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

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

  Study phs000178

• The molecular basis of T-PLL is an actionable perturbation of TCL1/ATM- and epigenetically instructed damage responses

  T-cell prolymphocytic leukemia (T-PLL) is a rare and poor-prognostic mature T-cell malignancy. To address its incomplete molecular concept, we integrated large-scale profiling data of alterations in gene expression, allelic copy number (CN), and nucleotide sequences in 111 well-characterized patients. Besides prominent signatures of T-cell activation and prevalent clonal variants, we also identified novel hot-spots for CN variability, fusion molecules, alternative transcripts, and progression-associated dynamics. The overall lesional spectrum of T-PLL is mainly annotated to axes of DNA damage responses, T-cell receptor/cytokine signaling, and histone modulation. We formulate a multi-dimensional model of T-PLL pathogenesis centered around a unique combination of TCL1 overexpression with damaging ATM aberrations as initiating core lesions. The effects imposed by TCL1 cooperate with compromised ATM towards a leukemogenic phenotype of impaired DNA damage processing. Dysfunctional ATM appears inefficient in alleviating elevated redox burdens and telomere attrition and in evoking a p53-dependent apoptotic response to genotoxic insults. As non-genotoxic strategies, synergistic combinations of p53 reactivators and deacetylase inhibitors reinstate such cell death execution.

  Study EGAS00001002744

• Disease Variant Landscape of a Large Multiethnic Population of Moyamoya Patients by Exome Sequencing

  We report a significant advance in Moyamoya disease (MMD) research through our unique access to a large MMD patient population combined with high depth exome sequencing and bioinformatics. We confirm a reported RNF213 founder mutation (FM), provide evidence for its ethnic specificity and identify novel variants in other genes that associate with Caucasian and non-RNF213 FM MMD. This work has a broader impact in vascular research by highlighting novel genetic aspects of cerebrovascular disease. Moyamoya Disease (MMD) is a rare cerebrovascular disorder characterized by unilateral or bilateral progressive stenosis or occlusion of the internal carotid artery, with frequent involvement of the anterior cerebral artery and the middle cerebral artery (MCA). Fragile collateral vessels subsequently develop and are particularly prone to hemorrhage. MMD patients are usually diagnosed angiographically following presentation of developmental delays, seizures, migraines, ischemia, and/or hemorrhage. The most common approach to re-establish normal blood flow is bypass surgery. While some progress has been made in identifying disease-associated genes, the underlying disease biology is not well understood. Moyamoya disease (MMD) is a rare cerebrovascular disease. Our unique access to a large MMD patient population combined with high depth exome sequencing and bioinformatics has led to a significant advance in the field. We confirm a reported RNF213 founder mutation (FM), provide evidence for its ethnic specificity and identify novel variants in other genes that associate with Caucasian and non-RNF213 FM MMD. This work has a broader impact in vascular research by highlighting novel genetic aspects of cerebrovascular disease. The 125 ethnically diverse, unrelated MMD patients were matched based on sex and broad ethnic category to 125 controls. Control DNA from the 1000 Genomes project was used to design a control Library by Personalis. 125 controls were selected from this Personalis Control DNA Library for our study. (The 1000 Genomes Project Consortium 2012). Genomic DNA was extracted using the Gentra Puregene kit (Qiagen, Valencia, CA). Libraries were prepared from approximately 3 µg of high quality genomic DNA (50-200 ng/µl) using Illumina TruSeq Genomic DNA High throughput Sample Prep Kits (Illumina, San Diego, CA) and exome enrichment (targeting 62Mb) was accomplished using the TruSeq Exome Target Enrichment kit (Illumina, San Diego, CA), all according to manufacturer's protocols. Target enrichment validation was confirmed by determining the concentration of the library by PicoGreen-based quantitation. Library yields ranged from 100-1000ng of DNA, a portion of which was run on the Bioanalyzer HS DNA chip (Agilent, Santa Clara, CA), with an average size of 300-550nt for DNA fragments. Exome Sequencing. Sequencing was performed using Illumina Hiseq2000 or HiSeq2500 sequencers with single lane, paired-end 2X100bp reads. DNA fragments were generated and amplified using Clonal Single Molecule Array technology (Illumina, San Diego, CA). The sequences were determined using the Clonal Single Molecule Array and Sequencing-by-Synthesis using Illumina's instrumentation and Reversible Terminator Chemistry. Each sequencing lane interrogated the DNA sequences of a pool of 3 individual sample libraries each carrying a unique index. Sequencing reads of at least 2x100bp in length for a total of approximately 8 Gb of sequence data were generated for each sample.

  Study phs001700

• Prediction of Resistance and Sensitivity to HER2 Targeted Therapy in the Neoadjuvant Setting

  Women with breast cancers that overexpress HER2 are at greater risk for disease progression and death than women whose tumors do not overexpress HER2. Trastuzumab, a recombinant humanized monoclonal antibody against the extracellular domain of the HER2 protein blocks downstream signaling of HER2 and substantially improves the efficacy of chemotherapy in women with metastatic and early-stage HER2-positive breast cancers. Because resistance to trastuzumab eventually results in progressive disease in the metastatic setting and contributes to recurrence following adjuvant trastuzumab-based therapy, it is important to develop agents other than trastuzumab that target HER2 signaling through different mechanisms of action. Lapatinib is an oral, small molecule, dual tyrosine kinase inhibitor of HER2 and EGFR. Lapatinib has shown a lack of cross-resistance with trastuzumab in preclinical studies and activity in women with HER2-positive, metastatic breast cancer that has progressed during trastuzumab treatment. Trastuzumab blocks the downstream signaling of HER2 by binding to the extracellular domain of the receptor. Lapatinib binds to the intracellular domains of HER2 and EGFR and prevents activation of downstream signaling pathways. Because of this different mechanism of action, lapatinib may be effective in trastuzumab-resistant disease. The study will also provide important safety information on trastuzumab and lapatinib combinations immediately following anthracycline exposure, and also provide an initial direct comparison of cardiac effects of trastuzumab and lapatinib when incorporated into a standard sequential adriamycin and cyclophosphamide (AC) followed by weekly paclitaxel neoadjuvant regimen. Availability of a second agent that can interrupt HER2-signaling pathways through completely different mechanisms than those of trastuzumab offers the potential for further improvement in the management of patients with HER2-overexpressing breast cancer in both the adjuvant and metastatic setting. Co-administration of both trastuzumab and lapatinib with chemotherapy may be important in improving outcomes in subsets of HER2-positive breast cancers. However, use of two inhibitors of the HER2 pathway will increase costs and may increase toxicity, so it will be important to identify the subsets of patients who would benefit from the dual therapy. Inhibition of HER2 with a single agent clearly is sufficient for many patients as evidenced by the results of the trastuzumab trials. Therefore, co-administration to unselected populations of women with HER2-positive breast cancers would not represent an optimal approach. Given the activity of lapatinib, it is likely that it will also be sufficiently active in inhibiting HER2-pathway activation in some patients to allow for its use as the sole inhibitor of the HER2 pathway. Different populations may also derive greater benefit from one of the HER2-blocking agents relative to the other. Identification of potential predictive factors for pathologic complete response to the combination or to either agent administered alone in neoadjuvant trials would provide important information for adjuvant trials designed to definitively address these important issues. This study will compare 3 combined chemotherapy regimens: AC followed by paclitaxel plus trastuzumab and lapatinib, AC followed by paclitaxel plus lapatinib, and AC followed by paclitaxel plus trastuzumab given before surgery to patients with HER2-positive breast cancer.Tumor samples were collected prior to and post-treatment, and RNA sequencing was performed with this version.

  Study phs003275

• Transcriptomics for IMMU-SCCHN1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with confirmed locally recurrent and/or metastatic squamous cell carcinoma of the head and neck (SCCHN) who receive first-line treatment with immune checkpoint inhibitors (ICI) or ICI combined with chemotherapy. This dataset contains transcriptomics data processed with the STAR pipeline. Two sequencing kits were employed: Roche KAPA for high-quality RNA samples and Takara SMART-Seq Stranded for samples with limited RNA quantities.

  Dataset EGAD50000002206

• eIMPACT Trial: Modernized Collaborative Care to Reduce the Excess CVD Risk of Older Depressed Patients

  Cardiovascular disease (CVD) is the #1 killer of American men and women, and its economic burden is substantial and on the rise. Adults with depression are at elevated risk of CVD events and poor CVD prognosis. Unfortunately, past clinical trials of depression treatments have not observed the anticipated cardiovascular benefits. A novel explanation for these null results is that the depression interventions in these trials, which all involved patients with preexisting CVD, were delivered too late in the natural history of CVD. Accordingly, the objective of this single-center, parallel-group, assessor-blinded randomized controlled trial was to determine whether successful depression treatment before, versus after, the onset of clinical CVD reduces CVD risk in depression. Primary care patients with depression and elevated CVD risk from a safety net healthcare system (N = 216, Mage = 59 years, 78% female, 50% Black, 46% with income <$10,000/year) were randomized 1:1 to 12 months of the eIMPACT intervention (a modernized collaborative stepped care program involving internet cognitive-behavioral therapy [CBT], telephonic CBT, and/or select antidepressants) or usual primary care for depression (primary care providers supported by embedded behavioral health clinicians and psychiatrists). The primary outcome was endothelial dysfunction (brachial flow-mediated dilation) at 12 months, and the secondary outcomes were self-reported depressive symptoms (Hopkins Symptom Checklist-20), autonomic dysfunction (high-frequency heart rate variability), systemic inflammation (interleukin-6 and high-sensitivity C-reactive protein), and platelet activation (β-thromboglobulin and platelet factor 4) at 12 months. The central hypothesis was that the eIMPACT intervention would improve endothelial dysfunction in depressed adults by decreasing depressive symptoms, autonomic dysfunction, systemic inflammation, and platelet activation. Primary results are posted to ClinicalTrials.gov at NCT02458690. The de-identified limited dataset available on BDC consists of only the individual phenotypic data needed to confirm this trial’s primary results – i.e., randomization status, primary and secondary outcome variables, and participant characteristics utilized in the hypothesis-testing models (age, sex, race, education, income, and systolic blood pressure). We also provide the SAS code to replicate the results of the hypothesis-testing models reported in Table 4 and Supplemental Tables 1-3 in the main outcomes paper (#2 in Selected Publications). Of note, the user will need to specify the file path for the dataset to run the SAS code. Instructions for requesting individual-level data are available on BDC at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BDC. For questions about availability, you may contact the BDC team at https://biodatacatalyst.nhlbi.nih.gov/contact.

  Study phs003283

• Comprehensive genetic analysis of uveal melanoma heterogeneity during metastatic progression

  Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Despite improvement of diagnosis and treatment of the primary tumor, there is no effective treatment of metastatic disease and approximately half of patients will die within one year or less following metastases detection. Tumor heterogeneity has been proposed as a key factor of drug resistance. However, it has been scarcely studied in UM. The present project aims searching for specific drivers of the metastatic progression, describing the genomic and transcriptomic landscape of metastatic UM, exploring tumor heterogeneity and investigating its role in drug resistance. Thus whole exome sequencing and transcriptomics have been performed on constitutional, primary tumor and metastatic samples from 28 UM patients.

  Dataset EGAD00001004554

• TK-EPN862 - Patient-dervied xenograft model of Posterior Fossa A Ependymoma - RNAseq

  Ependymomas (EPN) are the third most common malignant brain cancer in children. Treatment strategies for pediatric EPN have remained unchanged over recent decades, with 10-year survival rates stagnating at just 67% for children aged 0-14 years. Moreover, patients who survive treatment for the most common subtype of pediatric EPN (posterior fossa A (PFA)), often suffer long-term neurological side effects as a result of therapy. It is evident that there is a need for safer, more effective treatments for pediatric EPN patients. There are ten distinct subgroups of EPN, each with their own molecular and prognostic features. To identify and facilitate the testing of new treatments for EPN, in vivo laboratory models representative of the diverse molecular subtypes are required. Here, we describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of PFA EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using immunohistochemistry, DNA methylation profiling, whole genome sequencing (WGS) and RNA sequencing. Both patient and PDOX tumors classified as PFA EPN by methylation profiling, and shared similar histological features consistent with this molecular subgroup. RNA sequencing revealed that gene expression patterns were maintained across the primary and metastatic tumors, as well as the PDOX. Copy number profiling revealed gains of chromosomes 7, 8 and 19, and loss of chromosomes 2q and 6q in the PDOX and matched patient tumor. No clinically significant single nucleotide variants were identified, consistent with the low mutation rates observed in PFA EPN. Overexpression of EZHIP RNA and protein, a common feature of PFA EPN, was also observed. Despite the aggressive nature of the tumor in the patient, this PDOX was unable to be maintained past two passages in vivo. Others who have successfully developed PDOX models report some of the lowest success rates for EPN compared to other pediatric brain cancer types attempted, with loss of tumorigenicity not uncommon, highlighting the challenge in propagating these tumors in the laboratory. Here, we discuss our collective experiences with PFA EPN PDOX model generation and propose potential approaches to improve future success in establishing preclinical EPN models

  Study EGAS00001006844

• 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

• Polycystic Ovary Syndrome (PCOS) Genetics

  PCOS is a complex genetic disease reflecting the interplay of susceptibility genes and environmental factors. The cardinal reproductive feature of the syndrome, hyperandrogenemia, appears to play a direct role in the pathogenesis of the associated metabolic abnormalities. Male as well as female first-degree relatives have reproductive and metabolic phenotypes including increased prevalence rates of type 2 diabetes (T2D), metabolic syndrome (MBS) and other risk factors for cardiovascular disease (CVD). Northwestern University (NU) investigators lead a team that has extensive experience in phenotyping PCOS and in the genetic analysis of complex diseases including genome-wide association study (GWAS). Together with an expert group of collaborators from the Hershey Medical Center, and The University of Chicago, we have conducted a GWAS to identify PCOS susceptibility alleles using a large cohort of extensively and consistently phenotyped PCOS cases. Population controls for this study come from the NUgene project described below. NUgene project: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators.

  Study phs000368