PyMT-Maclow: A novel, inducible, murine model for determining the role of CD68 positive cells in breast tumor development

CD68+ tumor-associated macrophages (TAMs) are pro-tumorigenic, pro-angiogenic and are associated with decreased survival rates in patients with cancer, including breast cancer. Non-specific models of macrophage ablation reduce the number of TAMs and limit the development of mammary tumors. However, the lack of specificity and side effects associated with these models compromise their reliability. We hypothesized that specific and controlled macrophage depletion would provide precise data on the effects of reducing TAM numbers on tumor development. In this study, the MacLow mouse model of doxycycline-inducible and selective CD68+ macrophage depletion was crossed with the murine mammary tumor virus (MMTV)-Polyoma virus middle T antigen (PyMT) mouse model of spontaneous ductal breast adenocarcinoma to generate the PyMT-MacLow line. In doxycycline-treated PyMT-MacLow mice, macrophage numbers were decreased in areas surrounding tumors by 43%. Reducing the number of macrophages by this level delayed tumor progression, generated less proliferative tumors, decreased the vascularization of carcinomas and down-regulated the expression of many pro-angiogenic genes. These results demonstrate that depleting CD68+ macrophages in an inducible and selective manner delays the development of mammary tumors and that the PyMT-MacLow model is a useful and unique tool for studying the role of TAMs in breast cancer

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Variants of EVER1 and EVER2 (TMC6 and TMC8) and human papillomavirus status in patients with mucosal squamous cell carcinoma of the head and neck

Published online: 20 April 2016Purpose: There is a growing association of human papillomavirus (HPV) with some cases of mucosal squamous cell carcinoma of the head and neck (HNSCC), particularly of the oropharynx. Persistent oral HPV infection is believed to increase the likelihood of malignancy, and it is possible that host genetic factors can determine susceptibility to persistent HPV infection. Polymorphisms in the two EV genes (EVER1 and EVER2, also known as transmembrane channel protein (TMC) 6 and 8) have been identified as strong candidate genes, since a small number of critical mutations in these genes have been shown to cause profound and florid skin HPV infections, and some of them have been linked to susceptibility to cervical cancer. Methods: We sought to determine whether there was a difference in the frequency of single nucleotide polymorphisms (SNPs) in EVER1 (rs2613516, rs12449858) and EVER2 (rs7205422, rs12452890) between HNSCC patients with HPV-positive and HPV-negative tumors, and healthy controls. We used logistic regression to analyze SNPs in 219 patients with histologically confirmed primary SCC of the oropharynx, oral cavity, hypopharynx, or larynx, and 321 healthy controls. Results: We did not find any associations with the EVER1/ EVER2 SNPs and HPV status or being a HNSCC case or a control. Conclusions: The present data do not provide evidence for a role of genetic variations in EVER1 or EVER2 for HPV status of mucosal HNSCC or between HNSCC patients and controls.Annika Antonsson, Matthew H. Law, Rachel E. Neale, William B. Coman, David I. Pryor, Study of Digestive Health (SDH), Sandro V. Porceddu, David C. Whiteman (The SDH Study Group: Chief investigators Paul Drew, Glyn Jamieson (University of Adelaide) ; Clinical collaborator William Tam (Royal Adelaide Hospital)

Does polygenic risk influence associations between sun exposure and melanoma?: a prospective cohort analysis

Melanoma develops as the result of complex interactions between sun exposure and genetic factors. Data on these interactions from prospective studies are scant however. We aimed to quantify the association between ambient and personal ultraviolet (UV) exposure and incident melanoma in a large population‐based prospective study of men and women residing in a high ambient UV setting, and to examine potential gene‐environment interactions. Among participants with genetic data (n=15,373), 420 (2.7%) developed cutaneous melanoma (173 invasive, 247 in situ) during a median follow‐up time of 4.4 years. Country of birth, age at migration, having greater than 50 sunburns in childhood/adolescence and a history of keratinocyte cancer/actinic lesions were significantly associated with melanoma risk. An interaction with polygenic risk was suggested; among people at low polygenic risk, markers of cumulative sun exposure (as measured by actinic damage) were associated with melanoma. In contrast, among people at high polygenic risk, markers of high‐level early life ambient exposure (as measured by place of birth) were associated with melanoma (HR for born in Australia vs. overseas 3.16, 95% CI 1.39‐7.22). These findings suggest interactions between genotype and environment that are consistent with divergent pathways for melanoma development

Genetic variants for smoking behaviour and risk of skin cancer

Observational studies have suggested that smoking may increase the risk of cutaneous squamous cell carcinoma (cSCC) while decreasing the risks of basal cell carcinoma (BCC), and melanoma. However, it remains possible that confounding by other factors may explain these associations. The aim of this investigation was to use Mendelian randomization (MR) to test whether smoking is associated with skin cancer, independently of other factors. Two-sample MR analyses were conducted to determine the causal effect of smoking measures on skin cancer risk using genome-wide association study (GWAS) summary statistics. We used the inverse-variance-weighted estimator to derive separate risk estimates across genetic instruments for all smoking measures. A genetic predisposition to smoking initiation was associated with lower risks of all skin cancer types, although none of the effect estimates reached statistical significance (OR 95% CI BCC 0.91, 0.82–1.01; cSCC 0.82, 0.66–1.01; melanoma 0.91, 0.82–1.01). Results for other measures were similar to smoking initiation with the exception of smoking intensity which was associated with a significantly reduced risk of melanoma (OR 0.67, 95% CI 0.51–0.89). Our findings support the findings of observational studies linking smoking to lower risks of melanoma and BCC. However, we found no evidence that smoking is associated with an elevated risk of cSCC; indeed, our results are most consistent with a decreased risk, similar to BCC and melanoma