206 research outputs found

    Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

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    This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

    Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

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    Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

    Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

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    Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

    Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

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    Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

    Anti-inflammatory therapy with nebulised dornase alfa in patients with severe COVID-19 pneumonia A Randomised Clinical Trial

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    SARS-CoV2 infection causes severe, life-threatening pneumonia. Hyper-inflammation, coagulopathy and lymphopenia are associated with pathology and poor outcomes in these patients. Cell-free (cf) DNA is prominent in COVID-19 patients, amplifies inflammation and promotes coagulopathy and immune dysfunction. We hypothesized that cf-DNA clearance by nebulised dornase alfa may reduce inflammation and improve disease outcomes. Here, we evaluated the efficacy of nebulized dornase alfa in patients hospitalised with severe COVID-19 pneumonia. In this randomised controlled single-centre phase 2 proof-of-concept trial, we recruited adult patients admitted to hospital that exhibited stable oxygen saturation (≥94%) on supplementary oxygen and a C-reactive protein (CRP) level ≥30mg/L post dexamethasone treatment. Participants were randomized at a 3:1 ratio to receive twice-daily nebulised dornase alfa in addition to best available care (BAC) or BAC alone for seven days or until hospital discharge. A 2:1 ratio of historical controls to treated individuals (HC, 2:1) were included as the primary endpoint comparators. The primary outcome was a reduction in systemic inflammation measured by blood CRP levels over 7 days post-randomisation, or to discharge if sooner. Secondary and exploratory outcomes included time to discharge, time on oxygen, D-dimer levels, lymphocyte counts and levels of circulating cf-DNA. We screened 75 patients and enrolled 39 participants out of which 30 in dornase alfa arm, and 9 in BAC group. We also matched the recruited patients in the treated group (N=30) to historical controls in the BAC group (N=60). For the the primary outcome, 30 patients in the dornase alfa were compared to 69 patients in the BAC group. Dornase alfa treatment reduced CRP by 33% compared to the BAC group at 7-days (P=0.01). The dornase alfa group least squares mean CRP was 23.23 mg/L (95% CI 17.71 to 30.46) and the BAC group 34.82 mg/L (95% CI 28.55 to 42.47). A significant difference was also observed when only randomised participants were compared. Furthermore, compared to the BAC group, the chance of live discharge was increased by 63% in the dornase alfa group (HR 1.63, 95% CI 1.01 to 2.61, P=0.03), lymphocyte counts were improved (least-square mean: 1.08 vs 0.87, P=0.02) and markers of coagulopathy such as D-dimer were diminished (least-square mean: 570.78 vs 1656.96μg/mL, P=0.004). Moreover, the dornase alfa group exhibited lower circulating cf-DNA levels that correlated with CRP changes over the course of treatment. No differences were recorded in the rates and length of stay in the ICU or the time on oxygen between the groups. Dornase alfa was well-tolerated with no serious adverse events reported. In this proof-of-concept study in patients with severe COVID-19 pneumonia, treatment with nebulised dornase alfa resulted in a significant reduction in inflammation, markers of immune pathology and time to discharge. The effectiveness of dornase alfa in patients with acute respiratory infection and inflammation should be investigated further in larger trials

    Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose.

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    Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D

    Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

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    Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles

    Gene-Educational attainment interactions in a Multi-Population Genome-Wide Meta-Analysis Identify Novel Lipid Loci

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