective Immune Response to Thoracic Cancers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets