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• A Phase I Study of the Treatment of Recurrent Malignant Glioma with CAN-3110 (AKA rQNestin34.5v.2), a Genetically Engineered HSV-1 Virus

  This study is a "first-in-human" phase 1 trial of recurrent, IDH wild-type glioblastoma (rGBM) patients treated with CAN-3110 (aka rQNestin34.5v.2), a genetically modified oncolytic herpes simplex virus 1 (HSV-1). The study was a 3+3 dose-escalation study starting at a single intra-tumoral injection of 106 PFU CAN-3110 going up to 1010 PFU in half-log increments (cohorts 1-9). An expansion cohort (cohort 10) was treated with 109 PFU injected into up to 5 sites within a single tumor. Principal findings of the study conclude that:CAN-3110 was safe at all tested doses (no dose-limiting toxicities reached)HSV-1 seropositive patients survived significantly longer than HSV-1 seronegative patients in this study - longer than expected based on historical rGBM controlsHSV-1 seropositive patients were significantly more likely to clear CAN-3110 (HSV-1) antigen from their tumors based on immunohistochemical stainingCAN-3110 treatment-induced CD8+/CD4+ T-cell recruitment to injected tumor sites altered TCR beta diversity in both tumor infiltrating leukocytes (TILs) and PBMCs, and initiated changes in specific TCR beta clonotypes and immune gene signatures which were associated with post-treatment survival.Data provided in dbGaP for this study include:Bulk RNA-Seq data for 13 pre/post-treatment pairs of rGBM tumors corresponding to 12 unique patients (patient 28 was treated with CAN-3110 at two different timepoints). This data is provided as raw sequencing reads (.fastq), kallisto pseudoalignment transcript-level read counts (abundance.tsv files), gene-level abundance matrix (Gene_Level_Expression_Matrix.tsv), and immune gene signature scores matrix (Immune_Gene_Signatures_Matrix.tsv). Sequencing was performed as paired 150bp sequencing on NovaSeq6000 S4 Illumina flow cells. Library preparation varied by batch as described in the included RNAseq_Metadata.xlsx table.TCR beta DNA sequencing performed by Adaptive Biotechnologies for 21 pre/post-treatment pairs of rGBM tumors and PBMCs representing 20 unique patients (patient 28 was treated with CAN-3110 at two different timepoints). Note that all tumor pairs with TCR beta sequencing data also have available bulk RNA-Seq data. The TCR beta sequencing data is provided as an excel spreadsheet (TCRbeta_Sample_Overview.xslx), which provides a broad level summary of the sequencing on a per-sample basis (# of TCRbeta templates, T cell fraction, TCR beta diversity metrics, etc.) and as a large tab separated value file (Full_TCRbeta_Rearrangement_Details.tsv), which provides information per sample at the level of each unique TCR beta sequence (amino/nucleic acid sequence, VDJ gene usage, etc.).An excel table of de-identified metadata for the patients involved in this study.

  Study phs003378

• Cryptococcosis in Previously Healthy Adults

  Cryptococcus is a fungus that causes infections most commonly in immunocompromised patients, such as those with AIDS and solid organ transplant recipients and is currently responsible for an estimated 15% of all AIDS-related deaths globally. However, in developed countries, approximately one-third of cases fall outside these groups such that cryptococcal meningoencephalitis (CM) has become the most common cause of non-viral meningitis in the U.S. Within the U.S., approximately 15-20% have no readily identifiable immune defect and cryptococcal infection in these hosts has a mortality rate of 30-50% despite optimal antifungal therapy. NIH Clinical Study Protocol 93-I-0106 (NCT00001352). The objectives of this protocol can be broadly categorized as: 1) Characterize the immunologic and genetic mechanisms predisposing to disease acquisition; 2) Understand the post-infectious inflammatory response and distinguish its consequences from those directly due to fungal growth; and 3) Management of post-infectious neuro-inflammatory syndromes associated with cryptococcal meningitis (CM). This protocol recruits patients who have microbiological evidence of cryptococcal neurologic or non-CNS disease (typically pulmonary or bone). Cerebrospinal fluid (CSF) and blood samples collected during clinical care are used to measure serum and CNS cellular and soluble cytokines as well as to perform in-situ immunohistochemistry. CSF and blood samples are also collected to measure transcriptional (gene expression), proteomic (protein), and lipidomic (lipids) changes that occur throughout disease and treatment. Observational data detailing audiological, ophthalmological and neurocognitive deficits in these patients is also recorded. We have recently described a post-infectious inflammatory syndrome (PIIRS) associated with cryptococcal meningoencephalitis (CM) which can be best described as a neuro-inflammatory state during which CM patients present with altered mental status (Montreal Cognitive Assessment Score As part of the protocol, we treat patients for either microbiological failure or neuroinflammatory sequelae by conventional therapies utilizing FDA-approved pharmaceuticals at FDA-approved doses. To understand and identify the predominant biological pathways responsible for PIIRS neuroinflammation, we examined patient CSF at baseline (at time of PIIRS diagnosis) and post-treatment with different FDA-approved pharmaceuticals at FDA-approved doses compared to CSF from non-PIIRS donors. Upstream pathway analysis of differentially expressed genes in the CSF of PIIRS patients revealed a prominent role for the JAK/STAT pathway in the pathogenesis of PIIRS and support the use of the JAK inhibitor, ruxolinib, as a pathway-instructed therapy to treat PIIRS patients. Data available through dbGaP: single cell RNA sequencing (scRNA-seq) data from CSF cells of a patient with PIIRS at the time of diagnosis and following treatment with steroids (1g solumedrol/day) and NanoString transcriptional data from CSF cells of patients with PIIRS at the time of diagnosis compared to CSF cells from non-PIIRS donors.

  Study phs003871

• Shedding light over COVID-19 susceptibility and severity

  “One of the most remarkable features of SARS-CoV-2 infection is the variation in consequence ranging from asymptomatic to life-threatening” state the authors of the majestic Nature paper released in accelerated pre-view on July 2021. This variation in consequences represent not only an issue of burning medical relevance, but also a scientific and intellectual challenge for scientists around the globe. Researchers are known to be easily triggered by a challenge, and they recently demonstrated to be even better at double their efforts to focus on an urgent societal need. In this work led and coordinated by the COVID-19 Host Genetics Initiative (COVID-19 HGI), researchers gathered data from 49,562 cases and two million controls from 46 studies performed worldwide. The majority of the data has been deposited at the EGA, where 29 datasets have been collected under the study number EGAS00001005304. Data gathered from diverse sources conferred a huge robustness to the results. Genetic data from different ancestry origin were included in the analyses, even though data from patients with European origin was highly predominant (approx. 75%). Separated analysis of the different ancestries gave rise to relevant differences in the results, highlighting how important it is to take this aspect in consideration for future studies. Data from the individual studies was compiled into three categories according to COVID-19 severity for a meta-analysis: (a) critically ill cases (b) moderate or severe cases (c) all cases. They were paired with non-COVID-19-reported controls. All three analysis resulted in a total of 13 genetic loci associated with COVID-19 severity. Out of those, 9 loci associated specifically with critical illness, while 6 (a few overlapped) associated with moderated illness. Some genomic position matched previous findings. Interestingly, working with such an enormous amount of data allowed the depth to discriminate association with disease susceptibility vs severity, an issue of large medical relevance. Several loci mapped into biological relevant genes, like TYK2, PPP1R15A and FOXP4, previously implicated in immune response and/or lung functions. Researchers participating in the COVID-19 Host Genetics Initiative have worked in an unprecedented time-tight collaboration, prioritizing data sharing and collective results to tackle the pandemic affecting the world. The scientific community won’t miss any of the lessons to learn from this, and the EGA is ready to keep working to enable safe data sharing and fruitful collaborations.

  Blog covid-19-susceptibility-and-severity

• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

  Blog the-last-addition-to-the-list-of-clonal-evolution-studies-in-the-ega

• Discover Cancer Image Europe, the first release of the EUCAIM platform to fuel cancer research and data sharing

  The EUCAIM consortium and the European Commission have announced the release of Cancer Image Europe, a platform to fuel collaborative innovation and data sharing across Europe. This constitutes a major milestone in EUCAIM's development and an exciting step towards achieving the project's vision and goals. The platform aims to accelerate the pace of AI development and other data-intensive cancer research activities, constituting the basis for a fully-fledged infrastructure for sharing, reusing, and exploiting cancer imaging data, especially using AI techniques. A platform paving the way for the future of cancer diagnosis and treatment Cancer Image Europe brings benefits to researchers, clinicians, and AI innovators across Europe as it addresses the fragmentation of existing cancer image repositories. The platform gathers a public catalogue of cancer imaging datasets from the repositories of the EU-funded AI for Health Imaging projects. A distributed Atlas of Cancer Imaging with over 60 million anonymised cancer image data from over 100,000 patients will be established as part of future updates. The first version of Cancer Image Europe also includes a public catalogue of cancer imaging datasets, a federated searching tool to understand the information available in the federated providers, and full integration with Life Science Login. Moreover, it reuses and adds value to key components of EU-funded research projects and infrastructure in the field of cancer. The Public Catalogue When we talk about data re-use, data discovery is key. The EUCAIM public catalogue is the result of a systematic and collaborative process to which the EGA-CRG has actively contributed. This browsing interface gathers the metadata of the available cancer imaging datasets. This descriptive information will help researchers in their data retrieval. Our contribution to the Public Catalogue comes from our experience gained in the deployment of our federated network, the Federated EGA (FEGA), and as partners in projects such as EuCanImage (AI4HI initiative), in which we worked on the definition of common data models, ontologies and data standards. The EUCAIM Project EUCAIM is a cornerstone of the European Commission-initiated European Cancer Imaging Initiative, a flagship of the Europe's Beating Cancer Plan, which aims to foster innovation and deployment of digital technologies in cancer treatment and care, to achieve more precise and faster clinical decision-making, diagnostics, treatments, and predictive medicine for cancer patients. For more information about the first release of the Cancer Image Europe platform and the EUCAIM project, please visit cancerimage.eu.

  Blog discover-cancer-image-europe

• Heart Failure Network - Renal Optimization Strategies Evaluation in Acute Heart Failure and Reliable Evaluation of Dyspnea (HFN ROSE-BioLINCC)

  ObjectivesTo test the two independent hypotheses that when compared to placebo, addition of: (1) low dose dopamine; or (2) low dose nesiritide to diuretic therapy will enhance decongestion and preserve renal function in patients with acute heart failure and renal dysfunction.Background A primary treatment goal in acute heart failure is to achieve adequate decongestion while avoiding renal dysfunction and other side effects. Patients with acute heart failure and moderate or severe renal dysfunction are at risk for inadequate decongestion and worsening renal function - both of which are associated with worse outcomes. Renal adjuvant therapies that enhance decongestion and preserve renal function during treatment of acute heart failure are needed. Dopamine is an endogenous catecholamine which, at low doses, may selectively activate dopamine receptors and promote renal vasodilatation. Previous studies have suggested that the addition of low dose dopamine to diuretic therapy enhances decongestion and preserves renal function during diuretic therapy in acute heart failure; however, these studies were small with variable study designs and dopamine doses. B-type natriuretic peptide (BNP) is a cardiac peptide with vasodilating, renin and aldosterone inhibiting, natriuretic and diuretic properties. Nesiritide is human recombinant BNP approved for management of acute heart failure. The recommended dose lowers blood pressure and atrial pressures and produces modest improvement in dyspnea, but does not favorably impact clinical outcomes or renal function, potentially due to its hypotensive effects. Small studies using low dose nesiritide in acute heart failure and cardiac surgery patients have demonstrated favorable effects on urine output and renal function. Therefore, the Renal Optimization Strategies Evaluation (ROSE) trial was initiated to determine the benefits and safety of intravenous administration of low dose nesiritide or low dose dopamine in patients with congestive heart failure and kidney dysfunction. ParticipantsA total of 360 participants were enrolled. Design Participants were enrolled within 24 hours of hospital admission and randomized in a 1:1 ratio to the nesiritide or dopamine strategies. Participants randomized to the dopamine strategy were randomized in a double-blind 2:1 ratio to low dose dopamine (2 µg/kg/min for 72 hours infused via local guideline stipulated vascular access) or placebo. Participants randomized to the nesiritide strategy were randomized in a double-blind 2:1 ratio to low dose nesiritide (0.005 μg/kg/min for 72 hours infused via peripheral intravenous access without initial bolus) or placebo. All participants received open-label, intravenous loop diuretic treatment with a recommended total daily dose equal to 2.5x the total daily oral outpatient furosemide (or equivalent) dose at 7 days prior to admission up to a maximum of 600 mg/day. Participants naive to outpatient loop diuretics received 80 mg/day of intravenous furosemide. One-half of the total daily diuretic dose was administered as a bolus twice daily for at least 24 hours. Use of other medications and diuretic dosing after 24 hours were at the discretion of the clinician. All participants were placed on a 2000 mg sodium diet and 2000 cc fluid restriction. After the primary endpoint assessment at 72 hours, study drug was discontinued and subsequent treatment was at the clinician's discretion. The two co-primary endpoints were the 72-hour cumulative urinary volume as an index of decongestion efficacy and the change in cystatin-C from randomization to 72 hours as a measure of renal function preservation. Conclusions In participants with acute heart failure and renal dysfunction, neither low-dose dopamine nor low-dose nesiritide enhanced decongestion or improved renal function when added to diuretic therapy.

  Study phs003589

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. 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. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, 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 and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access 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 (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. Please consider the number of datasets that your submission consists of, for example, a case control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• University of Alabama at Birmingham (Xenotransplantation Project) - DAC

  This is a Data Access Committee (DAC) developed to govern access to sequence level data produced by the Porrett Lab, University of Alabama at Birmingham (Xenotransplantation Project), and headed by Dr. Jayme Locke, and Dr. Porrett M. Paige, Principal, and Co-Principal Investigators respectively. These data were largely used in the generation of the manuscript: “Spatiotemporal immune atlas of the first clinical-grade gene-edited pig-to-human kidney xenotransplant”. Access to these data will be granted to qualified investigators for appropriate use following data use approval from members of the DAC.

  Dac EGAC50000000188

• Bulk Iso-Seq from brain tissue and exosomes isolated from brain tissue using long-read PacBio sequencing of poly-adenylated transcripts

  Human brain sEVs were isolated from postmortem human brain tissue. RNA was extracted from both the source brain tissue and sEVs and prepared for long-read sequencing. cDNA was sequenced on the PacBio Sequel II. The data provided have undergone processing using the tool ccs (v6.0.0) to generate circula consensus reads. For further processing, it is recommended to follow the guidelines at https://isoseq.how/, starting with using lima to remove cDNA primers.

  Dataset EGAD50000000043

• Long Read sequencing data from 4 tumor/germline samples with non muscle invasive bladder cancer

  A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). Additionally, tumor/germline samples from 4 patients were sequenced using Oxford Nanopore technology. This dataset contains 8 BAM files corresponding to the Long read sequencing data.

  Dataset EGAD50000000731