5709 results for "canc*"

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• Stratifying and Targeting Pediatric Medulloblastoma through Genomics

  In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

  Study EGAS00001000273

• Prostate cancer ancestral genomic disparity

  Prostate cancer is characterised by significant global disparity; mortality rates in SubSaharan Africa are double to quadruple those in Eurasia. Hypothesising unknown interplay between genetic and non-genetic factors, tumour genome profiling envisages contributing mutational processes. Through whole-genome sequencing of treatment-naïve prostate cancer from 183 ethnically/globally distinct patients (African versus European), we generate the largest cancer genomics resource for Sub-Saharan Africa. Identifying ~2 million somatic variants, Africans carried the greatest burden. We describe a new molecular taxonomy using all mutational types and ethno39 geographic identifiers, including Asian. Defined as Global Mutational Subtypes (GMS) A–D, although Africans presented within all subtypes, we found GMS-B to be ‘African-specific’ and GMS-D ‘African-predominant’, including Admixed and European Africans. Conversely, Europeans from Australia, Africa and Brazil predominated within ‘mutationally-quiet’ and ethnically/globally ‘universal’ GMS-A, while European Australians shared a higher mutational burden with Africans in GMS45 C. GMS predicts clinical outcomes; reconstructing cancer timelines suggests four evolutionary trajectories with different mutation rates (GMS-A, low 0.968/year versus D, highest 1.315/year). Our data suggest both common genetic factors across extant populations and regional environmental factors contributing to carcinogenesis, analogous to gene-environment interaction defined here as a different effect of an environmental surrounding in persons with different ancestries or vice versa. We anticipate GMS acting as a proxy to intrinsic and extrinsic mutational processes in cancers, promoting global inclusion in landmark studies.

  Study EGAS00001006425

• Whole genome sequencing of tumour and matched normal from patients with family history of breast cancer

  Analysis of whole genome sequencing of tumour and matched normal from familial breast cancer patients who had previously undergone clinical germline testing for variation in the BRCA1 and BRCA2 genes. The patients were either carriers of BRCA1 or BRCA2 germline pathogenic variants or non-carriers high-risk individuals (non-BRCA1/2). Somatic mutational signatures were used to study tumour aetiology of these individuals.

  Study EGAS00001003305

• Genetic landscape of pediatric ependymoma

  We performed whole-genome sequencing of 42 paired tumor/normal EPD genomes and analyzed somatically acquired single nucleotide variations (SNVs), insertion/deletions, structural variations and copy number variations in the cancer genomes

  Study EGAS00001000254

• RNA-sequencing data from 195 B-cell precursor acute lymphoblastic leukemias and mate pair whole genome sequencing data from 15 B-cell precursor acute lymphoblastic leukemias

  Fusion genes are potent driver mutations in cancer. In this study, we delineate the fusion gene landscape in a consecutive series of 195 pediatric B-cell precursor acute lymphoblastic leukemia (BCP ALL). Using RNA-sequencing, we find in-frame fusion genes in 127 (65%) cases, including 27 novel fusions. We describe a new subtype characterized by recurrent IGH-DUX4 or ERG-DUX4 fusions, representing 4% of cases, leading to overexpression of DUX4 and frequently co-occurring with intragenic ERG deletions. Furthermore, we identify a subtype characterized by an ETV6-RUNX1-like gene expression profile and coexisting ETV6 and IKZF1 alterations. Taken together, this study provides a detailed overview of fusion genes in pediatric BCP ALL and adds new pathogenetic insights, which may improve risk stratification and provide novel therapeutic options in this disease.

  Study EGAS00001001795

• METABRIC

  The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (CNVs, SNPs) and acquired somatic copy number aberrations (CNAs) were associated with expression in 40% of genes, although the landscape was dominated by cis and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP, and MAP2K4. Unsupervised analysis of paired DNA/RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, ER-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration 0152hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 0152CNA-devoid sub-group and a Basal-specific chromosome 5 deletion-driven mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic copy number aberrations on the transcriptome.

  Study EGAS00000000083

• Single-cell RNA sequencing data using the BDRhapsody platform of human B-cell lymphoma. The data comprises both immune and stromal cells of 19 individuals (6 healthy control and 13 cancer patients)

  Study EGAS00001006986

• Yemen_and_Chad_Genotyping

  HumanOmni2.5-8 data from Chad and Yemen.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001231

• ICGC PanCancer Analysis of Whole Genomes

  Study EGAS00001001692

• TRACERx 100: RNAseq data from the first 100 TRACERx tumours

  TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). This study looks at the multi-region RNAseq data from the TRACERx100 cohort with high enough quality RNA available. There is RNAseq data from 164 regions (64 patients).

  Study EGAS00001003458

• The Precision Medicine in Liver Cancer across an Asia-pacific NETwork (PLANET) - Intratumoural immune heterogeneity

  The clinical relevance of immune landscape intratumoural heterogeneity (immune-ITH) and its role in tumour evolution remain largely unexplored. Here, we uncover significant spatial and phenotypic immune–ITH from multiple tumour sectors and decipher its relationship with tumour evolution and disease progression in hepatocellular carcinomas (HCC). Immune–ITH is associated with tumour transcriptomic-ITH, mutational burden, and distinct immune microenvironments. Tumours with low immune–ITH experience higher immunoselective pressure and escape via loss of heterozygosity in human leukocyte antigens and immunoediting. Instead, the tumours with high immune-ITH evolve to a more immunosuppressive/exhausted microenvironment. This gradient of immune pressure along with immune-ITH represents a hallmark of tumour evolution, which is closely linked to the transcriptome-immune networks that contributes to disease progression and immune inactivation. Remarkably, high immune-ITH and its transcriptomic signature are predictive for worse clinical outcome in HCC patients. This in-depth investigation of ITH provides evidence on tumour-immune co-evolution along HCC progression.

  Study EGAS00001003814

• Genomic and transcriptomic analysis of thymic epithelial tumors

  Study EGAS00001004227

• BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer

  Checkpoint immunotherapy combined with neoadjuvant chemotherapy improves complete pathologic response in a subset of breast cancer patients. Here, we applied single-cell profiling to tumor biopsies collected before and during anti-PD1 therapy. One-third of tumors exhibited proliferative T-cells expanding along CD8+ or CD4+ lineages, which were either characterized by increased cytotoxicity and exhaustion or improved T-helper function, respectively. Lineage tracing in non-expanding tumors revealed at which point in the lineage T-cells were impaired, while gene expression modeling along these lineages revealed novel genes and underlying transcription factors involved in T-cell expansion. Interestingly, different dendritic and myeloid cell phenotypes could either stimulate or inhibit expanding T-cells, while cell-to-cell communication revealed an integrated immune context highly predictive of T-cell expansion, consisting of immune-stimulatory/-inhibitory interactions between cancer and various immune cell types. Our data yield unprecedented insights into the dynamic changes underlying checkpoint immunotherapy response in breast cancer.

  Study EGAS00001004809

• Transcriptome Sequencing Data of High Grade Serous Ovarian Cancer

  Study EGAS00001004714

• Whole Genome Sequencing Data of High Grade Serous Ovarian Cancer

  Longitudinal whole genome sequencing data to study chemoresponse in ovarian cancer. These samples belong to an ongoing sample collection and the data already includes hundreds of samples from over 200 patients. Samples are from primary and metastatic tissues and ascites. All patients have been treated with platinum-taxane based chemotherapy and majority of them are diagnosed with high-grade serous ovarian cancer.

  Study EGAS00001006775

• IG-MYC rearrangement defines a high-risk subgroup of B-cell precursor acute lymphoblastic leukaemia

  Study EGAS00001005111

• RNA sequencing data of 142 samples from 142 patients with HER2+ breast cancer treated with letrozole or tamoxifen (SOLTI PAMELA trial)

  PAMELA (NCT01973660) is a non-randomized, open-label, multicentric, prospective translational research study in stage I-IIIA HER2+ breast cancer designed to evaluate the ability of the PAM50 intrinsic subtypes to predict pCR in the breast (pCRB) following 18 weeks of neoadjuvant lapatinib and trastuzumab (+/- hormonal therapy if hormone receptor [HR] positive). Patients with HR+ disease received letrozole (if postmenopausal) or tamoxifen (if pre-menopausal). The primary objective was to compare the pCRB rates of the HER2-E versus the non-HER2-E subtypes in the intent-to-treat population.

  Study EGAS00001006410

• EGA synthetic data

  Synthetic Data One of the limitations in genomics research is that human genomics data is not openly available; access must be controlled according to participant consent agreements and data protection regulations such as GDPR. Obtaining authorization to access such data can sometimes take a long time, resulting in delays to important research work. In this context, synthetic genomic and phenotype data can be useful resources for researchers to avoid these delays. Synthetic data are artificially generated datasets, often created with algorithms, which can be used without the need for authorization to test new products and tools, build technical demonstrators, validate data models, and train AI models. The EGA provides access to synthetic cohort datasets augmented with rich synthetic metadata that overcomes these real data usage restrictions. Whilst synthetic datasets are not included in the general EGA mandate and services, we can consider such submissions and evaluate their acceptance on the basis of their unique use cases not already covered by existing synthetic datasets. Access to synthetic data studies is managed by the EGA Helpdesk Data Access Committee. Study ID Title Located in EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples Central EGA EGAS00001005591 Synthetic data - Genome in a Bottle Central EGA EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 Central EGA EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Central EGA EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data Central EGA EGAS50000000086 Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023 Federated EGA Sweden EGAS50000000678 Synthetic - GDI synthetic data Federated EGA Spain

  Documentation synthetic-data

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? The most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• Projects

  Projects Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, the EGA provides an invaluable service to the worldwide biomedical research community. The teams leading the EGA are involved in several international partnerships and consortia in numerous scientific fields, where they contribute to ambitious projects. In addition to the project listed below, the EGA is in a long-standing partnership with the Global Alliance for Genomics and Health (GA4GH), as described on the dedicated page. On-going projects Project Duration Domain Funder Tags CANDLE | CANDLE project aims to conceptualise and advance the development of National Cancer Data Nodes (NCDNs) in European countries. These NCDNs will boost the reuse of cancer data for research, innovation and policy making, in order to improve diagnostics and treatment for cancer patients, as well as prevention and early detection. 2025-2028 Cancer Horizon Europe DOCUMENTATION EASIGEN-DS | The EASIGEN-DS project aims to conduct a design study to establish a new European Research Infrastructure on Advanced Genomics Technologies, EASIGEN. To develop an excellent scientific, technological and operational design, we will conduct landscape studies, stakeholder consultations, and community surveying. 2025-2028 Genomic and health data Horizon Europe DATA MANAGEMENT DOCUMENTATION INFRASTRUCTURE Go-IMPaCT | Go-IMPaCT will contribute sequenced genomes and provide infrastructure as part of IMPaCT-Cohort, one of the three fundamental pillars of the Precision Medicine Infrastructure associated with Science and Technology (IMPaCT) program in Spain. Along with the Genome of Europe (GoE) project, around 18.000 people will have their genomes sequenced, also contributing to Spain's commitments in 1+MG. Go-IMPaCT will fund the development of an EGA node to manage and share this genomic and phenoclinic data, laying the foundations for regional and ethnic genomic variability in Spain to be available for research purposes. The IMPaCT cohort is created with the spirit of being an open research tool, compatible with the rest of the health research ecosystem, and other international initiatives. 2025-2027 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS FAIR-FEGA | This project seeks to accelerate data depositions into FEGA, significantly increasing the data flow in and from FEGA nodes. It will build capacity within the FEGA nodes and increase awareness in a wide range of stakeholders, thus altogether achieving the ultimate goal of enhancing data reuse. The project will be carried out by a strategic consortium comprising seven ELIXIR nodes and two ELIXIR communities. 2025-2026 Not applicable ELIXIR ACCESS DISCOVERY DOCUMENTATION INFRASTRUCTURE METADATA STANDARDS FEGA-Connect | A consortium of six ELIXIR nodes plus the Polish FEGA node (in-kind contribution) joining forces to build a solid base to develop solutions for effective multi-omic sensitive data integration between FEGA nodes and other infrastructures and specialised Data repositories. We aim to promote a more coherent data deposition, discoverability and retrieval of multi-omics datasets, providing FAIRer data and consequently accelerating research. 2025-2026 Multi-omics data ELIXIR ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE METADATA STANDARDS IMPaCT-Data 2 | IMPaCT-Data 2 will develop a digital platform for the integration and modelling of biomedical data associated with IMPaCT (Precision Medicine Infrastructure associated with Science and Technology) projects in Spain. It will deploy a sustainable infrastructure that facilitates the integration, standardisation, interoperability and analysis of clinical, genomic, molecular and medical imaging data. This platform will be aligned with European projects such as Genome of Europe (GoE), the first project to make use of the European Genomic Data Infrastructure (GDI), and EUCAIM. IMPaCT-Data 2 will benefit from advanced Artificial Intelligence and High Computing Capacity Systems capabilities, offering robust and accessible tools for researchers from the National Health System in Spain. 2025-2026 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS SenSec | This project aims to establish a mechanism for orchestrating secure access to sensitive data hosted by the EGA, whether in Central EGA or any Federated Node, from Galaxy, a popular open-source, community-driven VRE (Virtual Research Environment) for bioinformatics analysis. Building on a previous prototype that enabled Galaxy users within Trusted Research Environments (TREs) to decrypt sensitive data for workflow execution without sharing private encryption keys, SenSec will expand this prototype into a comprehensive solution for secure data analysis in Galaxy, facilitating encrypted data access and transfer from FEGA/EGA repositories to designated TREs. 2025-2026 Genomic and health data; trusted research environment ELIXIR ACCESS DATA ANALYSIS ERDERA | The European Rare Disease Research Alliance (ERDERA) takes over EJPRD to deliver concrete health benefits to rare disease patients in the next decade by advancing prevention, diagnosis and treatment research. To leave no one behind, over 170 organisations championed by the European Union and member states are working hand in hand to make Europe a world leader in rare diseases research and innovation. 2024-2034 Rare diseases Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution ACCESS DATA ANALYSIS DISCOVERY INFRASTRUCTURE SYNTHIA | The aim of SYNTHIA is to deliver validated, reliable tools and methods for synthetic data generation (SDG). The tools will cover multiple data types including lab results, clinical notes, genomics, imaging and m-health data. SYNTHIA also hopes to make possible the generation of longitudinal data. 2024-2029 Genomic and health data; multi-omics; AI solutions Innovative Health Initiative (IHI) DATA ANALYSIS DATA MANAGEMENT INFRASTRUCTURE GoE | The Genome of Europe initiative aims to build a European network of national genomic reference cohorts of at least 500.000 citizens. These reference cohorts will be selected to be representative of the European population. 2024-2028 Large-scale genomic and health data Horizon Europe ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS HEREDITARY | HEREDITARY aims to transform the way we approach disease detection, prepare treatment response, and explore medical knowledge by building a robust, interoperable, trustworthy, and secure framework that integrates multimodal health data (including genetic data) while ensuring compliance with cross-national privacy-preserving policies. 2024-2027 Neurodegenerative disorders, gut-brain interplay Horizon Europe DATA MANAGEMENT DATA ANALYSIS EOSC-ENTRUST | The mission of EOSC-ENTRUST is to create a European network of trusted research environments for sensitive data and to drive European interoperability by joint development of a common blueprint for federated data access and analysis. 2024-2026 Trusted Research Environment Horizon Europe INFRASTRUCTURE EBV-MS | "Targeting Epstein-Barr Virus Infection for Treatment and Prevention of Multiple Sclerosis". The ambitious goals of the project are to answer the questions why only a few EBV infected persons develop MS, and define the underlying mechanism of this process, as well as clarify if targeting the EBV infection can prevent MS or improve the disease course. 2023-2028 Viral-host genetics; immune response; disease modelling; disease prevention; AI/ML solutions Horizon Europe DATA MANAGEMENT DATA ANALYSIS WISDOM | WELL-BEING IMPROVEMENT THROUGH THE INTEGRATION OF HEALTHCARE AND RESEARCH DATA AND MODELS WITHOUT BORDER FOR CHRONIC IMMUNE-MEDIATED DISEASES aims to deploy novel approaches for data processing, harmonisation, management, and secure data sharing and federated access for diseases like multiple sclerosis. Using an end-user guided approach, it will facilitate responsible and critical assessment of the use of AI in healthcare. 2023-2028 Chronic immune-mediated diseases Horizon Europe DATA MANAGEMENT INFRASTRUCTURE EUCAIM | EUropean Federation for CAncer IMages is a project that will build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of Artificial Intelligence in oncology. 2023-2027 Cancer Digital Europe Programme (DIGITAL) DISCOVERY CONTAGIO | CONTAGIO (COhorts Network To be Activated Globally In Outbreaks) aims to create coordination mechanisms to rapidly react to infectious disease (re-)emergence in low- and middle-income countries (LMICs). 2023-2026 Infectious Diseases European Commission - Horizon Europe ACCESS DATA MANAGEMENT DISCOVERY Youth-GEMs | Youth-GEMS (Gene Environment Interactions in Mental Health TrajectorieS of Youth) will conduct research into the genetic and environmental factors of mental health in young European people. 2022-2027 Mental health European Commission - Horizon Europe DATA MANAGEMENT DISCOVERY GDI | The European Genomics Data Infrastructure 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. 2022-2026 Genomic and health data European Commission - Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution DISCOVERY DOCUMENTATION INFRASTRUCTURE IMPaCT-T2D | The IMPaCT-T2D project aims at studying the complete genomes of a large cohort of patients with Type 2 Diabetes mellitus (T2D), using modern sequencing technologies and artificial intelligence (AI) in order to improve the stratification and pharmacological treatment in the context of precision medicine. 2022-2025 Cardiovascular and Complex Diseases Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE Completed projects Project Duration Domain Funder Tags EOSC4Cancer | EOSC4Cancer builds on existing projects, research outcomes and established community solutions to create the federated FAIR data, analysis and services infrastructure needed for European Cancer research programmes. 2022-2025 Cancer European Commission - Horizon Europe DISCOVERY EuCanImage | A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology. 2020-2025 AI Solutions in Oncology European Commission - H2020 Programme; "La Caixa" Foundation cofunds CRG's contribution DATA MANAGEMENT METADATA STANDARDS GenoMed4ALL | A consortium built to empower personalised medicine in the field of haematological diseases through the use of AI and the pooling of genomic and clinical data. 2020-2025 Hematological diseases European Commission - H2020 Programme DISCOVERY METADATA STANDARDS BY-COVID | The BeYond-COVID project aims to make COVID-19 data accessible to scientists in laboratories but also to anyone who can use it, such as medical staff in hospitals or government officials. Going beyond SARS-CoV-2 data, the project will provide a framework for making data from other infectious diseases open and accessible to everyone. 2021-2024 Infectious diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE IMPaCT-Data | IMPaCT-Data aims to create the infrastructure for secondary use of data from Spanish healthcare systems - electronic health records, medical imaging and genomic repositories - and contribute with the knowledge and methodology produced to the healthcare system. 2021-2024 Large-scale genomics and health dataSpanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE LaMarato | It is a project aimed at creating and developing a catalan interhospitalary network to interrogate genetic variants from thousands of genetic tests carried out in patients with rare diseases from the main catalan hospitals. 2021-2024 Genomic and health data Fundacio La Marato de TV3 (catalan foundation) DISCOVERY HealthyCloud | This consortium will contribute a Strategic Agenda towards the European Health Research and Innovation Cloud. The project will work in collaboration with a broad range of stakeholders to ensure that all voices are included and that the results are technically and ethically sound. 2021-2023 Not Applicable European Commission - H2020 Programme DOCUMENTATION B1MG | Beyond 1 Million Genomes aims to create a network of genetic and clinical data across Europe. The project provides coordination and support to the 1+ Million Genomes Initiative (1+MG). This initiative is a commitment of 24 EU countries, the UK and Norway to give cross-border access to one million sequenced genomes by 2022. 2020-2023 Not applicable European Commission - Horizon Europe DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS ELIXIR-CONVERGE | An alliance with the goal of Connecting and aligning ELIXIR Nodes to deliver sustainable FAIR life-science data management services. 2020-2023 Data Management and Infectious Diseases European Commission - H2020 Programme DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS IHCC | The International HundredK+ Cohorts Consortium aims to create a global platform for translational research ? informing the biological and genetic basis for disease and improving clinical care and population health. 2020-2022 Translational research NIH; The Wellcome Trust; CZI INFRASTRUCTURE METADATA STANDARDS PPCG | The Pan Prostate Cancer Group aims to harmonise and interrogate Whole Genome DNA Sequence data generated around the world from over 2000 men with prostate cancer, with associated transcriptome and methylome data to include men from different clinical categories, and ethnicities. This project is about providing breakthrough advances through analysis of a very large series of Whole Genome DNA data from prostate cancer contributed by many of the leading scientists and clinicians working in prostate cancer genomics. 2019-2024 Cancer Cancer Research UK DATA MANAGEMENT CINECA | Consortium providing a Federated solution enabling population-scale genomic and biomolecular data accessible across international borders accelerating research and improving the health of individuals resident across continents. 2019-2023 Large-scale Genomics and Health Data European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE EASI-Genomics | A project designed to provide easy access to cutting-edge DNA sequencing technologies to researchers from academia and industry, within a framework that ensures compliance with ethical and legal requirements, as well as FAIR and secure data management. 2019-2023 Next Generation Sequencing European Commission - H2020 Programme ACCESS EJP-RD | An European consortium built to create a comprehensive, sustainable ecosystem allowing a virtuous circle between research, care, and medical innovation. 2019-2023 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DOCUMENTATION METADATA STANDARDS EOSC-Life | EOSC-Life brings together the 13 Life Science research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research. The project will publish 'FAIR' data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). 2019-2023 Not applicable European Commission - H2020 Programme DOCUMENTATION EUCANCan | A federated network aiming at implementing a cultural, technological and legal integrated framework across Europe and Canada, to enable and facilitate the efficient sharing of cancer genomic data. 2019-2023 Cancer European Commission - H2020 Programme DATA MANAGEMENT METADATA STANDARDS The Federated EGA framework: supporting sensitive data management across the ELIXIR Nodes | This project is a direct continuation of the FHD IS with the goal to position the FEGA framework as the core infrastructure driver to support human data sharing for research. 2019-2023 Human genomic data ELIXIR INFRASTRUCTURE UK Biobank | UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. This project is to archive whole genome sequencing and other genetic data for UK Biobank participants. 2019-2023 Large-scale Genomics and Health Data The Wellcome Trust; UKRI; Amgen; AstraZeneca; GSK; Johnson & Johnson DATA MANAGEMENT INFRASTRUCTURE VEIS | The core mission of VEIS is to create an open ecosystem of technologies that will address and adapt to the requirements of the systems used to analyse and interpret -omics and clinical data in research and application environments in biomedicine. The aim of the project is to leverage the value of the EGA for both industry and society. 2019-2022 Oncology and Rare diseases Generalitat de Catalunya and European Regional Development Fund (ERDF) ACCESS DISCOVERY ELIXIR BEACON IS | This study follows on from a number of earlier activities that have established the ELIXIR Beacon Project. The main aim is to extend the Beacon protocol, developed at EGA, to become the reference ELIXIR Data Discovery product 2019-2021 Not applicable ELIXIR DISCOVERY ELIXIR FHD IS | This project coordinates the delivery of FAIR compliant metadata standards, interfaces, and reference implementation to support the federated ELIXIR network of human data resources. 2019-2021 Human genomic data ELIXIR INFRASTRUCTURE ELIXIR Rare Disease | The Rare Disease Community extends and generalises the system of access authorisation and high volume secure data transfer developed within the EGA. The goal of the Community is to create a federated infrastructure that will enable researchers to discover, access and analyse different rare disease repositories across Europe. It is doing this in partnership with other European infrastructure projects, namely RD-CONNECT, BBMRI-ERIC and E-Rare.2019-2021 Rare diseases ELIXIR INFRASTRUCTURE Solve-RD | Solve-RD - solving the unsolved rare diseases - is a research project funded by the European Commission. It echoes the ambitious goals set out by the International Rare Diseases Research Consortium (IRDiRC) to deliver diagnostic tests for most rare diseases by 2020. The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory for a large proportion of rare disease patients - the unsolved RD cases. For these unsolved rare diseases, we are unable to explain the etiology responsible for the disease phenotype, predict the individual disease risk and/or rate of disease progression, and/or quantitate the risk of relatives to develop the same disorder. 2018-2024 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT METADATA STANDARDS EuCanShare | An EU-Canada joint infrastructure for next-generation multi-Study Heart research. 2018-2022 Cardiovascular Diseases European Commission - H2020 Programme ACCESS METADATA STANDARDS

  Documentation about/projects-and-funders/projects

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Federated EGA

  Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please send an email to fega-info [at] lists [dot] ega-archive [dot] org. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about setting up a Federated EGA Node in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Node Operations guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior signing through its official channels.

  Documentation about/projects-and-funders/federated-ega

• Catalogue Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue What is in the EGA? The European Genome-phenome Archive (EGA) overview The EGA archives and distributes the results of several types of studies. Such studies include genome-wide association studies (GWAS), different purpose sequencing and molecular diagnosis assays among others. Moreover, these studies target many different kind of diseases from cancer to neurodegenerative alterations. We summarised all this data in the charts below. The statistics are computed daily based on an updated list of studies. Each pie chart depicts a different aspect of the studies: which disease type was studied, which type of sampling method was used and what was the scope of the genomic analysis. Number of studies per disease type Studies in the EGA by disease barHorizontalChart('disease-graph', 'https://stats.test.ega-archive.org/catalog/by_disease') Number of studies per technology type Studies in the EGA by technology barHorizontalChart('technology-graph', '/stats/studies/technology', 'Include dbGaP') Number of studies per sample type Studies in the EGA by samples barChart('samples-graph', '/stats/studies/sample-types', 'Include dbGaP')

  Documentation about/statistics/catalogue

• Multi-omic and functional analysis for classification and treatment of sarcomas with FUS-TFCP2 or EWSR1-TFCP2 fusions(H021/INF)

  Many rare cancers are not well understood, and pathogenesis-directed therapies are often lacking, resulting in poor patient outcomes. Leveraging two (inter)national precision oncology trials that enroll large numbers of rare cancers, we investigated the clinical, histopathologic, molecular, and functional characteristics of rhabdomyosarcoma (RMS) with fusions of FUS or EWSR1 to the TFCP2 transcription factor, a recently discovered ultra-rare entity whose classification, pathogenesis, and optimal treatment are unclear. Unusually for fusion-driven sarcomas, most cases had highly rearranged genomes, including chromothripsis, and signs of defective homologous recombination DNA repair. All tumors showed extreme expression of a truncated TERT variant and the ALK receptor tyrosine kinase, which was additionally affected by intragenic deletions and aberrant splicing, resulting in the expression of shortened variants in 58% of cases. Three ALK variants were oncogenic in immortalized cells, and patient-derived tumor cells expressing two variants responded to certain ALK inhibitors. Other recurrent alterations included CDKN2A/MTAP co-deletions and mutations in PAPPA2, encoding an IGFBP5-specific proteinase, in 67% and 25% of cases, respectively. DNA methylation analysis, along with 19 other soft-tissue sarcoma classes, revealed a close relationship with undifferentiated sarcoma but not other RMS subtypes, suggesting that TFCP2-rearranged RMS is a separate entity, possibly arising from a distinct cell of origin. Expression of TFCP2 fusions in immortalized human cells blocked late myogenic differentiation and significantly induced genes that were also highly expressed in patient tumors, including ALK, TERT, and two known regulators of skeletal muscle cells, IGFBP5 and PTH1R. Genome-wide chromatin profiling demonstrated direct binding of FUS-TFCP2 to the ALK and TERT loci outside their regular promoters, which correlated with the expression of alternative transcript variants. Finally, FUS-TFCP2 inhibited the repair of DNA double-strand breaks in immortalized cells, rendering them sensitive to treatment with cisplatin. This study provides insight into the pathogenesis and therapeutic vulnerabilities of a new RMS subtype and illustrates the value of linking comprehensive molecular diagnostics and functional annotation within multicenter consortia to improve the understanding and clinical management of rare cancers.

  Study EGAS00001006939

• Spatiotemporal Genomic Profiling of Intestinal Metaplasia Reveals Clonal Dynamics of Gastric Cancer Progression

  Intestinal metaplasia (IM) is a pre-malignant condition of the gastric mucosa associated with increased gastric cancer (GC) risk. We analyzed 1256 gastric samples (1152 IMs) from 692 subjects from a 10-year prospective study. We identified 26 IM driver genes in diverse pathways including chromatin regulation (ARID1A) and intestinal homeostasis (SOX9), largely occurring as subclonal events. Analysis of clonal dynamics between and within subjects, and also longitudinally across time, revealed that IM clones are likely transient but increase in size upon progression to dysplasia, with eventual transmission of genetic events to paired GCs. Single-cell and spatial profiling highlighted changes in tissue ecology and lineage heterogeneity in IM, including an intestinal stem-cell dominant cellular compartment linked to early malignancy. Expanded transcriptome profiling revealed expression-based molecular subtypes of IM, including a body-resident “pseudoantralized” subtype associated with incomplete histology, antral/intestinal cell types, ARID1A mutations, inflammation, and microbial communities normally associated with the healthy oral tract. We demonstrate that combined clinical-genomic models outperform clinical-only models in predicting IMs likely to progress. Our results raise opportunities for GC precision prevention and interception by highlighting strategies for accurately identifying IM patients at high GC risk and a role for microbial dysbiosis in IM progression.

  Study EGAS00001007067

• Multi-Layered Molecular Profiling Informs the Diagnosis and Targeted Therapy of Desmoplastic Small Round Cell Tumor

  Desmoplastic small round cell tumor (DSRCT) is an ultra-rare soft tissue sarcoma with dismal prognosis and limited therapeutic options. Here, we identified individual therapeutic targets in a cohort of 30 DSRCT patients by use of multilayered molecular profiling (genomic, transcriptomic, and (phospho-)proteomic analyses) within the DKFZ NCT DKTK MASTER program. 28 of the 30 patients received at least one personalized treatment recommendation. In line with the observation that DSRCTs are an entity with a low mutational load, most of the treatment recommendations were based on target gene or -protein expression. 13 patients received a recommended therapy, resulting in four partial responses, four stable diseases, and five progressive diseases. Eight of 13 patients received the multi tyrosine kinase inhibitor pazopanib, with mixed response. Based on high ERBB2 expression levels, two patients received the antibody drug conjugate trastuzumab deruxtecan (T-DXd), showed exceptional, long-lasting responses and are currently (8/2024) still on treatment and progression-free. We detected high ERBB2 protein expression in all DSRCT samples with proteome data (n = 9), which suggests that ERBB2 may be a particularly useful therapeutic target for DSRCT patients. For one of the two exceptional responders we did not detect a substantial ERBB2/HER2 overexpression, which may indicate that T-DXd could also be efficacious in DSRCT patients with low levels of HER2, as also described for breast cancer patients. In conclusion, multi-omic molecular profiling revealed personalized therapeutic targets in patients with DSRCT with HER2 targeting, with T-DXd as a promising therapeutic option for this ultra-rare and difficult-to-treat tumor entity.

  Study EGAS00001007934

• National Human Genome Research Institute Tumor Sequencing Project (TSP) - Lung Adenocarcinoma

  The Tumor Sequencing Project (TSP) Consortium is a collaboration among participants at the Baylor College of Medicine Human Genome Sequencing Center, the Broad Institute Genome Sequencing Platform, the Dana Farber Cancer Institute, the Memorial Sloan-Kettering Cancer Center, the Genome Sequencing Center and Siteman Cancer Center at Washington University, the M.D. Anderson Cancer Center and the University of Michigan Medical Center. The TSP Part A will pilot approaches to large-scale identification of genomic changes in tumors by sequencing the exonic regions of 623 genes in 188 specimens of adenocarcinoma of the lung, as well as using high density SNP genotyping arrays for high resolution identification of changes in chromosomal copy number. The TSP Part B will pilot approaches to tumor characterization of lung adenocarcinoma samples using next-generation sequencing technologies and benchmark those results against Part A data generated with ABI3730 instruments.

  Study phs000144

• Study of Addiction: Genetics and Environment (SAGE)

  This study is part of the Gene Environment Association Studies initiative (GENEVA) funded by the National Human Genome Research Institute. The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. The focus of this proposal is a case-control design of unrelated individuals for a genetic association study of addiction. Cases are defined as individuals with DSM-IV alcohol dependence (lifetime) and potentially other illicit drug dependence. In addition to the categorical diagnosis, we have data on ordinal measurements of number of DSM-IV symptoms for alcohol, nicotine, marijuana, cocaine, opiates and other drugs so that we will able to construct quantitative measurements of addiction severity over a wide range of substances. Controls are defined as individuals who have been exposed to alcohol (and possibly to other drugs), but have never met lifetime diagnosis for alcohol dependence or dependence on other illicit substances. Analyses that include refinement of the phenotype and incorporation of important demographic and environmental factors into association studies will be pursued. Cases and controls were selected from three large, complementary datasets: the Collaborative Study on the Genetics of Alcoholism (COGA), the Family Study of Cocaine Dependence (FSCD), and the Collaborative Genetic Study of Nicotine Dependence (COGEND). COGA: COGA was initiated in 1989 and is a large-scale family study that has had as its primary aim the identification of genes that contribute to alcoholism susceptibility and related characteristics. COGA is funded through the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Subjects were recruited from 7 sites across the U.S. Alcohol dependent probands were recruited from treatment facilities and assessed by personal interview. After securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through the alcohol dependent probands. Assessment involved a comprehensive personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Families with three or more first-degree relatives who were alcohol dependent were invited for more extensive testing, including neurophysiology evaluations (ERPs and EEGs) and a battery of neuropsychological assessments. Blood was obtained for genetic studies. Institutional Review Boards at all sites approved the protocols, including sharing in the NIAAA national repository. COGA has four Co-Principal Investigators Bernice Porjesz, Victor Hesselbrock, Howard Edenberg, and Laura Bierut. COGA includes nine different centers where data collection, analysis, and storage take place. The nine sites and Principal Investigators and Co-investigators are: University of Connecticut (Victor Hesselbrock); Indiana University (Howard Edenberg, John Nurnberger, Jr., Tatiana Foroud); University of Iowa (Samuel Kuperman); SUNY Downstate (Bernice Porjesz); Washington University in St. Louis (Laura Bierut, Alison Goate, John Rice); University of California at San Diego (Marc Schuckit); Howard University (Robert Taylor); Rutgers University (Jay Tischfield); Southwest Foundation (Laura Almasy). Q. Max Guo serves as the NIAAA Staff Collaborator. This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). Family Study of Cocaine Dependence (FSCD): This project was initiated in 2000 as a case-control family study of cocaine dependence funded through the National Institute on Drug Abuse (NIDA; PI: Laura Bierut). The primary goal was to increase the understanding of the familial and non-familial antecedents and consequences of cocaine dependence. Cocaine dependent individuals were systematically recruited from chemical dependency treatment units (both public and private; residential and outpatient) in the greater St. Louis metropolitan area. Community based control subjects were identified through a Missouri Driver's License Registry (maintained at Washington University for research purposes) and matched by age, race, gender, and residential zip code. As a supplement to this project, blood samples were collected for future genetic analysis and were included in the NIDA Genetics Consortium. Phenotypic data, DNA, and cell lines are in the NIDA Center for Genetics Studies. Collaborative Genetic Study of Nicotine Dependence (COGEND): COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design is a community based case-control family study. All subjects were recruited from Detroit and St. Louis. Nicotine dependent cases and non-dependent smoking controls were identified and recruited. In addition, one sibling for each case and control subject was recruited in a subset of the sample. Over 56,000 subjects aged 25-44 years were screened by telephone, over 3,100 subjects were personally interviewed, and over 2,900 donated blood samples for genetic studies. All three studies (COGA, COGEND, FSCD) include measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, educational attainment, religious participation, and family structure. Other important covariates and/or potential moderators of genetic effects include comorbid addictions and age at initiation of use for cigarettes, alcohol and drugs. The assessments also include measures of various life stressors, such as physical and sexual abuse, which have been implicated in gene-environment interactions for several disorders. Coding for both individual variables and indices has been standardized across studies. All subjects were assessed in person by trained research assistants. Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR), was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease"(HHSN268200782096C). Note for Publications Related to Study: The Study of Addiction: Genetics and Environment (SAGE) has not yet generated publications. Below is a listing of publications related to the three studies from which the SAGE sample was selected. COGA has over 228 publications listed at www.niaaagenetics.org This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to addiction through a large-scale genome-wide association study of DSM-IV alcohol dependent (and frequently illicit drug dependent) cases and non-dependent, unrelated control subjects of European and African American descent. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000092

• A Genome Wide Scan of Lung Cancer and Smoking

  The majority of cases of lung cancer are the culmination of a dynamic process that begins with smoking initiation, proceeds through dependency and smoking persistence, continues with lung cancer development and ends with progression to disseminated disease or response to therapy and survival. We are conducting a whole genome study of lung cancer and smoking to examine critical steps in lung cancer progression. This study is a genome-wide association study (GWAS) to investigate the genetic determinants of lung cancer risk. The study design efficiently allows identification of genes that also contribute to smoking persistence and outcome from lung cancer using a single GWAS of 5,900 subjects using the primary GENEVA dataset, derived from two studies. The first is the Environment and Genetics in Lung Cancer Etiology Study (EAGLE), a population-based, biologically intensive, case-control study from the Lombardy region of Italy including ~2000 newly diagnosed lung cancer cases and ~2000 age-, gender- and region- matched controls. The second is the Prostate, Lung, Colon and Ovary Study (PLCO) Cancer Screening Trial from which we have selected ~850 lung cancer cases and ~850 controls, also matched on age and gender. Understanding the basis for the well-established hereditary component of lung cancer and smoking persistence could provide new insights into etiology, prevention, and treatment, and have an enormous impact on public health. The same GWAS genotyping data in the two studies will be used to investigate the genetic determinants of smoking persistence. Specifically, we will analyze current smokers and former smokers from EAGLE and PLCO for diverse smoking phenotypes, including persistence of smoking as well as ever/never smoking comparisons, quitting attempts, and the Fagerström index of tobacco addiction. PLCO participants are all European-Americans and EAGLE involves subjects from Italy. EAGLE is a case-control study and contains 3937 phenotyped subjects. PLCO is a screening trial with a cohort design and contains 1651 phenotyped subjects. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to lung cancer and smoking through large-scale genome-wide association studies of population-based samples of lung cancer cases and controls. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000093

• Whole Genome Association Study of Systemic Lupus Erythematosus

  The goal of this collaborative study was to identify new genetic risk factors for Systemic Lupus Erythematosus (SLE). To do this we conducted a genome-wide scan by combining the resources and expertise from a number of SLE researchers to establish a large sample set comprising 1311 SLE cases and 3340 controls. The SLE case samples were genotyped from the following collections: 338 subjects from the Autoimmune Biomarkers Collaborative Network (ABCoN), an NIH/NIAMS funded repository, 141 subjects from the Multiple Autoimmune Disease Genetics Consortium (MADGC), 613 subjects from the University of California San Francisco (UCSF) Lupus Genetics, and 335 subjects from the University of Pittsburgh Medical Center (UPMC), plus 8 samples collected at The Feinstein Institute for Medical Research. A total of 3583 control samples were examined in the association analyses. As part of this project, 1861 control samples were selected and then genotyped from the New York Cancer Project (NYCP), based on self-described ethnicity, gender and age. In addition, genotype data from 1722 control samples (all self-described North Americans of European descent) were obtained from the publicly available iControlDB database (http://www.illumina.com/pages.ilmn?ID=231).

  Study phs000122

• Neuroblastoma Genome-Wide Association Study (NBL-GWAS)

  Neuroblastoma is a malignancy of the developing sympathetic nervous system that most commonly affects young children and is often lethal. The etiology of this embryonal cancer is not known. We have performed a whole genome scan for association of neuroblastoma with SNP genotypes and copy number variation to discover predisposition loci. We therefore initiated a genome-wide association study (GWAS) in 2007 focused on neuroblastoma patients identified through the Children's Oncology Group (COG; 238 member institutions). Control patients for this study are children cared for at the Children's Hospital of Philadelphia (CHOP) without a diagnosis of cancer. The study was designed to collect up to 5000 neuroblastoma cases and 10,000 controls and is powered to detect common susceptibility variants in Caucasian and African American patients. Whole genome genotyping is being performed on the Illumina HH550 SNP array.

  Study phs000124

• Cancer Genetic Markers of Susceptibility (CGEMS) Breast Cancer Genome-wide Association Study (GWAS) - Primary Scan: Nurses' Health Study - Additional Cases: Nurses' Health Study 2

  The initial stage of the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer genome-wide association study (GWAS) included genotyping 528,173 SNPs (Illumina HumanHap550) in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls from the Nurses' Health Study (NHS). Subsequently, incident invasive breast cancer cases from the Nurses' Health Study 2 (NHS2) cohort were genotyped using the Illumina HumanHap 610 quad. The NHS2 cases are younger than the cases in the first stage of the CGEMS breast cancer GWAS, which only included postmenopausal women. The NHS2 cases are a mix of pre- and postmenopausal women

  Study phs000147

• National Institutes of Health The Cancer Genome Atlas (TCGA)

  The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services. TCGA projects are organized by cancer type or subtype. Click here for a current list of cancer types selected for study in TCGA. Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the Genomic Data Commons (GDC). Data from TCGA projects are organized into two tiers: Open Access and Controlled Access. Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal. Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through dbGaP Authorized Access. Controlled Access data types consist of the following: Individual germline variant data (SNP .cel files) Primary sequence data (.bam files), which are available at GDC Clinical free text fields Exon Array files (for Glioblastoma and Ovarian projects only) NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access. Please go to this page: https://tcga-data.nci.nih.gov/docs/publications/ to access all data associated with TCGA tumor specific publications. The TCGA study is utilized in the following dbGaP substudies. To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000178 TCGA study. phs000854 Genome-wide Analysis of Noncoding Regulatory Mutations in Cancer

  Study phs000178

• High Density SNP Association Analysis of Melanoma: Case-Control and Outcomes Investigation

  This research builds upon an extensive resource of melanoma cases and hospital based controls collected over several years at the U.T. M.D. Anderson Cancer Center. The goal of this research is to identify novel susceptibility and outcome-related genes for melanoma using a systematic genome-wide association-based approach. Our goal is to conduct high-density SNP association and outcome studies. This dbGaP study contains samples from 2000 European ancestry cases and 1000 European ancestry controls using the Illumina OMNI1-Quad SNP chip. As a part of an ongoing R01 project, we have epidemiological data together with candidate gene results for 1000 of the melanoma cases and the controls. With regard to the outcome aspect of our design, as part of our melanoma Specialized Program of Research Excellence (SPORE) grant, our MelCore database contains comprehensive, prospectively maintained clinical information from all melanoma patients included in the study cohort, including primary tumor histopathology and staging information, standard and investigational blood tumor markers, details of surgical and systemic therapies, and extensive follow-up information, including time to relapse or recurrence, pattern of recurrence and survival duration. Finally, we intend to collaborate with the GenoMEL collaboration so we can jointly evaluate each other's findings. The goal of our analysis will be to identify novel genetic factors predisposing the development of melanoma, as well as genetic factors controlling melanoma stage at presentation, recurrence and progression. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to melanoma through large-scale genome-wide association studies of 2000 European ancestry cases and 1000 European ancestry controls. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000187

• Women's Health Initiative Clinical Trial and Observational Study

  The Women's Health Initiative (WHI) is a long-term national health study that has focused on strategies for preventing heart disease, breast and colorectal cancer, and osteoporotic fractures in postmenopausal women. The original WHI study included 161,808 postmenopausal women enrolled between 1993 and 1998. The Fred Hutchinson Cancer Research Center in Seattle, WA serves as the WHI Clinical Coordinating Center for data collection, management, and analysis of the WHI. The WHI has two major parts: a partial factorial randomized Clinical Trial (CT) and an Observational Study (OS); both were conducted at 40 Clinical Centers nationwide. The CT enrolled 68,132 postmenopausal women between the ages of 50-79 into trials testing three prevention strategies. If eligible, women could choose to enroll in one, two, or all three of the trial components. The components are: Hormone Therapy Trials (HT): This double-blind component examined the effects of combined hormones or estrogen alone on the prevention of coronary heart disease and osteoporotic fractures, and associated risk for breast cancer. Women participating in this component with an intact uterus were randomized to estrogen plus progestin (conjugated equine estrogens [CEE], 0.625 mg/d plus medroxyprogesterone acetate [MPA] 2.5 mg/d] or a matching placebo. Women with prior hysterectomy were randomized to CEE or placebo. Both trials were stopped early, in July 2002 and March 2004, respectively, based on adverse effects. All HT participants continued to be followed without intervention until close-out. Dietary Modification Trial (DM): The Dietary Modification component evaluated the effect of a low-fat and high fruit, vegetable and grain diet on the prevention of breast and colorectal cancers and coronary heart disease. Study participants were randomized to either their usual eating pattern or a low-fat dietary pattern. Calcium/Vitamin D Trial (CaD): This double-blind component began 1 to 2 years after a woman joined one or both of the other clinical trial components. It evaluated the effect of calcium and vitamin D supplementation on the prevention of osteoporotic fractures and colorectal cancer. Women in this component were randomized to calcium (1000 mg/d) and vitamin D (400 IU/d) supplements or a matching placebo. The Observational Study (OS) examines the relationship between lifestyle, environmental, medical and molecular risk factors and specific measures of health or disease outcomes. This component involves tracking the medical history and health habits of 93,676 women not participating in the CT. Recruitment for the observational study was completed in 1998 and participants were followed annually for 8 to 12 years. Extension Studies: The original protocol allowed for follow-up until March 2005, after which participants were invited to enroll in the first WHI Extension Study for follow-up through 2010. Participants were invited again to participate in the second WHI Extension Study with continued follow up from 2010 to at least 2015. As of March 31, 2011 there were 93,122 women enrolled in the second extension. In Extension Study 2, the overall WHI study population was divided into two new subsamples, the Medical Records Cohort (MRC) and the Self-Report Cohort (SRC). The MRC consists of all former hormone trial participants and all African American and Hispanic participants from all study components. The SRC consists of the remaining participants. The extent of outcome information collected differs between the two cohorts, with more extensive outcomes information collection on the MRC. As part of Extension Study 2, selected older WHI participants were invited to participate in an In Person Visit (a.k.a., Long Life Study) at their homes during which additional blood samples were collected and various measurements were taken (such as blood pressure, height, weight, waist circumference, grip strength, etc.). In October 2015, Extension Study 2 was renewed with continued follow-up planned through October 2020, pending annual contract review and renewal. Additional Information: The WHI website, https://www.whi.org/about/SitePages/About%20WHI.aspx has much more information about the study. For WHI data collection forms used over the years, please see https://www.whi.org/researchers/studydoc/SitePages/Forms.aspx. For additional dataset documentation, see https://www.whi.org/researchers/data/Pages/Available%20Data.aspx. For data preparation and use, please refer to 'WHI dbGaP Cohort Data Release Data Preparation Guide May 2018' for additional details about the WHI data. The WHI Cohort is utilized in the following dbGaP substudies. To view genotypes, analysis, expression data, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000200 WHI Cohort. phs000386 WHI SHARe phs000281 GO-ESP WHISP phs000315 WHI GARNET phs000503 WHISE phs000227 PAGE WHI phs000675 WHIMS+ phs000746 WHI Harmonized and Imputed GWAS phs001334 WHI Metabolomics of CHD phs001335 WHI BA23 phs001614 WHI LLS Phase III GWAS

  Study phs000200

• International Standards for Cytogenomic Arrays

  The International Standards for Cytogenomic Arrays (ISCA) Consortium is a rapidly growing group of clinical cytogenetics and molecular genetics laboratories committed to improving quality of patient care related to clinical genetic testing using new molecular cytogenetic technologies including array comparative genomic hybridization (aCGH) and quantitative SNP analysis by microarrays or bead chip technology. The ISCA Consortium is now working with the sequencing community to extend the goals of standardization, collaboration, and data sharing. To reflect this combined effort, we are becoming ICCG, or the International Collaboration for Clinical Genomics. Efforts of the Consortium include: Clinical Utility: The ISCA Consortium has made recommendations regarding the appropriate clinical indications for cytogenetic array testing (Miller et al. AJHG 2010, PMID: 20466091). Currently, discussions are focused on pediatric applications for children with unexplained developmental delay, intellectual disability, autism and other developmental disabilities. A separate committee has been developed to address appropriate cancer genetic applications (http://www.urmc.rochester.edu/ccmc/). Evidence-based standards for cytogenomic array design: The Consortium will develop recommendations for standards for the design, resolution and content of cytogenomic arrays using an evidence-based process and an international panel of experts in clinical genetics, clinical laboratory genetics (cytogenetics and molecular genetics), genomics and bioinformatics. This design is intended to be platform and vendor-neutral (common denominator is genome sequence coordinates), and is a dynamic process with input from the broader genetics community and evidence-based review by the expert panel (a Steering Committee with international representation). Public Database for clinical and research community: It is essential that a publicly available database be created and maintained for cytogenetic array data generated in clinical testing laboratories. This will be integrated into the current dbGaP database at NCBI/NIH and released through dbVar, and curated by a committee of clinical genetics laboratory experts. The very high quality of copy number data (i.e., deletions and duplications) coming from clinical laboratories combined with expert curation will produce an invaluable resource to the clinical and research communities. Standards for interpretation of cytogenetic array results: Using the ISCA Database, along with other genomic and genetics databases, the Consortium will develop recommendations for the interpretation and reporting of pathogenic vs. benign copy number changes as well as imbalances of uncertain clinical significance. Membership in the ISCA Consortium is open to all individuals and laboratories involved in cytogenetic array testing who are committed to free data sharing and to participation in a process to develop evidence-based standards and guidelines to improve patient care. ISCA is available through dbVar: http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd37/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd75/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd45/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd101/

  Study phs000205

• Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

  Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III “borrowed” GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

  Study phs000206

• Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer Genome-Wide Association Study (GWAS) - Primary Scan (Stage 1) - PLCO Screening Trial

  The Cancer Genetic Markers of Susceptibility (CGEMS) prostate cancer genome-wide association study (GWAS) included genotyping approximately 550,000 SNPs (Phase 1A with HumanHap300 and Phase 1B HumanHap240, both from Illumina, San Diego, CA) in 1,172 prostate cancer patients and 1,157 controls of European ancestry from the Prostate, Lung, Colon and Ovarian (PLCO, http://www.cancer.gov/prevention/plco/) Cancer Screening Trial. The original analysis published in Nature Genetics [PMID: 17401363] included 2,282 subjects. After improvement and revisions of the original analysis, 2,252 subjects were submitted to dbGaP.

  Study phs000207

• A Genome-Wide Association Study in Patients Experiencing Musculoskeletal Adverse Events on NCIC CTG Trial MA.27 Evaluating Aromatase Inhibitors as Adjuvant Therapy in Early Breast Cancer. A Collaboration Between the NIH Pharmacogenetics Research Network and the RIKEN Yokohama Institute Center for Genomic Medicine.

  Source of Patients The source of the patients for this genome-wide case control study was MA.27, which was conducted as a multi-cooperative group effort under the auspices of the NCI Breast Cancer Intergroup of North America. The NCIC Clinical Trials Group (CTG) serves as the coordinating group, with participation by the NCI-sponsored North Central Cancer Treatment Group, Eastern Cooperative Oncology Group (ECOG), Southwest Oncology Group, and Cancer and Leukemia Group B (CALGB). MA.27 involved postmenopausal women with histologically confirmed and completely resected invasive breast cancer with surgical margins clear of invasive carcinoma and DCIS in the following TMN categories (AJCC Version 6): pT1, pT2, pT3; pNx, pN0, pN1, pN2, pN3 (only when the sole basis is presence of 10 or more involved axillary nodes); MO. The primary tumor must have been ER and/or progesterone receptor positive. Patients were stratified by lymph node status at diagnosis, prior adjuvant chemotherapy, and trastuzumab use and were randomized to 5 years of adjuvant therapy with anastrozole or exemestane. The trial was activated on May 26, 2003, and reached its accrual objectives on July 31, 2008, after the randomization of 6827 North American patients, with the majority (79%) providing DNA and consent for genetic testing. Non-North American patients were also entered by the International Breast Cancer Study Group but they did not contribute DNA. From 2003 to December 21, 2004, patients also underwent a second randomization to celecoxib 400 mg twice daily or placebo but, after the entry of 1,622 patients, this treatment was discontinued because of reports of increased cardiovascular risk associated with celecoxib. Case Definition for Musculoskeletal Adverse Events Cases were defined to have had at least one of the following six MSAE): 1. pain - joint, 2. pain - muscle, 3. pain - bone, 4. arthritis, 5. joint - function, 6. musculoskeletal - other. Cases were required to have at least grade 3 toxicity, according to the NCI Common Terminology Criteria for Adverse Events v3.0, or go off treatment for any grade of the six MSAEs; and were required to have had an MSAE within the first two years (i.e., a MSAE occurring after 2 years was not be considered a case). Subjects were excluded from the case group who met the case definition while on celecoxib or in the three months after stopping celecoxib but subjects were allowed to be cases if they developed their first MSAE more than three months after stopping celecoxib. Study Design The design was a nested matched case-control study with two controls selected for each case. Random matching from the potential control group (i.e., risk-set sampling such that controls had at least the same length of followup as the timing of the case event) was done by matching the case on the following factors: 1. treatment arm (exemestane vs. anastrozole, blinded), 2. prior adjuvant chemotherapy (yes vs. no), 3. age at start of treatment (+/- 5 years), 4. Celecoxib allocation (yes vs. no). Attempts were made to exactly match each case with two controls according to the above criteria. If, however, controls could not be exactly matched, "close" matching was used to define a distance between each case and all potential controls in an optimal matching algorithm. Race is a potential confounding factor, and 94% of the MA.27 population is Caucasian, 3% are Black, 1% Asian, 1% American Indian, and 1% are of unknown race. Because there are so few non-Caucasian participants, was restricted to only Caucasians. Following identification of cases and controls, the list was sent to the CALGB Pathology Coordinating Office (PCO) at Ohio State University Medical Center for plating of the samples. Other patient factors that might influence the association of SNP genotypes with a MSAE, either as confounding factors or as effect modifiers, were included as investigational covariates. These include 1. body mass index (BMI), 2. bisphosphonate use, 3. fractures in past 10 years, 4. baseline ECOG performance status, 5. prior hormone replacement therapy, 6. prior adjuvant radiotherapy, and 7. prior taxane treatment. An Honest Broker Process was utilized to ensure patient confidentiality. Genotype Assays and Quality Control A GWAS was performed with the Illumina Human610-Quad at the RIKEN Center for Genomic Medicine. The DNA for all of the samples were adjusted to a concentration of 50 to 100 ng/µL in a final volume of 50 µL. The 96-well plates were designed to have a unique distribution pattern with x duplicates per plate to evaluate genotype concordance. Samples with <98% call rate were removed from analyses. SNPs with ambiguous call will be excluded using the following criteria: 1. low signal intensity of each allele, 2. low cluster separation score, 3. SNP call rate <0.98, 4. SNPs with Hardy Weinberg Equilibrium p-value <0.001 were excluded. X-chromosome SNPs were used to validate female sex and quality control measures were examined across the different submitting centers. For all pairs of subjects, the percentage of matching genotypes were computed to screen for unexpected duplicate samples. All SNPs found to be associated with MSAE were subjected to visual inspection of the cluster plots to verify that the genotype clustering was adequate.

  Study phs000210

• National Cancer Institute (NCI) TARGET: Therapeutically Applicable Research to Generate Effective Treatments

  The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative seeks to accelerate research in novel marker and drug development, along with understanding the molecular basis of pediatric malignancy, through identification of genomic changes associated with the following childhood cancers: Acute Lymphoblastic Leukemia (ALL) - A fast-growing type of blood cancer in which too many immature white blood cells are found in the blood and bone marrow. Acute Myeloid Leukemia (AML), including AML Induction Failure cases (AML-IF) - Another type of blood cancer marked by too many myeloblasts, an alternate type of immature white blood cell, are found in the blood and bone marrow. Upon diagnosis, AML patients without high-risk genetic markers undergo standard chemotherapy regimen, called the primary induction phase, to eliminate most cancer cells and induce a remission state. Clear Cell Sarcoma of the Kidney (CCSK) - A rare type of kidney cancer, in which the inside of the cells looks clear when viewed under a microscope. Clear cell sarcoma can spread from the kidney to other organs, most commonly the bone, but also including the lungs, brain, and soft tissues of the body. Neuroblastoma (NBL) - Cancer of cells of the sympathetic nervous system. Cancer Model Systems (MDLS), including PPTP - MDLS includes acute leukemia cell lines and xenografts with matched primary and/or relapsed tumor information, NBL cell lines and xenografts with matched controls, kidney tumor cell lines, normal brain tissues, and embryonic stem cell lines that were sequenced for some TARGET disease projects. Osteosarcoma (OS) - A cancer of the bone that primarily affects children and adolescents. Rhabdoid Tumor (RT) - A rare, fast-growing cancer that usually forms in the kidney or central nervous system (the brain and spinal cord) but can also form in soft tissues in other areas of the body. Rhabdoid tumors tend to spread quickly, are hard to treat, and have a poor prognosis. Wilms' Tumor (WT) - A cancer of cells in the kidney that can spread to the liver, lung and lymph nodes. More information can be found about each TARGET subproject by following the links on the top of this page. Together these cancers account for the majority of the more than 10,000 childhood cancer cases diagnosed in the United States each year. TARGET has employed a set of advanced and complementary genome analysis technologies, including large scale 2nd and 3rd generation genome sequencing, to strategically characterize alterations in both gene expression and in genomic structure (such as deletions and amplification) that are involved in childhood cancers. The goal of this coordinated effort has been to create a comprehensive genomic and transcriptomic profile of each cancer. Integrated analysis of the TARGET data has identified those genes that are either altered in their expression level or mapped to the chromosome regions of deletion/amplification/translocation, as these genes represent strong candidates for therapeutic targeting. To learn more about the TARGET project, which is now completed, visit the website at https://www.cancer.gov/ccg/research/genome-sequencing/target. TARGET data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the TARGET Publication Pages at the GDC (Please see the "Supplemental Links" section of any TARGET publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original TARGET research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data - gene expression, copy number variation, epigenetics, targeted sequencing), as well as higher level data generated by the GDC. Please see the TARGET Use and Publication Guidelines for updated details on the sharing of any TARGET substudy data, including how to cite TARGET.

  Study phs000218

• Population Architecture using Genomics and Epidemiology (PAGE): Multiethnic Cohort (MEC)

  The Multiethnic Cohort (MEC) has established a large biorepository of blood and urine (N=67,000) and cryopreserved lymphocytes (N=15,000) linked to extensive, prospectively collected risk factors (e.g., diet, smoking, physical activity), biomarkers and clinical data for five racial/ethnic groups. This cohort study of over 215,000 men and women in Hawaii and California is unique in that it is population-based and includes large representations of older adults (45-75 yrs at baseline) for five US racial/ethnic groups (Japanese Americans, African Americans, European Americans, Latinos and Native Hawaiians) at varying risks of chronic diseases. Within the PAGE investigation, the MEC proposes to study: 1) diseases for which we have DNA available for large numbers of cases and controls (breast, prostate, and colorectal cancer, diabetes, and obesity); 2) important cancers that are less common (e.g., lung, pancreas, endometrial cancers, NHL) but for which we propose to pool our data with other funded groups; 3) common traits that are risk factors for these diseases (e.g., body mass index/weight, waist-to-hip ratio, height) and 4) relevant disease-associated biomarkers (e.g., fasting insulin and lipids, steroid hormones). The specific aims are: 1) To determine the population-based epidemiologic profile (allele frequency, main effect, heterogeneity by disease characteristics) of putative causal variants in the five racial/ethnic groups in the MEC; 2) for variants displaying effect heterogeneity across ethnic/racial groups, we will utilize differences in LD to identify a more complete spectrum of associated variants at these loci; 3) investigate gene x gene and gene x environment interactions to identify modifiers; 4) examine the associations of putative causal variants with already measured intermediate phenotypes (e.g., plasma insulin, lipids, steroid hormones); and 5) for variants that do not fall within known genes, start to investigate their relationships with gene expression and epigenetic patterns in small genomic studies. This study is part of the Population Architecture using Genomics and Epidemiology (PAGE) study phs000356.

  Study phs000220

• Development and Use of Network Infrastructure for High-Throughput GWA Studies

  The "Development and Use of Network Infrastructure for High-Throughput GWA Studies" project is collaboration between Group Health Cooperative (GH), the University of Washington (UW), and the Fred Hutchinson Cancer Research Center (FHCRC). It is one of 5 sites participating in The Electronic Medical Records and Genomics (eMERGE) Network funded by the National Human Genome Research Institute (NHGRI) with additional funding from the National Institute of General Medical Sciences (NIGMS). The overall eMERGE project is designed to assess whether linking biorepositories of patients in healthcare delivery systems with electronic medical records (EMRs) is an efficient strategy for high-throughput genome wide association (GWA) studies and whether information can be pooled across sites. The primary phenotype in the Group Health/UW Aging and Dementia eMERGE study project is dementia. Phenotyped participants originated from four population-based sources. Seattle-area members of GH (a large integrated health care system in Washington State) consented and enrolled in 1) the University of Washington Alzheimer's Disease Patient Registry (ADPR) and 2) the Adult Changes in Thought (ACT) study, 3) Marshfield Clinic Personalized Medicine Research Project (PMRP), a population-based DNA, plasma and serum biobank of 20,000 adults, 4) Vanderbilt's BioVU, a de-identified DNA biobank. The ADPR (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is a population-based registry of incident dementia cases designed to identify all new dementia cases within GH from 1987 to 1996. Potential dementia cases were identified through referrals from primary care physicians, mental health services, and neurologists, as well as review of CT scans, neurology, emergency room, geriatrician, and mental health clinic logs, and hospital discharge diagnoses. Medical history, physical, laboratory testing, and neuropsychological testing were performed on all consenting potential cases. These data were reviewed by a consensus conference including a neuropsychologist, study physicians, and an epidemiologist. After discussion of the case, dementia status is assigned using DSM-III-R criteria. Alzheimer's disease status was assigned using the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria. The study base of the ADPR population was stable with an attrition rate of less than 1%/year. The ACT study (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is an ongoing community-based cohort study designed to determine the incidence of Alzheimer's Disease (AD), other types of dementia, and cognitive impairment and to determine risk factors for these conditions. The original cohort of 2,581 was enrolled in 1994-1996. An expansion cohort of 811 was enrolled in 2000-2002. Continuous enrollment to keep 2,000 persons enrolled and at-risk for dementia outcomes was begun in 2005. To date the study has accrued more than 4,000 participants. Dementia-free participants are enrolled in this cohort and evaluated every two years by study personnel with many evaluations including an assessment of cognitive functioning using the Cognitive Abilities Screening Instrument (CASI), which is an extended version of the Mini-Mental State Examination that has been widely used in epidemiological studies of the elderly. Persons with CASI scores lower than 86 or in whom interviewers suspect unusual cognitive decline are followed up with dementia diagnostic evaluations which include physical and neurological examinations and neuropsychological tests. Medical records are reviewed, and laboratory tests and neuroimaging are obtained if not available. The dementia diagnoses and dementia subtypes are determined during a consensus conference attended by the examining clinicians and other study physicians, a neuropsychologist, and research nurse. After discussion of the case, dementia status is assigned using DSM-IV criteria. Alzheimer's disease status was assigned using NINCDS-ADRDA criteria. Individuals diagnosed with dementia are seen again one year following diagnosis using the same procedures. Individuals not diagnosed with dementia are returned to the pool of at-risk individuals and are seen at their next routinely scheduled biennial visit. The ACT sub-sample is stable; for the original cohort, median enrollment in GH was 19 years prior to joining the ACT study, and 85% of the cohort has ≥ 10 years of GH enrollment. DNA for the ADPR participants were obtained through a companion study, Genetic Differences in Cases and Controls (PI: Walter Kukull; NIH/NIA R01 AG007584). DNA obtained through both studies were extracted from blood using Gentra Systems Puregene methods. DNA concentration is determined by UV optical density. All samples are checked for quality by OD 260/280 ratio. For long-term storage, samples are aliquoted and stored at -70°C. The Marshfield study, Personalized Medicine Research Project (PMRP), a population-based DNA, plasma and serum biobank of 20,000 adults. PMRP was started in 2002 as a 3 Phase project. Completed in April 2004, the objectives of Phase I were to build and develop a large population-based biobank with DNA, plasma and serum samples to facilitate genomics research. Phase 1 was also to educate, inform and consult with the Marshfield Epidemiologic Study Area population and communities concerning potential studies, create the DNA foundation of the personalized medicine database and build the bioinformatics tools to store securely and analyze genotypic and phenotypic data. Phase I tasks included operation of the ethics and security advisory board, scientific advisory board and community advisory group. More than 18,000 residents aged 18 and above from 19 different zip codes surrounding Marshfield, WI were invited to participate in Phase I. After providing written informed consent, participants completed brief questionnaires that included questions about demographics, some environmental exposures, family history of disease, and adverse drug reactions, as well as family members living in the area. Participants provided 50ml of blood from which DNA was extracted and plasma and serum samples were stored. The informed consent process allowed access to electronic medical records and included language about non-disclosure of personal research results. A tick-off box was included so that participants could either allow or decline subsequent recontact for future research studied. The objectives of Phase II are to create the phenotypic database, establish the scientific and administrative infrastructure to support genetic mapping of the DNA and the initial discovery projects and genotype a sufficient portion of the genetic material to support these discovery projects. The objectives of Phase III are to expand the discovery projects, complete the genotyping of the genetic database and expand physician/health care provider education and community consultation. The Vanderbilt BioVU model uses discarded blood leftover from clinical care and de-identified clinical information, both of which are tracked with a Research Unique Identifier that is permanently disconnected from the medical record number used to generate it. The Vanderbilt IRB determined that the project does not qualify as "human subject" research under §46.102(f)(1)(2). The program has been reviewed by OHRP twice (the latest immediately prior to launch), and the determination of the Vanderbilt IRB that the project is consistent with the 8/10/2004 guidance has been validated. The model includes a simple opt out mechanism developed for patients who do not wish to have their samples included. Planning (including community involvement, ethics and IRB discussions, focus groups, development of operating procedures and pilot studies to assess them) began in May 2004, and the resource commenced sample collection in February 2007 and as of May 2009 contains approximately 56,000 samples. The current resource will be available to all Vanderbilt investigators who sign a Data Use Agreement. The resource represents Vanderbilt's broad patient population and thus provides a potential resource for research in a range of common and rare diseases as well as drug response or medication safety. BioVU patient protection model: There has been extensive involvement by the ethics community and the IRB to put in place procedures to provide privacy protection. The overall protection plan is multifaceted and includes a combination of technology and policy: (1) creation of the SD described above; (2) return of only the specific clinical data items requested by investigators (rather than the complete de-identified chart); (3) submission of any specific proposed project to the IRB and to a separate protocol review committee, both of which must approve; (4) implementing a Data Use Agreement that permits only approved queries and data analyses, specifically prohibits attempts at re-identification (at the risk of institutional sanctions), and mandates redeposit of all genetic information generated in a research study back into a genomic database; and (5) tracking and auditing all use.

  Study phs000234

• National Cancer Institute Cancer Genome Characterization Initiative (CGCI)

  The Office of Cancer Genomics at the National Cancer Institute sponsored a series of studies as part of the Cancer Genome Characterization Initiative (CGCI) to assess novel emerging sequencing technologies in cancer. The CGCI program included comprehensive characterization of the genetic aberrations found in different pediatric and/or adult tumors. CGCI characterized a number of B-cell non-Hodgkin lymphomas (including diffuse large B-cell lymphoma (DLBCL) from patients with and without HIV+ infection, follicular lymphoma (FL), as well as adult and pediatric Burkitt lymphomas), and additional HIV-associated tumors (including DLBCL, lung, and cervical cancers). All data from these projects is available at the NCI Genomic Data Commons. Individual project descriptions are available by disease on the substudy pages (links are included at the top of this page). Brief summaries are as follows: Non-Hodgkin Lymphoma (NHL) - CGCI investigators probed genomic alterations more deeply than had previously been possible by using state-of-the-art RNA sequencing (mRNA-seq) and whole genome shotgun sequencing (WGS) coupled with leading edge bioinformatics, data management and analysis approaches. The project sequenced tumor DNA and/or RNA from 117 NHL tumor samples and 10 cell lines. This included the genomes or exomes of 1 Follicular Lymphoma (FL) and 13 diffuse large B-cell lymphoma (DLBCL) cases, all with matched constitutional DNA sequenced to comparable depths, RNA-sequencing (mRNA-seq) of 92 DLBCL, 12 FL and 8 B-cell NHL cases with other histologies and 10 DLBCL-derived cell lines. The DLBCL cases and cell-lines are from the two major subtypes of DLBCL: germinal center B-cell (GCB) and activated B-cell (ABC). HIV+ Tumor Molecular Characterization Project (HTMCP) - This project was a joint effort of the Office of Cancer Genomics (OCG) and the Office of HIV and AIDS Malignancy (OHAM). Its goals were to characterize HIV-associated cancers (obtained from HIV-infected patients) and compare them to the same types of cancers from patients without HIV infection. Investigators performed 30X genome sequencing of 100 cases of paired tumor and germline DNA, along with transcriptome sequencing in each of 3 types of HIV+ tumors (DLBCL, lung and cervical cancers). These platforms allow discovery of mutations both in coding and non-coding genomic regions, gene expression and genomic alterations (including translocations, insertions and deletions). Comparing tumors of cancer patients both with and without HIV-infection provides insight into the potential function of this virus in certain cancers. Burkitt Lymphoma Genome Sequencing Project (BLGSP) - This project was a collaborative effort between the National Cancer Institute and the Foundation for Burkitt Lymphoma Research to develop a databank of the many alterations found in Burkitt lymphoma (BL), an uncommon type of Non-Hodgkin lymphoma that occurs most often in children and young adults. The goal of the BLGSP was to explore potential genetic changes in patients with BL that could lead to better prevention, detection and treatment of the cancer. The project characterized the alterations of the tumors' genomes (with matched normal as control) and transcriptomes by sequencing the DNA and RNA of each case. Using the data generated, the ultimate goals of the project was to discover the molecular changes that are present in BL patients and then determine how those changes correlate with treatment regimen and outcome. CGCI data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the CGCI Publication Pages at the GDC (Please see the "Supplemental Links" section of any CGCI publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original CGCI research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data), as well as higher level data generated by the GDC. Please see the CGCI Use and Publication Guidelines for updated details on the sharing of any CGCI substudy data, including how to cite CGCI.To learn more about the CGCI studies, please visit the CGCI Program website.

  Study phs000235

• Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis

  Monozygotic (MZ) twins have been widely employed for dissection of the relative contributions of genetics and environment in disease. In multiple sclerosis (MS), an autoimmune demyelinating disease that commonly causes neurodegeneration and disability in young adults, disease discordance in MZ twins has been interpreted to indicate environmental importance in pathogenesis. However, genetic and epigenetic differences between MZ twins have been described, challenging the accepted experimental paradigm in disambiguating effects of nature and nurture. Here, we report the genome sequences of one MS-discordant MZ twin pair and messenger RNA (mRNA) transcriptome and epigenome sequences of CD4+ lymphocytes from three MS-discordant, MZ twin pairs. No reproducible differences were detected between co-twins among ~3.6 million single nucleotide polymorphisms (SNPs) or ~0.2 million insertion-deletion polymorphisms (indels). Nor were any reproducible differences observed between siblings of the three twin pairs in HLA haplotypes, confirmed MS-susceptibility SNPs, copy number variations, mRNA and genomic SNP and indel genotypes, or expression of ~19,000 genes in CD4+ T cells. Only two to 176 differences in methylation of ~2 million CpG sites were detected between siblings of the three twin pairs, in contrast to ~800 differences in methylation between T cells of unrelated individuals and several thousand differences between tissues or normal and cancerous tissues. In the first systematic effort to estimate sequence variation among MZ co-twins, we did not find evidence for genetic, epigenetic or transcriptome differences that explained disease discordance. These are the first female, twin and autoimmune disease genome sequences reported.

  Study phs000239

• Whole Genome Sequencing of a Triple Negative Breast Cancer Patient: Matched Primary Tumor, Normal, Metastasis and Xenograft samples

  Breast Cancer Subject Participant ID 700064 (Source Sample names: 6888 and 206). We used massively parallel DNA sequencing technologies to screen entire genomes, in an unbiased manner, for genetic changes associated with tumor growth and metastasis. We describe the complete genome sequence analysis of four DNA samples from a 44-year old African-American patient with basal-like breast cancer: peripheral blood, the primary tumor, a brain metastasis that developed within a year of initial therapy, and a first-passage xenograft derived from the primary tumor. A total of 50 validated mutations were discovered within coding, RNA, or splice site sequences. Of these, 20 mutations were abundantly present in all three tumors, including mutations in CSMD1 and JAK2. These two genes subsequently were found to be mutated in other breast tumors. The metastasis contained two de novo mutations not present in the primary tumor, and was significantly enriched for 20 shared mutations, suggesting that they may be involved in the metastatic process. The xenograft contained no unique coding, RNA, or splice site mutations and retained all primary tumor mutations, albeit at different frequencies. However, a significant increase in copy number alterations was observed in the xenograft as compared to the primary tumor. We validated 28 large deletions and six inversions, as well as seven translocations in at least one of the three tumor samples. Among them, a 26 kb deletion in MECR was solely identified, assembled, and validated in the brain metastasis and two overlapping large deletions on chromosome 5 encompassing CTNNA1, a potential tumor suppressor gene, were identified in all three tumors. The differential mutation frequencies and structural variation patterns between primary and metastatic tumors suggest that metastatic tumors may arise from minor subpopulations of cells within the primary. Namely, the metastatic and xenografting processes apparently select for cells harboring a distinct subset of the primary tumor mutation repertoire.

  Study phs000245

• Genetic Determinants of Viral Clearance in HCV-Infected Populations

  The World Health Organization estimates there are 170 million persons with HCV infection worldwide, and an estimated 4 million persons are infected in the United States. Persistent HCV infection can cause cirrhosis and hepatocellular cancer. Long term disease consequences do not occur in persons who spontaneously recover. It is not known why 10-40% recover from hepatitis C (and are not at increased risk of cirrhosis and cancer), but that knowledge would contribute to efforts to treat and/or prevent HCV infection. The overall purpose of this study is to investigate the host genetic basis for recovery from hepatitis C virus (HCV) infection. There is strong evidence that host genetic differences determine HCV recovery. After accidental exposure to the same HCV inoculum, some persons recover while others develop persistent infection. Recovery from HCV infection is an outstanding phenotype for genetic studies since both viral exposure and viral recovery can be ascertained with high sensitivity and specificity and since there are no known viral or environmental determinants. The study group is comprised of an ethnically and geographically diverse international population. All cohorts comprising this study group of 3350 individuals are well characterized. This case-control study is comprised of approximately 40% clearance individuals who are matched by age and HIV status within their respective cohorts at a ratio of 1:1 or 1:2 with HCV chronically infected individuals.

  Study phs000248

• CALGB 80303:Genome-Wide Association Study of Advanced Pancreatic Cancer Patients - A Randomized Phase III trial of Gemcitabine Plus Bevacizumab versus Gemcitabine Plus Placebo in Patients with Advanced Pancreatic Cancer

  CALGB 80303 was a randomized, phase III study in advanced pancreatic cancer patients treated with Gemcitabine plus either Bevacizumab or placebo. No difference in overall survival (OS) was observed between the two arms. We prospectively collected germline DNA and conducted a genome-wide association study (GWAS).

  Study phs000250

• National Human Genome Research Institute (NHGRI) GENEVA Genome-Wide Association Study of Venous Thrombosis (GWAS of VTE)

  Overview: Our overall long-term goal is to determine risk factors for the complex (multifactorial) disease, venous thromboembolism (VTE), that will allow physicians to stratify individual patient risk and target VTE prophylaxis to those who would benefit most. In this genome-wide association case-control study (1300 cases and 1300 controls) we hope to identify susceptibility variants for VTE. Mutations within genes encoding for important components of the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways are risk factors for VTE. We hypothesize that other genes within these four pathways or within other pathways also are VTE disease-susceptibility genes. Therefore, we performed a genome wide association (GWA) screen and analysis using the Illumina 660W platform to identify SNPs within 1,300 clinic-based, non-cancer VTE cases primarily from Minnesota and the upper Midwest USA, and 1300 clinic-based, unrelated controls frequency-matched on patient age, gender, myocardial infarction/stroke status and state of residence. This is a subset of a slightly larger candidate gene study using 1500 case-control pairs to identify haplotype-tagging SNPs (ht-SNPs) in a large set of candidate genes (n~750) within the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways. Study Populations. Cases. VTE cases were consecutive Mayo Clinic outpatients with objectively-diagnosed deep vein thrombosis (DVT) and/or pulmonary embolism (PE) residing in the upper Midwest and referred by Mayo Clinic physician to the Mayo Clinic Special Coagulation Laboratory for clinical diagnostic testing to evaluate for an acquired or inherited thrombophilia, or to the Mayo Clinic Thrombophilia Center. Any person contacted to be a control but discovered to have had a VTE was evaluated for inclusion as a case. Cases were primarily residents from Minnesota, Wisconsin, Iowa, Michigan, Illinois, North or South Dakota, Nebraska, Kansas, Missouri and Indiana. A DVT or PE was categorized as objectively diagnosed when (a) confirmed by venography or pulmonary angiography, or pathology examination of thrombus removed at surgery, or (b) if at least one non-invasive test (compression duplex ultrasonography, lung scan, computed tomography scan, magnetic resonance imaging) was positive. A VTE was defined as: Proximal leg deep vein thrombosis (DVT), which includes the common iliac, internal iliac, external iliac, common femoral, superficial [now termed "femoral"] femoral, deep femoral [sometimes referred to as "profunda" femoral] and/or popliteal veins. (Note: greater and lesser saphenous veins, or other superficial or perforator veins, were not included as proximal or distal leg DVT). Distal leg DVT (or "isolated calf DVT"), which includes the anterior tibial, posterior tibial and/or peroneal veins. (Note: gastrocnemius, soleal and/or sural [e.g., "deep muscular veins" of the calf] vein thrombosis was not included as distal leg DVT). Arm DVT, which includes the axillary, subclavian and/or innominate (brachiocephalic) veins. (Note: jugular [internal or external], cephalic and brachial vein thrombosis was not included in "arm DVT"). Hepatic, portal, splenic, superior or inferior mesenteric, and/or renal vein thrombosis. (Note: ovarian, testicular, peri-prostatic and/or pelvic vein thrombosis was not included). Cerebral vein thrombosis (includes cerebral or dural sinus or vein, saggital sinus or vein, and/or transverse sinus or vein thrombosis). Inferior vena cava (IVC) thrombosis Superior vena cava (SVC) thrombosis Pulmonary embolism Patients with VTE related to active cancer, antiphospholipid syndrome, inflammatory bowel disease, vasculitis, a rheumatoid or other autoimmune disorder, a vascular anomaly (e.g., Klippel-Trénaunay syndrome, etc.), heparin-induced thrombocytopenia, or a mechanical cause for DVT (e.g., arm DVT or SVC thrombosis related to a central venous catheter or transvenous pacemaker, portal and/or splenic vein thrombosis related to liver cirrhosis, IVC thrombosis related to retroperitoneal fibrosis, etc.), with hemodialysis arteriovenous fistula thrombosis, or with prior liver or bone marrow transplantation were excluded. Controls. A Mayo Clinic outpatient control group was prospectively recruited for this study. Controls were frequency-matched on the age group (18-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+ years), sex, myocardial infarction/stroke status, and state of residence distribution of the cases. We selected clinic-based controls using a controls' database of persons undergoing general medical examinations in the Mayo Clinic Departments of General Internal Medicine or Primary Care Internal Medicine. Additionally persons undergoing evaluation at the Mayo Clinic Sports Medicine Center, and the Department of Family Medicine were screened for inclusion as controls. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to venous thrombosis through large-scale genome-wide association studies of 1,300 clinic-based, VTE cases and 1300 clinic-based, unrelated controls. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000289

• Whole genome and transcriptome sequencing of lung cancer patients and cell lines at Genentech

  Version 1 Whole genome sequencing was applied to tumor and adjacent normal lung tissue in an individual non-small-cell lung cancer patient. We present an analysis of somatic changes identified throughout the tumor genome, including single-nucleotide variants, copy number variants, and large-scale chromosomal rearrangements. Over 50,000 high-confidence single-nucleotide variants were identified, revealing evidence of substantial smoking-related DNA damage as well as distinct mutational pressures within the tumor resulting in uneven distribution of somatic mutations across the genome. Version 2 Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and 3 lung tumor/normal pairs. Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Smoker and never-smoker cancer samples exhibit distinguishable patterns of mutations. A number of epigenetic regulators are frequently altered by mutations or copy number changes. A systematic survey of splice-site mutations identified over 100 splice site mutations associated with cancer specific aberrant splicing, including mutations in several known cancer-related genes. Differential usages of splice isoforms were also studied. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers.

  Study phs000299

• A Genome-Wide Association Study in Participants Experiencing Breast Cancer Events in High-Risk Postmenopausal Women Receiving Selective Estrogen Receptor Modulators on NSABP Trials P-1 and P-2. A Collaboration Between the NIH Pharmacogenetics Research Network and the RIKEN Yokohama Institute Center for Genomic Medicine

  The primary aim of this study is to identify genes related to the occurrence of breast events, defined as occurrence of invasive breast cancer or ductal carcinoma in situ (DCIS), in women at high risk of developing breast cancer who have received a Selective Estrogen Receptor Modulator (SERM - tamoxifen or raloxifene) on the National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 or P-2 trials. Cases and controls were selected from the tamoxifen arm in the P-1 and from the tamixifen and raloxifene arms in the P-2 trial. For the P-1 trial, cases and controls were required to be 50 years of age or older at time of entry. Cases were females who experienced an invasive breast cancer or DCIS on P-1 or P-2. Controls were females who did not experience an invasive breast cancer or DCIS. A nested matched case-control design was used, with matching on the following factors: 1) trial and treatment arm (P-1 tamoxifen, P-2 tamoxifen, P-2 raloxifene); 2) age at trial entry (when controls could not be exactly matched on age, we incremented the age of matching by +/- 1 year, in sequence until a match was obtained without exceeding +/- 5 years); 5-year predicted breast cancer risk based on the Gail model ( <2.00%, 2.01-3.00, 3.01-5.00, >5.01), 3) history of lobular carcinoma in situ (yes vs. no); 4) history of atypical hyperplasia in breast (yes vs. no); 5) time on study (controls must be on study at least as long as the time to diagnosis of the breast event for the case). Because 94.2% of the participants on P-1 and P-2 treated with tamoxifen or raloxifene were Caucasian, this study was restricted to only Caucasians. Two cases and two controls were randomly chosen as duplicates for quality control of genotype concordance. The DNA samples were plated into 96-well plates, with cases and controls randomly distributed across the plates. A Caucasian parent-child CEPH trio from the HapMap project was included to check for Mendelian transmission of alleles. Genotypes were determined by the RIKEN Center for Genomic Medicine with the Illumina Human610-Quad.

  Study phs000305

• A Multiethnic Genome-wide Scan of Prostate Cancer

  Multiple GWA studies of prostate cancer conducted in European White populations are ongoing. These studies will continue to have a dramatic impact on our understanding of the contribution of common genetic variation on inter-individual susceptibility to this common cancer. Important questions that will remain unanswered, however, are whether all common risk alleles for prostate cancer will be revealed in studies limited to populations of European ancestry. A comprehensive examination of common genetic variation in men of Japanese, Latino, and African ancestry will be required to understand population differences in disease risk and to reveal the full spectrum of causal alleles that exist in these populations. Further, genetic and environmental diversity is likely to contribute to ethnic heterogeneity of genetic effects. Elucidating gene x gene and gene x environment interactions is also likely to provide knowledge that may be critical for understanding the contribution of genetic susceptibility to racial/ethnic disparities in prostate cancer incidence and for translating the findings from GWA studies into interventions. In this study we plan to undertake a genome-wide association study (GWAS) of prostate cancer in the Multiethnic Cohort (MEC) Study. We propose the following hypotheses: (a) that inherited DNA variation influences risk of prostate cancer; (b) that many of the causal alleles will be outside known "candidate genes" requiring an agnostic, comprehensive search; and (c) that performing this search in a multi-ethnic cohort is more powerful than a study limited to a single population to reveal the full range of causal alleles relevant to the U.S. population. The version 1 release of this dataset will include genotype data for the Japanese and Latino populations in the study. The version 2 release will include data for the African ancestry population along with the Japanese and Latino subjects. The version 3 release will include fully-cleaned genotype data for all three populations. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to prostate cancer through large-scale genome-wide association studies of a well-characterized multi-ethnic cohort. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center and at the University of Southern California. Data cleaning and harmonization were performed at the GEI-funded GENEVA Coordinating Center at the University of Washington. As an add-on to this GWAS we performed a targeted re-sequencing of all known prostate cancer risk loci in the samples from the MEC. Sequencing was performed in Dr. Reich's lab at Harvard Medical School.

  Study phs000306

• Discovery of Non-ETS Gene Fusions in Human Prostate Cancer using Next Generation RNA Sequencing

  Half of prostate cancers harbor gene fusions between TMPRSS2 and members of the ETS transcription factor family. To date little is known about the presence of non-ETS fusion events in prostate cancer. We employed next-generation transcriptome sequencing (RNA-Seq) in order to explore the whole transcriptome of 25 human prostate cancer samples for the presence of chimeric fusion transcripts. We generated more than 1 billion sequence reads and used a novel computational approach (FusionSeq) in order to identify novel gene fusion candidates with high confidence. In total, we discovered and characterized seven new cancer-specific gene fusions, two involving the ETS genes ETV1 and ERG, and five involving non-ETS genes such as CDKN1A (p21), CD9 and IKBKB (IKK-beta), genes known to exhibit key biological roles in cellular homeostasis or assumed to be critical in tumorigenesis of other tumor entities, as well as the oncogene PIGU and the tumor suppressor gene RSRC2. The novel gene fusions are found to be of low frequency but interestingly, the non-ETS fusions were all present in prostate cancer harboring the TMPRSS2-ERG gene fusion. Future work will focus on determining if the ETS rearrangements in prostate cancer are associated or directly predispose to a rearrangement prone phenotype.

  Study phs000310

• FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data

  We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts such as misalignments or random pairing of transcript fragments and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including 8 cancers with and without known rearrangements.

  Study phs000311

• The Genomic Complexity of Primary Human Prostate Cancer

  Seven primary prostate cancers and their paired normal counterparts were DNA-sequenced on a massively parallel sequencing platform. This approach was taken to investigate the genomes for mutations and genomic alterations that would be undetectable by lower-resolution methods. As result, tumors containing chains of balanced rearrangements that occurred within or adjacent to known cancer genes were discovered.

  Study phs000330

• A Genome-Wide Association Study of Lung Cancer Risk

  Three genetic loci for lung cancer risk have been identified by genome-wide association studies (GWAS), but inherited susceptibility to specific histologic types of lung cancer is not well established. We conducted a GWAS of lung cancer and its major histologic types genotyping 515,922 single nucleotide polymorphisms (SNPs) in 5,739 incident lung cancer cases and 5,848 controls from one population-based case-control study and three cohort studies. Results were combined with summary data from 10 additional studies for a total of 13,300 cases and 19,666 controls of European descent. Four studies also provided histology data for replication resulting in 3,333 adenocarcinomas (AD), 2,589 squamous cell carcinomas (SQ), and 1,418 small cell carcinomas (SC). In analyses by histology, rs2736100 (TERT) on chromosome 5p15.33, was associated with risk of adenocarcinoma (OR=1.23, 95%CI=1.13-1.33, P=3.02x10-7), but not other histologic types (OR=1.01, P=0.84, and OR=1.00, P=0.93, for SQ and SC, respectively). This finding was confirmed in each replication study and overall meta-analysis (OR=1.24, 95%CI=1.17-1.31, P=3.74x10-14 for AD and OR=0.99, P=0.69 and OR=0.97, P=0.48, for SQ and SC, respectively). Other previously reported association signals on 15q25 and 6p21 were also refined, but no additional loci reached genome-wide significance. In conclusion, a lung cancer GWAS identified a distinct hereditary contribution to adenocarcinoma. Note: The lung study dataset to be released to dbGaP and caBIG excludes 47 individuals from the PLCO cohort who consented to participate only in cancer research projects and 22 individuals because of updated QC. Thus, the released dataset is derived from 11517 subjects, 5699 cases and 5818 controls. After the updated QC, the dataset to be released to dbGaP and caBIG includes 506062 SNPs.

  Study phs000336

• The genomic complexity of early T-cell progenitor acute lymphoblastic leukemia

  CREST The accurate identification of structural variations using whole-genome DNA sequencing data generated by next-generation sequencing technology is extremely difficult. To address this challenge, we have developed CREST, an algorithm that uses sequencing reads with partial alignments to the reference human genome (so-called soft-clipped reads) to directly map the breakpoints of somatic structural variations. We applied CREST to paired tumor/normal whole genome sequencing data from five cases of T-lineage acute lymphoblastic leukemia (T-ALL). A total of 110 somatic structural variants were identified, >80% of which were validated by genomic PCR and Sanger sequencing. The validated structural variants included 31 inter-chromosomal translocations, 19 intra-chromosomal translocations, one inversion, 22 deletions and 16 insertions. A comparison of the results generated with CREST to those obtained using the traditional paired-end discordant mapping methods demonstrate CREST to have a much higher sensitivity and specificity. In addition, application of CREST to publicly available whole-genome sequencing data from the human melanoma cancer cell line COLO-829 demonstrated the identification of 50 novel structural variations not detected using the standard methods, 20 of which were selected for validation with a 90% success rate. These data demonstrate that direct mapping of soft-clipped reads offers an improved method for detecting structural variants at the nucleotide level of resolution. T-ALL Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases and assessed the frequency of the identified somatic mutations in 94 T-cell acute lymphoblastic leukaemia cases. ETP ALL was characterized by activating mutations in genes regulating cytokine receptor and RAS signalling (67% of cases; NRAS, KRAS, FLT3, IL7R, JAK3, JAK1, SH2B3 and BRAF), inactivating lesions disrupting haematopoietic development (58%; GATA3, ETV6, RUNX1, IKZF1 and EP300) and histone-modifying genes (48%; EZH2, EED, SUZ12, SETD2 and EP300). We also identified new targets of recurrent mutation including DNM2, ECT2L and RELN. The mutational spectrum is similar to myeloid tumours, and moreover, the global transcriptional profile of ETP ALL was similar to that of normal and myeloid leukaemia haematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL.

  Study phs000340

• Genome-wide association study for Bladder Cancer Risk

  This study funded by the National Cancer Institute (NCI) involves conducting a genome-wide association study of common genetic variants to identify markers of susceptibility to bladder cancer. This bladder GWAS has led to the discovery of three novel regions in the genome associated with bladder cancer risk. Cases were defined as individuals having histologically confirmed primary carcinoma of the urinary bladder, including carcinoma in situ (ICD-0-2 topography codes C67.0-C67.9 or ICD9 codes 188.1-188.9). Scan data were obtained from two case-control studies carried out in Spain and the United States (specifically, those in the Maine and Vermont components of the New England Bladder Cancer Study) and three prospective cohort studies in Finland and the United States (specifically Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, and The American Cancer Society Cancer Prevention Study II Nutrition Cohort). We used data from 591,637 single nucleotide polymorphisms 3,532 affected individuals (cases) and 5,119 controls of European descent and replication including 8382 cases and 48275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P ≈ 8 x 10-12) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 x 10-11) on maps to CCNE1 and rs11892031 (P = 1 x 10-7) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3 (Rothman N et al., Nature Genetics, 2010, PMID: 20972438). Through meta-analysis with the MD Anderson Texas Bladder Cancer Study (TXBCS), we also identified a novel susceptibility locus that mapped to a region of 18q12.3, marked by rs7238033 (P = 8.7 x 10(-9); allelic odds ratio 1.20 with 95% CI: 1.13-1.28) and two highly correlated SNPs, rs10775480/rs10853535 (r(2)= 1.00; P = 8.9 x 10(-9); allelic odds ratio 1.16 with 95% CI: 1.10-1.22) (Garcia-Closas M et al, Human Molecular Genetics, 2011) For NCI-GWAS2, we performed genotyping on cases and controls for the New Hampshire component of the New England Bladder Cancer Study (NEBCS-NH). For the majority of new bladder cancer cases, we genotyped only cases from four case-control studies, the Los Angeles Bladder Cancer Study (LABCS), the French Center for Research on Prostate Diseases (CeRePP), the French Bladder Study (FBCS) and the Brescia Bladder Cancer Study (BBCS). We used existing control data from four cohort studies already genotyped and subjected to rigorous quality control metrics: the European Prospective Investigation Into Cancer and Nutrition Study (EPIC), Womens Health Initiative (WHI), Health Professionals Follow-up Study (HPFS), Nurses Health Study (NHS), which have been a part of Cancer Genetic Markers of Susceptibility (CGEMS). Meta-analysis of NCI-GWAS1, NCI-GWAS2 and a previously reported GWAS TXBCS-GWAS along with taqman replication, identified two new loci: rs10936599 on 3q26.2 (P = 4.53 x 10(-9)) and rs907611 on 11p15.5 (P = 4.11 x 10(-8)). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 x 10(-7)) and rs4510656 on 6p22.3 (P = 6.98 X 10(-7)); these require further studies for confirmation (Figueroa J et al, Human Molecular Genetics, 2013).

  Study phs000346

• Fred Hutchinson Cancer Research Center - Whole-Exome Sequencing of Hereditary Prostate Cancer Families

  The specific aim of this study is to identify hereditary prostate cancer (HPC) susceptibility genes using a novel study design, whereby whole-exome sequencing will be undertaken on multiple affected relatives from 19 HPC families, in which ≥ 3 affected relatives were diagnosed with clinically aggressive and/or early onset prostate cancer (PC). While whole-exome sequencing of unrelated affected individuals would result in hundreds of candidate disease variants, this family-based, aggressive/early onset phenotype approach will provide an enriched genetic background for discovery and significantly reduce the number of candidate mutations that will require follow-up. Findings from this pilot study will immediately be followed-up to confirm whether candidate mutations found in each family segregate with disease in the remaining unscreened relatives. As part of this pilot study, we aim to: Perform whole-exome sequencing on 80 affected and 11 unaffected relatives from 19 HPC families that have multiple men diagnosed with an aggressive and/or early onset disease phenotype using the Illumina HiSeq platform; and, Analyze sequencing data using BWA, SAMtools and SeattleSeq to prioritize candidate HPC mutations that segregate with aggressive and/or early onset disease in affected relatives.

  Study phs000350

• National Cancer Institute Genome-Wide Association Study of Renal Cell Carcinoma

  The National Cancer Institute (NCI) genome-wide association study (GWAS) of renal cell carcinoma (RCC) was conducted to investigate common genetic variants associated with RCC risk. The GWAS includes 1,453 RCC cases and 3,531 controls of European background from 4 studies (3 cohort, 1 case-control), scanned using the Illumina InfiniumHumanHap 550, 610 and 660W chips. This project was supported by the Intramural Research Program of the National Institutes of Health and NCI. Data from this GWAS were pooled with those from another GWAS of RCC (2,639 cases and 5,392 controls) conducted in Europe by the International Agency for Research on Cancer and the Centre National de Gènotypage. Findings from this collaboration are described in an upcoming report (Purdue et al. Nature Genetics 2011;43(1):60-65; PMID: 21131975). Only data from the NCI scan are included in this dbGaP submission.

  Study phs000351

• The genomic complexity of sporadic and inherited retinoblastoma with a matched orthotopic xenograft

  Retinoblastoma is a pediatric cancer of the developing retina. All retinoblastomas are believed to initiate with biallelic inactivation of the RB1 gene. To identify subsequent genetic lesions in retinoblastoma, we performed whole genome sequencing of tumor and normal DNA of 4 children with retinoblastoma and one matched orthotopic xenograft. Both alleles of RB1 were inactivated in the tumor samples. 3 of the patients had sporadic retinoblastoma and one patient had inherited retinoblastoma. Overall, there were few single nucleotide changes in coding regions of the genome and some of the tumors had few chromosomal lesions. There were very few new genetic lesions in the xenograft compared to the primary tumor. These data suggest that the genome in retinoblastoma is more stable than previously believed and there are relatively few recurrent genetic lesions in known cancer pathways other than the RB1 pathway.

  Study phs000352

• Genome-Wide Association Studies in Upper Gastrointestinal Cancers (Asian)

  This project aims to conduct genome-wide association studies (GWAS) in two anatomically different upper gastrointestinal (UGI) cancer sites in two populations with distinctly different disease rates and genetic profiles. One population has very high rates for both esophageal squamous cell carcinomas (ESCCs) and gastric cancers (GCs) and is comprised of Asians (the "Asian UGI GWAS"), while the other population has low rates of ESCC and GC and includes non-Asians from the Americas, Europe, and Australia (the "Non-Asian UGI GWAS"). Study participants for the Asian UGI GWAS reported here (Illumina 660W Quad chip) were drawn from 2 studies, the Shanxi Upper Gastrointestinal Cancer Genetics Project (Shanxi) and the Linxian Nutrition Intervention Trial (NIT), a prospective cohort, and included a total of 1898 ESCCs, 1625 GCs, and 2100 controls. For the 2nd phase (8 TaqMan SNPs), additional subjects from Shanxi and NIT as well as subjects from the Shanghai Men's Health Study (SMHS), the Shanghai Women's Health Study (SWHS), and the Singapore Chinese Health Study (SCHS) were also included (217 ESCCs, 615 GCs, 1202 controls). Altogether 2115 ESCCs, 2240 GCs, and 3302 controls were genotyped in this study.

  Study phs000361

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395

• Sequence Analysis of Mutations and Translocations Across Breast Cancer Subtypes

  This study combined whole exome (103 samples) and whole genome (22 samples) sequencing over a total of 108 breast tumors and matched normal DNA to identify novel mutations and translocations. Samples were subjected to paired-end Illumina sequencing with goal of 30x coverage of tumor/normal for whole genomes and 100x tumor/normal coverage for whole exomes. From these sequences, we used various computational techniques to identify somatic point mutations, insertion/deletions and structural rearrangements in these tumors. From these data, we identified new insights into the rates of background mutations in these cancers, novel recurrent mutated genes, and multiple gene rearrangements. One of these rearrangements appears to be a recurrent event in breast cancer.

  Study phs000369

• The Mutational Landscape of Head and Neck Squamous Cell Carcinoma

  This study was the first-known large-scale effort to uncover the mutational spectrum of head and neck cancers. We analyzed whole-exome sequence from 92 tumor-normal pairs and retained 74 of them for significance analysis. The majority exhibited a mutational profile consistent with tobacco exposure; human papilloma virus sequence was detectable in 15% of cases. In addition to identifying previously known HNSCC genes (TP53, CDKN2A, PTEN, PIK3CA, and HRAS), the analysis revealed many genes not previously implicated in this malignancy. At least 30% of cases harbor mutations in genes (such as NOTCH1, IRF6, TP63) that regulate squamous differentiation, implicating alterations in this process as a major driver of HNSCC carcinogenesis. Altogether, the results suggest that large-scale exome sequencing may illuminate fundamental tumorigenic mechanisms with important therapeutic implications.

  Study phs000370

• Whole Genomic Sequencing of Nine Primary Colorectal Adenocarcinoma Tumor/Germline Pairs

  This study was the first-known effort to complete the complete genome sequencing of primary colorectal adenocarcinomas and the matched germline genome. Nine colorectal adenocarcinomas selected on the basis of having chromosomal instability were subjected to 'shotgun' Illumina sequencing with 101-bp paired end reads to an approximate goal of 30x coverage of tumor and of normal. From these sequences, we used various computational techniques to identify somatic point mutations, insertion/deletions and structural rearrangements in these tumors. From these data, we identified new insights into the rates of background mutations in these cancers, new spectrums of structural alterations including the identification of a novel in-frame fusion gene.

  Study phs000374

• Genome-Wide Association Study of Breast Cancer in the African Diaspora - the ROOT study

  The paucity of data on the genetic epidemiology of breast cancer for racial/ethnic groups other than those of European ancestry hinders the development of innovative interventions to reduce health disparities. Women in the African Diaspora experience a disproportionate burden of pre-menopausal breast cancer in comparison to all other races for reasons that remain unknown and understudied. This higher proportion of early-onset breast cancer might suggest a stronger genetic component in these populations. Genome-wide association studies (GWAS) have revealed several genetic loci that confer risk of breast cancer. Because all GWAS started the discovery stage in women of European ancestry and replicated mainly in women of European ancestry, we propose a novel approach for a GWAS in indigenous African women to identify alleles associated with breast cancer risk which will then be replicated in other populations. This innovative design builds on our current understanding of the etiologic heterogeneity in breast cancer and the distribution of breast cancer molecular subtypes which differ between women of African ancestry and women of European ancestry. The major objective of the proposed studies is to get to the "root" causes of breast cancer by identifying breast cancer risk alleles in a pooled sample of women of African ancestry and to replicate our findings in other populations. To achieve this objective, we conducted a case control study of breast cancer in women of African ancestry, including Africans living in Nigeria, African Americans and African Barbadians. We will genotype ~3800 individuals using the Illumina HumanOmni2.5-Quad platform. We will conduct both standard and novel genetic analyses of the data to map genes associated with breast cancer susceptibility, verify genotyping and carry out fine-mapping studies in genes or regions showing association with breast cancer risk, and replicate in other African American and non-African American populations. By pooling unique resources from studies throughout the African Diaspora, this study has the potential to identify risk alleles in several genes that contribute to increased breast cancer risk and may have implications for early detection, prognosis and treatment of breast cancer in ALL women. This should ultimately lead to improved outcomes for those who suffer a disproportionate burden of early-onset breast cancer.

  Study phs000383

• Epigenetic Profiling of Human Colorectal Cancer

  DNA methylation, together with chromatin modifications, constitute the epigenome that functions to regulate gene expression and genome integrity. DNA methylation alterations are ubiquitous in human cancers, as many genes acquire DNA methylation in a cancer-specific manner. DNA methylation at these sites in the genome of cancer cells not only serves as a marker for tumor identification, but together with gene mutation and gene expression data, can also be used to describe subsets of tumors of the same organ source. We have previously shown distinct human colorectal cancer subtypes based on DNA methylation differences. Correlating these DNA methylation differences with clinical co-variates will serve to further understand how these distinct subtypes are generated. We have collected 100 colorectal tumor tissues (for which clinical information is known) and have obtained (unprotected) genome-wide DNA methylation and chromatin modification information for each sample for the purposes of identifying and classifying unique tumor subtypes of colorectal cancers. In addition, we have determined mutations of key genes relevant to colorectal cancer as well as gene expression profiles. We will use the clinical data for each de-identified sample to correlate with the DNA methylation, mutation and gene expression information so as to understand the driving forces behind these distinct colorectal subtypes. We have selected the most promising tumors for whole-genome bisulfite sequencing using next-generation sequencing technology to obtain complete maps of colon cancer methylomes. Researchers will be unable to identify the subjects because the samples and associated information have been de-identified and anonymized by the tissue source site. In addition, upon receipt by the USC Epigenome Center, we have assigned new random identifiers for each sample. The data generated using these new codes are not traceable to the patient identity.

  Study phs000385

• Division of Cancer Epidemiology and Genetics (DCEG) Imputation Reference Dataset

  We have built a new resource for imputation of SNPs for existing and future genome-wide association studies (GWAS), known as the Division of Cancer Epidemiology and Genetics (DCEG) Reference Set. The first build of the data set includes 728 cancer-free individuals of European descent from three large prospectively sampled studies, 98 African-American individuals from the Prostate, Lung, Colon, and Ovary Cancer Screening Trial (PLCO), 74 Chinese individuals from a Chinese clinical trial in Shanxi, China (SHNX), and 349 unrelated individuals from the HapMap Project (see Molecular Data Section for details on arrays used). The final harmonized dataset includes 2.8 million autosomal polymorphic SNPs on 1,249 subjects after rigorous quality control metrics were applied.

  Study phs000396

• The Genetic Architecture of Smoking and Smoking Cessation

  This study includes samples from two projects: Collaborative Genetic Study of Nicotine Dependence (COGEND; PI: Laura Bierut) and University of Wisconsin Transdisciplinary Tobacco Use Research Center (UW-TTURC; PI: Timothy Baker). Data are available for an additional 1420 COGEND subjects through the Study of Addiction: Genetics and Environment (SAGE), dbGaP study accession phs000092. The majority of these subjects are independent from the current study, but there is a small amount of overlap between the two samples (n=29 subjects) for quality control purposes. It should be noted that the case definition in the SAGE study is DSM-IV alcohol dependence. The case definition in the current study is nicotine dependence by a current score of 4 or greater on the Fagerström Test for Nicotine Dependence (FTND). The overall goal of this project is to identify and characterize genetic variants that contribute to the development of nicotine dependence, related smoking behaviors, and smoking cessation. The COGEND sample includes unrelated cases and controls for a genetic association study of nicotine dependence. Cases are defined by a commonly used definition of nicotine dependence, a current score of 4 or more (maximum score of 10) on the Fagerström Test for Nicotine Dependence (FTND). Control status is defined as an individual who smoked at least 100 cigarettes during their lifetime, yet never became dependent (lifetime FTND=0). By selecting controls who smoked, those genetic effects that are specific to nicotine dependence can be examined. The UW-TTURC sample includes nicotine dependent smokers from three smoking cessation studies. Subjects had to smoke at least 10 cigarettes per day (confirmed smoking by an alveolar carbon monoxide (CO) level greater than 9) and report being motivated to quit smoking. Participants were excluded based on evidence of psychosis history, clinically significant depression symptoms, other severe mental illness, or contraindications to smoking cessation medications. COGEND: COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design is a community based case-control family study. Nicotine dependent cases and non-dependent, smoking controls were identified and recruited from Detroit and St. Louis. In addition, one sibling for each case and control subject was recruited in a subset of the sample. More than 54,000 subjects aged 25-44 years were screened by telephone; more than 3,100 subjects were personally interviewed; and more than 2,900 subjects donated blood samples for genetic studies. UW-TTURC: The UW-TTURC was initiated in 2001 as a study of nicotine dependence and smoking cessation treatment. The second round of UW-TTURC was initiated in 2005 as a study of efficacy of smoking cessation and long term outcomes. Nicotine dependent smokers seeking cessation treatment were identified and recruited from Madison and Milwaukee, WI. Over 9,000 adult smokers were screened by telephone; 2,575 individuals were enrolled and randomized to treatment conditions that involved use of different smoking cessation medications. Participants from the UW-TTURC smoking cessation clinical trials had the option of participating in a genetic substudy, and approximately 2,000 donated blood samples for genetic studies. The goal of the genetic studies of smokers seeking cessation treatment is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes including cessation, withdrawal, and relapse. Both studies (COGEND and UW-TTURC) include measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, and educational attainment. Information on nicotine dependence, as assessed by the Fagerström Test for Nicotine Dependence (FTND) is available for all subjects. In addition, participants also completed the Nicotine Dependence Syndrome Scale (NDSS; Shiffman et al., 2004) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68; Piper et al, 2004). Coding for both individual variables and indices has been standardized across studies. All subjects were assessed in person by trained research assistants. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to the genetic architecture of smoking through large-scale genome-wide association studies of two well-characterized cohorts. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000404

• Whole Exome Sequencing for Colorectal Cancer

  This project was designed to use next generation sequencing technology to screen the protein coding regions of the genome for low frequency variants in a panel of high-risk colorectal adenocarcinoma cases. Blood and cell-line DNA for colorectal cancer patients and a subset of quality control samples that had existing whole exome sequence data were analyzed using Illumina HiSeq sequencers. Samples from this project were from participants in the Women's Health Initiative (WHI) and the Diet, Activity, and Lifestyle Study (DALS).

  Study phs000410

• Temporal Dissection of Tumorigenesis in Primary Cancers

  The earliest genetic abnormalities in cancer represent a unique opportunity for timely clinical diagnosis. Classic deep sequencing of tumors identifies many aberrations acquired later in cancer progression. In this study, data regarding simple mutation and chromosomal aberration were integrated to trace the evolution of cutaneous squamous cell carcinomas and ovarian adenocarcinomas. Only after the second allele of TP53 was lost did the genome enter a window of extreme genomic vulnerability, in both cancer types, eventually acquiring more than 100,000 mutations in skin cancers. Inactivating Notch mutations were also identified as prevalent secondary changes. These results add context to the idea of TP53 mutation as dominant negative and occurring later in tumorigenesis.

  Study phs000418

• Whole Exome Sequencing of Chronic Lymphocytic Leukemia

  The somatic genetic basis of chronic lymphocytic leukemia (CLL), a common and clinically heterogenous adult leukemia, remains poorly understood. Massively parallel sequencing technology now provides a method for systematic discovery of genetic alterations that underlie disease, and for uncovering new therapeutic targets and biomarkers. In study version 2 we presented a dataset consisting of DNA sequencing from 169 CLL samples (with matched germline controls). Samples were collected from patients displaying a wide range of characteristics representing the broad clinical spectrum of CLL. Understanding the mutational landscape of CLL provides a starting point for systematic analyses to address fundamental questions in CLL, including how mutated genes alter cellular networks and phenotypes, and thereby contribute to disease heterogeneity. Intratumoral heterogeneity plays a critical role in tumor evolution. To define the contribution of DNA methylation to heterogeneity within tumors, we performed genome-scale bisulfite sequencing of >100 primary chronic lymphocytic leukemias (CLLs; data presented in study version 3). Compared with 26 normal B cell samples, CLLs consistently displayed higher intrasample variability of DNA methylation patterns across the genome, which appears to arise from stochastically disordered methylation in malignant cells. Transcriptome analysis of bulk and single CLL cells revealed that methylationdisorder was linked to low-level expression. Disordered methylation was further associated with adverse clinical outcome. We therefore propose that disordered methylation plays a similar role to that of genetic instability, enhancing the ability of cancer cells to search for superior evolutionary trajectories.

  Study phs000435

• Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression

  Noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease and to reveal uncharacterized aspects of tumor biology. Here we discover 121 unannotated prostate cancer-associated ncRNA transcripts (PCATs) by ab initio assembly of high-throughput sequencing of polyA+ RNA (RNA-Seq) from a cohort of 102 prostate tissues and cells lines. We characterized one ncRNA, PCAT-1, as a prostate-specific regulator of cell proliferation and show that it is a target of the polycomb repressive complex 2 (PRC2). We further found that patterns of PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1-repressed target genes. Taken together, our findings suggest that PCAT-1 is a transcriptional repressor implicated in a subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes.

  Study phs000443

• Prostate Cancer Genome Sequencing Project

  Prostate cancer is a prevalent cause of cancer morbidity and mortality in men. In order to characterize the full range of somatic mutations in protein-coding genes that may drive the growth of prostate cancer, we sequenced the exonic regions of genomic and tumor DNA from over 100 patients with high-risk primary prostate cancer. Using hybrid capture and paired end DNA sequencing, we identified mutations in several novel putative prostate cancer genes. We interrogated copy number changes across tumor genomes using high-density SNP arrays, and identified a molecular subtype of cancer characterized by mutation of the ubiquitin ligase subunit SPOP and copy number loss at specific genomic loci.

  Study phs000447

• Melanoma Genome Sequencing Project

  Melanoma is the most prevalent cause of skin cancer morbidity and mortality. In order to characterize the full range of somatic mutations that may drive the growth of melanoma, we are sequencing tumor and normal DNA from a set of roughly 150 melanomas. For the majority of samples (approximately 90% of the cases) mutations in protein coding genes will be assessed in the exonic DNA of tumor-normal pairs using hybrid capture and paired-end DNA sequencing. Additionally, in a few DNA samples (approximately 10% of the cases) the entire genomes will be analyzed to assess the possible contribution of complex structural rearrangements contributing to oncogenesis. The sequencing data are supplemented by copy number profiling on the same tumors using high-density SNP arrays. Integration of these approaches will enable the unbiased and comprehensive characterization of both known and novel recurrent DNA alterations that arise in melanoma.

  Study phs000452

• Exome Recapture and Sequencing of Prospectively Characterized Clinical Specimens From Cancer Patients

  The study cohort comprises samples from patients whose tumors have been prospectively characterized as part of their care at Memorial Sloan Kettering Cancer Center. This includes a variety of distinct tumor type and molecular subtypes.

  Study phs001783

• Estrogen Receptor Positive Breast Cancer: Aromatase Inhibitor Response Study

  Highly variable outcomes are observed in patients with estrogen receptor positive (ER+) breast cancer who undergo preoperative estrogen deprivation therapy with aromatase inhibitors (AI). In this study, 46 baseline tumor and normal genomes and 31 baseline tumor/normal exomes of participants selected from two clinical trials of neoadjuvant AI therapy on ER+ breast cancer were sequenced to identify somatic alterations that correlate with response to AI, to screen for therapeutic targets and to elucidate the genetic landscape of ER+ breast cancer. From the same set of patients we later performed deep genomic characterization of a subset of matched primary tumors after four months of AI therapy, generating comprehensive information about the range of changes that occur when ER+ breast cancers are subjected to estrogen deprivation. This data includes whole genome sequence and transcriptome data. To better understand tumor heterogeneity and the evolution of resistance to estrogen-deprivation therapy, a subset of these tumours, along with 38 additional cases were sequenced to greater depth using targeted capture with a gene panel.

  Study phs000472

• Identification of Cancer Predisposition Genes in Breast Cancer Families

  Breast cancer has a significant genetic component. The strongest epidemiologic risk factor is family history of the disease. Familial studies have identified a number of genes contributing to increased risk. Of these, the highly penetrant BRCA1 and BRCA2 genes account for the majority of families in whom disease-causal mutations have been identified. However, mutations in other genes have been associated with predisposition to breast or breast and ovarian cancer including (CHEK2, ATM, PALB2, RAD51C, RAD51D). Together these genes account for less than 20% of excess familial breast cancer risk and do not explain ~50% of high-risk families referred for genetic testing. This significant gap in our understanding of the genetic basis of breast cancer poses a significant barrier to our ability to identify women at increased risk, who would benefit greatly from appropriate clinical management. To identify additional breast cancer predisposition genes whole exome sequencing of familial high-risk breast cancer patients was undertaken. DNA samples from 50 pairs of breast cancer patients from high-risk breast cancer families with no pathogenic mutations in the BRCA1 or BRCA2 breast cancer predisposition genes were subjected to whole exome sequencing. The goal was to select candidate predisposition genes for further validation studies based on sharing of putative protein inactivating mutations among both members of each family and/or the frequency of inactivating mutations among the 50 families.

  Study phs000480

• Functionally Active Copy Number Variants Associated with Prostate Cancer Risk

  Prostate cancer is a leading cause of cancer death in males throughout the world and has the largest estimated effect of heritability among the most common tumor types. Copy Number Variants (CNVs) are a recently recognized class of human germline polymorphisms (Iafrate AJ, et al. (2004) Nat Genet 36, 949-951; Sebat J, et al. (2004) Science 305, 525-528.) and are associated with a variety of human diseases, including cancer. This study characterized 1,903 individuals from the Tyrol Early Prostate Cancer Detection Program (Bartsch G, et al. (2008) BJU Int 101, 809-816) using a computational framework (Banerjee S, et al. (2011) PLoS One 29;6(3)). The study results establish non-coding and coding germline CNVs as significant risk factors for prostate cancer susceptibility and implicate their role in disease development and progression. The study "Identification of functionally active, low frequency copy number variants at 15q21.3 and 12q21.31 associated with prostate cancer risk," Demichelis F, Setlur SR, Proc Natl Acad Sci U S A. 2012 Apr 24;109(17):6686-91.

  Study phs000487

• Genomic Sequencing of Lung Adenocarcinoma

  Lung carcinoma is the leading cause of cancer death in the United States and world-wide; lung adenocarcinoma is the most common cause of lung cancer. Pilot studies of lung adenocarcinoma with hybrid-capture based whole exome sequencing will enable us to identify new targets for therapy and improve diagnosis. We will analyze a blend of whole exome and whole genome sequencing data as well as copy number and somatic mutation calls for 200 tumor and matched normal controls. When completed this study will represent the most comprehensive lung adenocarcinoma genome dataset to date.

  Study phs000488

• Medulloblastoma exome sequence analysis

  Medulloblastomas are the most common malignant brain tumors in children. Identifying and understanding the genetic events that drive these tumors is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. Recently, our group and others described distinct molecular subtypes of medulloblastoma based on transcriptional and copy number profiles. Here, we utilized whole exome hybrid capture and Illumina sequencing to identify somatic mutations across the coding regions of 92 primary medulloblastoma/normal pairs. Overall, medulloblastomas exhibit low mutation rates consistent with other pediatric tumors, with a median of 0.35 non-silent mutations per megabase. We identified twelve genes mutated at statistically significant frequencies, including previously known mutated genes in medulloblastoma such as CTNNB1, PTCH1, MLL2, SMARCA4 and TP53. Recurrent somatic mutations were identified in an RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR, and LDB1, to our knowledge novel findings in medulloblastoma and all cancer. We show that mutant DDX3X potentiates transactivation of a TCF promoter and enhances cell viability in combination with mutant but not wild type beta-catenin. Together, our study reveals the alteration of Wnt, Hedgehog, histone methyltransferase and now N-CoR pathways across medulloblastomas and nominates the RNA helicase DDX3X as a component of pathogenic beta-catenin signaling in medulloblastoma. "Reprinted from 'MEDULLOBLASTOMA EXOME SEQUENCING UNCOVERS SUBTYPE-SPECIFIC SOMATIC MUTATION', with permission from Nature"

  Study phs000504

• Genomic Sequencing of Pediatric Rhaboid Cancers

  In this study, we sequenced the exomes of 35 rhabdoid tumors, highly aggressive cancers of early childhood. This study is part of a larger effort to characterize pediatric cancers as part of the Slim Initiative for Genomic Medicine (SIGMA) project.

  Study phs000508

• Genome Sequencing of Hepatocellular Carcinoma at The Human Genome Sequencing Center of Baylor College of Medicine (HGSC-BCM)

  Hepatocellular carcinoma (HCC) accounts for 85-90% of primary liver cancers. We have focused on three major HCC etiologies:hepatitis C virus (HCV), hepatitis B virus (HBV), and nonviral causes. The onset and progression of cancer is driven by extensive rearrangement and mutation of the genome. We combined our capability to capture and enrich exome DNA with the next generation sequencing capacity to allow us to detect and characterize the somatic mutation profile of patients with HCC. Patient samples were collected by the Liver Center, Division of Abdominal Transplantation in the Baylor College of Medicine Department of Surgery. Sequencing of HCC is one of the NHGRI Center Initiated Projects in progress in the Human Genome Sequencing Center at Baylor College of Medicine.

  Study phs000509

• Rearrangements of the MAST Kinase and Notch Gene Families in Breast Cancer

  Paired-end transcriptome sequencing was performed on a panel of breast cancer cell lines and tissues and a set of benign cell line and tissue controls. Analyses of the paired end sequences were performed and chimeric transcripts derived from gene rearrangement events were identified. Sequencing was performed on Illumina GAII and HiSeq 2000 platforms with read lengths from 40 to 100 bases.

  Study phs000513

• Genome Sequencing of Pancreatic Ductal Adenocarcinoma at The Human Genome Sequencing Center of Baylor College of Medicine (HGSC-BCM)

  Pancreatic ductal adenocarcinomas (PDAC) is the 11th most common cancer in the USA but the 4th in fatalities. The onset and progression of cancer is driven by extensive rearrangement and mutation of the genome. We combined our capability to capture and enrich exome DNA with the next generation sequencing capacity to allow us to detect and characterize the somatic mutation profile of 24 patients with PDAC. Patient samples were collected by the Elkins Pancreatic Center in the Baylor College of Medicine Department of Surgery. Sequencing of the pancreatic ductal adenocarcinoma is one of the NHGRI Center Initiated Projects in progress in the Human Genome Sequencing Center at Baylor College of Medicine. These data are also contributed to an ICGC study and will be published with the ICGC collaborators.

  Study phs000516

• GWAS in African Americans, Latinos and Japanese

  The Multiethnic Cohort Study is a population-based prospective cohort study (n=215,251) 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 study subjects. 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 estrogen receptor status is also obtained through these registries. Blood sample collection in the MEC began in 1994 and targeted incident breast cancer cases and a random sample of study participants to serve as controls for genetic analyses. Subjects are frequency matched on age at blood draw and ethnicity. The following number of breast cancer cases and controls were genome-wide scanned as part of this study: African Americans, 473 cases and 464 controls; Japanese: 885 cases and 822 controls; Latinos, 520 cases and 544 controls.

  Study phs000517

• Study of Melanoma Risk in Australia and the United Kingdom

  The results from a small number of melanoma GWAS have been published. Initial studies identified several pigmentation- and nevus-associated loci that mediate an effect on melanoma risk, however, these studies were somewhat limited in that sample number (hence power) was low, they used first-generation low density SNP arrays, or used pools of DNA samples. Subsequently, additional melanoma GWAS have been completed, including a "Phase 2" study by the International Melanoma Genetics Consortium (GenoMEL), a full GWAS of the Australian sample resource previously used in a pooled-DNA GWAS, and study conducted through the MD Anderson Cancer Center and the Brigham and Women's Hospital. The groups who performed these studies have recently shared data and replicated a number of new melanoma susceptibility loci. Aside from the identification of novel loci, there are two additional notable outcomes of these studies. Firstly, an examination of the observed versus expected test statistics (Q-Q plot) from the Australian study, even after removing data from SNPs within known replicated susceptibility loci, reveals that there remains an excess of positive results. Similarly, the latest GenoMEL study finds almost three times as many SNPs reaching p-values between 10-4 and 10-5 as would be expected by chance. In the light of other studies of complex traits [1], these data suggest that studies based on additional melanoma samples from the same population are likely to identify an even larger "polygenic tail" and should prove invaluable in identifying and characterizing the underlying loci. A second observation, is that the effect size for replicated associations differs very little between GWAS. Given the strong effect of ultraviolet radiation (UVR) on risk of melanoma this might be expected to affect penetrance, particularly in the Australian and English samples, which are ethnically similar, but have very different sun exposures. We hypothesize that those SNPs whose effects are strongly mediated by UVR exposure may have smaller marginal effects on risk and are therefore harder to detect initially, suggesting a whole category of genetic effects that are unexplored.Indeed, most melanoma GWAS performed to date used samples from both European and Australian populations in discovery and or replication, and thus, existing studies have been poorly powered to detect associations with a wide difference in effect between these low- and high-UVR populations. Here, we wish to pursue this hypothesis further and plan to genotype additional cases drawn from the English and Australian populations. This will serve two complementary objectives: firstly, it will greatly increase our collective power to detect additional new melanoma risk alleles; secondly, it will highlight associations that are stronger specifically within the context of either low-UVR or high-UVR exposure environments. We posit that these data will be invaluable, not just for identifying novel melanoma-associated loci, but also for addressing gene-environment interactions. We have available a collection of valuable resources to help fulfill our aims. These include a considerable number of well-characterized and ungenotyped Australian and UK melanoma cases, as well as existing genotype data from sets of Australian and UK cases and population-based controls. We propose the following Specific Aims: To extend our current collective melanoma GWAS by genotyping a large number of additional UK and Australian cases. To jointly analyze these data and identify new melanoma risk loci. To determine whether pre-existing and new melanoma risk alleles show different degrees of association between the English and Australian populations, and assess whether these differences might be due to differences in sunlight exposure.

  Study phs000519

• Expressed Pseudogenes in the Transcriptional Landscape of Human Cancers

  In this study, we describe a systematic analysis of pseudogene 'transcription' from an RNA-Seq resource of 293 samples, from 13 cancer and normal tissue types. We observed a highly prevalent, genome-wide expression of pseudogenes that could be categorized as universally expressed or lineage- and/or cancer-specific. We also explored disease subtype specificity and functions of selected expressed pseudogenes. We provide evidence that transcribed pseudogenes are a significant contributor to the transcriptional landscape of cells and are positioned to play significant roles in cellular differentiation and cancer progression. Our work provides a transcriptome resource that enables high-throughput analyses of pseudogene expression.

  Study phs000525

• Whole Genome and Exon Capture Sequencing of Bladder Cancers

  One primary bladder cancer and paired peripheral blood sample were subjected to whole genome sequencing on an Illumina HiSeq 2000 platform. This technology was utilized to investigate the genetic basis of a durable remission of metastatic bladder cancer in a patient treated with everolimus, a drug that inhibits the mTOR (mammalian target of rapamycin) signaling pathway. Among the somatic mutations found was a loss-of-function mutation in TSC1 (Tuberous Sclerosis Complex 1), a regulator of mTOR pathway activation. Targeted sequencing using an exon capture and sequencing assay was performed on 13 tumors derived from patients on the same everolimus trial as the index patient and the sequencing data from these tumors is included. TSC1 mutation status was correlated with response to everolimus. The index patient responder tumor and peripheral blood DNA were also subjected to exon capture and sequencing.

  Study phs000535

• Exome Sequencing of Pleuropulmonary Blastoma

  Pleuropulmonary blastoma (PPB) is a rare, aggressive pediatric cancer arising from the lung or pleural cavity. In this study, we sequenced and analyzed the exomes of 15 PPB matched tumor and normal pairs. This study is part of a larger effort to characterize pediatric cancers as part of the Slim Initiative for Genomic Medicine (SIGMA) project.

  Study phs000543

• A Genome-Wide Association Study in Breast Cancer Patients from the Prospectively Randomized SUCCESS Trial

  The SUCCESS-A Study (http://www.success-studie.de) is a randomized Phase III study of response to treatment of early primary breast cancer with adjuvant therapy after surgical resection. The primary outcome is disease-free survival and secondary outcomes are overall survival and toxicity. The study design includes comparison of two pairs of treatment regimes performed in sequence. The first randomization contrasts treatment with and without Gemcitabine (3 cycles of Epirubicin-Fluorouracil-Cyclophosphamide(FEC)-chemotherapy, followed by 3 cycles of Docetaxel(D)-chemotherapy versus 3 cycles of Epirubicin-Fluorouracil-Cyclophosphamide(FEC), followed by 3 cycles of Gemcitabine-Docetaxel(DG)-chemotherapy). The second randomization contrasts treatment with Zoledronate for two versus five years. Patients were recruited from 2005 to 2007 and a total of 3754 patients were included from 250 study sites across Germany. Genome-wide SNP microarray data and phenotype outcomes for the first phase of the study (treatment with and without Gemcitabine) are included in the GARNET (Genomics and Randomized Trials Network) pharmacogenomic study in this dbGaP submission. (The Zoledronate treatments are still underway). SUCCESS-A is a an open-label, multicenter, 2x2 factorial design, randomized controlled, Phase III study comparing the disease free survival after randomization in patients treated with 3 cycles of Epirubicin-Fluorouracil-Cyclophosphamide(FEC)-chemotherapy, followed by 3 cycles of Docetaxel(D)-chemotherapy versus 3 cycles of Epirubicin-Fluorouracil-Cyclophosphamide(FEC), followed by 3 cycles of Gemcitabine-Docetaxel(DG)-chemotherapy, and to compare the disease free survival after randomization in patients treated with 2 years of Zoledronate versus 5 years of Zoledronate in patients with early primary breast cancer. Patients were required to have histopathological proof of axillary lymph node metastases (pN1-3) or high risk node negative, defined as: pT>2 or histopathological grade 3, or age , ≤ 35 or negative hormone receptor, but were not allowed to have evidence of distant disease. Patients were entered into the study no later than 6 weeks after complete resection of the primary tumor. No antineoplastic treatment other than surgical treatment, the defined cytotoxic and endocrine treatment, and radiotherapy was allowed prior to study entry and during the course of the study. After surgery, leading to complete resection of the invasive and intraductal components of the primary tumor, patients were randomized to one of the following two treatments for the first phase of the project (which is included in this GARNET pharmacogenomics study): AA: 3 cycles of 5-Fluorouracil 500 mg/m² i.v. body surface area and Epirubicin 100 mg/m² i.v. and Cyclophosphamide 500 mg/m² i.v., (FEC100), each administered on day 1, repeated on day 22, subsequently followed by 3 cycles of Docetaxel 75 mg/m² body surface area i.v. (D), and Gemcitabine 1000 mg/m² i.v. (30 min infusion) (G), administered on day 1, followed by Gemcitabine 1000 mg/m² i.v. (30 min infusion) on day 8, repeated on day22 AB: 3 cycles of 5-Fluorouracil 500 mg/m² i.v. body surface area and Epirubicin 100 mg/m² i.v. and Cyclophosphamide 500 mg/m² i.v., (FEC100), each administered on day 1, repeated on day 22, subsequently followed by 3 cycles of Docetaxel 100 mg/m² body surface area i.v. (D), administered on day 1, repeated on day 22 A second randomization was made for assigning patients to one of the following two subsequent treatments for the second phase of the project (NOT included in the pharmacogenetic study in this dbGaP submission): BA: Zoledronic acid 4 mg i.v., every 3 months for the duration of two years, subsequently followed by zoledronic acid 4 mg i.v., every 6 months for the duration of additional three years BB: Zoledronic acid 4 mg i.v., every 3 months for the duration of two years Patients with positive hormone receptor status (≥10% positively stained cells for estrogen and/or progesterone) of the primary tumor were recommended to receive Tamoxifen treatment 20 mg p.o. per day for 2 years, after the end of chemotherapy . Subsequent to chemotherapy, postmenopausal patients with positive hormone receptor status were recommended to be treated with Anastrozole (Arimidex®) 1 mg p.o. for additional 3 years, premenopausal patients will continue Tamoxifen treatment for those additional 3 years. In addition to tamoxifen, all patients with positive hormone receptor status of the primary tumor and under the age of 40 or restart of menstrual bleeding within 6 months after the completion of cytostatic treatment or with premenopausal hormone levels as defined below were recommended to receive Goserelin (Zoladex®) 3.6 mg subcutaneously every 4 weeks over a period of 2 years following chemotherapy. Premenopausal endocrine status will be assumed, if the following serum levels are met: LH < 20 mIE/ml, FSH < 20 mIE/ml and E2 > 20 pg/ml. Endocrine therapy will start after the end of chemotherapy. All patients with breast conserving therapy or more than 3 axillary lymph node metastases or in the following cases after mastectomy will receive adjuvant radiotherapy: T3/T4-carcinoma, T2-carcinoma > 3 cm, multicentric tumor growth, lymphangiosis carcinomatosa or vessel involvement, involvement of the pectoralis fascia or a safety margin < 5 mm. Radiotherapy was usually performed after the chemotherapy and parallel to the beginning of the zoledronic acid therapy. Initially, DNA was extracted for performing SNP microarray assays using blood collected at baseline prior to the first randomization. However, the first set of SNP microarray results indicated a quality problem with approximately 50% of the DNA samples. A majority of the problematic samples were replaced with DNA extracted from redrawn blood. The remaining problematic samples were 'restored' using an Illumina FFPE Restoration procedure. A total of 3322 subjects (1638 from arm AA and 1684 from arm AB) were successfully genotyped. Among those, 1771 were from original blood samples, 1092 from redrawn blood samples and 459 from restored blood samples. The SUCCESS-A study is part of the Genomics and Randomized Trials Network (GARNET, http://www.garnetstudy.org) funded by the National Human Genome Research Institute (NHGRI). The overarching goal is to identify novel genetic factors that contribute to disease-free survival for breast cancer patients using different forms of chemotherapy through large-scale genome-wide association studies of treatment response in randomized clinical trials. The study was conducted at the University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Germany. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were performed at the GARNET Coordinating Center at the University of Washington.

  Study phs000547

• Genome-Wide Characterization of Pancreatic Adenocarcinoma Patients Using Next Generation Sequencing

  In this study, we whole genome sequenced tumor/normal pairs from three pancreatic adenocarcinoma patients to separately characterize each patient with respect to somatic alterations. For 2 patients for whom tumor RNA was available, we also performed RNA sequencing to evaluate gene expression changes. While additional sequencing is needed to improve our understanding of the disease, the information acquired from this study contributes to our knowledge base on pancreatic cancer and helps to establish a foundation for identifying and developing more efficacious treatments for patients.

  Study phs000550

• Mutational Landscape of Lethal Castrate Resistant Prostate Cancer

  Exome sequencing of matched pairs of tumor / normal genomic DNA was performed from high risk localized prostate cancer or lethal, metastatic, castrate resistant prostate cancer (CRPC). Exome libraries were prepared using Illumina Paired_End Genome DNA Sample Prep Kit and captured using Agilent SureSelect Capture Library or Roche EZ Exome capture library. Sequencing was performed on Illumina GAII and HiSeq 2000 platforms in paired end mde, with 80 base pair reads from the final library fragments. Copy number alterations and somatic mutations were identified.

  Study phs000554

• Population Architecture Using Genomics and Epidemiology (PAGE): Epidemiologic Architecture for Genes Linked to Environment (EAGLE) - BioVU Cancer Project

  As part of Population Architecture using Genomics and Epidemiology PAGE study (Phase I), the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE I) project accessed both epidemiologic- and clinic-based collections. The epidemiologic-based collection of EAGLE I included the National Health and Nutritional Examination Surveys (NHANES), ascertained between 1991-1994 (NHANES III), 1999-2002, and 2007-2008. NHANES is a population-based cross-sectional survey now conducted every year in the United States to assess the health status of Americans at the time of ascertainment and to assess trends over the years of survey. Genetic NHANES consists of 19,613 DNA samples linked to thousands of variables including demographics, health and lifestyle variables, physical examination variables, laboratory variables, and exposures. NHANES is diverse with almost one-half of the samples (46.4%) coming from self-reported Mexican Americans and non-Hispanic blacks. In contrast to NHANES, BioVU is a clinic-based collection of >150,000 DNA samples from Vanderbilt University Medical Center linked to de-identified electronic medical records (EMRs). Approximately 12% of BioVU's overall DNA sample collection is from African American, Hispanic, and Asian patients. The overall goals of PAGE I and EAGLE I were broad and several-fold: Replicate genome-wide association study (GWAS)- identified variants in European Americans; Identify population-specific and trans-population genotype-phenotype associations; Identify genetic and environmental modifiers of these associations. NHANES is an excellent resource for the study of quantitative traits associated with common human diseases. However, given that the age range of NHANES spans childhood to late adulthood and not all diseases are surveyed, NHANES is less useful for the study of adult-onset diseases such as major cancers. Therefore, under American Recovery and Reinvestment Act (ARRA) funding, EAGLE as part of PAGE I defined eight major cancers sites for genetic analysis in BioVU, Vanderbilt's biorepository linked to de-identified EMRs. The eight major cancers defined for this study included melanoma, breast, ovarian, prostate, colorectal, lung, endometrial, and Non-Hodgkin's lymphoma (NHL). Cancer cases were defined using a combination of ICD-9 codes and tumor registry entries. Controls include BioVU participants without cancer and encompassing the age and gender distributions of cancer cases. Targeted genotyping of GWAS-identified variants for these diseases (124 SNPs) and ancestry informative markers (128 AIMs) was performed by the Center for Human Genetics Research Vanderbilt DNA Resources Core. After quality control, a total of 116 cancer-associated SNPs and 122 AIMs were available for downstream analyses.

  Study phs000559

• The Genomics of Pilocytic Astrocytoma Formation in Neurofibromatosis Type 1

  Neurofibromatosis type 1 (NF1) inherited cancer predisposition syndrome is one of the most common autosomal dominant tumor predisposition syndromes in which affected individuals develop brain tumors. These low-grade glial neoplasms (pilocytic astrocytomas) typically arise in children younger than 7 years of age and are hypothesized to result from a combination of germline and acquired somatic NF1 tumor suppressor gene mutations. In this study, whole genome sequence analysis was performed on three NF1-associated pilocytic astrocytoma tumors (NF1-PA) and matched normal blood samples to establish the genomic landscape of NF1-PA. These data support the existence of multiple distinct mechanisms (mutation, LOH, and methylation) underlying somatic NF1 inactivation in NF1-PA tumors.

  Study phs000563

• Identification of Recurrent NAB2-STAT6 Gene Fusions in Solitary Fibrous Tumor by Integrative Sequencing

  Transcriptome sequencing of solitary fibrous tumors / hemangiopericytomas from a variety of anatomic sites revealed recurrent gene fusions between two genes, NAB2 and STAT6. All SFTs examined exhibited an in-frame fusion transcript encoding a fusion protein containing the EGR1 interaction domain of NAB2 with the transcriptional activation domain of STAT6. Functional testing of the fusion alleles confirmed the conversion of the wt NAB2 repressor into a transcriptional activator. A range of individual fusion junctions can be detected by next-generation sequencing acro the sample set, highlighting the suitability of this method in the diagnostic characterization of SFTs. This study indentified the pathognomonic alteration in solitary fibrous tumors and illuminates a pathway towards targeted therapeutics for this cancer.

  Study phs000567

• Genomic Sequencing of Solitary Fibrous Tumors

  Solitary fibrous tumors (SFTs) are rare mesenchymal tumors that can be benign or malignant. In this study, we sequenced and analyzed the exomes of 17 SFT matched tumor and normal pairs. This study appertains to a large endeavor to characterize cancers as part of the Slim Initiative for Genomic Medicine in the Americas (SIGMA) project.

  Study phs000568

• Small Intestine Neuroendocrine Tumors (Carcinoid Tumors)

  The diagnosed incidence of small bowel neuroendocrine tumors (NETs) is increasing. While patients with localized disease can be treated surgically, those with metastatic disease currently have few treatment options. The success of biologically targeted therapies in other malignancies has led to interest in the molecular alterations underlying the pathogenesis of these NETs. To identify genetic aberrations in small intestine NETs, we generated copy number profiles from 31 primary and metastatic tumors and performed whole-exome sequencing on a subset of 29 primary small intestine NETs and 24 metastatic NETs in parallel with normal blood DNA. Whole-genome sequencing data was generated on 15 tumor/normal pairs and 5 primary/metastasis/normal trios. The global genetic landscape of small bowel NETs is relatively quiet. Consistent with previous studies, the overwhelming majority of tumors were characterized by loss of chromosome 18 and, to a lesser extent, other chromosome arm gains and losses. In stark contrast to arm-level alterations, recurrent high-level focal amplifications and deletions were much less prevalent in these tumors. High-throughput mutation screening and exome sequencing revealed similarly low rates of somatic mutation in NETs (median of 0.77 non-silent mutations per megabase (Mb) of coding DNA) compared to other recent cancer exome sequencing efforts. Our analysis of this cohort identified only a single, statistically significant recurrent somatic mutation targeting the cyclin-dependent kinase inhibitor gene, CDKN1B, encoding p27.

  Study phs000579

• 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