Search Results - EGA European Genome-Phenome Archive

Combining a Universal Telomerase Based Cancer Vaccine with Ipilimumab in Patients with Metastatic Melanoma - Five-year Follow up of a Phase I/IIa Trial

Background Ipilimumab improves survival for patients with metastatic malignant melanoma. Combining a therapeutic cancer vaccine with ipilimumab may increase efficacy by providing enhanced anti-tumor immune responses. UV1 consists of three synthetic long peptides from human telomerase reverse transcriptase (hTERT). These peptides comprise epitopes recognized by T cells from cancer patients experiencing long-term survival following treatment with a first-generation hTERT vaccine, and generate long-lasting immune responses in cancer patients when used as monotherapy. The objective of this trial was to investigate the safety and efficacy of combining UV1 with ipilimumab in metastatic melanoma. Patients and methods In this phase I/IIa, single center trial [NCT02275416], patients with metastatic melanoma received repeated UV1 vaccinations, with GM-CSF as an adjuvant, in combination with ipilimumab. Patients were evaluated for safety, efficacy and immune response. Immune responses against vaccine peptides were monitored in peripheral blood by measuring antigen-specific proliferation and IFN-γ production. Results Twelve patients were recruited. Adverse events were mainly diarrhea, injection site reaction, pruritus, rash, nausea and fatigue. Ten patients showed a Th1 immune response to UV1 peptides, occurring early and after few vaccinations. Three patients obtained a partial response and one patient a complete response. Overall survival was 50% at 5 years. Conclusion Treatment was well tolerated. The rapid expansion of UV1-specific Th1 cells in the majority of patients indicates synergy between UV1 vaccine and CTLA-4 blockade. This may have translated into clinical benefit, encouraging the combination of UV1 vaccination with standard of care treatment regimes containing ipilimumab/CTLA-4 blocking antibodies.

Study EGAS00001005253

Cross-site Concordance Evaluation of Tumor DNA and RNA Sequencing Platforms of CIMAC-CIDC Network

The Cancer Immune Monitoring and Analysis Centers (CIMACs) and the Cancer Immunologic Data Commons (CIDC)  has engaged in efforts to harmonize Whole-exome (WES) and RNA-sequencing (RNA-seq) data from three different experimental platforms (MD Anderson, NCI MoCha lab, and Broad Institute) and are using a series of developed pipelines to process the early trial samples. To evaluate the consistency of tumor WES and RNA-seq profiling platforms across different centers, the CIMACs-CIDC conducted a systematic harmonization study. DNA and RNA were centrally extracted from fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) non-small cell lung carcinoma (NSCLC) tumors and distributed to three centers for WES and RNA-seq profiling

Study phs002295

Atezolizumab Plus Personalized Neoantigen Vaccination in Patients with Urothelial Cancer: a Phase 1 Trial

This is a phase 1 single center clinical trial evaluating a multi-peptide personalized neoantigen vaccine, PGV001, in combination with atezolizumab for patients with urothelial cancer. Patients were enrolled in either the adjuvant or metastatic setting. Those in the adjuvant setting included patients considered high risk of recurrence (pathological stage ypT2-T4 or ypN+ for those treated with prior neoadjuvant chemotherapy or pT3-T4 or pN+ for those not treated with prior neoadjuvant chemotherapy). Those in the metastatic setting included patients progressing after prior chemotherapy or those with at least stable disease after chemotherapy treated as “switch maintenance” therapy. The primary endpoint was feasibility (including the number of neoantigens identified per patient, vaccine production time, the proportion of patients consenting to the vaccine development phase for whom a vaccine product was prepared, and the proportion of patients eligible for the treatment phase who completed the priming cycle 1 of PGV001 plus atezolizumab) and safety. Secondary endpoints included the objective response rate, progression-free survival or disease-free survival, overall survival, and immunomodulatory effects of treatment. Urothelial tumor tissue and normal tissue or peripheral blood specimens were utilized for DNA sequencing. HLA typing was performed utilizing genomic DNA isolated from whole blood, and RNA sequencing was performed utilizing tumor tissue specimens. Neoantigen prediction and final selection for incorporation into the vaccine was determined by the OpenVax computational pipeline. 12 patients were initially enrolled into the vaccine development phase and 10 patients initiated the treatment phase. PGV001 was administered with poly-ICLC and tetanus peptide on subsequent days for up to 10 doses, including an initial priming cycle 1. Atezolizumab was administered every 3 weeks for up to 1 year in the adjuvant setting or 2 years in the metastatic setting. The vaccine was feasible and safe, the median vaccine production time was 20.3 weeks, and 100% of patients that initiated the treatment phase completed the priming cycle. The most common treatment-related adverse events were local grade 1 injection site reactions, fever, and fatigue. Three of the 4 patients in the adjuvant setting remained disease-free at median follow-up of 39.5 months. There was a 40% objective response rate among patients with measurable disease in the metastatic setting. The single patient treated as “switch maintenance” therapy remained with no evidence of disease at 39.4 month follow-up. All patients demonstrated on-treatment neoantigen-specific T cell reactivity, as measured by ELISPOT and flow cytometry assays. Overall, PGV001 plus atezolizumab was feasible, safe, and warrants further investigation. Whole exome sequencing from tumor tissue and normal tissue or whole blood specimens and RNA sequencing from tumor tissue specimens will be available through dbGaP.

Study phs003922

How to upload GPG files

Uploading files Users that holds an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. In order to manage the available resources, we enforce a limit of 10Tb per submission account at any one time. Please do not exceed this limit. INBOX FTP The FTP is only compatible with files encrypted using the EGACryptor tool Before uploading Once your submission files have been prepared using the EGAryptor, the resulting encrypted files and associated md5sum files can be uploaded to your submission account using Aspera or FTP. The EGA is a shared, public service with limited resources. In order to manage the available resources, EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. If you are approaching this limit please contact contact EGA Helpdesk so that we can advise on how to register the associated metadata and trigger the archiving of files, so that you can continue with your submission. If we note that your submission account increases above 10Tb on a consistent base your password will be changed until metadata is associated. Aspera Download Aspera Using Aspera FTP FTP windows FTP Linux / Unix FTP client (Filezilla) FTP and TLS Troubleshooting Troubleshooting Aspera Download Aspera is a commercial file transfer protocol that may provide faster transfer speeds than ftp especially over longer distances. The Aspera ascp command line client. Please select Aspera Connect. The ascp command line client is distributed as part of the aspera connect highperformance transfer browser plugin and is free to use, without registration. The minimum required version of the IBM Aspera Cli is V4. Further instructions. Using the Aspera ascp command line program The location of the ascp program in the filesystem: Mac: on the desktop go cd /Applications/Aspera\ Connect.app/Contents/Resources/ there you'll see the command line utilities where you're going to use ascp. Windows: the downloaded files are a bit hidden. For instance, in Windows 7 the ascp.exe is located in the users home directory in: AppData\Local\Programs\Aspera\Aspera Connect\bin\ascp.exe Linux: should be in your user's home directory, cd /home/username/.aspera/connect/bin/ there you'll see the command line utilities where you're going to use ascp. Your command should look similar to this: ascp -P33001 -O33001 -QT -l300M -L- /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path If you wish to upload several files without being requested the password, please use the below command : ASPERA_SCP_PASS=ega-box-password ascp -P33001 -O33001 -QT -l300M /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path/ Explanation of parameters l300M option sets the upload speed limit to 30MB/s. You may wish to lower this value to increase the reliability of the transfer. L option is for printing logs out while transferring files to upload can be a file mask (e.g. '/homes/submitter/*.srf) or a list of files. ega-box-N is your submission account login. Add k2 switch for transfer restarts Check the command line transfer usage for more configuration details. Using FTP to upload your prepared files Use your preferred ftp client. For example, lftp is a popular choice for Linux and Mac users. Use binary mode for file transfers. Use ftp.ega.ebi.ac.uk as the target host. Login with your ega-box username and password. Upload files to your private ega-box upload area. Depending on your network setting you might wish to start FTP in passive or active mode. Using default FTP command line client in Windows Start the command line interpreter: press WinR, type cmd, hit enter Enter ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Use exit command to exit the command line interpreter. Using default FTP command line client in Linux / Unix Open a terminal and type ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Using FTP client FileZilla We recommend the use of FileZilla, a free FTP client . FileZilla is open source software distributed free of charge under the terms of the GNU General Public License. Use the following connection details (File - Site Manager) and add yoursubmission account username and password: Using FTP client FileZilla Select the files you wish to upload and then select upload: Using FTP client Filezilla Using LFTP with TLS We recommend the following to force the use of a secure connection. lftp > set ftp:ssl-force yes We also recommend setting the following for not encrypting the bulk data itself for performance reasons (theauthentication will still be encrypted): lftp > ftp:ssl-protect-data no In order to verify the certificate,the recommended way would be to use the CA certificates from your machine. To do that use this command in lftp adjusting the path to your ca-certificates location. lftp > ssl:ca-file "/etc/ssl/certs/ca-certificates.crt" If that is not possible or certificates are old and you can't update them, you can download the certificates needed from Quo Vadis Digital Repository The two certificates to download in PEM format are: QuoVadis Root CA2 G3 QuoVadis EV SSL ICA G3 Then you can concatenate them in a file one after the other and save it as lftp-certificates.pem Once this is done you have to point the ssl:ca-file variable to the path lftp > set ssl:ca-file "/path/to/lftp-certificates.pem" Also note that you can save this configuration at ~/.lftp/rc Another option is to download the certificates and add them to the ca-certificates of your machine. For example: In RHEL7 and cenots and others box the process to add the certificates globally is: Download the two certificates (in PEM or DER format, doesn't matter) and save them to "/etc/pki/ca-trust/source/anchors/" Run "update-ca-trust extract" Another less secure option is to turn off certificate verification with the following command: lftp > set ssl:verify-certificate false Troubleshooting If you are having problems with Aspera connection timeouts, it can be down to either one of the following. Transfers cannot start the connection fails instantly. Ensure that TCP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewall. The connection is made, transfers are started, but 0 bytes (0%) are uploaded for each file. Ensure that UDP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewal

Documentation submission/data/uploading-files/ftp

NHLBI TOPMed: SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS)

Subpopulations and Intermediate Outcome Measures in COPD Study Description Subpopulations and intermediate outcome measures in COPD study (SPIROMICS) supports the prospective collection and analysis of phenotypic, biomarker, genetic, genomic, and clinical data from subjects with COPD for the purpose of identifying subpopulations and intermediate outcome measures. It is funded by the National Heart, Lung, and Blood Institute and is coordinated by the University of North Carolina at Chapel Hill. Molecular fingerprinting and extensive subject phenotyping will be performed to identify disease subpopulations and to identify and validate surrogate markers of disease severity, which will be useful as intermediate outcome measures for future clinical trials. Secondary aims are to clarify the natural history of COPD, to develop bioinformatic resources that will enable the utilization and sharing of data in studies of COPD and related diseases, and to create a collection of clinical, biomarker, radiographic, and genetic data that can be used by external investigators for other studies of COPD. Participating Institutions: Research participants for SPIROMICS will be enrolled, phenotyped, and followed at twelve SPIROMICS Clinical Centers: Columbia University, Temple University, Johns Hopkins University, Wake Forest University, University of Michigan, University of Illinois at Chicago, University of Iowa, University of Utah, National Jewish Health, University of California at San Francisco, and University of California at Los Angeles. The University of North Carolina at Chapel Hill serves as the Genomics and Informatics Center. The Radiology Reading Center is based at the University of Iowa. The PFT Reading Center is based at the University of California at Los Angeles. Study Design: SPIROMICS is a prospective cohort study that enrolled approximately 2,981 participants at twelve clinical centers over five years. Participants are distributed across four enrollment strata (i.e., Never-smokers, Smokers without COPD, Mild/Moderate COPD, and Severe COPD). Study Visits: Participants have up to four in-person visits (Baseline and Annual Clinic Visits at years 1, 2, 3 after Baseline). Study questionnaires and spirometry are completed at all main study visits. Blood and urine samples are collected at visits 1, 2, and 4. Sputum samples are collected at Visit 1. The CT scans are completed and Baseline and Visit 2. Participants also receive quarterly follow-up calls to assess health status and determine if an exacerbation has occurred. Substudies Bronchoscopy and Immunophenotyping Substudy The Bronchoscopy Substudy will enroll 50 subjects per site, for a total of 300 participants. These participants will be recruited across all four study strata. This substudy includes two study visits. During the first visit, sputum samples are collected for Immunophenotyping analysis at the Immunophenotyping Core Lab based at the University of Michigan. Participants then return for a second visit during which samples are collected via bronchoscopy, including bronchoalveolar lavage, epithelial brushings and bronchial biopsies. Immunophenotyping analysis is also conducted on BAL and blood collected during the bronchoscopy study visit. Repeatability Substudy The entire baseline clinic visit was repeated on 98 volunteers to determine reliability of measurement procedures. All baseline study-related procedures and questionnaires, including the CT scan, were re-administered and new samples of blood, urine, saliva, and sputum were collected. Field center staff processed these biospecimen samples according to the same protocol. Exacerbation Substudy The Exacerbation Substudy is a prospective, longitudinal observational study of up to 400 participants, which will allow the assessment of participant-driven health care utilization (HCU) events and symptom-defined exacerbation events over time. HCU-driven events are defined by change in medical treatment in response to a perceived COPD Exacerbation. Symptom-based events will be defined by using EXACT-PRO (EXacerbations of Chronic Pulmonary Disease Tool - Patient Reported Outcome), a daily symptom diary developed to measure the frequency, severity, and duration of exacerbations in clinical trials. All participants are provided with a PDA on which to complete the diary. Participants reporting a possible COPD exacerbation will be brought into the study clinic for a study visit to collect biological specimens and questionnaire data. The overall objectives of the Exacerbation Substudy are to: Obtain clinical data and specimens on SPIROMICS participants before and during an acute COPD exacerbation defined by Health care utilization triggered by the subject, or Symptomatic change (triggered by an EXACT defined threshold) Describe symptomatic changes occurring around HCU and symptom-defined (EXACT) events and their relationship to clinical and specimen data, Characterize the non-exacerbation "stable" state in COPD using the EXACT, including: Symptom variability (EXACT), Clinical data and specimen parameters during a stable state (baseline), The relationship between clinical and specimen data and symptom severity and variability. Explore the characteristics of stable patients, defined as those who do not experience HCU or symptom-defined (EXACT) events during the sub-study period, using baseline clinical data and specimens, and compare these characteristics with patients who experience HCU and/or symptom-defined events. Examine the relationship between HCU and symptom-defined exacerbation frequency during the first one-year period of follow-up for exacerbations and health outcomes, including 12-month change in lung function and COPD health status, and longer-term morbidity and mortality, with the latter derived from long-term data from the larger SPIROMICS study.

Study phs001927

Human Dorsal Root Ganglion RNA Landscape Profiling for Neuropathic and Chronic Pain

Bulk and spatial RNA sequencing were performed on human Dorsal Root Ganglia (DRGs), peripheral sensory nerves and nodose ganglia and relative gene abundances were calculated. Various analyses were performed: Human DRG gene expression profiles were contrasted with a panel of gene expression profiles of relevant tissues in human and mouse (integrating, among other sources, datasets from ENCODE and GTex) in order to identify. DRG-enriched gene expression, co-expression modules of DRG-expressed genes, and key transcriptional regulators in humans. Contrasting the human and mouse DRG transcriptomes to identify DRG-enriched gene expression patterns that were conserved between human and mouse, identifying putative cell types of expression of these genes, and potential known drugs that might target the corresponding gene products. Characterization of non-coding RNA profile of human and mouse DRGs. Characterization of DRG-enriched alternative splicing and alternative transcription start site usage based transcript variants in humans and mouse, and the overlap between these two species. Contrasting of human DRG and GTex human tibial nerve samples to identify putative axonally transported mRNAs in sensory neurons. Human DRG and peripheral nerves transcriptomes from donors suffering from neuropathic and/or chronic pain were contrasted with controls to identify. Differentially expressed genes, pathways and regulators path play a potential role in neuronal plasticity, electrophysiological activity, immune signaling and response. Predictive models (Random Forests) were built to jointly predict the sex and pain state of samples based on information contained solely in autosomal gene expression profile. Gene co-expression modules were identified and gene set enrichment analysis performed.to identify sample - pathway associations, and to broadly characterize plasticity in human DRG cell types. Spatial transcriptomics was performed on human DRGs and nodose ganglia to obtain near single-neuron resolution. We identified distinct clusters of human DRG sensory neurons. We characterized the expression of different gene families (e.g., GPCRs, sodium channels, potassium channels, etc.). We investigated differences between male and female neurons.

Study phs001158

eMERGE Network Phase III Clinical Sequencing: eMERGEseq Panel

The Electronic Medical Records and Genomics (eMERGE) Network is a National Institutes of Health (NIH)-organized and funded consortium of U.S. medical research institutions. The primary goal of the eMERGE Network is to develop, disseminate, and apply approaches to research that combine biorepositories with electronic medical record (EMR) systems for genomic discovery and genomic medicine implementation research. eMERGE was announced in September 2007 and began its third phase in September 2015. eMERGE III consists of nine study sites, two central sequencing and genotyping facilities, and a coordinating center. eMERGE Phase III aims to: 1) sequence and assess the phenotypic implication of rare variants in a custom designed eMERGEseq panel consisting of 109 genes (including 56 ACMG actionable finding list genes and the top 6 genes from each site relevant to their specific aims), as well as approximately 1400 SNPs; 2) assess the phenotypic implications of these variants by developing, validating and implementing new phenotype algorithms, 3) integrate genetic variants into EMRs to inform clinical care; and 4) create community resources. Included in this study are: ~24,000 eMERGE participants from 10 eMERGE III study sites. Corresponding demographics, body mass index measurements. Top PheWAS codes generated from a collated list of ICD codes from all study sites. Study sites and participants include: Cincinnati Children's Hospital Medical Center (CCHMC): Cincinnati Children's Hospital Medical Center (CCHMC) is a not-for-profit hospital and research center pioneering breakthrough treatments, providing outstanding family-centered patient care and training healthcare professionals for the future, and dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. We bring a comprehensive electronic health record (EPIC), a deidentified i2b2 data warehouse of 680K patient records, a biobank with >261,000 consents that allow return of results to >84,000 patients and guardians who have provided DNA samples, and hundreds of faculty and senior staff who make genomics or informatics an active focus of their research. CCHMC will help the eMERGE III Steering Committee identify genes for the eMERGE III targeted sequencing panel, provide 3,000 DNA samples from CCHMC patients to be sequenced, review targeted gene panels from clinical care at CCHMC for somatic mosaicism and reinterpretation, and further develop and disseminate a software workflow suite for sequence analysis. We will also extend our work generating phenotype algorithms using heuristic and machine learning methods to many new childhood diseases. We will develop tools to evaluate adolescent return of results preferences, examine the ethical and legal obligations and potential to reanalyze results, and develop clinical decision support for phenotyping, test ordering, and returning sequencing results. Children's Hospital of Philadelphia (CHOP): The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP), and one of the world's largest genetics research programs, with to state-of-the-art high-throughput sequencing and genotyping technology. Our primary goal is to translate basic research findings to medical innovations. We aim to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders, including asthma, autism, epilepsy, pediatric cancer, learning disabilities, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. Participants were recruited from the CAG biorepository (n>450,000), specifically from >100,000 CHOP pediatric patients and family members, which is enriched for rare-diseases (n>12,000). Center for Applied Genomics, The Children's Hospital of Philadelphia We gratefully thank all the children and their families who enrolled in this study, and all individuals who donated blood samples for research purposes. Genotyping for this project was performed at the Center for Applied Genomics and supported by an Institutional Development Award from The Children's Hospital of Philadelphia. Sequencing was supported by the National Institutes of Health through an award from the National Human Genome Research Institute's Electronic Medical Records and Genomics (eMERGE) program (U01HG008684). Columbia University: The goal of the Columbia eMERGE III project is to develop methods for integrating genomic data in EHRs and to study the impact of such genomic informatics interventions on the health of a diverse, underserved urban adult English- and Spanish-speaking patient population in Northern Manhattan served by Columbia University Medical Center/New York-Presbyterian Hospital system. The study group is 2500 patients recruited from diverse clinics and community outreach centers of self-reported White (~61%), Asian (~11%), African-American (~11%), American Indian/Alaska Native (<1%) racial and Hispanic (~33%) ethnic backgrounds. There are two subgroups in the study cohort - a retrospective group (N=1052) that includes patients from oncology and nephrology clinics, and a prospective one (N=1448) that includes healthy individuals as well as participants with diverse medical conditions. Confirmed pathogenic variants in 70 selected genes will be returned to participants and their healthcare providers through the EHR integration. Participants are able to choose the results they receive and will have the freedom to meet with a genetic counselor and a geneticist to review results. The impact of genetic testing on clinical care is determined by periodic monitoring of EHRs. Geisinger: Samples and phenotype data in this study were provided by the Geisinger MyCode® Community Health Initiative. Participants are recruited across the Geisinger System via online consents or in-person consents at a hospital or clinic visit. Enrollment is ongoing with over 100,000 individuals currently consented. Partners Healthcare (Harvard University): The Partners HealthCare Biobank is a large research program designed to help researchers understand how people's health is affected by their genes, lifestyle, and environment. This large research data and sample repository provides access to high-quality, consented blood samples to help foster research, advance our understanding of the causes of common diseases, and advance the practice of medicine. For the Partners research community (Massachusetts General Hospital and Brigham and Women's Hospital), the Biobank provides: Banked samples (plasma, serum, and DNA) collected from consented patients Blood samples that were discarded after clinical testing in the Crimson Cores maintained in the Brigham and Women's Hospital and Massachusetts General Hospital Pathology Departments Sample handling and preparation services Link to the biobank data to the Partners Research Patient Data Registry (RPDR) a research instance of our electronic clinical chart Data access through our research portal. To date, over 70,000 Partners patients have given their consent to enroll, give a blood sample, receive research results and agreed to be re-contacted for additional research studies. The Biobank has enabled Partners investigators to compete for nationally recognized grants in personalized medicine such as a clinical electronic Medical Records and Genomics network (eMERGE) site and the national All of US program. The Biobank currently supports over 120 Partners investigators and over 130 million dollars in NIH research. Kaiser Permanente Washington/ (KPWA) / University of Washington (UW): KPWA participants were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N=2,500.) NWIGM is based at the University of Washington and co-managed by the University of Washington and KPWA. The purpose of the NWIGM biorepository is to build infrastructure and resources to carry out a broad range of future genetic research. KPWA members enrolled in the biorepository are asked to provide informed consent to providing a DNA sample for storage in the NWIGM biorepository. The consent is purposefully broad to serve the dual purpose of reducing the burden on researchers who wish to use this biorepository and the IRB committees who will be responsible for reviewing these requests in the future. Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in KPWA's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at KPWA. 1) Demographics - participants with self-reported race as Asian ancestry were prioritized and selected to enrich for non-European ancestry. The KPWA eMERGE cohort includes N=1,245 members of Asian ancestry. 2) Participants were also selected for a history of colorectal cancer (N=1,255), in order to allow us to enrich germline pathogenic variants. Mayo Clinic: The Return of Actionable Variants Empirical (RAVE) Study was approved by the Mayo Clinic IRB. We recruited 2537 participants from Mayo Clinic biobanks in Rochester, MN, who had hypercholesterolemia or colon polyps, thereby enriching for Familial hypercholesterolemia (FH) and monogenic causes of colorectal cancer (CRC). Additional eligibility criteria were: 1) residents of Southeast MN who were alive and aged 18-70 years; 2) LDL-C level >155 or >120 mg/dl while on lipid-lowering therapy; 3) no known cause of secondary hyperlipidemia; and 4) no cognitive impairment or dementia that would compromise their ability to give written informed consent. Based on these criteria, we identified 5270 eligible patients and obtained informed consent from 3030 participants. Recruitment was conducted in waves and utilized mailed recruitment packets consisting of a study brochure, a written informed consent form, a baseline psychosocial questionnaire, and a return postage-paid envelope. DNA of 2537 participants was sent for CLIA-certified targeted sequencing of 109 genes including genes associated with FH and CRC. Targeted sequencing and genotyping was performed in a Central Laboratory Improvement Amendment (CLIA)-certified laboratory. Northwestern University: Samples and data used in this study were obtained from patients from Northwestern Medicine, an integrated healthcare system, formed through a partnership of Northwestern Memorial HealthCare and Northwestern University Feinberg School of Medicine. Participants include a retrospective cohort from the Northwestern Pharmacogenomics Study, funded through the eMERGE II project, NHGRI (3U01HG006388-02S1) and a prospective cohort from the Genetic Testing and Your Health Study, funded through the eMERGE III project, NHGRI (U01HG008673). Patients were eligible to participate if they were18 years or older and see a physician at Northwestern Medicine. Patients consented to genetic testing and to allow their results to be placed in their electronic medical record. Vanderbilt University Medical Center: Vanderbilt University Medical Center (VUMC) participants were enrolled in the eMERGE Network through the Vanderbilt Genome-Electronic Records (VGER) project. Patients were provided the appropriate consent to receive clinically relevant genetic results (N=2,700). Participants were eligible if aged 21 or over, had a healthcare provider at VUMC, and visited the provider at least 3 times in the past 3 years. Meharry Medical College: Inclusion of ethnic groups in genomic research is critical to identify possible reasons for health disparities. African-Americans are being enrolled in various outpatient clinics of Nashville General Hospital at Meharry, an inner city hospital primary serving a poorer patient group. A total of 500 African Americans with four cancer types demonstrating health disparities in this population - prostate, colon, breast, lung are identified and approached by clinical research coordinators. The purpose of the study is to determine if any genetic information can be identified from these patients who have or are at high risk of one of these disparate cancers. All participants provide written informed consent and HIPAA authorization to provide blood samples for broad research use and permission to access data in their hospital electronic medical record for research now and in the future. An extensive demographic profile is obtained and entered into a REDCap database. Blood samples are obtained for a panel of alleles from extracted DNA at Baylor. In addition, de-identified coded samples are processed and stored in a central biorepository for further DNA, RNA and proteomic analyses. The survey and phlebotomy are performed at the time of the initial contact and agreement to participate. Nearly all patients approached willingly agree to participate for potential benefit to themselves, family members, or humankind. Little concern is voiced of providing samples for genetic analysis. Study investigators will share results with the participants and providers if testing does not indicate high risk. Results indicating increased risk or actionable alleles for the patient and/or family will be returned by a genetic counselor. Monitoring of the patients' health in this cohort will continue to be followed in the EMR to identify any future associations that might explain health disparities in African Americans. Proposals will be reviewed from investigators to study the genetic or proteomic samples as well as the clinical and demographic information in the repository. Please note that this version of the dataset has a handful of mismatches between genotyped and provided sex. Data with the following IDs should be removed prior to analysis: 420252874213744142412243424569384245694642672223

Study phs001616

HTAN MCL Pre-Cancer Atlas Pilot Project - Targeted Sequencing Development Study

The HTAN-MCL Pre-Cancer Atlas Pilot Project (PCAPP) is the result of a collaboration between the seven members of the MCL consortium. Across four organ types, PCAPP's goal is to collect and profile pre-malignant lesions for gene expression, DNA mutations, single-cell gene expression and immune-environment. Most PML are small in size and only available come from formalin fixed paraffin embedded archived tissue. The primary goal of PCAPP is to 1) understand the logistical challenges of PML specimen collection, 2) document technical limitations of the assays that are specific to the PML and 3) overcome them to support the generation of a more comprehensive Pre-Cancer Atlas in the future. The current upload provides RNA and DNA sequencing from participants with DCIS who were studied at the University of San Diego and the University of Vermont. Description of the overall study: A. Background/Significance One of the critical barriers to developing new approaches for cancer detection and prevention is the lack of understanding of the key molecular and cellular changes that cause cancer initiation and progression. Unlike the extensive work that has been done profiling advanced stage tumors, few studies have comprehensively profiled the molecular alterations found in precancerous tissues. Premalignant lesions are currently characterized by histologic changes that precede the development of invasive carcinoma1,2.These lesions can often be identified in regions surrounding an invasive tumor or in biopsies taken from patients undergoing diagnostic evaluation for suspicion of cancer. Currently, limited metrics exist to identify lesions that will likely progress to carcinoma and require intervention from those that will naturally regress or remain stable3,4. Characterization of the molecular alterations in premalignant lesions and the corresponding changes in the microenvironment would hasten the development of biomarkers for early detection and risk stratification as well as suggest preventive interventions to reverse or delay the development of cancer. Our pilot study will establish the feasibility of transcriptomic, genomic and immune profiling of FFPE premalignant lesions from multiple organ sites, collected and profiled with uniform SOPs across multiple institutions within the MCL consortium. We will characterize the molecular alterations in precancerous lesions and the corresponding microenvironment in four major organ sites, in order to uncover the molecular and cellular determinants of premalignancy, and establish standardized sequencing and immunohistochemistry protocols on FFPE precancerous tissue. We will also evaluate the technical feasibility of single nuclei sequencing of small FFPE pre-cancer lesions. Successful completion of the proposed pilot study will set the stage for expansion and development of a comprehensive Pre-Cancer Atlas (PCA) as part of the NCI's moonshot.B. Specific Aims Aim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples (and flash frozen tissue where available) and compare the genomic/transcriptomic alterations within and across organ sites. C. ApproachAim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). MethodsI. Patient Population/Sample Collection: Overview of the sites collecting PML tissue from the respective organs is provided in Table 1 and a full description of the biospecimens to be obtained is described in detail for each organ type below. Table 1. Breakdown of cohort by tissue type and collection site.Organ siteBreastLungPancreasProstateType of PMLDCISAAH, Squamous Dysplasia/CISIPMNsPINCollection of PatientsUCSF/UCSDUVMBU*/UCLAVanderbilt/MoffittMDACC*JHUStanford*# of Patients201920 (10 of each type)20 (10 of each type)242020Total patients per Organ39402440Note: single nuclei/cell RNA-Seq will be performed on 4-5 FFPE samples from each of the organ types 1. DCIS lesions from breast tissue: DCIS lesions will be collected from 39 patients (20 from UCSF/UCSD & 19 from UVM) with primary low or high-grade DCIS diagnosed from a breast core biopsy. Subsequent resected lumpectomy or mastectomy tissues will be prospectively sampled in the vicinity of the prior biopsy site using multiple approaches: 1) Live cells (heterogeneous mix) will be obtained as a cell scrape slurry from the lesion surface or by fine needle aspirate (FNA); 2) For a subset of specimens where size is sufficient, a block of breast tissue with DCIS will be fresh-frozen; 3) The remainder of the specimen will be taken for routine formalin-fixation and paraffin-embedding (FFPE). The FFPE sample will be annotated to identify the matched FFPE tissue block adjacent to the fresh-frozen sample and will be sectioned for use in bulk and single nuclei sequencing . We will dissect DCIS, adjacent normal and when available, associated carcinoma. In addition, when possible, normal tissue will be collected from a tissue block lacking lesions as well as collection of blood. A subset of patients (n = 5 | FFPE, flash frozen and fresh) will be sent to the Broad Institute for single nuclei/cell sequencing.2. AAH and squamous dysplastic/CIS lesions from airway and lung tissue: For squamous cell lung cancer, we will collect endobronchial biopsies from abnormal airway regions identified on autofluroscence bronchoscopy or identify PMLs in the margins of resected lung tissue. We will study 20 patients (5 each from BU/UCLA/Vanderbilt/Moffitt) with pre-invasive squamous lesions (moderate-severe dysplasia or carcinoma in situ (CIS)) identified on pathologic examination. LCM of the premalignant region and adjacent normal epithelium will be performed as well as the invasive tumor for those collected from the resection margin (n=5 from UCLA). On a subset of lesions collected at bronchoscopy (n=5), we will collect additional biopsies that will be flash frozen and fresh for single nuclei and cell sequencing, respectively, performed at the Broad Institute. In parallel to the work at the Broad, BU will perform single cell RNA-seq on these freshly cell sorted tissues (n = 5). Blood will be collected on all patients for genomic studies. For lung adenocarcinoma, we will collect resected FFPE lung tissues from 20 patients (10 from UCLA and 10 from Vanderbilt/Moffitt) with early stage lung adenocarcinoma that harbor atypical adenomatous (AAH) premalignant lesions in the resection margin. We will LCM multiple AAH regions (3-5 per patient) as well as adjacent regions of normal epithelium and invasive adenocarcinoma. In addition, blood will be collected on all patients for genomic studies. 3. IPMNs from pancreatic tissue: For pancreatic cancer PML, we will collect low and high grade lesions from 24 patients representing macroscopic Intraductal Papillary Mucinous Neoplasms (IPMN) (n=24) from surgically resected specimens along with blood samples. Archival FFPE specimens of microscopic PanIN lesions, occurring multi-focally adjacent to invasive PDAC, and archival IPMN lesions (with or without associated invasive cancer), along with the adjacent normal tissue, will undergo LCM and utilized for bulk DNA and RNA sequencing. If matched frozen tissues are available for a subset of these FFPE samples, we will bank for comparison of profiles. Because IPMNs are macroscopic lesions, they provide an opportunity for obtaining the samples fresh and therefore can be used for single cell sequencing (in contrast to PanINs). Therefore, 5 freshly obtained IPMNs will be used for the single cell RNA sequencing studies performed at both the Broad Institute and MDACC, and the matched FFPE and/or frozen sections from these lesions (obtained from the adjacent PML) will be sent to Broad Institute as a pilot to assess "single nuclei" RNA sequencing.4. PINs from prostate tissue: For prostate cancer PML, there will be 40 samples of Prostatic Intraepithelial Neoplasia (PIN) collected between the Stanford and JHU sites (20 cases per site). At the Stanford site, 20 prostate specimens detected by PSA screening who have/will undergo surgery (radical prostatectomy) for clinically localized disease will make up the final cohort. The age range of the participants would be 40-75, and we anticipate that 18 will be Caucasian, 1 Asian and 1 Latino or African American based on the practice demographics practice at Stanford. Clinical and MRI data will also be collected for these samples. We will collect low grade (e.g. Gleason score of 6/Grade group 1; n=10) and high grade (Gleason score 4+3=7 or higher/Grade group 3 or higher; n=10) PINs from FFPE samples that have prostate carcinoma. In addition to obtaining LCM archival samples of low and high grade PIN, we will also obtain normal prostatic epithelial from the peripheral, central and transition zones as well as multiple samples of prostate carcinoma in order to obtain the spectrum of Gleason grades in the carcinoma as needed. LCM samples will be used for bulk DNA and RNA sequencing. In addition, single cells will be dissected from FFPE samples to prepare single cell RNA seq libraries using techniques developed at Stanford, and FFPE tissue will be sent to the Broad for single nuclei sequencing. When available, flash frozen and fresh samples from these prostates will be archived and prepared for single nuclei and cell sequencing, respectively, at the Broad Institute and at Stanford (single cell only). JHU will also capture 10 cases (5 grade group 1 and 5 grade group 2) of high grade PIN, normal and invasive adenocarcinoma using frozen sections from fresh frozen tissues. When possible these will be from the same patients as the FFPE samples. Since frozen sections can be quite challenging to morphologically determine high grade PIN from normal epithelium, for these samples we will perform a number of additional tissue-based characterizations. These will include a multicolor combined basal cells (p63 and CK903) and PIN/carcinoma markers (AMACR) referred to in the cocktail as "PIN4", c-MYC (referred to as MYC) protein5, by IHC and mRNA by in situ hybridization (AM De Marzo, Q Zheng unpublished observations), telomere length by in situ hybridization6 and the 5'ETS/45S rRNA7. For these slides, the whole slides will be scanned with a Hammamatsu Nanozoomer with a 40x objective and regions of interest will be annotated as a guide for LCM.II. Laser-capture microdissection (LCM): FFPE tissue blocks will be sectioned at 7μm thickness and serial sections will be stained with H&E. LCM will be performed utilizing standard LCM systems, such as Leica LMD7000 and ArcturusXT at each site. Regions of premalignancy will be dissected and RNA/DNA will be extracted from microdissected cells using the Qiagen All Prep DNA/RNA FFPE Kit. Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples and compare the genomic/transcriptomic alterations within and across organ sites. Rationale: There have been limited studies characterizing the genomic and transcriptomic landscape of premalignant lesions associated with breast, pancreatic, lung or prostate cancers. Characterizing the molecular determinants of premalignant disease that are unique and shared across multiple organs will enable new candidate biomarkers for early detection and novel therapeutic strategies for early intervention. MethodsBulk RNA-seq of LCM FFPE tissue: All participating sites will perform bulk RNA-seq in accordance with SOPs developed at BU. In brief, total RNA will be isolated from LCM'd lesion and associated microenvironment tissue using the Qiagen All Prep DNA/RNA FFPE Kit and quality will be assessed with the Agilent Bioanalyzer. Libraries will be generated with the Illumina TruSeq Access kit (for FFPE samples). They will be sequenced on the Illumina HiSeq2500 with 75base-pair paired-end reads. Quality of FASTQ files will be assessed with FastQC. Reads will be aligned to the human genome with STAR and gene-level and isoform-level expression will be quantified with RSEM. Splice junction saturation, transcript integrity, and biotype distributions will be calculated for each sample with RSeQC. DESeq2 and EdgeR will be used to identify associations between gene expression profiles and clinical variables while controlling for confounding covariates. BU will serve as an RNA-seq Core to assess reproducibility of FFPE RNA-seq methods across sites. We will perform RNA-seq according to the SOP listed above on a subset of samples for each organ type (total n ~ 20). Bulk seq of DNA from FFPE tissue: All participating sites will perform targeted or whole exome-seq (WES) in accordance with SOPs. In brief, DNA from laser captured material will be isolated using the Qiagen All Prep DNA/RNA FFPE Kit and undergo stringent quality control to ensure high quality input material for genomic profiling. Purified DNA (ideally 100-200 ng) will be used for library preparation and amplification, followed by next generation sequencing using standard protocols distributed by CDMG. Exome-seq methods are considered standardized, thus we will not need a DNA-seq Core to assess reproducibility across sites. We anticipate local centers will use Illumina paired end reads, following the following general approach. 1) DNA library preparation: Paired-end libraries will be prepared following the manufacturer's protocols (Illumina and Agilent), fragmented to 150-200 bp 2) Capture of targeted exome: Whole exome capture will be carried out using the protocol for Agilent's SureSelect Human All Exon kit. Purified capture products will be amplified using the SureSelect GA PCR primers (Agilent) for 12 cycles. 3) Sequencing will be carried out for the captured libraries using at least 100 bp paired-end reads. To achieve high level sensitivity and accuracy for detecting all the mutations in the whole exome, each sample will be sequenced at 200X mean depth. 4) Read mapping and alignment and variant analysis: Sequence short reads will be aligned to a reference genome (NCBI human genome assembly build 38) using BWA-MEM. Local realignment of aligned reads will be performed using Genome Analysis Toolkit (GATK).Data QC: To ensure scientific rigor and consistency among sites in RNA and DNA processing we will include a preliminary analysis of steps in processing and analysis. Protocols for extraction of high quality RNA and DNA from formalin fixed paraffin embedded (FFPE) tissues, which will be used extensively in these studies continue to improve and may have variable implementation among the sites participating in this study. To evaluate consistency of preliminary steps in processing and downstream analyses, we will initially distribute slides from one large FFPE fixed cancer of origin from prostate, breast, lung and pancreatic cancer. Analysis of these samples will allow us to review the DNA and RNA characteristics (yield, purity and strand length) among sites. Downstream analysis of these same samples will also allow us to compare among sites the consistency of variant calls among centers. We will be able to identify if there are some times of calls (such as small insertion deletions) that are more variable among centers versus other types of calls (such as relative gene expression or single base pair substitutions) that we expect to be less variable and to characterize the reliability of findings across sites. We are also including a 5% blind duplicate analysis of RNA sequencing. Samples will be analysed by the participating genomics cores without knowledge of the phenotype. RNA seq and CNA analyses are normalized for batch effects. We will also compare the observed sex to the self-reported sex as based on RNA profiles and exome sequencing of X chromosome genes as another check for processing accuracy and sample management. D. References 1. Wacholder, S. Precursors in Cancer Epidemiology: Aligning Definition and Function. Cancer Epidemiol. Prev. Biomark. 22, 521-527 (2013). PMID: 23549395.2. Berman, J. J. Precancer: The Beginning and the End of Cancer. (Jones & Bartlett Learning, 2011).3. Nasiell, K., Nasiell, M. & Vaćlavinková, V. Behavior of moderate cervical dysplasia during long-term follow-up. Obstet. Gynecol. 61, 609-614 (1983). PMID: 6835614.4. Merrick, D. T. et al. Persistence of Bronchial Dysplasia Is Associated with Development of Invasive Squamous Cell Carcinoma. Cancer Prev. Res. (Phila. Pa.) 9, 96-104 (2016). PMID: 26542061.5. Gurel, B. et al. Nuclear MYC protein overexpression is an early alteration in human prostate carcinogenesis. Mod. Pathol. Off. J. U. S. Can. Acad. Pathol. Inc 21, 1156-1167 (2008). PMID: 18567993.6. Meeker, A. K. et al. Telomere shortening is an early somatic DNA alteration in human prostate tumorigenesis. Cancer Res. 62, 6405-6409 (2002). PMID: 12438224.7. Guner, G. et al. Novel Assay to Detect RNA Polymerase I Activity In Vivo. Mol. Cancer Res. MCR 15, 577-584 (2017). PMID: 28119429.

Study phs002225

Privacy Notice for EGA Web user

Privacy Notice for EGA Public Website This Privacy Notice explains what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. 1. Who controls your personal data and how to contact us? European Genome- Phenome Archive - EGA offers a service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects, jointly managed by European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI) and Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG). EMBL-EBI and CRG represent joint Data Controllers’ of processing of your personal data. They and their Data protection officers may be contacted for data protection queries and for exercising your rights under Section 8. You may contact EMBL-EBI, represented by Mallory Freeberg, by: email at: mfreeberg@ebi.ac.uk , or post at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: telephone at +49 6221 387-8590, email at dpo@embl.org , or post at EMBL Heidelberg, Data protection officer, Meyerhofstraße 1, 69117 Heidelberg, Germany. You may contact CRG, whose EGA team is represented by dr. Jordi Rambla de Argila, by: email at jordi.rambla@crg.eu, or post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), Dr.Aiguader 88, PRBB Building, 08003 Barcelona, Spain. CRG Data protection officer may be contacted by: email at dpo@crg.eu post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain. 2. Which is the lawful basis for processing personal data? For providing you access to the website and for offering support whilst using the website, we need your consent under Article 5 (2) of the IP 68 (equivalent to Article 6 (1)(a) of the GDPR). For monitoring your activities on the website, we process your personal data on the grounds of important public interest. Such legal basis is found in Article 5(1)(a) of EMBL Internal Policy No 68 on General Data Protection (hereinafter IP 68), which is equivalent to Article 6 (1)(e) of the EU General Data Protection Regulation (hereinafter GDPR) and upon which personal data are processed for the achievement of the aims laid down in 1973 agreement establishing EMBL, such as the promotion of the cooperation in the fundamental research, in the development of advanced instrumentation and in advanced teaching in molecular biology and dissemination of information. 3. What personal data is collected from users of the service? How do we use this personal data? We collect the following personal data from you: IP addressesDate and time of a visit to the service website Operating systemAmount of data transmittedBrowserEmail address (only when support is requested by the user) The data controller will use your personal data for the following purposes: To provide the user access to the serviceTo better understand the needs of the users and guide future improvements of the serviceTo monitor website activities according to and in compliance with the Terms of UseTo better understand the needs of the website visitors and guide future improvements of the serviceTo conduct and monitor website security activitiesTo create anonymous usage statistics 4. Who will have access to your personal data? The personal data will be disclosed to: Authorised staff in the data controller’s institutions acting on data controller`s behalf and instructions. 5. 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The joint Data Controllers provide these rights regarding your personal data You have the right to: Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration.Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period.Request information to understand data processing activities when the results of these activities are applied to you.Object at any time to the processing of your personal data unless we can demonstrate that we have legitimate reasons to process your personal data.Request free of charge and without excessive delay rectification or erasure of your personal data if we have not been processing it respecting the data protection policies of the respective controllers. It must be clarified that rights under points 4 and 5 are only available whenever you need support whilst using our website. For other processing based on the grounds of important public interest you cannot exercise your rights to object, rectify or erase your personal data according to the Article 13(2)(a)(b) of IP 68 (equivalent to Article 17(3)(b)(d) and Article 21(6) of the GDPR). 8. Supervisory authority If you wish to complain against the processing of your personal data, you may do so by post at: EMBL Heidelberg, Data Protection Committee, Meyerhofstraße 1, 69117 Heidelberg, Germany, or Autoritat Catalana de Protecció de Dades (Catalan Data Protection Authority), C/Rosselló 214, Esc A, 1r 1a, Barcelona 08008, Spain. 9. CookiesWe use cookies and similar technologies that are strictly necessary for the correct functioning and security of the EGA website (for example, to enable core site features and to help protect the service). These cookies do not store information that directly identifies you.You can set your browser to block or delete cookies. Please note that if you disable strictly necessary cookies, parts of the website may not function properly.Published on February 6, 2019

Documentation data-protection/privacy-notice/ega-website

Resuscitation Outcomes Consortium (ROC) Amiodarone, Lidocaine or Neither for Out-Of-Hospital Cardiac Arrest Due to Ventricular Fibrillation or Ventricular Tachycardia (ALPS)(ROC-ALPS-BioLINCC)

Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Objective To compare the effects of amiodarone, lidocaine, and placebo on survival to hospital discharge after out-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation or pulseless ventricular tachycardia. Background Ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT) are common causes of out-of-hospital cardiac arrest, but are considered the most responsive to shock and therefore the most treatable. Nonetheless, most defibrillation attempts do not result in sustained return of spontaneous circulation, and VF or VT may persist or recur after shock. There is also evidence that longer durations of VF or VT are associated with decreases in the likelihood of resuscitation. Amiodarone and lidocaine are commonly used to promote successful defibrillation of shock-refractory VF or VT and prevent recurrences. Previous trials have shown amiodarone to be more effective than placebo or lidocaine for return of spontaneous circulation and survival at hospital admittance. This study sought to further extend the research and examine whether amiodarone would improve survival to hospital discharge and neurologic outcomes, as compared to placebo or lidocaine. Participants 3,026 eligible participants were enrolled, with 974 assigned to amiodarone, 993 assigned to lidocaine, and 1,059 assigned to placebo. An additional 1,627 participants that received a study intervention, but did not meet eligibility criteria, were included in analysis of the intention-to-treat population. Design The study interventions (amiodarone, lidocaine, and saline) were packaged in indistinguishable sealed kits and randomly distributed in to Emergency Medical Services (EMS) providers in a 1:1:1 ratio, stratified by participating site and agency. Each kit contained three syringes, and each syringe held 3 ml of colorless fluid containing 150 mg of amiodarone, 60 mg of lidocaine, or normal saline. Participants with out-of-hospital cardiac arrest were treated in accordance with local EMS protocols, in compliance with American Heart Association (AHA) guidelines. If VF or VT persisted or recurred after one or more shocks, eligible participants received a vasopressor and the masked kit containing amiodarone, lidocaine, or placebo. Approximating current clinical practice, the initial dose consisted of two syringes administered by rapid bolus. If the estimated body weight of the patient was less than 100 lbs., then one syringe was used. If VF or VT persisted, standard resuscitation measures, additional shocks, and an additional syringe of the study drug were administered. At that point the trial interventions were completed and standard interventions for advanced life support were employed. Upon arrival at the hospital, providers were notified of the patient's enrollment in the trial and encouraged to provide usual care in accordance with AHA guidelines, including open-label amiodarone or lidocaine if necessary. Components of hospital care were monitored but not standardized by the trial protocol. Participants, providers, and trial personnel were blinded to the trial drug assignments, with the exception of treating physicians if emergency un-blinding was required for care. Data from pre-hospital patient care records, CPR process measures, and hospital medical records were collected. The primary outcome of the trial was survival to hospital discharge, and the secondary outcome was survival with favorable neurologic status at discharge, defined as a score on the modified Rankin scale of 3 or less. Conclusions Neither amiodarone nor lidocaine resulted in a significantly higher rate of survival to hospital discharge or favorable neurologic outcome, as compared to placebo, among participants with out-of-hospital cardiac arrest due to initial shock-refractory ventricular fibrillation or pulseless ventricular tachycardia.

Study phs003784

How to upload Crypt4GH files

Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated, you will be able to request a submitter role. [Optional] Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g., ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs) for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username. If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days. Specific to using keys to authenticate Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

Documentation submission/data/uploading-files/inbox

Distribution of data using Live Distribution

Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

Documentation access/download/files/live-outbox

Implementation, Adoption, and Utility of Family History in Diverse Care Settings

The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

Study phs001641

DNA Methylation Analysis of Prostate Cancer Cell Lines and Tissues Using Next Generation Sequencing

Aberrant DNA methylation changes are known to occur during prostate cancer progression beginning with precursor lesions. Utilizing fifty nanograms of genomic DNA in Methylplex-Next Generation Sequencing (M-NGS) we mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2). Peaks were located from mapped reads obtained in each sequencing run using a Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(http://www.sph.umich.edu/csg/qin/HPeak). The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. Promoter CGI methylation gradually increased from -12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues and approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues. We observed distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. Overall Design: We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were sequenced twice on Illumina next generation sequencing platform to access overall repeatability of M-NGS.

Study phs000597

Osteoporotic Fractures in Men (MrOS)

The Osteoporotic Fractures in Men Study (MrOS) is a multi-center prospective, longitudinal, observational study of risk factors for vertebral and all non-vertebral fractures in older men, and of the sequelae of fractures in men. The original specific aims of the study include: (1) to define the skeletal determinants of fracture risk in older men, (2) to define lifestyle and medical factors related to fracture risk, (3) to establish the contribution of fall frequency to fracture risk in older men, (4) to determine to what extent androgen and estrogen concentrations influence fracture risk, (5) to examine the effects of fractures on quality of life, (6) to identify sex differences in the predictors and outcomes of fracture, (7) to collect and store serum, urine and DNA for future analyses as directed by emerging evidence in the fields of aging and skeletal health, and (8) define the extent to which bone mass/fracture risk and prostate diseases are linked. The MrOS study population consists of 5,994 community dwelling, ambulatory men aged 65 years or older from six communities in the United States (Birmingham, AL; Minneapolis, MN; Palo Alto, CA; Monongahela Valley near Pittsburgh, PA; Portland, OR; and San Diego, CA). Inclusion criteria were designed to provide a study cohort that is representative of the broad population of older men. The inclusion criteria were: (1) ability to walk without the assistance of another, (2) absence of bilateral hip replacements, (3) ability to provide self-reported data, (4) residence near a clinical site for the duration of the study, (5) absence of a medical condition that (in the judgment of the investigator) would result in imminent death, and (6) ability to understand and sign an informed consent. To qualify as an enrollee, the participant had to provide written informed consent, complete the self-administered questionnaire (SAQ), attend the clinic visit, and complete at least the anthropometric, DEXA, and vertebral X-ray procedures. There were no other exclusion criteria. The baseline examination was completed over a 25-month period from March 2000 to April 2002. Participants completed questionnaires regarding medical history, medications, physical activity, diet, alcohol intake, and cigarette smoking. Objective measures of anthropometric, neuromuscular, vision, strength, and cognitive variables were obtained at a clinic visit. Skeletal assessments included DEXA, calcaneal ultrasound, and vertebral radiographs. Vertebral and proximal femoral QCT was performed on a subset (65%) of participants. Serum, urine, and whole blood specimens were collected. During the study period, participants complete a tri-annual questionnaire every four months that obtains information concerning the occurrence of incident falls and fractures. Follow-up is over 99% complete. All reported fractures are confirmed using physician review of radiology reports or study radiologist review of x-rays. All deaths are also centrally adjudicated using death certificates and recent hospitalization records. In additional to the baseline study visit and tri-annual questionnaires, all surviving active participants were invited to follow-up visits and questionnaires. A second comprehensive clinic visit was completed between March 2005 and May 2006 or about 4.5 years after baseline. Most baseline measures, including vertebral radiographs, were repeated at the second visit. A third comprehensive clinic visit was completed between March 2007 and March 2009 or about 7.0 years after baseline. Many baseline measures were repeated at the third visit. A measure of energy expenditure was also obtained utilizing an accelerometer based armband device. Surviving participants have also completed two extensive mailed questionnaires designed to updated information obtained at clinic visits. The first questionnaire was mailed between baseline and visit 2 between July 2002 and March 2004. The second questionnaire was mailed after the third questionnaire between March 2009 and February 2011. The MrOS Sleep Study, an ancillary study of the parent MrOS cohort, was conducted between December 2003 and March 2005 and recruited 3,135 MrOS participants for a comprehensive sleep assessment. Men were screened for nightly use of mechanical devices during sleep including pressure mask for sleep apnea (CPAP or BiPAP), mouthpiece for snoring or sleep apnea, or oxygen therapy and were generally excluded. The sleep visit included objective measures of sleep using actigraphy and polysomnography. Other measures at the sleep visit include neuropsychiatric measures, performance measures, health related questionnaires, routine exam measures previously completed at MrOS clinic visits and a urine and serum specimen collection. During the sleep study period, participants complete a tri-annual questionnaire every four months that contains information about incident cardiovascular events. All reported events are centrally adjudicated utilizing hospital records, study ECGs and other supporting documentation. The Hip Osteoarthritis study, another ancillary study of the parent MrOS cohort, was completed at the second clinic visit between March 2005 and May 2006. Participants were asked for consent for a hip x-ray and osteoarthritis measurements were obtained from these x-rays. DNA was extracted from baseline whole blood samples for use in genetic research. Consent for use of DNA was obtained through written consent. Raw GWAS data is housed at the study's coordinating center and not released as part of the dbGAP release. The raw data can be requested from the study's coordinating center. Only QC cleaned Plink files are available on dbGAP.

Study phs000373

ICGC Data Access Compliance Office

To access Controlled Access ICGC data at the EGA you must first apply for access on the DACO form. Instructions on this process is available from this web site: http://docs.icgc.org/portal/access/

Dac EGAC00001000010

Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

Study EGAS50000000156

ELLIPSE Prostate Cancer Meta-Analysis and Genotyping

The ELLIPSE Consortium is an international effort to discover risk loci for prostate cancer. It includes the meta-analysis of existing GWAS data as well as novel GWAS, exome, and iCOGS genotyping. The GWAS meta-analysis includes the following cases and controls from studies of European ancestry: UK GWAS stage 1 (Illumina Infinium HumanHap 550 Array: 1854 cases and 1894 controls), UK GWAS stage 2 (Illumina iSELECT: 3706 cases and 3884 controls), CAPS1 (Affymetrix GeneChip 500K: 474 cases and 482 controls), CAPS2 (Affymetrix GeneChip 5.0K: 1458 cases and 512 controls), BPC3 (Illumina Human610 Illumina: 2068 cases and 3011 controls), PEGASUS (HumanOmni2.5: 4600 cases and 2941 controls). The OMNI 2.5M genotyping was conducted for 977 prostate cancer cases from UKGPCS. The Exome SNP array genotyping was conducted for 4741 subjects from UKGPCS. The iCOGs genotyping was conducted for 10366 subjects which includes the Multiethnic Cohort (n=1648) and UKGPCS (n=8718). Below is a description of each study that contributed to the meta-analysis of men of European ancestry. Information about the studies that contributed to the multiethnic meta-analysis can be found on the associated study page and also in Conti et al (Nature Genetics, PMID:33398198).   UK GWAS Stage 1 (UK1) and Stage 2 (UK2): The UK Genetic Prostate Cancer Study (UKGPCS) was first established in 1993 and is the largest prostate cancer study of its kind in the UK, involving nearly 189 hospitals. We are based at The Institute of Cancer Research in Sutton, Surrey, and collaborate with the Royal Marsden NHS Foundation Trust. Our aim is to find genetic changes which are associated with prostate cancer risk. Our target is to recruit 26,000 gentlemen into the UKGPCS by 2017. Men are eligible to take part if they fit into at least one of the following groups: They have been diagnosed with prostate cancer at 60 years of age or under (up to their 61st birthday). They have been diagnosed with prostate cancer and a first, second or third degree relative where at least one of these men were diagnosed with prostate cancer at 65 years of age or under. They are affected and have 3 or more cases of prostate cancer on one side of their family. They are a prostate cancer patient at the Royal Marsden NHS Foundation Trust. We have to date recruited around 16,000 men on whom we have germline DNA and clinical data at diagnosis. The UK GWAS is based on genotyping of 541,129 SNPs in 1,854 individuals with clinically detected (non-PSA-screened) prostate cancer (cases) and 1,894 controls. 43,671 SNPs showing strong evidence of association in stage 1 were followed up by genotyping a further 3,268 cases and 3,366 controls from UK and Melbourne in stage2. CAPS1 and CAPS2: The CAPS (Cancer of the Prostate in Sweden) study represents a large Swedish population-based cancer study, comprising 3,161 cases and 2,149 controls, recruited between 2001 and 2003. Biopsy confirmed prostate cancer cases were identified and recruited from four out of six regional cancer registries in Sweden, diagnosed between July 2001 and October 2003. Clinical data including TNM stage, Gleason grade and PSA levels at time for diagnosis were retrieved through record linkage to the National Prostate Cancer Registry. Control subjects, who were recruited concurrently with case subjects, were randomly selected from the Swedish Population Registry and matched according to the expected age distribution of cases (groups of 5-year intervals) and geographic region. Whole blood was collected from all individuals for extraction of genomic DNA. A GWAS was conducted in two parts. In the first phase (CAPS1) 498 cases and 502 controls were genotyped, in the second phase 1,483 cases and 519 controls were genotyped. Genotyping was performed using the GeneChip Human Mapping 500K (CAPS1) and 5.0K (CAPS2) Array Set from Affymetrix (Santa Clara, CA). The National Cancer Institute Breast and Prostate Cancer Cohort Consortium, BPC3: BPC3 was a consortium of prospective cohort studies investigating genetic and gene-environmental risk factors for breast and prostate cancer. Each study selected cases and controls for this study as described below. The clinical criteria defining advanced prostate cancer (Gleason = 8 or stage C/D) were either obtained from medical records or cancer registries. The Gleason score source was either surgical specimens (radical prostatectomy or autopsy) or the diagnostic biopsy (needle biopsy or TURP). When multiple Gleason scores were available the surgical value was used. PLCO was removed from the analysis as the samples were included in the Pegasus GWAS described below. In total 2,473 advanced prostate cancer cases and 3,534 controls were included in the analysis following QC. ATBC, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: ATBC was a randomized, placebo-controlled primary prevention trial to investigate whether α-tocopherol or ß-carotene supplementation reduced the incidence of lung or other cancers in male smokers. Between 1985 and 1988, 29,133 men ages 50 to 69 years were enrolled in the trial from Finland and randomized to supplementation (50 mg α-tocopherol, 20mg ß-carotene, or both) or placebo. Men with a prior history of cancer, other than non-melanoma skin cancer or carcinoma in situ, were excluded from participating. Incident cancer cases are identified through linkage with the Finnish Cancer Registry, which has ~100% ascertainment of cancer cases nationwide. Cases included 249 men diagnosed with advanced prostate cancer (Gleason = 8 or stage C/D) from 1985 to 2003 with DNA available. Controls were 1,271 men selected previously for a GWAS of lung cancer in ATBC without a diagnosis of prostate cancer. CPSII, Cancer Prevention Study II: CPSII is a cohort study started in 1982 to investigate the relationship between dietary, lifestyle and other etiologic factors and cancer mortality. Approximately 1.2 million men and women enrolled in the study from 50 states in the U.S. In 1992, a subset of these participants (n= ~184,000) were enrolled in the CPSII Nutrition Cohort to examine the relationship between dietary and other exposures and cancer incidence. Blood samples were drawn from approximately 39,376 members of the Nutritional Cohort from 1998 to 2001, and buccal cells were collected from 69,467 members from 2001 to 2002. Cancer cases are identified by self-report through follow-up questionnaires followed by verification through medical records and/or linkage to state cancer registries as well as death certificates. A total of 660 advanced prostate cancer cases (Gleason = 8 or stage III/IV) with a source of DNA were identified for this study. Controls were 660 men matched on ethnicity, date of birth, sample collection date and DNA type. EPIC, European Prospective Investigation into Cancer and Nutrition: EPIC is a prospective study designed to investigate both genetic and non-genetic risk factors for different forms of cancer. Study participants were almost all white Europeans. Approximately 500,000 individuals (150,000 men) in EPIC were recruited between 1992 and 2000, from 23 centers in 10 European countries. Overall approximately 400,000 subjects also provided a blood sample at recruitment. The methods of recruitment and details of the study design are described in detail elsewhere. In brief, study participants completed an extensive questionnaire on both dietary and nondietary data at recruitment. The present study includes subjects from advanced prostate cancer cases (Gleason = 8 or stage III/IV) matched to controls based on study center, length of follow-up, age at enrollment (± 6 months), fasting and time of day of blood collection (± 1 hour). The advanced prostate cancer subjects were from 8 of the 10 participating countries: Denmark, Germany, Greece, Italy, the Netherlands, Spain, Sweden and the United Kingdom (UK). France and Norway were not included in the current study because these cohorts only included female subjects. All participants gave written consent for the research and approval for the study was obtained from the ethical review board from all local institutions in the regions where participants had been recruited for the EPIC study. HPFS, Health Professionals Follow-up Study: HPFS began in 1986 and is an ongoing prospective cohort study of 51,529 United States male dentists, optometrists, osteopaths, podiatrists, pharmacists, and veterinarians 40 to 75 years of age. The baseline questionnaire provided information on age, marital status, height and weight, ancestry, medications, smoking history, disease history, physical activity, and diet. At baseline the cohort was 97% white, 2% Asian American, and 1% African American. The median follow-up through 2005 was 10.5 years (range 2-19 years). Self-reported prostate cancer diagnoses were confirmed by obtaining medical and/or pathology records. Prostate cancer deaths are either reported by family members in response to follow-up questionnaires, discovered by the postal system, or the National Death Index. Questionnaires are sent every two years to surviving men to update exposure and medical history. In 1993 and 1994, a blood specimen was collected from 18,018 men without a prior diagnosis of cancer. Prostate cancer cases are matched to controls on birth year (+/-1) and ethnicity. Controls are selected from those who are cancer-free at the time of the case’s diagnosis, and had a prostate-specific antigen test after the date of blood draw.   MEC, Multiethnic Cohort: The Multiethnic Cohort Study is a population-based prospective cohort study that was initiated between 1993 and 1996 and includes subjects from various ethnic groups - African Americans and Latinos primarily from Californian (great Los Angeles area) and Native Hawaiians, Japanese-Americans, and European Americans primarily from Hawaii. State drivers’ license files were the primary sources used to identify study subjects in Hawaii and California. Additionally, in Hawaii, state voter’s registration files were used, and, in California, Health Care Financing Administration (HCFA) files were used to identify additional African American men. All participants (n=215,251) returned a 26-page self-administered baseline questionnaire that obtained general demographic, medical and risk factor information. In the cohort, incident cancer cases are identified annually through cohort linkage to population-based cancer Surveillance, Epidemiology, and End Results (SEER) registries in Hawaii and Los Angeles County as well as to the California State cancer registry. Information on stage and grade of disease are also obtained through the SEER registries. Blood sample collection in the MEC began in 1994 and targeted incident prostate cancer cases and a random sample of study participants to serve as controls for genetic analyses. PHS, Physicians Health Study:PHS was a randomized trial of aspirin and ß carotene for cardiovascular disease and cancer among 22,071 U.S. male physicians ages 40-84 years at randomization; none had a cancer diagnosis at baseline. The original trial ended, but the men are followed. From 1982 to 1984, blood samples were collected from 14,916 physicians before randomization. Participants are sent yearly questionnaires to ascertain endpoints. Whenever a physician reports cancer, we request permission to obtain the medical records, and cancers are confirmed by pathology report. We obtain death certificates and pertinent medical records for all deaths. Follow-up for nonfatal outcomes in PHS is over 97% complete, and for mortality, over 99%. PLCO, Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial:PLCO is a multicenter, randomized trial to evaluate screening methods for the early detection of prostate, lung, colorectal and ovarian cancer. Between 1993 and 2001, over 150,000 men and women ages 55-74 years were recruited from ten centers in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, D.C.). Men randomized to the screening arm underwent prostate cancer screening with prostate-specific antigen (PSA) annually for six years and digital rectal exam annually for four years. Blood specimens were collected from participants randomized to the screening arm of the trial, and buccal cell specimens were obtained from participants randomized to the control arm. Cases included 754 men diagnosed with advanced prostate cancer (Gleason = 8 or stage III/IV) from either arm of the trial. Of these cases, 317 were genotyped previously as part of Cancer Genetic Markers of Susceptibility (CGEMS), a GWAS for prostate cancer. Controls included 1,491 men without a diagnosis of prostate cancer from the screening arm of the PLCO trial. All subjects provided informed consent to participate in genetic etiology studies of cancer and other traits. This study was approved by the institutional review boards at the ten centers and the National Cancer Institute. PLCO was removed from the meta-analysis of the BPC3 studies as a consequence of PEGASUS below. PEGASUS, Prostate cancer Genome-wide Association Study of Uncommon Susceptibility loci: Pegasus is a genome-wide association nested within the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. PLCO is a multicenter, randomized trial to evaluate screening methods for the early detection of prostate, lung, colorectal and ovarian cancer. Between 1993 and 2001, over 150,000 men and women ages 55-74 years were recruited from ten centers in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, D.C.). Men randomized to the screening arm underwent prostate cancer screening with prostate-specific antigen annually for six years and digital rectal exam annually for four years. Blood specimens were collected from participants randomized to the screening arm of the trial, and buccal cell specimens were obtained from participants randomized to the control arm. Cases included 4,598 men of European ancestry diagnosed with prostate cancer from either arm of the trial and controls included 2,941 men of European ancestry without a diagnosis of cancer from the screening arm, matched on age and year of randomization. All subjects provided informed consent, and the study approved by the institutional review board at the National Cancer Institute. Funding:This work was supported by the GAME-ON U19 initiative for prostate cancer (ELLIPSE): U19 CA148537. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233, U01-CA98710, U01-CA98216, and U01-CA98758, and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). The ATBC study and PEGASUS was supported in part by the Intramural Research Program of the NIH and the National Cancer Institute. Additionally, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, N01-RC-37004 and HHSN261201000006C from the National Cancer Institute, Department of Health and Human Services. CAPS: The Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden was supported by the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (grant: K2010-70X-20430-04-3), the Swedish Cancer Foundation (grant: 09-0677), the Hedlund Foundation, the Söderberg Foundation, the Enqvist Foundation, ALF funds from the Stockholm County Council. Stiftelsen Johanna Hagstrand och Sigfrid Linnér’s Minne, Karlsson’s Fund for urological and surgical research. We thank and acknowledge all of the participants in the Stockholm-1 study. We thank Carin Cavalli-Björkman and Ami Rönnberg Karlsson for their dedicated work in the collection of data. Michael Broms is acknowledged for his skillful work with the databases. KI Biobank is acknowledged for handling the samples and for DNA extraction. Hans Wallinder at Aleris Medilab and Sven Gustafsson at Karolinska University Laboratory are thanked for their good cooperation in providing historical laboratory results. UKGPCS would like to acknowledge the NCRN nurses and Consultants for their work in the UKGPCS study. We thank all the patients who took part in this study. This work was supported by Cancer Research UK (grants: C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835). We would also like to thank the following for funding support: Prostate Research Campaign UK (now Prostate Cancer UK), The Institute of Cancer Research and The Everyman Campaign, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The MEC was supported by NIH grants CA63464, CA54281 and CA098758. 

Study phs001120

ARGO_GWAS

Osteoarthritis (OA) is the most common form of arthritis affecting ~40% of people over the age of 70. OA is characterised by cartilage degeneration in the synovial joints and presents one of the leading causes of chronic disability and impaired quality of life in the developed world.The ARGO study is an ongoing collection of well-phenotyped OA patients undergoing total joint replacement and is based in Greece.

Study EGAS00001000917

OncoArray: Prostate Cancer

Original description of the study: From ELLIPSE (linked to the PRACTICAL consortium), we contributed ~78,000 SNPs to the OncoArray. A large fraction of the content was derived from the GWAS meta-analyses in European ancestry populations (overall and aggressive disease; ~27K SNPs). We also selected just over 10,000 SNPs from the meta-analyses in the non-European populations, with a majority of these SNPs coming from the analysis of overall prostate cancer in African ancestry populations as well as from the multiethnic meta-analysis. A substantial fraction of SNPs (~28,000) were also selected for fine-mapping of 53 loci not included in the common fine-mapping regions (tagging at r2>0.9 across ±500kb regions). We also selected a few thousand SNPs related with PSA levels and/or disease survival as well as SNPs from candidate lists provided by study collaborators, as well as from meta-analyses of exome SNP chip data from the Multiethnic Cohort and UK studies. The Contributing Studies: Aarhus: Hospital-based, Retrospective, Observational. Source of cases: Patients treated for prostate adenocarcinoma at Department of Urology, Aarhus University Hospital, Skejby (Aarhus, Denmark). Source of controls: Age-matched males treated for myocardial infarction or undergoing coronary angioplasty, but with no prostate cancer diagnosis based on information retrieved from the Danish Cancer Register and the Danish Cause of Death Register. AHS: Nested case-control study within prospective cohort. Source of cases: linkage to cancer registries in study states. Source of controls: matched controls from cohort ATBC: Prospective, nested case-control. Source of cases: Finnish male smokers aged 50-69 years at baseline. Source of controls: Finnish male smokers aged 50-69 years at baseline BioVu: Cases identified in a biobank linked to electronic health records. Source of cases: A total of 214 cases were identified in the VUMC de-identified electronic health records database (the Synthetic Derivative) and shipped to USC for genotyping in April 2014. The following criteria were used to identify cases: Age 18 or greater; male; African Americans (Black) only. Note that African ancestry is not self-identified, it is administratively or third-party assigned (which has been shown to be highly correlated with genetic ancestry for African Americans in BioVU; see references). Source of controls: Controls were identified in the de-identified electronic health record. Unfortunately, they were not age matched to the cases, and therefore cannot be used for this study. Canary PASS: Prospective, Multi-site, Observational Active Surveillance Study. Source of cases: clinic based from Beth Israel Deaconness Medical Center, Eastern Virginia Medical School, University of California at San Francisco, University of Texas Health Sciences Center San Antonio, University of Washington, VA Puget Sound. Source of controls: N/A CCI: Case series, Hospital-based. Source of cases: Cases identified through clinics at the Cross Cancer Institute. Source of controls: N/A CerePP French Prostate Cancer Case-Control Study (ProGene): Case-Control, Prospective, Observational, Hospital-based. Source of cases: Patients, treated in French departments of Urology, who had histologically confirmed prostate cancer. Source of controls: Controls were recruited as participating in a systematic health screening program and found unaffected (normal digital rectal examination and total PSA < 4 ng/ml, or negative biopsy if PSA > 4 ng/ml). COH: hospital-based cases and controls from outside. Source of cases: Consented prostate cancer cases at City of Hope. Source of controls: Consented unaffected males that were part of other studies where they consented to have their DNA used for other research studies. COSM: Population-based cohort. Source of cases: General population. Source of controls: General population CPCS1: Case-control - Denmark. Source of cases: Hospital referrals. Source of controls: Copenhagen General Population Study CPCS2: Source of cases: Hospital referrals. Source of controls: Copenhagen General Population Study CPDR: Retrospective cohort. Source of cases: Walter Reed National Military Medical Center. Source of controls: Walter Reed National Military Medical Center ACS_CPS-II: Nested case-control derived from a prospective cohort study. Source of cases: Identified through self-report on follow-up questionnaires and verified through medical records or cancer registries, identified through cancer registries or the National Death Index (with prostate cancer as the primary cause of death). Source of controls: Cohort participants who were cancer-free at the time of diagnosis of the matched case, also matched on age (±6 mo) and date of biospecimen donation (±6 mo). EPIC: Case-control - Germany, Greece, Italy, Netherlands, Spain, Sweden, UK. Source of cases: Identified through record linkage with population-based cancer registries in Italy, the Netherlands, Spain, Sweden and UK. In Germany and Greece, follow-up is active and achieved through checks of insurance records and cancer and pathology registries as well as via self-reported questionnaires; self-reported incident cancers are verified through medical records. Source of controls: Cohort participants without a diagnosis of cancer EPICAP: Case-control, Population-based, ages less than 75 years at diagnosis, Hérault, France. Source of cases: Prostate cancer cases in all public hospitals and private urology clinics of département of Hérault in France. Cases validation by the Hérault Cancer Registry. Source of controls: Population-based controls, frequency age matched (5-year groups). Quotas by socio-economic status (SES) in order to obtain a distribution by SES among controls identical to the SES distribution among general population men, conditionally to age. ERSPC: Population-based randomized trial. Source of cases: Men with PrCa from screening arm ERSPC Rotterdam. Source of controls: Men without PrCa from screening arm ERSPC Rotterdam ESTHER: Case-control, Prospective, Observational, Population-based. Source of cases: Prostate cancer cases in all hospitals in the state of Saarland, from 2001-2003. Source of controls: Random sample of participants from routine health check-up in Saarland, in 2000-2002 FHCRC: Population-based, case-control, ages 35-74 years at diagnosis, King County, WA, USA. Source of cases: Identified through the Seattle-Puget Sound SEER cancer registry. Source of controls: Randomly selected, age-frequency matched residents from the same county as cases Gene-PARE: Hospital-based. Source of cases: Patients that received radiotherapy for treatment of prostate cancer. Source of controls: n/a Hamburg-Zagreb: Hospital-based, Prospective. Source of cases: Prostate cancer cases seen at the Department of Oncology, University Hospital Center Zagreb, Croatia. Source of controls: Population-based (Croatia), healthy men, older than 50, with no medical record of cancer, and no family history of cancer (1st & 2nd degree relatives) HPFS: Nested case-control. Source of cases: Participants of the HPFS cohort. Source of controls: Participants of the HPFS cohort IMPACT: Observational. Source of cases: Carriers and non-carriers (with a known mutation in the family) of the BRCA1 and BRCA2 genes, aged between 40 and 69, who are undergoing prostate screening with annual PSA testing. This cohort has been diagnosed with prostate cancer during the study. Source of controls: Carriers and non-carriers (with a known mutation in the family) of the BRCA1 and BRCA2 genes, aged between 40 and 69, who are undergoing prostate screening with annual PSA testing. This cohort has not been diagnosed with prostate cancer during the study. IPO-Porto: Hospital-based. Source of cases: Early onset and/or familial prostate cancer. Source of controls: Blood donors Karuprostate: Case-control, Retrospective, Population-based. Source of cases: From FWI (Guadeloupe): 237 consecutive incident patients with histologically confirmed prostate cancer attending public and private urology clinics; From Democratic Republic of Congo: 148 consecutive incident patients with histologically confirmed prostate cancer attending the University Clinic of Kinshasa. Source of controls: From FWI (Guadeloupe): 277 controls recruited from men participating in a free systematic health screening program open to the general population; From Democratic Republic of Congo: 134 controls recruited from subjects attending the University Clinic of Kinshasa KULEUVEN: Hospital-based, Prospective, Observational. Source of cases: Prostate cancer cases recruited at the University Hospital Leuven. Source of controls: Healthy males with no history of prostate cancer recruited at the University Hospitals, Leuven. LAAPC: Subjects were participants in a population-based case-control study of aggressive prostate cancer conducted in Los Angeles County. Cases were identified through the Los Angeles County Cancer Surveillance Program rapid case ascertainment system. Eligible cases included African American, Hispanic, and non-Hispanic White men diagnosed with a first primary prostate cancer between January 1, 1999 and December 31, 2003. Eligible cases also had (a) prostatectomy with documented tumor extension outside the prostate, (b) metastatic prostate cancer in sites other than prostate, (c) needle biopsy of the prostate with Gleason grade ≥8, or (d) needle biopsy with Gleason grade 7 and tumor in more than two thirds of the biopsy cores. Eligible controls were men never diagnosed with prostate cancer, living in the same neighborhood as a case, and were frequency matched to cases on age (± 5 y) and race/ethnicity. Controls were identified by a neighborhood walk algorithm, which proceeds through an obligatory sequence of adjacent houses or residential units beginning at a specific residence that has a specific geographic relationship to the residence where the case lived at diagnosis. Malaysia: Case-control. Source of cases: Patients attended the outpatient urology or uro-onco clinic at University Malaya Medical Center. Source of controls: Population-based, age matched (5-year groups), ascertained through electoral register, Subang Jaya, Selangor, Malaysia MCC-Spain: Case-control. Source of cases: Identified through the urology departments of the participating hospitals. Source of controls: Population-based, frequency age and region matched, ascertained through the rosters of the primary health care centers MCCS: Nested case-control, Melbourne, Victoria. Source of cases: Identified by linkage to the Victorian Cancer Registry. Source of controls: Cohort participants without a diagnosis of cancer MD Anderson: Participants in this study were identified from epidemiological prostate cancer studies conducted at the University of Texas MD Anderson Cancer Center in the Houston Metropolitan area. Cases were accrued in the Houston Medical Center and were not restricted with respect to Gleason score, stage or PSA. Controls were identified via random-digit-dialing or among hospital visitors and they were frequency matched to cases on age and race. Lifestyle, demographic, and family history data were collected using a standardized questionnaire. MDACC_AS: A prospective cohort study. Source of cases: Men with clinically organ-confined prostate cancer meeting eligibility criteria for a prospective cohort study of active surveillance at MD Anderson Cancer Center. Source of controls: N/A MEC: The Multiethnic Cohort (MEC) is comprised of over 215,000 men and women recruited from Hawaii and the Los Angeles area between 1993 and 1996. Between 1995 and 2006, over 65,000 blood samples were collected from participants for genetic analyses. To identify incident cancer cases, the MEC was cross-linked with the population-based Surveillance, Epidemiology and End Results (SEER) registries in California and Hawaii, and unaffected cohort participants with blood samples were selected as controls MIAMI (WFPCS): Prostate cancer cases and controls were recruited from the Departments of Urology and Internal Medicine of the Wake Forest University School of Medicine using sequential patient populations as described previously (PMID:15342424). All study subjects received a detailed description of the study protocol and signed their informed consent, as approved by the medical center's Institutional Review Board. The general eligibility criteria were (i) able to comprehend informed consent and (ii) without previously diagnosed cancer. The exclusion criteria were (i) clinical diagnosis of autoimmune diseases; (ii) chronic inflammatory conditions; and (iii) infections within the past 6 weeks. Blood samples were collected from all subjects. MOFFITT: Hospital-based. Source of cases: clinic based from Moffitt Cancer Center. Source of controls: Moffitt Cancer Center affiliated Lifetime cancer screening center NMHS: Case-control, clinic based, Nashville TN. Source of cases: All urology clinics in Nashville, TN. Source of controls: Men without prostate cancer at prostate biopsy. PCaP: The North Carolina-Louisiana Prostate Cancer Project (PCaP) is a multidisciplinary population-based case-only study designed to address racial differences in prostate cancer through a comprehensive evaluation of social, individual and tumor level influences on prostate cancer aggressiveness. PCaP enrolled approximately equal numbers of African Americans and Caucasian Americans with newly-diagnosed prostate cancer from North Carolina (42 counties) and Louisiana (30 parishes) identified through state tumor registries. African American PCaP subjects with DNA, who agreed to future use of specimens for research, participated in OncoArray analysis. PCMUS: Case-control - Sofia, Bulgaria. Source of cases: Patients of Clinic of Urology, Alexandrovska University Hospital, Sofia, Bulgaria, PrCa histopathologically confirmed. Source of controls: 72 patients with verified BPH and PSA<3,5; 78 healthy controls from the MMC Biobank, no history of PrCa PHS: Nested case-control. Source of cases: Participants of the PHS1 trial/cohort. Source of controls: Participants of the PHS1 trial/cohort PLCO: Nested case-control. Source of cases: Men with a confirmed diagnosis of prostate cancer from the PLCO Cancer Screening Trial. Source of controls: Controls were men enrolled in the PLCO Cancer Screening Trial without a diagnosis of cancer at the time of case ascertainment. Poland: Case-control. Source of cases: men with unselected prostate cancer, diagnosed in north-western Poland at the University Hospital in Szczecin. Source of controls: cancer-free men from the same population, taken from the healthy adult patients of family doctors in the Szczecin region PROCAP: Population-based, Retrospective, Observational. Source of cases: Cases were ascertained from the National Prostate Cancer Register of Sweden Follow-Up Study, a retrospective nationwide cohort study of patients with localized prostate cancer. Source of controls: Controls were selected among men referred for PSA testing in laboratories in Stockholm County, Sweden, between 2010 and 2012. PROGReSS: Hospital-based, Prospective, Observational. Source of cases: Prostate cancer cases from the Hospital Clínico Universitario de Santiago de Compostela, Galicia, Spain. Source of controls: Cancer-free men from the same population ProMPT: A study to collect samples and data from subjects with and without prostate cancer. Retrospective, Experimental. Source of cases: Subjects attending outpatient clinics in hospitals. Source of controls: Subjects attending outpatient clinics in hospitals ProtecT: Trial of treatment. Samples taken from subjects invited for PSA testing from the community at nine centers across United Kingdom. Source of cases: Subjects who have a proven diagnosis of prostate cancer following testing. Source of controls: Identified through invitation of subjects in the community. PROtEuS: Case-control, population-based. Source of cases: All new histologically-confirmed cases, aged less or equal to 75 years, diagnosed between 2005 and 2009, actively ascertained across Montreal French hospitals. Source of controls: Randomly selected from the Provincial electoral list of French-speaking men between 2005 and 2009, from the same area of residence as cases and frequency-matched on age. QLD: Case-control. Source of cases: A longitudinal cohort study (Prostate Cancer Supportive Care and Patient Outcomes Project: ProsCan) conducted in Queensland, through which men newly diagnosed with prostate cancer from 26 private practices and 10 public hospitals were directly referred to ProsCan at the time of diagnosis by their treating clinician (age range 43-88 years). All cases had histopathologically confirmed prostate cancer, following presentation with an abnormal serum PSA and/or lower urinary tract symptoms. Source of controls: Controls comprised healthy male blood donors with no personal history of prostate cancer, recruited through (i) the Australian Red Cross Blood Services in Brisbane (age range 19-76 years) and (ii) the Australian Electoral Commission (AEC) (age and post-code/ area matched to ProsCan, age range 54-90 years). RAPPER: Multi-centre, hospital based blood sample collection study in patients enrolled in clinical trials with prospective collection of radiotherapy toxicity data. Source of cases: Prostate cancer patients enrolled in radiotherapy trials: CHHiP, RT01, Dose Escalation, RADICALS, Pelvic IMRT, PIVOTAL. Source of controls: N/A SABOR: Prostate Cancer Screening Cohort. Source of cases: Men >45 yrs of age participating in annual PSA screening. Source of controls: Males participating in annual PSA prostate cancer risk evaluations (funded by NCI biomarkers discovery and validation grant), recruited through University of Texas Health Science Center at San Antonio and affiliated sites or through study advertisements, enrolment open to the community SCCS: Case-control in cohort, Southeastern USA. Prospective, Observational, Population-based. Source of cases: SCCS entry population. Source of controls: SCCS entry population SCPCS: Population-based, Retrospective, Observational. Source of cases: South Carolina Central Cancer Registry. Source of controls: Health Care Financing Administration beneficiary file SEARCH: Case-control - East Anglia, UK. Source of cases: Men < 70 years of age registered with prostate cancer at the population-based cancer registry, Eastern Cancer Registration and Information Centre, East Anglia, UK. Source of controls: Men attending general practice in East Anglia with no known prostate cancer diagnosis, frequency matched to cases by age and geographic region SNP_Prostate_Ghent: Hospital-based, Retrospective, Observational. Source of cases: Men treated with IMRT as primary or postoperative treatment for prostate cancer at the Ghent University Hospital between 2000 and 2010. Source of controls: Employees of the University hospital and members of social activity clubs, without a history of any cancer. SPAG: Hospital-based, Retrospective, Observational. Source of cases: Guernsey. Source of controls: Guernsey STHM2: Population-based, Retrospective, Observational. Source of cases: Cases were selected among men referred for PSA testing in laboratories in Stockholm County, Sweden, between 2010 and 2012. Source of controls: Controls were selected among men referred for PSA testing in laboratories in Stockholm County, Sweden, between 2010 and 2012. PCPT: Case-control from a randomized clinical trial. Source of cases: Randomized clinical trial. Source of controls: Randomized clinical trial SELECT: Case-cohort from a randomized clinical trial. Source of cases: Randomized clinical trial. Source of controls: Randomized clinical trial TAMPERE: Case-control - Finland, Retrospective, Observational, Population-based. Source of cases: Identified through linkage to the Finnish Cancer Registry and patient records; and the Finnish arm of the ERSPC study. Source of controls: Cohort participants without a diagnosis of cancer UGANDA: Uganda Prostate Cancer Study: Uganda is a case-control study of prostate cancer in Kampala Uganda that was initiated in 2011. Men with prostate cancer were enrolled from the Urology unit at Mulago Hospital and men without prostate cancer (i.e. controls) were enrolled from other clinics (i.e. surgery) at the hospital. UKGPCS: ICR, UK. Source of cases: Cases identified through clinics at the Royal Marsden hospital and nationwide NCRN hospitals. Source of controls: Ken Muir's control- 2000 ULM: Case-control - Germany. Source of cases: familial cases (n=162): identified through questionnaires for family history by collaborating urologists all over Germany; sporadic cases (n=308): prostatectomy series performed in the Clinic of Urology Ulm between 2012 and 2014. Source of controls: age-matched controls (n=188): age-matched men without prostate cancer and negative family history collected in hospitals of Ulm WUGS/WUPCS: Cases Series, USA. Source of cases: Identified through clinics at Washington University in St. Louis. Source of controls: Men diagnosed and managed with prostate cancer in University based clinic. Acknowledgement Statements: Aarhus: This study was supported by the Danish Strategic Research Council (now Innovation Fund Denmark) and the Danish Cancer Society. The Danish Cancer Biobank (DCB) is acknowledged for biological material. AHS: This work was supported by the Intramural Research Program of the NIH, National Cancer Institute, Division of Cancer Epidemiology and Genetics (Z01CP010119). ATBC: This research was supported in part by the Intramural Research Program of the NIH and the National Cancer Institute. Additionally, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, N01-RC-37004, HHSN261201000006C, and HHSN261201500005C from the National Cancer Institute, Department of Health and Human Services. BioVu: The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU which is supported by institutional funding and by the National Center for Research Resources, Grant UL1 RR024975-01 (which is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06). Canary PASS: PASS was supported by Canary Foundation and the National Cancer Institute's Early Detection Research Network (U01 CA086402) CCI: This work was awarded by Prostate Cancer Canada and is proudly funded by the Movember Foundation - Grant # D2013-36.The CCI group would like to thank David Murray, Razmik Mirzayans, and April Scott for their contribution to this work. CerePP French Prostate Cancer Case-Control Study (ProGene): None reported COH: SLN is partially supported by the Morris and Horowitz Families Endowed Professorship COSM: The Swedish Research Council, the Swedish Cancer Foundation CPCS1 & CPCS2: Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev Ringvej 75, DK-2730 Herlev, DenmarkCPCS1 would like to thank the participants and staff of the Copenhagen General Population Study for their important contributions. CPDR: Uniformed Services University for the Health Sciences HU0001-10-2-0002 (PI: David G. McLeod, MD) CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study II cohort. CPS-II thanks the participants and Study Management Group for their invaluable contributions to this research. We would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. EPIC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by the Danish Cancer Society (Denmark); the Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation, Greek Ministry of Health; Greek Ministry of Education (Greece); the Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); the Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF); the Statistics Netherlands (The Netherlands); the Health Research Fund (FIS), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, Spanish Ministry of Health ISCIII RETIC (RD06/0020), Red de Centros RCESP, C03/09 (Spain); the Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skåne and Västerbotten, Fundacion Federico SA (Sweden); the Cancer Research UK, Medical Research Council (United Kingdom). EPICAP: The EPICAP study was supported by grants from Ligue Nationale Contre le Cancer, Ligue départementale du Val de Marne; Fondation de France; Agence Nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES). The EPICAP study group would like to thank all urologists, Antoinette Anger and Hasina Randrianasolo (study monitors), Anne-Laure Astolfi, Coline Bernard, Oriane Noyer, Marie-Hélène De Campo, Sandrine Margaroline, Louise N'Diaye, and Sabine Perrier-Bonnet (Clinical Research nurses). ERSPC: This study was supported by the DutchCancerSociety (KWF94-869,98-1657,2002-277,2006-3518, 2010-4800), The Netherlands Organisation for Health Research and Development (ZonMW-002822820, 22000106, 50-50110-98-311, 62300035), The Dutch Cancer Research Foundation (SWOP), and an unconditional grant from Beckman-Coulter-HybritechInc. ESTHER: The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. The ESTHER group would like to thank Hartwig Ziegler, Sonja Wolf, Volker Hermann, Heiko Müller, Karina Dieffenbach, Katja Butterbach for valuable contributions to the study. FHCRC: The FHCRC studies were supported by grants R01-CA056678, R01-CA082664, and R01-CA092579 from the US National Cancer Institute, National Institutes of Health, with additional support from the Fred Hutchinson Cancer Research Center. FHCRC would like to thank all the men who participated in these studies. Gene-PARE: The Gene-PARE study was supported by grants 1R01CA134444 from the U.S. National Institutes of Health, PC074201 and W81XWH-15-1-0680 from the Prostate Cancer Research Program of the Department of Defense and RSGT-05-200-01-CCE from the American Cancer Society. Hamburg-Zagreb: None reported HPFS: The Health Professionals Follow-up Study was supported by grants UM1CA167552, CA133891, CA141298, and P01CA055075. HPFS are grateful to the participants and staff of the Physicians' Health Study and Health Professionals Follow-Up Study for their valuable contributions, as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. IMPACT: The IMPACT study was funded by The Ronald and Rita McAulay Foundation, CR-UK Project grant (C5047/A1232), Cancer Australia, AICR Netherlands A10-0227, Cancer Australia and Cancer Council Tasmania, NIHR, EU Framework 6, Cancer Councils of Victoria and South Australia, and Philanthropic donation to Northshore University Health System. We acknowledge support from the National Institute for Health Research (NIHR) to the Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden Foundation NHS Trust. IMPACT acknowledges the IMPACT study steering committee, collaborating centres, and participants. IPO-Porto: The IPO-Porto study was funded by Fundaçäo para a Ciência e a Tecnologia (FCT; UID/DTP/00776/2013 and PTDC/DTP-PIC/1308/2014) and by IPO-Porto Research Center (CI-IPOP-16-2012 and CI-IPOP-24-2015). MC and MPS are research fellows from Liga Portuguesa Contra o Cancro, Núcleo Regional do Norte. SM is a research fellow from FCT (SFRH/BD/71397/2010). IPO-Porto would like to express our gratitude to all patients and families who have participated in this study. Karuprostate: The Karuprostate study was supported by the the Frech National Health Directorate and by the Association pour la Recherche sur les Tumeurs de la ProstateKarusprostate thanks Séverine Ferdinand. KULEUVEN: F.C. and S.J. are holders of grants from FWO Vlaanderen (G.0684.12N and G.0830.13N), the Belgian federal government (National Cancer Plan KPC_29_023), and a Concerted Research Action of the KU Leuven (GOA/15/017). TVDB is holder of a doctoral fellowship of the FWO. LAAPC: This study was funded by grant R01CA84979 (to S.A. Ingles) from the National Cancer Institute, National Institutes of Health. Malaysia: The study was funded by the University Malaya High Impact Research Grant (HIR/MOHE/MED/35). Malaysia thanks all associates in the Urology Unit, University of Malaya, Cancer Research Initiatives Foundation (CARIF) and the Malaysian Men's Health Initiative (MMHI). MCCS: MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553, and 504711, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. MCC-Spain: The study was partially funded by the Accion Transversal del Cancer, approved on the Spanish Ministry Council on the 11th October 2007, by the Instituto de Salud Carlos III-FEDER (PI08/1770, PI09/00773-Cantabria, PI11/01889-FEDER, PI12/00265, PI12/01270, and PI12/00715), by the Fundación Marqués de Valdecilla (API 10/09), by the Spanish Association Against Cancer (AECC) Scientific Foundation and by the Catalan Government DURSI grant 2009SGR1489. Samples: Biological samples were stored at the Parc de Salut MAR Biobank (MARBiobanc; Barcelona) which is supported by Instituto de Salud Carlos III FEDER (RD09/0076/00036). Also sample collection was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d'Oncologia de Catalunya (XBTC). MCC-Spain acknowledges the contribution from Esther Gracia-Lavedan in preparing the data. We thank all the subjects who participated in the study and all MCC-Spain collaborators. MD Anderson: Prostate Cancer Case-Control Studies at MD Anderson (MDA) supported by grants CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388. MDACC_AS: None reported MEC: Funding provided by NIH grant U19CA148537 and grant U01CA164973. MIAMI (WFPCS): ACS MOFFITT: The Moffitt group was supported by the US National Cancer Institute (R01CA128813, PI: J.Y. Park). NMHS: Funding for the Nashville Men's Health Study (NMHS) was provided by the National Institutes of Health Grant numbers: RO1CA121060. PCaP only data: The North Carolina - Louisiana Prostate Cancer Project (PCaP) is carried out as a collaborative study supported by the Department of Defense contract DAMD 17-03-2-0052. For HCaP-NC follow-up data: The Health Care Access and Prostate Cancer Treatment in North Carolina (HCaP-NC) study is carried out as a collaborative study supported by the American Cancer Society award RSGT-08-008-01-CPHPS. For studies using both PCaP and HCaP-NC follow-up data please use: The North Carolina - Louisiana Prostate Cancer Project (PCaP) and the Health Care Access and Prostate Cancer Treatment in North Carolina (HCaP-NC) study are carried out as collaborative studies supported by the Department of Defense contract DAMD 17-03-2-0052 and the American Cancer Society award RSGT-08-008-01-CPHPS, respectively. For any PCaP data, please include: The authors thank the staff, advisory committees and research subjects participating in the PCaP study for their important contributions. For studies using PCaP DNA/genotyping data, please include: We would like to acknowledge the UNC BioSpecimen Facility and LSUHSC Pathology Lab for our DNA extractions, blood processing, storage and sample disbursement (https://genome.unc.edu/bsp). For studies using PCaP tissue, please include: We would like to acknowledge the RPCI Department of Urology Tissue Microarray and Immunoanalysis Core for our tissue processing, storage and sample disbursement. For studies using HCaP-NC follow-up data, please use: The Health Care Access and Prostate Cancer Treatment in North Carolina (HCaP-NC) study is carried out as a collaborative study supported by the American Cancer Society award RSGT-08-008-01-CPHPS. The authors thank the staff, advisory committees and research subjects participating in the HCaP-NC study for their important contributions. For studies that use both PCaP and HCaP-NC, please use: The authors thank the staff, advisory committees and research subjects participating in the PCaP and HCaP-NC studies for their important contributions. PCMUS: The PCMUS study was supported by the Bulgarian National Science Fund, Ministry of Education and Science (contract DOO-119/2009; DUNK01/2-2009; DFNI-B01/28/2012) with additional support from the Science Fund of Medical University - Sofia (contract 51/2009; 8I/2009; 28/2010). PHS: The Physicians' Health Study was supported by grants CA34944, CA40360, CA097193, HL26490, and HL34595. PHS members are grateful to the participants and staff of the Physicians' Health Study and Health Professionals Follow-Up Study for their valuable contributions, as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. PLCO: This PLCO study was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIHPLCO thanks Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention at the National Cancer Institute, the screening center investigators and staff of the PLCO Cancer Screening Trial for their contributions to the PLCO Cancer Screening Trial. We thank Mr. Thomas Riley, Mr. Craig Williams, Mr. Matthew Moore, and Ms. Shannon Merkle at Information Management Services, Inc., for their management of the data and Ms. Barbara O'Brien and staff at Westat, Inc. for their contributions to the PLCO Cancer Screening Trial. We also thank the PLCO study participants for their contributions to making this study possible. Poland: None reported PROCAP: PROCAP was supported by the Swedish Cancer Foundation (08-708, 09-0677). PROCAP thanks and acknowledges all of the participants in the PROCAP study. We thank Carin Cavalli-Björkman and Ami Rönnberg Karlsson for their dedicated work in the collection of data. Michael Broms is acknowledged for his skilful work with the databases. KI Biobank is acknowledged for handling the samples and for DNA extraction. We acknowledge The NPCR steering group: Pär Stattin (chair), Anders Widmark, Stefan Karlsson, Magnus Törnblom, Jan Adolfsson, Anna Bill-Axelson, Ove Andrén, David Robinson, Bill Pettersson, Jonas Hugosson, Jan-Erik Damber, Ola Bratt, Göran Ahlgren, Lars Egevad, and Roy Ehrnström. PROGReSS: The PROGReSS study is founded by grants from the Spanish Ministry of Health (INT15/00070; INT16/00154; FIS PI10/00164, FIS PI13/02030; FIS PI16/00046); the Spanish Ministry of Economy and Competitiveness (PTA2014-10228-I), and Fondo Europeo de Desarrollo Regional (FEDER 2007-2013). ProMPT: Founded by CRUK, NIHR, MRC, Cambride Biomedical Research Centre ProtecT: Founded by NIHR. ProtecT and ProMPT would like to acknowledge the support of The University of Cambridge, Cancer Research UK. Cancer Research UK grants (C8197/A10123) and (C8197/A10865) supported the genotyping team. We would also like to acknowledge the support of the National Institute for Health Research which funds the Cambridge Bio-medical Research Centre, Cambridge, UK. We would also like to acknowledge the support of the National Cancer Research Prostate Cancer: Mechanisms of Progression and Treatment (PROMPT) collaborative (grant code G0500966/75466) which has funded tissue and urine collections in Cambridge. We are grateful to staff at the Welcome Trust Clinical Research Facility, Addenbrooke's Clinical Research Centre, Cambridge, UK for their help in conducting the ProtecT study. We also acknowledge the support of the NIHR Cambridge Biomedical Research Centre, the DOH HTA (ProtecT grant), and the NCRI/MRC (ProMPT grant) for help with the bio-repository. The UK Department of Health funded the ProtecT study through the NIHR Health Technology Assessment Programme (projects 96/20/06, 96/20/99). The ProtecT trial and its linked ProMPT and CAP (Comparison Arm for ProtecT) studies are supported by Department of Health, England; Cancer Research UK grant number C522/A8649, Medical Research Council of England grant number G0500966, ID 75466, and The NCRI, UK. The epidemiological data for ProtecT were generated though funding from the Southwest National Health Service Research and Development. DNA extraction in ProtecT was supported by USA Dept of Defense award W81XWH-04-1-0280, Yorkshire Cancer Research and Cancer Research UK. The authors would like to acknowledge the contribution of all members of the ProtecT study research group. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Department of Health of England. The bio-repository from ProtecT is supported by the NCRI (ProMPT) Prostate Cancer Collaborative and the Cambridge BMRC grant from NIHR. We thank the National Institute for Health Research, Hutchison Whampoa Limited, the Human Research Tissue Bank (Addenbrooke's Hospital), and Cancer Research UK. PROtEuS: PROtEuS was supported financially through grants from the Canadian Cancer Society (13149, 19500, 19864, 19865) and the Cancer Research Society, in partnership with the Ministère de l'enseignement supérieur, de la recherche, de la science et de la technologie du Québec, and the Fonds de la recherche du Québec - Santé.PROtEuS would like to thank its collaborators and research personnel, and the urologists involved in subjects recruitment. We also wish to acknowledge the special contribution made by Ann Hsing and Anand Chokkalingam to the conception of the genetic component of PROtEuS. QLD: The QLD research is supported by The National Health and Medical Research Council (NHMRC) Australia Project Grants (390130, 1009458) and NHMRC Career Development Fellowship and Cancer Australia PdCCRS funding to J Batra. The QLD team would like to acknowledge and sincerely thank the urologists, pathologists, data managers and patient participants who have generously and altruistically supported the QLD cohort. RAPPER: RAPPER is funded by Cancer Research UK (C1094/A11728; C1094/A18504) and Experimental Cancer Medicine Centre funding (C1467/A7286). The RAPPER group thank Rebecca Elliott for project management. SABOR: The SABOR research is supported by NIH/NCI Early Detection Research Network, grant U01 CA0866402-12. Also supported by the Cancer Center Support Grant to the Cancer Therapy and Research Center from the National Cancer Institute (US) P30 CA054174. SCCS: SCCS is funded by NIH grant R01 CA092447, and SCCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA68485). Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; Arkansas Department of Health, Cancer Registry, 4815 W. Markham, Little Rock, AR 72205. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SCPCS: SCPCS is funded by CDC grant S1135-19/19, and SCPCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA68485). SEARCH: SEARCH is funded by a program grant from Cancer Research UK (C490/A10124) and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. SNP_Prostate_Ghent: The study was supported by the National Cancer Plan, financed by the Federal Office of Health and Social Affairs, Belgium. SPAG: Wessex Medical ResearchHope for Guernsey, MUG, HSSD, MSG, Roger Allsopp STHM2: STHM2 was supported by grants from The Strategic Research Programme on Cancer (StratCan), Karolinska Institutet; the Linné Centre for Breast and Prostate Cancer (CRISP, number 70867901), Karolinska Institutet; The Swedish Research Council (number K2010-70X-20430-04-3) and The Swedish Cancer Society (numbers 11-0287 and 11-0624); Stiftelsen Johanna Hagstrand och Sigfrid Linnérs minne; Swedish Council for Working Life and Social Research (FAS), number 2012-0073STHM2 acknowledges the Karolinska University Laboratory, Aleris Medilab, Unilabs and the Regional Prostate Cancer Registry for performing analyses and help to retrieve data. Carin Cavalli-Björkman and Britt-Marie Hune for their enthusiastic work as research nurses. Astrid Björklund for skilful data management. We wish to thank the BBMRI.se biobank facility at Karolinska Institutet for biobank services. PCPT & SELECT are funded by Public Health Service grants U10CA37429 and 5UM1CA182883 from the National Cancer Institute. SWOG and SELECT thank the site investigators and staff and, most importantly, the participants who donated their time to this trial. TAMPERE: The Tampere (Finland) study was supported by the Academy of Finland (251074), The Finnish Cancer Organisations, Sigrid Juselius Foundation, and the Competitive Research Funding of the Tampere University Hospital (X51003). The PSA screening samples were collected by the Finnish part of ERSPC (European Study of Screening for Prostate Cancer). TAMPERE would like to thank Riina Liikanen, Liisa Maeaettaenen and Kirsi Talala for their work on samples and databases. UGANDA: None reported UKGPCS: UKGPCS would also like to thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. UKGPCS should also like to acknowledge the NCRN nurses, data managers, and consultants for their work in the UKGPCS study. UKGPCS would like to thank all urologists and other persons involved in the planning, coordination, and data collection of the study. ULM: The Ulm group received funds from the German Cancer Aid (Deutsche Krebshilfe). WUGS/WUPCS: WUGS would like to thank the following for funding support: The Anthony DeNovi Fund, the Donald C. McGraw Foundation, and the St. Louis Men's Group Against Cancer.

Study phs001391

Multiple_Malignancy_Familial_Comparison

Patient (who has had multiple malignancies) has previously been found to harbour a pathogenic p53 variant which is probably mosaic. This finding is based on exome sequencing performed elsewhere. In this study we will resequence the locus in question to ascertain whether the variant is indeed mosaic.

Study EGAS00001000333

Single cell analysis reveals new evolutionary complexity in uveal melanoma

Uveal melanoma (UM) is a rare eye cancer that is highly aggressive and leads to metastatic death in up to half of patients. Here, we conduct droplet-based single-cell RNA sequencing of 59,915 neoplastic and non-neoplastic cells from 8 primary and 3 metastatic UMs. Single-cell VDJ sequencing of RNA is conducted to determine clonally expanded B/T- cell populations. Single-cell DNA sequencing of 1 primary and 1 metastasis is utilized.

Study phs001861

Synthetic - GDI synthetic data

Synthetic - The Genomic Data Infrastructure (GDI) project is enabling access to genomic and related phenotypic and clinical data across Europe. It is doing this by establishing a federated, sustainable and secure infrastructure to access the data. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure the GDI project (funding: EC H2020 under grant agreement number 101081813) is acknowledged.

Study EGAS50000000678

EXCEED Study

EXCEED is a longitudinal population-based cohort which facilitates investigation of genetic, environmental and lifestyle-related determinants of a broad range of diseases and of multiple morbidity through data collected at baseline and via electronic healthcare record linkage. Recruitment has taken place in Leicester, Leicestershire and Rutland since 2013 and is ongoing, with approximately 10,000 participants aged 30-69 to date. The population of Leicester is diverse and additional recruitment from the local South Asian community is ongoing.

Study EGAS00001003499

GOSH_Tumour_Embryology_Paediatric_Behjati_RNA_Managed_Access

The aim of the study is to study thyroid development and function, and how this is altered in Down Syndrome and Thyroid Cancer.

Study EGAS00001006737

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