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

    12 January 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.

    9 January 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

    9 January 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.

  • 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.

    25 December 2017

    OBJECTIVE: 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. PATIENTS AND METHODS: 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. RESULTS: 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]). CONCLUSION: 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 will focus on the analysis of large, complex, heterogeneous data sets for research into the causes and consequences, prevention and treatment of disease. Research will be conducted in 4 general themes: genomics, population health, infectious disease surveillance, and methodology (including informatics, statistics, and engineering). Big Data methods could transform 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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