464 results for "GitHub+primate+acoustics+baboon+Zenodo+10.5281+Gui*"

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Spatial transcriptomics analysis of triple negative breast cancers

Spatial transcriptomics analysis of triple negative breast cancers Both bulk sequencing and ST were performed Counts, images etc are available at https://zenodo.org/doi/10.5281/zenodo.8135721
Dataset EGAD50000000686
Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

Patients with metastatic urothelial cancer (n=29) were treated with atezolizumab (anti-PD-L1). All 29 patients had T-cell receptor sequencing (TCR-seq) of the pre-treatment blood; 24 also had TCR-seq on at least one post-treatment blood sample. 24 patients had TCR-seq of their pre-treatment tumors. TCR-seq data can be found at http://doi.org/10.5281/zenodo.546110. 26 patients also had RNAseq of their tumors and whole exome sequencing (WES) of their tumors and matched normal blood. WES results for one sample were excluded from our analysis after failing to meet coverage requirements.
Study phs001743
RNA sequencing of end-stage kidney disease patients with COVID-19

This dataset contains paired RNA sequencing data for end-stage kidney disease (ESKD) patients on dialysis. There are two cohorts. The first includes 179 samples from 51 COVID-19 patients recruited during the initial phase of the COVID-19 pandemic (April-May 2020) and 55 non-infected ESKD patients as controls. 17 patients initially recruited as controls as part of the Wave 1 cohort were later infected with COVID-19 in January-March 2021. We acquired a total of 90 samples during the acute infection and convalescent samples for 12 of the 17 patients following the acute COVID-19 episode. RNA-seq counts and full clinical metadata for these cohorts are available without restriction from Zenodo (https://doi.org/10.5281/zenodo.6497251).
Dataset EGAD00001009752
scRNAseq of aplastic anemia and healthy immune cells co-cultured with autologous HSPCs

This dataset contains 8 batches of bone marrow or peripheral blood cells of patients with aplastic anemia and bone marrow cells of healthy controls. The samples from different experimental conditions have been multiplexed with Totalseq-C antibodies (feature barcoding). To demultiplex the samples, see instructions in 10.5281/zenodo.2590196 and https://github.com/janihuuh/aa_manu. Each sample is analysed with Chromium V(D)J and 5' Gene Expression Platform v2 (10X Genomics). The raw data includes fastq files for Gene expression, feature barcodes and V(D)J Expression. The processed data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress).
Dataset EGAD00001012120
Spatially resolved antigen receptor and gene expression data from breast cancer patients

The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.
Dataset EGAD00001011061
Spatially resolved antigen receptor and gene expression data from human tonsil tissue

The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.
Dataset EGAD00001011062
CRUK-ICGC Prostate Cancer Group Study

Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the International Cancer Genome Consortium Prostate Cancer UK consortium (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry. Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data. The original aims of the project were: 1.To understand the significance of multifocal prostate cancer. 2.To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting. 3.To understand the molecular basis of development and spread of castration-resistant and metastatic disease 4.To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. Please see https://doi.org/10.5281/zenodo.4022439 for links between sample IDs reported in literature and EGA IDs.
Study EGAS00001000262
Psoriasis Patient PBMC scRNA-seq data

This dataset contains raw and processed data of four scRNA-seq PBMC samples from four Psoriasis patients (Pso3 = male, Pso4 = female, Pso7 = female, Pso8 = male). Raw sample libraries were prepared with 10x 5'-kit v2 multiplexing the individual samples using TrueSeq barcoded antibodies to stain the cells. Resulting sequencing data consists of four FASTQs (2 x antibody multiplex to demultiplex the samples + 2 x transcriptome data containing the typical cell x UMI barcode data for count matrix generation). The raw sequencing data was processed with cellranger 6.0.1 with the GRCh38 3.0.0 reference and cell to sample assignment was subsequently done with couhto 1.1.0 (https://github.com/dmalzl/counhto) employing the same algorithm as cellranger multi (couldn't be used here though as multi does not handle 5'-chemistry data). Processed data contains all main outputs of cellranger count in raw text format or in h5 formats including the used feature reference for (i.e. antibody barcode to sample assignment) as well as the computed cell to sample assignment computed by counhto. For further details including all used code in the analysis of the data please consult https://doi.org/10.5281/zenodo.13846873
Dataset EGAD50000001102
DAC-2023-07-05-Ritz (DAC-007) - Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study

Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study This is only the location for the raw data and any check if the dataset may be relevant please refer to the MF-DAC Zenodo community (https://zenodo.org/records/11237107).
Study EGAS50000000780
Beacon v2

Beacon v2: a tool for data discovery Motivation In the era of data-driven health research and personalised medicine, human genomic data has become extremely valuable. These are also identifiable data, as they carry information pointing to a specific individual as well as their own family; and as such, they must be protected. This makes data discovery particularly challenging: this is where “Beacon” comes in. A “Beacon” is an API aiming to enable the search of genomic variants and associated information without jeopardising the privacy of the dataset. Here, we refer to its current version, namely version 2 (v2). Definition Beacon v2 is a term that can refer to different aspects. The EGA is playing a central role in the following aspects: The Beacon v2 protocol is a Global Alliance for Health and Genomics standard. The Beacon v2 Reference Implementation (B2RI) is an “out-of-the-box” Beacon instance developed with ELIXIR, which facilitates Beacon deployment. The EGA Beacon(s) are Beacons following the v2 standard and using the B2RI, deployed on top of data hosted at the EGA and allowing for their discovery. Resources Depending on whether you are visiting us a stakeholder (you need more general information about Beacon), a deployer /implementer (you want to have your own Beacon instance), or an EGA user (you want to query Beacon and start browsing data), you will be interested in the following resources: Your role Beacon aspect Documentation type Stakeholder Beacon v2 protocol Beacon website Beacon page on the GA4GH website Deployer/Implementer Beacon v2 protocol Read the docs: Beacon v2 standard technical description GitHub repository Beacon v2 standard Beacon v2 Reference Implementation Read the docs: B2RI technical description GitHub repository B2RI Guide to deploy Beacon using B2RI EGA user EGA Beacon(s) API in construction UI in construction
Documentation about/projects-and-funders/beacon
ST lobular manuscript dataset

Spatial transcriptomics analysis for 43 breast cancer lobular samples. Note that there are 2 runs per sample Images and read counts will be made availalble on Zenodo upon publication.
Dataset EGAD50000001467
DAC-2023-07-05-Ritz (DAC-007)

Full description of the dataset and all necessary information on metadata may be found under (https://zenodo.org/records/11237107): DAC-2023-07-05-Ritz (DAC-007), raw data in EGA, metadata in Zenodo (https://zenodo.org/records/11237107)
Dataset EGAD50000001147
Construction of Thousands of Single Cell Genome Sequencing Libraries Using Combinatorial Indexing

Single-cell genome sequencing has proven valuable for the detection of somatic variation, particularly in the context of tumor evolution. Current technologies suffer from high library construction costs which restrict the number of cells that can be assessed and thus impose limitations on the ability to measure heterogeneity within a tissue. Here, we present Single cell Combinatorial Indexed Sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single cell libraries for somatic copy number variant detection. We constructed libraries for 16,698 single cells from a combination of cultured cell lines, primate frontal cortex tissue, and two human adenocarcinomas, including a detailed assessment of subclonal variation within a pancreatic tumor. "Reprinted from Vitak, S.A., et. al. 2017 with permission from Nature Methods."
Study phs001268
Dataset Rdata segmentations

This data set contains an .Rdata file for all the processed segmentation profiles from 81 lpWGS samples included used in downstream analyses from Github repository Evo_history_CACRC. Lastly, there is an .Rdata object with 50 segmentations for 50 total samples from 25 sporadic SNP adenomas used in the comparison with colitis samples.
Dataset EGAD00010001586
Human CD4+ T cells regulate peripheral immune responses in rheumatoid arthritis via IGFL2

To elucidate the role of the immunoregulatory mechanisms of human CD4+ T cells, we analyzed CD4+ T cells in PBMC, synovium and synovial fluid using single-cell RNA sequencing (scRNA-seq). We identified insulin-like growth factor (IGF) like family member 2 (IGFL2), an evolutionary novel paralogous inflammatory factor found exclusively in primates. ScRNA-seq of RA synovium showed that IGFL2 is specifically expressed by CD4+ T cells, predominantly within peripheral helper T (Tph) cells, with upregulation correlating to disease severity. IGFL2 promotes TGF-��-induced CXCL13 production in CD4+ T cells. In addition, IGFL2 activates NF-��B signaling and induces monocyte gene signatures associated with pathogenic macrophages from RA synovial tissue, including CXCL10 and CXCL9. Notably, blood IGFL2 protein levels significantly correlate with RA disease severity and effectively discriminate RA patients. These findings highlight the substantial involvement of IGFL2 in RA pathogenesis, emphasizing how human CD4+ T cells exert pleiotropic regulation of local immune responses via this novel primate-specific soluble factor.
Study JGAS000727
Origins and functional consequence of somatic mitochondrial DNA mutations

Recent sequencing studies have extensively explored the somatic alterations present in the nuclear genomes of cancers. Although mitochondria control energy metabolism and apoptosis, the origins and impact of cancer-associated mutations in mitochondrial DNA (mtDNA) are unclear. Here, we analysed somatic alterations in mtDNA from 1,675 tumors across 31 histologies. We identified 1,907 somatic substitutions, which exhibited dramatic replicative strand bias, predominantly C>T and A>G on the mitochondrial heavy strand. This strand-asymmetric signature differs from those found in nuclear cancer genomes but matches the inferred germline process shaping primate mtDNA sequence content. Numbers of mtDNA mutations showed considerable heterogeneity across tumor types. Missense mutations were selectively neutral and often gradually drifted towards homoplasmy over time. In contrast, mutations resulting in protein truncation undergo negative selection and were almost exclusively heteroplasmic. Our findings indicate that the endogenous mutational mechanism has far greater impact than any other external mutagens in mitochondria, and is fundamentally linked to mtDNA replication.
Study EGAS00001000968
Single-cell bam files and RNA sequencing of viral RNA stocks

244 infected single-cell alveolar bam files, 48 empty well bam files, and 52 RNA sequencing of amplicons (4 SARS-CoV-2 variants with 12 batches and 4 viral variants pool samples). 244 alveolar single cells were captured over 12 experimental batches and experimental condition is written in metadata uploaded as "infected_cells_final_revision.csv". on github (https://github.com/twkim-0510/SARS-CoV-2_viral_competition). Each bam file name corresponds to the sample_name column of the metadata.
Dataset EGAD00001009711
Star2xml: metadata converter into XML

Star2xml: metadata converter into XML Welcome to the landing page for star2xml - a tool developed by the European Genome-phenome Archive (EGA) that enables users to convert metadata into XML format that is compliant with EGA's programmatic submission system. For starters, if you are not sure what metadata is in this context, you may want to explore our brief metadata introduction. . Star2xml is useful for users who have already submitted their data to the EGA and want to also submit their metadata programmatically without having to create the XML files manually. The need for these XMLs containing metadata is explained in the EGA's metadata schemas and programmatic submission guidelines. In summary, star2xml allows for users to deposit metadata in a tabular format (e.g. excel spreadsheet) and transform it into a compliant XML format. This saves time in large submissions, where the sheer size of metadata makes it difficult to manage. For smaller submissions, it is recommended to use EGA's Submitter Portal (SP) instead. The installation of the tool is straightforward, and it contains detailed documentation on how to use it in its GitHub repository . While star2xml can help users convert metadata formats, it does not validate the resulting metadata against EGA's schemas. Therefore, after creating your XMLs, you should still validate your metadata before submitting it to EGA, as described in the documentation for programmatic metadata validation.
Documentation tools/star2xml
Spatial transcriptomics analysis of triple negative breast cancers

While triple negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigated TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We performed an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures led to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identified nine TNBC ecotypes, further validated in external datasets. Several ecotypes were associated with disease outcome and characterized by potentially actionable features. In this work, we provideThis is the largest TNBC series providing a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy. All the processed data needed to replicate the results from the publication (counts, images, clinical data, etc) are available on Zenodo: https://zenodo.org/records/14204217
Study EGAS50000000475
PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

This dataset contains Visium spatial transcriptomics data for the PCA Atlas study EGAS00001008332. It includes: - Raw Visium FASTQ runs (EGAR00004172272–EGAR00004172283) for all libraries. - spaceranger alignment BAM analyses for those libraries where BAM output was generated. - visium.csv: a slide-level mapping table linking Visium slide samples to EGA sample accessions, sequencing runs/experiments and spaceranger BAM analyses. - visium_tma.csv: a TMA mapping table describing how mini-TMA slides relate slide-level Visium libraries to tissue-level samples/cores. Together these objects provide the raw and aligned Visium data and the technical/biological mappings needed to understand and reuse the spatial transcriptomics component of the PCA Atlas. High resolution histology images are available from the following Zenodo repository, along with additional spaceranger outs for each slide: - https://zenodo.org/records/17411292
Dataset EGAD00001015796
EGA QuickView

EGA QuickView Secure and Remote Access to EGA Files What is EGA QuickView? EGA QuickView is a FUSE file system that allows secure and remote access to EGA files. It is a hybrid of sshfs and crypt4ghfs, which means it uses ssh to connect to the EGA distribution servers, downloads data in Crypt4GH format, and transparently decrypts those files. It is compatible with Linux and macOS 12. Why use EGA QuickView? EGA QuickView is a highly useful and convenient tool for anyone who needs to access EGA files remotely and securely. The software offers a quick and convenient way to browse through EGA files without the need for complete downloads, thus saving bandwidth and time. Whether you are a medical professional requiring access to sensitive patient data or a research scientist working on a project that involves EGA files, EGA QuickView provides a highly useful solution. The software boasts advanced security features, ensuring that your data is safe and protected at all times. Get started with EGA QuickView To get started with EGA QuickView, visit the EGA QuickView GitHub repository for more information and installation instructions.
Documentation access/download/visualisation/ega-quickview
EGA FUSE Client

EGA FUSE Client What is EGA FUSE Client? The EGA FUSE Client is a Java Native Runtime (JNR) based Filesystem in Userspace (FUSE) client that allows users to access authorised EGA files by presenting them in a virtual directory. This means that users can use authorised EGA files like regular files without having to download them first. The EGA FUSE Client is a useful tool for researchers who need to access and work with large amounts of EGA data. Why use EGA FUSE Client? The EGA FUSE Client provides several benefits for researchers working with EGA data: Efficient use of storage space: With the EGA FUSE Client, users can access authorised EGA files without having to download them first. This means that users can save storage space on their local machines. Faster access to data: Since users can access authorised EGA files directly through the EGA FUSE Client, they can work with the data more quickly than if they had to download the files first. Simplified workflow: The EGA FUSE Client presents authorised EGA files in a virtual directory, allowing users to work with the files like regular files. This simplifies the workflow for researchers who need to access and work with large amounts of EGA data. Get started with EGA FUSE Client To get started with EGA FUSE Client visit the EGA FUSE Client GitHub repository for more information and installation instructions.
Documentation access/download/visualisation/fuse-client
PyEGA3 download client

PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.
Documentation access/download/files/pyega3
EGA metadata schema

EGA Schemas In this page, you will find information on how the European Genome-phenome Archive (EGA) manages its metadata standards using both XML Schema Definition (XSD) and JavaScript Object Notation (JSON) formats. If you are not sure what this means, you may want to explore our brief metadata introduction. This information may be of your interest if you are planning to learn more about how the EGA is built and how to wrap around it for other processes. Nevertheless, if you are a common user (e.g. submitter or requester), you would not have to worry about these schemas nor their format, since they are implemented in user-friendly ways for you. Metadata standards are rules that define how to format and structure data of metadata objects (i.e. entities), like EGA's samples or experiments in a consistent manner. These objects are the nodes of the metadata model of the EGA (Figure 1). Figure 1. Diagram of EGA's metadata model. The model's building blocks are objects (e.g. sample), which can reference each other (e.g. an experiment referencing the used samples). Once your files are uploaded, they can also be referenced by Runs and Analyses. The submission object is an object itself that compiles many others. At EGA, we inherit our metadata schemas from the European Nucleotide Archive (ENA), and we have expanded them to include bespoke objects such as "Policy", "Dataset", and "DAC" (Data Access Committee) for our specific use-case: handling sensitive human data. See below a list of all our metadata objects and some context for each. Metadata Object EGA accession Description Examples of metadata fields Study EGAS… Information about the study Study type, study title, study abstract… Sample EGAN… Information about the used samples in the experiment or analysis Taxon ID, scientific name, biological sex, phenotype… Experiment EGAX… Information about the performed experiment Used libraries, sequencing platform, reference to the used samples… Analysis EGAZ… Contains information about the analysis Type of analysis, used assembly, reference sequence… Run EGAR… The run holds information about the files containing the raw reads generated in a run of sequencing Platform, spot descriptor, raw file references… DAC EGAC… Contains information about the Data Access Committee (DAC) DAC contacts, contact emails… Policy EGAP… Contains the Data Access Agreement (DAA) and policy which its usage complies with Policy text, data use ontologies (DUO) codes… Dataset EGAD… Contains the collection of runs/analysis to be subject to controlled access Dataset type, compilation of Run's and Analysis' IDs There are two different sets of schemas, based on their formats, in which the EGA accepts metadata: XSDs (for XML files) and JSON Schemas (for JSON files). EGA's XML Schema Definition . When programmatic submissions are pushed through the European Bioinformatics Institute (EBI) system, XML format is used. The schemas that are applied for this format are defined in XML Schema Definition (XSD) files, which can be found at ENA's GitHub repository. You can find more information on how to validate and submit your data programmatically in our programmatic submission documentation. Furthermore, see at our GitHub repository some XML examples with either made-up values (similar to what you would submit) or descriptive values for each field (just for documentation). EGA's JSON Metadata Schemas . When programmatic submissions are pushed through the Centre for Genomic Regulation (CRG) system, JSON format specifications are used instead. See the full JSON specifications for further details. In conclusion, the EGA metadata schemas are crucial for maintaining the quality and consistency of submitted data. By understanding and following the rules outlined in these schemas, you can ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource. Sample checklists Besides the standards in our schemas, we have another layer called Sample checklists. These are another system that the EGA inherits from ENA, and specifies what attributes are required or allowed for a sample object. The EGA uses these checklists to enforce that, for example, a sample object has the three mandatory attributes: subject ID, sex and phenotype of the individual the sample was taken from. When you submit to EGA, our checklist is automatically selected by default.
Documentation submission/metadata/ega-schema
How to encrypt files with Crypt4gh

File preparation Due to the processes used at the EGA for file archival the use of non-alphanumeric characters in a filename will cause issues in archival. By convention whitespaces in filenames are to be avoided and should be replaced with the underscore character (_). Before encrypting your files please make sure that any files that will be uploaded to EGA do not use special characters such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | & Crypt4gh EGACryptor Files encrypted with Crypt4gh must be uploaded via INBOX Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated you will be able to request a submitter role. Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g. ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. Encrypting your files Please note that you can also encrypt your files by uploading them directly to the "to-encrypt" folder in your upload area If your connection is unstable, please encrypt them using Crypt4gh The EGA encryption of this inbox is based on Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git Save now the following Crypt4GH public key, into a file, say ingestion.pubkey. __EGA_INGESTION_PUBKEY__ Encrypt a given file with the following command: crypt4gh encrypt --recipient_pk ingestion.pubkey < file_to_encrypt > encrypted_file.c4gh The command reads the file from stdin (with < ) and output the encrypted version to stdout (with > ). Replace file_to_encrypt and encrypted_file.c4gh with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write.
Documentation submission/data/file-preparation/crypt4gh
Distribution of data using Live Distribution

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

We will shortly be deprecating the Java-based download client also known as API V2. The Java client began its life at the back end of 2014, and during its time has seen many developments that have served our users well. During its tenure, the Java client enabled distribution of over 6.5 million files totalling nearly 15 petabytes of data to researchers around the globe. Recent increases in genomic and phenotypic data file sizes, the number of datasets archived in the EGA, the number of users downloading data, and the diversity of download locations has meant that at times the Java client struggled to distribute data efficiently. In response, our team of developers have built a new download client with more robust features and better functionality and reliability. Enter stage left our shiny new API V3.1! The new API V3.1 will replace both API V2 and API V3. The download client for the new API V3.1 is written in Python, and the code is openly available on Github. This Python client makes https calls to the EGA AAI (https://ega.ebi.ac.uk:8443/) and to the EGA Data API (https://ega.ebi.ac.uk:8052), both of which must be accessible from the client location. In addition to general updates to improve performance, the API V3.1 has three new features enhancing the EGA distributions services. First, requested data are now downloaded over secure https connections rather than http, meaning that data can be delivered unencrypted. Instead of separately requesting, downloading, and decrypting data using the Java client, data can now be requested and downloaded using a single command without the need for decrypting. The API V3.1 also automatically verifies each file’s unencrypted md5 after download to ensure the file has downloaded correctly. Second, the new API supports segmenting and automatic resuming. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the new resume feature, downloads - both segmented and continuous stream - will automatically resume if they encounter any errors or the connection is interrupted. These features result in both a faster and more reliable download experience. Finally, the new API V3.1 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file saving both time and storage space. The new features of the Python-based API V3.1 represent significant improvements in security, speed, reliability, and flexibility that are sure to provide a better data download experience. To take full advantage of these features, users must update to Python 3.X. Considering the changes in the data structures with the new API, the upgrade to a new version of the Python client is also mandatory. Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 -U We are excited for users to try out the new features and take advantage of the multifariously growing EGA distribution services for human biomedical omics data. If you have any questions or feedback, please email the EGA Helpdesk team. We'd love to hear from you!
Blog new-ega-data-download-client
Programmatic submission based on XML

Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.
Documentation submission/metadata/submission/programmatic-submission-xml
Papua New Guinean Genome Diversity Commitee
Dac EGAC00001002177
Papua New Guinean Y chromosome Diversity Commitee
Dac EGAC00001002531
Paired-end RNA-seq analysis of the TERT promoter mutant GBM cell lines

This dataset contains 9 RNA-seq BAM files. RNA was derived from TERT promoter mutant GBM cell lines and sequenced on an Illumina HiSeq4000 sequencer with paired-end reads and an average read length of 50 base pairs. Reads were aligned with TopHat (v2.0.14) using a GENCODE V19 transcriptome-guided alignment.
Dataset EGAD00001005435
3q-capture DNA sequencing of primary AMLs with 3q26 rearrangements

This dataset contains DNA sequencing of the chromosome 3q region in 28 primary AML cases with 3q26 rearrangements (3q26-rearranged AML). Genomic DNA was fragmented using the Covaris shearing device (Covaris), and sample libraries were assembled following the TruSeq DNA Sample Preparation Guide (Illumina). After ligation of adapters and an amplification step, target sequences of chromosomal regions 3q21.1-q26.2 were captured using custom in-solution oligonucleotide baits (Nimblegen SeqCap EZ Choice XL). The design of target sequences was based on the human genome assembly hg19: chr3q21.1:126036241-130672290 - chr3q26.2:157712147-175694147. Amplified captured sample libraries were paired-end sequenced (2x100 bp) on the HiSeq 2500 platform (Illumina) and aligned against the hg19 reference genome using the Burrows-Wheeler Aligner (BWA).
Dataset EGAD00001006820
Comprehensive NGS Profiling to Enable Detection of ALK Gene Rearrangements and MET Amplifications in Non-Small Cell Lung Cancer

We evaluate the analytical performance of the PGDx™ elio™ tissue complete assay, a 505 gene next-generation sequencing (NGS) tissue-based assay, that has now been FDA-cleared for use by physicians to help guide treatment decisions for cancer patients, using a NSCLC cohort of 38 patients.
Dataset EGAD50000000016
AVENIO Expanded ctDNA panel sequence alignments (BAMs)

The AVENIO ctDNA Expanded Kit is a next-generation sequencing (NGS) liquid biopsy assay with a 77 gene panel (192 kb) containing genes in U.S. National Comprehensive Cancer Network (NCCN) Guidelines and emerging cancer biomarkers. This pan-cancer assay was applied to 100 plasma samples from patients with lung cancer undergoing treatment in the OSCILLATE trial. After 150 bp paired-end sequencing, reads were aligned to the human genome reference with the AVENIO Oncology Analysis Software. These files are the sorted non-deduplicated alignments generated by the analysis software used for subsequent variant, indel and CNV calling.
Dataset EGAD50000000138
Access to dataset, "Feasibility of Functional Precision Medicine for Guiding Treatment of Relapsed/Refractory Pediatric Cancers"

Access to sequencing data from project.
Dac EGAC50000000124
SNP Genotype Data: Subpopulations of Filipino, Malaysian, and Papua New Guinea

In the context of the study 'Exploring Adaptive Phenotypes for the Human Calcium-Sensing Receptor Polymorphism R990G,' this repository contains 11 VCF files. Each file includes genetic data from 22 autosomes, representing distinct subpopulations. The subpopulations are as follows: Malaysian: Aeta (15 samples) Agta (11 samples) Batak (10 samples) Mamanwa (6 samples) Filipino: Jehai (13 samples) Kintaq (6 samples) Temiar (5 samples) Papua New Guinea: Highlands (2 samples) Koinambe (10 samples) Kosipe (10 samples) Sepik (2 samples) The numbers in parentheses indicate the sample size for each respective subpopulation.
Study EGAS50000000044
MediMer: A versatile do-it-yourself peptide-receptive MHC class I multimer platform for tumor neoantigen-specific T cell detection

Peptide-loaded MHC class I (pMHC-I) multimers have revolutionized our capabilities to monitor disease-associated T cell responses with high sensitivity and specificity. To improve the discovery of T cell receptors (TCR) targeting neoantigens of individual tumor patients with recombinant MHC molecules, we developed a peptide-loadable MHC class I platform termed MediMer. MediMers are based on soluble disulfide-stabilized β2-microglobulin/heavy chain ectodomain single-chain dimers (dsSCD) that can be easily produced in large quantities in eukaryotic cells and tailored to individual patients’ HLA allotypes with only little hands-on time. Upon transient expression in CHO-S cells together with ER-targeted BirA biotin ligase, biotinylated dsSCD are purified from the cell supernatant and are ready to use. We show that CHO-produced dsSCD are free of endogenous peptide ligands. Empty dsSCD from more than 30 different HLA-A,B,C allotypes, that were produced and validated so far, can be loaded with synthetic peptides matching the known binding criteria of the respective allotypes, and stored at low temperature without loss of binding activity. We demonstrate the usability of peptide-loaded dsSCD multimers for the detection of human antigen-specific T cells with comparable sensitivities as multimers generated with peptide-tethered β2m-HLA heavy chain single-chain trimers (SCT) and wild-type peptide-MHC-I complexes prior formed in small-scale refolding reactions. Using allotype-specific, fluorophore-labeled competitor peptides, we present a novel dsSCD-based peptide binding assay capable of interrogating large libraries of in silico predicted neoepitope peptides by flow cytometry in a high-throughput and rapid format. We discovered rare T cell populations with specificity for tumor neoepitopes and epitopes from shared tumor-associated antigens in peripheral blood of a melanoma patient including a so far unreported HLA-C*08:02-restricted NY-ESO-1-specific CD8+ T cell population. Two representative TCR of this T cell population, which could be of potential value for a broader spectrum of patients, were identified by dsSCD-guided single-cell sequencing and were validated by cognate pMHC-I multimer staining and functional responses to autologous peptide-pulsed antigen presenting cells. By deploying the novel CHO producer cell-based dsSCD MHC-I MediMer platform, we hope to significantly improve success rates for the discovery of personalized neoepitope- specific TCR in the future by being able to also cover rare HLA allotypes.
Study EGAS50000000065
AVENIO Expanded ctDNA panel sequence alignments (BAMs) from plasma DNA of lung cancer patients from the OSCILLATE trial

The AVENIO ctDNA Expanded Kit is a next-generation sequencing (NGS) liquid biopsy assay with a 77 gene panel (192 kb) containing genes in U.S. National Comprehensive Cancer Network (NCCN) Guidelines and emerging cancer biomarkers. This pan-cancer assay was applied to 100 plasma samples from patients with lung cancer undergoing treatment in the OSCILLATE trial. After 150 bp paired-end sequencing, reads were aligned to the human genome reference with the AVENIO Oncology Analysis Software. These files are the sorted non-deduplicated alignments generated by the analysis software used for subsequent variant, indel and CNV calling.
Study EGAS50000000103
Sensitive circulating tumor DNA based residual disease detection in epithelial ovarian cancer

We analyzed 264 plasma samples collected between June 2016 and September 2021 from 63 epithelial ovarian cancer patients using tumor-guided plasma cell-free DNA analysis to detect residual disease after treatment.
Dataset EGAD50000000360
Bulk Iso-Seq from brain tissue and exosomes isolated from brain tissue using long-read PacBio sequencing of poly-adenylated transcripts

Human brain sEVs were isolated from postmortem human brain tissue. RNA was extracted from both the source brain tissue and sEVs and prepared for long-read sequencing. cDNA was sequenced on the PacBio Sequel II. The data provided have undergone processing using the tool ccs (v6.0.0) to generate circula consensus reads. For further processing, it is recommended to follow the guidelines at https://isoseq.how/, starting with using lima to remove cDNA primers.
Dataset EGAD50000000043
Comprehensive NGS Profiling to Enable Detection of ALK Gene Rearrangements and MET Amplifications in Non-Small Cell Lung Cancer

We evaluate the analytical performance of the PGDx™ elio™ tissue complete assay, a 505 gene next-generation sequencing (NGS) tissue-based assay, that has now been FDA-cleared for use by physicians to help guide treatment decisions for cancer patients, using a NSCLC cohort of 38 patients.
Study EGAS50000000010
Cross-Sectional Characterization of Idiopathic Bronchiectasis

This GDMCC protocol will study adult patients with non-CF, idiopathic bronchiectasis, whose genetic etiologies are not known. Idiopathic bronchiectasis is reportedly more common in females with certain tall, thin body types and associated with environmental organisms, such as nontuberculous mycobacterium (NTM). The other susceptibility factors predisposing to bronchiectasis or acquisition of NTM are unclear. The study will attempt to broaden the understanding of this disease by comparing gender-associated factors and NTM status. A relatively equal number of both females/males and NTM/non-NTM infected subjects will be enrolled. Approximately 300 people may be screened to find 260 eligible subjects, since a small number (e.g., 40 patients) may be diagnosed with PCD, vCF, or other known etiology as an explanation for the bronchiectasis. This single-visit protocol will use a systematic approach to characterize the physical features, radiographic patterns, and associated lower airway microbial flora. There is no natural history of disease course follow-up component to this protocol. Participants will have one outpatient clinic visit for evaluation with a physical examination including detailed body size measurements, medical history, collection of blood samples for routine lab tests and genetic analyses, and a chest X-ray if no recent one is available. Participants will also have tests of lung function, and measurement of a gas called nitric oxide in the nose. Participants whose initial tests show abnormal results may also be asked to have a nasal scrape to collect cell samples and/or a skin sweat test to measure salt concentrations. Participants will also have a sputum specimen collected during the visit and will be asked to collect two additional early morning sputum samples and a mouth rinse at home within 2 weeks of the clinic visit, and mail the sample collection materials to the research team. Careful evaluation and characterization of the physical and clinical characteristics will guide the genetic characterization of idiopathic bronchiectasis, and likely lead to an improved diagnostic approach. Identification of disease causing genes may provide new therapeutic targets.
Study phs001279
Discovering the Genetic Basis of Human Neuroblastoma: A Gabriella Miller Kids First Pediatric Research Program (Kids First) Project

The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Children with disseminated neuroblastoma have a very high risk of treatment failure and death despite receiving intensified chemotherapy, radiation therapy and immunotherapy. The long-term goal of our research program is to ultimately improve neuroblastoma cure rates by first comprehensively defining the genetic basis of the disease. The central hypothesis to be tested here is that neuroblastoma arises largely due to the epistatic interaction of common and rare heritable DNA variation. Here we will perform a comprehensive whole genome sequencing of 563 quartets of neuroblastoma patient germline and diagnostic tumor DNAs and germline DNAs from both parents. The case series was recently collected through a Children's Oncology Group epidemiology clinical trial and is robustly annotated with complete demographic (age, sex, race, ethnicity), clinical (e.g. age at diagnosis, stage, risk group), epidemiologic (parental dietary and exposure questionnaire) and biological (e.g. tumor MYCN status and multiple other tumor genomic measures) co-variates. Subjects were consented for genetic research and DNA is immediately available for shipment for sequencing. We propose Illumina-based whole genome sequencing in the 593 "trio" germline samples (Aim 1; due to missing parent: 487 full neuroblastoma triads, 106 child-single parent dyads = 1673 whole genome sequences) and matched diagnostic tumor DNA (Aim 2; N=366) at 30x sequencing depth (N=2039 whole genome sequences). Also in Aim 2 we will perform whole exome (100x) and RNA sequencing on the 366 tumor DNA and 228 tumor RNA samples from this cohort. Finally, we propose a pilot study of structural variation using long-range sequencing in 10 non-overlapping tumor samples chosen based on potentially relevant chromosomal alterations discovered with conventional NGS. Thus, a total of 2277 individual samples and 2655 sequences will be generated. We will use our established analytic pipeline that is currently being used to study the germline genomes of all cases sequenced through the NCI supported Therapeutically Applicable Research to Generate Effective Treatments program. We plan a three-stage analytic approach, first focusing on classic de novo and inherited Mendelian damaging alterations. We will next integrate our extensive epigenomic data from human neuroblastoma cell lines and genome-wide association study data (N=5,703 neuroblastoma cases to date) to guide a comprehensive assessment of noncoding variants that influence tumor initiation with a recently established analytic pipeline. Finally, we will utilize the tumor DNA analyses to inform relevance via somatic gain or loss of function effects at the sequence and/or copy number levels. All data generated in this project will be immediately placed into the Genomic Data Commons (GDC) and we will compute within this environment by importing our analytic pipelines into the GDC. These data will be fully integrated into the Kids First Data Resource and freely shared with all academically qualified petitioners. This comprehensive data set derived from a large and richly phenotyped series of neuroblastoma DNA quartets will be integrated with existing germline and/or tumor genomic data from over 6,000 neuroblastoma subjects (but none with matched patient-parent germline sequencing data) to provide an unparalleled opportunity to comprehensively discover the genetic basis of neuroblastoma.
Study phs001436
International Cancer Proteogenomics Consortium (ICPC): Proteogenomics of East-Asian Breast Cancer

The incidence of invasive breast cancer is rapidly increasing in East Asia, enriching in younger patients and luminal disease. We presented a deep proteogenomic landscape of a prospectively enrolled early-stage cohort in Taiwan to uncover the etiology, age-related subtype and oncogenic vulnerabilities underlying the heterogeneous disease. Multilayer proteogenomic architecture revealed distinct endogenous and environmental carcinogen-associated mutagenesis, linking APOBEC (apolipoprotein-B mRNA editing enzyme, catalytic polypeptide-like) cytidine deaminase and DBAC (dibenz[a,j] acridine) signatures to significantly elevated hormone biosynthesis and chemical carcinogenesis in younger patients, and identifying a novel subset of high mutation burden and immune escape potentially beneficial from combined epigenetic therapy and immunotherapy. A proteomics-informed classification resolves luminal heterogeneity, defines high-risk recurrent luminal disease and nominates biomarkers and actionable druggable pathways. Furthermore, proteogenomics profile distinguished a young luminal population susceptible to exogenous carcinogen and having DNA repair deficiency. This study illuminates the age-related molecular subtypes and oncogenic vulnerabilities, providing a proteogenomics-transformative guide for patient stratification and precision therapeutics beyond clinical staging.
Study phs003150
Ghana Breast Health Study

The Ghana Breast Health Study (GBHS) was a multi-disciplinary, population-based case-control study conducted in Accra and Kumasi, Ghana that was designed to investigate breast cancer etiology in the West Africa region. The study enrolled 1,126 invasive breast cancer cases and 2,106 frequency matched population-based controls using census-based sampling. GHBS participants were between the ages of 18 to 74 years of age. Breast cancer cases were recruited at the time they presented with lesions suspicious of breast cancer at three hospitals: Korle Bu Teaching Hospital (Accra), Komfo Anoyke Teaching Hospital (Kumasi) and Peace and Love Hospital (Kumasi). These hospitals are the primary providers of treatment for breast cancer in Ghana, allowing for cases in our study to represent most breast cancers diagnosed in these areas during the study period. The following publications describe the GBHS in detail: 1) Brinton LA et al. Design considerations for identifying breast cancer risk factors in a population-based study in Africa. Int J Cancer 2017;140:2667-2677 (PMID:28295287). 2) Nyante SJ et al. Recruiting population controls for case-control studies in sub-Saharan Africa: The Ghana Breast Health Study. PloS one 2019;14:e0215347 (PMID:30990841)Among subjects with an available source of germline DNA genotyping was performed using the Infinium Global Screening Array-24 per the manufacturer’s guidelines using the Infinium automated protocol. We excluded probable duplicates and close relatives, and samples with a call rate < 95% or samples with extreme heterozygosity. Female sex was verified by Identifiler using X chromosome heterozygosity and the X chromosome inbreeding coefficient F statistics. The GLU genetics’ ld.tagzilla module was used to confirm African ancestry. To resolve more detailed population substructure, principal components analysis was conducted among all unrelated subjects using the EIGENSOFT v7.2.1. Imputation was performed using the Michigan imputation server Minimac3 with 1K genomes phase3 as reference panel.
Study phs002387
Joslin Study on the Genetics of Type 2 Diabetes

Between 1993 and 2003, families were recruited in the Joslin Study on the Genetics of Type 2 Diabetes for the presence of an autosomal dominant mode of inheritance of diabetes. Recruiting and screening of families occurred through probands who were receiving medical care at the Joslin Clinic (Boston, MA). Screening of families included 1) a proband and at least one sibling with type 2 diabetes, 2) diabetes occurring in at least three generations, 3) diabetes inherited on one side of the prospective family. Probands had to have a diabetes diagnosis between the age range from 10 to 60 years. Demographic data, clinical data, and family history were collected from participating family members, along with blood and urine samples. This study includes genetic and phenotypic data from one family examined in Simeone, Wilkerson, et. al. (NPJ Genomic Medicine 2022, PMID: 35869090). Genotype data for 14 family members and whole genome sequencing data for 6 individuals were generated with the goal of identifying a potential genetic cause of disease in this family. To de-identify this family and protect confidentiality, information on sex in phenotype data and variants identified in X, Y, and MT chromosomes have been removed in compliance with IRB guidelines.
Study phs002959
Whole Exome Sequencing and RNA-Seq to Characterize the Somatic Breast Cancer Landscape Among Latinas in California

Somatic mutational profiles are increasingly being used to guide treatment. However, there is a dearth of data from non-European populations. To address this gap, we conducted paired tumor/normal whole exome paired-end sequencing and tumor RNA-Seq on 146 breast tumors from 140 Hispanic/Latina (H/L) women with breast cancer. We characterized somatic mutations, copy number alterations, tumor subtypes [Prediction Analysis of Microarray 50 (PAM50)], Catalogue Of Somatic Mutations In Cancer (COSMIC) mutation signatures and expression profiles, and compared them to breast tumors from non-Hispanic White (White) women in The Cancer Genome Atlas (TCGA). Using MutSigCV, we found that the most commonly mutated genes, including TP53, RUNX1, PTEN, PIK3CA, MAP3K1, GATA3, CDH1 and CBFB, were similar to those in breast tumors from non-Hispanic white women. Novel findings were that outliers of BLTP2 (aka KIAA0100) over-expression and COSMIC mutational signature 16 were more common in tumors from H/L women than White women. We also identified that the known germline APOBEC3A/B deletion variant was significantly more common in H/L women and was associated with indigenous American ancestry. Data available through dbGaP will be whole-exome sequencing (WES) and RNA sequencing (RNA-Seq) data, as well as phenotypic data, including estrogen receptor, progesterone receptor, and HER2 receptor status, tumor histology, stage, and grade, and age at diagnosis.
Study phs003218
NHLBI Recipient Epidemiology Donor Evaluation Study (REDS)-III - Red Blood Cell Omics (RBC-Omics) Study

The Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) is an initiative launched by the National Heart, Lung, and Blood Institute (NHLBI) with the goal to improve transfusion medicine practice, ensure safety and reduce risks. Extensive variation exists in donor iron metabolism, hemoglobin production, and hemolysis. Despite this variation, current blood banking guidelines regulating blood donation frequency, donation volume, and storage time and conditions remain uniform for all donors. The RBC-Omics study proposed to investigate if this approach is appropriate. Specifically, the RBC-Omics study: Established a multi-ethnic cohort of blood donors with well-characterized demographic, behavioral, and donation history. Developed a database linking donations from these blood donors to outcomes in transfusion recipients. Identify genetic factors impacting hemoglobin, donation history, ferritin, and iron metabolism. Identify markers and clinical factors associated with the iron-related disorders pica and restless leg syndrome (RLS). Define the genetic and metabolic basis for donor-specific differences in spontaneous, osmotic and oxidative hemolysis at the end of storage. RBC-Omics study has 13,403 blood donors over the age of 18 that were recruited from December 2013 to December 2015 at four REDS-III blood centers: the American Red Cross (Farmington, CT), the Institute for Transfusion Medicine (ITxM, Pittsburgh, PA), Blood Center of Wisconsin (Milwaukee, WI), and the Blood Centers of the Pacific (San Francisco, CA). All blood donors are healthy volunteers who passed donation screening and were not anemic. The samples were genotyped with a customized Affymetrix Axiom array called the TM-Array. The TM-array was specifically designed to cover genetic variations related to transfusion medicine. Also copy number polymorphisms in the alpha globin, beta globin and Rh gene clusters were added. The genetic variations among different ethnicity groups were considered as well.
Study phs001955
Genomic Predictors of Combat Stress Vulnerability and Resilience

The Genomic Predictors of Combat Stress Vulnerability and Resilience Study was designed to probe the likely hereditary basis for risk or resilience to develop PTSD and other trauma spectrum disorders. The overall guiding hypothesis was that genomic variation gives rise to risk/susceptibility traits that, when actuated by traumatic environmental stimuli, such as combat, give rise to PTSD and other stress-related phenotypes. Two studies designed to identify risk and resilience factors for combat-induced, stress-related symptoms are being conducted by our group: The Marine Resiliency Study (MRS) is a prospective PTSD study with longitudinal follow-up (pre- and post-exposure to combat stress) of US Marines bound for deployment to Iraq or Afghanistan. Extensive phenotyping includes 3 domains: Psychosocial, Psychophysiologic, and Biophysiologic. The biological and physiological measures collected were chosen in part due to their potential to serve as intermediate phenotypes for stress-related disorders. A second, cross-sectional study involves a cohort of combat-exposed active duty or previously deployed service members (CAVC), including PTSD cases and controls with comparable psychosocial and clinical phenotypes. Little information is available about the factors that explain why some trauma survivors develop stress disorders and some do not. It is hoped that the insights gained from this approach will improve understanding of the genetic contributors to PTSD, and potentially provide novel diagnostic tests and therapeutic approaches to this currently enigmatic and difficult-to-manage condition.
Study phs000864
eMERGE Network's Multi-Center Pilot of Pharmacogenetic Sequencing in Clinical Practice

eMERGE-PGx is a multi-site test of the concept that sequence information can be coupled to electronic medical records (EMRs) for use in healthcare. The promise of personalized medicine - health care guided by each individual's biological characteristics - is being fostered by increasingly powerful and economical methods to acquire clinically relevant biomarkers from large numbers of people. One therapeutic area that seems especially ripe for an early test of the personalized medicine concept is pharmacogenomics (PGx) - the idea that individual variation in drug response includes a genomic component. Drug response variation is an accepted feature of virtually all drug treatments, and contemporary molecular biologic tools continue to identify key genes mediating drug metabolism, transport, and targets. Importantly, common variation in these genes is an increasingly well-recognized contributor, sometimes with large effects, to variation in drug responses. As a result, recommendations for genotype-guided therapy are increasing. These evidence-based recommendations, if implemented in health care practice, could reduce adverse drug events and improve time to therapeutic response. Through eMERGE-PGx, we are developing strategies for the optimal implementation of genetic sequence data into the clinical environment with the ultimate goal of improving patient care. Site and participants include: Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. The PGx population selected for sequencing with the PGRNseq panel at CHOP is 1,650 children from CAG's biorepository with well-documented drug-related severe adverse events (SAEs) or EHR-based drug response profiles. SAEs were extracted from EPIC records and from CHOP's Adverse Event (AE) database, which documents every AE at CHOP. These AEs are classified by a medical review panel according to the causal relationship with the suspected drug into 'doubtful', 'possible', and 'probable'. Individuals with events classified as probable, severe and objective, were selected for sequencing. The drugs more frequently associated with adverse events are antibiotics, antineoplastics, immunosuppressants and psychotropic drugs. This cohort constitutes 50% of the target population. The remaining subjects were selected using EHR-based algorithms that we have developed and validated at CAG for identifying patients not responding to ADHD medication (primarily atomoxetine) and patients refractory to antiepileptic treatment from responders. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): 811 CCHMC samples were obtained from children, adolescents or young adults exposed to medication or at risk for needing medication of study interest. 55% of participants were exposed to one or more opioids and their DNA source was a CCHMC study-specific biobank; while 27% of participants were at risk for needing an opioid for surgical pain management and were newly recruited. The remainder of the cohort was exposed to methylphenidate and their DNA samples were obtained from a CCHMC study-specific biobank. The focus of Boston Children's Hospital eMERGE PGx project is on individuals with epilepsy. Samples were taken from a current pharmacogenomics study already in place through which DMET analysis was run and used as confirmation for PGRN-Seq results. A total of 109 samples were sent for PGRN-Seq analysis at University of Washington. The remaining 141 epilepsy samples were from Children's Hospital of Philadelphia and underwent testing with PGRN-Seq at CHOP. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All data are from Geisinger patients who consent to participate in the MyCode project. MyCode participants agree to provide biological samples for broad research use, including genomic analysis, and for linking of sample data to information in the participant's Geisinger health record. The consent also permits sharing of de-identified data for research purposes. Group Health(GH)/University of Washington (UW): Potential GH participants for the PGx project were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~6300). Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF,) or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if a participant had any laboratory event of creatine kinase (CK) > 1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): For this study, 750 subjects were selected and enrolled into PGx based on Vanderbilt's algorithm designed to enrich for patients who are most likely to receive one of three common drugs (Clopidogrel, Warfarin or Simvastatin) in the next 2-3 years. These patients were sent a letter of invitation and description of the PGx project. Follow-up phone calls were made, and interested subjects came in for a one time meeting to discuss the project and go through the informed consent with the research coordinator. If they were interested they signed the consent and HIPAA forms and gave blood. Subjects were chosen and enrolled into PGx independently of previous biobank participation. Mayo Clinic: The Right Drug, Right Dose, Right Time - Using Genomic Data to Individualize Treatment (The RIGHT Protocol) enrolled 1013 patients to test the hypothesis that prescribers could deliver genome-guided drug therapy at the point-of-care by using pharmacogenomic data preemptively integrated in the electronic medical record. Complete details regarding the study population have been previously described (Bielinski et al., 2014). Icahn School of Medicine at Mount Sinai School (Mt Sinai): Our study site is the Primary Care Associates (PCA) practice group of the Mount Sinai Faculty Practice Associates (FPA) of the Mount Sinai Medical Center in New York City. This practice has 12 physician providers. All patient encounters are documented and managed with EpicCare ambulatory electronic medical record. Active PCA Patients eligible for enrollment fulfilled the following criteria: a) age 50 or older receiving clinical care at Mount Sinai FPA PCA practice with at least one practice encounter within 18 months prior to commencement of enrollment; b) no history or current use of clopidogrel, warfarin, or simvastatin. Eligible patients were invited to participate through de novo recruitment by letter sent by their provider. Interested patients were screened for eligibility and enrolled to participate in the eMERGE PGX study on site by a dedicated research coordinator. In addition to de novo enrollment from clinical practice, patients of FPA PCA who had previously enrolled in Mount Sinai's BioMe Biobank program AND fulfilled eligibility criteria as stated under a) and b) were identified by chart review and samples sequenced at CIDR using PGRNseq platform (N=300). PGRNseq data from 291 samples passed stringent quality control and are included in the current data set. Furthermore, 56 of these patients carrying known and validated 'actionable' variants affecting prescribing of clopidogrel, warfarin, and/or simvastatin were enrolled in the eMERGE PGX study following invitation through recontacting by the Principal Investigator of the BioMe Program. Northwestern University: Participants for this study were recruited from the General Internal Medicine (GIM) clinic at Northwestern Medical Group (NMG). Patients were selected for invitation to participate if they had been seen a minimum of two times over the last four years, having a high likelihood to receive a prescription for warfarin, Plavix, or a statin, and are seeing a physician who has agreed to allow their patients to be contacted for the study. We utilized an algorithm developed at Vanderbilt and tailored to our population which uses our EHR to estimate the probability that individuals will receive a prescription for warfarin, Plavix, or a statin in the next three years. Participants were sent a letter explaining the study prior to their GIM appointment and offered participation at the time of their visit. Participants were consented on-site and blood drawn after consent was obtained. The GIM clinic consists of 39 primary care physicians who provide approximately 80,000 patient encounters per year. As with any large primary care clinic, a significant proportion of patients in GIM clinic suffer from a variety of chronic health conditions, such as diabetes, hypertension, and coronary artery disease. Over 50,000 individuals have been seen by GIM doctors in the past 5 years; 11,562 of these patients have evidence of a statin prescription in the EHR, 3,436 have evidence of a warfarin prescription, and 1,872 have evidence of a Plavix prescription. Vanderbilt University: The more than 1000 participants enrolled into Vanderbilt's eMERGE PGx study were newly recruited from the Cardiology and Internal Medicine Clinics and the Hillsboro Medical Group within Vanderbilt University Medical Center (VUMC). Patients were selected based on a predictive algorithm estimating the patient's likelihood of receiving Clopidogrel, Warfarin, and/or Simvastatin. The algorithm identifies primarily older middle-aged patients, and the mean age of the study group is 74. The cohort is approximately 45% female with 75% of subjects self-identified as EA and 24% as AA. Subjects were consented in person by study personnel following a routine clinic visit and an introduction to the study staff by their doctor. VUMC is a comprehensive health care facility dedicated to patient care, research, and the education of health care professionals. Translational research into the causes and treatment of disease as well as studying fundamental biological properties is the primary focus of discovery at Vanderbilt. Clinical research is conducted in Vanderbilt University Hospital, the Nashville Veterans Administration Hospital, Meharry General Hospital and in their associated outpatient clinics. These hospitals and clinics, all associated with the Vanderbilt system, each have full time Vanderbilt faculty and medical housestaff and provide clinical care and participate in research programs. The Vanderbilt Clinic is comprised of more than 95 adult outpatient specialty practices and received over 1.5 million ambulatory visits in 2012-13. The Vanderbilt Heart and Vascular Institute offers a comprehensive heart program offering diagnosis, medical treatment, minimally invasive therapies, surgical intervention and disease management, tailored to each individual's unique needs. All programs within the Vanderbilt Clinic have survival figures that surpass the national average.
Study phs000906
Genome-Wide Association Study of HCC in Non-Asian USA Population

This GWAS of HCC relied on existing biological and data resources from 7 USA sites and one Canadian site which allows for the integration of genetic and environmental data. Total of 2199 case patients and 1103 non cancer controls were genotyped. Case patients are defined as patients with pathological or radiological diagnosis of HCC with and without chronic hepatitis C virus infection. Study was restricted to Caucasian population without prior infection with chronic HBV infection. All DNA samples were extracted from peripheral blood. Study population of the participated sites are: Site-1: University of Texas MD Anderson Cancer Center A hospital based-case-control study was initiated at UT MD Anderson Cancer Center in 2000 and approved by the Institutional Review Board of The University of Texas M. D. Anderson Cancer Center. Study design was previously described in detailed (1-3). Written informed consent for an interview and for a biological sample was obtained from each participant. At time of diagnosis prior to treatment exposure. Case patients were recruited from the population of patients with newly diagnosed HCC who were evaluated and treated at the institution's gastrointestinal medical and surgical oncology outpatient clinics. Inclusion criteria were as follows: a pathologically confirmed diagnosis of HCC, U.S. residency, and the ability to communicate in English. Patients with concurrent or past history of other types of cancers were excluded. Control subjects were healthy spouses of patients with cancers other than liver, gastrointestinal, lung, or head and neck (smoking-related cancers) undergoing treatment at our institution. Eligibility criteria for control subjects were the same as those for patients, except that control subjects could not have ever had cancer. A short, structured questionnaire was used to screen potential control subjects on the basis of eligibility criteria. Control subjects and patients were recruited simultaneously. Case patients and control subjects were interviewed by well-trained interviewers who followed a written protocol to guide ascertainment and reduce surveillance, interviewer, and recall bias. No proxy interviews were conducted. The interviewers used a structured and validated questionnaire to collect information about demographic characteristics and potential risk factors for HCC such as personal smoking history, alcohol use, medical history, occupational history, and family history of cancer. Blood samples from cases and controls were tested for HBV and HCV. HCV antibodies, hepatitis B surface antigen, and antibodies to hepatitis B core antigen were detected by use of a third-generation enzyme-linked immunosorbent assay (ELISA) (Abbott Laboratories, North Chicago, IL). Important clinical information were retrieved from patients' medical records. Total of 1188 case patients and 278 controls from MD Anderson study were genotyped. Site-2: Mayo Clinic Case patients and controls included in the study from Mayo clinic were recruited as part of Mayo International Hepatobiliary Neoplasia Register and Biorepository. Only USA patients and controls were participated. All cases and controls signed informed consent indicating their willingness to participate in genetic studies. Epidemiological and clinical data were collected from participants and retrieved from medical records. Total of 522 HCC case patients and 182 controls were genotyped in this study. Site-3: Toronto University Molecular Epidemiology of Hepatobiliary Tumors (HBT study, CAPCR 09-0289) is a hospital-based study that includes 1710 patients diagnosed with liver, bile duct or gallbladder adenocarcinoma (Hepatocellular carcinoma - HCC; cholangiocarcinoma - CCA; and gall bladder adenocarcinoma -GBCa) recruited at University Health Network (UHN) between 2009 and 2018. HBT study is a prospective study of cancer patients that collects self-reported demographic and epidemiological data, medical records/clinical information and blood samples for research purposes. Eligible patients recruited between 2009 and March 15 2016 were selected to participate in the Genome-Wide Association Study in Patients with Hepatocellular Carcinoma (GWAS in HCC, CAPCR 15-9943-CE), a sub-study of the HBT study. Eligible prospective patients recruited after March 15 2016 signed the Addendum to the HBT consent form and became eligible to participate in the GWAS in HCC study. Total of 271 cases and 21 controls were genotyped from Toronto University Site-4: University of Pittsburgh The design of the study has been described previously (4-6). Briefly, this population-based study enrolled 120 HCC patients and 230 matched controls from black, Hispanic, and non-Hispanic white residents of Los Angeles County who were between 18 and 74 years of age at diagnosis from January 1995 through December 2001. HCC Cases were identified through the Los Angeles County Cancer Surveillance Program. We sought to recruit up to two control subjects per case from the neighborhoods where HCC patients resided at the time of diagnosis, who were matched to the index case by sex, age (within 5 years), and race (Hispanic white, non-Hispanic white, black). Blood samples (plasma and buffy coat), medical and lifestyle factors were collected from all consenting participants. Total of 65 cases and 60 controls were genotyped in this study Site-5: South Western Dallas Under IRB approval, HCC Cases and controls were prospectively collected since 2015 from the outpatient Liver clinics from Parkland Health and Hospital System and UT Southwestern Medical Center, two large health systems in Dallas TX. A Total of 31 cases and 29 controls were genotyped from site 5. Site-6: Columbia University HCC patients from Columbia are those recruited as part of the Herbert Irving Comprehensive Cancer Center Database Shared Resource which seeks to recruit all cancer patients for potential future studies. The PI is Dr. Katherine Crew AAAL5871. This resource collects sociodemographic, lifestyle and clinical data on patients as well as a blood sample. Those who indicated on their consent form that they would participate in genetic studies were included. Total of 79 case patients with HCC were genotyped from Columbia University. Site-7: University of Michigan The study population included from University of Michigan were included from a prospective study of with chronic HCV infection recruited in Ann Arbor Patients who had under- gone liver transplantation, known coinfection with HIV, life expectancy <12 months due to extra-hepatic illnesses, or were receiving HCV treatment at enrolment, were excluded. Protocol, surveys, and data forms were developed where each enrolled patient completed the same questionnaire All patients provided written informed consent before enrolment in the study. The study was approved by the institutional review board or ethics committee at the University of Michigan. Detailed study description was previously published (7). Total of 44 cases and 347 controls were genotyped Site-8: Veteran Administration Medical Center in Houston This research including written informed consent form was jointly approved by Institutional Review Boards for the Baylor College of Medicine and the Michael E. DeBakey VA Medical Center in Houston, Texas. Study details have been previously published (8). Briefly, we prospectively recruited consecutive HCV-infected veterans prior to their previously scheduled HCV clinic visit at a large tertiary care VA medical center between May 1, 2009 and December 31, 2012. Patients completed a research assistant (RA) administered survey interrogating medical and risk factor history including lifetime alcohol use, had anthropometric measurements taken, and completed a fasting venipuncture for performance of the FibroSure-ActiTest as a measure of hepatic pathology. We restricted our current analysis to individuals who were: (1) White male veterans between 18 and 70 years; (2) had no history of HCC, liver transplant, decompensated liver disease including ascites, dementia, or psychosis; (3) were serologically-confirmed to have chronic HCV and to be negative for both HIV and active HBV infection; (4) were not currently receiving anti-HCV pharmacotherapy; and (5) had FibroSURE testing shown F3/F4 fibrosis consistent with cirrhosis.
Study phs001744
Genetic History of Neandertal and Denisovan Introgression into Melanesian Individuals

This study describes high-coverage whole-genome sequencing of 35 individuals sampled from the Bismarck Archipelago of Northern Island Melanesia, Papua New Guinea as described in PMID: 26989198
Study phs001085
Drug screening of patient-derived organoids from colorectal peritoneal metastases

Colorectal cancer (CRC) patients with peritoneal metastases (CRPM) have limited treatment options and the lowest CRC survival rates. We trialed the possibility of organoid directed precision treatment for CRPM patients. CRPM organoids (peritonoids) isolated from patients underwent next-generation sequencing and medium-throughput drug panel testing ex vivo to identify specific drug sensitivities for each patient. We measured the utility of such a service including: success of peritonoid generation, time to cultivate peritonoids, reproducibility of the medium-throughput drug testing, and documented changes to clinical therapy as a result of the testing. Peritonoids were successfully generated and validated from 68% (19/28) of patients undergoing standard care. Genomic and drug profiling was completed within 8 weeks and a formal report ranking drug sensitivities was provided to the medical oncology team upon failure of standard care treatment. This resulted in a treatment change for 2 patients, one of whom had a partial response despite previously progressing on multiple rounds of standard care chemotherapy. The barrier to implementing this technology in Australia is the need for drug access and funding for off-label indications. In conclusion, our approach is feasible, reproducible and can guide novel therapeutic choices in this poor prognosis cohort, where new treatment options are urgently needed. This platform is relevant to many solid organ malignancies.
Study phs002023
University of Pennsylvania CAR T Cell Responding and Non-responding Patients

The adoptive transfer of autologous T cells genetically modified to express a CD19-specific, 4-1BB/CD3zeta-signaling chimeric antigen receptor (CAR; CTL019) has shown remarkable activity in patients with B acute lymphoblastic leukemia. Similar therapy can induce long-term remissions for relapsed/refractory chronic lymphocytic leukemia (CLL) patients, but in only a small subset of subjects. The determinants of response and resistance to CTL019 therapy of CLL are not fully understood. We employed next generation sequencing of RNA (RNA-seq) to identify predictive indicators of response to CTL019 treatment. We performed RNA-seq on leukapheresis and manufactured infusion product T cells from patients with heavily pre-treated and high-risk disease. To characterize potency, we also performed RNA-seq on the cellular infusion product after CAR-specific stimulation. Our findings indicate that durable remission in CLL is associated with gene expression signatures of early memory T cell differentiation (e.g., STAT3), while T cells from poorly- or non-responding patients exhibited elevated expression of key regulators of late memory as well as effector T cell differentiation, apoptosis, aerobic glycolysis, hypoxia and exhaustion. These gene expression signatures, along with additional immunological biomarkers, may be used to identify which patients are most likely to respond to cellular therapies and suggest manufacturing modifications that might potentiate the generation of maximally efficacious infusion products. In a follow-up study, we report multiplex CRISPR-Cas9 gene editing on T cells in humans to test the hypothesis that disrupting genes encoding the endogenous T cell receptor (TCR), TCRα (TRAC) and TCRβ (TRBC), as well as PD-1 (PDCD1) would enhance the efficacy of autologous T cells engineered to express a transgenic NY-ESO TCR. We used single cell RNA-seq to comprehensively characterize the transcriptomic phenotype of this T cell product and its evolution over time in a patient with sarcoma. Notably, this patient's transferred T cells that persisted in vivo demonstrated increases in expression of genes associated with a central memory state (IL7R, TCF7). These findings may guide future trials involving administration of genetically redirected, genome -edited T cells.In this follow-up study, we investigated whether CRISPR-Cas9-mediated disruption of PDCD1 and/or CTLA-4 could restore CART dysfunction in healthy donors and/or CLL patients. We show that depletion of CTLA-4 rescues the function of T cells from leukemia patients that previously failed CART cell treatment. We performed single-cell RNA sequencing and characterized the transcriptomic landscape of CTLA-4 KO CART19 cells that showed higher proliferation and anti-tumor efficacy by permitting unopposed CD28 signaling and maintenance of CAR expression on the T cell surface under stress-test conditions of chronic antigen exposure. Our findings reveal that selective disruption of CTLA-4 invigorates dysfunctional patient T cells, providing a strategy for increasing patient responses to CART cell therapy.
Study phs001707
Mechanism of Action of Vitamin E in NAFLD

We conducted a single-center prospective mechanistic randomized trial (clinicaltrials.gov NCT01792115). Adult patients with NAFLD were enrolled between May 2013 and November 2016. All patients underwent a 12 weeks run-in period of lifestyle intervention. Following the run-in phase, patients underwent a baseline evaluation including blood tests, glucose tolerance testing, measurement of liver fat content by 1H-magnetic resonance spectroscopy (MRS) and a percutaneous liver biopsy. Patients with no evidence of NAFLD on the entry liver biopsy ended their participation in the study at that point. Patients with histological confirmation of NAFLD (either NAFL or NASH) on the baseline biopsy were randomized at a 1:1:1 ratio to receive one of 3 doses of natural vitamin E (RRR-alpha-tocopherol, Pharmavite), 200, 400 or 800 IU/d, for 24 weeks, and were followed at a 1-8 week intervals in a parallel assigned design. While on vitamin E treatment, patients were encouraged to continue and adhere to the lifestyle and dietary guidelines of the run-in phase. On week 4 of vitamin E treatment, a repeat MRS and liver biopsy were performed. On week 24, a final MRS was performed to assess treatment response. The primary endpoint was the reduction of liver fat content by MRS from baseline (immediately prior to starting vitamin E) to week 24 of treatment, and a response was defined as a relative reduction of ≥25% in liver fat content by MRS between baseline and week 24. Early changes in the hepatic transcriptome were investigated at week 4 comparing non-responders to responder in a nested design.
Study phs001930
Oncoarray Consortium - Lung Cancer Studies

The study was conducted under the auspices of the Transdisciplinary Research In Cancer of the Lung (TRICL) Research Team, which is a part of the Genetic Associations and MEchanisms in ONcology (GAME-ON) consortium, and associated with the International Lung Cancer Consortium (ILCCO). Ethics: All participants provided written informed consent. All studies were reviewed and approved by institutional ethics review committees at the involved institutions. Sequencing data are derived from four substudies. The substudies that contributed include Harvard, Liverpool, Toronto, and IARC. The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study is a randomized primary prevention trial including 29,133 male smokers enrolled in Finland between 1985 and 1993. Participants ranged between ages of 50 to 69 at enrollment and were randomized in a factorial design to take either 50 milligrams of d-alpha tocopheryl acetate (Vitamin E), 20 mg of all-trans-beta-carotene, both or placebo. The study continued to monitor cancer incidence through 2012 and total mortality through December 2013. The CAncer de PUlmon en Asturias Study (CAPUA) is a hospital-based case-control study conducted in Asturias, Spain by the University of Oviedo. Lung cancer cases were recruited in three main hospitals of Asturias, following an identical protocol from 2002 to 2012. Eligible cases were incident cases of histologically confirmed lung cancer between 30 and 85 years of age and residents in the geographical area of each participating hospital. Controls were selected from patients admitted to those hospitals with diagnoses unrelated to the exposures of interest and individually matched by ethnicity, gender, age (± 5 years) and hospital. Epidemiologic data were collected personally through computer-assisted questionnaires by trained interviewers during the first hospital admission. Structured questionnaires collected information on sociodemographic characteristics, recent and prior tobacco use, environmental exposure (air pollution and passive smoking), diet, personal and family history of cancer, and occupational history from each participant. Peripheral blood samples (or mouthwash samples when they refused to donate blood) were collected from all participants. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The Canadian Screening Study includes the nested case-control samples from 3 screening programs: IELCAP-Toronto: Ever smokers of more than 10 pack-years age 50 and above were eligible for the I-ELCAP screening program since 2003, and a total of 4782 individuals have been enrolled in the Greater Toronto Area. Participants were administered a LDCT scan along with a standard study questionnaire at baseline. Blood samples were systematically collected at baseline since 2006. Participants who had an abnormality in a CT scan were followed up every 1 to 2 years. The screening program was organized by the Princess Margaret Hospital. PanCan: Ever smokers between the ages of 50-75 with no previous history of invasive cancer are eligible to participate in the study. The study was carried out across Canada in Vancouver, Calgary, Hamilton, Toronto, Ottawa, Quebec, Halifax, and St. John's. A total of 2537 smokers have been screened from 2008 to 2011. All study participants completed a detailed questionnaire, spirometry, collection of blood specimens for biomarker measurement and LDCT at baseline. All participants are followed for a minimum of 3 years. On yearly follow up, an updated shorter questionnaire is administered, blood is collected and CT scans are performed. Blood samples are available from all 2537 individuals. BCCA Screening Program: From 1990 to 2007, 4274 smokers above 40 years old who had smoked 20 pack-years or more were enrolled at BCCA. Upon enrollment, subjects completed a questionnaire for their lifestyle and medical history. Baseline spirometry was conducted using a flow-sensitive spirometer in accordance with the American Thoracic Society recommendations. Since 2000, a LDCT was obtained in 2440 individuals. The participants were followed prospectively to determine whether they developed lung cancer. A total of 9759 individuals participated in the CT screening program in Canada from these 3 programs. The samples included in this project is based on a subset of nested lung cancer case-control pairs based on 1:2 ratio. The Carotene and Retinol Efficacy Trial (CARET) was a randomized, double-blind, placebo-controlled trial of the cancer prevention efficacy and safety of a daily combination of 30 mg of beta-carotene and 25,000 IU of retinyl palmitate in 18,314 persons at high risk for lung cancer. CARET began in 1985, and the intervention was halted in January 1996, 21 months ahead of schedule, with the twin conclusions for definitive evidence of no benefit and substantial evidence of a harmful effect of the intervention on both lung cancer incidence and total mortality. CARET continued to follow and collect endpoints on their participants through 2005. Pathology reports and medical records were reviewed to confirm cancer endpoints, and death certificates obtained to capture cause of death. During the active intervention phase of CARET, serum, plasma, whole blood, and lung tissue specimens were collected on participants. These biospecimens make up the CARET Biorepository. For the OncoArray Project, CARET provided DNA extracted from whole blood of lung cancer cases and controls matched on age at baseline (± 4 years), sex, race, baseline smoking status, history of occupational asbestos exposure (asbestos vs heavy smoker), and year of enrollment (2-year intervals). The European Prospective Investigation into Cancer and Nutrition (EPIC) study is a multi-center cohort study involving 521,000 study participants from 10 European countries. The current study involved EPIC participants from 7 countries (Greece, Netherlands, UK, France, Germany, Spain, and Italy), including 1223 incident lung cancer cases and 1249 smoking matched controls. The Kentucky Lung Cancer Research Initiative is a study conducted by the Markey Cancer Center Cancer Center and the University of Kentucky using a population-based, case-control framework to study the extraordinarily high rates of lung cancer in Southeastern, Appalachian Kentucky. Cancer cases were recruited from the Kentucky Cancer Registry at the time of diagnosis and controls were recruited from a random digit dialing process from the same region. Study accrual began in January 5, 2012 and completed on September 5, 2014 and 520 subjects were recruited in a 4:1 ratio of controls: cases from Appalachian Kentucky. Of the 520 subjects recruited, 231 are included in the OncoArray analysis, including all 93 cancer cases, and 123 controls. Newly diagnosed lung cancer cases and controls underwent blood, toenail (for trace element analysis), urine, buffy coat, water, soil, and radon collection, residence GPS mapping, as well as an extensive epidemiologic, occupational, and health history questionnaire (Clinical Trials.gov Identifier: NCT01648166). The Harvard Lung Cancer Study (HLCS) is a case-control study based at Mass General Hospital (MGH) in Boston, Massachusetts from 1992 to 2004. Details of the study were described previously. Briefly, eligible cases included any person over the age of 18 years with a diagnosis of primary lung cancer that was further confirmed by an MGH lung pathologist. Controls were recruited from the friends or spouses of cancer patients or the friends or spouses of other surgery patients in the same hospital. Potential controls were excluded from participation if they had a diagnosis of any cancer (other than non-melanoma skin cancer). Interviewer-administered questionnaires, a modified version of the standardized American Thoracic Society respiratory questionnaire, collected information on demographics, medical history, family history of cancer, smoking history, and a detailed work history, including job titles and tasks. Genome-wide genotype data were first generated using Illumina Human 610-Quad BeadChips and then imputed by MACH against the 1000 Genome Project dataset (http://browser.1000genomes.org/index.html). The Institutional Review Board of MGH and the Human Subjects Committee of the Harvard School of Public Health approved the study. The Israel study (NICCC-LCA) is an ongoing case-control study of newly diagnosed lung cancer cases of any histology and population age/sex/ethnicity-matched "healthy" controls. All participants undergo face-to-face interviews, provide a venous blood sample (separated into DNA, Sera, lymphocytes) after signing an IRB-approved form. Histology reports, FFPE blocks and clinical follow-up are available for most cancer cases. The MD Anderson Cancer Center (MDACC) Study. Lung cancer cases and frequency-matched controls were ascertained from a large ongoing case-control study at the University of Texas MD Anderson Cancer Center (UTMDACC) since 1991. Detailed study description was provided previously (Spitz et al 2007). In brief, cases were newly-diagnosed and histologically confirmed lung cancer patients recruited from UTMDACC. Controls were healthy individuals without a history of cancer (except for nonmelanoma skin cancer) and recruited from the Kelsey-Seybold Clinics, the largest private multispecialty physician group in the Houston metropolitan area. Controls were frequency-matched to cases on age (±5 years), sex, and race/ethnicity. After providing written informed consent, each study participants completed an in-person interview by staff interviewers to collect information on demographics, smoking status, etc. Blood samples were also drawn from all the study participants. This study was approved by institutional review boards of UTMDACC and Kelsey-Seybold Clinics. The Malmö Diet and Cancer Study (MDCS) is a population-based prospective cohort study that recruited men and women aged at 44 to 74 years old of living in Malmö, Sweden between 1991 and 1996. The main goal of the MDCS is to study the impact of diet on cancer incidence and mortality. It consists of a baseline examination including dietary assessment, a self-administered questionnaire, anthropometric measurements and collection of blood samples. A total of 165 incident lung cancer cases and 174 individually smoking-matched controls were available for this analysis. The Multiethnic Cohort (MEC) Study includes 215,251 men and women aged 45-74 years at recruitment, primarily from five ethnic/racial groups - African Americans and Latinos mostly recruited from CA (mainly from Los Angeles County) and Japanese Americans, Native Hawaiians and whites (mostly recruited from HI). The cohort was assembled in 1993-1996 by mailing a self-administered questionnaire to persons identified primarily through driver's license files. The baseline questionnaire obtained information on demographics, anthropometry, smoking history, medical and reproductive histories, family history of cancer, diet and physical activity. Incident cancer cases are identified by regular linkage with the State of California Cancer Registry and the Hawaii Tumor Registry, both members of the SEER Program of the NCI. In 2001-2006, a prospective biorepository was assembled by collecting a pre-diagnostic blood specimen from 67,594 surviving MEC members. At the time of blood collection a short questionnaire was administered that included information on smoking during the previous 15 days. For this study, cases were all lung cancer cases incident to blood draw and diagnosed before December 2012. For each case, a control was selected among unaffected MEC participants who were alive at time of the case's diagnosis and matched on study site, sex, race/ethnicity, age (age at diagnosis for cases; age at blood collection for controls), and date of blood collection. The Mount-Sinai Hospital-Princess Margaret Study (MSH-PMH) was conducted in the greater Toronto area from 2008 to 2013. Lung cancer cases were recruited at the hospitals in the network of the University of Toronto. Controls were selected randomly from individuals registered in the family medicine clinics databases and were frequency matched with cases on age and sex. All subjects were interviewed, and information on lifestyle risk factors, occupational history and medical and family history was collected using a standard questionnaire. Tumors were centrally reviewed by the reference pathologist, a member of the International Association for the Study of Lung Cancer (IASLC) committee, and a second pathologist in the University Health Network. If the reviews conflicted, a consensus was arrived at after discussion. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The New England Lung Cancer Study (NELCS) is a population-based case-control study of lung cancer among residents of Northern and Central New Hampshire counties and the bordering region of Vermont. Cases with histologically confirmed primary incident lung cancer were identified from 2005 to 2007 using the New Hampshire State Cancer Registry and the Dartmouth-Hitchcock Medical Center (DHMC) Tumor Registry. Control participants were identified using a commercial database and matched to lung cancer cases within 5-year age groups, sex and county. Genomic DNA was isolated from blood or buccal specimens provided by consenting participants. The study complied with requirements of the Dartmouth College's Committee for Protection of Human Subjects. The Nijmegen Lung Cancer Study. The Netherlands patients with lung cancer were identified through the population-based cancer registry of the Netherlands Comprehensive Cancer Organisation in Nijmegen, the Netherlands. Patients who were diagnosed in one of three hospitals (Radboud University Medical Center, Canisius Wilhelmina Hospital in Nijmegen, and Rijnstate Hospital in Arnhem) since 1989 and who were still alive at April 15th, 2008 were recruited for a study on gene-environment interactions in lung cancer. 458 patients gave informed consent and donated a blood sample. This case series was expanded with 94 patients to a total of 552 by linking three other studies to the population-based cancer registry in order to identify new occurrences of lung cancer among the participants of these other studies. All three other studies (i.e., POLYGENE, the Nijmegen Biomedical Study, and the Radboudumc Urology Outpatient Clinic Epidemiology Study) were initiated to study genetic risk factors for disease and participants to these studies gave general informed consent for DNA-related research and linkage with disease registries. Information on histology, stage of disease, and age at diagnoses was obtained through the cancer registry. Lifestyle information was collected through a structured questionnaire and whole blood for DNA isolation was collected by the regional thrombosis services. The cancer-free controls (46% males) were selected from participants of the "Nijmegen Biomedical Study" (NBS), an age- and sex-stratified random sample of the general population of the municipality of Nijmegen, The Netherlands. All participants provided extensive lifestyle information by structured questionnaires and blood samples for DNA isolation, serum and plasma. All controls are of self-reported European descent. The study protocols of the NBS were approved by the Institutional Review Board of the Radboudumc and all study subjects signed a written informed consent form. The Northern Sweden Health and Disease Study (NSHDS) encompasses several prospective cohorts. The current study involves participants from the Västerbotten Intervention Project (VIP), a sub-cohort within NSHDS. VIP is an ongoing prospective cohort and intervention study intended for health promotion of the general population of the Västerbotten County in northern Sweden. VIP was initiated in 1985 and all residents in the Västerbotten County were invited to participate by attending a health check-up at 40, 50 and 60 years of age. Participants were asked to complete a self-administered questionnaire including various demographic factors such as education, smoking habits, physical activity and diet. In addition, height and weight were measured and participants were asked to donate a fasting blood sample for future research. A total of 243 incident lung cancer cases and 266 individually smoking-matched controls were available for this analysis. Norway National Institute of Occupational Health Study. Early-stage NSCLC cases and healthy controls at the time of enrollment were Caucasians of Norwegian origin and were recruited from the same geographical region (Western Norway). The patients were enrolled in the study, whenever practically feasible among patients admitted for lung cancer at the Haukeland University Hospital in Bergen, Norway. The informed written consents covering analysis of molecular and genetic markers was signed by the patients prior to surgery. Only patients with histologically confirmed early-stage NSCLC were included in our study. The subjects included in this project are a subgroup recruited into the project "lung cancer genetics" at NIOH. The controls were recruited from the same geographical region of Western Norway and frequency-matched with cases on cumulative smoking dose (pack-years). Pack-years smoked [( 20 cigarettes per day) x years smoked] were calculated to indicate the cumulative smoking dose. The Cases and controls were interviewed using similar questionnaires and were categorized as never smokers, ex-smokers or current smokers. Never smokers are subjects indicating having smoked less than 100 cigarettes in their life time. Ex-smokers were defined as those having quitted at least 1 year before sampling, and current smokers were those indicating that they were smokers at the time of sampling. The project has been approved by the Regional Committee for Medical and Health Research Ethics in Southern Norway in accordance with the WMA Declaration of Helsinki. The ethical approval covered access to the NSCLC databank. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Study, a randomized trial aimed at evaluating the efficacy of screening in reducing cancer mortality, recruited approximately 155,000 men and women age 55 to 74 years from 1992 to 20014. Screening for lung cancer among participants in the intervention arm included a chest x-ray at baseline followed by either three annual x-rays (for current or former smokers at enrollment) or two annual x-rays (for never smokers); participants in the control arm received routine health care. Screening-arm participants provided data on sociodemographic factors, smoking behavior, anthropometric characteristics, medical history, and family history of cancer, as well as blood samples annually for the first 6 years of the study (baseline T0 and T1 through T5). Lung cancers were ascertained through annual questionnaires mailed to the participants, and positive reports were followed up by abstracting medical records or death certificates. Follow-up in the trial as of July 2009 was 96.7%. Patients were excluded because of missing baseline questionnaire, previous history of any cancer, diagnosis of multiple cancers during follow-up, missing smoking information at baseline, missing consent for utilization of biologic specimens for etiologic studies, or unavailability/insufficient quantity of serum or DNA specimens. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT) is an ongoing study conducted in Sheffield from 2006 and due to complete recruitment in 2016. The study recruited pathologically confirmed lung cancer cases diagnosed at age 60 years or younger and family matched controls. Lung cancer cases diagnosed at ages older than 60 years were recruited if they reported a family history of lung cancer. The cases and matched controls were recruited through several major cancer treatment centers, however, the majority were recruited in North Trent. All participants completed a detailed lifestyle questionnaire which included questions about occupational exposures, education, medical history and family history of cancer and lung disease. Participants also donated blood samples for DNA extraction. The ReSoLuCENT study has been funded by the Sheffield Hospitals Charity, Sheffield ECMC and Weston Park Hospital Cancer Charity. First degree relatives were removed from the sample deposited to dbGaP. The Roy Castle Lung Study of Liverpool Lung Project (LLP) is a case-control and cohort study which has recruited over 11,500 individuals since 1996 from the Liverpool region in the UK. Detailed epidemiological and clinical data is collected with associated specimens (i.e. tumor tissue, blood, plasma, sputum, bronchial lavage and oral brushings). The participants have completed a detailed lifestyle questionnaire at recruitment, with repeat questionnaires at intervals; updated data on clinical outcome and hospital events are collected through the Health and Social Care Information Center (including Office of National Statistics mortality data, Cancer Registry and Health Episode Statistics). The project is registered on the UK National Institute for Health Research (NIHR) lung cancer portfolio and has all the required ethical approvals and sponsorship arrangements in place. The lung tumors were reviewed by the reference pathologist. The Seoul Bundang Lung Cancer Study was conducted between 2005 and 2010 to discover genetic and environmental factors related with lung cancer development. Lung cancer cases were recruited at the Seoul National University Hospital in Bundang. Controls were selected randomly from individuals participated in health check-up program and were frequency matched with cases on age and sex. All subjects were interviewed, and information on lifestyle risk factors, occupational history and medical and family history was collected using a standard questionnaire. Tumors were reviewed by the pathologists in the hospital. If the reviews conflicted, a consensus was arrived at after discussion. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The Shanghai Cohort Study (SCS) consisted of 18,244 men in Shanghai, China, who were 45-64 years old at the time of enrollment during 1986-1989. Approximately 80% of eligible men participated in the study. At the time of recruitment, each cohort subject was interviewed in-person by a trained nurse interviewer using a structured questionnaire that included background information, history of tobacco and alcohol use, current diet, and medical history. At the completion of the interview, the nurse collected a 10 ml blood and a single void urine specimen from the study participant. The buccal cell samples were collected from all surviving cohort members (~15,000) in the 2001-2002 follow-up interviews. The cohort has been followed for the occurrence of cancer and death through routine ascertainment of new cases from the population-based Shanghai Cancer Registry and Shanghai Vital Statistics Units. To maximize the cancer findings and minimize the loss of follow-up, we contacted each surviving cohort member annually. Retired nurses visit the last known address of each living cohort member and record details of the interim health history of the cohort member. As of December 31, 2014, cumulatively 612 (3.4%) original subjects were lost to follow-up, and 574 (3.1%) refused to our continued follow-up interview. A nested case-control study of incident lung cancer cases within the Shanghai Cohort Study was used to examine the association between serum levels of vitamin B6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. Briefly, 516 lung cancer cases were identified among cohort participants with available serum samples as of 12/31/2006. For each case, we randomly selected one control subject from all cohort members who were free of cancer and alive at the time of cancer diagnosis of the index case. Controls were matched to the index case by age at enrollment (±2 years), date of biospecimen collection (±1 month) and neighborhood of residence at recruitment, and smoking status (current, former and never smokers) as established previously for other studies. For former smokers, cases and controls were further matched by years since quitting smoking (<10 vs ≥10 years). One serum vial per subject was retrieved from biorepository and all serum samples were sent to the laboratory (B-vital) for measurements. DNA samples of 250 lung cancer cases and 250 matched controls were available for the present study. The Singapore Chinese Health Study (SCHS) cohort consisted of 63,257 Chinese men and women in Singapore when they were 45-74 years old at the time of enrollment between April 1993 and December 1998. At recruitment, each study subject was interviewed in person by a trained interviewer using a structured questionnaire that emphasized current diet assessed via a validated, 165-item food frequency questionnaire. The questionnaire also requested information on demographics, lifetime use of tobacco, incense use, current physical activity, usual sleep duration, reproductive history (women only), occupational exposure, medical history, and family history of cancer. Blood or buccal cell, and spot urine samples were collected first from a random 3% sample of cohort participants in April 1994, and extended to all surviving cohort participants starting in January 2000. Overall approximately 60% of eligible cohort participants donated biospecimens. The cohort has been passively followed for death and cancer occurrence through regular record linkage with the population-based Singapore Cancer Registry and the Singapore Registry of Births and Deaths. Migration out of Singapore, especially among housing estate residents, was negligible. As of latest update, only 55 individuals from this cohort were known to be lost to follow-up due to migration and other reason. A nested case-control study of incident lung cancer cases within the Singapore Chinese Health Study was used to examine the association between serum levels of vitamin B6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. As of 12/31/2011, 422 lung cancer cases were identified among cohort participants with available prediagnostic plasma samples. For each case, one control subject was randomly selected from all eligible cohort members who were alive and free of cancer on the date of cancer diagnosis of the index case. The control subject was individually matched to the index case by gender, dialect group (Hokkien, Cantonese), age at enrollment (±3 years), date of baseline interview (±2 year), date of biospecimen collection (±6 months), and smoking status (current, former, and never smokers). For current smokers, cases and controls were further matched by number of cigarettes per day (<15, ≥15 cigarettes/day). For former smokers, cases and controls were further matched by years since quitting smoking (<10, ≥10 years). One plasma aliquot per subject was retrieved from the biorepository and all plasma samples were sent to the laboratory (B-vital) for measurements, and one aliquot of DNA per subject for the present study. The International Agency for Research on Cancer (IARC) L2 Study. Lung cancer cases and controls were recruited through a multicentric case-control study coordinated by the IARC in Russia, Poland, Serbia, Czech Republic, and Romania from 2005 to 2013. Cases were incident cancer patients collected from general hospitals. Controls were recruited from individuals visiting general hospitals and out-patient clinics for disorders unrelated to lung cancer and/or its associated risk factors, or from the general population. Information on lifestyle risk factors, medical and family history was collected from subjects by interview using a standard questionnaire. All study participants provided written informed consent. The current study included 1,133 lung cancer cases and 1,117 controls genotyped on the Oncoarray. The Washington State University Lung Cancer Study is a hospital case-control study of 511 subjects with newly-diagnosed (within 1 year of diagnosis) lung cancer and 820 race-, sex- and age-matched controls. Lung cancer cases were recruited from lung cancer clinics within the H. Lee Moffitt Cancer Center while controls were recruited from the Lifetime Cancer Screening Center, a H. Lee Moffitt Cancer Center affiliate. None of the controls were diagnosed with any form of cancer at the time of screening. Detailed questionnaire data and oral buccal cells were collected for all subjects. The Total Lung Cancer (TLC) Study is a hospital-based study that included 458 lung cancer patients recruited for Moffitt Cancer Center's Total Cancer Care™ protocol between April 2006 and August 2010. Total Cancer Care™ is a multi-institutional observational study of cancer patients that prospectively collects self-reported demographic and clinical data, medical record information and blood samples for research purposes. All patients used in this cohort were recruited from the Thoracic Oncology Clinic at the Moffitt Cancer Center. The Vanderbilt Lung Cancer Study (BioVU) is a case-control study nested within the Vanderbilt University Medical Center biobank, BioVU. BioVU is a biorepository of DNA extracted from blood drawn from patients seeking routine clinical care at Vanderbilt University Medical Center and linked to de-identified electronic health records for research purposes. Lung cancer cases and controls were identified from BioVU participants in February 2014. Lung cancer cases were identified from the Vanderbilt tumor registry. All specimens undergo pathologic review for determination of morphology. Coding of histology was based on SEER Program Coding Guidelines. Controls were randomly selected from BioVU participants, excluding cancer patients, and were matched to cases on age (± 5 years), sex, and race. Relevant covariates were identified from electronic health records using natural language processing. Genomic DNA was extracted based on a standard protocol.
Study phs001273
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
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
HTAN MCL Pre-Cancer Atlas Pilot Project - Targeted Sequencing Development Study

The HTAN-MCL Pre-Cancer Atlas Pilot Project (PCAPP) is the result of a collaboration between the seven members of the MCL consortium. Across four organ types, PCAPP's goal is to collect and profile pre-malignant lesions for gene expression, DNA mutations, single-cell gene expression and immune-environment. Most PML are small in size and only available come from formalin fixed paraffin embedded archived tissue. The primary goal of PCAPP is to 1) understand the logistical challenges of PML specimen collection, 2) document technical limitations of the assays that are specific to the PML and 3) overcome them to support the generation of a more comprehensive Pre-Cancer Atlas in the future. The current upload provides RNA and DNA sequencing from participants with DCIS who were studied at the University of San Diego and the University of Vermont. Description of the overall study: A. Background/Significance One of the critical barriers to developing new approaches for cancer detection and prevention is the lack of understanding of the key molecular and cellular changes that cause cancer initiation and progression. Unlike the extensive work that has been done profiling advanced stage tumors, few studies have comprehensively profiled the molecular alterations found in precancerous tissues. Premalignant lesions are currently characterized by histologic changes that precede the development of invasive carcinoma1,2.These lesions can often be identified in regions surrounding an invasive tumor or in biopsies taken from patients undergoing diagnostic evaluation for suspicion of cancer. Currently, limited metrics exist to identify lesions that will likely progress to carcinoma and require intervention from those that will naturally regress or remain stable3,4. Characterization of the molecular alterations in premalignant lesions and the corresponding changes in the microenvironment would hasten the development of biomarkers for early detection and risk stratification as well as suggest preventive interventions to reverse or delay the development of cancer. Our pilot study will establish the feasibility of transcriptomic, genomic and immune profiling of FFPE premalignant lesions from multiple organ sites, collected and profiled with uniform SOPs across multiple institutions within the MCL consortium. We will characterize the molecular alterations in precancerous lesions and the corresponding microenvironment in four major organ sites, in order to uncover the molecular and cellular determinants of premalignancy, and establish standardized sequencing and immunohistochemistry protocols on FFPE precancerous tissue. We will also evaluate the technical feasibility of single nuclei sequencing of small FFPE pre-cancer lesions. Successful completion of the proposed pilot study will set the stage for expansion and development of a comprehensive Pre-Cancer Atlas (PCA) as part of the NCI's moonshot.B. Specific Aims Aim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples (and flash frozen tissue where available) and compare the genomic/transcriptomic alterations within and across organ sites. C. ApproachAim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). MethodsI. Patient Population/Sample Collection: Overview of the sites collecting PML tissue from the respective organs is provided in Table 1 and a full description of the biospecimens to be obtained is described in detail for each organ type below. Table 1. Breakdown of cohort by tissue type and collection site.Organ siteBreastLungPancreasProstateType of PMLDCISAAH, Squamous Dysplasia/CISIPMNsPINCollection of PatientsUCSF/UCSDUVMBU*/UCLAVanderbilt/MoffittMDACC*JHUStanford*# of Patients201920 (10 of each type)20 (10 of each type)242020Total patients per Organ39402440Note: single nuclei/cell RNA-Seq will be performed on 4-5 FFPE samples from each of the organ types 1. DCIS lesions from breast tissue: DCIS lesions will be collected from 39 patients (20 from UCSF/UCSD & 19 from UVM) with primary low or high-grade DCIS diagnosed from a breast core biopsy. Subsequent resected lumpectomy or mastectomy tissues will be prospectively sampled in the vicinity of the prior biopsy site using multiple approaches: 1) Live cells (heterogeneous mix) will be obtained as a cell scrape slurry from the lesion surface or by fine needle aspirate (FNA); 2) For a subset of specimens where size is sufficient, a block of breast tissue with DCIS will be fresh-frozen; 3) The remainder of the specimen will be taken for routine formalin-fixation and paraffin-embedding (FFPE). The FFPE sample will be annotated to identify the matched FFPE tissue block adjacent to the fresh-frozen sample and will be sectioned for use in bulk and single nuclei sequencing . We will dissect DCIS, adjacent normal and when available, associated carcinoma. In addition, when possible, normal tissue will be collected from a tissue block lacking lesions as well as collection of blood. A subset of patients (n = 5 | FFPE, flash frozen and fresh) will be sent to the Broad Institute for single nuclei/cell sequencing.2. AAH and squamous dysplastic/CIS lesions from airway and lung tissue: For squamous cell lung cancer, we will collect endobronchial biopsies from abnormal airway regions identified on autofluroscence bronchoscopy or identify PMLs in the margins of resected lung tissue. We will study 20 patients (5 each from BU/UCLA/Vanderbilt/Moffitt) with pre-invasive squamous lesions (moderate-severe dysplasia or carcinoma in situ (CIS)) identified on pathologic examination. LCM of the premalignant region and adjacent normal epithelium will be performed as well as the invasive tumor for those collected from the resection margin (n=5 from UCLA). On a subset of lesions collected at bronchoscopy (n=5), we will collect additional biopsies that will be flash frozen and fresh for single nuclei and cell sequencing, respectively, performed at the Broad Institute. In parallel to the work at the Broad, BU will perform single cell RNA-seq on these freshly cell sorted tissues (n = 5). Blood will be collected on all patients for genomic studies. For lung adenocarcinoma, we will collect resected FFPE lung tissues from 20 patients (10 from UCLA and 10 from Vanderbilt/Moffitt) with early stage lung adenocarcinoma that harbor atypical adenomatous (AAH) premalignant lesions in the resection margin. We will LCM multiple AAH regions (3-5 per patient) as well as adjacent regions of normal epithelium and invasive adenocarcinoma. In addition, blood will be collected on all patients for genomic studies. 3. IPMNs from pancreatic tissue: For pancreatic cancer PML, we will collect low and high grade lesions from 24 patients representing macroscopic Intraductal Papillary Mucinous Neoplasms (IPMN) (n=24) from surgically resected specimens along with blood samples. Archival FFPE specimens of microscopic PanIN lesions, occurring multi-focally adjacent to invasive PDAC, and archival IPMN lesions (with or without associated invasive cancer), along with the adjacent normal tissue, will undergo LCM and utilized for bulk DNA and RNA sequencing. If matched frozen tissues are available for a subset of these FFPE samples, we will bank for comparison of profiles. Because IPMNs are macroscopic lesions, they provide an opportunity for obtaining the samples fresh and therefore can be used for single cell sequencing (in contrast to PanINs). Therefore, 5 freshly obtained IPMNs will be used for the single cell RNA sequencing studies performed at both the Broad Institute and MDACC, and the matched FFPE and/or frozen sections from these lesions (obtained from the adjacent PML) will be sent to Broad Institute as a pilot to assess "single nuclei" RNA sequencing.4. PINs from prostate tissue: For prostate cancer PML, there will be 40 samples of Prostatic Intraepithelial Neoplasia (PIN) collected between the Stanford and JHU sites (20 cases per site). At the Stanford site, 20 prostate specimens detected by PSA screening who have/will undergo surgery (radical prostatectomy) for clinically localized disease will make up the final cohort. The age range of the participants would be 40-75, and we anticipate that 18 will be Caucasian, 1 Asian and 1 Latino or African American based on the practice demographics practice at Stanford. Clinical and MRI data will also be collected for these samples. We will collect low grade (e.g. Gleason score of 6/Grade group 1; n=10) and high grade (Gleason score 4+3=7 or higher/Grade group 3 or higher; n=10) PINs from FFPE samples that have prostate carcinoma. In addition to obtaining LCM archival samples of low and high grade PIN, we will also obtain normal prostatic epithelial from the peripheral, central and transition zones as well as multiple samples of prostate carcinoma in order to obtain the spectrum of Gleason grades in the carcinoma as needed. LCM samples will be used for bulk DNA and RNA sequencing. In addition, single cells will be dissected from FFPE samples to prepare single cell RNA seq libraries using techniques developed at Stanford, and FFPE tissue will be sent to the Broad for single nuclei sequencing. When available, flash frozen and fresh samples from these prostates will be archived and prepared for single nuclei and cell sequencing, respectively, at the Broad Institute and at Stanford (single cell only). JHU will also capture 10 cases (5 grade group 1 and 5 grade group 2) of high grade PIN, normal and invasive adenocarcinoma using frozen sections from fresh frozen tissues. When possible these will be from the same patients as the FFPE samples. Since frozen sections can be quite challenging to morphologically determine high grade PIN from normal epithelium, for these samples we will perform a number of additional tissue-based characterizations. These will include a multicolor combined basal cells (p63 and CK903) and PIN/carcinoma markers (AMACR) referred to in the cocktail as "PIN4", c-MYC (referred to as MYC) protein5, by IHC and mRNA by in situ hybridization (AM De Marzo, Q Zheng unpublished observations), telomere length by in situ hybridization6 and the 5'ETS/45S rRNA7. For these slides, the whole slides will be scanned with a Hammamatsu Nanozoomer with a 40x objective and regions of interest will be annotated as a guide for LCM.II. Laser-capture microdissection (LCM): FFPE tissue blocks will be sectioned at 7μm thickness and serial sections will be stained with H&E. LCM will be performed utilizing standard LCM systems, such as Leica LMD7000 and ArcturusXT at each site. Regions of premalignancy will be dissected and RNA/DNA will be extracted from microdissected cells using the Qiagen All Prep DNA/RNA FFPE Kit. Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples and compare the genomic/transcriptomic alterations within and across organ sites. Rationale: There have been limited studies characterizing the genomic and transcriptomic landscape of premalignant lesions associated with breast, pancreatic, lung or prostate cancers. Characterizing the molecular determinants of premalignant disease that are unique and shared across multiple organs will enable new candidate biomarkers for early detection and novel therapeutic strategies for early intervention. MethodsBulk RNA-seq of LCM FFPE tissue: All participating sites will perform bulk RNA-seq in accordance with SOPs developed at BU. In brief, total RNA will be isolated from LCM'd lesion and associated microenvironment tissue using the Qiagen All Prep DNA/RNA FFPE Kit and quality will be assessed with the Agilent Bioanalyzer. Libraries will be generated with the Illumina TruSeq Access kit (for FFPE samples). They will be sequenced on the Illumina HiSeq2500 with 75base-pair paired-end reads. Quality of FASTQ files will be assessed with FastQC. Reads will be aligned to the human genome with STAR and gene-level and isoform-level expression will be quantified with RSEM. Splice junction saturation, transcript integrity, and biotype distributions will be calculated for each sample with RSeQC. DESeq2 and EdgeR will be used to identify associations between gene expression profiles and clinical variables while controlling for confounding covariates. BU will serve as an RNA-seq Core to assess reproducibility of FFPE RNA-seq methods across sites. We will perform RNA-seq according to the SOP listed above on a subset of samples for each organ type (total n ~ 20). Bulk seq of DNA from FFPE tissue: All participating sites will perform targeted or whole exome-seq (WES) in accordance with SOPs. In brief, DNA from laser captured material will be isolated using the Qiagen All Prep DNA/RNA FFPE Kit and undergo stringent quality control to ensure high quality input material for genomic profiling. Purified DNA (ideally 100-200 ng) will be used for library preparation and amplification, followed by next generation sequencing using standard protocols distributed by CDMG. Exome-seq methods are considered standardized, thus we will not need a DNA-seq Core to assess reproducibility across sites. We anticipate local centers will use Illumina paired end reads, following the following general approach. 1) DNA library preparation: Paired-end libraries will be prepared following the manufacturer's protocols (Illumina and Agilent), fragmented to 150-200 bp 2) Capture of targeted exome: Whole exome capture will be carried out using the protocol for Agilent's SureSelect Human All Exon kit. Purified capture products will be amplified using the SureSelect GA PCR primers (Agilent) for 12 cycles. 3) Sequencing will be carried out for the captured libraries using at least 100 bp paired-end reads. To achieve high level sensitivity and accuracy for detecting all the mutations in the whole exome, each sample will be sequenced at 200X mean depth. 4) Read mapping and alignment and variant analysis: Sequence short reads will be aligned to a reference genome (NCBI human genome assembly build 38) using BWA-MEM. Local realignment of aligned reads will be performed using Genome Analysis Toolkit (GATK).Data QC: To ensure scientific rigor and consistency among sites in RNA and DNA processing we will include a preliminary analysis of steps in processing and analysis. Protocols for extraction of high quality RNA and DNA from formalin fixed paraffin embedded (FFPE) tissues, which will be used extensively in these studies continue to improve and may have variable implementation among the sites participating in this study. To evaluate consistency of preliminary steps in processing and downstream analyses, we will initially distribute slides from one large FFPE fixed cancer of origin from prostate, breast, lung and pancreatic cancer. Analysis of these samples will allow us to review the DNA and RNA characteristics (yield, purity and strand length) among sites. Downstream analysis of these same samples will also allow us to compare among sites the consistency of variant calls among centers. We will be able to identify if there are some times of calls (such as small insertion deletions) that are more variable among centers versus other types of calls (such as relative gene expression or single base pair substitutions) that we expect to be less variable and to characterize the reliability of findings across sites. We are also including a 5% blind duplicate analysis of RNA sequencing. Samples will be analysed by the participating genomics cores without knowledge of the phenotype. RNA seq and CNA analyses are normalized for batch effects. We will also compare the observed sex to the self-reported sex as based on RNA profiles and exome sequencing of X chromosome genes as another check for processing accuracy and sample management. D. References 1. Wacholder, S. Precursors in Cancer Epidemiology: Aligning Definition and Function. Cancer Epidemiol. Prev. Biomark. 22, 521-527 (2013). PMID: 23549395.2. Berman, J. J. Precancer: The Beginning and the End of Cancer. (Jones & Bartlett Learning, 2011).3. Nasiell, K., Nasiell, M. & Vaćlavinková, V. Behavior of moderate cervical dysplasia during long-term follow-up. Obstet. Gynecol. 61, 609-614 (1983). PMID: 6835614.4. Merrick, D. T. et al. Persistence of Bronchial Dysplasia Is Associated with Development of Invasive Squamous Cell Carcinoma. Cancer Prev. Res. (Phila. Pa.) 9, 96-104 (2016). PMID: 26542061.5. Gurel, B. et al. Nuclear MYC protein overexpression is an early alteration in human prostate carcinogenesis. Mod. Pathol. Off. J. U. S. Can. Acad. Pathol. Inc 21, 1156-1167 (2008). PMID: 18567993.6. Meeker, A. K. et al. Telomere shortening is an early somatic DNA alteration in human prostate tumorigenesis. Cancer Res. 62, 6405-6409 (2002). PMID: 12438224.7. Guner, G. et al. Novel Assay to Detect RNA Polymerase I Activity In Vivo. Mol. Cancer Res. MCR 15, 577-584 (2017). PMID: 28119429.
Study phs002225
Treatment of Pulmonary Hypertension and Sickle Cell Disease with Sildenafil Therapy (Walk-PHaSST)

Pulmonary arterial hypertension (PAH) is a progressive condition characterized by narrowing or stiffening pulmonary arterioles resulting in increased pulmonary blood pressure and reduced delivery of oxygenated blood to the body. It is a common complication of sickle cell disease and initially presents with the symptom of shortness of breath (dyspnea) on exertion. As the condition worsens, other symptoms such as dizziness, lower extremity edema, and chest pain can develop. The drug, sildenafil, works by relaxing blood vessels in the lungs which reduces pulmonary blood pressure and allows more oxygenated blood to circulate. Increased levels of oxygenated blood allows individuals with PAH to tolerate more activity, but guidelines for using sildenafil in patients with PAH and sickle cell disease were unavailable at the time of the Walk-PHaSST trial.Participants were screened for the existence of pulmonary hypertension with a six minute walk test and a Doppler echocardiogram that assessed TRV, diastolic function, and valvular and systolic function. Subjects with TRV ≥ 2.7 m/s received further clinical evaluation for possible causes of pulmonary hypertension. Other screening data included medical history, a physical exam, and standard laboratory testing. For individuals with moderate to severe pulmonary hypertension (TRV ≥ 3.0), a cardiac catheterization was done at the baseline and week 16 data collection periods.Subjects eligible for the main intervention trial based on screening results were randomized in a 1:1 double blind fashion to receive sildenafil or placebo for 16 weeks. Subjects received 20 mg of oral sildenafil or matching placebo 3 times daily for 6 weeks, followed by 40 mg 3 times daily for 4 weeks, followed by 80 mg 3 times daily for 6 weeks, as tolerated. Participants could also receive other therapies as needed to manage sickle cell and related complications. The primary outcome measure of the trial was change in the six minute walk test, a standard indicator of a person's heart and lung function and exercise capacity, from baseline to week 16. After completing the study treatment (or placebo), participants could choose to be part of the open-label follow-up phase of the study and continue to be assessed for up to one year.The study was intended to screen about 1000 subjects and randomize 132 subjects, however it was terminated early due to the unforeseen increase in adverse events in participants treated with sildenafil as compared to placebo. When the study was stopped, 33 participants had completed the trial. Subjects continued to be monitored, but were instructed to taper sildenafil treatment over three to seven days.There was no evidence that treatment with sildenafil impacted the six minute walk distance from baseline to week 16. In addition, treatment with sildenafil appeared to increase rates of hospitalization due to sickle cell disease pain.Due to in part to the early termination of the trial, the majority of subject data was collected from the screening phase of the study (n=720), as opposed to the main intervention trial (n=74).Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.
Study phs002383
Strabismus, CCDD and other anomalies

Strabismus is one of the most common ophthalmological diseases affecting our population. It has many detrimental effects for affected individuals, including functional visual loss from amblyopia, secondary psychosocial difficulties, and limited employment opportunities. Strabismus also imposes a significant economical burden on society from the cost of screening programs, surgical correction, and lost productivity. Congenital incomitant strabismus is a broad term that encompasses congenital ocular motility disorders with restricted movement in one or more directions of gaze, and includes various forms of Duane's retraction syndrome, horizontal gaze palsy, congenital fibrosis of extraocular muscles, and Moebius syndrome. Incomitant strabismus together with a group of related disorders such as congenital ptosis and congenital facial palsy have been collectively redefined as congenital cranial dysinnervation disorders (CCDDs). This redefinition stemmed from previous studies in the Engle lab showing that these disorders can be caused by mutations in genes encoding transcription factors critical to ocular cranial motor neuron development and by genes that encode proteins essential to the proper growth and guidance of developing axons. Thus, while these disorders account for only a small percent of strabismus, understanding their genetic etiology has led to our ability to provide genetic testing and counseling, and has provided the first insights into the molecular etiologies of strabismus. While the Engle lab has identified all seven CCDD genes published to date, our cohort still includes >100 familial CCDD pedigrees that are mutation-negative. We continue to utilize co-segregation analysis and whole exome or whole genome sequencing technologies to identify new disease genes in this mutation-negative cohort.
Study phs000478
eMERGE Network Phase III Clinical Sequencing: eMERGEseq Panel

The Electronic Medical Records and Genomics (eMERGE) Network is a National Institutes of Health (NIH)-organized and funded consortium of U.S. medical research institutions. The primary goal of the eMERGE Network is to develop, disseminate, and apply approaches to research that combine biorepositories with electronic medical record (EMR) systems for genomic discovery and genomic medicine implementation research. eMERGE was announced in September 2007 and began its third phase in September 2015. eMERGE III consists of nine study sites, two central sequencing and genotyping facilities, and a coordinating center. eMERGE Phase III aims to: 1) sequence and assess the phenotypic implication of rare variants in a custom designed eMERGEseq panel consisting of 109 genes (including 56 ACMG actionable finding list genes and the top 6 genes from each site relevant to their specific aims), as well as approximately 1400 SNPs; 2) assess the phenotypic implications of these variants by developing, validating and implementing new phenotype algorithms, 3) integrate genetic variants into EMRs to inform clinical care; and 4) create community resources. Included in this study are: ~24,000 eMERGE participants from 10 eMERGE III study sites. Corresponding demographics, body mass index measurements. Top PheWAS codes generated from a collated list of ICD codes from all study sites. Study sites and participants include: Cincinnati Children's Hospital Medical Center (CCHMC): Cincinnati Children's Hospital Medical Center (CCHMC) is a not-for-profit hospital and research center pioneering breakthrough treatments, providing outstanding family-centered patient care and training healthcare professionals for the future, and dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. We bring a comprehensive electronic health record (EPIC), a deidentified i2b2 data warehouse of 680K patient records, a biobank with >261,000 consents that allow return of results to >84,000 patients and guardians who have provided DNA samples, and hundreds of faculty and senior staff who make genomics or informatics an active focus of their research. CCHMC will help the eMERGE III Steering Committee identify genes for the eMERGE III targeted sequencing panel, provide 3,000 DNA samples from CCHMC patients to be sequenced, review targeted gene panels from clinical care at CCHMC for somatic mosaicism and reinterpretation, and further develop and disseminate a software workflow suite for sequence analysis. We will also extend our work generating phenotype algorithms using heuristic and machine learning methods to many new childhood diseases. We will develop tools to evaluate adolescent return of results preferences, examine the ethical and legal obligations and potential to reanalyze results, and develop clinical decision support for phenotyping, test ordering, and returning sequencing results. Children's Hospital of Philadelphia (CHOP): The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP), and one of the world's largest genetics research programs, with to state-of-the-art high-throughput sequencing and genotyping technology. Our primary goal is to translate basic research findings to medical innovations. We aim to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders, including asthma, autism, epilepsy, pediatric cancer, learning disabilities, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. Participants were recruited from the CAG biorepository (n>450,000), specifically from >100,000 CHOP pediatric patients and family members, which is enriched for rare-diseases (n>12,000). Center for Applied Genomics, The Children's Hospital of Philadelphia We gratefully thank all the children and their families who enrolled in this study, and all individuals who donated blood samples for research purposes. Genotyping for this project was performed at the Center for Applied Genomics and supported by an Institutional Development Award from The Children's Hospital of Philadelphia. Sequencing was supported by the National Institutes of Health through an award from the National Human Genome Research Institute's Electronic Medical Records and Genomics (eMERGE) program (U01HG008684). Columbia University: The goal of the Columbia eMERGE III project is to develop methods for integrating genomic data in EHRs and to study the impact of such genomic informatics interventions on the health of a diverse, underserved urban adult English- and Spanish-speaking patient population in Northern Manhattan served by Columbia University Medical Center/New York-Presbyterian Hospital system. The study group is 2500 patients recruited from diverse clinics and community outreach centers of self-reported White (~61%), Asian (~11%), African-American (~11%), American Indian/Alaska Native (<1%) racial and Hispanic (~33%) ethnic backgrounds. There are two subgroups in the study cohort - a retrospective group (N=1052) that includes patients from oncology and nephrology clinics, and a prospective one (N=1448) that includes healthy individuals as well as participants with diverse medical conditions. Confirmed pathogenic variants in 70 selected genes will be returned to participants and their healthcare providers through the EHR integration. Participants are able to choose the results they receive and will have the freedom to meet with a genetic counselor and a geneticist to review results. The impact of genetic testing on clinical care is determined by periodic monitoring of EHRs. Geisinger: Samples and phenotype data in this study were provided by the Geisinger MyCode® Community Health Initiative. Participants are recruited across the Geisinger System via online consents or in-person consents at a hospital or clinic visit. Enrollment is ongoing with over 100,000 individuals currently consented. Partners Healthcare (Harvard University): The Partners HealthCare Biobank is a large research program designed to help researchers understand how people's health is affected by their genes, lifestyle, and environment. This large research data and sample repository provides access to high-quality, consented blood samples to help foster research, advance our understanding of the causes of common diseases, and advance the practice of medicine. For the Partners research community (Massachusetts General Hospital and Brigham and Women's Hospital), the Biobank provides: Banked samples (plasma, serum, and DNA) collected from consented patients Blood samples that were discarded after clinical testing in the Crimson Cores maintained in the Brigham and Women's Hospital and Massachusetts General Hospital Pathology Departments Sample handling and preparation services Link to the biobank data to the Partners Research Patient Data Registry (RPDR) a research instance of our electronic clinical chart Data access through our research portal. To date, over 70,000 Partners patients have given their consent to enroll, give a blood sample, receive research results and agreed to be re-contacted for additional research studies. The Biobank has enabled Partners investigators to compete for nationally recognized grants in personalized medicine such as a clinical electronic Medical Records and Genomics network (eMERGE) site and the national All of US program. The Biobank currently supports over 120 Partners investigators and over 130 million dollars in NIH research. Kaiser Permanente Washington/ (KPWA) / University of Washington (UW): KPWA participants were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N=2,500.) NWIGM is based at the University of Washington and co-managed by the University of Washington and KPWA. The purpose of the NWIGM biorepository is to build infrastructure and resources to carry out a broad range of future genetic research. KPWA members enrolled in the biorepository are asked to provide informed consent to providing a DNA sample for storage in the NWIGM biorepository. The consent is purposefully broad to serve the dual purpose of reducing the burden on researchers who wish to use this biorepository and the IRB committees who will be responsible for reviewing these requests in the future. Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in KPWA's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at KPWA. 1) Demographics - participants with self-reported race as Asian ancestry were prioritized and selected to enrich for non-European ancestry. The KPWA eMERGE cohort includes N=1,245 members of Asian ancestry. 2) Participants were also selected for a history of colorectal cancer (N=1,255), in order to allow us to enrich germline pathogenic variants. Mayo Clinic: The Return of Actionable Variants Empirical (RAVE) Study was approved by the Mayo Clinic IRB. We recruited 2537 participants from Mayo Clinic biobanks in Rochester, MN, who had hypercholesterolemia or colon polyps, thereby enriching for Familial hypercholesterolemia (FH) and monogenic causes of colorectal cancer (CRC). Additional eligibility criteria were: 1) residents of Southeast MN who were alive and aged 18-70 years; 2) LDL-C level >155 or >120 mg/dl while on lipid-lowering therapy; 3) no known cause of secondary hyperlipidemia; and 4) no cognitive impairment or dementia that would compromise their ability to give written informed consent. Based on these criteria, we identified 5270 eligible patients and obtained informed consent from 3030 participants. Recruitment was conducted in waves and utilized mailed recruitment packets consisting of a study brochure, a written informed consent form, a baseline psychosocial questionnaire, and a return postage-paid envelope. DNA of 2537 participants was sent for CLIA-certified targeted sequencing of 109 genes including genes associated with FH and CRC. Targeted sequencing and genotyping was performed in a Central Laboratory Improvement Amendment (CLIA)-certified laboratory. Northwestern University: Samples and data used in this study were obtained from patients from Northwestern Medicine, an integrated healthcare system, formed through a partnership of Northwestern Memorial HealthCare and Northwestern University Feinberg School of Medicine. Participants include a retrospective cohort from the Northwestern Pharmacogenomics Study, funded through the eMERGE II project, NHGRI (3U01HG006388-02S1) and a prospective cohort from the Genetic Testing and Your Health Study, funded through the eMERGE III project, NHGRI (U01HG008673). Patients were eligible to participate if they were18 years or older and see a physician at Northwestern Medicine. Patients consented to genetic testing and to allow their results to be placed in their electronic medical record. Vanderbilt University Medical Center: Vanderbilt University Medical Center (VUMC) participants were enrolled in the eMERGE Network through the Vanderbilt Genome-Electronic Records (VGER) project. Patients were provided the appropriate consent to receive clinically relevant genetic results (N=2,700). Participants were eligible if aged 21 or over, had a healthcare provider at VUMC, and visited the provider at least 3 times in the past 3 years. Meharry Medical College: Inclusion of ethnic groups in genomic research is critical to identify possible reasons for health disparities. African-Americans are being enrolled in various outpatient clinics of Nashville General Hospital at Meharry, an inner city hospital primary serving a poorer patient group. A total of 500 African Americans with four cancer types demonstrating health disparities in this population - prostate, colon, breast, lung are identified and approached by clinical research coordinators. The purpose of the study is to determine if any genetic information can be identified from these patients who have or are at high risk of one of these disparate cancers. All participants provide written informed consent and HIPAA authorization to provide blood samples for broad research use and permission to access data in their hospital electronic medical record for research now and in the future. An extensive demographic profile is obtained and entered into a REDCap database. Blood samples are obtained for a panel of alleles from extracted DNA at Baylor. In addition, de-identified coded samples are processed and stored in a central biorepository for further DNA, RNA and proteomic analyses. The survey and phlebotomy are performed at the time of the initial contact and agreement to participate. Nearly all patients approached willingly agree to participate for potential benefit to themselves, family members, or humankind. Little concern is voiced of providing samples for genetic analysis. Study investigators will share results with the participants and providers if testing does not indicate high risk. Results indicating increased risk or actionable alleles for the patient and/or family will be returned by a genetic counselor. Monitoring of the patients' health in this cohort will continue to be followed in the EMR to identify any future associations that might explain health disparities in African Americans. Proposals will be reviewed from investigators to study the genetic or proteomic samples as well as the clinical and demographic information in the repository. Please note that this version of the dataset has a handful of mismatches between genotyped and provided sex. Data with the following IDs should be removed prior to analysis: 420252874213744142412243424569384245694642672223
Study phs001616
Metagenomics-Guided Pathogen Discovery in Travelers' Diarrhea

Traveler's diarrhea (TD) is caused by enterotoxigenic Escherichia coli (ETEC), other pathogenic gram-negative pathogens, norovirus and some parasites. Nevertheless, standard diagnostic methods fail to identify pathogens in more than 30% of TD patients, so it is predicted that new pathogens or groups of pathogens may be causative agents of disease. A comprehensive metagenomic study of the fecal microbiomes from 23 TD patients and seven healthy travelers was performed, all of which tested negative for the known etiologic agents of TD in standard tests. Metagenomic reads were assembled and the resulting contigs were subjected to semi-manual binning to assemble independent genomes from metagenomic pools. Taxonomic and functional annotations were conducted to assist identification of putative pathogens. We extracted 560 draft genomes, 320 of which were complete enough to be enough characterized as cellular genomes and 160 of which were bacteriophage genomes. We made predictions of the etiology of disease in individual subjects based on the properties and features of the recovered cellular genomes. Three subtypes of samples were observed. First were four patients with low diversity metagenomes that were predominated by one or more pathogenic E. coli strains. Annotation allowed prediction of pathogenic type in most cases. Second, five patients were co-infected with E. coli and other members of the Enterobacteriaceae, including antibiotic resistant Enterobacter, Klebsiella, and Citrobacter. Finally, several samples contained genomes that represented dark matter. In one of these samples we identified a TM7 genome that phylogenetically clustered with a strain isolated from wastewater and carries genes encoding potential virulence factors. We also observed a very high proportion of bacteriophage reads in some samples. The relative abundance of phage was significantly higher in healthy travelers when compared to TD patients. Our results highlight that assembly-based analysis revealed that diarrhea is often polymicrobial and includes members of the Enterobacteriaceae not normally associated with TD and have implicated a new member of the TM7 phylum as a potential player in diarrheal disease.
Study phs001352
The spectrum of somatic mutations in high-risk acute myeloid leukemia with -7/del(7q)

Patients with myeloid malignancies bearing high-risk cytogenetic abnormalities lack effective therapies and have a poor overall survival. -7/del(7q) is identified in half of high-risk myeloid neoplasms. We recently identified CUX1 to be a haploinsufficient myeloid tumor suppressor gene located within the commonly deleted segment of 7q22. Here we identify the spectrum of somatic mutations that co-occur with loss of CUX1 and chromosome 7 in patients with de novo acute myeloid leukemia (AML) or a therapy-related myeloid neoplasm. -7/del(7q) leukemias have a distinct mutational profile characterized by low frequencies of alterations in major leukemogenic pathways, including genes encoding transcription factors, cohesin, and DNA-methylation-related proteins. In contrast, RAS pathway activating mutations occurred in 40% of -7/del(7q) samples, a significantly higher frequency than other AMLs and higher than previously reported. As targeted therapeutics advance, our data provide guidance for which pathways are most relevant in the treatment of adverse-risk myeloid leukemia.
Study phs000759
CIDR: The Role of Rare Coding Variation in Prostate Cancer in Men of African Ancestry - RESPOND Project 2

In RESPOND Project 2, we seek to identify rare genetic factors that are associated with prostate cancer (PCa) risk and aggressiveness in men of African ancestry (AA). We will conduct exome sequencing of 15,000 prostate cancer cases and 5,000 controls from the RESPOND cohort and the African Ancestry Prostate Cancer Consortium (AAPC) with cases selected based on risk categories: high-risk (stage T3/T4 or Gleason 8+ or PSA>20 ng/ml), intermediate-risk (stage T2b/T2c or Gleason 7 or PSA 10-20 ng/ml) and low-risk disease (stage T1/T2a and Gleason ≤ 6 and PSA<10 ng/ml). We expect the findings from this Project to significantly advance knowledge of susceptibility to aggressive PCa and racial/ethnic disparities in PCa risk, and to guide the development of future preventive, early detection and prognostic measures for AA men. The first phase of the study will include exome sequence data for approximately 7,500 cases and 5,000 controls from AAPC. The second phase of the study will include exome data for 5,000 cases from RESPOND and AAPC.ACKNOWLEDGMENTS and CONTRIBUTING SITES Multiethnic Cohort (MEC): The MEC and the genotyping in this study were supported by National Institutes of Health (NIH) grants CA63464, CA54281, CA1326792, CA148085, and HG004726. Cancer incidence data for the MEC and Los Angeles Study of Aggressive Prostate Cancer (LAAPC) studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with federal funds from the National Cancer Institute (NCI)/NIH/Department of Health and Human Services (DHHS) under Contract No. N01-PC-35139, and the California Department of Health Services as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant 1U58DP000807-3 from the Centers for Disease Control and Prevention (CDC). Ghana Prostate Study (GPS): The Ghana Prostate Study was funded by the Intramural Program of the NCI/NIH/DHHS under Contract No. HHSN261200800001E. Men of African Descent and Carcinoma of the Prostate (MADCaP): We thank all MADCaP study participants. This work is a product of the MADCaP network. This work was supported by NCI/NIH grant U01CA184374 to Timothy Rebbeck and National Institute of General Medical Sciences (NIGMS) MIRA grant R35GM133727 to Joseph Lachance. Additional funding includes a seed grant from the Integrated Cancer Research Center at Georgia Institute of Technology. UGANDA: The UGANDA study was supported by NIH grant R01CA165862. Southern Community Cohort (SCCS) is funded by NIH grant CA092447. SCCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt Ingram Cancer Center (CA68485). Data on SCCS cancer cases were provided by Alabama Statewide Cancer Registry, Kentucky Cancer Registry, Office of Cancer Surveillance at Tennessee Department of Health, Florida Cancer Data System, Central Cancer Registry at North Carolina Division of Public Health, Georgia Comprehensive Cancer Registry, Louisiana Tumor Registry, Mississippi Cancer Registry, South Carolina Central Cancer Registry, Virginia Cancer Registry at Virginia Department of Health, and Cancer Registry at Arkansas Department of Health. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries (NPCR)/Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the NPCR/CDC. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. Karuprostate: The Karuprostate study was supported by the French National Health Directorate and by the Association pour la Recherche sur les Tumeurs de la Prostate. Séverine Ferdinand, Marc Romana. The North Carolina - Louisiana Prostate Cancer Project (PCaP) is carried out as a collaborative study supported by the Department of Defense contract DAMD 17-03-2-0052. The authors thank the staff, advisory committees and research subjects participating in the PCaP study for their important contributions. We would also like to acknowledge the UNC BioSpecimen Facility and the LSUHSC Pathology Lab for our DNA extractions, blood processing, storage and sample disbursement (https://genome.unc.edu/bsp). Gene-Environment Interaction in Prostate Study (GECAP) was supported by NIH grant ES011126. King County Prostate Cancer Study (KCPCS) was supported by NIH grants CA056678, CA082664, and CA092579, with additional support from the Fred Hutchinson Cancer Research Center. We thank the participants in these studies, and Ms. Suzanne Kolb for help with study management. The Los Angeles Study of Aggressive Prostate Cancer (LAAPC) was funded by grant 99-00524V-10258 from the Cancer Research Fund, under Interagency Agreement #97-12013 (University of California contract #98-00924V) with the Department of Health Services Cancer Research Program. Cancer incidence data for the MEC and LAAPC studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with Federal funds from the NCI/NIH/DHHS under Contract No. N01-PC-35139, and the California Department of Health Services as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant 1U58DP000807-3 from the CDC. Prostate Cancer Studies at MD Anderson (MDA) was supported by grants CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388.
Study phs002637
The Genetic Landscape of Metastasis and Recurrence in Head and Neck Squamous Cell Carcinoma

Recurrence and/or metastasis occur in more than half of patients with head and neck squamous cell carcinoma (HNSCC) and pose the greatest threats to long-term survival. The genetic alterations underlying recurrent/metastatic HNSCC are unknown. This study represents the first whole exome sequencing (WES) cohort study of patient-matched tumor pairs in recurrent or metastatic HNSCC. Synchronous lymph node metastases are genetically more similar to their paired index primary tumors than metachronous recurrent tumors are. Newly arisen mutations in recurrent or metastatic tumors, may have therapeutic implications. Several genes were found to be mutated in multiple metastatic or recurrent tumors, but not in their respective primaries, including C17orf104, ITPR3, and DDR2. DDR2 mutations have been shown to confer enhanced sensitivity to Src-family kinase (SFK) inhibitors in other malignancies. Similarly, we found HNSCC cell lines harboring endogenous and engineered DDR2 mutations to be more sensitive to the SFK inhibitor dasatinib, than those with WT DDR2. This study outlines the first compendium of somatic mutations in primary, metastatic and/or recurrent HNSCC cancers that arise in individual patients; and demonstrates how such data can be used to interrogate potential predictive/prognostic biomarkers to inform and guide personalized therapy.
Study phs001007
Evaluating genetic ancestry and self-reported ethnicity in the context of carrier screening

Current professional society guidelines recommend genetic carrier screening be offered on the basis of ethnicity, or when using expanded carrier screening panels, they recommend to compute residual risk based on ethnicity. We investigated the reliability of self-reported ethnicity in 9125 subjects referred to carrier screening. We identified several discrepancies between the two sources of self-reported ethnicity and genetic ancestry. As a result, carrier rates and residual risks provided for patient decision-making are impacted if using self-reported ethnicity. We recommend the routine use of pan-ethnic carrier screening panels in reproductive medicine. Furthermore, the use of an ancestry model would allow better estimation of carrier rates and residual risks.
Study phs001482
National Eye Institute (NEI) Age-Related Eye Disease Study (AREDS)

The Age-Related Eye Disease Study (AREDS) was initially designed as a long-term multi-center, prospective study of the clinical course of age-related macular degeneration (AMD) and age-related cataract. In addition to collecting natural history data, AREDS included a clinical trial of high-dose vitamin and mineral supplements for AMD and a clinical trial of high-dose vitamin supplements for cataract. AREDS participants were 55 to 80 years of age at enrollment and had to be free of any illness or condition that would make long-term follow-up or compliance with study medications unlikely or difficult. On the basis of fundus photographs graded by a central reading center, best-corrected visual acuity and ophthalmologic evaluations, 4,757 participants were enrolled in one of several AMD categories, including persons with no AMD. The clinical trials for AMD and cataract were conducted concurrently. AREDS participants were followed on the clinical trials for a median time of 6.5 years. Subsequent to the conclusion of the clinical trials, participants were followed for an additional 5 years and natural history data were collected. The AREDS research design is detailed in AREDS Report 1. AREDS Report 8 contains the mainline results from the AMD trial; AREDS Report 9 contains the results of the cataract trial. Blood samples were also collected from 3,700+ AREDS participants for genetic research. Genetic samples from 600 AREDS participants (200 controls, 200 Neovascular AMD cases, and 200 Geographic Atrophy cases) were selected using data available in March 2005 and then were evaluated with a genome-wide scan. These data, as well as selected phenotypic data, were made available in the dbGaP. DNA from AREDS participants, which is currently being stored in the AREDS Genetic Repository, is available for research purposes. However, not all of the 3,700+ AREDS participants who submitted a blood sample currently have DNA available. In addition to including the data from the genome-wide scan on the 600 original samples, this second version of the AREDS dbGaP database provides a comprehensive set of data tables with extensive clinical information collected for the 4,757 participants who participated in AREDS. The tables include information collected at enrollment/baseline, during study follow-up, fundus and lens pathology, nutritional estimates, quality of life measures and measures of morbidity and mortality. In November 2010, over 72,000 high quality fundus and lens photographs of 595 AREDS participants (of the original 600 selected for the genome-wide scan) were made available in the AREDS dbGaP. In addition to the genome-wide scan data, the fundus and lens grading data for these participants are also available through the AREDS dbGaP. Details about the ocular photographs that are available may be found in the document "Age-Related Eye Disease Study (AREDS) Ocular Photographs". In January 2012, a measure of daily sunlight exposure was added in a separate "sunlight" table. Furthermore, the "followup" table has been revised. The visual acuity for the right eye was inadvertently missing at odd-numbered visits (01, 03, 05, etc.). This data is now part of the table. In February 2014 over 134,500 high-quality fundus photographs (macular field F2) of 4613 AREDS participants were added to the existing AREDS dbGaP resource. The AREDS dbGaP image archive already contains over 72,000 high quality fundus and lens photographs of 595 AREDS participants for whom dbGaP-accessible genotype data exist. Information about the available ocular photographs found in the document "Age-Related Eye Disease Study (AREDS) Ocular Photographs" has been updated with an addendum. It is hoped that this resource will better help researchers understand two important diseases that affect an aging population. These data may be applied to examination and inference on genetic and genetic-environmental bases for age-related diseases of public health significance and may also help elucidate the clinical course of both conditions, generate hypotheses, and aid in the design of clinical trials of preventive interventions. Definitions of Final AMD Phenotype Categories Please see phd001138.1 for a detailed description of how AREDS participants' final AMD phenotype was categorized. User's Guide for AREDS Phenotype Data A detailed User's Guide for the AREDS phenotype data is available. This User's Guide is meant to be a comprehensive document which explains the complexities of the AREDS data. It is recommended that all researchers using AREDS phenotype data make use of this User's Guide.
Study phs000001
Maintenance of Genome Sequence Integrity in Long- and Short-lived Rodent Species

In this study, we compared somatic mutation frequencies and spectra of bleomycin-treated and control fibroblasts of 5 species, i.e., mouse, guinea pig, blind mole-rat, naked mole-rat and humans, by using single-cell whole-genome sequencing. Sequencing data of the human cells are deposited here in the dbGaP. Sequencing data of the rodent cells are in the SRA database, under accession ID: PRJNA753541.
Study phs002610
Complete Genomics Deep Whole-Genome Sequencing of Circulating Tumor Cells from a Patient with Metastatic Breast Cancer

Circulating tumor cells (CTCs) are considered to be informative non-invasive biopsy for the early detection, diagnosis or therapy guidance of cancer. CTCs also contain most of the heterogeneity found across multiple metastatic sites. In this study, we combine recently improved CTC isolation methods, resulting in almost pure CTCs, with advanced whole genome sequencing (WGS) based on Long Fragment Read (LFR) technology to demonstrate, for the first time, the research and clinical utility of an accurate, comprehensive, phased, and quantitative genomic analysis of CTCs. In particular, genomes of 34 CTCs, collected at two different time points from a patient whose metastatic breast cancer ultimately became resistant to standard treatments, were analyzed as 3072 barcoded sub-genomic compartments of long DNA. An average read coverage of 23X per cell enabled the high-resolution detection of somatic variants, cancer copy number variations (CNVs) and structural variants, including 133 CNVs shorter than 1Mb and an early chromothripsis-like event.
Study phs001028
Genetic Testing to Understand and Address Renal Disease Disparities

People of African ancestry (Blacks) have increased risk of kidney failure due to numerous socioeconomic, environmental, and clinical factors. Two variants in the APOL1 gene (GeneID: 8542) are now thought to account for much of the racial disparity associated with hypertensive kidney failure in Blacks. One in 7 AAs carry this risk allele, but it is nearly absent in other populations; the variants protect against infection with African trypanosomiasis which is endemic to Africa. However, this knowledge has not been translated into clinical care to help improve patient outcomes and address disparities. It is unknown whether patient or clinician knowledge of genetic risk impacts patient care (i.e., renal surveillance, BP medication use/intensification) or outcomes such as BP control and CKD. GUARDD is a randomized trial to evaluate the effects and challenges of incorporating genetic risk information into primary care. Hypertensive, nondiabetic, adults with self-reported African ancestry, without kidney dysfunction, are recruited from diverse clinical settings and randomized to undergo APOL1 genetic testing at baseline (intervention) or at one year (waitlist control). Prior to enrolling their patients, providers are educated about genomics and APOL1. Guided by a genetic counselor, trained staff return APOL1 results to patients and provide low-literacy educational materials. Real-time clinical decision support tools alert clinicians of their patients' APOL1 results and associated risk status at the point of care. Our academic - community clinical partnership designed a study to generate information about the impact of genetic risk information on our primary outcome - patient care (renal surveillance by clinicians, of serum creatinine and/or urine albumin tests. with a sub-aim of reduction of systolic blood pressure). Secondary outcomes include impact on primary outcomes at 12 months, psycho-behavioral differences of patients between groups and over time, clinician knowledge, attitudes and beliefs at baseline and 12 months, and differences in outcomes between those tested and not tested. GUARDD will help establish the effective implementation of APOL1 risk informed management of hypertensive patients at high risk of CKD, and will provide a robust framework for future endeavors to implement genomic medicine in diverse clinical practices. It will also add to the important dialog about factors that contribute to and may help eliminate racial disparities in kidney disease.
Study phs001620
A Genomic Approach to Warfarin Dose Prescription in Admixed Caribbean Hispanics

Warfarin is an oral anticoagulant and one of the most used drugs in the world accounting for up to 1.5% of prescriptions globally. Warfarin inhibits vitamin K epoxide reductase complex 1 (VKORC1), an enzyme responsible for the vitamin K recycling that is required for the activation of clotting factors. Warfarin is prescribed at an average dose of 5 mg/day for most of the indications but significant inter-patient variability has been observed. Single nucleotide variations at the VKORC1 (GeneID: 79001) and CYP2C9 (GeneID: 1559) genes alone explain from 28 to 35% of variability in warfarin dose requirements and up to 56% when clinical factors (age, gender, body surface area, diabetes and heart valve) are incorporated into regression analysis. However, genetic-guided dosing algorithms are mostly derived from studies in Europeans. The objective of the present study was to identify genetic polymorphisms that can explain variability in warfarin dose requirements among Caribbean Hispanics of Puerto Rico. VKORC1 (GeneID: 79001) and CYP2C9 (GeneID: 1559), the most important genetic predictors of warfarin response, were sequenced using Next Generation sequencing. Other candidate genes in DMET Plus Assay were assessed to identify genetic variants with relevance in warfarin dose requirements among Puerto Ricans. Our study used the Extreme Discordant Phenotype approach to perform a case-control association analysis that were confirmed with univariate and conditioned regressions. Accordingly, patients were stratified based on stable warfarin doses as sensitive (<4 mg/day), control (4-6 mg/day) and resistant (>6 mg/day). Genetic variants associated with warfarin response among the study cohort were used to perform a multivariate regression analyses to develop a genetic-guided dosing algorithm tailored for Puerto Ricans. The CYP2C9rs2860905 SNP was found to be strongly associated with warfarin dose requirements in Puerto Ricans. The CYP2C9 rs2860905 SNP tags four haplotypes that represent the trihybrid admixture of Puerto Ricans. Therefore, haplotypes harboring the rs2860905 variant are more informative in predicting warfarin dose among Puerto Ricans than the common single segregating SNPs (i.e., CYP2C9*2 and CYP2C9*3) relevant to Europeans. DMET Plus array confirmed the strong association of VKORC1 (GeneID: 79001) and CYP2C9 (GeneID: 1559) with warfarin dose requirements, but also identified other polymorphisms in CES2 (GeneID: 8824) and ABCB1 (GeneID: 5243) associated with warfarin resistance (>6 mg/day). Ancestry analysis showed that Puerto Ricans from Veteran Affairs Caribbean Healthcare System (VACHS) using warfarin had higher Native American proportions than Puerto Ricans from the 1000 Genomes project; however, this difference was not significant. Incorporation of rs2860905 in a regression model (R2=0.60 MSE=0.38) that also included additional genetic predictors (i.e., VKORC1-1639G>A; CYP2C9 rs1856908; ABCB1 rs10276036; CES2 rs4783745) and non-genetic factors (i.e., hypertension, diabetes and age) showed better prediction of warfarin dose requirements than CYP2C9*2 and CYP2C9*3 combined (partial R2=0.132 versus 0.023 and 0.007, respectively, p<0.001). Interestingly, deep vein thrombosis and diabetes were found associated with high warfarin dose requirements among Puerto Ricans. Our results support the use of CYP2C9 rs2860905 along with other genetic markers [e.g., VKORC1(GeneID: 79001 )] and clinical covariates to predict warfarin dose in Puerto Ricans. Although our findings need further replication, this study contributes to the field of Pharmacogenetics and to improve anticoagulation therapy among Puerto Ricans.
Study phs001496
The Spatio-Temporal Evolution of Multiple Myeloma from Baseline to Relapse-Refractory States

In multiple myeloma, the spatio-temporal evolution patterns of cancer cells in the bone marrow remain largely unknown. Here, we applied spatial-longitudinal sequencing, including a total of 179 samples collected from 25 patients during a follow-up of up to 14 years. Our study highlights focal bone lesions (FL) as hotspots of tumor evolution and provides possible explanations for their negative prognostic impact. We show a close phylogenetic relationship between baseline FL and relapse clones. In patients with ≥3 FDG-PET-positive FL at diagnosis, relapse was driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion was typically seen (p<0.01). Applying imaging-guided sampling, we identified a novel evolutionary pattern with unique treatment-resistant sub-clones at distinct locations. We observed multiple selective clonal sweeps, resistant clones that can be hidden over years, and epigenetic modifier mutations as a new class of relapse aberrations. Here we show that imaging can enhance tumor evolution studies and response assessment. The sophisticated evolution mechanisms in myeloma and presence of single cells that can drive relapse even after 10 years of remission suggest that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.
Study phs002625
Gabriella Miller Kids First Pediatric Research Program in Craniofacial Microsomia

The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Craniofacial microsomia (CFM), also termed hemifacial microsomia or oculo-auricular-vertebral spectrum, is the third most common congenital craniofacial condition. CFM comprises a variable phenotype and the most common features include malformations of the ear (i.e. microtia) and lower jaw (i.e. mandibular hypoplasia) on one or both sides. Microtia in the absence of other anomalies is believed to represent the mildest form of CFM. The cause of CFM is unknown for most affected individuals. We have established a cohort through previous studies and collected DNA to identify the genetic contributions to CFM, which could facilitate diagnosis, tailored treatments and guide prevention strategies. Successful completion of this proposal will advance knowledge in the genetic architecture of susceptibility to CFM and will provide insight about the biological mechanisms underlying craniofacial development. The results from this study have the potential to further research on the etiology of other craniofacial conditions, and the pathogenesis of typical and atypical craniofacial development.
Study phs002130
Rare Cancer Tumors Project

A large proportion of common cancers affecting patients around the world have been selected for comprehensive cancer genome studies. Further efforts will be needed to tackle the remaining tumor types, including the rare forms of cancers. Although rare, these cancers tend to be more aggressive and fast growing with an early recurrence following initial chemotherapy and poor prognosis. Besides, patients diagnosed with rare cancers may have difficulty finding a physician knowledgeable in treating their type of cancer. While sample collection is a major challenge, the integrated genomic analyses would identify novel causative genes in these rare cancers, shed new light on the biology of the rare cancers, as well as guide novel targeted cancer therapies. Through efficient collaboration, the Human Genome Sequencing Center (HGSC) at Baylor College of Medicine (BCM) has collected/is expected to collect 20 different types of rare cancers, 15-30 cases each. Whole-exome sequencing and high-resolution SNP array analysis were/will be performed for all cases and whole-genome sequencing was designed for a selected subset of the cases. The Rare Cancer Tumors Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in this sub-study, please click on the following sub-study below or in the "Sub-studies" section of this top-level study page phs000725 Rare Cancer Tumors Cohort. phs000754 Intracranial Germ Cell Tumors phs000861 Craniopharyngioma Tumors phs000859 Sezary Syndrome Genomic Analysis
Study phs000725
Impact of Race and Genetic Factors on Beta Blocker Effectiveness in Heart Failure

Heart Failure (HF) continues to be an enormous public health problem, despite the many advances in its pharmacotherapy over the past 25 years. The availability of multiple therapeutic options for HF requires individualization of therapy. However, there are currently insufficient tools to guide the optimal selection of therapy. The expansion of human genomic information has led to exciting new opportunities to improve the selection of drug therapy. In order to evaluate whether genetic variation impacts outcomes and drug response in heart failure, we performed a cohort study of patients with heart failure. Our overall plan was to recruit patients who have heart failure and receive treatment through HAP-HFMG. This allowed us to collect clinical outcomes, and quantify medication adherence via electronic pharmacy claims data. We collected whole blood samples for genetic analysis. We then tested whether genetic variants can predict patient outcomes (survival) in the context of their medical therapy (drug exposure). The primary medications of interest were beta blockers. Outcomes were modeled overall, accounting for the amount of exposure to the agent, and adjusting for baseline characteristics.
Study phs001501
Molecular Genetics of Heroin Dependence in China

Our principal goal was to determine the chromosomal locations of genetic variations associated with increased vulnerability to heroin dependence. This goal was attained by identifying specific DNA markers that are genetically linked to heroin dependence. We collected blood and diagnostic information (using a structured diagnostic interview) from 1230 people (sib-pairs) having DSM-IV defined heroin dependence as well as from their parents and other affected and unaffected siblings, when possible. Genotype and clinical data was archived using database software. We conducted a multipoint linkage analysis using the guidelines of Lander and Kruglyak to assert statistical significance, and followed up on regions of interest with a dense set of markers to evaluate candidate genes.
Study phs001213
Breast Cancer Genome Guided Therapy Study (BEAUTY)

The Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective neoadjuvant clinical study enrolling 140 women with Stage I-III breast cancer. The goals of this study were to 1) perform comprehensive sequencing of germline and tumor tissues (exome and RNA seq), 2) identify genomic alterations and perturbed pathways associated with response/resistance to standard chemotherapy, and 3) develop patient-derived xenograft (PDX) for testing of therapeutic regimens chosen on the basis of alterations identified by genetic sequencing.
Study phs001050
National Cancer Institute (NCI) Adding Hispanics to Ongoing GWAS in Colorectal Cancer

Genome-wide association studies (GWAS) of colorectal cancer (CRC) have been instrumental in identifying a number of common susceptibility loci in Non Hispanic (NH)-White populations, and a NCI priority is to extend GWAS findings to other populations to address racial/ethnic disparities in cancer susceptibility. Currently, GWA studies of CRC in NH-Whites, Japanese and African-Americans are ongoing. We propose a complementary study to address this critical research area in Hispanics. Hispanics represent the fastest growing ethnic population in the U.S. and have been largely understudied in terms of genetic susceptibility to cancer. There are noted differences in incidence, survival and mortality in CRC by ethnic/racial groups. Hispanics often present with CRC at a younger age and have a significantly greater incidence of stage IV tumors or metastatic disease compared to NH-Whites. We propose to conduct a large, cost-efficient, population-based GWAS in Hispanics by building upon existing NIH-funded resources, the Colon Cancer Family Registry (Colon CFR) and the Multiethnic Cohort Study (MEC). We plan to recruit 2,500 Hispanic men and women diagnosed with CRC between 01/2008 to present using cancer registries in California, physican referrals and familial referrals. Risk factor/diet questionnaires, pathology reports, Oragene saliva samples (for genotyping), optional blood samples (for genotyping and biometric analysis) and tumor blocks (for MSI testing) will be collected using methodologies developed in the Colon CFR/MEC. Cases of CRC in the MEC (currently 473; anticipated 600 at end) will also be included. Population-based Hispanic individuals without a diagnosis of CRC participating in other GWA studies in the MEC (n=3,900, U01HG004726, Haiman) will be used as controls. We will genotype all 3,100 cases using the Illumina 1M array and use available genotype and epidemiologic data collected on 3,900 controls. Our statistical analyses will include: single-SNP and haplotype effects, gene-environment interactions and heterogeneity by MSI, tumor subtype and family history of CRC. We will replicate findings in a second-stage using CRC cases and controls from Mexico (1,000 cases and 1,000 controls, EU FP7 funding, CHIBCHA, Carvajal-Carmona/Tomlinson). We will also examine heterogeneity of the risk estimates by ethnicity/race by leveraging GWA data on NH-Whites (2,142 cases, 1,909 controls, U01 CA122839, Casey), (4,000 cases, 6,000 NH-White controls, UK-CHIBCHA, Tomlinson), Colombians (2,000 cases and 2,000 controls, CHIBCHA), Japanese (1,000 cases and 1,000 controls) and African-Americans (1,500 cases and 1,500 controls, R01CA126895, Le Marchand). We will genotype replicated significant SNPs in our main and combined analysis in several Hispanic populations (note: studies funded by EU or NIH for data collection but not GWAS), including 800 Puerto Ricans, 2,000 Brazilians, 2,000 Argentineans and 3,000 Spanish/Portuguese, to assess generalizability of findings. We will examine the differences in inflammatory gene transcription dynamics in leukocytes (from blood sample collection) by fatigue level (as assessed from study questionnaire data). This study will have a high impact by addressing the key question of racial/ethnic disparities related to genetic susceptibility to CRC, will provide translational guidelines on biological mechanisms during the cancer survivorship period to increase quality of life among cancer survivors, and will enable further growth and investment into research among Hispanics by providing a resource of genetic data and biospecimens, which is lacking.
Study phs001193
Genomic Profiling of Relapsed and Refractory Childhood Cancers

Through clinical trials focused on molecularly guided therapy in pediatric cancers (NCT01355679, NCT01802567, NCT02162732), the Beat Childhood Cancer Consortium performed paired tumor/normal whole exome sequencing and/or tumor mRNA sequencing for 202 pediatric or adolescent young adult patients with rare, relapsed and refractory cancers (including neuroblastoma, Ewing sarcoma, osteosarcoma, rhabdomyosarcoma, ependymoma, glioma, and other rare solid tumors). A subset of these patients had multiple biopsies sequenced, including longitudinal profiling.
Study phs002238
Optimizing Primary Stroke Prevention in Children with Sickle Cell Anemia (STOP II)

The STOP II trial evaluated whether prophylactic transfusion in patients with sickle cell disease and high risk of stroke can be safely halted after 30 months of treatment during which patients became low risk for stroke.Stroke causes substantial morbidity in children with sickle cell disease. To prevent first strokes, the Stroke Prevention Trial in Sickle Cell Anemia (STOP) used prophylactic transfusions in children who were identified by transcranial Doppler (TCD) ultrasonography as being at high risk for stroke. This strategy reduced the incidence of stroke among such children from 10% per year to less than 1% per year, leading to recommendations for TCD screening and prophylactic transfusion for children with abnormal velocities on ultrasonography. Despite the reduced risk of stroke, long-term use of transfusions can cause adverse side effects, such as iron overload or alloimmunization. However, cessation of transfusions is associated with recurrence of stroke, and at the time of the STOP II trial, there were no clinical or laboratory indicators to guide the duration of prophylaxis. Therefore the STOP II trial was initiated to determine whether transfusions could be limited by monitoring patients with TCD examinations after transfusions were halted and resuming transfusions if the examination indicated a high risk of stroke.The trial was halted for safety concerns after 79 of a planned 100 children were randomized. Discontinuation of transfusion for the prevention of stroke in children with sickle cell disease resulted in a high rate of reversion to abnormal blood-flow velocities on Doppler studies and stroke incidence.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.
Study phs002386
Center for Common Disease Genomics [CCDG] - Cardiovascular: Genetics of Coronary Heart Disease - Characterizaton of Coronary Prone Pedigrees

The primary cohort includes samples from 499 men and 154 women who survived a myocardial infarction, percutaneous transluminal angioplasty, or coronary artery bypass grafting (CABG) by age 55 years for men or age 65 years for women. Patients were seen at least six months after their acute event. Samples from another 488 men and 541 women that showed a nearly identical age, gender, and measured blood pressure distribution were collected as population-based controls. These controls tended to live in urban Salt Lake City, where the vast majority of the population is Caucasian. Blood samples were collected in the morning after 12h to 16h of fasting and prepared according to guidelines of the Lipid Research Clinic's program Manual of Laboratory Operations. All samples have serum, DNA and/or white blood cells, and a host of measurements (e.g. standard clinical chemistries, LDL and HDL cholesterol, triglycerides, glucose, HbA1C, insulin, blood pressures, anthropometric data, information on medical conditions and drug use, etc.). Additional details about the cohort or sample preparation were published previously. Here we present in total 1,475 patients that were successfully sequenced. This number includes 228 women, 419 men, and 7 of unreported gender survivors as well as 339 women and 482 men that served as population-based controls.
Study phs001901
Sequencing to Guide Cancer Care (CanSeq)

The overall goal of the CanSeq U01 project is to study the impact of whole-exome sequencing (WES) on the clinical care of cancer patients and oncology provider practices. The aims of Project 1 are to implement and establish the feasibility of germline and somatic WES in patients with advanced solid tumors (lung and colon); to develop a framework for interpreting and reporting for exome sequencing data; to determine the proportion of patients with "actionable items" compared to existing technologies; and to report on the percentage of patients in whom unique WES findings led to a clinical action. The aims of Project 2 are to implement a production-scale platform for WES from archival (FFPE) material; to identify biologically relevant somatic and germline alterations existing in tumor/normal DNA from individual patients; to produce an evidence-based list of clinically "actionable" genetic alterations; and to develop inferential models that predict the utility of tumor genomic data within the larger clinical context. The goals of Project 3 are to describe the impact of information derived through WES on cancer patients; to test the hypothesis that patients will want to receive information about all potentially informative somatic and germline variants; to study patients' understanding of disclosed genomic information; and to describe the experiences of oncology providers as they implement WES into cancer care delivery.
Study phs001075
VCRC - Giant Cell Arteritis Longitudinal Study

The development and validation of accurate biological markers and predictors of disease activity and outcome for Giant Cell Arteritis, a form of idiopathic vasculitis, would have a major positive impact on the clinical care of patients with this rare disease, be an important insight into the design of clinical trials and feasibility of new drug development, and provide important insight into the pathogenesis of this condition. Current measures of disease activity, including erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), while helpful in selected settings, are mostly considered highly flawed and markers whose poor correlation with disease flares makes them inadequate to help guide therapy. Similarly, ESR and CRP are clearly unhelpful as diagnostic tests for vasculitis. The current state of clinical investigation for vasculitis relies heavily upon determination of disease status solely by clinical presentation and investigator opinion. Thus, discovery and validation of more precise markers that quantifiably measure activity, predict disease course, and correlate with an important biological process, would be of great importance. Knowledge to be gained from this study could potentially be highly important. Discovery of effective biomarkers of vasculitis and assessment of long term disease course and complications by the collection of clinical and diagnostic imaging studies proposed could lead to better care and less toxic treatment regimens. Furthermore, insight into the pathophysiology of vasculitis could be gained and this might lead to better treatments and improved outcomes in terms of reduction of vascular and treatment related complications. Ideally, excellent biomarkers reflect a sophisticated understanding of the pathophysiology of a disease.
Study phs000588
Genome-wide Association Study and Meta-Analysis of Ewing Sarcoma

We combined a set of 122 French Ewing sarcoma (EWS) cases from the Institut Curie, 19 EWS cases from the National Cancer Institute (NCI) Center for Cancer Research (CCR), and 29 EWS cases from the NCI Bone Disease and Injury Study. All EWS cases were confirmed by medical record review and the presence of a specific EWSR1-ETS translocation were noted when data was available. Each participant provided informed consent and each participating study was approved by the Institutional Review Boards of their study center. DNA was extracted using standard methods from blood, saliva (Oragene), or buccal cells. All de novo genotyping of EWS cases was performed at the NCI CGR on the Illumina OmniExpress-24 v1.1 array. Genotyping was performed according to manufacturer's guidelines using the Infinium HD Assay automated protocol.
Study phs001549
Tumor Fraction Guided Cell-Free DNA Profiling in Metastatic Cancer Patients

Cell-free DNA (cfDNA) profiling is increasingly used to guide cancer care, yet mutations are not always identified. The ability to detect somatic mutations in plasma depends on both assay sensitivity and the fraction of circulating DNA in plasma that is tumor-derived (i.e cfDNA tumor fraction). We hypothesized that cfDNA tumor fraction could inform the interpretation of negative cfDNA results and guide the choice of subsequent assays of greater genomic breadth or depth. Plasma samples collected from 118 metastatic cancer patients were analyzed with cf-IMPACT, a modified version of the FDA-authorized MSK-IMPACT tumor test that can detect genomic alterations in 410 cancer-associated genes. Shallow whole genome sequencing (sWGS) was also performed in the same samples to estimate cfDNA tumor fraction based on genome-wide copy number alterations using z-score statistics. The results show that cfDNA tumor fraction can inform the interpretation of negative cfDNA results and guide the selection of subsequent sequencing platforms that are most likely to identify clinically-relevant genomic alterations.
Study phs002290
Rare Diseases Clinical Research Network (RDCRN) Vasculitis Clinical Research Consortium (VCRC) Longitudinal Protocol for Takayasu's Arteritis

The development and validation of accurate biological markers and predictors of disease activity and outcome for Takayasu's arteritis, a form of idiopathic vasculitis, would have a major positive impact on the clinical care of patients with this rare disease, be an important advance in the design of clinical trials and feasibility of new drug development, and provide important insight into the pathogenesis of this condition. Current measures of disease activity, including erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), while helpful in selected settings, are mostly considered highly flawed and markers whose poor correlation with disease flares makes them inadequate to help guide therapy. Similarly, ESR and CRP are clearly unhelpful as diagnostic tests for vasculitis. The current state of clinical investigation for vasculitis relies heavily upon determination of disease status solely by clinical presentation and investigator opinion. Thus, discovery and validation of more precise markers that quantifiably measure activity, predict disease course, and correlate with an important biological process, would be of great importance. Knowledge to be gained from this study could potentially be highly important. Discovery of effective biomarkers of vasculitis and assessment of long term disease course and complications by the collection of clinical and diagnostic imaging studies proposed could lead to better care and less toxic treatment regimens. Furthermore, insight into the pathophysiology of vasculitis could be gained and this might lead to better treatments and improved outcomes in terms of reduction of vascular and treatment related complications. Ideally, excellent biomarkers reflect a sophisticated understanding of the pathophysiology of a disease.
Study phs000589
eMERGE: Genetics of Complex Pediatric Disorders from the Center for Applied Genomics

The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP) with the primary goal of translating basic research findings to medical innovations. The mission of CAG is to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders. We aim to discover genetic causes for the most prevalent diseases of childhood including asthma, autism, diabetes, epilepsy, obesity, schizophrenia, pediatric cancer, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. The CAG is one of the world's largest genetics research programs in pediatrics, and the only center at a pediatric hospital to have established a large-scale biobank of genotyped samples. The electronic Medical Records and Genomics (eMERGE) Network is a consortium of 9 clinical sites with EMR linked DNA biobanks, including Northwestern University and its NUgene biobank, funded by the NHGRI (National Human Genome Research Institute) to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 100,000 individuals using EMR-derived phenotypes and DNA from linked biorepositories. Using electronic phenotyping methods, the consortium has been and is using DNA samples from all participating sites to explore the genetic determinants of approximately 80 phenotypes, including both diseases and traits, for which the electronic phenotyping algorithms have or are being published on PheKB.org.
Study phs001165
Transcriptional Profiling of Macrophages In Situ in Metastatic Melanoma Reveals Localization-Dependent Phenotypes and Function

Therapeutic interventions modulating immune function at the tumor site could improve outcomes in cancer. Here we analyzed metastatic melanoma, a tumor type highly responsive to T-cell based therapies, and found that the tumor-infiltrating T cells are localized closely to CD14+ monocytes/macrophages rather than to melanoma cells. Using novel immunofluorescence-guided laser capture micro-dissection, we analyzed transcriptomes of CD3+ T cells, CD14+ monocytes/macrophages and melanoma cells in non-dissociated tissue. CD14+ cells localized in tumors beds displayed specific transcriptional signatures distinct from CD14+ cells that were localized in tumor stroma.The 8 distinct metastatic melanoma samples are from the Cooperative Human Tissue Network (CHTN). 64 RNA-seq files are available from 16 intraCD14+, 16 stromaCD14+, 16 cancer cells, 8 intraCD3+ and 8 stromaCD3+ cells. 2 LCM harvests were completed for the CD14+ and cancer cells on two sections of the samples. One harvest was done for the CD3+ samples.The processed data (TPM/raw counts tables) are available on GEO under GSE180124. 
Study phs002564
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
GMKF: Kids First Pediatric Research Program on Congenital Cranial Dysinnervation Disorders and Related Birth Defects

The Gabriella Miller Kids First Pediatric Research Program (Gabriella Miller Kids First Pediatric Research Program) (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). Both, childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. Whole Genome Sequence (WGS) and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Goals of this ongoing study are to identify novel "congenital cranial dysinnervation disorder" (CCDD) genes and define the role of the wildtype and mutant genes in normal and aberrant development. The umbrella term (CCDD) refers to congenital birth defects with malformation of one or more cranial nerves, typically resulting in limitations of eye and/or face movement. Examples of CCDDs include congenital fibrosis of the extraocular muscles (CFEOM), congenital ptosis, Duane retraction syndrome (DRS), horizontal gaze palsy with progressive scoliosis (HGPPS), congenital 3rd, 4th or 6th nerve palsies, Moebius syndrome (MBS), and hereditary congenital facial paresis (HCFP). In some cases, anosmia, and disorders of hearing, sucking, chewing, swallowing, and breathing may also be classified as CCDDs. CCDDs can be accompanied by additional birth defects such as intellectual and social disabilities, developmental delays, limb anomalies, and cardiac, GI, and GU disorders. The genetic basis of multiple CCDDs has been determined, and the gene mutations typically alter cranial motor neuron identity or function, or perturb axon growth and guidance. Despite these successes, the genetic etiologies of many inherited CCDDs remain unidentified.
Study phs001247
Barrett's and Esophageal Adenocarcinoma Genetic Susceptibility Study (BEAGESS)

BEAGESS is a genome-wide association study (GWAS) which takes advantage of the extensive data collected by investigators in the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON, http://beacon.tlvnet.net), representing many of the high-quality population-based and other epidemiologic studies of BE and EA in the world. The overall goal of our research is to evaluate the influence of genetic susceptibility on risk of EA and its main precancerous condition, BE. The primary aims of the study are to identify tagSNPs most strongly associated with risk of esophageal adenocarcinoma (EA) and Barrett's esophagus (BE). In the secondary aims we will evaluate the extent to which the most significant associations vary according to key environmental and host factors for these conditions, including gender, obesity and cigarette smoking. These results will guide additional exploratory analyses examining ways in which individual SNP results might be aggregated to shed light in the importance of specific pathways relevant to the etiology of BE and EA, and to identify possible epistatic effects.
Study phs000869
Implementation, Adoption, and Utility of Family History in Diverse Care Settings

The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia
Study phs001641
Kids First Pediatric Research Study in Familial Predisposition to Hematopoietic Malignancies (SJFAMILY-HM)

The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Acute lymphoblastic leukemia (ALL) is the commonest childhood tumor and a leading cause of cancer death in children, adolescents and young adults. Hodgkin and non-Hodgkin lymphoma are also important hematologic malignancies (HM) that occur in children. Each are genetic diseases with growing evidence for a germline predisposition of both familial and sporadic cases, however the inherited genetic basis of ALL/lymphoma are poorly understood. Such knowledge is essential to gain mechanistic insight into the basis of tumor formation, and to guide genetic counseling and genetic management. Here we have assembled a large collection of familial HM kindreds, and extended recurrence cohorts of ALL and HL which will be used to identify the genetic basis of familial HM, examine the frequency of germline variants in sporadic ALL and HL, and to integrate inherited and somatic genomic data. These studies have high potential to provide fundamental new insights into the inherited genetic basis of HM, to provide important information to guide clinical management, and to provide an invaluable public resource of genomic data.
Study phs001738
HCI-PDX Trial Center for Breast Cancer Therapy

The HCI-PDX Trial Center for Breast Cancer Therapy study is a collection of patient-tumor samples that are serially established as patient-derived tumor xenografts (human tumor in mouse) and matching organoids. For this study, we describe valuable methods and resources to generate organoids and use them as cost-effective models for precision drug screens. This study leveraged whole-exome sequencing, RNAseq, and SNP-array data to illustrate how the heterogeneity of tumors is preserved in xenograft and organoid models. Sequencing files are provided for whole-exome and RNAseq data. Processed copy number variant calls from the SNP arrays are also available. A full description of the study can be found in the article entitled, "A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology," written by Guillen et al., 2022 in Nature Cancer (PMID: 35221336).
Study phs002479
Ghana Prostate Study

Participants were recruited through the Ghana Prostate Study-a population-based component, and a clinical component. The population-based component was a probability sample designed using the 2000 Ghana Population and Housing Census data in an attempt to recruit approximately 1,000 men aged 50-74 years in the Greater Accra region (~3 million people), which successfully recruited 1,037 healthy men between 2004 and 2006 with a response percentage of 98.8 %. Consented individuals underwent an in-person interview, and within 7 days had a digital rectal examination (DRE) and provided an overnight fasting blood sample for prostate-specific antigen (PSA) testing, biomarker assays, and genetic analysis. Subjects who had a positive screen by PSA (>2.5 ng/ml) or DRE underwent a transrectal ultrasound-guided biopsy. A total of 73 histologically confirmed prostate cancer cases were identified through the population-based screening component of the Ghana Prostate Study and were included in the case population in the published GWAS (Cook et al., Human genetics, 2013). From the remaining 964 screen-negative individuals, 836 had at least 20 μg DNA extracted and available for analysis, and 500 of these were matched to cases for analysis by age (in 5-year categories). In the Ghana Prostate Study, we recruited 676 prostate cancer cases at Korle Bu Teaching Hospital in Accra, Ghana, between 2008 and 2012. All consented cases were interviewed and provided an overnight fasting blood sample. At the time of selection for this analysis we had recruited 582 prostate cancer cases, from which we selected 427 for analysis. Combined with the 73 cases diagnosed through the population-based component of the study, this yielded 500 available prostate cancer cases for analysis.
Study phs000838
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
University of Texas PDX Development and Trial Center Grant

The goal for the University Texas PDX Development and Trial Center (UTPDTC) is to optimize personalized biomarker-based cancer therapy and identify effective targeted drugs based on the molecular characteristics of each tumor. Our short-term goals are to establish a biobank of clinically, and molecularly-annotated Patient-Derived Xenografts (PDXs) and to use PDXs as a platform for preclinical drug development and biomarker discovery. The primary goal for UTPDTC investigators will be to develop PDX trial strategies for preclinical testing of single agents and drug combinations. These models will allow the determination of the optimal treatments (single drugs or combinations) that should be tested in clinical trials in increasingly individualized, molecularly defined subsets of tumors. The goal of the Patient-Derived Xenograft Core is to provide high-quality clinically relevant and molecularly annotated PDX models for the research projects proposed in the University of Texas PDX Development and Trial Center (UTPDTC) grant application and to the research activity of the NCI PDX Development and Trial Centers Research Network (PDXNet) by leveraging PDX resources at our institutions and developing new PDX models from human cancer specimens using rigorous quality standards so that the models can be used to guide clinical trial development. The PDX models developed and/or characterized by the PDX Core will be available to other cancer researchers through the PDXNet and NCI Patient-Derived Models Repository (PDMR).
Study phs001980
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
CSER: Genomic Sequencing to Aid Diagnosis in Pediatric and Prenatal Practice: Examining Clinical Utility, Ethical Implications, Payer Coverage, and Data Integration in a Diverse Population

Congenital abnormalities and developmental disorders affect 3-5% of live born infants and children. Despite advances in both pre- and post-natal treatment, the utility of genetic testing in diagnosing the etiology underlying such conditions in order to guide management has been frustratingly limited. Traditional genetic testing with specific gene tests, or even gene panels, is diagnostic in only a small percentage of cases. Recent technological advances in next generation sequencing (NGS) have led to the ability to sequence and interpret the entire exome relatively quickly, allowing a diagnosis in 25-30% or more of cases of developmental disorders when other genetic tests have not yielded a result. Although whole exome sequencing (WES) holds great promise for improved diagnosis leading to better clinical outcomes, challenges remain in determining how best to apply and utilize sequence data. Fulfilling the promise of WES also requires investigation of ELSI (ethical, legal, social) concerns, given skepticism in some communities that research will benefit them; economic considerations that ultimately determine access to and equitable use of WES; and a need to share clinical genetic results with families and across health care systems to enable better prognostication and management of rare conditions in community settings. We propose a Program in Prenatal and Pediatric Genomic Sequencing (P3EGS) at UCSF to examine the diagnostic and clinical utility of WES. P3EGS will recruit and study affected individuals and their parents, including pregnancies in which the fetus has a confirmed structural anomaly and children with previously undiagnosed developmental disorders that are likely of genetic etiology. Following consent and collection of standardized phenotypic data, the families will undergo WES as part of clinical care. To achieve diversity, patient ascertainment and recruitment will occur at four UCSF sites that serve a broad range of underrepresented minorities (target of 75%) and span the full socio-economic spectrum, including the underserved. Our specific aims will: 1) examine the clinical utility of WES, including assessment of a variety of health-related and reproductive outcomes, in undiagnosed individuals; 2) address ethical, social and economic issues in the delivery of genomic sequencing results to ancestrally and economically diverse populations through (2.1) a mixed methods, longitudinal empirical study of clinical interactions and experiences, (2.2) an economic analysis of insurance coverage, price and reimbursement of multigene tests, and (2.3) creation of an Ethics Advisory Board to respond to emerging issues and establishment of authentic stakeholder engagement; and 3) pilot a user-friendly web-based patient/provider application integrating genomic and clinical data as a shared evidence base to support result communication, interpretation and clinical decision making; the application will be based on the “Bioscreen” model created and successfully implemented at UCSF.
Study phs002324

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Spatial transcriptomics analysis of triple negative breast cancers

Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

RNA sequencing of end-stage kidney disease patients with COVID-19

scRNAseq of aplastic anemia and healthy immune cells co-cultured with autologous HSPCs

Spatially resolved antigen receptor and gene expression data from breast cancer patients

Spatially resolved antigen receptor and gene expression data from human tonsil tissue

CRUK-ICGC Prostate Cancer Group Study

Psoriasis Patient PBMC scRNA-seq data

DAC-2023-07-05-Ritz (DAC-007) - Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study

Beacon v2

ST lobular manuscript dataset

DAC-2023-07-05-Ritz (DAC-007)

Construction of Thousands of Single Cell Genome Sequencing Libraries Using Combinatorial Indexing

Dataset Rdata segmentations

Human CD4+ T cells regulate peripheral immune responses in rheumatoid arthritis via IGFL2

Origins and functional consequence of somatic mitochondrial DNA mutations

Single-cell bam files and RNA sequencing of viral RNA stocks

Star2xml: metadata converter into XML

Spatial transcriptomics analysis of triple negative breast cancers

PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

EGA QuickView

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EGA metadata schema

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Papua New Guinean Genome Diversity Commitee

Papua New Guinean Y chromosome Diversity Commitee

Paired-end RNA-seq analysis of the TERT promoter mutant GBM cell lines

3q-capture DNA sequencing of primary AMLs with 3q26 rearrangements

Comprehensive NGS Profiling to Enable Detection of ALK Gene Rearrangements and MET Amplifications in Non-Small Cell Lung Cancer

AVENIO Expanded ctDNA panel sequence alignments (BAMs)

Access to dataset, "Feasibility of Functional Precision Medicine for Guiding Treatment of Relapsed/Refractory Pediatric Cancers"

SNP Genotype Data: Subpopulations of Filipino, Malaysian, and Papua New Guinea

MediMer: A versatile do-it-yourself peptide-receptive MHC class I multimer platform for tumor neoantigen-specific T cell detection

AVENIO Expanded ctDNA panel sequence alignments (BAMs) from plasma DNA of lung cancer patients from the OSCILLATE trial

Sensitive circulating tumor DNA based residual disease detection in epithelial ovarian cancer

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

Comprehensive NGS Profiling to Enable Detection of ALK Gene Rearrangements and MET Amplifications in Non-Small Cell Lung Cancer

Cross-Sectional Characterization of Idiopathic Bronchiectasis

Discovering the Genetic Basis of Human Neuroblastoma: A Gabriella Miller Kids First Pediatric Research Program (Kids First) Project

International Cancer Proteogenomics Consortium (ICPC): Proteogenomics of East-Asian Breast Cancer

Ghana Breast Health Study

Joslin Study on the Genetics of Type 2 Diabetes

Whole Exome Sequencing and RNA-Seq to Characterize the Somatic Breast Cancer Landscape Among Latinas in California

NHLBI Recipient Epidemiology Donor Evaluation Study (REDS)-III - Red Blood Cell Omics (RBC-Omics) Study

Genomic Predictors of Combat Stress Vulnerability and Resilience

eMERGE Network's Multi-Center Pilot of Pharmacogenetic Sequencing in Clinical Practice

Genome-Wide Association Study of HCC in Non-Asian USA Population

Genetic History of Neandertal and Denisovan Introgression into Melanesian Individuals

Drug screening of patient-derived organoids from colorectal peritoneal metastases

University of Pennsylvania CAR T Cell Responding and Non-responding Patients

Mechanism of Action of Vitamin E in NAFLD

Oncoarray Consortium - Lung Cancer Studies

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

Women's Health Initiative Clinical Trial and Observational Study

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

Treatment of Pulmonary Hypertension and Sickle Cell Disease with Sildenafil Therapy (Walk-PHaSST)

Strabismus, CCDD and other anomalies

eMERGE Network Phase III Clinical Sequencing: eMERGEseq Panel

Metagenomics-Guided Pathogen Discovery in Travelers' Diarrhea

The spectrum of somatic mutations in high-risk acute myeloid leukemia with -7/del(7q)

CIDR: The Role of Rare Coding Variation in Prostate Cancer in Men of African Ancestry - RESPOND Project 2

The Genetic Landscape of Metastasis and Recurrence in Head and Neck Squamous Cell Carcinoma

Evaluating genetic ancestry and self-reported ethnicity in the context of carrier screening

National Eye Institute (NEI) Age-Related Eye Disease Study (AREDS)

Maintenance of Genome Sequence Integrity in Long- and Short-lived Rodent Species

Complete Genomics Deep Whole-Genome Sequencing of Circulating Tumor Cells from a Patient with Metastatic Breast Cancer

Genetic Testing to Understand and Address Renal Disease Disparities

A Genomic Approach to Warfarin Dose Prescription in Admixed Caribbean Hispanics

The Spatio-Temporal Evolution of Multiple Myeloma from Baseline to Relapse-Refractory States

Gabriella Miller Kids First Pediatric Research Program in Craniofacial Microsomia

Rare Cancer Tumors Project

Impact of Race and Genetic Factors on Beta Blocker Effectiveness in Heart Failure

Molecular Genetics of Heroin Dependence in China

Breast Cancer Genome Guided Therapy Study (BEAUTY)