Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

All clinically-relevant blood components transmit prion disease following a single blood transfusion: a sheep model of vCJD

Variant CJD (vCJD) is an incurable, infectious human disease, likely arising from the consumption of BSE-contaminated meat products. Whilst the epidemic appears to be waning, there is much concern that vCJD infection may be perpetuated in humans by the transfusion of contaminated blood products. Since 2004, several cases of transfusion-associated vCJD transmission have been reported and linked to blood collected from pre-clinically affected donors. Using an animal model in which the disease manifested resembles that of humans affected with vCJD, we examined which blood components used in human medicine are likely to pose the greatest risk of transmitting vCJD via transfusion. We collected two full units of blood from BSE-infected donor animals during the pre-clinical phase of infection. Using methods employed by transfusion services we prepared red cell concentrates, plasma and platelets units (including leucoreduced equivalents). Following transfusion, we showed that all components contain sufficient levels of infectivity to cause disease following only a single transfusion and also that leucoreduction did not prevent disease transmission. These data suggest that all blood components are vectors for prion disease transmission, and highlight the importance of multiple control measures to minimise the risk of human to human transmission of vCJD by blood transfusion

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

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

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

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

3D genomics across the tree of life reveals condensin II as a determinant of architecture type

We investigated genome folding across the eukaryotic tree of life. We find two types of three-dimensional(3D) genome architectures at the chromosome scale. Each type appears and disappears repeatedlyduring eukaryotic evolution. The type of genome architecture that an organism exhibits correlates with theabsence of condensin II subunits. Moreover, condensin II depletion converts the architecture of thehuman genome to a state resembling that seen in organisms such as fungi or mosquitoes. In this state,centromeres cluster together at nucleoli, and heterochromatin domains merge. We propose a physicalmodel in which lengthwise compaction of chromosomes by condensin II during mitosis determineschromosome-scale genome architecture, with effects that are retained during the subsequent interphase.This mechanism likely has been conserved since the last common ancestor of all eukaryotes.C.H. is supported by the Boehringer Ingelheim Fonds; C.H., Á.S.C., and B.D.R. are supported by an ERC CoG (772471, “CohesinLooping”); A.M.O.E. and B.D.R. are supported by the Dutch Research Council (NWO-Echo); and J.A.R. and R.H.M. are supported by the Dutch Cancer Society (KWF). T.v.S. and B.v.S. are supported by NIH Common Fund “4D Nucleome” Program grant U54DK107965. H.T. and E.d.W. are supported by an ERC StG (637597, “HAP-PHEN”). J.A.R., T.v.S., H.T., R.H.M., B.v.S., and E.d.W. are part of the Oncode Institute, which is partly financed by the Dutch Cancer Society. Work at the Center for Theoretical Biological Physics is sponsored by the NSF (grants PHY-2019745 and CHE-1614101) and by the Welch Foundation (grant C-1792). V.G.C. is funded by FAPESP (São Paulo State Research Foundation and Higher Education Personnel) grants 2016/13998-8 and 2017/09662-7. J.N.O. is a CPRIT Scholar in Cancer Research. E.L.A. was supported by an NSF Physics Frontiers Center Award (PHY-2019745), the Welch Foundation (Q-1866), a USDA Agriculture and Food Research Initiative grant (2017-05741), the Behavioral Plasticity Research Institute (NSF DBI-2021795), and an NIH Encyclopedia of DNA Elements Mapping Center Award (UM1HG009375). Hi-C data for the 24 species were created by the DNA Zoo Consortium (www.dnazoo.org). DNA Zoo is supported by Illumina, Inc.; IBM; and the Pawsey Supercomputing Center. P.K. is supported by the University of Western Australia. L.L.M. was supported by NIH (1R01NS114491) and NSF awards (1557923, 1548121, and 1645219) and the Human Frontiers Science Program (RGP0060/2017). The draft A. californica project was supported by NHGRI. J.L.G.-S. received funding from the ERC (grant agreement no. 740041), the Spanish Ministerio de Economía y Competitividad (grant no. BFU2016-74961-P), and the institutional grant Unidad de Excelencia María de Maeztu (MDM-2016-0687). R.D.K. is supported by NIH grant RO1DK121366. V.H. is supported by NIH grant NIH1P41HD071837. K.M. is supported by a MEXT grant (20H05936). M.C.W. is supported by the NIH grants R01AG045183, R01AT009050, R01AG062257, and DP1DK113644 and by the Welch Foundation. E.F. was supported by NHGR