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• Epstein-Barr Virus status drives morphological and molecular intra-tumour heterogeneity in gastric cancer: insights from a case report and literature review

  This case report describes a rare case of bi-phenotypic gastric cancer with two distinct, but clonally related, histological components. The first component, associated with Epstein-Barr virus (EBV) infection, exhibited the morphological features of gastric carcinoma with lymphoid stroma, suggesting that EBV, as an effective immunogenic factor, may trigger a prominent immune response within the tumour microenvironment. The second component, which was EBV-negative, displayed tubular/papillary morphology and features of increased biological aggressiveness, such as high-grade areas and lymphatic invasion. Immunohistochemical and molecular studies confirmed that, despite the differing morphologies and immunophenotypes, both components were clonally related, with especially the EBV-negative area showing more complex DNA aberrations, reminiscent of Chromosomally instable (CIN) lesions. This case describes clonally related EBV-positive and negative components within a single gastric cancer, contributing to a better understanding of EBV role in tumour heterogeneity and progression and highlighting the impact of EBV loss on tumour behaviour.

  Study EGAS50000000706

• Bulk RNA sequencing of day 2 and day 7 biopsies from the tuberculin skin test in people with latent tuberculosis, and of day 2 biopsies from saline controls

  We used the tuberculin skin test (TST) as human challenge model to study the temporal evolution of the immune response to Mycobacterium tuberculosis (Mtb) antigens in vivo. Study participants comprised healthy HIV seronegative adults, 18-60 years of age. Latent tuberculosis (TB) infection was defined as immune memory for Mtb-specific antigens identified by positive peripheral blood IFNg release assays, but no clinical or radiological evidence of active TB. Two units tuberculin each were injected intradermally into the contralateral forearms of participants. After 2 days (n=216) or 7 days (n=158), 3 mm skin punch biopsies were taken from the injection sites and processed for whole genome transcriptional profiling by bulk RNA sequencing. The time points reflect maximum clinical inflammation (day 2) and maximum T cell infiltration (day 7) of the TST. TST samples were compared to skin biopsies taken two days after control injection of saline in a separate group of individuals (n=33), comprising healthy volunteers as well as patients with active or latent TB, ranging from 18-75 years of age. This dataset includes 407 samples from 256 individuals (n=33 saline, n=223 with Day 2 and/or Day 7 TST). Skin biopsies were collected into RNAlater (Qiagen) for RNA extraction. Total RNA was obtained with the Precellys Evolution homogenizer (Bertin Instruments) and Qiagen RNeasy mini kit. RNA was subjected to DNase treatment to remove contaminating genomic DNA using a TURBO DNA-free kit (Ambion, Life Technologies). The KAPA mRNA HyperPrep Kit (Roche Diagnostics) was used to construct stranded mRNA-Seq libraries from up to 500 ng intact total RNA. Paired-end sequencing was performed on the Illumina Nextseq using the Nextseq 500/550 High Output 75 cycle kit (Illumina). Runs were demultiplexed using bcl2fastq by Illumina.

  Dataset EGAD50000001208

• Data Use Ontology (DUO) at EGA

  For those of you that follow the updates of GA4GH you will have seen the unanimous approval of the steering committee for DUO to be included in its suite of technical standards for the sharing of genomic and health related data. DUO has three main features: Each term has been generated by the community and includes a human readable definition that can be expanded where necessary. It is a machine-readable file that encodes how that data can be used and how a researcher intends to use the data. Can be implemented alongside an advanced search algorithm which would allow authenticated users to query and gain access to datasets pertaining to their research. e.g., an industry researcher working on cancer could potentially be matched to any dataset that is allowed for commercial use and for cancer research and offered the opportunity to fetch them automatically. So, what does this mean for EGA? EGA as a driver project has adopted this standard and will be utilizing it for two main purposes in the first instance. i) It will allow EGA users to instantly identify what terms they will need to agree to as part of any Data Access Agreements. This will save valuable time for both applicants and Data Access Committees alike - as you will be able to see if you are likely to be able to access the data based on your research intentions and working background. ii) In the future it will allow users to be able to search for data based on Data Use Ontology terms e.g., you could search for all data that can be used for General Research Use (GRU). As EGA moves forwards submitters will be asked to align their data access policies with DUO so that their dataset(s) can be tagged appropriately on our web pages. If you would like to add DUO to any of your existing datasets, or to any future datasets, do please just let us know.

  Blog data-use-ontology

• The spatial organization of intratumor heterogeneity andevolutionary trajectories of metastases in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) has one of the poorest survival amongst cancers. Using multi-regional sampling of nine resected HCC with different etiologies, we find that phylogenetic relationships of these sectors show diverse levels of genetic sharing spanning early to late diversification. Unlike the variegated pattern found in colorectal cancers, a large proportion of HCC display a clear Isolation-By-Distance pattern where spatially closer sectors are genetically more similar. Two resected intra-hepatic metastases showed genetic divergence occurring before and after primary tumor diversification respectively. Interestingly, metastatic tumors had much higher variability than their primary tumors, suggesting that intra-hepatic metastasis is accompanied by rapid diversification at the distant location. The presence of co-existing mutations offers the possibility of drug repositioning for HCC treatment. Taken together, these insights into intra-tumor heterogeneity allow for a comprehensive understanding of the evolutionary trajectories of HCC and suggest novel avenues for personalized therapy.

  Study EGAS00001001603

• A comprehensive assessment of somatic mutation detection in cancer using whole genome sequencing

  As whole genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Using tumor-normal sample pairs from two different types of cancer, chronic lymphocytic leukemia and medulloblastoma, we conducted a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines, and even validation methods. Here we show that using PCR-free methods and increasing sequencing depth to ~100x showed benefits, as long as the tumor:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artifact-prone nature of the raw data and lack of standards for dealing with the artifacts. However, armed with the benchmark mutation set we have created, we show that many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

  Study EGAS00001001539

• Gut metagenome associations with extensive digital health data in a volunteer-based EstMB cohort

  Microbiome research is starting to move beyond the exploratory phase towards interventional trials and therefore well-characterized cohorts will be instrumental for generating hypothesis and providing new knowledge. As part of the Estonian Biobank, we established the Estonian Microbiome Cohort which includes stool, oral and plasma samples from 2,509 participants and is supplemented with multi-omic measurements, questionnaires, and regular linkages to national electronic health records. In this study, we analyzed stool data from deep metagenomic sequencing together with rich phenotyping, including 71 diseases, 136 medications, 21 dietary questions, 5 procedures, and 19 other factors. The data revealed numerous relationships with different microbiome features. Additionally, the long-term antibiotic usage, independent from recent administration, has a significant impact on the microbiome composition, partly explaining the common associations between diseases. Overall, this study extends the understanding of microbiome–host interactions and facilitates the development of microbiome-related studies.

  Study EGAS00001005900

• Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs drug target discovery in ankylosing spondylitis

  Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterised by enthesitis of the spine and sacroiliac joints. Genome-wide association studies have revealed >100 genetic associations but their functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We report differential disease signatures for specific genomic regions from individual -omic modalities and multi-omic integration, showing enrichment at genetically associated loci and evidence for altered monocyte function in AS. We link putative functional SNPs with cognate genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1. We also show how disease-specific functional genomic data can be integrated with GWAS evidence to enhance therapeutic target discovery. This study combines multiple epigenetic and transcriptional analysis with GWAS to identify cell types, candidate drug target genes and transcriptional regulation of likely pathogenic relevance.

  Study EGAS00001006233

• Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

  Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. Strikingly, we observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19.

  Study EGAS00001006778

• Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs drug target discovery in ankylosing spondylitis

  Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterised by enthesitis of the spine and sacroiliac joints. Genome-wide association studies have revealed >100 genetic associations but their functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We report differential disease signatures for specific genomic regions from individual -omic modalities and multi-omic integration, showing enrichment at genetically associated loci and evidence for altered monocyte function in AS. We link putative functional SNPs with cognate genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1. We also show how disease-specific functional genomic data can be integrated with GWAS evidence to enhance therapeutic target discovery. This study combines multiple epigenetic and transcriptional analysis with GWAS to identify cell types, candidate drug target genes and transcriptional regulation of likely pathogenic relevance.

  Study EGAS00001006945

• An isogenic human iPSC model unravels neurodevelopmental abnormalities in SMA

  Whether neurodevelopmental defects underlie the selective neuronal death that characterizes neurodegenerative diseases is becoming an intriguing question. To address it, we focused on the motor neuron (MN) disease Spinal Muscular Atrophy (SMA), caused by reduced levels of the ubiquitous protein SMN. Taking advantage of the first isogenic human induced pluripotent stem cell-derived SMA model that we have generated and a spinal cord organoid system, here we report that the relative and temporal expression of early neural progenitor and MN markers is altered in SMA. Furthermore, the corrected isogenic controls only partially reverse these abnormalities. These findings raise the relevant clinical implication that SMN-increasing treatments might not fully amend SMA pathological phenotypes. The approach we have taken demonstrates that the discovery of new disease mechanisms is greatly improved by using human isogenic models. Moreover, our study implies that SMA has a developmental component that might trigger the MN degeneration.

  Study EGAS00001007259

• Fetal origins of malignant germ cell tumours

  Germ cell tumours (GCTs) are a collection of benign and malignant neoplasms derived from primordial germ cells (PGCs). They are uniquely able to generate embryonic and extraembryonic tissues, which in malignant GCTs carries prognostic and therapeutic significance. The developmental pathways underpinning GCT initiation and histogenesis are incompletely understood. Here, we studied the phylogenetic and transcriptional diversity of 15 malignant gonadal GCTs and four normal testis biopsies by sequencing 131 whole genomes and 416 transcriptomes from 14 gonadal histologies, excised by laser capture microdissection. Our findings demonstrate that tumours were initiated by whole genome duplication likely in embryogenesis, within ~5-8 cell divisions post-PGC specification, followed by chromosome 12p gains associated with invasive disease. Of note, 12p imbalances were not only generated through GCT-typical isochromosomes, but also through non-isochromosomic configurations. Whilst tumours developed along homogenous phylogenetic pathways, they spawned manifold tissues independent of genetic subclonal diversification. A key feature of GCT tissues was the expression of fetal-specific genes. The transcriptional diversity notwithstanding, we found universal transcriptional elements correlated with hallmark 12p gains. Overall, our study reveals stereotyped phylogenies and transcriptomes underpinning the development of GCT that originate in fetal life and may lend themselves to therapeutic manipulation.

  Dataset EGAD00001007037

• The NCAA-DoD Concussion Assessment, Research, and Education (CARE) Consortium

  Study Description: Established in 2014, the U.S. Department of Defense (DoD) and the National Collegiate Athletic Association (NCAA) funded the Concussion Assessment, Research and Education (CARE) Consortium to inform science, clinical care and public policy related to concussion and repetitive head impact exposure (HIE) in U.S. Military Service Academy (MSA) cadets and collegiate student-athletes. The primary aims of the initial study were to: (1) establish a multisite research consortium to characterize the effects of concussion and repetitive head impact exposure on brain health, (2) characterize the clinical sequelae and natural history of concussion, (3) characterize the effect of concussion and repetitive head impacts on brain structure and brain function. The 30 member institutions of the CARE Consortium (26 civilian universities/colleges and 4 military service academies) agreed to invite all varsity athletes to undergo multimodal clinical assessments at preseason baseline, and at 5 additional timepoints after concussion diagnosis. At the service academies, non-varsity athlete cadets/midshipmen were also studied. Assessment Categories At Each Time Point Baseline: • Demographics • Personal and Family History • Neurocognitive Assessment • Neurological Status • Postural Stability • Symptoms ACUTE POST-INJURY FOLLOW-UP (CARE 1.0) (24 hrs; 48hrs; Asymptomatic; Unrestricted Return to Play (RTP); 6 mos) • Neurocognitive Assessment • Neurological Status • Postural Stability • Symptoms CUMULATIVE & PERSISTENT EFFECTS. (CARE 2.0) In-Person Exit Visit; Post-grad Online • Demographics • Personal and Family History • Neurocognitive Assessment • Neurological Status • Postural Stability • Symptoms • PROs • MROs (at academies only) LONG-TERM EFFECTS (CARE/SALTOS Integrated (CSI))Annual Online Assessment (at MSAs only); In-Person Research Visits • Demographics • Symptoms • Psychological health • PROs • MROs (at academies only)A subset of athletes at 6 of the CARE institutions underwent additional assessments including multimodal MRI, proteomic and genomic biomarker characterization, and head impact quantification (helmet-based sensors). Assessment time points for the ARC/pARC Neurocognitive and Behavioral Testing was done at Baseline, <48hrs Post-Injury, Cleared for Return to Play Progression (Asymptomatic), Unrestricted Return to Play, 7 days following Return to Play, and at >60 days after final game of junior year until end of collegiate career.Blood biomarker collection was done at Baseline, <48hrs Post-Injury, Cleared for Return to Play Progression (Asymptomatic), 7 days following Return to Play, and at >60 days after final game of junior year until end of collegiate career. Multi-modal MRI studies were done at Baseline, <48hrs Post-Injury, Cleared for Return to Play Progression (Asymptomatic), 7 days following Return to Play, and at >60 days after final game of junior year until end of collegiate career. However, please note that only athletes from one study performance site completed a multi-modal MRI study at baseline.

  Study phs002175

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• Powerful Identification of Cis-regulatory SNPs in Human Primary Monocytes Using Allele-Specific Gene Expression

  The aim of this study was to compare the power to detect associations between SNPs using cis-eQTL mapping and ASE analysis (allele specific expression).

  Study EGAS00000000119

• Exome sequencing of patients with Ewings sarcoma

  Cancer is driven by mutations in the genome. We will uncover the mutations that give rise to Ewing's sarcoma, a bone tumour that largely affects children. We will use second generation Illumina massively parallel sequencing, and bespoke software, to characterise the genomes and transcriptomes of Ewing,s sarcoma tumours.

  Dataset EGAD00001000333

• SNP_array

  33 ETMR samples were genotyped using Illumina HumanOmni2.5M array. Hybridization and scanning was done according to the manufacturer's instructions (Illumina). Copy number (Log R) and B allele frequency estimates were obtained sing the Genotyping module (v1.9.4) in GenomeStudio v2011.1 (Illumina). Normalized and log2-transformed copy number measurements were imported from genomestudio and analysed using R package CopyNumber to identify segments with similar copy number.

  Dataset EGAD00010001667

• Colorectal cancer GWAS on the Spanish population

  Data on Affymetrix 6.0 arrays for Genome-Wide Association Study of colorectal cancer in the Spanish population. Additionally, geographical origin for each sample is provided, which constitutes the largest to-date Spanish genomic sample population

  Dataset EGAD00010001715

• Whole-exome sequencing and microRNA profiling predicted relapse risk of stage I lung adenocarcinomas for the study and all the datasets.

  This dataset contains PAIR files and processed somatic copy number alteration value for array CGH of stage I lung adenocarcinomas from Asian patients. In total, there are 111 patients and 222 samples, including 111 tumor samples and 111 adjacent normal samples.

  Dataset EGAD00010001902

• methylation_bc_cervix

  Raw methylation data from cervical samples in individuals with breast cancer.

  Dataset EGAD00010002081

• NSCCG_CRC_GWAS

  Genotype data of 7,281 individuals with colorectal cancer from the National Study of Colorectal Cancer Genetics (NSCCG) study. Individuals genotyped on the Illumina OncoArray. Data provided in plink format and has not been quality controlled. Control samples used were obtained from the PRACTICAL and BCAC consortia, and are available through the respective Data Access Coordination Committees (http://practical.icr.ac.uk and http://bcac.ccge.medschl.cam.ac.uk/)

  Dataset EGAD00010002184

• Single-cell RNA-sequencing of peripheral blood mononuclear cells from patients with achalasia

  The data published here contains single-cell RNA-sequencing (scRNAseq) data as obtained using the 3' scRNAseq using Chromium Single Cell 3’ Reagent from 10X Genomics on peripheral blood mononuclear cells (PBMC) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000250

• AmsterdamUMC Data Access Committee for the study entitled "Carboxylesterase 1 mediates a distinctive metabolic profile of dendritic cells to attain an inflammatory phenotype"

  The current data access committee reviews and assesses data requests of researchers that seek to gain access to data presented in the manuscript entitled "Carboxylesterase 1 mediates a distinctive metabolic profile of dendritic cells to attain an inflammatory phenotype" as performed at the AmsterdamUMC, Amsterdam, Netherlands.

  Dac EGAC50000000178

• Korean WGS

  We sequenced the genomes of 141 Korean never-smoker lung adenocarcinoma patients, excluding EGFR and ALK alterations. We utilized the TruSeq DNA Library Prep Kit and performed by the Illumina NovaSeq 6000 instrument, yielding sequencing paired reads of approximately 150 bp in size. Afterward, we processed the FASTQ files using the GATK Best Practice pipeline.

  Dataset EGAD50000000346

• RNA Sequencing of AD OM cells exposed to traffic-related air pollutants

  Total RNA sequencing of olfactory mucosa (OM) cells derived from individuals diagnosed with Alzheimer's disease exposed to traffic-related ultrafine particles (UFPs) for 24-h and 72-h in submerged cultures. The UFPs used for exposures were: A0, A20 and Euro6. Exposures were compared to the corresponding blank samples.

  Dataset EGAD50000000648

• Hi-C in breast healthy, primary cancer and metastatic tissues

  Hi-C libraries have been prepared by enzymatic digestion with MboI restriction enzyme and sonication (Covaris). Illumina single-indexig primers have been use to amplify ligated fragments. Hi-C experiments have been performed on 10 slices (50um thick) of fresh-frozen tissues or 10x10e6 Pleural Effusion cells. Raw paired-end fastq.gz files are provided.

  Dataset EGAD50000000643