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• The identification of genetic vulnerabilities in head and neck cancers for the development of novel treatments.

  The global cancer burden is expected to double by the year 2030 and this will disproportionately affect the Asian continent. Head and neck squamous cell carcinoma (HNSCC) is endemic in Asia. It is the sixth most common cancer worldwide and the second most common in Malaysia, with approximately 600,000 new cases per year globally. Longterm survival remains less than 50%, underscoring the need for better therapeutic strategies. In this proposal we aim to bring new technological advancements in the identification of critical genes in HNSCC and the identification of compounds for the development of new therapies in HNSCC. We propose to genomically characterise 16 unique Asian H&N cancer cell lines, to classify sub groups where identification of essential genes that can be targeted for tumor control can be applied in a more rational way. Further, we propose to use zebrafish, an established drug discovery platform to identify promising pathway-specific compounds that could be further studied using CRISPR/Cas9 lethality screens and biological models to better identify novel therapeutic strategies to combat this major health problem in Asia.

  Study EGAS00001002682

• SINGLE_CELL_ANALYSIS_OF_IN_VITRO_ERYTHROPOIESIS

  We are developing a protocol to differentiate mouse and human induced pluripotent stem (IPS) and embryonic stem (ES) cells towards the haematopoietic pathway to generate erythrocytes in vitro. This system has many applications such as the study of the role of specific genes and human polymorphisms in infectious diseases such as malaria, as well as haematological diseases such as myelodysplastic syndrome. The nature of the in vitro differentiation process means that a heterogeneous population of cells is generated. In order to understand the types of cells produced with our protocol, we have performed a single cell analysis, which has the power to reveal the different populations of cells and their characteristics. For this, a cDNA library has been made that needs to be sequenced to obtain the gene expression profiles of the different cells. With this information we will be able to assess the quality of the differentiation protocol and improve it in order to produce better cells for the downstream applications.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000764

• Biomarker Screen for Efficacy of Oncolytic Virotherapy in Patient-derived Pancreatic Cancer Cultures

  Pancreatic ductal adenocarcinoma (PDAC) is a tumour entity with unmet medical need. To assess the therapeutic potential of oncolytic virotherapy (OVT) against PDAC, different oncolytic viruses (OVs) are currently investigated in clinical trials. However, systematic comparisons of these different OVs in terms of efficacy against PDAC and biomarkers predicting therapeutic response are lacking. We screened fourteen patient-derived PDAC cultures for their sensitivity to five clinically relevant OVs, namely serotype 5 adenovirus Ad5-hTERT, herpes virus T-VEC, measles vaccine strain MV-NIS, reovirus jin-3, and protoparvovirus H 1PV using live cell analysis, quantification of viral genome/gene expression, cell viability as well as cytotoxicity assays. Sensitivity was correlated with transcriptome profiles to identify potential predictive biomarkers for response to OVT. Patient-derived PDAC cultures showed individual response patterns to OV treatment. Twelve of fourteen cultures were responsive to at least one OV, with no single OV proving superior or inferior across all cultures. Known host factors for distinct viruses were retrieved as potential biomarkers. Compared to the basal-like molecular subtype, the quasi-mesenchymal subtype of PDAC was more sensitive to H-1PV, jin-3, and T-VEC. Expression of viral entry receptors did not correlate with sensitivity to OV treatment. Rather, cellular pathways controlling immunological, metabolic, and proliferative signalling appeared to determine outcome. For instance, high baseline expression of interferon-stimulated genes (ISGs) correlated with relative resistance to oncolytic measles virus, whereas cGAS expression was associated with exceptional response. Combination treatment of MV-NIS with a cGAS inhibitor improved tumour cell killing in several PDAC cultures.

  Study EGAS00001007001

• CRU-Ukrainian National Research Center for Radiation Medicine Trio Study

  Data on transgenerational effects following nuclear accidents are important for understanding fully the consequences of parental exposure to ionizing radiation. Few studies to date have had adequate statistical power to detect effects of the magnitude expected based on animal data, and most have not been of low-dose, protracted exposures associated with nuclear accidents and their aftermath. Although, to date, scant use has been made of the new genomic technologies, in Chernobyl-exposed areas of Ukraine and Belarus, excess minisatellite mutations have been seen in children born after the accident. We propose a study of parent-child trios in which at least one parent was exposed to Chernobyl radiation as a clean-up worker (mean dose>=100 mGy) and/or evacuee from a contaminated area (mean >=50 mGy). The specific aims are to investigate the transgenerational and de novo mutation rates of the spectrum of genetic variants in trios, in particular looking at effects in children and mapping them to possible parental origin of the chromsoome. Together with long-term collaborators at the Research Center for Radiation Medicine (RCRM) in Kiev, epidemiologic data will be collected for up to 450 trios of parents with preconceptional doses and their unexposed offspring. We will use state-of-the-art genomic technologies to characterize the landscape of the genomes of the trios to determine whether parental radiation exposure is associated with genetic mutations transmitted to the offspring, by examining de novo mutation rates, minisatellite mutations, copy number alterations, and variations in telomere length. The analysis will be conducted in peripheral blood and/or buccal samples (when blood is not available) from complete father-mother-child trios. Doses to the gonads from the time of the accident to the time of conception will be reconstructed for all parents using existing records supplemented by interview data. Trio subjects will be selected from representative populations exposed to radiation from Chernobyl who are under active follow-up in the Clinico-Epidemiologic Registry at RCRM. To help identify specific effects of paternal and maternal radiation exposure, we will initially select sets of trio subjects in five categories: (1) exposed father, unexposed mother; (2) unexposed father, exposed mother; (3) both parents exposed; (4) both parents unexposed; and (5) a group of high dose "emergency workers" with acute radiation syndrome. All trio members will be invited to the RCRM outpatient clinic for collection of a 20 ml blood sample (or buccal cells for those who refuse phlebotomy). Both parents will be asked to complete a general questionnaire to obtain demographic and lifestyle data. Then one or both will complete detailed dosimetry questionnaires, based on forms used in previous collaborations with RCRM and administered by specially trained interviewers. Once 50 trios have been recruited (10 from each of the 5 exposure categories), we will conduct an interim evaluation of participation rates, sample collection and quality, and dose reconstruction in order to modify the protocol as needed. The analytical approach will be to correlate the extent, especially for de novo events of genetic alterations in the offspring with parental pre-conceptional radiation dose overall and by parental origin. The statistical power in relation to de novo mutations is very high, in excess of 90%, but somewhat lower for trends in minisatellite mutations. Study findings will contribute importantly to knowledge of the heritable effects of moderate- and low-dose radiation exposure in humans and to radiation risk projection. Eventually data from the Trio Study may be shared with the international community through dbGap.

  Study phs001163

• About

  About What's the EGA? The European Genome-phenome Archive (EGA) is a global network for permanent archiving and sharing of personally identifiable genetic, phenotypic, and clinical data generated for the purposes of biomedical research projects or in the context of research-focused healthcare systems. Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, we aim to advance biomedical research and promote personalised medicine worldwide by enabling discovery of and access to human genomic and health research data. The EGA contains data collected from individuals whose consent agreements authorise data release for specific research use to bona fide researchers. We ensure strict security measures to control access to the data and maintain patient confidentiality. With expertise in data management and technical infrastructure, we promote FAIR data reuse and enable researchers to share their data securely. By leveraging public funding and our strategic partnerships, the EGA provides a free service for permanent data storage, data discovery, and secure data access. In addition, we foster a federated network to provide transnational access to human research data in compliance with legal frameworks. For additional information about the EGA, please contact: Helen Parkinson and Mallory Freeberg EMBL European Bioinformatics Institute Arcadi Navarro, Roderic Guigó, and Jordi Rambla Center for Genomic Regulation History The European Genome-phenome Archive was launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. With the signing of a memorandum of understanding in 2013 and a formal agreement in 2016, the EGA became a joint project of EMBL-EBI and the Centre for Genomic Regulation (CRG). The two institutes work together to support the EGA services, including supporting submissions, website, strategic leadership, and data infrastructure developments. In 2022, the Federated EGA was officially launched with the signature of the first five countries: Finland, Germany, Norway, Spain, and Sweden. With more than 20 additional nodes worldwide preparing to join, the Federated EGA aims to become the largest human omics data sharing initiative towards understanding human health and disease. EGA overview If you're a researcher you may need to deposit, manage, or access genomic data in a secure and regulated way. The European Genome-phenome Archive (EGA) is a platform that facilitates these processes, ensuring that sensitive data is stored and shared in accordance with legal and ethical regulations. Submission process To start a submission, you need to become an EGA submitter. For that, you’ll need to sign a Data Processing Agreement (DPA) with us, that defines the terms and conditions under which your data will be processed and shared within the EGA system. The access to each study is controlled by its Data Access Committee (DAC). The DAC is responsible for managing data access requests and ensuring that the release of data is in accordance with the General Data Protection Regulation (GDPR). Please, note that once your data is released, all public metadata related to your study and dataset(s) will be searchable on the EGA website. However, the files are only accessible under controlled access, which means that a DAC has to agree to a previous data access request. Request process It is possible to submit an access request to data stored at the EGA. The DAC assigned to the study will assess the request and, if approved, grant access to the data. Requesters must provide sufficient justification for your request and comply with the intended data usage in order to get access to it. Each dataset is covered by a Data Access Agreement (DAA) that defines the terms and conditions of use for the specified dataset/s. The DAA is created and provided by the DAC, and must be signed by the individual requesting access to the given dataset/s. Download process Once the request for access is approved, the data and metadata can be downloaded. The EGA offers various download options to fit the needs: it is possible to preview files without downloading them, download specific files of interest, or even download terabytes of data. Overall, the EGA provides a secure and regulated environment for depositing, managing, and accessing human data. Regardless of the role - submitter, DAC member, or requester - the EGA provides assistance for each while ensuring that sensitive data is managed in an ethical and responsible manner. More information on how EGA handles data is available in the EGA dataflow.

  Documentation about/ega

• Genome-Wide Association Study of Celiac Disease

  Celiac disease (gluten-sensitive enteropathy, celiac sprue) is a common disease with significant morbidity and mortality. It is caused by sensitivity to the dietary protein gluten, resulting in a chronic enteropathy in the small intestine. Celiac disease is now recognized to be a common disease, with reports that the disease frequency is 1:133 in the United States, similar to European estimates. There is recent evidence to suggest that the incidence of the disease is rising. Occult disease is frequently present with minimal classic symptoms or signs. The ratio of symptomatic to asymptomatic celiac disease is estimated to be 1:7. Some complications of celiac disease include lymphoma, osteoporosis, anemia, miscarriages, seizures, vitamin deficiencies, and co-occurrence of other autoimmune diseases. The only treatment is a gluten-free diet, so that recurrence of symptoms and complications may occur after minor dietary indiscretions. Identifying the underlying genetic causes of celiac disease may allow us to identify susceptible individuals, as well as advance new ways to prevent the disease and to treat it once it occurs. Several genome-wide linkage studies and one genome-wide association study of celiac disease have been conducted. Other than the common locus at HLA, few putative celiac disease loci have been replicated between studies, suggesting that the disease is heterogeneous and complex. A portion of the genetic predisposition can be attributed to HLA, but the etiology is likely due to a number of rare, high penetrant genes (as would be identified in linkage analysis) and to more common, low penetrant genes (as would be identified in association studies). The objective of this study is to identify new loci that increase risk to develop celiac disease. We will capitalize on existing North American resources, including three large collections of celiac cases and controls (Aim 1) and an independent set of celiac cases and their families (Aim 3). We propose a comprehensive multi-stage approach, with the following specific aims. In Aim 1, we will conduct a genome-wide association (GWA) study of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). The GWA will include1900 disease cases and 3400 matched controls, genotyped using the Illumina Human 610-quad chip. Statistical analyses will be performed to test for associations with celiac disease. In Aim 2, we will conduct a combined analysis of our GWA dataset from Aim 1 and a previous GWA dataset to identify additional low-penetrance loci. In Aim 3, we will attempt to replicate significant findings from the GWA study in two independent sample sets. At the conclusion of this study, we expect to have validated a number of SNPs and CNPs from regions in the genome that alter the risk of developing celiac disease. These SNPs may prove useful at both the clinical and research level. The data from the study will be shared with the scientific community.

  Study phs000274

• dbGaP Collection: NHLBI Heart Failure Related dbGaP Data (No IRB requirement)

  The National Heart, Lung, and Blood Institute (NHLBI) Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research has concluded. The NHLBI will continue to make this collection of data available to the research community.Past information regarding the Data Collection for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research is below.The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health (NIH), is inviting novel Solutions for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research. The goal of the challenge is to foster innovation in computational analysis and machine learning approaches utilizing large-scale NHLBI-funded datasets to identify new paradigms in heart failure research. The challenge aims to address the need for new open source disease models that can define sub-categorizations of adult heart failure to serve as a springboard for new research hypotheses and tool development in areas of heart failure research from basic to clinical settings. NHLBI has a history of making considerable investments in the creation of deep data resources including: long-standing, deeply-phenotyped epidemiological cohorts, innovative clinical trials, and large-scale precision medicine efforts that have generated whole genome sequencing and "other omics" data for more than one-hundred thousand individuals. To provide challenge participants with streamlined access to data across NHLBI's numerous studies containing heart failure data, the NHLBI has created this dbGaP study collection. Data access for this collection is controlled by NHLBI's Data Access Committee. Challenge participants are required to follow the use restrictions and acknowledgement instructions from the original dataset(s). Please note that the data in this collection are not harmonized across studies or otherwise altered from the original study. The NHLBI Big Data Analysis Challenge: Heart Failure Data Collection contains all NHLBI studies currently in dbGaP that contain data that may be relevant to research on heart failure. The studies in this collection are approved for General Research Use (GRU), Health/Medical/Biomedical Use (HMB), or Disease-Specific Use (DS) that permits research on heart failure. These studies span a variety of study designs, inclusion and exclusion criteria, sample sizes, and provide a wide breadth and depth of phenotype data on study participants. The available genomic data in these studies also varies, including genotyping arrays, sequencing (targeted, exome, whole genome), and additional -omic data (e.g., RNA or metabolite profiles). Please refer to each study's individual accession page to learn more about how study data were collected. Challenge participants are reminded that in addition to dbGaP, NHLBI's Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) contains other studies that have collected data relevant to heart failure and may wish to utilize BioLINCC data in the development of their Solution. Please refer to the individual accession page for each study in this collection to learn more about the history of each study.

  Study phs001991

• INCLUDE Data Hub: NDA GUIDs for Down Syndrome Research

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality of life for people with Down syndrome. The INCLUDE Project Data Coordinating Center (DCC) and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions. The INCLUDE Data Hub includes an innovative researcher data repository, a portal, and a cloud-based platform for data analysis. The portal provides access to de-identified demographics and clinical metadata, as well as multi-omics datasets, including whole genome sequences, transcriptomes, proteomes, and metabolomes. The INCLUDE Data Hub uses a 'registered access' model, whereby users can access the portal and associated data after agreeing to terms of use. From the Data Hub, users can also reach 'controlled access' data (e.g., whole genome sequences) only for those studies for which they have obtained approval by an NIH Data Access Committee through the corresponding dbGaP study. A thoughtful linkage strategy to identify duplicate participants across studies enables researchers to merge multiple datasets and data types to create richer datasets and reduce costly redundancies in large-scale data generation (e.g., -omics) while mitigating potential risks introduced by linkage.Based on recommendations from the NICHD Office of Data Science and Sharing, which completed a comprehensive assessment of existing Privacy Protecting Record Linkage (PPRL) and associated data governance solutions, the INCLUDE DCC and the associated NIH Steering Committee have implemented NIMH Data Archive Global Unique Identifiers (NDA GUIDs) in the INCLUDE Data Hub. The NDA GUID Tool is a government-owned and operated PPRL technology that enables researchers to link data from multiple sources to the same individual without revealing personally identifiable information (PII). With the NDA GUID Tool, the same participant information will return the same GUID whenever or wherever it is entered. The GUID itself is not personally identifiable information or protected health information. Sharing data with NDA GUIDs allows approved researchers to link together data on a single participant, even if the data were collected at different locations or through different studies or are distributed from different data repositories. This dbGaP study enables researchers to access an INCLUDE GUID Mapping File that includes all available NDA GUIDs and their associations to study-specific Participant IDs across studies in the INCLUDE Data Hub. This dbGaP study only distributes NDA GUIDs associated with the INCLUDE Data Hub datasets. To access the associated controlled-access datasets, add the corresponding dbGaP studies to your Data Access Request. A list of these studies is available on the INCLUDE Data Hub Portal: https://portal.includedcc.org/studies.

  Study phs003678

• 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

• Use of new methodologies to achieve a thorough molecular characterization in pediatric acute leukemia

  In childhood leukemia, a better genetic characterization is needed to define new clinic-biological entities, refine the risk-stratification of patients and help to reduce the toxicity by applying a risk-directed therapy. This project aims to obtain information that will allow us to personalize the management of pediatric patients with leukemia as much as possible, so that we can offer a complete diagnosis, a sensitive follow-up and a more targeted treatment to the real risk of each case. The use of massive genome analysis technologies such as transcriptome sequencing (RNA-Sequencing) will allow a comprehensive biological characterization of pediatric leukemia patients, especially those cases that we have not been able to characterize by conventional diagnostic techniques. We believe that the RNA-Seq technique will allow us to identify new biomarkers that will be useful for: 1) defining new biological entities with their own clinical behavior; 2) refining the stratification of patients into risk groups; 3) finding targets for a better follow-up of MRD and 4) identifying therapeutic targets for the application of targeted therapies to reduce toxicity. PI: Camós-Guijosa, Mireia; Vega-García, Nerea Funding: PI21/00213 Instituto de Salud Carlos III (ISCIII)

  Study EGAS50000000701

• Detection of cancers three years prior to diagnosis using plasma cell-free DNA

  A major goal of early cancer detection is to identify subclinical disease when the tumor burden is low, so that treatments are more effective. But how early can cancers be detected prior to clinical signs or symptoms? This question can be answered only through the evaluation of participants whose clinical course has not been altered by the study itself. We here describe such an evaluation, performed on prospectively collected plasma samples from the Atherosclerosis Risk in Communities (ARIC) study, including 26 participants diagnosed with cancer and 26 matched controls. At the index time point, eight of these 52 participants scored positively with a multicancer early detection (MCED) blood test. All eight of these participants were diagnosed with cancer within 4 months after blood collection. In six of these 8 participants, we were able to assess an earlier plasma sample collected 3.1 to 3.5 years prior to clinical diagnosis. In four of these six participants, the same mutations detected by the MCED test could be identified, but at 8.6 to 79-fold lower levels. These results demonstrate that it is possible to detect circulating tumor DNA (ctDNA) more than three years prior to clinical diagnosis, and provide benchmark sensitivities required for the success of ctDNA-based tests for this purpose.

  Study EGAS00001008068

• _WGS__Somatic_mutation_in_skin_epidermis__SMS_

  Previously we have sequenced 2mm2 biospies of human epithelia at high depth using a custom bait set. This has identified a range of mutations. Some of the biospies are predicted to be clonal and we would like to perform WGS to give an insight into mutational burden, and CNV which may be associated with these mutations.

  Study EGAS00001004464

• Chondrosarcoma_Targeted_Sequencing_Study

  This Study uses a focused bait pull down library method to target findings of Chondrosarcoma whole genome and whole exome sequencing studies in order to validate findings. This method will also be used on a larger set of tumour only samples in order to find precedence of these finding in a larger set of patients.

  Study EGAS00001000277

• Genomic Characterization of Metastatic Castration Resistant Prostate Cancer

  Biopsies of castration resistant prostate cancer metastases were subjected to whole genome sequencing (WGS), along with RNA-sequencing (RNA-Seq). The overarching goal of the study is to illuminate molecular mechanisms of acquired resistance to therapeutic agents, and particularly androgen signaling inhibitors, in the treatment of metastatic castration resistant prostate cancer (mCRPC).

  Study phs001648

• De novo detection of somatic variants

  The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. This dataset contains scDNA-seq data for 3 ovarian cancer samples from the Tumor Profiler Study used to validate LongSom. LongSom is a computational workflow leveraging high-quality long-read scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct tumor clonal heterogeneity.

  Dataset EGAD50000001292

• Whole genome sequence: cardiomyopathy, 1 HCM patient

  In this study, we analyzed the genomic information by whole genome sequencing using peripheral blood samples from patients with various cardiovascular diseases including severe heart failure to identify genetic variants related to the pathology of cardiovascular diseases. The purpose is to elucidate the underlying mechanism and develop new therapeutic interventions for cardiovascular disease.

  Study JGAS000704

• Whole genome sequence: cardiomyopathy, 1 ARVC patient

  In this study, we analyzed the genomic information by whole genome sequencing using peripheral blood samples from patients with various cardiovascular diseases including severe heart failure to identify genetic variants related to the pathology of cardiovascular diseases. The purpose is to elucidate the underlying mechanism and develop new therapeutic interventions for cardiovascular disease.

  Study JGAS000705

• RNA-seq analysis of BMP-stimulated glioma initiating cells

  Bone morphogenetic protein (BMP) signaling has been shown to suppress progression of glioblastoma by promoting astrocytic differentiation; however, roles of BMP signaling in cell proliferation and apoptosis of glioblastoma cells remains to be elucidated. We here performed RNA-seq analysis of glioma initiating cells to identify transcriptional target(s) of BMP.

  Study JGAS000077

• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• Multi-region RNA sequencing of tumour regions and tumour-adjacent normal lung tissue

  TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC).

  Study EGAS00001006517

• Identification of drug resistance genes in melanoma by small RNAs expression analysis

  We aim to sequence the small RNAs of 22 human melanoma cell lines in biological triplicate in order to define the microRNAs expression profile of each cell line. The data will be correlated to the mutation status and the sensitivity to a panel of drugs in order to identify genes whose deregulation is associated to drug resistance This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001003245

• Identification of drug resistance genes in melanoma by mRNA gene expression

  We aim to sequence the mRNA transcriptome of 22 human melanoma cell lines in biological triplicate in order to define the gene expression profile of each cell line. The data will be correlated to the mutation status and the sensitivity to a panel of drugs in order to identify genes whose deregulation is associated to drug resistance This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001003244

• MPN_Tissue_WGS_NanoSeq

  An exploration of the somatic mutational landscape of a patient with myeloproliferative neoplasm (MPN) and squamous cell carcinoma (SCC). In particular this study aims to identify mutational signatures present in SCC lesion compared to a region of healthy skin, especially searching for the presence of signatures specific to hydroxycarbamide from previous chemotherapy treatment.

  Study EGAS00001007761

• Wistar PDX Development and Trial Center

  Overall the outcomes of patients with metastatic melanoma have improved dramatically over the last decade due to an improved understanding of the molecular drivers of this disease. In particular, multiple targeted therapy regimens have been approved for patients with a BRAFV600E/K mutation, which are present in ~50% of cutaneous melanomas. These treatments achieve clinical responses in ~80% of patients with a BRAFV600E/K mutation, thus providing proof-of-concept of the therapeutic potential for personalized therapeutic strategies. However, most of the patients will progress within 2 years of starting those therapies. Further, currently there are no targeted therapies that have been shown to be effective in patients with a wild-type BRAF. Thus, there are unmet clinical needs to develop treatments that prevent or overcome resistance to existing therapies for patients with a BRAFV600E/K mutation, and that are effective in patients without a BRAF mutation. In order to facilitate the development of new therapeutic strategies, over the last 5 years we have led a major effort to develop a broad collection of PDX models to reflect the clinical, histological, and genetic heterogeneity of this disease. Our collection of PDX models represents one of the largest collections for any human malignancy, and our initial testing demonstrates that the collection accurately recapitulates the oncogenic drivers and molecular heterogeneity that is observed in patients. This collection also includes a subset of PDX established from patients with acquired resistance to targeted therapies that have been maintained on those agents in vivo to sustain their resistant phenotype. Together these efforts have generated a robust resource to develop, refine, and prioritize new personalized combinatorial therapies for patients. Thus, we propose to establish a multi-disciplinary and multi-institutional PDTC Program focused on the use and continued expansion of our robust melanoma PDX collection to identify new therapeutic approaches that fill important clinical gaps in this disease.

  Study phs002432

• Phenotypic and Genotypic Study of Keratoconus

  Keratoconus, a disease in which the cornea is abnormally steep and thin, affects approximately 1 in 2000 individuals and usually begins in the second decade of life. Keratoconus is a progressive disorder that results in loss of vision that cannot be alleviated by contact lenses or glasses alone and is a leading indication for corneal transplantation in developed countries (Udar et al., 2006, PMID: 16877401). Keratoconus has a strong genetic component, and while several causative genes are clearly suspected, the underlying pathogenic processes have not yet been identified. The goal of our research is to identify genes that harbor mutations contributing to keratoconus and underlying biochemical processes relevant to keratoconus pathogenesis and to develop a better understanding of the etiology of keratoconus, leading to improved detection, treatment, and management. We will enroll isolated keratoconus along with familial cases and unaffected relatives when available. The subject population will consist of individuals diagnosed with keratoconus and unaffected relatives in families. Individuals/families will be ascertained through support groups and clinical collaborators. The identities of the study participants will be known only to Dr. Chakravarti, the study coordinator, physicians in the team and the post-doctoral fellow(s) working directly on the project. DNA, lymphocytes, and lymphoblastoid cell lines may be prepared from the blood samples for future use. Molecular analyses using markers and sequencing and statistical analyses of these data will be used to identify regions of human chromosomes where putative keratoconus disease genes reside. Analyses of coding DNA sequences within genes from keratoconus subjects will be compared to published control sequences to identify disease specific variants. Additionally, to understand disease pathogenesis, subsets of patients may be asked to provide tear samples. Study subjects will not directly benefit from participation and results will not be disclosed to participants nor their health care providers, unless medically relevant.

  Study phs003168