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• The genomic diversity of Taiwanese Austronesian groups: implications for the ‘Into and Out of Taiwan’ models

  Study EGAS00001006911

• CLUC complete genomics dataset

  Complete genomics data for VCaP and PC346c.

  Dataset EGAD00001002069

• GFD viral enrichment sequencing

  sequencing of libraires enriched for viral particles

  Dataset EGAD00001007638

• AHA_Uganda_Study_Epigenome_Cases

  Case samples for AHA Uganda Study of Illumina Methylation EPIC array data

  Dataset EGAD00010002837

• AHA_Uganda_Study_Epigenome_Controls

  Control samples for AHA Uganda Study of Illumina Methylation EPIC array data

  Dataset EGAD00010002836

• CIDR, NCI, NIDA Sequencing of Targeted Genomic Regions Associated with Smoking

  This study includes samples from two projects: Collaborative Genetic Study of Nicotine Dependence (COGEND; PI: Laura Bierut) and Genetic Study of Nicotine Dependence in African Americans (AAND; PI: Laura Bierut and Eric Johnson). The majority of the COGEND subjects included in the current study overlap with the two datasets already available on dbGaP. GWAS data are available for COGEND subjects through the Study of Addiction: Genetics and Environment (SAGE), dbGaP study accession phs000092. It should be noted that the case definition in the SAGE study is DSM-IV alcohol dependence. GWAS data are available for additional COGEND subjects through The Genetic Architecture of Smoking and Smoking Cessation, dbGaP study accession phs000404. The overall goal of this project is to apply deep sequencing to key genomic regions associated with nicotine dependence in order to accelerate the discovery of variation in molecular pathways that govern the development of nicotine dependence. The sample includes unrelated cases and controls of European American and African American descent. Cases are defined by a commonly used definition of nicotine dependence, a current score of 4 or more (maximum score of 10) on the Fagerstrom Test for Nicotine Dependence (FTND). Control status is defined as an individual who smoked at least 100 cigarettes during their lifetime, yet never became dependent (lifetime FTND<2). By selecting controls who smoked cigarettes, we focus on those genetic effects that are specific to the development of nicotine dependence. COGEND: COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal was to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design was a community based case-control study. Nicotine dependent cases and non-dependent, smoking controls were identified and recruited from Detroit and St. Louis. More than 54,000 subjects aged 25-44 years were screened by telephone; more than 3,100 subjects were personally interviewed; and more than 2,900 subjects donated blood samples for genetic studies. AAND: AAND was initiated in 2009 to identify and characterize genetic determinants of nicotine dependence in a large African American population. Community-based recruitment of approximately 100,000 people was conducted to ascertain 1,000 African American nicotine dependent cases and 1,000 African American non-dependent, smoking controls. All subjects were between the ages of 25-44. Subjects were screened by telephone; if they qualified as a case or control, they completed the same interview that was used in COGEND and donated a blood sample. Both studies (COGEND and AAND) included measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, and educational attainment using the Semi-Structured Assessment for the Genetics of Nicotine Dependence. Information on nicotine dependence, as assessed by the Fagerstrom Test for Nicotine Dependence (FTND) is available for all subjects. In addition, participants also completed the Nicotine Dependence Syndrome Scale (NDSS; Shiffman et al., 2004) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68; Piper et al, 2004). All subjects were assessed in person by trained research assistants.

  Study phs000813

• Ongoing Replication Stress Tolerance and Clonal T Cell Responses Distinguish Liver and Lung Recurrence and Patient Outcomes in Pancreatic Ductal Adenocarcinoma

  Patients with metastatic pancreatic ductal adenocarcinoma (PDAC) survive longer when disease spreads to the lung but not to the liver. We generated overlapping, multi-omic datasets to identify molecular and cellular features that distinguish patients whose disease develops liver metastasis (liver cohort) from those whose disease progression results in lung metastasis without liver metastases (lung cohort). Lung cohort patients generally survived longer than liver cohort patients, independent of tumor subtype. We developed a pORG gene signature that distinguishes primary tumors in the liver and lung cohorts. We identified ongoing replication stress (RS) response pathways in high pORG/liver cohort tumors, while low pORG/lung cohort tumors had greater densities of lymphocytes and shared T cell clonal responses. Our study demonstrates that liver-avid PDAC is associated with tolerance to ongoing RS, limited tumor immunity, and less favorable outcomes; whereas low RS, lung-avid/liver-averse tumors are associated with active tumor immunity that may account for favorable outcomes. As expected, we found high frequencies of KRAS, TP53, CDKN2A, and SMAD4 gene alterations in our tumor samples. High pORG activity (GSVA scores) in our primary tumors appear to be positively correlated with alteration frequencies in both TP53 and CDKN2A. We did not see a correlation with alterations in KRAS as almost all the tumor samples have KRAS alterations. From a de-identified dataset of 1,873 patients diagnosed with and/or treated for PDAC at our institution between 2004 and 2020, we identified 422 patients for which we had specimens with sequencing data (N=374) and/or specific evidence of disease metastasis site(s) from the OHSU cancer registry or disease-relevant computed tomography (CT) scans to allow cohort classification. Note that our study includes RNA-Seq, DNA-Seq panel, and TCR-Seq (T-cell Receptor Sequencing) data, but that this dbGaP submission just includes the patients/samples with RNA-Seq and/or DNA-Seq panel data. Therefore, this submission includes 290 samples from 278 patients. TCR sequence data is available on the Adaptive Biotechnologies platform. Clinical course timepoints, patient demographics, stage, grade, nodal involvement, resection margins, and angiolymphatic invasion were provided as deidentified data by the OHSU cancer registry with quality control data verification in a subset by pathologists (BB and TM). We reviewed all available computed tomography (CT) scans for all patients with primary tumor resection dates recorded by the cancer registrar, with tumor samples analyzed by RNA-seq, DNA-seq, or TCR-seq, and/or with additional information indicating metastatic spread (e.g., metastatic samples received for related studies). We abstracted the site of all lesions proven to be metastatic by biopsy and/or that clearly increased in size during progression or decreased in size during treatment as long as a radiologist described the lesion as “likely”, “suspicious for”, “concerning for”, or “favor” metastasis. Clinical imaging was reviewed by a radiologist (AG) to validate patient assignments to the liver, lung, and neither liver nor lung (other recurrence site) cohorts. Time to recurrence was calculated from the earliest of either the recurrence date provided by the OHSU cancer registry, or the date of earliest lesion abstracted from CT reports. The subject attributes reported in this submission were up to date at the time of our data freeze, which occurred July 2021.

  Study phs003597

• Heart Failure Network - Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure (HFN RELAX-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. Related StudyEcho images are available through HFN RELAX-ImagingObjectivesThe RELAX trial tested the hypothesis that chronic phosphodiesterase type-5 inhibitor therapy with sildenafil would improve exercise capacity and clinical status in heart failure patients with normal ejection fraction, as compared to placebo. Background Heart failure (HF) with preserved ejection fraction (HFpEF) is a common and highly morbid condition that is characterized by chronic exercise intolerance, progressive functional decline and a high rate of readmission. At the time of the RELAX trial, clinical trials of renin-angiotensin system antagonists had not demonstrated improvement in outcomes or clinical status in HFpEF, and effective therapies were needed. Phosphodiesterase type-5 (PDE-5) metabolizes the nitric oxide (NO) and natriuretic peptide (NP) systems' second messenger cyclic guanosine monophosphate (cGMP), and thus may limit beneficial NO and NP actions in the heart, vasculature and kidney. Pre-clinical studies suggest that inhibition of PDE-5 reverses adverse cardiac structural and functional remodeling and enhances vascular, neuroendocrine and renal function. In clinical studies, PDE-5 inhibitor therapy improved exercise tolerance and clinical status in patients with idiopathic pulmonary arterial hypertension and in patients with HF and reduced ejection fraction (HFrEF). A small, single-center study in HFpEF observed improved hemodynamics, left ventricular (LV) diastolic function, right ventricular (RV) systolic function, LV hypertrophy and lung function with chronic PDE-5 inhibition as compared to placebo. In aggregate, these studies suggested the potential for PDE-5 inhibition to ameliorate several key pathophysiological perturbations in HFpEF, and thus improve exercise capacity and clinical status. ParticipantsA total of 216 participants were enrolled in the trial with 113 in the Sildenafil group and 103 in the placebo group. Design Participants who met screening criteria underwent baseline studies including a history and physical examination, cardiopulmonary exercise test (CPXT), six-minute walk distance, Minnesota Living with Heart Failure Questionnaire (MLWHFQ), echocardiography, cardiac magnetic resonance imaging, and phlebotomy for biomarkers. Subjects were then randomly assigned, in a 1:1 ratio, to either the sildenafil or placebo intervention group. The study drug was administered orally at 20 mg three times daily (TID) for 12 weeks. If the dose was well tolerated at 12 weeks, it was increased to 60 mg TID for another 12 weeks. If side effects developed, study staff could recommend discontinuation or return to a lower or previously tolerated dose of study drug. Sildenafil levels 2 hours after a scheduled dose of study drug were obtained at 12 and 24 weeks. The primary endpoint was exercise capacity determined by change in peak oxygen consumption during the CPXT after 24 weeks of therapy. Secondary endpoints included change in six-minute walk distance at 12 and 24 weeks, change in peak oxygen consumption at 12 weeks, and a three tier score reflective of clinical status where patients were ranked based on time to death (lowest tier), time to cardiovascular or cardiorenal hospitalization (middle tier), and change in the MLWHFQ for patients alive without cardiovascular or cardiorenal hospitalization after 24 weeks (highest tier). Conclusions Chronic phosphodiesterase type-5 inhibitor therapy with sildenafil for 24 weeks did not alter exercise capacity or clinical status compared to placebo in patients with heart failure and preserved ejection fraction.

  Study phs003565

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

  Blog fega-and-gdi

• Durvalumab Plus Tremelimumab Alone or in Combination with Low-Dose or Hypofractionated Radiotherapy in Metastatic Non-Small-Cell Lung Cancer Refractory to Previous PD(L)-1 Therapy: an Open-Label, Multicentre, Randomised, Phase 2 Trial

  Brief SummaryThis randomized phase II trial studies the side effects of durvalumab and tremelimumab and to see how well they work with or without high or low-dose radiation therapy in treating patients with colorectal or non-small cell lung cancer that has spread to other parts of the body (metastatic). Immunotherapy with durvalumab and tremelimumab, may induce changes in body's immune system and may interfere with the ability of tumor cells to grow and spread. Radiation therapy uses high energy x-rays to kill tumor cells and shrink tumors. Giving durvalumab and tremelimumab with radiation therapy may work better in treating patients with colorectal or non-small cell lung cancer.Detailed DescriptionPRIMARY OBJECTIVES:I. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 (durvalumab) and tremelimumab alone or with high or low-dose radiation in non-small cell lung cancer (NSCLC). (NSCLC Cohort) II. To compare the overall response (excluding the irradiated lesion[s]) between combined checkpoint blockade with MEDI4736 and tremelimumab alone or combined checkpoint blockade with low or high dose radiation. (NSCLC Cohort) III. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 and tremelimumab with high or low-dose radiation. (Colorectal Cohort) IV. To determine the overall response rate (excluding the irradiated lesion[s]) with combined checkpoint blockade with MEDI4736 and tremelimumab with either low or high dose radiation. (Colorectal Cohort)SECONDARY OBJECTIVES:I. To estimate median progression-free survival and overall survival. (NSCLC Cohort) II. To determine local control within the irradiated field(s) and abscopal response rates. (NSCLC Cohort) III. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells and overall response. (NSCLC Cohort) IV. To explore changes in PD-L1 expression, circulating T-cell populations, T-cell infiltration, ribonucleic acid (RNA) expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (NSCLC Cohort) V. To estimate median progression-free survival and overall survival. (Colorectal Cohort) VI. To determine local control within the irradiated field and abscopal response rates. (Colorectal Cohort) VII. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+ CD8+ T-cells and overall response. (Colorectal Cohort) VIII. To evaluate changes between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells induced by targeted low or high dose radiation. (Colorectal Cohort) IX. To explore changes in circulating T-cell populations, T-cell infiltration, RNA expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (Colorectal Cohort)OUTLINE: Patients are assigned to 1 of 2 cohorts. NOTE: This Study is only for Cohort 1. Please see phs003294 for Cohort 2COHORT 1: Patients with NSCLC are randomized to 1 of 3 arms.ARM A: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.ARM C: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses.COHORT 2: Patients with CRC are randomized to 1 of 2 arms.ARM A: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.After completion of study treatment, patients are followed for up to 12 weeks.Official TitleA Phase 2 Study of MEDI4736 (Durvalumab) and Tremelimumab Alone or in Combination With High or Low-Dose Radiation in Metastatic Colorectal and NSCLC Conditions Metastatic Colorectal CarcinomaMetastatic Lung Non-Small Cell CarcinomaStage IV Colorectal Cancer AJCC v7Stage IV Lung Non-Small Cell Cancer AJCC v7Stage IVA Colorectal Cancer AJCC v7Stage IVB Colorectal Cancer AJCC v7 Intervention/Treatment Biological: DurvalumabRadiation: Radiation TherapyBiological: Tremelimumab Other Study ID Numbers NCI-2016-01325NCI-2016-01325 ( Registry Identifier ) (REGISTRY: CTRP (Clinical Trial Reporting Program))17-71910021 ( Other Identifier ) (OTHER: Dana-Farber - Harvard Cancer Center LAO)10021 ( Other Identifier ) (OTHER: CTEP) Molecular Technologies / AssaysCyTOFIHCmIFOlinkRNAseqWES

  Study phs003295