• Appraising the relevance of DNA copy number loss and gain in prostate cancer using whole genome DNA sequence data.

    3 July 2018

    A variety of models have been proposed to explain regions of recurrent somatic copy number alteration (SCNA) in human cancer. Our study employs Whole Genome DNA Sequence (WGS) data from tumor samples (n = 103) to comprehensively assess the role of the Knudson two hit genetic model in SCNA generation in prostate cancer. 64 recurrent regions of loss and gain were detected, of which 28 were novel, including regions of loss with more than 15% frequency at Chr4p15.2-p15.1 (15.53%), Chr6q27 (16.50%) and Chr18q12.3 (17.48%). Comprehensive mutation screens of genes, lincRNA encoding sequences, control regions and conserved domains within SCNAs demonstrated that a two-hit genetic model was supported in only a minor proportion of recurrent SCNA losses examined (15/40). We found that recurrent breakpoints and regions of inversion often occur within Knudson model SCNAs, leading to the identification of ZNF292 as a target gene for the deletion at 6q14.3-q15 and NKX3.1 as a two-hit target at 8p21.3-p21.2. The importance of alterations of lincRNA sequences was illustrated by the identification of a novel mutational hotspot at the KCCAT42, FENDRR, CAT1886 and STCAT2 loci at the 16q23.1-q24.3 loss. Our data confirm that the burden of SCNAs is predictive of biochemical recurrence, define nine individual regions that are associated with relapse, and highlight the possible importance of ion channel and G-protein coupled-receptor (GPCR) pathways in cancer development. We concluded that a two-hit genetic model accounts for about one third of SCNA indicating that mechanisms, such haploinsufficiency and epigenetic inactivation, account for the remaining SCNA losses.

  • Pan-cancer analysis of homozygous deletions in primary tumours uncovers rare tumour suppressors.

    29 June 2018

    Homozygous deletions are rare in cancers and often target tumour suppressor genes. Here, we build a compendium of 2218 primary tumours across 12 human cancer types and systematically screen for homozygous deletions, aiming to identify rare tumour suppressors. Our analysis defines 96 genomic regions recurrently targeted by homozygous deletions. These recurrent homozygous deletions occur either over tumour suppressors or over fragile sites, regions of increased genomic instability. We construct a statistical model that separates fragile sites from regions showing signatures of positive selection for homozygous deletions and identify candidate tumour suppressors within those regions. We find 16 established tumour suppressors and propose 27 candidate tumour suppressors. Several of these genes (including MGMT, RAD17, and USP44) show prior evidence of a tumour suppressive function. Other candidate tumour suppressors, such as MAFTRR, KIAA1551, and IGF2BP2, are novel. Our study demonstrates how rare tumour suppressors can be identified through copy number meta-analysis.

  • The genetic underpinnings of body fat distribution

    3 July 2018

    © 2017 Informa UK Limited, trading as Taylor & Francis Group. Introduction: Obesity, defined as a body mass index (BMI) ≥ 30 kg/m 2 , has reached epidemic proportions; people who are overweight (BMI > 25 kg/m 2 ) or obese now comprise more than 25% of the world’s population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered: The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed > 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous sy stem in obesity-related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary: Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro, will be crucial in translating genome-wide association signals to the causal mechanisms driving disease.

  • If you come from a well-known organisation, I will trust you: Exploring and understanding the community's attitudes towards healthcare research in Cambodia.

    3 July 2018

    To explore Cambodian community members' understanding of and attitudes towards healthcare research.This qualitative study generated data from semi-structured interviews and focus group discussions. This study was conducted at a non-governmental paediatric hospital and in nearby villages in Siem Reap province, Cambodia. A total of ten semi-structured interviews and four focus group discussions were conducted, involving 27 participants. Iterative data collection and analysis were performed concurrently. Data were analysed by thematic content analysis and the coding structure was developed using relevant literature.Participants did not have a clear understanding of what activities related to research compared with those for routine healthcare. Key attitudes towards research were responsibility and trust: personal (trust of the researcher directly) and institutional (trust of the institution as a whole). Villagers believe the village headman holds responsibility for community activities, while the village headman believes that this responsibility should be shared across all levels of the government system.It is essential for researchers to understand the structure and relationship within the community they wish to work with in order to develop trust among community participants. This aids effective communication and understanding among all parties, enabling high quality ethical research to be conducted.

  • Lowering LDL cholesterol reduces cardiovascular risk independently of presence of inflammation.

    3 July 2018

    Markers of inflammation, including plasma C-reactive protein (CRP), are associated with an increased risk of cardiovascular disease, and it has been suggested that this association is causal. However, the relationship between inflammation and cardiovascular disease has not been extensively studied in patients with chronic kidney disease. To evaluate this, we used data from the Study of Heart and Renal Protection (SHARP) to assess associations between circulating CRP and LDL cholesterol levels and the risk of vascular and non-vascular outcomes. Major vascular events were defined as nonfatal myocardial infarction, cardiac death, stroke or arterial revascularization, with an expanded outcome of vascular events of any type. Higher baseline CRP was associated with an increased risk of major vascular events (hazard ratio per 3x increase 1.28; 95% confidence interval 1.19-1.38). Higher baseline LDL cholesterol was also associated with an increased risk of major vascular events (hazard ratio per 0.6 mmol/L higher LDL cholesterol; 1.14, 1.06-1.22). Higher baseline CRP was associated with an increased risk of a range of non-vascular events (1.16, 1.12-1.21), but there was a weak inverse association between baseline LDL cholesterol and non-vascular events (0.96, 0.92-0.99). The efficacy of lowering LDL cholesterol with simvastatin/ezetimibe on major vascular events, in the randomized comparison, was similar irrespective of CRP concentration at baseline. Thus, decisions to offer statin-based therapy to patients with chronic kidney disease should continue to be guided by their absolute risk of atherosclerotic events. Estimation of such risk may include plasma biomarkers of inflammation, but there is no evidence that the relative beneficial effects of reducing LDL cholesterol depends on plasma CRP concentration.

  • The Association of Serum Free Light Chains With Mortality and Progression to End-Stage Renal Disease in Chronic Kidney Disease: Systematic Review and Individual Patient Data Meta-analysis.

    3 July 2018

    To clarify the associations between polyclonal serum free light chain (sFLC) levels and adverse outcomes in patients with chronic kidney disease (CKD) by conducting a systematic review and individual patient data meta-analyses.On December 28, 2016, we searched 4 databases (MEDLINE, Embase, CINAHL, and PubMed) and conference proceedings for studies presenting independent analyses of associations between sFLC levels and mortality or progression to end-stage renal disease (ESRD) in patients with CKD. Study quality was assessed in 5 domains: sample selection, measurement, attrition, reporting, and funding.Five prospective cohort studies were included, judged moderate to good quality, involving 3912 participants in total. In multivariable meta-analyses, sFLC (kappa+lambda) levels were independently associated with mortality (5 studies, 3680 participants; hazard ratio [HR], 1.04 [95% CI, 1.03-1.06] per 10 mg/L increase in sFLC levels) and progression to ESRD (3 studies, 1848 participants; HR, 1.01 [95% CI, 1.00-1.03] per 10 mg/L increase in sFLC levels). The sFLC values above the upper limit of normal (43.3 mg/L) were independently associated with mortality (HR, 1.45 [95% CI, 1.14-1.85]) and ESRD (HR, 3.25 [95% CI, 1.32-7.99]).Higher levels of sFLCs are independently associated with higher risk of mortality and ESRD in patients with CKD. Future work is needed to explore the biological role of sFLCs in adverse outcomes in CKD, and their use in risk stratification.

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    The Big Data Institute (BDI) is a new, interdisciplinary research centre that focuses on the analysis of large, complex, heterogeneous data sets for research into the causes and consequences, prevention and treatment of disease. Big Data methods are transforming the scale (breadth, depth and duration) and efficiency (data accumulation, storage, processing and dissemination) of large-scale clinical research. The work of the BDI requires people and projects that span traditional departmental boundaries and scientific disciplines, supported by technical resources to handle the vast quantities of data they generate.

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