The compositional and metabolic responses of gilthead seabream (Sparus aurata) to a gradient of dietary fish oil and associated n-3 long-chain PUFA content

The authors express their gratitude to the technical team at the BioMar Feed Trial Unit, Hirtshals, in particular, Svend Jørgen Steenfeldt for expert care of the experimental subjects, for training and supervision provided by laboratory staff at Nutrition Analytical Services and Molecular Biology at the Institute of Aquaculture, University of Stirling, UK. S. J. S. H’s. PhD was co-funded by BioMar and the Marine Alliance for Science and Technology Scotland. BioMar provided the experimental feeds, trial facilities and fish, and covered travel expenses. V. K. and J. T. designed and executed the nutritional trial and all authors contributed to planning the analyses. V. K., J. T. and S. J. S. H. carried out the sampling. O. M., D. R. T and S. A. M. M. supervised the lead author. M. B. B. provided training in molecular biology to S. J. S. H. who carried out all analytical procedures. S. J. S. H. analysed all of the data and prepared the manuscript. Subsequently the manuscript was shared between all authors who made amendments, contributions and recommendations. The authors declare that there are no conflicts of interestPeer reviewedPublisher PD

Effects of inaccuracies in arterial path length measurement on differences in MRI and tonometry measured pulse wave velocity

Abstract Background Carotid-femoral pulse wave velocity (cf-PWV) and aortic PWV measured using MRI (MRI-PWV) show good correlation, but with a significant and consistent bias across studies. The aim of the current study was to evaluate whether the differences between cf.-PWV and MRI-PWV can be accounted for by inaccuracies of currently used distance measurements. Methods One hundred fourteen study participants were recruited into one of 4 groups: Type 2 diabetes melltus (T2DM) with cardiovascular disease (CVD) (n = 23), T2DM without CVD (n = 41), CVD without T2DM (n = 25) and a control group (n = 25). All participants underwent cf.-PWV, cardiac MRI and whole body MR angiography(WB-MRA). 90 study participants also underwent aortic PWV using MRI. cf.-PWVEXT was performed using a SphygmoCor device (Atcor Medical, West Ryde, Australia). The true intra-arterial pathlength was measured using the WB-MRA and then used to recalculate the cf.-PWVEXT to give a cf.-PWVMRA. Results Distance measurements were significantly lower on WB-MRA than on external tape measure (mean diff = −85.4 ± 54.0 mm,p < 0.001). MRI-PWV was significantly lower than cf.-PWVEXT (MRI-PWV = 8.1 ± 2.9 vs. cf.-PWVEXT = 10.9 ± 2.7 ms−1,p < 0.001). When cf.-PWV was recalculated using the inter-arterial distance from WB-MRA, this difference was significantly reduced but not lost (MRI-PWV = 8.1 ± 2.9 ms−1 vs. cf.-PWVMRA 9.1 ± 2.1 ms−1, mean diff = −0.96 ± 2.52 ms−1,p = 0.001). Recalculation of the PWV increased correlation with age and pulse pressure. Conclusion Differences in cf.-PWV and MRI PWV can be predominantly but not entirely explained by inaccuracies introduced by the use of simple surface measurements to represent the convoluted arterial path between the carotid and femoral arteries

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

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

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

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

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

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

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

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

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation