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• Single-Cell Analysis of CD19-Specific CAR T Cell Treatment of Relapsed/Refractory CD19+ Acute Lymphoblastic Leukemia

  By leveraging single-cell transcriptome and T cell receptor (TCR) sequencing, we aimed to track the transcriptional signatures of chimeric antigen receptor (CAR) T cell clonotypes throughout the course of treatment and furthermore identify molecular patterns leading to potent CAR T cell cytotoxicity. The data presented in this study encompass blood and bone marrow samples from patients ≤ 21 years of age with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) participating in the SJCAR19 phase I/II clinical trial (NCT03573700). In brief, patients enrolled in the clinical trial received either 1 x 10^6 (dose level 1) or 3 x 10^6 (dose level 2) per kilogram of body weight following successful generation of autologous CAR T cell products and lymphodepleting chemotherapy. Peripheral blood was drawn from each participant every week until week 4 post-infusion, at week 6 or 8, and month 3 or 6 if feasible. At week 4 post-infusion, blood marrow was also collected from participants. Total T cells (CD3+) were sorted from each post-infusion sample, as well as the pre-infusion CAR T cell products, and processed through 10x Genomics’ single-cell gene expression and V(D)J sequencing platform using the standard protocol. We identified a unique and unexpected transcriptional signature in a subset of pre-infusion CAR T cells that shared TCRs with post-infusion cytotoxic effector CAR T cells. Functional validation of cells with even a subset of these pre-effector markers demonstrated their immediate cytotoxic potential and resistance to exhaustion. The data deposited into dbGaP include all raw single-cell expression and V(D)J sequencing generated from samples obtained from 16 study participants.

  Study phs002966

• Epigenetics of Cocaine and Nicotine Addiction

  This project characterizes DNA methylation and gene expression changes that occur in the human brain, specifically in neurons from the rostral striatum. Major advances from NIDA funded initiatives for noninvasive neuroimaging studies have made it possible to study neuroanatomical, neurochemical and functional changes in the human brain that contribute to the vulnerability to abuse drugs, together with the neurotoxic consequences of years of drug misuse. Animal models have been developed to explain the fundamental behavioral and biological mechanisms of addiction, including reward, tolerance and dependence. From these studies, we learned that cocaine abuse not only alters the epigenetic status of genes, but also induces particular epigenetic modifications depending on the frequency of the drug's administration. Certain genes are switched on by infrequent (short-term exposure) administration, while others are switched on only after chronic administration (addiction/dependence). Animal studies have also suggested that cocaine-seeking habits modeling chronic cocaine addiction in humans depend upon dopamine-dependent serial connectivity linking the ventral (nucleus accumbens) with the dorsal striatum (caudate nucleus). The primary goal of this study is to identify DNA methylation and gene expression changes that occur in the transition from recreational cocaine use to cocaine addiction. High throughput sequencing studies were designed to investigate unique postmortem human brain specimens from individuals that met criteria for cocaine dependence, as compared to unaffected age-matched controls. Brain biospecimens were available from the University of Miami Brain Endowment BankTM, from a collection of phenotypically well-characterized postmortem tissues sampled from chronic cocaine abusers that came to autopsy. This biobank of postmortem brain specimens and annotated genomic data serve as a research resource to support NIDA's scientific mission.

  Study phs001377

• Genetic Aberrations and Subclonal Structure Impact Chronic Lymphocytic Leukemia

  Large-scale whole-exome sequencing (WES) of primary tumor samples enables the unbiased discovery of recurrent putative driver events and patterns of clonal evolution. We report the identification of 44 recurrently mutated genes and 11 recurrent CNVs through the WES of 538 chronic lymphocytic leukemia (CLL) and matched germline DNAs. These include previously unrecognized cancer drivers (e.g., RPS15, IKZF3), and collectively identify nuclear export, MYC activity and MAPK signaling as central pathways affected by somatic mutation in CLL. A clonality analysis of this large dataset further enabled the reconstruction of temporal relationships between these driver events. Several drivers were associated with shorter progression-free survival (PFS) in 280 samples that were collected prior to uniform treatment with front line chemo-immunotherapy, with mature follow up of greater than 10 years. Direct comparison between matched pretreatment and relapse CLL from 59 samples demonstrated marked clonal evolution occurring in more than 95% of these patients. Distinct patterns of clonal evolution in relationship to specific gene alteration were observed, suggesting a hierarchy of fitness amongst mutations. Thus, large WES datasets of clinically informative samples enable the discovery of novel driver genes as well as the network of relationships between the drivers and their impact on disease relapse and clinical outcome.Additionally, we performed RNA-seq for 268 CLL samples (including 26 follow-up samples) and used them to identify expression subtypes of CLL. RRBS for 30 of these samples was also generated. In an integrative analysis of genetic, transcriptomic, and epigenetic data, incorporating known and newly identified subtypes of CLL, we built new models to improve patient prognostication.

  Study phs000922

• Center Common Disease Genomics [CCDG] - Cardiovascular: Partners Biobank

  This study is a part of NHGRI's Center for Common Disease Genomics, a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will perform whole genome sequencing of 140K individuals and whole exome sequencing of 225K individuals over the course of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. CAD is the leading cause of death in the world and common variant association studies show that our understanding of the underlying molecular mechanisms is incomplete (e.g, >2/3 of the 63 common variants previously mapped for CAD do not directly relate to known risk factors). CAD is representative of a class of common diseases with onset at middle age. When CAD occurs early in life, there is a stronger inherited component. In addition, there is empirical evidence that a burden of rare coding alleles in individual genes contribute to risk for CAD. Source: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOCAD_STROKE_FINAL.pdf For this study, The Broad Institute of MIT and Harvard generated genetic data for ~13,500 individuals from the Partners Biobank, including using whole exome sequencing and whole genome genotyping (Infinium Global Screening Array), with 550 samples from patients with myocardial infarction, a type of EOCAD. Partners Biobank, launched in 2010, is a biobank composed of samples from Partners hospitals, including Massachusetts General Hospital and Brigham and Women's Hospital. The Biobank has enrolled >100K individuals to study how genes, lifestyle, and other factors affect people's health and contribute to disease.

  Study phs002018

• Epigenetic Profiling of Human Colorectal Cancer

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

  Study phs000385

• Enhancing Open Data Sharing for Functional Genomics Experiments: Measures to Quantify Genomic Information Leakage and File Formats for Privacy Preservation

  This study was designed to develop methods that address the privacy risk associated with genetic variants obtained from genomic sequencing experiments. The preponderance of readily available genetic sequencing experiments has enabled the generation of datasets whose target purposes range from the genotyping of individuals for medical and forensic analyses, to the acquisition of molecular information for biological model development. However, by virtue of the experimental procedure, the raw sequences are tagged with small bits of patients' private variant information. In addition, depending on the protocol, these experiments may identify sequences from exogenous species. Raw human and microbial sequences can be analyzed as a whole to learn both general facts about biology, but can also be used to determine sensitive information about an individual. This presents a privacy conundrum for data sharing.To address privacy concerns, we have developed several mathematical and computational approaches to quantify the risk of disclosure of sensitive information from genetic sequence data, and to sanitize the associated reads in a manner that optimally maintains the utility of the data. In addition, we have generated environmental samples containing genetic information from consented individuals to evaluate the risk posed by such data. These samples were then used to assess the efficacy of the computational methods. Human-derived and -associated samples that are damaged or obscured by environmental factors represent the scope of samples used for experimentation in this study. Samples include, but were not limited to, swabs of contacted objects and glass slides coated in saliva from consented individuals. Blood samples were also acquired from the same consented individuals to create gold-standard comparison datasets.

  Study phs003166

• Whole Exome Sequencing of Head and Neck Patients-Derived Tumor Organoids and Formalin Fixed Paraffin Embedded Tumor Tissue

  In this study, tissue specimens were procured from head and neck squamous cell carcinoma (HNSCC) patients undergoing routine diagnostic procedures as part of their standard of care. For surgical cases, only excess tissue remaining after allocation for essential pathological diagnosis was utilized for research purposes. We aimed to validate the genomic fidelity of patient-derived organoids (PDOs) compared to their originating tumors. Whole exome sequencing was performed on four paired sets of PDOs and matched surgical specimens preserved as formalin-fixed paraffin-embedded (FFPE) samples. The objective was to demonstrate that the in vitro PDO models maintain the genomic profile of the patient-specific tumors, thus supporting their potential application in identifying efficacious personalized treatment regimens. Comparative analyses of single nucleotide variants (SNVs) and copy number variants (CNVs) between matched PDO and FFPE pairs were conducted to assess global genomic similarity and shared HNSCC-relevant variants. The results indicated that the PDOs closely recapitulated the mutational landscape of the resected tumors from which they were derived. This finding supports the validity of the employed in vitro culture conditions for potential use in precision medicine approaches for HNSCC. The data submitted to dbGaP comprise eight whole exome sequencing profiles of matched pairs of HNSCC FFPE and PDO samples from four subjects. Novogene (Novogene Co., Ltd, CA, USA) generated these profiles from 1.0μg genomic DNA per sample, using the Agilent SureSelect Human All Exon kit (Agilent Technologies, CA, USA). Deposited files include raw BAM files of reads aligned to human genome Hg19 using BWA (v.0.7.8-r455), sorted with Samtools (v1.0), and filtered for duplicate reads using Picard (v1.111). Additionally, we've deposited VCF files of SNV variant calls from GATK (v3.8).

  Study phs003755

• Rapid Early Action for Coronary Treatment (REACT-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: This multicenter controlled community study developed and evaluated the impact of a community educational intervention program on participant delay time from onset of symptoms of an acute myocardial infarction (AMI) to arrival at a hospital emergency department.Background: Although early reperfusion or thrombolytic therapy can reduce morbidity and mortality following an AMI, delayed access to medical care in participants is relatively common. Mean delay times from symptom onset to hospital arrival range from more than 4 hours to 24 hours, and the largest component of prolonged delay is participant recognition and action.Participants: A total of 20 communities from 5 field centers in the U.S. were pair-matched (10 pairs) according to geographic proximity and demographic characteristics. After initiation of a 4 month baseline surveillance period, one community in each pair was randomly selected to receive the intervention. The baseline surveillance period was followed by an 18 month community intervention and surveillance period. The community surveillance captured a total of 59,944 adults aged 30 years or older presenting to hospital emergency departments with chest pain, of whom 20,364 met study criteria for suspected acute coronary heart disease (CHD) at admission and discharged with a CHD diagnosis.Conclusions: Delay times were decreased in the intervention and reference communities. The results showed that the multicomponent community intervention program did not differentially reduce delay time from onset of AMI symptoms to arrival at a hospital, but did significantly increase the use of Emergency Medical Services by these participants in the intervention communities. (PMID:10872014).

  Study phs003885

• HeLa Cell Genome Sequencing Studies

  HeLa is a human epithelial adenocarcinoma cell line that was derived from biopsy specimens of a 31-year old patient named Henrietta Lacks who was being treated for cervical cancer at The Johns Hopkins University Hospital in 1951. The specimens were obtained without her knowledge or consent just before her death in 1951. HeLa is the oldest and most widely used human cell line and has been an extraordinarily important resource for researchers throughout the world. HeLa cells have served as a standard for understanding many fundamental biological processes, such as testing the polio vaccine, establishing basic techniques for cloning and in vitro fertilization, identifying the cause of cervical cancer (HPV), and advancing the development of anti-cancer drugs.This study contains all authorized whole genome sequence data of the HeLa cell line from datasets currently in dbGaP. These data have been approved for health, medical, and/or biomedical research purposes. Access to these data can be granted for one year. Accessible data will include the studies listed on this page and any additional authorized datasets that become available during this one-year period. The HeLa Genome Data Access Working Group of the Advisory Committee to the Director (ACD) will review requests from the research community for access to these datasets and assess whether the requests align with the terms of use defined in the HeLa Genome Data Use Agreement. The Working Group's findings will be reported to the ACD, and the ACD will make recommendations to the NIH Director about whether a request should be approved or disapproved. The NIH Director will decide whether access to the data will be granted.

  Study phs000640

• 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!

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