The Missed Patient With Diabetes: How access to health care affects the detection of diabetes

OBJECTIVE—This study examined the association between access to health care and three classifications of diabetes status: diagnosed, undiagnosed, and no diabetes

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Physical activity levels and differences in the prevalence of diabetes between the United States and Canada

Objective: To examine the American–Canadian difference in physical activity and its association with diabetes prevalence. Methods: We used cross-sectional data from nationally representative samples of adults (8688 persons aged ≥ 18 years) participating in the 2004 Joint Canada/U.S. Survey of Health. Using data on up to 22 activities in the past 3 months, we defined 3 physical activity groups (in metabolic equivalents-hours/day) as low (< 1.5), moderate (1.5–2.9), and high (≥ 3.0). We employed logistic regression models in our analyses. Results: Self-reported diabetes prevalence was 7.6% in the U.S. and 5.4% in Canada. The prevalence of low physical activity was considerably higher in the U.S. (70.9%) than in Canada (52.3%), while levels of moderate and high physical activity were higher in Canada (24.6% and 23.1%, respectively) than in the U.S. (14.3% and 14.8%, respectively). Using nationality (Canada as reference) to predict diabetes status, the adjusted odds ratio was 1.48 (95%CI, 1.22–1.79), and became 1.38 (95%CI, 1.15–1.66) when additionally adjusting for physical activity level. We estimate that 20.8% of the U.S.-Canada difference in diabetes prevalence is associated with physical activity. Conclusions: The difference in the prevalence of diabetes between U.S. and Canadian adults may be partially explained by differences in physical activity between the two countries