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• Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET)

  STARNET is a genetics of RNA expression study of multiple disease-relevant tissues obtained from living patients with cardiovascular disease. Tissue samples are obtained from blood, atherosclerotic-lesion-free internal mammary artery (MAM) and atherosclerotic aortic root (AOR), subcutaneous fat (SF), visceral abdominal fat (VAF), skeletal muscle (SKLM), and liver (LIV) during open thorax surgery of 600 coronary artery disease (CAD) patients. All patients gave written informed consent. The inclusion criterion was eligibility for coronary artery by-pass graft (CABG) surgery. Patients with other severe systemic diseases, such as active systemic inflammatory disease or cancer, were excluded. The primary clinical end points were the SYNTAX score based on the extent of coronary atherosclerosis assessed from preoperative angiograms. The STARNET patients are Caucasians (31% females); 32% had diabetes, 75% had hypertension, and 67% had hyperlipidemia; and 33% had an MI before age 60. By New York Heart Association criteria, 45% were class I, 42% class II, 9% class III, and 1% class IV. TYPES AND RNA SEQUENCING: 566 DNA genotype and 3577 RNA-seq profiles from seven tissues from 600 STARNET CABG patients passed quality control (on average 511 RNA-seq profiles/tissue). DNA was genotyped with the OmniExpress Exome array (Illumina, ~900k SNPs) and imputed to a total of 14,098,063 DNA variant calls (6,245,505 with minor allele frequency >5%). The STARNET subjects mainly overlap with Caucasian of Northern European (Finnish) descent. RNA sequencing was performed using the HighSeq2000 platform, poly-A (LIV, SKLM, VAF, SF and blood) and ribo-zero (AOR, MAM) protocols with 50-100 bp read lengths, single end to 15-30 million read depth.

  Study phs001203

• Transcriptional_profiling_of_tauopathies_in_human_IPS_derived_neurons

  This project is a pilot study, in collaboration with Maria Grazia Spillantini and Mariangela Iovino (Cambridge Centre for Brain Repair), to investigate the utility of IPS-derived neurons for the study of neurodegenerative disorders. Our aim is to characterise the transcriptional consequences of tauopathies using neurons derived from differentiated IPSCs as a model system. We will use IPS cells derived from six individuals, four with known mutations in the tau protein, 2 without. RNA will be extracted at Day 0 and Day 65 of differentiation by which time the neuronal tauopathy is apparent. RNA will be extracted and the transcriptome of each line characterised using RNAseq. We will then search for genes that are differentially expressed between the transcriptomes of individuals with tau mutations versus those in controls. My lab will analyse the RNAseq data, comparing both affected and controls and both time-points, to establish candidate genes. Darren Logan’s lab, along with our collaborators, will experimentally verify and further investigate these genes in additional lines and animal models. From this analysis we will generate a list of candidate genes that are differentially expressed between cases and controls. This study will not only help us understand the molecular basis of tauopathies, but also identify gene candidates for biomarkers of neurodegenerative disease. It will serve as a proof of principle for future planned studies into generating and transcriptomically analysing an allelic series of Tau mutations in IPSCs with a controlled genetic background.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000382

• Functional Mapping of AKT Signaling and Biomarkers of Response From the FAIRLANE Trial of Neoadjuvant Ipatasertib Plus Paclitaxel for Triple-Negative Breast Cancer

  Purpose: Despite extensive genomic and transcriptomic profiling, it remains unknown how signaling pathways are differentially activated and how tumors are differentially sensitized to certain perturbations. Here, we aim to characterize AKT signaling activity and its association with other genomic or immunohistochemistry-based PI3K/AKT pathway biomarkers as well as the clinical activity of ipatasertib (AKT inhibitor) in the FAIRLANE trial. Experimental Design: In FAIRLANE, 151 patients with early triple-negative breast cancer were randomized 1:1 to receive paclitaxel with ipatasertib or placebo for 12 weeks prior to surgery. Adding ipatasertib did not increase pathologic complete response rate and numerically improved overall response rate by magnetic resonance imaging (MRI). We used reverse-phase protein microarrays (RPPA) to examine the total level and/or phosphorylation states of over 100 proteins in various signaling or cell processes including PI3K/AKT and mTOR signaling. 125 baseline and 127 on-treatment samples were evaluable by RPPA, with 110 paired samples at both time points. Results: Tumors with genomic/protein alterations in PIK3CA/AKT1/PTEN were associated with higher levels of AKT phosphorylation. In addition, phosphorylated(p)AKT levels exhibited a significant association with enriched clinical benefit of ipatasertib, and identified patients who received benefit in the absence of PIK3CA/AKT1/PTEN alterations. Ipatasertib treatment led to a down-regulation of AKT/mTORC1 signaling, which was more pronounced among the tumors with PIK3CA/AKT1/PTEN alterations or among the responders to the treatment. Conclusions: We showed that the high baseline pAKT levels are associated with the alterations of PI3K/AKT pathway components and enriched benefit of ipatasertib in TNBC.

  Study EGAS00001005892

• The clinical utility of genomics in childhood cancer extends beyond targetable mutations

  Survival of children with relapsed cancer remains unacceptably low. Cancer genomic profiling may identify new therapeutic options, enhance diagnostic accuracy, and improve understanding of tumors’ origins and continued evolution. We deeply sequenced 864 cancer-associated genes and the complete genomes and transcriptomes for 300 pediatric and adolescent/young adult (AYA) patients with poor prognosis or rare tumors. Using integrative somatic-germline analyses, we assessed the clinical utility of cancer genomics in this population. We analyzed tumor mutational signatures and burden to interrogate the relevance of germline variants in DNA repair genes and the impact of therapeutic exposures on tumor evolution. We also evaluated the frequency of changes to tumor drivers and therapeutic targets at relapse in those with serial samples. Integrative analysis yielded clinically actionable variants in a majority of patients. Improved diagnostic accuracy led to modified management in a subset. Therapeutically targetable variants were of unanticipated timing and type, with many of these derived from the germline. Enrichment in corroborating mutational signature 3 (‘BRCAness’) in patients with germline homologous recombination defects suggests they are drivers of pediatric cancers and potential targets for PARP inhibition. We show that mutation burden, which may in part be driven by prior therapeutic exposures, was significantly elevated in a minority of patients. Over one-third of patients with sequential samples analyzed showed a change in therapeutically-targetable drivers. Comprehensive somatic-germline cancer genomic profiling is useful at multiple points in the care trajectory for pediatric/AYA patients, supporting its integration into early clinical management. Re-biopsy at the time of relapse should be considered, and specific attention to mutational burden is indicated. Defective DNA repair genes in the germline may represent important targetable drivers.

  Study EGAS00001006034

• Long-term temporal stability of peripheral blood DNA methylation alterations in patients with inflammatory bowel disease.

  Introduction There is great current interest in the potential application of DNA methylation alterations in peripheral blood as biomarkers of susceptibility, progression, and treatment response in inflammatory bowel disease (IBD). To date, the intra-individual stability of peripheral blood leukocyte (PBL) methylation in IBD patients, a key consideration in biomarker development, has not been characterised. Here, we studied the long-term stability of all probes located on the Illumina HumanMethylation EPIC BeadChip array. Methods We followed a cohort of 46 adult IBD patients (36 Crohn’s disease (CD), 10 ulcerative colitis (UC), median age 44 (IQR: 27-56), 50% female) that received standard care without any intervention at the Amsterdam UMC. Paired PBL samples were collected at two time points with a median 7 (range: 2-9) years in between. Differential methylation and intra-class correlation (ICC) analyses were used to identify time-associated differences and temporally stable CpGs, respectively. Results Around 60% of all CpG loci on the EPIC array presented poor (ICC <0.50); 119,388 (≈14%) and 41,274 (≈5%) showed good (ICC ≥0.75) or excellent (ICC ≥0.90) intra-individual stability. Functional over-representation analyses of the stable methylated CpGs implicated genes involved in cell-cell signaling, adhesion and neurogenesis. Focusing on previously identified consistently differentially methylated positions indicated that 22 CD-, 11 UC-, and 24 IBD-associated loci demonstrated good stability (ICC ≥0.75) over time, where we observed a marked stability of CpG loci associated to the HLA genes. Conclusion Our data provide insight into the long-term stability of the PBL DNA methylome within an IBD context, facilitating the selection of biologically relevant and robust IBD-associated epigenetic biomarkers with increased potential for independent validation.

  Study EGAS00001006501

• A transcriptomic approach to understand patient susceptibility to pneumonia after abdominal surgery

  Rationale Nosocomial infections are a major healthcare challenge, developing in over 20% of patients aged 45 or over undergoing major-abdominal surgery, with postoperative pneumonia associated with an almost five-fold increase in 30-day mortality. Objectives To describe immune-pathways and gene-networks altered following major-abdominal surgery and identify transcriptomic patterns associated with postoperative pneumonia. Methods and Measurements From a prospective consecutive cohort (n=150) undergoing major-abdominal surgery whole-blood RNA was collected preoperatively and at three time-points postoperatively (2-6, 24 and 48hrs). Twelve patients diagnosed with postoperative pneumonia and 27 matched patients remaining infection-free were identified for analysis with RNA-sequencing. Main Results Compared to preoperative sampling, 3,639 genes were upregulated and 5,043 downregulated at 2-6hrs. Pathway-analysis demonstrated innate-immune activation with neutrophil-degranulation and Toll-like-receptor signalling upregulation alongside adaptive-immune suppression. Cell-type deconvolution of preoperative RNA-sequencing revealed elevated S100A8/9-high neutrophils alongside reduced naïve CD4 T-cells in those later developing pneumonia. Preoperatively, a gene-signature characteristic of neutrophil-degranulation was associated with postoperative pneumonia acquisition (P=0.00092). A previously reported Sepsis Response Signature (SRSq) score, reflecting neutrophil-dysfunction and a more dysregulated host response, at 48hrs postoperatively, differed between patients subsequently developing pneumonia and those remaining infection-free (P=0.045). Analysis of the novel neutrophil gene-signature and SRSq scores in independent major-abdominal surgery and polytrauma cohorts indicated good predictive performance in identifying patients suffering later infection. Conclusions Major-abdominal surgery acutely upregulates innate-immune pathways while simultaneously suppressing adaptive-immune pathways. This is more prominent in patients developing postoperative pneumonia. Preoperative transcriptomic signatures characteristic of neutrophil-degranulation and postoperative SRSq scores may be useful predictors of subsequent pneumonia risk.

  Study EGAS00001007229

• Dynamics of circulating tumor DNA in acute myeloid leukemia (AML) patients who undergo allogeneic transplantation

  Allogeneic hematopoietic cell transplantation (allo-HCT) is the only curative option for many patients with acute myeloid leukemia (AML). With the increasing use of non-myeloablative conditioning regimens, it is widely assumed that the cytotoxic conditioning itself does not eradicate all leukemia cells. Instead, the donor-derived immune cells are believed to eliminate residual leukemia cells over time. This so-called graft-versus-leukemia (GvL) is postulated to be the primary mechanism by which allo-HCT achieves sustained remissions in patients with AML. However, the precise quantification of GvL effects and its contribution to leukemia eradication has not yet been demonstrated by sequential, highly sensitive measurements of residual disease at various time points following allo-HCT. In the current study, we employed a novel, highly sensitive and specific plasma-based assay to track mutations in the leukemia cells of 14 patients before and after allo-HCT. If the primary therapeutic benefit of allo-HCT is truly mediated through a GvL effect, we hypothesized that leukemia cells would persist following cytotoxic conditioning and would often be reduced after post-transplant immunosuppression was discontinued. In the majority of patients (11 of 14, 79%), the conditioning used for allo-HCT did not eradicate the leukemia cells. In seven of these 11 patients, the leukemia cells persisted until immunosuppression was discontinued, at which time the persistent leukemia cells were reduced or eradicated. These results demonstrate the feasibility of using circulating tumor DNA (ctDNA) as a dynamic marker of GvL as well as minimal residual disease for patients with AML undergoing alloHCT, and have substantial implications for their management.

  Study EGAS00001007969

• Tubulointerstitial fibrosis is the histological hallmark of chronic kidney disease (CKD). Hypoxia and inflammation (i.e., interleukin (IL)-1β signalling) are independent mediators of tubulointerstitial fibrosis. However, the physiological response of human kidney tubular cells to IL-1β/IL-1RI signalling under the hypoxic conditions of CKD is poorly understood and remains a clinical imperative for therapeutic targeting. This study reports that hypoxia and IL-1β act in synergy to trigger cell cycle arrest/cellular senescence of ex vivo patient-derived primary proximal tubular epithelial cells (PTECs).

  Hypoxia and interleukin (IL)-1β are independent mediators of tubulointerstitial fibrosis, the histological hallmark of chronic kidney disease (CKD). Here, we examine how hypoxia and IL-1β act in synergy to augment maladaptive proximal tubular epithelial cell (PTEC) repair in human CKD. Ex vivo patient-derived PTECs were cultured under normoxic (21% O2) or hypoxic (1% O2) conditions in the absence or presence of IL-1β and examined for maladaptive repair signatures. Hypoxic PTECs incubated with IL-1β displayed a discrete transcriptomic profile distinct from PTECs cultured under hypoxia alone, IL-1β alone or under normoxia. Hypoxia+IL-1β-treated PTECs had 692 ‘unique’ differentially expressed genes (DEGs) compared to normoxic PTECs, with ‘cell cycle’ the most significantly enriched KEGG pathway based on ‘unique’ down-regulated DEGs (including CCNA2, CCNB1 and CCNB2). Hypoxia+IL-1β-treated PTECs displayed signatures of cellular senescence, with reduced proliferation, G2/M cell cycle arrest, elevated senescence-associated β-galactosidase (SA-β-gal) activity and increased production of pro-inflammatory/fibrotic senescence-associated secretory phenotype (SASP) factors compared to normoxic conditions. Treatment of Hypoxia+IL-1β-treated PTECs with either a senolytic drug combination, quercetin+dasatinib, or a type I IL-1 receptor (IL-1RI) neutralizing antibody attenuated senescent cell burden. Our data identify a mechanism whereby hypoxia in combination with IL-1β/IL-1RI signalling trigger PTEC senescence, providing novel therapeutic and diagnostic check-points for restoring tubular regeneration in human CKD.

  Study EGAS00001007904

• Spatial transcriptomic data of breast cancer

  Fastq files from spatial transcriptomic of breast cancer coming from 8 Breast cancer sections. Sample preparation: frozen BC samples were chosen based on tissue structure and RNA quality (RIN > 8). The “Visium Spatial Tissue Optimization Slide and Reagent Kit” (10X Genomics; #PN-1000193) was then used to optimize permeabilization conditions for BC tissues. Briefly, sections were fixed, stained and then permeabilized at different time points to capture mRNA, and the reverse transcription was performed to generate fluorescently labeled cDNA. The permeabilization time that resulted in the highest fluorescence signal with the lowest background diffusion was chosen. The best permeabilization time for BC tissue was 18 min. Cryostat sections of 10 μm of thickness were cut and placed on Visium Spatial Gene Expression slides (10X Genomics, PN-1000184). The slide was incubated for 1 min at 37°C, then fixed with methanol for 30 min at -20°C followed by Hematoxylin and Eosin (H&E) staining and images were taken under a high-resolution microscope. After imaging, the coverslip was detached by holding the slide in water and the slide was mounted in a plastic slide cassette. The spatial gene expression process, including tissue permeabilization, second strand synthesis and cDNA amplification, was performed according to the manufacturer’s instructions (10X Genomics; #CG000239). cDNA quality was next assessed using Agilent High sensitivity DNA Kit (Agilent, #5067-4626). The spatial gene libraries were constructed using Visium Spatial Library Construction Kit (10X Genomics, PN-1000184).

  Dataset EGAD50000000322

• Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT-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 The Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT) was designed to evaluate the hypothesis that amiodarone or a conservatively programmed shock-only, single-lead implantable cardioverter defibrillator (ICD) would decrease the risk of death from any cause in a broad population of patients with mild-to-moderate heart failure. Background Patients with congestive heart failure (CHF) can die suddenly and unpredictably from arrhythmia despite the use of proven medical therapies, such as beta-blockade. Two approaches have been developed to prevent sudden death among patients with CHF: therapy with amiodarone and therapy with an ICD. However, most of the mortality data on amiodarone and ICD therapy at the time of the study had been obtained in clinical trials performed after myocardial infarction in patients without CHF or those with ventricular arrhythmias. Thus, more data was needed to guide these therapies in patients who did not meet these criteria. Participants There were a total of 2521 participants. 847 were randomly assigned to placebo, 845 to amiodarone, and 829 to ICD therapy. Design Before randomization, participants underwent electrocardiography, a 6-minute walk test, 24-hour ambulatory electrocardiography, liver and thyroid function studies, and chest radiography. Participants were required, if clinically reasonable, to receive treatment with a beta-blocker and an angiotensin-converting-enzyme inhibitor, as well as an aldosterone antagonist, aspirin, and statins, when appropriate. Participants were randomly assigned in equal proportions to receive placebo, amiodarone, or a single-chamber ICD programmed to shock-only mode. Participants assigned to amiodarone or matching placebo began therapy as outpatients immediately after randomization. Placebo and amiodarone were administered as a loading dose of 800 mg/day for one week, 400 mg/day for three weeks, and then as a daily maintenance dosage according to weight and bradycardia status. Participants assigned to ICD therapy received their device a median of three days after randomization. The goal of ICD therapy was to treat only rapid, sustained ventricular tachycardia or ventricular fibrillation. Dual-chamber or biventricular devices, or antitachycardia or rate-response pacing therapies were not permitted. The ICD was uniformly programmed to have a detection rate of 187 beats per minute or more. Antibradycardia pacing was initiated only if the intrinsic rate decreased to less than 34 beats per minute. Median follow-up was 45.5 months. The primary end point was death from any cause. Secondary outcome measures included cardiac mortality, arrhythmic mortality, morbidity, quality of life, and incremental cost-effectiveness of the interventions. Each active treatment arm was compared to the placebo drug arm. Conclusions In participants with NYHA class II or III CHF and LVEF of 35% or less, amiodarone had no favorable effect on survival, whereas single-lead, shock-only ICD therapy reduced overall mortality by 23%.

  Study phs003654

• Resuscitation Outcomes Consortium (ROC) Trauma Epidemiologic Registry (Trauma Epistry) (ROC-Trauma Epistry-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.Available Data: This dataset only includes traumatic injury participants. For ROC cardiac arrest Epistry data please see: ROC-Cardiac Epistry 1 and 2 and ROC-Cardiac Epistry 3.Objectives: To build a prospective population-based registry of participants with out-of-hospital traumatic injury responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of people that experience traumatic injury; to more fully establish the burden of traumatic injury; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Traumatic injury is a major public health problem generating substantial morbidity, mortality, and economic burden on society. The majority of seriously injured persons are initially evaluated and cared for by prehospital providers, however the effect of EMS systems, EMS clinical care, and EMS interventions on trauma patient outcomes is largely unknown. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region.The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Previous trauma registries have generally focused primarily on hospitalized patients with limited prehospital information. Registries may also exclude trauma cases at far ends of the spectrum, such as those who die in the field or in a non-trauma center and/or patients that are treated and released. These limitations do not allow for detailed, outcome-based assessments of EMS system factors necessary to define prehospital resuscitation best practices. Therefore there was a need for standardized data collection of out-of-hospital traumatic injuries matched to hospital-based outcomes in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 13,830 traumatic events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital traumatic injuries in the ROC regions. Cases were identified through review of emergency response system records including dispatch centers, EMS ground agencies, and air medical services. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Sites submitted data using a web-based interface or batch uploads (Newgard, et al., 2008, PMID: 18482792).

  Study phs003809

• MATISSE bulk RNA-sequencing data

  Tumor availability: Bulk RNA sequencing was performed on tumor biopsies of 48 patients at baseline and 45 patients across time points (baseline n=48, week 1 n=38, week 2 n=34, week 4 n=37), including lymph node metastasis that were sequenced in addition to the primary tumor. Tumor RNA was extracted from formalin-fixed paraffin-embedded (FFPE) primary tumor and lymph node metastasis sections containing at least 10% viable tumor cells present in the sample, except for on-treatment samples with a complete pathological response in which we isolated samples with at least 10% immune-infiltration with no viable tumor cells. A pathologist (LS) scored the tumor percentage and indicated the most tumor-dense region on a hematoxylin and eosin (H&E) stain slide for subsequent RNA isolation. According to the manufacturer's protocol, five to 10 FFPE slides (10 µm) were used for RNA isolation using the AllPrep DNA/RNA FFPE isolation kit (Qiagen, 80234) and the QIAcube. Genetic Diagnostics and Sequencing Services (CeGaT) performed RNA sequencing in Germany. Data processing: Demultiplexing of the sequencing reads was performed with Illumina bcl2fastq (v2.20). Adapters of the reads (Pico v2 SMART adapter, first three nucleotides of the second sequencing read) were trimmed with Skewer (v0.2.2), without quality trimming. RNA-sequencing data was aligned to GRCh38 (Ensembl v109) using STAR (v2.7.9) in 2-pass mode with default settings. Gene counts were generated with the HTSeq (v2.0.2). FastQC (v0.12.1) and MultiQC (v1.14) were used for assessing data quality, on FASTQ files and intermediate processing steps.

  Dataset EGAD50000001470

• Nala TAS-LRS PGx Study

  Sequencing was performed on the PromethION Flow Cell R10 (M version) using the P2 Solo Sequencer (MinION release 24.02.16). The library was divided into three portions and loaded in a tapered manner across three time points. At the first time point, the initial portion of the library was loaded, and sequencing began in whole-genome mode (without adaptive sampling), to monitor QC (N50 within the expected range). After one hour, adaptive sampling was activated. A custom BED file was used to define the target regions (N=326). Each region was extended by 20 kb upstream and downstream, and overlapping regions were merged into non-redundant intervals using bedtools43, covering a total of 1.3% of the human genome. At the second time point (20–24 hours after sequencing began, or when fewer than 2000 pores remained active), the flow cell was washed following ONT’s Flow Cell Wash Kit protocol, and the second portion of the library was loaded. At the third time point (40–48 hours), the second portion of the library was retrieved, and merged with the third portion. The flow cell was washed and reloaded with the mixed library. POD5 files were basecalled using Dorado v0.8.1 in super-accuracy (SUP) mode with the dna_r10.4.1_e8.2_400bps_SUP@v5.0.0. Reads from the initial 1 h Whole Genome Sequencing (WGS) and subsequent 72 h adaptive sampling were filtered for Q-scores >10. Passing reads were demultiplexed using Dorado’s demux function, then combined per sample and mapped to the GRCh38 reference genome using Minimap2 v2.22. Files were converted to CRAM using samtools.

  Dataset EGAD50000001609

• Prediction of Trastuzumab Benefit in Adjuvant Breast Cancer: Gene Expression Profiling in NSABP B31

  Background National Surgical Adjuvant Breast and Bowel Project (NSABP) trial B-31 suggested the efficacy of adjuvant trastuzumab, even in HER2-negative breast cancer. This finding prompted us to develop a predictive model for degree of benefit from trastuzumab using archived tumor blocks from B-31. Methods Case subjects with tumor blocks were randomly divided into discovery (n = 588) and confirmation cohorts (n = 991). A predictive model was built from the discovery cohort through gene expression profiling of 462 genes with nCounter assay. A predefined cut point for the predictive model was tested in the confirmation cohort. Gene-by-treatment interaction was tested with Cox models, and correlations between variables were assessed with Spearman correlation. Principal component analysis was performed on the final set of selected genes. All statistical tests were two-sided. Results Eight predictive genes associated with HER2 (ERBB2, c17orf37, GRB7) or ER (ESR1, NAT1, GATA3, CA12, IGF1R) were selected for model building. Three-dimensional subset treatment effect pattern plot using two principal components of these genes was used to identify a subset with no benefit from trastuzumab, characterized by intermediate-level ERBB2 and high-level ESR1 mRNA expression. In the confirmation set, the predefined cut points for this model classified patients into three subsets with differential benefit from trastuzumab with hazard ratios of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100), 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449), and 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442; P interaction between the model and trastuzumab < .001). Conclusions We developed a gene expression-based predictive model for degree of benefit from trastuzumab and demostrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47). ** Reprinted from J Natl Cancer Inst;2013;105:1782-1788 with permission from Oxford University Press.

  Study phs000826

• POPRES: Population Reference Sample

  POPRES version 1 and 2 Technological and scientific advances, stemming in large part from the Human Genome and HapMap projects, have made large-scale, genome-wide investigations feasible and cost-effective. These advances have the potential to dramatically impact drug discovery and development by identifying genetic factors that contribute to variation in disease risk as well as drug pharmacokinetics, treatment efficacy, and adverse drug reactions. In spite of the technological advancements, successful application in biomedical research would be limited without access to suitable sample collections. To facilitate exploratory genetic research, we have assembled a DNA resource from a large number of subjects participating in multiple studies throughout the world. This resource was initially genotyped using the Affymetrix 500K SNP panel. This project includes nearly 6,000 subjects of African American, East Asian, South Asian, Mexican, and European origin. POPRES version 3 The discovery and development of novel drugs is challenging and with high attrition rates. Selection of the right target and optimal indications for novel therapeutics represent key decision points in this process, and are often hampered by our limited understanding of the biology of the target in humans. Building a robust body of knowledge of variation within drug target genes has in several cases increased the probability of success for novel therapeutics. Our knowledge of variation within drug target genes and their influence on traits of medical interest is still very limited. In an effort to fill this gap, we resequenced the exons of 202 known or suspected drug target genes in over 16,000 well-phenotyped individuals. A total of 863,883 bases were targeted, including 351 kb of coding and 323 kb of untranslated exon regions. The sequenced subjects included 3,381 from two studies that were part of the Population Reference Sample (POPRES) project: CoLaus (N = 2,059) and LOLIPOP (N = 1,322). Genotyping data generated through the targeted next generation sequencing is available through the dbGaP.

  Study phs000145

• Heart Failure Network - Effectiveness of Ultrafiltration in Treating People with Acute Decompensated Heart Failure and Cardiorenal Syndrome (HFN CARRESS - BioLINCC)

  ObjectiveThe CARRESS trial examined the effectiveness of ultrafiltration compared with a strategy of diuretic-based stepped pharmacologic therapy on renal function and weight loss in patients with heart failure who have worsening renal function and persistent congestion.BackgroundAcute cardiorenal syndrome is defined as worsening renal function in patients with acute decompensated heart failure. It occurs in 25 to 33% of patients and is associated with poor outcomes. Current heart failure treatments focus on removing excess fluid buildup, however administration of intravenous diuretics may contribute to worsening renal function. Venovenous ultrafiltration is one alternative therapy that may be effective in patients with acute decompensated heart failure complicated by acute cardiorenal syndrome and persistent congestionSubjectsA total of 188 patients were randomized.DesignHospitalized subjects were randomly assigned to receive either ultrafiltration therapy or pharmacologic therapy. Ultrafiltration was performed at a fluid-removal rate of 200 ml per hour and loop diuretics were discontinued for the duration of the intervention. The addition of intravenous vasodilators or positive inotropic agents was prohibited, unless they were deemed to be necessary as rescue therapy. Patients assigned to stepped pharmacologic therapy received intravenous diuretics to manage congestion and maintain a urine output of 3 to 5 liters per day. In both groups, the assigned treatment strategy was to be continued until the signs and symptoms of congestion in the patient were reduced as much as possible. The primary end points were baseline changes in serum creatinine and weight as assessed at 96 hours after treatment assignment.ConclusionsThe use of a stepped pharmacologic-therapy algorithm was superior to a strategy of ultrafiltration for the preservation of renal function, and there was a similar amount of weight loss between the two groups. Ultrafiltration was associated with a higher rate of adverse events (Bart, et al., 2012, PMID: 23131078).

  Study phs003510

• The National Myelodysplastic Syndromes (MDS) Study

  Myelodysplastic syndromes (MDS) are the most common cause of adult bone marrow failure in the US and occur predominantly in people over the age of 60. Much of the morbidity and mortality for MDS is due to complications from low blood counts (e.g., infections and bleeding). MDS is caused by acquired DNA mutations that occur in a patient's blood cells as they grow older. Accumulating evidence suggests that MDS patients may traverse through a series of antecedent hematopoietic conditions defined by, i) clonal hematopoiesis (i.e., detectable somatic mutations in a person's blood cells) without low blood counts, a condition that affects up to 10% of individuals 70 years old and is termed Clonal Hematopoiesis of Indeterminate Potential (CHIP), or ii) clonal hematopoiesis with low blood counts (i.e., cytopenias) but no evidence of bone marrow dysplasia, termed Clonal Cytopenia of Undetermined Significance (CCUS). Nearly all CCUS patients will develop MDS over a ten year period, highlighting the prognostic importance of this disease. A better understanding of the genetic mutations, including non-coding and structural variants (SVs), occurring in CCUS and MDS could provide valuable insight into the natural history of MDS development. The NHLBI is conducting a longitudinal, observational study to create a standardized clinical dataset linked to a prospectively collected, consistently processed, and well-characterized biospecimen repository derived from approximately 1750 participants with suspected or newly diagnosed MDS or MDS/myeloproliferative neoplasm (MPN) overlap. These volunteers will donate biological specimens (like bone marrow and blood) and medical information, including details of their symptoms, how they perceive their quality of life, the treatments they receive, and what their doctor says about the severity of their illness over time. This information will be collected at pre-determined time points for the duration of the study. Study participants may be followed for life.

  Study phs002714

• Somatic Mutations and Their Etiological Determinants for Breast Cancer in African American Women (B-CAUSE Study)

  The breast cancer health disparity between women of African ancestry (AA) and European ancestry (EA) remains a major public health challenge in the United States. AA women are afflicted by higher rate of triple-negative breast cancer (TNBC) and bear the highest mortality rate of all populations from the disease. Even within TNBC, some data suggest that AA women have poorer outcomes than EA women. Mounting evidence points to population differences in cancer biology, a fundamental question that remains unsettled. The disparity also manifests in representativeness in tumor sequencing projects, due to the low inclusion of AAs in genomic studies. A recent analysis of The Cancer Genome Atlas (TCGA) data revealed that the number of AA cases sequenced was sufficient only for studying frequently mutated genes (≥10%), and the power was worse for TNBC as a separate disease entity. As a result, the breast tumor mutational landscape elucidated to date is derived mostly from EA cases, and may not necessarily represent AA cancer genomes. Moreover, as discovery potential for new driver genes has come close to a plateau, cancer in AAs derived from distinct genetic and environmental contexts may provide a powerful venue for discovering mutations that are rare in EA cases. This is showcased in recent studies in colon and prostate cancer. Thus, we carried out a collaborative project, B-CAUSE, to characterize the mutational landscape of AA breast cancer genomes by pooling resources from five established studies, including the Women's Circle of Health Study (WCHS), the Black Women's Health Study (BWHS), the Southern Community Cohort Study (SCCS), the Nashville Breast Health Study (NBHS), and the Southern Tri-State Breast Health Study (STSBHS). This effort has created the largest AA-specific breast tumor mutation dataset to date in an efficient manner. The data deposited here are from the whole-exome sequencing (WES) of matched tumor-normal pairs of 462 TNBC in AA women from the B-CAUSE Study.

  Study phs003962

• Single cell eQTL map of T cell activation

  We isolated naive and memory CD4+ T cells from 119 healthy individuals and stimulated the cells using anti-CD3/anti-CD28 coated beads. We profiled gene expression using single cell RNA-seq (10X-Genomics 3’ v2 kit) at resting state and three time points of activation (16h, 40h and 5 days post stimulation) and mapped expression quantitative trait loci.

  Dataset EGAD00001008197

• Longitudinal profiling of the immune response to Plasmodium vivax in naive hosts by RNA-sequencing

  Plasmodium vivax offers unique challenges for control and elimination, and may prove a tougher hurdle to overcome than Plasmodium falciparum. And yet compared to P. falciparum we know very little about the innate and adaptive immune responses that need to be harnessed to reduce disease and transmission. We recently generated a blood bank of a new clonal field isolate of P. vivax (PvW1) for human challenge studies and used systems immunology tools to track the host response throughout infection and convalescence. As part of this study, RNA-sequencing was used to resolve changes in whole blood gene expression through time in 6 volunteers (7-9 time-points per volunteer). In summary, these data show that P. vivax induces two distinct transcriptional programmes in whole blood during and after infection. During infection, transcriptional profiling reveals the rapid mobilisation of an emergency myeloid response, which leads to systemic inflammation and the recruitment of all major T cell subsets into lymphoid tissues. Six days after infection, this innate response subsides and a transcriptional signature of proliferation is revealed. This most likely represents widespread activation of lymphocytes, which return to the circulation after parasite clearance - transcriptional profiling of T cells at this time-point could therefore reveal the outcomes of critical cell-cell interactions that take place within the spleen during infection. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ .

  Dataset EGAD00001006924

• Genome-Wide Association Studies of Prematurity and Its Complications

  Preterm labor resulting in the delivery of a premature child is a complex problem with an enormous impact on individuals, families and society. An estimated 500,000 children are born prematurely in the U.S. each year, and 5 million worldwide die annually of prematurity and its complications. Prematurity is also the single largest contributor to disability-adjusted life years, a measure of the lifetime impact of a disease. Despite the importance of the problem and its disproportionate occurrence in poor and minority populations, its underlying etiology (or etiologies) remains unknown; the single best predictor for preterm delivery is a previous preterm birth. The largest single cause of prematurity is spontaneous preterm labor, and suspected triggers for this include infection, stress, poor nutrition and genetic factors. Family and twin studies provide strong evidence that genetic factors underlie about 40% of the risk for prematurity. A major challenge in studying genetic factors in prematurity is maternal/uterine factors, fetal/placental factors, or both may influence risk. Thus, any approach to studying preterm birth should account for both infant and maternal risk, environmental covariates and interactions. The Danish National Birth Cohort (DNBC) is a well-established, prospective cohort that enrolled women early in pregnancy, prior to any adverse pregnancy outcomes, to minimize bias in data collection and sampling. See details at: http://www.ssi.dk/English. The DNBC followed over 100,000 pregnancies beginning in the first trimester and has extensive biological material and epidemiologic data on health outcomes in both mother and child. The current study posted on dbGaP contains data from a genome-wide case/control study using approximately 1,000 preterm mother-child pairs from the DNBC most with spontaneous onset of labor or preterm premature rupture of membranes (PPROM), along with 1,000 control pairs where the child was born at ~40 weeks' gestation. After data cleaning some small changes in case/control status and other variables resulted in minor changes in numbers of cases or controls in certain categories. Environmental variables are being used as covariates in the analysis. To replicate positive findings, we are using additional mother-child pairs from the DNBC and from the general Danish population, as well as 2200 samples coming primarily from an African-American population known to have high rates of preterm labor and delivery. This group includes over 1000 mostly very low birthweight infants, over 500 infant term controls, 326 term mother/baby pairs and 180 preterm mother/baby pairs. The data from these additional cohorts will be available in separate dbGaP postings. The study is expected to yield a better understanding of the biology of parturition, identify common genetic factors that play a role in preterm birth and its complications and suggest environmental modifications that can prolong gestation, with the goal of improving both neonatal and adult outcomes. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to prematurity and its complications through large-scale genome-wide association studies of a well-characterized cohort of Danish mothers and babies. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000103

• Hepatitis C Antiviral Long-term Treatment Against Cirrhosis (HALT-C)

  Reprinted from http://www.haltctrial.org/ Purpose The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial is a randomized controlled trial designed to evaluate the safety and efficacy of long-term use of pegylated interferon for the treatment of chronic hepatitis C in patients who failed to respond to previous interferon therapy. The HALT-C Trial was developed to determine whether prolonged interferon therapy altered histological and clinical outcomes in a group of patients who had failed to eradicate hepatitis C virus with previous interferon treatment. Study Hypotheses In patients with chronic hepatitis C and bridging fibrosis who failed to eradicate the virus with previous interferon therapy, long-term treatment with interferon is safe and can prevent progression to cirrhosis. In patients with cirrhosis secondary to chronic hepatitis C who failed to eradicate the virus with previous interferon therapy, long-term treatment with interferon is safe and can reduce the risks of hepatic decompensation or of hepatocellular carcinoma. Study Design 1145 patients with chronic HCV and advanced hepatic fibrosis (Ishak stage 3-6) who failed to respond to previous treatment with interferon were enrolled at 10 clinical centers and entered into a Lead-in phase. They were treated with a combination of pegylated interferon (Pegasys®, Hoffmann-La Roche) 180 µg/week and ribavirin (1000-1200 mg/day) for 24 weeks. Patients who had no detectable HCV-RNA at week 20 continued on combination therapy until week 48. 662 patients who did not clear virus were randomly assigned at week 24 to either continue treatment with pegylated interferon alone (90 µg/week) for an additional 42 months, or to have treatment discontinued. All patients were followed at 3-month intervals following randomization. Liver biopsy was performed at baseline and after 1.5 and 3.5 years of treatment. Because of slower than expected enrollment and the approval by the FDA of peginterferon alfa-2b after the start of the trial, we modified the study protocol in three ways. First, criteria for admission to the trial were liberalized to allow patients to enter the trial with lower platelet and white blood cell counts than had been initially considered safe or tolerable. Second, 151 Lead-in patients and those continuing on therapy after 24 weeks who demonstrated return of viremia during or after their 48-week treatment period (called "Breakthrough" or "Relapse" patients, respectively) were allowed to return to enter the randomized trial. Third, 237 patients treated with peginterferon alfa-2b (or with peginterferon alfa-2a in licensing trials) outside the HALT-C Trial who in other respects met all study criteria, having received the equivalent of Trial Lead-in period therapy, were allowed to enter the long-term trial as "Express" patients. A total 1050 patients were randomized. Those patients who completed Month 48 were offered an "extended follow-up (observation only)" until October 2009. These visits will primarily be to identify outcome events, and to provide information to patients concerning the current status of the trial. Some questionnaires, blood tests, and an ultrasonogram will be performed. Quarterly (every 3 months) Interval history of complications, adverse events Current medications Brief physical examination Laboratory tests: liver panel, CBC, INR, AFP Child-Pugh Score Stored serum Annual Complete physical examination Ultrasound of liver 1.5 years (M24 visit, middle of study) Liver biopsy: formalin fixed histology, frozen liver tissue (subset of patients) 3.5 years (M48, end of study) Liver biopsy: formalin fixed histology, frozen liver tissue (subset of patients) Endoscopy: evaluate esophageal varices and portal hypertension After Month 48 Observation only (no treatment) to determine clinical outcomes Clinic visit every 6 months with current medications, brief PE, liver panel, CBC, AFP, stored Serum Ultrasound of liver every 6 months Outcome Variables Primary outcome variables to be assessed in the two groups of patients include: Development of cirrhosis on liver biopsy (progression of Ishak fibrosis score by 2 points or more) Development of hepatic decompensation, as shown by: Sustained increase in the Child-Turcotte-Pugh score to 7 points or higher Variceal hemorrhage Ascites Spontaneous bacterial peritonitis Hepatic encephalopathy Development of hepatocellular carcinoma Death Secondary outcomes include quality of life, serious adverse events, events requiring dose reductions, and development of presumed hepatocellular carcinoma.

  Study phs000430

• Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC)

  In 2016 we established the Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC) funded by the Ice Bucket Challenge Grant administered by the Motor Neurone Disease Research Institute of Australia. The goals of the SALSA-SGC are to collect biological samples from clinics across Australia with matched in depth clinical and self-report phenotypes and to generate multiple levels of genetic and genomic data. In this first data generation exercise of the SALSA-SGC the majority of the samples were collected prior to the formal establishment of SALSA-SGC from clinics across Australia.Briefly, the cohort includes the University of Sydney’s Australian Motor Neuron Disease DNA Bank (MND Bank) cohort recruited April 2000 to June 2011), with study protocol approved by the Sydney South West Area Health Service Human Research Ethics Committee (HREC). Cases were recruited from around Australia via state-based MND associations with diagnosis verified by a neurologist. The remainder of the cases were recruited from clinics across Australia between 2015 and 2017 under HREC approvals from Royal Brisbane and Women’s Hospital, Macquarie University Multidisciplinary Motor Neurone Disease Clinic,  Calvary Health Care Bethlehem in Melbourne , Fiona Stanley Hospital in Perth, and from 2016 under HREC approvals at each site for the sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC). The ALS cases were diagnosed with definite or probable ALS according to the revised El Escorial criteria. Some controls were recruited as either partners or friends of patients, healthy individuals free of neuromuscular diseases. We are providing GWAS and MWAS data in this dataset. Individual level GWAS data were generated using Illumina Infinium CoreExome-24 version 1.1 chips for N= 846 cases and N=665 controls. Individual MWAS data was generated using the Illumina Human methylation 450K array for N=782 cases and N=613 controls. There 1315 individuals where GWAS and MWAS data has been generated and is available. Further information on these data sets can be found: Paper 1: Restuadi, R, Garton, FC, Benyamin, B, Lin, T, et al. Amyotrophic Lateral Sclerosis Genetic Correlation with Cognitive Performance, educational attainment and schizophrenia: evidence from polygenic risk score analysis. (submitted) Paper 2: Nabais, MF, Lin, T, Benyamin, B et al. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. 2020. NPJ genomic medicine. 5(10). Files provided in this submission include: GWAS: This folder contains QCed genotype for the Australian ALS case-control cohort. Contains PLINK files for genotyping data (not imputed yet). The individuals selected here have: good consistency on phenotype data ethics approval registered as part of sporadic ALS studies unrelated by GRM cut-off 0.05 No ancestry QC yet MWAS: This folder contains the IDAT and post-QC normalized DNAm (beta) for the Australian ALS case-control cohort. 2019_AUS_ALS_PCTG_DNAm.tar.gz - IDATS for 1315 individuals analyzed in the MWAS study normalized_beta_values - Binary files (created with the OSCA software) containing information on the individuals, probes and the DNAm (beta) values obtained after QC phenotype_file - contains all the covariates analysed in the MWAS including: case-control status, coded 0 = Control and 1 = ALS, predicted age, predicted cell-type proportions, predicted smoking scores, slide and chip position and sex Important Notes: The DNAm data were normalized together with samples that were not part of this ALS case/control study and thus, the normalization procedure may not be 100% reproducible using only the IDAT files uploaded here. Summary data has been made publicly available and can be accessed directly: Data collection and sample processing were performed at several clinics across Australia. Genotyping and DNA methylation arrays were performed by the Human Studies Unit, at the Institute for Molecular Bioscience (University of Queensland). Quality control of the genotypic, phenotypic and DNA methylation data was done by the Program of Complex Traits Genomics, at the Institute for Molecular Bioscience (University of Queensland).

  Study phs002068

• Effectiveness and Safety of Intermittent Antimicrobial Therapy for the Treatment of New Onset Pseudomonas Aeruginosa Airway Infection in Young Patients With Cystic Fibrosis (EPIC-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: To investigate the efficacy and safety of 4 antipseudomonal treatments in children with cystic fibrosis (CF) with recently acquired Pseudomonas aeruginosa (PA) infection.Background: CF is an inherited disease that causes mucus to build up in the lungs and digestive tract, which can cause lung infections and digestive problems. It is the most common type of chronic lung disease in children and young adults and may result in early death. There is no cure for this disease. The primary cause of death in individuals with CF is progressive obstructive pulmonary disease associated with chronic Pseudomonas aeruginosa (PA) infection. PA infection can occur early in life and can become highly resistant to antibiotics. Once an individual has been diagnosed with chronic PA infection, it is almost impossible to manage effectively. The need exists for an effective treatment to control and eliminate PA infection. Past research has shown that if PA infection is treated early, there is a greater likelihood that it may be eliminated completely.Participants: There were 304 participants.Design: Participants were randomized to 1 of 4 antibiotic regimens for 18 months (six 12-week quarters) between December 2004 and June 2009. Participants randomized to cycled therapy received tobramycin inhalation solution (300 mg twice a day) for 28 days, with oral ciprofloxacin (15-20 mg/kg twice a day) or oral placebo for 14 days every quarter, while participants randomized to culture-based therapy received the same treatments only during quarters with positive P aeruginosa cultures. The primary end points were time to pulmonary exacerbation requiring intravenous antibiotics and proportion of PA - positive cultures.Conclusions: No difference in the rate of exacerbation or prevalence of PA positivity was detected between cycled and culture-based therapies. Adding ciprofloxacin produced no benefits (Arch Pediatr Adolesc Med 2011; 165(9):847-856).

  Study phs004067

• Acute Respiratory Distress Syndrome Clinical Network: Fluid and Catheter Treatment Trial (ARDSNet FACTT-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.Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-FACTT include plasma, DNA, and serum. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives: This study evaluated the benefits and risks of Pulmonary Artery Catheters (PACs) in patients with established acute lung injury in a trial comparing hemodynamic management guided by a PAC with hemodynamic management guided by a central venous catheter (CVC) using an explicit management protocol.Background: Optimal fluid management in patients with acute lung injury is unknown. Diuresis or fluid restriction may improve lung function but could jeopardize extrapulmonary organ perfusion.Participants: This randomized study compared a conservative and a liberal strategy of fluid management using explicit protocols applied for seven days in 1000 patients with acute lung injury. The primary end point was death at 60 days. Secondary end points included the number of ventilator-free days and organ-failure-free days and measures of lung physiology. Design: Participants were randomly assigned to a strategy involving either conservative or liberal use of fluids with concealed allocation in permuted blocks of eight with the use of an automated system. Participants were simultaneously and randomly assigned to receive either a pulmonary-artery catheter or a central venous catheter in a two-by-two factorial design.Conclusions: There was no significant difference in the primary outcome of 60-day mortality; however, the conservative strategy of fluid management was associated with improved lung function and shortened the duration of mechanical ventilation and intensive care without increasing non-pulmonary organ failures. These results support the use of a conservative strategy of fluid management in patients with acute lung injury. (NEJM June 15, 2006; Vol 354, No. 24, pp 2564-75; NEJM May 25, 2006; Vol 354, No. 21, pp 2213-24)

  Study phs004165

• Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study

  Background: Follicular lymphoma (FL) is an indolent, yet incurable B-cell malignancy. A subset of patients experience increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immuno-chemotherapy. The nature of tumor clonal dynamics leading to these clinical endpoints is poorly understood and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the timecourse of FL clinical histories. Methods and Findings: Using a combination of whole genome sequencing, targeted deep sequencing and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in n=15 transformation patients and n=6 progressors, and measured their change in abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimen. This pattern was independent of time to transformation and treatment modality. By contrast, progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immuno-chemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in a validation cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical endpoints. We found 12 genes were more commonly mutated in transformed samples than in preceding FL, including TP53, B2M, CCND3, GNA13, S1PR2 and P2RY8. Moreover, 10 genes were more commonly mutated in diagnostic specimens of early progressors including TP53, BTG1, MKI67 and XBP1. Conclusions: Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implication for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies.

  Study EGAS00001001709

• Bulk RNA sequencing of day 2 and day 7 biopsies from the tuberculin skin test in people with latent tuberculosis, and of day 2 biopsies from saline controls

  We used the tuberculin skin test (TST) as human challenge model to study the temporal evolution of the immune response to Mycobacterium tuberculosis (Mtb) antigens in vivo. Study participants comprised healthy HIV seronegative adults, 18-60 years of age. Latent tuberculosis (TB) infection was defined as immune memory for Mtb-specific antigens identified by positive peripheral blood IFNg release assays, but no clinical or radiological evidence of active TB. Two units tuberculin each were injected intradermally into the contralateral forearms of participants. After 2 days (n=216) or 7 days (n=158), 3 mm skin punch biopsies were taken from the injection sites and processed for whole genome transcriptional profiling by bulk RNA sequencing. The time points reflect maximum clinical inflammation (day 2) and maximum T cell infiltration (day 7) of the TST. TST samples were compared to skin biopsies taken two days after control injection of saline in a separate group of individuals (n=33), comprising healthy volunteers as well as patients with active or latent TB, ranging from 18-75 years of age. This dataset includes 407 samples from 256 individuals (n=33 saline, n=223 with Day 2 and/or Day 7 TST). Skin biopsies were collected into RNAlater (Qiagen) for RNA extraction. Total RNA was obtained with the Precellys Evolution homogenizer (Bertin Instruments) and Qiagen RNeasy mini kit. RNA was subjected to DNase treatment to remove contaminating genomic DNA using a TURBO DNA-free kit (Ambion, Life Technologies). The KAPA mRNA HyperPrep Kit (Roche Diagnostics) was used to construct stranded mRNA-Seq libraries from up to 500 ng intact total RNA. Paired-end sequencing was performed on the Illumina Nextseq using the Nextseq 500/550 High Output 75 cycle kit (Illumina). Runs were demultiplexed using bcl2fastq by Illumina.

  Dataset EGAD50000001208

• Transcriptional profiling of tauopathies in human IPS-derived neurons (2019-08-21)

  This project is a pilot study, in collaboration with Maria Grazia Spillantini and Mariangela Iovino (Cambridge Centre for Brain Repair), to investigate the utility of IPS-derived neurons for the study of neurodegenerative disorders. Our aim is to characterise the transcriptional consequences of tauopathies using neurons derived from differentiated IPSCs as a model system. We will use IPS cells derived from six individuals, four with known mutations in the tau protein, 2 without. RNA will be extracted at Day 0 and Day 65 of differentiation by which time the neuronal tauopathy is apparent. RNA will be extracted and the transcriptome of each line characterised using RNAseq. We will then search for genes that are differentially expressed between the transcriptomes of individuals with tau mutations versus those in controls. My lab will analyse the RNAseq data, comparing both affected and controls and both time-points, to establish candidate genes. Darren Logan’s lab, along with our collaborators, will experimentally verify and further investigate these genes in additional lines and animal models. From this analysis we will generate a list of candidate genes that are differentially expressed between cases and controls. This study will not only help us understand the molecular basis of tauopathies, but also identify gene candidates for biomarkers of neurodegenerative disease. It will serve as a proof of principle for future planned studies into generating and transcriptomically analysing an allelic series of Tau mutations in IPSCs with a controlled genetic background. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005277

• Mutational dynamics of triple negative breast cancer over neoadjuvant chemotherapy treatment reveal frequent whole genome duplication events

  Background Triple-negative breast cancer (TNBC) is associated with poor survival rate and high genomic instability, generating complex tumour genomes. However, the processes generating this complexity are poorly studied over time. Here, we study the temporal dynamics of TNBC somatic mutations, revealing major transitions in tumour genome evolution, from diagnostic biopsies, through treatment, to cancer remission or recurrence. Methods Deep whole exome sequencing and CUTseq, a reduced representation whole genome sequencing approach, were performed in parallel, to comprehensively identify short nucleotide variants (SNVs), copy number alterations (CNAs) and aneuploidies genome-wide. We profiled (N=74) tumour samples from 22 patients across the course of disease, including spatially diverse samples from many tumours, allowing us to observe the gain and loss of candidate driver variants over time. Results As expected, tumour infiltrating lymphocyte (TIL) levels and residual cancer burden (RCB) score were negatively correlated, with good responses to neoadjuvant chemotherapy (NACT) seen in pre-treatment samples with high infiltration. In general, SNV mutational burden remained stable across disease progression and RCB classes. Recurrent SNVs across the cohort implicated several known TNBC driver genes in disease progression and response to treatment, with TP53, MICA, CYP2D6, BRCA1 and BRCA2 frequently altered. However, the presence of candidate driver SNVs in these genes often varied throughout a patient’s treatment, including the loss of those seen in a given driver gene in pre-treatment samples, but gain of novel variants in the same gene at later time points. Dramatic structural dynamics were seen in all tumours, including abundant CNA including chromosome arm aneuploidies seen in pre-treatment samples, followed by frequent loss of these alterations post-treatment, and their re-emergence at recurrence. Whole genome duplication (WGD) events appear to drive these dynamics, with a higher frequency of pre-treatment WGD seen in patients with the best response to NACT. Conclusions Comprehensive longitudinal profiling demonstrates the complex interplay of SNVs and copy number alterations during TNBC progression, leading to diverse evolutionary trajectories and patient outcomes. Complex mutational patterns emerge as important features during progression, with WGD events emerging as potential candidate biomarkers of response at both tumour establishment and recurrence.

  Study EGAS00001008261

• Angelman, Rett, Prader-Willi Syndrome Consortium (ARP) Rett Syndrome Natural History Protocol

  This observational study will involve the comprehensive assessment of the clinical features of Rett syndrome with this Rare Diseases Clinical Research Network. A clinical database of 1350 individuals with Rett syndrome including MECP2 mutation status for the purpose of elaborating phenotype-genotype correlations will be established. Eligible participants will be enrolled in the natural history study for 11 years from the 4 clinic sites currently involved with Rett syndrome (BCM, GGC, and UAB, CHB) as well as from the several thousand registrants in the International Rett Syndrome Foundation registry who will be invited to the nearest consortium site involved in Rett syndrome. Potential participants are expected to have had determination of their MECP2 mutation status, but will not be excluded should no mutation be present. All individuals who meet criteria and are currently receiving care from study sites will be invited to participate (N>600). Additional participants will be invited through the assistance of the International Rett Syndrome Foundation (IRSF). All individuals with RS registered with IRSF will be invited to participate in this study via the IRSF website and the IRSF newsletter. Potential participants will be apprised of the participating study sites and invited to contact one of the sites based on feasibility of travel. Inasmuch as the enrollment goal of 1350 participants is 40% of the current IRSF registry, we believe this will be a representative sampling. Bias can certainly occur based on travel requirements, level of interest, and other factors. However, inclusion of such a large sample should minimize sampling bias. No travel expenses will be covered, so it is likely that participants will be those with geographic proximity to Houston, Greenwood, Birmingham, or Boston. From the sample pool described above, the aim is to conduct the observational longitudinal (natural history) study on up to 1350 participants with classic Rett syndrome and variant forms of Rett syndrome, that is, participants who fully meet the established clinical criteria for Rett syndrome and those who meet some but not all of these criteria. All participants expressing MECP2 mutations irrespective of phenotype will be eligible for enrollment. For each of the eight common genetic mutations as well as those with large scale deletions, the prevalence of the following RS characteristics will be estimated for each age category and overall: seizure presence and frequency, ambulation status, scoliosis, ECG abnormality, breathing irregularities while awake, feeding difficulties, growth as measured in percentiles from NCHS standards curves, the presence of osteopenia as evidenced by bone fracture, and the presence of gallbladder disease. Both incidence and prevalence estimates will be made using a combination of data obtained at the regularly scheduled clinical visits and that obtained from the medical history. Incidence estimates will be calculated using time-to-event analyses (e.g., Kaplan-Meier survival curves) with appropriate calculations of standard errors and 95% confidence intervals. Cox proportional hazard regression models will be used to allow control for other factors, and t-tests will be calculated on the coefficients themselves. Appropriate transformations on explanatory variables will be used as deemed necessary in Cox models. Prevalence estimates will be calculated from estimates of proportions with exact confidence intervals. For a sample size of 275, the standard error of the estimate will not exceed 0.03 (3%) and for a sample size of 60, the standard error of the estimate will not exceed 0.06 (6%).

  Study phs000574

• Add Health: Longitudinal Study of a Nationally Representative Sample of Adolescents in Grades 7-12 in the United States during the 1994-95 School Year, Followed into Adulthood with Five Interviews/Surveys in 1995, 1996, 2001-02, 2008, and 2016-18

  The National Longitudinal Study of Adolescent to Adult Health [Add Health] is an ongoing longitudinal study of a nationally representative U.S. cohort of more than 20,000 adolescents in grades 7-12 (aged 12-19 years) in 1994 followed into adulthood with five interviews/surveys in 1995, 1996, 2001-02, 2008, and 2016-18. Add Health was designed to understand how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. Add Health contains unprecedented environmental, behavioral, psychosocial, biological, and genetic data from early adolescence and into adulthood on a large, nationally representative cohort with unprecedented racial, ethnic, socioeconomic, and geographic diversity. Add Health has a large, multidisciplinary user base of over 50,000 researchers around the world who have published over 3,400 research articles. Add Health is housed at the Carolina Population Center of the University of North Carolina at Chapel Hill. Add Health datasets are distributed according to a tiered data disclosure plan designed to protect the data from the risk of direct and indirect disclosure of respondent identity. Add Health's large sample size, population diversity and rich longitudinal database of psychosocial, physical, and contextual data will permit investigation of an exceptionally broad range of phenotypes with known genetic variation. Prospective longitudinal measures are available to document change over time in each of these phenotypes, as well as change in the social environment and life experiences, making the Add Health sample ideal for understanding genetic linkages with health and behavior across the life course. The original design of Add Health included important features for understanding biological processes in health and developmental trajectories across the life course of young people, including an embedded genetic sample with more than 3,000 pairs of adolescents with varying biological resemblance (e.g., twins, full sibs, half sibs, and adolescents who grew up in the same household but have no biological relationship), testing of saliva and urine for sexually transmitted infections and HIV, and biomarkers of cardiovascular health, metabolic processes, immune function, renal function, and inflammation. Add Health therefore has critical objective indicators of health status and disease markers in young adulthood, well before chronic illness or its complications emerge in later adulthood. Because DNA has been collected on the full sample at Wave IV, it is possible to link genetic profiles with social, behavioral, and biological measures over time from adolescence into adulthood. Add Health sampled the multiple environments in which young people live their lives, including the family, peers, school, neighborhood, community, and relationship dyads, and provides independent and direct measurement of these environments over time. Add Health contains extensive longitudinal information on health-related behavior, including life histories of physical activity, involvement in risk behavior, substance use, sexual behavior, civic engagement, education, and multiple indicators of health status based on self-report (e.g., general health, chronic illness), direct measurement (e.g., overweight status and obesity), and biomarkers. No other data resource with this expanse of genotype and phenotype data on a large nationally representative longitudinal sample with race, ethnic, socioeconomic, and geographic diversity exists. A complete reference guide on study design and accomplishments can be found on the Add Health website: https://cdr.lib.unc.edu/concern/articles/6t053j27s.

  Study phs001367

• Implementation of the GDPR

  Data Protection 1 About the EGA The European Genome-phenome Archive (EGA) was formally launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. In late 2012, with the signing of a memorandum of understanding (and subsequent formal agreement in 2016) between EMBL-EBI and the Centre for Genomic Regulation (CRG), the EGA formally became a joint project of the two institutes. The two institutes work together to support the EGA services, including supporting submissions, web site, strategic leadership, and data infrastructure developments. 2 EMBL-EBI & GDPR The EGA is co-managed by EMBL-EBI and CRG. EMBL-EBI is an international organisation established by treaty and has certain privileges and immunities (e.g. exemptions from the application of national law) and also may self-regulate its activities (e.g. establish its own institutional legal framework) within the framework of its founding act of 1973. The General Data Protection Regulation (GDPR) is a European Union (EU) regulation that legislates how organisations can share and process personal data of EU citizens. EMBL places great value in maintaining collaboration with researchers who are subject to GDPR. For that reason, it is of utmost importance for EMBL to handle data received from those collaborators in a secure and responsible manner. Mindful of its public mandate and the sensitivity of the data it handles, EMBL has always ensured a high level of data protection in its activities. Since the introduction of GDPR in May 2018, EMBL has established its internal policy on General Data Protection (IP68), exercising its right to self-regulate its operations,., IP 68 establishes a robust personal data protection framework that provides for data protection principles, enforceable data subject rights and oversight and redress mechanisms offering a level of protection comparable with GDPR. 3 CRG & GDPR The Centre for Genomic Regulation (CRG) is an international biomedical research institute of excellence, created in July 2000 and mainly participated by the Catalan Government. It is a non-profit foundation and its mission is to discover and advance knowledge for the benefit of society, public health and economic prosperity. The CRG is a CERCA center. CERCA is the collective organisation for all research centres of excellence in Catalonia. CERCA ensures these centres develop successfully by promoting synergies and strategic cooperation improving their visibility and the impact of their research and promoting the dialogue amongst both public and private stakeholders. As a legal entity based in Spain and operating within the EU, the CRG ensures the compliance with the GDPR and the legal regulations on personal data protection applicable at the national level, as well as any other legislation that may replace, modify or supplement the above-mentioned in terms of personal data protection. 4 EGA & GDPR EGA GDPR Schema 4.1 Genetic and phenotypic data Within GDPR, there are two main actors: data controllers and data processors. Data controllers are persons or entities which determine the purposes and means that the personal data may be processed, e.g. companies, researchers, or universities. For EGA, the data controller is ultimately the data producer and the submitter(s) who submit the data to EGA. The data controller also creates a Data Access Committee (DAC) who will decide on data access permissions at EGA. Data processors are the persons or entities which process the data on behalf of a data controller. With regard to GDPR, EGA is a data processor as it processes data as instructed by the data controller. GDPR applies to any organisation which accesses personal data from an individual within the EU. Under GDPR, personal data is defined as any data that is identifiable, including names and email addresses as well as health-related and genetic data. EGA does not accept personally identifiable data except genetic and phenotypic data, so all other data submitted to EGA, such as names and addresses, must be pseudonymised. GDPR requires that data controllers implement data protection principles, such as data minimisation, to minimise the risk of data leakage, and protect the rights of the data subjects. As a data processor, EGA has a set of security policies that are followed to minimise the risk of unauthorised data access or data loss. In its role as a data processor, EGA requires all submitters to sign a Data Processing Agreement (DPA) when the submission account is first created. This agreement is only required to be signed once per submitter, and will remain valid for future submissions to EGA. 4.2 Other personal data The EGA also collects personal data as part of our interactions with submitters, data access committees, and researchers accessing data distributed by EGA. The below privacy notices explain what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. Privacy Notices for EGA Title Version Last Updated EGA Data Access Committee Account Privacy Notice for EGA Data Access Committee Account 1.0 February 6, 2019 EGA User Account Privacy Notice for EGA User Account 1.0 February 6, 2019 EGA Helpdesk Service Privacy Notice for EGA Helpdesk Service 1.0 February 6, 2019 EGA Website Service Privacy Notice for EGA Website Service 1.0 February 6, 2019 Documentation Title Version Description EGA Security Overview Security Document 1.1 The EGA Security Document provides an overview of EGA’s practices in ensuring the security of data stored at EGA. EGA Data Processing Agreement Data Processing Agreement 1.5 The Data Processing Agreement must be completed and returned as part of the submission process. Please note that this document is non-negotiable. Authorised Submitters Authorised Submitters Formulary 1.0 The Authorised Submitters Form must be completed and returned as part of the submission process. Please list all those that should have access to the submission account in order to submit to the EGA should be detailed here. Dispute Resolution Any controversy or claim arising out of, or relating to, the DPA (including the enforceability or breach thereof, any question regarding its existence, validity or termination) or relating to the EGA Service shall be resolved using the internal dispute resolution mechanisms of EGA including those related to Data Protection. The EGA’s internal dispute resolution mechanism has the following procedure: EGA OPERATIONAL PHASE: Meetings between EGA staff and the Data Controller.LEGAL MANAGEMENT PHASE: Meetings between legal teams of EMBL, CRG and the Data Controller. DIRECTION MANAGEMENT PHASE: Negotiation between the legal representatives of EMBL, the CRG and the Data Controller. If the internal dispute resolution mechanism doesn’t resolve the controversy or claim the next phase is:ARBITRATION PHASE: Resolution by arbitration under the WIPO Expedited Arbitration Rules (“Rules”).

  Documentation about/privacy-notice

• Genome-Wide associations of Lung Health Study (LHS)

  The 'Genome-Wide Associations Environmental Interactions in the Lung Health Study' at Johns Hopkins University aims to test for association between lung function decline as a primary outcome associated with chronic obstructive pulmonary disease (COPD) using banked DNA and phenotype data on 4,287 European Americans from the longitudinal, multicenter Lung Health Study (LHS). The broad goals of the LungGO/ESP-GO falls into two general categories: (i) discovery of all variants (i.e., common and rare) in all protein-coding regions of the human genome (i.e., the exome) conferring risk to complex pulmonary diseases including COPD, in a subset of the LHS cohort. The Johns Hopkins University LHS cohort offers a unique opportunity to elucidate genetic variants that cause COPD. The Lung Health Study I was a randomized multicenter clinical trial with 5887 participants carried out from October 1986 to April 1994, designed to test the effectiveness of smoking cessation and bronchodilator administration in smokers aged 35 to 60 with mild lung function impairment. Participants were randomly assigned to one of three groups: usual care, who received no intervention smoking intervention with the inhaled bronchodilator ipratroprium bromide smoking intervention with an inhaled placebo. The effect of intervention was evaluated by the rate of decline of forced expiratory volume in one second (FEV1). For the GWAS, only the subset of European American LHS participants for whom lung function data from three time points or more are available. Thus, the GWAS represents 73% of the 5,887 volunteers who participated in the LHS study. Importantly, LHS subjects included had similar demographics (including age, gender and BMI) and rates of lung function decline (mean annual change in FEV1% predicted: -0.96 %/yr vs. -0.99 %/yr, p=0.57) compared with those not included in the GWAS, reflecting little selection bias for our primary outcome. They were, however, more likely to have quit smoking after 5 years. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to lung function through large-scale genome-wide association studies of smokers enrolled in a multicenter clinical trial. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000335

• NSIGHT BabySeq Project

  The Genomic Sequencing for Childhood Risk and Newborn Illness (BabySeq Project) is a research study exploring the use of genomic sequencing in newborns. The National Institutes of Health is funding this study. Investigators enrolled 240 healthy infants and their parents from the Brigham and Women's Hospital (BWH) Well Newborn Nursery and 240 sick infants and their parents at Boston Children's Hospital (BCH) or the BWH Neonatal Intensive Care Unit (NICU). A small blood sample was taken from each infant and genome sequencing may have been performed. Whole-exome (WES) was performed at the Broad Institute. Six weeks later, the results were returned and explained. Over 12 months, the investigators studied the experiences of parents and pediatricians of infants who receive sequencing to help understand how best to use genomics in pediatric care. Parents were surveyed at four points over the 12 months after enrollment: baseline, immediately post-disclosure (approximately 6 weeks after enrollment), 3 months post-disclosure, and at 10 months post-disclosure. Primary outcome measures included: (1) downstream health care costs attributable to BabySeq Project disclosure measured as days of inpatient care; (2) Parents' distress using validated scales and a novel item assessing blame; (3) parent-child relationship using validated scales; (4) Parents' relationship using validated and novel measures of marital satisfaction; (5-9) downstream health care utilization attributable to BabySeq project disclosure measured as the number of health care provider visits, per-patient counts for number of medications at 10 months, number of emergency room visits, number of outpatient lab tests, and per-patient means for healthcare costs in U.S. dollars. A secondary outcome was whether there was a change in perceived utility toward genomic sequencing. Findings of BabySeq include: (1) interest in newborn genomic testing is high among parents of healthy children and the majority of couples had similar levels of interest; (2) parents reported several motivations to receive and reasons to decline adult-onset only results from newborn sequencing; (3) 88% of newborns had at least one recessive carrier variant that could be relevant to their parents' future reproductive planning; (4) 5% of babies had an atypical pharmacogenomic variant related to how they might process medications used in childhood; (5) parent surveys using validated measures showed no evidence that newborn genomic sequencing caused increased psychological distress, even if the baby had a disease risk identified; and (6) 11% of newborn babies in the study had unanticipated monogenic disease risks.

  Study phs002093

• Heart Failure Network: Diuretic Optimization Strategies Evaluation in Acute Heart Failure (HFN DOSE-BioLINCC)

  ObjectivesThe DOSE study sought to evaluate the most effective dosing (high vs. low) and administration (continuous infusion vs. intermittent boluses) combination of the diuretic Furosemide in the treatment of patients with acute decompensated heart failure.BackgroundAcute decompensated heart failure is the most common cause of hospital admissions among patients older than 65 years of age and is responsible for more than 1 million hospitalizations annually in the United States. Intravenous loop diuretics are an essential component of current treatment and are administered to approximately 90% of patients who are hospitalized with heart failure. Despite decades of clinical experience with these agents, prospective data to guide the use of loop diuretics are sparse, and current guidelines are based primarily on expert opinion. As a result, clinical practice varies widely with regard to both the mode of administration and the dosing.SubjectsA total of 308 patients were enrolled between March 2008 and November 2009 at 26 clinical sites in the United States and Canada. DesignThe DOSE study was a prospective, randomized, double-blind, controlled trial with a 2-by-2 factorial design. Patients were randomly assigned to either a low-dose strategy (total intravenous furosemide dose equal to their total daily oral loop diuretic dose in furosemide equivalents) or a high-dose strategy (total daily intravenous furosemide dose 2.5 times their total daily oral loop diuretic dose in furosemide equivalents) and to administration of furosemide either by intravenous bolus every 12 hours or by continuous intravenous infusion.The study treatment, with group assignments concealed, was continued for up to 72 hours. At 48 hours, the treating physician had the option of adjusting the diuretic strategy on the basis of the clinical response. An assessment of biomarkers, including creatinine, cystatin C, and N-terminal pro-brain natriuretic peptide, was performed at a central core laboratory at baseline, 72 hours, and 60 days. Patients were followed for clinical events to day 60. The coprimary end points were patients' global assessment of symptoms, quantified as the area under the curve (AUC) of the score on a visual-analogue scale over the course of 72 hours, and the change in the serum creatinine level from baseline to 72 hours.ConclusionsAmong patients with acute decompensated heart failure, there were no significant differences in patients' global assessment of symptoms or in the change in renal function when diuretic therapy was administered by bolus as compared with continuous infusion or at a high dose as compared with a low dose. (Felker et. al., 2011, PMID: 21366472)

  Study phs003524

• Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT-BioLINCC)

  Accessing Data: Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.Objective: To determine whether an amino-terminal pro-B-type natriuretic peptide (NT-pro-BNP)-guided treatment strategy improves clinical outcomes vs usual care in high-risk patients with heart failure (HF) and reduced ejection fraction (HFrEF).Background: Heart failure is a common disorder. Standard treatment for HF includes diuretics to control fluid, and drugs called "neurohormonal antagonists" (such as beta-blockers and ACE-inhibitors) to help the heart work more efficiently. The natriuretic peptides, specifically NT-pro-BNP, are biomarkers that reflect HF severity and are significantly associated with adverse outcomes in HF. Smaller studies have evaluated adjusting HF therapy based on natriuretic peptide levels (“guided therapy”) with inconsistent results.Participants: A total of 894 participants were enrolled at 45 sites in the United States and Canada, with 446 randomized to the NT-pro-BNP-guided strategy treatment group and 448 randomized to the usual care treatment group.Design: GUIDE-IT was a multicenter randomized clinical trial with participants randomized to either the NT-pro-BNP-guided therapy strategy or usual care. Given the nature of the study intervention, treatment assignment was not blinded. For participants randomized to the NT-pro-BNP-guided strategy, clinicians were instructed to titrate HF therapy to target an NT-pro-BNP level After an initial visit at 2 and 6 weeks, visits occurred every 3 months throughout the remainder of the study. After therapy adjustment for HF (whether driven by NT-pro-BNP levels or clinical reasons), participants had a 2-week follow-up visit for reassessment. All participants in either group also had blinded NT-pro-BNP concentrations measured in a core laboratory at each study visit. The primary end point was the composite of time-to-first HF hospitalization or cardiovascular mortality. Secondary end points included all-cause mortality, total hospitalizations for HF, days alive and not hospitalized for cardiovascular reasons, the individual components on the primary end point, and adverse events. Conclusions: The guided strategy intervention was stopped in July of 2016 (median follow-up of 15 months) after the study met prespecified inefficacy criteria. Compared to usual care, a strategy of NT-pro-BNP-guided therapy was not more effective in improving time-to-first HF hospitalization or cardiovascular mortality in high-risk participants with HFrEF.

  Study phs003621

• Multiple Myeloma Genomic Study (MMGS)

  Multiple Myeloma (MM) is a plasma cell dyscrasia characterized by bone marrow (BM) infiltration and lytic bone lesions. Recent studies of massive parallel sequencing of tumor cells obtained from the BM of patients with MM have demonstrated significant clonal heterogeneity in MM. Despite this remarkable clonal heterogeneity, it could be envisioned that such clonal diversity may be even higher since single BM samples only represent a small fraction of the whole BM compartment, and the pattern of BM infiltration in MM is typically patchy. In addition, BM biopsies are painful and cannot be repeated multiple times during the course of therapy, indicating a need for less invasive methods to molecularly characterize MM patients and monitor disease progression during the therapy. Thus, optimal characterization of circulating tumor cells (CTCs) may represent a non-invasive method to capture relevant mutations present in PC clones. In addition, MM almost always progresses from precursor states of monoclonal gammopathy of undetermined significance (MGUS)/smoldering multiple myeloma (SMM) to overt MM. However, some patients rapidly progress from MGUS/SMM to overt MM (progressors) with a rate of progression of up to 70% over 5 years, while others remain indolent with minimal progression over the same time period (non-progressors). Although many patients are diagnosed with earlier phases of disease, most patients do not receive treatment until their disease progresses, at which time they have overt end-organ damage. This concept of initiating therapy at the time of symptomatic disease is analogous to initiating therapy in patients with solid tumors only after the development of measurable metastatic disease. It is therefore not surprising that cure is not achieved for most patients with MM. Interestingly, studies have demonstrated that MGUS/SMM clones may already harbor chromosomal alterations (Ig loci or hyperdiploidy) and that progression to MM is mainly due to expansion of clones that were already present in the early stages of MGUS/SMM. However, the biological factors that discriminate progressors from non-progressors in MGUS/SMM are not well known. Therefore, our overarching hypothesis is that an effective therapeutic intervention will result from defining genomic and transcriptomic markers that are associated with disease progression. We believe, therefore, that focused research studies that define molecular mechanisms of clonal evolution in MGUS/SMM/MM will identify novel biomarkers of disease progression and help develop therapeutic agents that prevent or delay progression from MGUS to overt MM. Indeed, by eradicating the disease at the precursor stages, MM may become a preventable disease. Recently, a new term called Clonal Hematopoiesis of Indeterminate Potential (CHIP) has been proposed to describe asymptomatic individuals with hematologic malignancy-associated somatic mutations. Those individuals do not fulfill any diagnostic criteria for any hematological malignancy yet they have a tendency to progress into myelodysplastic syndrome (MDS) or myeloid or lymphoid neoplasia at a rate of around 0.5-1% per year, similar to MGUS. The frequency of CHIP and role of HSCs mutations in enhancing acquisition of somatic mutations in MM plasma cells, allowing progression following treatment, has not been studied. Investigating the dysregulated pathways in early progenitor cells would allow us to understand the reasons of progression and establish novel therapeutic and potentially preventive strategies. This study dissects genomic and transcriptomic characteristics of clonal evolution from MGUS/SMM to MM as well as the characteristics of the tumor microenvironment/immune cells/peripheral blood. Our hypothesis is that molecular biomarkers will be strong predictors of progression from MGUS/SMM to MM and will allow for the development of novel therapeutic agents that prevent or delay this progression. We aim to define genomic and transcriptomic markers that lead to progression from MGUS/SMM to MM in tumor cells, blood biopsies (cell free DNA and circulating tumor cells), and the tumor microenvironment.

  Study phs001323

• 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

• The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension

  Study Design: The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension contains N=797 participants. At the time of recruitment individuals ranged from 15-84 years of age and had varying ethnic backgrounds (Whites, African-Americans and Hispanics). The cohort was developed to study the genetic basis of autonomic dysfunction in human essential hypertension (reviewed in Zhang et al. Curr Hypertens Rep. 2011 13: 36-45. PMID: 21104344) and contains cross-sectionally collected phenotypes related to blood pressure regulation.  Phenotypes: Phenotype data specifically focuses on the sympathetic branch of the autonomic system, which is a key regulator of blood pressure. Data includes circulating concentrations of the secretory "quantum" (catecholamines, neuropeptides, chromogranins) which influence vascular responses to sympatho-adrenal activation resulting in blood pressure. Other measures on the twins and a subset of family members include a standard urine panel with absolute concentrations of potassium, sodium, uric acid, phosphorous; DynaPulse acquired heart rate and blood pressure measures including systolic and diastolic blood pressure, mean arterial pressure, heart rate, Left Ventricular (LV) ejection time, LV contractility, cardiac output, stroke volume data including; and absolute concentrations of plasma aldosterone, endothelin, leptin, and insulin. Phenotype data is included for the 697 individuals sequenced here and 100 family members.  Whole Genome Sequence Data Generation: Whole genome sequencing (20X coverage) was conducted on plasma-derived DNA. The WGS was generated using the Illumina HiSeq X Ten sequencing platform with variants called by Human Longevity, Inc (HLI; Ref 1). Preparation of sample libraries was conducted by mechanical shearing of gDNA (100-800 bp), isolation of DNA fragments 300-500bp in size, followed by library PCR amplification employing high-fidelity, low-bias PCR to amplify library fragments consisting of appropriate adapter sequences on both ends. BAM files were created by alignment of sequence reads to the reference genome (GRCh38) using iSAAC-01.14.02.06 (Python2.7). Note: The user may want to realign the sequence data and recall variants. In total, the WGS dataset contains 30 monozygotic twin pairs represented by one member of the twin pair, 133 monozygotic twin pairs with both individuals sequenced, 7 dizygotic twin pairs represented by one member of the twin pair, 67 dizygotic twin pairs with both individuals sequenced, 8 twin pairs with undetermined zygosity (both individuals sequenced) and 1 triplet with mother (all individuals sequenced). The dataset also contains sequences of 260 family members of twins including their mother, father, non-twin sibling, and offspring. Table 1 describes the composition of families ("sibling" refers to a non-twin sibling in the cohort). Note: We suggest users check gender and family relatedness. Table 1: Description of the families with sequence data                                                                                                               Units with:Twin PairsTwin (no pairs)No twinSibshipsMother and Father511Mother only1222Father only113One or more siblings22133Mother and siblings132Father and siblings(s)211Offspring of twins11Non twin siblings2Sib with unknown relative1Only one member of8The UCSD/O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension is now being managed at the UCSD School of Medicine's Institute for Genomic Medicine (IGM). The TSP Resource has collected a wide range of information (phenotypic, genotypic, demographic, metabolomic) and reagents (blood, plasma, urine, DNA, human induced pluripotent stem cells). For additional information about the resource, including other data types available as well as references and links to data from studies with previous collaborators, please go to the resource website at IGM [http://igm.ucsd.edu/research/twins-resource.shtml] or contact Dr. Brinda Rana at UCSD [bkrana@ucsd.edu]. 

  Study phs002230

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX.Further information about EGA can be found on https://ega-archive.org and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access Policy Your Data Access Policy provides the terms and conditions of data use, this is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. 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. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• Prevention and Early Treatment of Acute Lung Injury Network - Vitamin D to Improve Outcomes by Leveraging Early Treatment (PETAL VIOLET-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from PETAL VIOLET include Plasma and Whole Blood. Please note that use of biospecimens in genetic research is subject to a tiered consent. Specimens may not be used to produce commercial products.Objectives: To evaluate the effect of short-term vitamin D supplementation on mortality among critically ill patients with a vitamin D deficiency.Background: Observational data indicate that vitamin D deficiency is common among critically ill patients and constitutes a potentially modifiable risk factor associated with longer lengths of stay in the hospital and intensive care unit (ICU), lung and other organ injury, prolonged mechanical ventilation, and death. In a previous phase 2 trial, vitamin D supplementation administered to vitamin D-deficient, critically ill patients was associated with lower observed mortality than placebo at 28 days and at 6 months. Because of the need for a larger, phase 3 trial, the PETAL-VIOLET study was initiated to determine if early administration of high-dose vitamin D3 would reduce all-cause mortality among critically ill patients with a vitamin D deficiency.Participants: 1360 patients underwent randomization, 690 were assigned to the vitamin D group and 668 were assigned to the placebo group. Of the 1078 patients confirmed to have a vitamin D deficiency by liquid chromatography-tandem mass spectrometry (LC-MS-MS), 538 had been assigned to the vitamin D group and 540 had been assigned to the placebo group.Design: PETAL-VIOLET was a multicenter, double-blind, placebo-controlled, phase 3 trial. Patients were enrolled within 12 hours after the clinician's decision to admit the patient to the ICU from the emergency department, hospital ward, operating room, or outside facility. Patients were tested for vitamin D deficiency, with a threshold of plasma 25-hydroxyvitamin D level of less than 20 ng per milliliter. Patients were randomly assigned in a 1:1 ratio, stratified according to site, to receive either a single enteral (administered orally or through a nasogastric or orogastric tube) dose of 540,000 IU of vitamin D3 or matched placebo, in liquid form, administered within 2 hours after randomization.Conclusions: After the first interim analysis, the data and safety monitoring board recommended that the trial be stopped for futility.A single 540,000 IU enteral dose of vitamin D3 administered early during critical illness rapidly corrected vitamin D deficiency but did not provide an advantage over placebo with respect to mortality or other clinically important end points.

  Study phs003879

• Exploratory novel biomarker and resistance mechanism of milademetan, an MDM2 inhibitor, in MDM2 amplified intimal sarcoma from an open-label phase 1b/2 trial ���NCCH1806/MK004���

  Amplified Murine double minute 2 (MDM2) is found in > 70% of intimal sarcoma, known as one of the ultra-rare sarcomas. Milademetan (DS3032, RAIN-32) is a novel, specific, small-molecule MDM2 inhibitor that disrupts MDM2 and the tumor suppressor protein p53 interactions in tumor cells. We conducted a phase 1b/2 trial (Trial registration No: JMA-IIA00402) in patients with amplified MDM2 wild-type TP53 intimal sarcoma as a sub-study under the nationwide large registry for rare cancers in Japan (MASTERKEY Project). Eleven patients were enrolled, and ten were included in the efficacy analysis. Two (20%) patients had durable responses for > 15 months. Milademetan provided clinical benefits in patients with amplified MDM2 intimal sarcoma. Predictive biomarkers other than amplified MDM2 and acquired resistance mechanisms for milademetan are unknown. Whole-exome and RNA sequencing analyses of pre-treatment tissue samples were conducted to identify determinants of response. Genomic alterations were analyzed for 10 patients, and gene expression was analyzed for 9 patients using their pre-treatment tissue samples. Targeted sequencing of cell-free DNA (cfDNA) samples (liquid biopsy) was also conducted sequentially at three points [before treatment with milademetan (baseline), at Cycle 2 Day1, and at the time of disease progression] to identify determinants of response and resistance. From whole-exome and RNA sequencing analyses of pre-treatment tissue samples, we could not find any molecular pathways associated with the anti-tumor activity of milademetan. Focusing on 8 genes (CDK4, CDKN2A, CDKN2B, EGFR, ERBB3, MDM2, PDGFRA, TP53) known to be frequently affected in intimal sarcoma and 10 genes (AKT1, ATM, BBC3, CDKN1A, CDKN1C, CHEK2, MDM4, PMAIP1, PPM1D, TWIST1) reported to be associated with MDM2 inhibitor responses; we found that anti-tumor activity correlated with amplified TWIST1 (p-value = 0.028) and negatively with CDKN2A loss (p-value = 0.071). Eight of the 10 patients had their cfDNA collected sequentially at baseline, at Cycle 2 Day1 and disease progression; however, one did not consent to the exploratory analysis study using cfDNA, and one had cfDNA collected at baseline but not at disease progression due to ongoing treatment. Of the eight patients, TP53 mutations in cfDNA were detected in one and five patients at baseline and disease progression, respectively. The cfDNA allele frequency of TP53 mutations increased with disease progression. CDKN2A loss and amplified TWIST1 could be associated with the anti-tumor activity of milademetan in patients with MDM2 amplified intimal sarcoma. Acquired TP53 mutations were detected in sequential liquid biopsies as loss-of-function mutations, and these TP53 mutations might compromise the anti-tumor activity.

  Study JGAS000619

• Best Practices for DACs

  Best Practices Data access committees (DACs) are institutional safeguards responsible for ensuring a balance between data protection and accessibility. However, there are no procedural standards that apply across DACs, which can lead to inconsistencies in their reviews and compromise their quality and effectiveness. Standardising DAC processes can foster trust and mutual recognition, paving the way for greater coordination, collaboration, and delegation between DACs and other oversight bodies to improve the efficiency of data access without sacrificing protection. To provide guidance, the following are suggestions for actions a DAC might take upon receiving a data access request: Aim to respond to all initial requests in less than 2 weeks. Failure to do so may result in follow-up emails from the EGA, journals, and ultimately dataset withdrawal. First, check that the data/EGAD number is consistent with your data submission. In other words, ensure that the requester has contacted the correct DAC. Verify that the user will be using the data within the terms of consent by asking them to sign up to the terms within the DAA. Ensure that data users who are granted access to the data comply with the terms of a Data Access Agreement (DAA) and to use the data only in approved ways. Look for an institutional email address for the requester. Search for evidence that the requester is "appropriately qualified/bona fide" for using the data, for example on PubMed, Research Gate, LinkedIn, etc. Confirm that the affiliated organisation is real and that the requester still works there. Inquire with the requester about who should have accounts created at the EGA under the terms of the agreement. If a negative decision is made, promptly communicate it to the requester and support it with the terms of the Data Access Policy that the requester has not met or cannot meet. Keep the information in the DAC up to date. If you leave your institution and are unable to manage data access requests on their behalf, you should add your replacement as a new contact in the DAC. Assist with data access requests for any further questions related to your data. The EGA can only check information that has been deposited in the repository. If the user has a specific question that the EGA cannot answer, we will redirect the user to the DAC. All the EGA studies and datasets referenced in a publication under your DAC must be publicly searchable on the EGA website before the paper is released. If possible, describe the permitted purposes for subsequent research projects, including associated limits and conditions, for all resources hosted in a repository using a common ontology, such as the GA4GH Data Use Ontology (DUO). It is considered best practice to try and release data only to those with an institutional email address. This gives reassurance to the EGA, research participants, and the general public that an appropriate individual is accessing and using the data. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security.

  Documentation access/data-access-committee/best-practices

• The NCAA-DoD Concussion Assessment, Research, and Education (CARE) Consortium

  Study Description: Established in 2014, the U.S. Department of Defense (DoD) and the National Collegiate Athletic Association (NCAA) funded the Concussion Assessment, Research and Education (CARE) Consortium to inform science, clinical care and public policy related to concussion and repetitive head impact exposure (HIE) in U.S. Military Service Academy (MSA) cadets and collegiate student-athletes. The primary aims of the initial study were to: (1) establish a multisite research consortium to characterize the effects of concussion and repetitive head impact exposure on brain health, (2) characterize the clinical sequelae and natural history of concussion, (3) characterize the effect of concussion and repetitive head impacts on brain structure and brain function. The 30 member institutions of the CARE Consortium (26 civilian universities/colleges and 4 military service academies) agreed to invite all varsity athletes to undergo multimodal clinical assessments at preseason baseline, and at 5 additional timepoints after concussion diagnosis. At the service academies, non-varsity athlete cadets/midshipmen were also studied. Assessment Categories At Each Time Point Baseline: • Demographics • Personal and Family History • Neurocognitive Assessment • Neurological Status • Postural Stability • Symptoms ACUTE POST-INJURY FOLLOW-UP (CARE 1.0) (24 hrs; 48hrs; Asymptomatic; Unrestricted Return to Play (RTP); 6 mos) • Neurocognitive Assessment • Neurological Status • Postural Stability • Symptoms CUMULATIVE & PERSISTENT EFFECTS. (CARE 2.0) In-Person Exit Visit; Post-grad Online • Demographics • Personal and Family History • Neurocognitive Assessment • Neurological Status • Postural Stability • Symptoms • PROs • MROs (at academies only) LONG-TERM EFFECTS (CARE/SALTOS Integrated (CSI))Annual Online Assessment (at MSAs only); In-Person Research Visits • Demographics • Symptoms • Psychological health • PROs • MROs (at academies only)A subset of athletes at 6 of the CARE institutions underwent additional assessments including multimodal MRI, proteomic and genomic biomarker characterization, and head impact quantification (helmet-based sensors). Assessment time points for the ARC/pARC Neurocognitive and Behavioral Testing was done at Baseline, <48hrs Post-Injury, Cleared for Return to Play Progression (Asymptomatic), Unrestricted Return to Play, 7 days following Return to Play, and at >60 days after final game of junior year until end of collegiate career.Blood biomarker collection was done at Baseline, <48hrs Post-Injury, Cleared for Return to Play Progression (Asymptomatic), 7 days following Return to Play, and at >60 days after final game of junior year until end of collegiate career. Multi-modal MRI studies were done at Baseline, <48hrs Post-Injury, Cleared for Return to Play Progression (Asymptomatic), 7 days following Return to Play, and at >60 days after final game of junior year until end of collegiate career. However, please note that only athletes from one study performance site completed a multi-modal MRI study at baseline.

  Study phs002175

• 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

• Localised colon cancer WES study contaning WBCs, tissue and plasma samples at different time points

  Decision-making based on circulating tumor DNA (ctDNA) status following curative-intent surgery in stage II-III colon cancer (CC) is where the strongest clinical evidence lies. Commercial or academic assays are predominantly used to detect ctDNA, aiming to reveal minimal residual disease (MRD) and identify patients at high risk of recurrence. However, while current tests demonstrate exceptional specificity in detecting MRD among patients who later experience recurrence, their sensitivity is notably limited, particularly immediately after surgery when decisions regarding ACT are crucial. Additionally, up to 70% of patients with stage II-III colon cancer fail to clear ctDNA after standard adjuvant treatment (ACT) and subsequently experience recurrence. In this study, we performed whole exome sequencing (WES) of ctDNA at different timepoints in relapsed CC patients using two independent cohorts, alongside transcriptomic and proteomic analyses of metastases to gain a deeper understanding of progression mechanisms. An assay based on a tumor-agnostic approach from WES could enhance sensitivity in detecting MRD disease compared to current assays. Immune evasion appears to be the primary driver of progression in the localized CC setting, facilitated by a functional mutational burden at relapse. This suggests that immunotherapy could extend its efficacy to patients with microsatellite stability, broadening treatment options. Moreover, organoid modeling affirms the promising potential of targeted therapy to surpass conventional treatments in eradicating MRD.

  Study EGAS50000000204

• Cincinnati Children's Hospital Medical Center (CCHMC) - eMERGE Phase IIIA Data

  This submission includes genotyping or sequencing data from separate cohorts, each is described in separate paragraphs below. Extreme early onset obesity Obesity is a serious epidemic condition and on the rise in the United States. Today, nearly one out of three children is overweight or obese in this country. According to the Center for Disease Control, 35.7% of American adults and 17% of American children are obese. The medical costs associated with obesity are estimated to be in the billions. Without a doubt, interplay of additive genetic effects and common environmental effects influence this complex disease. However, despite being exposed to so-called "obesogenic environment", a large proportion of the population remains of normal weight. These observations suggest that innate, non-environmental, factors make some individuals more susceptible to obesity providing support for biological mechanisms, and thus genetic factors, to underlie the individual's response to the obesogenic environment. In young children with severe obesity the relative role of genetics and in utero programming are likely to outweigh the short duration of environmental and lifestyle exposures. This group is therefore an ideal one to study as they are likely enriched for variants that influence the risk of developing obesity. The purpose of this project is to further study and understand obesity in childhood and to develop a repository of samples for future studies into obesity. Eosinophilic Esophagitis (EoE) Eosinophilic Esophagitis (EoE) is one of the manifestations of eosinophilic gastrointestinal inflammation which have profound effects on a patient's health and development. Results of epidemiologic studies performed through our center demonstrate that eosinophil-associated gastrointestinal disease is not an uncommon entity. While the epidemiology of eosinophilic esophagitis has not been thoroughly studied until recently, there appears to be a significant increase in the diagnosis of EoE in the last decade. Based on our research, this mainly reflects increased disease recognition, but there is also a bona-fide increase in disease incidence which coincides with the increasing incidence of asthma and allergic diseases in the industrialized world. In addition, many patients with intractable symptoms thought in the past to represent atypical GERD or other disorders are now being recognized as having EoE. Diagnosis of EoE requires endoscopy and biopsies to document the characteristic histologic findings of esophageal eosinophilia. In general, this study proposed to elucidate the mechanisms underlying eosinophil growth, survival, migration, and function, and to investigate and further characterize the pathophysiology of, clinical manifestations of, and spectrum of disease severity of eosinophilic esophagitis in humans. The de-identified genotyping and genome wide association data generated as part of this research will be used for further genome research. Familial Sample Repository (FSR) and Directed Sample Repository (DSR) De novo mutations could cause many diseases, which has been demonstrated in mental retardation, autism and many rare genetic disorders. Family-based studies have a variety of advantages over case/control studies, including the elimination of analysis artifacts related to population stratification, the detection of genes that act through a recessive mechanism of inheritance and validation that the trait is not transmitted from a parent, something not possible using a case/control design. Additionally, DNA from families can be used to identify de novo mutations suggesting strong candidate causal polymorphisms. For this project, samples will be collected from families on an on-going basis. Families may be recruited because the patient either has a disease which is thought to be of genetic origin or from the general patient population to serve as controls or future identified diseases. Some phenotypes under study include fibroblastic rheumatism, diaphragmatic hernia, polymicrogyria, severe congenital neutropenia, primary sclerosing cholangitis and staph infection. CLRR-Cincinnati Lupus Registry and Repository Systemic lupus erythematosus (SLE) is a complex, partially understood autoimmune disorder. Genetic origins for SLE are supported by high heritability (> 66%), familial aggregation, increased monozygotic twin concordance, genetic linkages, and candidate gene genetic association, including HLA genes, Fc receptors, and complement components. Relevant environmental factors likely include infections (Epstein-Barr virus), therapeutics, personal habits (smoking), and diet. To continue a research resource facility for collection of well-characterized pedigrees containing a proband with systemic lupus erythematosus we develop this repository. Juvenile Idiopathic Arthritis (JIA) Juvenile Idiopathic Arthritis (JIA) is a debilitating complex genetic disorder characterized by inflammation of the joints and other tissues and shares histopathological features with other autoimmune diseases. It is considered complex genetic traits. There are more than 50,000 children with JIA in the USA, approximately 1 per 1000 births, which is about the same incidence as juvenile diabetes. It is believed that genes in the major histocompatibility complex (MHC) play a role in defining genetic risk, and it can be hypothesized that loci in other chromosomal regions are involved in conferring risk in JIA. These candidate chromosomal regions can be identified using genome-wide association analyses. The long-term goal is a comprehensive understanding of the genetic basis of these disabling arthropathies for which the molecular basis is not presently understood. These data will contribute to a national resource for the study of autoimmunity in children. Better Outcomes for Children-Cytogenetics Since 2007, more than 4000 samples, enriched with various rare or common genetic diseases as well as specific chromosomal abnormalities such as deletions and duplications have been genotyped for the purpose of subsequent GWAS and Phewas analyses and uncovering main genetic effects.

  Study phs001011

• Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA)

  The Resource for Genetic Epidemiology Research on Aging (GERA) Cohort was created by a RC2 "Grand Opportunity" grant that was awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics (AG036607; Schaefer/Risch, PIs). The RC2 project enabled genome-wide SNP genotyping (GWAS) to be conducted on a cohort of over 100,000 adults who are members of the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), and participating in its RPGEH. The purpose of the RPGEH is to facilitate research on the genetic and environmental factors that affect health and disease by linking together clinical data from electronic health records, survey data on demographic and behavioral factors, and environmental data from various sources, with genetic data derived from biospecimens collected from participants. At the time of the award of the RC2 project in late 2009, the RPGEH had established a cohort of about 140,000 individuals who had answered a detailed survey, provided saliva samples for extraction of DNA, and given broad consent for the use of their data in studies of health and disease. To maximize the diversity of the resulting sample, the GERA cohort was formed by including all racial and ethnic minority participants with saliva samples (N = 20,925; 19%); the remaining participants were drawn sequentially and randomly from white non-Hispanic participants (89,341; 81%). A total of 110,266 participant samples were included to ensure that at least 100,000 were successfully assayed. The resulting GERA cohort is 42% male, 58% female, and ranges in age from 18 to over 100 years old with an average age of 63 years at the time of the RPGEH survey (2007). The sample is ethnically diverse, generally well-educated with above average income. Approximately 69% of the participants are married or living with a partner. Length of membership in KPNC averages 23.5 years. UCSF and RPGEH investigators worked with the genomics company Affymetrix to design four custom microarrays for genotyping each of the four major race-ethnicity groups included in the GERA Cohort, described in detail in Hoffmann et al., 2011a and 2011b. Following genotyping and quality control procedures, and after removal of invalid, discordant, or withdrawn samples, about 103,000 participants were successfully genotyped. The resulting genotypic data were linked to survey data and data abstracted from the electronic medical records. As described below, all RPGEH participants were mailed new consent forms with explicit discussion of the placement of data in the NIH-maintained dbGaP. About 77% of participants returned completed consent forms, resulting in a final sample size of 78,486 participants in the GERA Cohort with data for deposit into dbGaP. Origins of the RPGEH GERA Cohort The goal in creating the RPGEH GERA cohort was to create a large, multiethnic, and comprehensive population-based resource for research into the genetic and environmental basis of common age-related diseases and their treatment, and factors influencing healthy aging and longevity. The GERA Cohort consists of a diverse cohort of more than 100,000 adults who are members of the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), and participating in its Research Program on Genes, Environment and Health (RPGEH). KPNC is an integrated health care delivery system with a population of about 3.3 million people in northern California. The membership of KPNC is representative of the general population in the 14 county area in which facilities are located, although the membership is underrepresented for the extremes of income at both ends of the spectrum. The RPGEH utilizes the longitudinal electronic health records (EHR) of KPNC to obtain clinical, laboratory, imaging and pharmacy information on all cohort members, to which personal demographic, behavioral and health characteristics have been added through member surveys. The GERA Cohort comprises a subsample of the RPGEH participant cohort, and was created through the RC2 award from the NIA, NIMH, and NIH Common Fund as described above. GERA Study Design The GERA Cohort is a subsample, as described above, of the longitudinal cohort enrolled in the Kaiser Permanente RPGEH. The RPGEH cohort includes about 400,000 survey participants of whom about 200,000 have provided broad consent and a sample of saliva or blood for use in studies of genetic and environmental factors in health and disease. The GERA Cohort was developed from a mailed survey sent to all adult members of KPNC who had been members for two years or more in 2007. All survey respondents were contacted and asked to complete a consent form; those who completed consent forms were asked to provide a saliva sample. Additional male participants were added to the RPGEH through inclusion of the Northern California sample of the California Men's Health Study (CMHS) cohort of about 40,000 men from KPNC, ages 45-69 years old at the time of the CMHS survey in 2002-2003. The CMHS participants contributed about 15,400 saliva samples to the RPGEH and were eligible for inclusion in the GERA Cohort. CMHS participants were included according to the same sampling design as for the RPGEH cohort as a whole. Specifically, all minority participants were selected for inclusion in order to maximize representation of minorities in the GERA Cohort, and Non-Hispanic White participants were selected at random to complete the sample of 110,266 GERA Cohort participants. GERA Genotypic Data High-density genotyping was conducted at UCSF using custom designed Affymetrix Axiom arrays, as described in Hoffmann et al. (2011a; 2011b). To maximize genome-wide coverage of common and less common variants, four specific arrays were designed for individuals of Non-Hispanic White (EUR), East Asian (EAS), African-American (AFR), and Latino (LAT) race/ethnicity. There was broad overlap among the SNPs on the arrays, which were designed using a hybrid greedy imputation algorithm (Hoffmann et al., 2011b) applied to genotype information validated by Affymetrix from the 1000 Genomes Project. However, in order to capture low frequency variants specific to particular race-ethnicity groups, SNP content varies between arrays. A more detailed description of the process of genotyping and results is included in Genotyping of DNA Samples. Description of the analyses of population structure and development of principal components for adjustment of population structure is included in Population Structure Analysis. GERA Phenotypic Data RPGEH and CMHS Survey Data. The sources of data on demographic and behavioral factors deposited in dbGaP for the GERA Cohort are the RPGEH and CMHS surveys. Data on common demographic factors such as gender, race/ethnicity, marital status, and education and on behavioral factors such as smoking, alcohol consumption, and body mass index, have been cleaned, edited, reconciled between the two surveys, and compiled into summary indices, where appropriate, for deposition into dbGaP. A more complete description of the survey variables is included in Survey Variables Documentation. Please note that the terms of use of the GERA Cohort Data, as specified in the Data Use Certification (DUC), prohibit the use of survey variables as outcomes in analyses. For example, a genome-wide association study (GWAS) of education or smoking is not permitted as specified by the DUC. Only health conditions can be used as outcome variables in analyses. Health Conditions derived from Kaiser Permanente Electronic Medical Records. Data on the occurrence of health conditions in participants in the GERA Cohort have been derived from summarizing ICD-9 coded diagnoses in Kaiser Permanente's electronic medical records. An algorithm that aggregates specific ICD-9 codes into appropriate diagnostic groups for selected conditions is applied to outpatient and inpatient databases; see Disease and Conditions Definitions Documentation for details. The criterion for including a condition as "present" for a participant is the occurrence of two or more diagnoses within a diagnostic category occurring on separate days. Two or more is used as the criterion in order to reduce false positives due to mistakes or rule-out diagnoses. When compared with validated disease registries, the criterion of 2+ diagnoses yields high specificity and good sensitivity. ICD-9 codes in the electronic records are specified in several ways. For outpatient visits occurring during the period 1995 to 2006, diagnoses were assigned by the treating physician who endorsed specific diagnoses on an optically scanned list that varied by specialty. Beginning in 2006 with the advent of an integrated, fully electronic medical record, outpatient diagnoses are made by physicians/ providers using a pull down menu. Discharge diagnoses from inpatient stays are specified by physicians and coded by specially trained coders. Databases of ICD-9 codes for diagnoses assigned at outpatient visits, or as one of the discharge diagnoses following inpatient stays, are complete and available for all KPNC members dating back to 1995. Although the average length of KPNC membership among GERA cohort members is 23.5 years in 2007, not all have been members since 1995, so the history for some conditions, such as those that are not chronic or recurrent, may not be complete for all cohort members. The year of first membership in KPNC is included as a variable in the list of survey variables, enabling investigators to estimate the number of years of observation of each Cohort member. RPGEH Access and Collaborations Website and Procedures The RPGEH maintains a web portal for inquiries and applications for collaboration and access to data. The url is: https://rpgehportal.kaiser.org/. RPGEH has an application process and an Access Review Committee that reviews applications for collaboration and use. For more details, please contact RPGEH through the website.

  Study phs000674

• 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

• Health and Retirement Study (HRS)

  Introduction to V2: This data release comprises data from the V1 release combined with approximately 3,000 additional samples, collected during the HRS 2010 field period. The 2010 data include samples from a random half of the new cohort enrolled in 2010 along with a significant expansion of the minority sample. Description: The University of Michigan Health and Retirement Study (HRS) is a longitudinal panel study that surveys a representative sample of approximately 20,000 people in America over the age of 50 every two years. Supported by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration, the HRS explores the changes in labor force participation and the health transitions that individuals undergo toward the end of their work lives and in the years that follow. The study collects information about income, work, assets, pension plans, health insurance, disability, physical health and functioning, cognitive functioning, and health care expenditures. Through its unique and in-depth interviews, the HRS provides an invaluable and growing body of multidisciplinary data that researchers can use to address important questions about the challenges and opportunities of aging. Because of its innovation and importance, the HRS has become the model and hub for a growing network of harmonized longitudinal aging studies around the world. Origins of the HRS. As the population ages it is increasingly important to obtain reliable data about aging and topics that are relevant to a range of policy issues in aging. To address this need, the National Institutes on Aging (NIA) established a cooperative agreement with the University of Michigan Institute for Social Research to collect such data. The HRS launched data collection in 1992 and has re-interviewed the original sample of respondents every two years since then. By adding new cohorts and refreshing the sample, the HRS has grown to become the largest, most representative longitudinal panel study of Americans 50 years and older. HRS Study Design. The target population for the original HRS cohort includes all adults in the contiguous United States born during the years 1931-1941 who reside in households, with a 2:1 oversample of African-American and Hispanic populations. The original sample is refreshed with new birth cohorts (51-56 years of age) every six years. The sample has been expanded over the years to include a broader range of birth cohorts as well. The target population for the AHEAD survey consists of United States household residents who were born in 1923 or earlier. Children of the Depression (CODA) recruits households born 1924-1930, War Babies 1942-47, Early Boomers 1948-53, and Mid-Boomers 1954-59. Data collection includes a mixed mode design combining in-person, telephone, mail, and Internet. For consenting respondents, HRS data are linked at the individual level to administrative records from Social Security and Medicare claims. Genetic Research in the HRS. The HRS has genotyped 2.5 million single nucleotide polymorphisms (SNPs) on respondents using Illumina's Human Omni2.5-Quad (Omni2.5) BeadChip. The genotyping was performed by the NIH Center for Inherited Disease Research (CIDR). Saliva was collected on half of the HRS sample each wave starting in 2006. In 2006, saliva was collected using a mouthwash collection method. From 2008 onward, the data collection method switched to the Oragene kit. Saliva completion rates were 83% in 2006, 84% in 2008, and 80% in 2010 among new cohort enrollees. HRS Phenotypic data. Phenotypic data are available on a variety of dimensions. Health measures include physical/psychological self-report, various health conditions, disabilities, cognitive performance, health behaviors (smoking, drinking, exercise), physical performance and anthropomorphic measures, and biomarkers (HbA1c, Total Cholesterol, HDL, CRP, Cystatin-C). Data are also available on health services including utilization, insurance and out-of-pocket spending with linkage to Medicare records. Economic measures include employment status/history, earnings, disability, retirement, type of work, income by source, wealth by asset type, capital gains/debt, consumption, linkage to pensions, Social Security earnings/benefit histories. There is also extensive information on family structure, proximity, transfers to/from of money, time, social and psychological characteristics, as well as a wide range of demographics. Performance on a cognitive test combining immediate and delayed word recall was selected as an example trait for the dbGaP data release. In the immediate word recall task the interviewer reads a list of 10 nouns to the respondent and asks the respondent to recall as many words as possible from the list in any order. After approximately five minutes of asking other survey questions, the respondent is asked to recall the nouns previously presented as part of the immediate recall task. The total recall score is the sum of the correct answers to these two tasks, with a range of 0 to 20. Researchers who wish to link to other HRS measures not in dbGaP will be able to apply for access from HRS. A separate Data Use Agreement (DUA) will be required for linkage to the HRS data. See the HRS website (http://hrsonline.isr.umich.edu/gwas) for details.

  Study phs000428

• Better Outcomes for Children: GWAS from Cincinnati Children's Hospital Medical Center (CCHMC) - eMERGE Phase II data - (dbGaP Deposit 1)

  This submission includes genotyping data from 6 separate cohorts (named A-E here), each is described in separate paragraphs below. Cohort A) Autism is a neurodevelopmental disorder that affects 1.5 million people in the United States, at an estimated lifetime cost of $4 million. It is part of Pervasive Developmental Disorder characterized by impairments in communication, language and reciprocal social interaction, and by unusual patterns of restricted and repetitive interests or behaviors. Autism, or autistic disorder, is the most severe form of the disease which has a wide range of symptom severity encompassed by the more inclusive term, Autism Spectrum Disorder (ASD). The prevalence of Autistic Disorder is currently estimated to be 4 per 1000, affecting 4 times more boys than girls. Susceptibility to autism is clearly attributable to genetic factors but the extensive clinical and genetic heterogeneity within the disorder have hindered efforts at genetic dissection. Identifying the genetic variants that contribute to this highly heritable disease is crucial to advancing research on autism. Reported concordance rates among monozygotic twins range from 60% − 90% and heritability of autism has been estimated to be 90% or greater. Identifying genetic variants that contribute to this highly heritable disease is crucial to advancing research on autism. Cohort B) The long-term objective of this research is to determine the molecular genetic basis of Cardiovascular (CV) disease in the young; this study seeks to identify gene mutations that cause "structural heart defects". Every year in the USA, 32,000 individuals are born with CV malformations; approximately 20% die during the first year of life. Another 30,000 young people develop a disturbance of cardiac rhythm or conduction. In addition, the life expectancy of another 40,000 young people is shortened by other forms of CV disease. It is becoming more apparent that in spite of the success in diagnosis and treatment, very little is known about the causes of CV disease in the young; understanding the causes will permit insight into the pathophysiologic basis of disease and allow definition of disease risk - two critical elements for disease prevention. Disease prevention and/or risk modification promises to be the new frontier in the management of CV disease in the young. Cohort C) Eosinophilic Esophagitis (EE) is one of the manifestations of eosinophilic gastrointestinal inflammation which have profound effects on a patient's health and development. Results of epidemiologic studies performed through our center demonstrate that eosinophil-associated gastrointestinal disease is not an uncommon entity. While the epidemiology of eosinophilic esophagitis has not been thoroughly studied until recently, there appears to be a significant increase in the diagnosis of EE in the last decade. Based on our research, this mainly reflects increased disease recognition, but there is also a bona-fide increase in disease incidence which coincides with the increasing incidence of asthma and allergic diseases in the industrialized world. In addition, many patients with intractable symptoms thought in the past to represent atypical GERD or other disorders are now being recognized as having EE. Diagnosis of EE requires endoscopy and biopsies to document the characteristic histologic findings of esophageal eosinophilia. In general, this study proposed to elucidate the mechanisms underlying eosinophil growth, survival, migration, and function, and to investigate and further characterize the pathophysiology of, clinical manifestations of, and spectrum of disease severity of eosinophilic esophagitis in humans. Cohort D) Juvenile idiopathic arthritis (JIA) is a debilitating complex genetic disorder characterized by inflammation of the joints and other tissues and shares histopathological features with other autoimmune diseases. It is considered a complex genetic disorder. There are more than 50,000 children with JIA in the USA, approximately 1 per 1000 births, which is about the same incidence as juvenile diabetes. It is believed that genes in the major histocompatibility complex (MHC) play a role in defining genetic risk, and it can be hypothesized that loci in other chromosomal regions are involved in conferring risk in JIA. These candidate chromosomal regions can be identified using genome wide association analyses. The long-term goal is a comprehensive understanding of the genetic basis of these disabling arthropathies for which the molecular basis is not presently understood. The results of recent GWAS for Caucasian JIA were recently published in Arthritis Rheum. 2012 Feb 21 (Published online, PMID=22354554). Cohort E) Childhood Absence Epilepsy (CAE) is characterized by very frequent (several to many per day) absence seizures in an otherwise normal child with an EEG usually demonstrating 3 Hz bilateral, synchronous, symmetrical spikewaves pattern with normal background activity. CAE occurs in 10% to 15% of all children with epilepsy with an annual incidence of 6.3 to 8/100,000 in children <15 years of age. Females are more affected than males. Epilepsy onset is typically between the ages of 4 to 8 years with a peak incidence of 6 to 7 years. CAE does not start after age 13 years. The primary objective of this study is: To identify the optimal anticonvulsant (i.e. the antiepileptic drug (AED) with highest rate of seizure control and lowest incidence of treatment limiting toxicity) used for the initial treatment of children with Childhood Absence Epilepsy (CAE). The secondary objectives are: To determine the pharmacogenetic and other non-heritable factors underlying the interindividual variation in anticonvulsant response efficacy and toxicity. To define and contrast the effects of ethosuximide, lamotrigine, and valproic acid monotherapy on cognition (attention), behavior and quality of life in children with Childhood Absence Epilepsy. Cohort F) Cincinnati Controls. These are local healthy children selected to be representative of the population of Cincinnati with respect to gender and ancestry. Cohort G) Cytogenetics cases are ascertained by the clinical request to obtain cytogenetic analysis. The platform used included genotyping data which are then available for genome wide association analysis

  Study phs000494

• CIDR: NCI Genome Wide Predictors of Survival in Colorectal Cancer

  CIDR/Molecular Correlates: This study genotyped archived blood samples from colorectal cancer cases participating in clinical trials. The goals of this project were to identify genetic variants associated with survival outcomes treatment and treatment-related severe adverse events among patients with colorectal cancer. Another goal was to examine the impact of adding information on germline genetic loci to existing prognostic models. MOSAIC: Multicenter International Study of Oxaliplatin/ 5FU-LV in the Adjuvant Treatment of Colon Cancer (MOSAIC). A randomized, open label efficacy trial to evaluate the FOLFOX regimen versus LV5FU2 in the adjuvant treatment of stage II and III colon cancer. The primary end point was disease-free survival. CPT.ES1.604: Randomized phase 3 study of weekly irinotecan plus high-dose 5-fluorouracil (FUIRI) versus biweekly irinotecan plus 5-fluorouracil/leucovorin (FOLFIRI) as first-line chemotherapy for patients with metastatic CRC. The aim of this study was to demonstrate that a regimen without leucovorin (LV) (FUIRI) is not inferior to the standard FOLFIRI (response rate). 03-TTD-01: The purpose of this phase III, multicenter, randomized, open-label study is to evaluate the safety and efficacy of combination therapy with capecitabine and oxaliplatin (XELOX) vs. oxaliplatin and 5-fluorouracil in continous infusion (5-FU CI) as first line treatment in advanced or metastatic colorectal cancer. The primary outcome measure is time to disease progression. Secondary outcome measures are to determine safety of combination, objective response rate, time to onset of response, duration of response, time to treatment failure, one year survival time, and overall survival time. 03-TTD-01: The purpose of this phase III, multicenter, randomized, open-label study is to evaluate the safety and efficacy of combination therapy with capecitabine and oxaliplatin (XELOX) vs. oxaliplatin and 5-fluorouracil in continous infusion (5-FU CI) as first line treatment in advanced or metastatic colorectal cancer. The primary outcome measure is time to disease progression. Secondary outcome measures are to determine safety of combination, objective response rate, time to onset of response, duration of response, time to treatment failure, one year survival time, and overall survival time. N0147: Randomized phase III clinical trial for adjuvant therapy in stage III colon cancer patients enrolled at institutions across North America. Originally designed to compare three different chemotherapy regimens, and then later expanded to evaluate adding cetuximab to each original arm (leading to six total arms). Further modification later added pre-screening for KRAS mutation status, with the resulting goal to assess the potential benefit of cetuximab added to the modified sixth version of the FOLFOX regimen (mFOLFOX6) in patients with resected stage III wild-type KRAS colon cancer. Main outcome of interest is disease-free survival in patients with wild-type KRAS, while secondary end points included overall survival and toxicity. N9741: Randomized phase III trial to compare the effectiveness of various combination chemotherapy regimens in treating patients who have advanced, recurrent, or metastatic colorectal cancer that cannot be treated with surgery or radiation therapy. C-08: A Phase III Clinical Trial Comparing Infusional 5-Fluorouracil (5-FU), Leucovorin, and Oxaliplatin (mFOLFOX6) Every Two Weeks With Bevacizumab to the Same Regimen Without Bevacizumab For the Treatment of Patients With Resected Stages II and III Carcinoma of the Colon. The primary aim of the trial was to compare the relative efficacy of mFOLFOX6 + bevacizumab with that of mFOLFOX6 alone in prolonging disease-free survival and the secondary aim was to compare the relative efficacy of mFOLFOX6 + bevacizumab with that of mFOLFOX6 alone in prolonging survival.

  Study phs001290

• T2D-GENES Project 2: San Antonio Mexican American Family Studies

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to T2D in multiple ethnic groups using next generation sequencing. T2D-GENES Project 2 is a complex pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence (WGS) information from 1,043 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas. The major objectives of this study are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. The sampled individuals are obtained from two studies: the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), collectively referred to as the San Antonio Mexican American Family Studies (SAMAFS). The strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The final data set includes whole genome sequence data for 607 individuals. After quality control, 585 sequenced individuals provide data for family based imputation, using Merlin linkage analysis software, into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. Extensive phenotype data is provided for 1048 individuals. These include 5 sequenced individuals who do not belong to any of the 20 large pedigrees. Phenotype information was collected between 1991 and 2011 in the two contributing longitudinal studies. SAFHS participants may have information from up to 5 visits, and SAFDGS participants may have up to 4 visits. The clinical variables reported are coordinated with T2D-GENES Project 1 (multi-ethnic exome sequencing) and include T2D status and age at diagnosis, glycemic traits (fasting and 2 hour glucose and insulin), blood pressure, blood lipids (total cholesterol, HDL cholesterol, calculated LDL cholesterol and triglycerides), clinical chemistry (cystatin c, glutamic acid decarboxylase antibody titer (GadAb), creatinine, adiponectin and leptin). Glycated hemoglobin (HbA1c) was not measured for these individuals and insulin C-peptide is not included in this data set. Additional phenotype data include the medication status at each visit, classified in four categories as any current use of diabetes, hypertension or lipid-lowering medications, and, for females, current use of female hormones. Anthropometric measurements include age, sex, height, weight, hip circumference, waist circumference and derived ratios. Each phenotype variable has an initial summary column containing the most recent non-missing measurement for each individual, followed by the five potential time points for each individual, the number of non-missing measurements, and the age and year for the most recent non-missing measurement. For historical reasons, the order in which variables are presented on the dbGaP web site differs from their order in the data download file. When reading the comment fields for each variable, please note that commas are omitted to support data exchange in .csv format.

  Study phs000462

• Systolic Blood Pressure Intervention Trial (SPRINT-BioLINCC)

  Available DataThe available data include all elements of the previously released SPRINT Primary Outcome Paper (SPRINT-POP) data, the full SPRINT clinical data including the MRI and MIND data, and select ancillary study data (Ambulatory Blood Pressure Monitoring, APOL1, Acute Kidney Injury, ASK, Heart, FAST, PWV, Renal Resistance, Biomarkers, Plasma AD).ObjectiveThe Systolic Blood Pressure Trial (SPRINT) was conducted to test the hypothesis that treating systolic blood pressure to a target of less than 120 mm Hg, as compared to a target of less than 140 mm Hg, would reduce the incidence of cardiovascular disease.BackgroundHypertension is a highly prevalent condition among adults and is a leading risk factor for myocardial infarction and stroke. Further, isolated systolic hypertension is the most common form of hypertension in adults over 50 years of age. Observational studies have shown a monotonic increase in cardiovascular risk with systolic blood pressures above 115 mm Hg; however, general population clinical trials have only documented the benefits of lowering systolic blood pressure to a target of 150 mm Hg. A 2007 expert panel sponsored by the National Heart, Lung, and Blood Institute designated the hypothesis that lowering the systolic blood pressure goal to a level SubjectsA total of 9361 participants were enrolled, with 4,678 randomized to the intensive-treatment group and 4,683 randomized to the standard-treatment group.DesignSPRINT was a randomized, single blinded (outcome adjudicators were blinded to treatment assignment) treatment trial with participants randomized to a systolic blood-pressure target of either less than 140 mm Hg (the standard-treatment group) or less than 120 mm Hg (the intensive-treatment group). Following randomization, baseline hypertensive regimens were adjusted in accordance with study treatment algorithms established for each group. The study formulary included all major classes of antihypertensive agents. Investigators could prescribe other antihypertensive medications, but the use of drug classes with the strongest evidence for reduction in cardiovascular outcomes was encouraged. This included thiazide-type diuretics as the first-line agent, loop diuretics for participants with advanced chronic kidney disease, and beta-adrenergic blockers for participants with coronary artery disease. Medications for participants in the intensive-treatment group were adjusted on a monthly basis to target a systolic blood pressure of less than 120 mm Hg. Medications for participants in the standard-treatment group were adjusted to target a systolic blood pressure of 135 to 139 mm Hg, and the dose was reduced if systolic blood pressure was less than 130 mm Hg on a single visit or less than 135 mm Hg on two consecutive visits. Lifestyle modification was encouraged as part of the management strategy.Participants were seen monthly for the first 3 months and every 3 months thereafter. Demographic data were collected at baseline. Clinical and laboratory data were obtained at baseline and every 3 months thereafter. A structured interview was used in both groups every 3 months to obtain self-reported cardiovascular disease outcomes. Medical records and electrocardiograms were obtained for documentation of events. Incidences of hypotension, syncope, injurious falls, electrolyte abnormalities, and bradycardia that were evaluated in an emergency department were included in adverse event reporting. Occurrences of acute kidney injury or acute renal failure requiring hospitalization were also monitored. The primary outcome was a composite outcome of myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes.ConclusionsThe blood pressure intervention was stopped in August of 2015 (median follow-up of 3.26 years) after the cardiovascular outcome results exceeded the boundary for efficacy at two consecutive time points. Compared with a systolic blood pressure target of less than 140 mm Hg, an intensive systolic blood pressure target of 120 mm Hg resulted in lower rates of fatal and nonfatal major cardiovascular events and death from any cause. Significantly higher rates of some adverse events were observed in the intensive-treatment group.

  Study phs003483

• Systolic Blood Pressure Intervention Trial (SPRINT-Imaging)

  Available DataSystolic Blood Pressure Intervention Trial, SPRINT-Imaging, provides access to ECG signals data from the SPRINT clinical trial. The clinical phenotyping and outcomes data from the trial are associated with SPRINT-BioLINCC, phs003483.ObjectiveThe Systolic Blood Pressure Trial (SPRINT) was conducted to test the hypothesis that treating systolic blood pressure to a target of less than 120 mm Hg, as compared to a target of less than 140 mm Hg, would reduce the incidence of cardiovascular disease. BackgroundHypertension is a highly prevalent condition among adults and is a leading risk factor for myocardial infarction and stroke. Further, isolated systolic hypertension is the most common form of hypertension in adults over 50 years of age. Observational studies have shown a monotonic increase in cardiovascular risk with systolic blood pressures above 115 mm Hg; however, general population clinical trials have only documented the benefits of lowering systolic blood pressure to a target of 150 mm Hg. A 2007 expert panel sponsored by the National Heart, Lung, and Blood Institute designated the hypothesis that lowering the systolic blood pressure goal to a level SubjectsA total of 9361 participants were enrolled, with 4,678 randomized to the intensive-treatment group and 4,683 randomized to the standard-treatment group. DesignSPRINT was a randomized, single blinded (outcome adjudicators were blinded to treatment assignment) treatment trial with participants randomized to a systolic blood-pressure target of either less than 140 mm Hg (the standard-treatment group) or less than 120 mm Hg (the intensive-treatment group). Following randomization, baseline hypertensive regimens were adjusted in accordance with study treatment algorithms established for each group. The study formulary included all major classes of antihypertensive agents. Investigators could prescribe other antihypertensive medications, but the use of drug classes with the strongest evidence for reduction in cardiovascular outcomes was encouraged. This included thiazide-type diuretics as the first-line agent, loop diuretics for participants with advanced chronic kidney disease, and beta-adrenergic blockers for participants with coronary artery disease. Medications for participants in the intensive-treatment group were adjusted on a monthly basis to target a systolic blood pressure of less than 120 mm Hg. Medications for participants in the standard-treatment group were adjusted to target a systolic blood pressure of 135 to 139 mm Hg, and the dose was reduced if systolic blood pressure was less than 130 mm Hg on a single visit or less than 135 mm Hg on two consecutive visits. Lifestyle modification was encouraged as part of the management strategy. Participants were seen monthly for the first 3 months and every 3 months thereafter. Demographic data were collected at baseline. Clinical and laboratory data were obtained at baseline and every 3 months thereafter. A structured interview was used in both groups every 3 months to obtain self-reported cardiovascular disease outcomes. Medical records and electrocardiograms were obtained for documentation of events. Incidences of hypotension, syncope, injurious falls, electrolyte abnormalities, and bradycardia that were evaluated in an emergency department were included in adverse event reporting. Occurrences of acute kidney injury or acute renal failure requiring hospitalization were also monitored. The primary outcome was a composite outcome of myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes. ConclusionsThe blood pressure intervention was stopped in August of 2015 (median follow-up of 3.26 years) after the cardiovascular outcome results exceeded the boundary for efficacy at two consecutive time points. Compared with a systolic blood pressure target of less than 140 mm Hg, an intensive systolic blood pressure target of 120 mm Hg resulted in lower rates of fatal and nonfatal major cardiovascular events and death from any cause. Significantly higher rates of some adverse events were observed in the intensive-treatment group.

  Study phs003566

• Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION-BioLINCC)

  Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.ObjectivesThe HF-ACTION study examined whether exercise training reduces a composite endpoint of all-cause mortality or all-cause hospitalization for patients with left ventricular systolic dysfunction and heart failure symptoms. Background Heart failure (HF) is a major and growing cardiovascular syndrome that is the end result of many cardiovascular disorders. Although evidence-based drug and device therapies decrease mortality, hospitalizations, and HF symptoms and improve quality of life, many patients treated with these regimens often remain burdened by dyspnea and fatigue, diminished exercise tolerance, reduced quality of life, recurrent hospitalizations, and early mortality. Whereas rest was traditionally recommended for HF patients, there has been evidence that physical deconditioning may play a key role in the progression of symptoms and poor outcomes. Previous studies have shown positive effects of exercise training on exercise capacity, quality of life, and biomarkers and suggest that exercise training might improve survival and decrease HF hospitalizations. Nonetheless, there remains a safety concern regarding exercise training in HF and a large scale, multicenter, controlled clinical trial was needed to provide definitive clinical outcome data. Participants A total of 2,331 participants were randomized. 2,130 consented to share data with non-commercial entities, and 1,753 consented to share for commercial purposes. Design All participants underwent baseline cardiopulmonary exercise testing. Test results were reviewed by investigators to identify significant arrhythmias or ischemia that would prevent safe exercise training, to determine appropriate levels of exercise training, and to establish training heart rate ranges. Demographics, socioeconomic status, past medical history, current medications, physical exam, and the most recent laboratory tests were obtained prior to randomization. All participants received detailed self-management educational materials which included information on medications, fluid management, symptom exacerbation, sodium intake, and physical activity. Participants randomized to the exercise training arm first participated in a structured, group-based, supervised exercise program, with a goal of 3 sessions per week for a total of 36 sessions in 3 months. The primary exercises were walking, treadmill, or stationary cycling. Exercise was initiated at 15 to 30 minutes per session at a heart rate corresponding to 60% of heart rate reserve, and was increased after 6 sessions to 30 to 35 minutes duration and 70% of heart rate reserve. Participants began home-based exercise after completing 18 supervised sessions and were to fully transition to home exercise after 36 supervised sessions. The target training regimen for home exercise was 5 times per week for 40 minutes at a heart rate of 60% to 70% of heart rate reserve. Participants randomized to the usual care group were not provided with a formal exercise prescription. To provide comparable levels of attention from study personnel, all participants were called every 2 weeks for the first 9 months, monthly until 24 months of follow-up, and quarterly thereafter. Participants returned for clinic visits every 3 months for the first 2 years of participation and yearly thereafter for up to 4 years. Follow-up assessments included cardiopulmonary exercise testing, a 6-minute walk test, and a physical activity questionnaire. The primary end point was a composite of all-cause mortality or all-cause hospitalization. Secondary end points included all-cause mortality, the composite of cardiovascular mortality or cardiovascular hospitalization, and the composite of cardiovascular mortality or HF hospitalization. Conclusions After adjustment for highly prognostic predictors of the primary end point, exercise training was associated with modestly significant reductions for both all-cause mortality or hospitalization and cardiovascular mortality or heart failure hospitalization.

  Study phs003599

• All you need to know about our new DAC Portal v2

  An updated version to boost the management of DACs (Data Access Committees), policies, and data access requests. In September of 2023, we launched new services for all EGA users, including a DAC Portal. Since then, our team has been working to refine it and improve the user experience. The version 2 of our DAC Portal is now ready, with a range of new features aimed at streamlining your workflow and boosting productivity. If you are interested in learning more about the latest version of our DAC Portal, then you are in the right place! In this post, you will find information about the implemented features and more. The full documentation is available on our website. Moreover, we highly recommend exploring the DAC Portal through the Take the Tour. This said, let's get cracking! Customise your data access requests table This table offers the possibility to tailor information about the data access requests by making visible or hiding different columns regarding the Dataset Persistent Identifier, the Organisation or the requestor's name s, among others. You can choose which columns to see, depending on your needs and interests. Additionally, we have implemented two new features columns with two new functionalities, DAC Comment and Expiration Date. You can find more details in the following sections! In this table, all data access requests are displayed by default. With the latest version of the Portal, you can now apply filters, combine them, and save them for future use. Leave internal comments on pending requests and visible to all the DAC Members We understand that many Data Access Committees (DACs) cannot instantly approve or deny data access requests. Often, there's an intermediary phase where discussions and the signing of legal documents are necessary. To facilitate this process, we've introduced the DAC comment feature! When you navigate to the DAC column, you'll find two tabs: "User" and "DAC". Under "User", you'll see the message provided by the requester at the time of requesting access to the data. As a DAC member/admin, you can then add an internal comment in the "DAC" tab. These comments are visible only to DAC members/admins, ensuring confidentiality. Requesters won't have access to DAC comments. By adding a DAC comment to a pending request, we'll know that you've begun reviewing it. This allows you to filter pending requests without DAC comments to focus on genuinely new requests, and it prevents you from receiving email notifications about ongoing data requests. Set expiration dates for permissions and revoke them automatically The latest update to the DAC Portal introduces a new feature allowing you to set expiration dates for new access requests. For instance, if you anticipate approving a data access request but only wish to grant access for a limited time, you can now specify an expiration date for the granted permissions. Once this date is reached, the permissions will automatically be revoked. By default, permissions are granted indefinitely without an expiration date. However, if you ever need to revoke permissions, you can easily do so from the History page. Audit your metadata objects Managing and auditing your metadata objects will be easier from now on as the new DAC Portal enhances and facilitates your role as a Data Controller. This is possible thanks to the tables available in each of the primary tabs visible on the homepage: DACs, Policies and Datasets. Starting with DACs, this section allows to check the policies linked to a specific DAC. In Policies, you can obtain a list of the datasets connected to a particular policy. In the Dataset tab, it is possible to group datasets by either DAC or Policy, as well as the release status. Now you have the option of deprecating metadata objects that you don’t want to use anymore, or that were created by mistake. You will find the deprecate button on the EDIT page of the DAC, as well as for registered policies. You can find more information here. What's neat about this feature is that once a DAC or policy is deprecated, it becomes hidden within the DAC Portal. This ensures that you only see information that is consistently up to date! Define your preferences regarding email notifications For pending requests, you will be able to decide how often you want to get the notifications: daily, weekly or fortnightly. Remember, you will not receive email notifications for pending requests with a DAC comment! Furthermore, you have the flexibility to decide which notifications you wish to receive, such as invitations to join a new DAC or notifications regarding DAC acceptance by the Helpdesk, among other options. Check the User preference to see the complete list of available notification settings! What’s next: incoming DAC Portal API In addition to the new DAC Portal, we are excited to announce the release of the DAC API! This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can consult the DAC API specification. This post was written by Aina Jené and Ana T. Alonso.

  Blog new-dac-portal-v2

• Submission FAQ

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

  Documentation submission/metadata/submission/FAQ

• Metadata Public API

  Metadata Rest Endpoints Common Aspects Functionality Identifiers Requests and Responses Request Response Querying Querying by Object Cross-Querying by Object Common Aspects Functionality The Metadata REST API allows EGA users to request metadata from the EGA. Using this api you will be able to obtain the publicly available information from EGA study, sample, experiment, run, analysis, policy, dac and dataset. The API also allows for objects to be cross-referenced in order to obtain for example all the datasets linked to a dac. Identifiers EGA objects can be identified by their unique accession. These are ID's displayed everywhere, shared among all EGA locations and specific for each data type (More information on the list below) EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID Server Request and Response Request All the endpoints implement these two HTTP methods, GET and HEAD. Moreover, there is an authentication layer that allows to disclose the private information about each object if the user runs the endpoint with their token and has permissions to the related dataset. HTTP Method Description GET Returns the objects and their public information (including private information if the user has permissions) HEAD Returns the count of objects to be returned Response API responses are diverse but they always return a status call. Below you have a summary on what these codes mean and some tips that can help you troubleshooting them: HTTP Status Code Description Resolution 200 OK No error handling necessary 206 Partial Content Expected status code when paginating the results. No error handling necessary 400 BAD REQUEST Request incorrectly formulated 401 UNAUTHORISED/FORBIDDEN You do not have permissions over the object you are trying to access. / The authorised user is not permitted to make the given request. 404 NOT FOUND The object/resource you are trying to access does not exist. 500 INTERNAL SERVER ERROR This is a server-side error, try later. If the issue persists, contact the Heldpesk. Querying Querying by Object In the below documentation, worked examples can be found on how to use the API, to query about each unique EGA object. https://metadata.ega-archive.org/analyses/{id} https://metadata.ega-archive.org/dacs/{id} https://metadata.ega-archive.org/datasets/{id} https://metadata.ega-archive.org/runs/{id} https://metadata.ega-archive.org/samples/{id} https://metadata.ega-archive.org/studies/{id} Worked example for the EGA data access committee(DAC) EGAC00001000514 https://metadata.ega-archive.org/dacs/EGAC00001000514 Points to notice Please, be advised that all EGA objects can be queried by using the same endpoint. For example: https://metadata.ega-archive.org/datasets Returns all the datasets registered at the EGA. https://metadata.ega-archive.org/datasets?limit=5&offset=15 Returns 5 results skipping the initial 15 results. Cross-Querying by Object The EGA REST API Metadata also allows for the crossquerying of public metadata. For example, should I want to query the datasets included in the ICGC DAC: https://metadata.ega-archive.org/dacs/EGAC00001000010/datasets Below, you can find worked examples on how to perform the call to the API. https://metadata.ega-archive.org/datasets/EGAD00001000645/analyses https://metadata.ega-archive.org/datasetsEGAD00001000645/files https://metadata.ega-archive.org/datasets/EGAD00001000620/runs https://metadata.ega-archive.org/datasets/EGAD00001000620/samples https://metadata.ega-archive.org/samples/EGAN00001092114/datasets https://metadata.ega-archive.org/files/EGAF00000056146/dacs https://metadata.ega-archive.org/files/EGAF00000056146/datasets https://metadata.ega-archive.org/files/EGAF00000056146/studies There are more endpoints included in this category. Please, find all the possibilities (green tick) in the table below. Table The table displays in a ordered fashion the objects that can be crossreferenced using the metadata API. BY Analysis Dac Dataset Experiment Policy Run Sample Study File Analysis ✔ ✔ Dac ✔ ✔ Dataset ✔ ✔ ✔ ✔ Experiment ✔ ✔ ✔ ✔ Policy ✔ Run ✔ ✔ ✔ Sample ✔ ✔* ✔ Study ✔ ✔ ✔ ✔ ✔ ✔* File ✔ *: These cross queries do not include data from Array-File submissions. Check out the SPEC

  Documentation discovery/metadata/public-metadata-api

• How to request data

  Request Data Access If you're looking to access data from the EGA for your research, you are in the right documentation page! In this page you will find a step by step guide on how to request data archived at the EGA, as well as some frequently asked questions you might find useful. When requesting data, you'll need to provide information about your research project and intended use of the data. Keep in mind that your data access request may require additional approvals or agreements depending on the datasets and data providers involved. Step by step guide Register yourself as an EGA user. Validate your account. A validation link will be sent to your email to activate your EGA user account. Log into the EGA page. Search the dataset of interest from the dataset catalogue or search bar. Click on “Request access” Add a comment requesting these files and send your request Your request is successfully registered, and the DAC will receive a notification about your request In addition to submitting your request, you should also contact the DAC directly via e-mail. You can access the DAC's contact information from the relevant dataset page. Frequently Asked Questions (FAQ) Which is the role of the EGA in the data access process? The European Genome-phenome Archive (EGA) facilitates the secure distribution under controlled access of personally identifiable genetic and phenotypic data. Data Access Committee (DAC) is a body of one or more individuals who are responsible for data release to external requestors, based on participant consent and/or National Research Ethics terms. How do I request access to multiple datasets? To request access for multiple datasets managed by the same Data Access Committee (DAC), please start by visiting the DAC catalogue to locate the DAC of your choice. Once you've found the relevant DAC, proceed to its dedicated page where you'll find a "Request access" button. After logging in to the EGA website, you'll be presented with a comprehensive list of datasets under the chosen DAC. Select the datasets you're interested in, and then compose a message indicating your access request. Click the "Send" button to submit your request, which will include the chosen datasets. Please be sure to review the data usage policies for the requested datasets, as policies can vary among datasets managed by the same DAC. I am a co-applicant. Will I be given a joint access account? Each applicant needs to create an EGA account themselves, with unique login details. User log-in details are for individual use only and should never be shared, under any circumstances. EGA services are only provided to EGA account holders. Therefore, the co-applicant will send a formal request to the DAC. What happens if a co-applicant leaves or a new member needs to be added to an existing DAC application? The appropriate Data Access Committee (DAC) should be contacted and informed about the required changes so that the application can be updated. I have changed my place of work since my EGA account was created. Can I still have access to data? You must contact the relevant Data Access Committee (DAC). The DAC did not reply to my data access request. How should I proceed? There is no estimated time for DAC response. Please allow up to four weeks for a response. In the meantime, please send them a weekly reminder of the status of your request by emailing them directly.If the DAC becomes unresponsive, meaning that they fail to communicate with a requestor within the specified time frame, please contact the EGA Helpdesk team. I have an account with the EGA and would like access to further datasets. How should I proceed? You need to reapply to the relevant Data Access Committee (DAC). Once the DAC has approved your application your account will be updated and an email will be sent to notify you of a change made to your account. How do I download files of the datasets I have been granted access to? The preferred method for data download and decryption is the EGA Secure Download Client. Please carefully review the provided documentation. How do I download metadata? Metadata is retrieved from the EGA webpage; you will not need to use any download client. Be informed that you will only be able to download metadata of your approved datasets. For such download, you should first log in the EGA webpage with your credentials. Then browse the metadata's dataset you have gained access and you will find a metadata download button. You may also want to have a look our metadata schema. I have forgotten or misplaced my user account password; how do I get a new one? Click on “Log in”, located in the top right corner of the EGA homepage. Then click “Forgot password”, type in your registered institutional email address, and instructions for resetting your password will be sent to you. Please check both regular and junk/spam folders. I was sent a link to set a password for my download account, but it seems like the link has expired. What should I do? The procedure is the same as the one for a forgotten password, explained in the previous question.

  Documentation access/request-data/how-to-request-data

• National Cancer Institute (NCI) Biospecimen Pre-analytical Variables (BPV) Analysis

  The Biospecimen Pre-analytical Variables (BPV) Program is a National Cancer Institute-sponsored study to systematically assess the effects of pre-analytical factors on the molecular profile of biospecimens. A robust biospecimen collection infrastructure was established to prospectively collect biospecimens using rigorous standard operating procedures to control for most variables while introducing experimental conditions to study specific biospecimen handling issues, including the cold ischemic time (delay to formalin fixation), time in formalin, freezing methods, and storage temperatures and durations. RNA and DNA from biospecimens collected under these conditions was analyzed on multiple molecular platforms. The potential effects of these pre-analytical conditions on protein integrity and detection of metabolites were also examined. Data from this study will be used to develop evidence-based biospecimen standard operating procedures and best practices for fit-for-purpose collection, processing, and storage of biospecimens. The BPV 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 sub-studies below or in the "Substudies" box located on the right hand side of this top-level study page phs001304 BPV Cohort. The substudy links will be active once they are released by dbGaP. Preanalytical Impacts on Global Metabolite Profiling - plasma (MassSpec by Metabolon) This study was to evaluate the impact of the storage temperature (s) (-80°C and LN2 vapor) and the length of storage on human plasma quality using LC-MS/MS (liquid-chromatography-mass spectrometry/mass spectrometry) based global metabolite profiling. The study includes 240 plasma samples collected from 40 donors. Investigate the effect of the delay to fixation on the proteome and phosphoproteome -FFPE (MassSpec by Caprion). The study is to do proteome and phosphoproteome analysis on Delay to fixation was carried out using FFPE tumor samples from colon and ovarian cancer patients comparing 2, 3, and 12hr delay to fixation to the 1hr time point. The study includes 100 samples 20 donors. Investigate the effect of storage conditions of tumor specimens on the proteome and phosphoproteome profiling- Frozen tissue and plasma (MassSpec by Caprion). The study was to evaluate the effects of storage conditions on tumor specimens. Plasma samples from 40 cancer patients stored at two different temperatures (-80°C and LN2) for a given period (0-2, 6-8, and 12-14 months) were evaluated. Frozen kidney tumor samples from 20 patients were compared for effects of different snap frozen (dry ice vs. LN2) and storage temperatures (-80°C and LN2). The study includes 100 tissue and 240 plasma samples from 60 donors. Preanalytical Impacts on Genomic Sequencing by Next Generation Sequencing (NGS) technology (mRNA/miRNA and WES by Expression Analysis). The goal of the study is to determine the effects of cold ischemic delay-to-fixation (4 time points) and formalin preservation (FFPE) on the nature and quality of genomic profiles using the matched freshly frozen sample as the gold standard, which including WES, RNAseq. The study includes 395 samples from 37 donors. Preanalytical Impacts on Copy Number Variation (CNV) Detection by aCGH technology (aCGH by Georgetown University). This study was to use aCGH to evaluate the effect of variation in cold ischemia time and time in formalin fixation on CNV in DNA extracted from kidney cancer specimens. The study includes 235 samples from 40 donors. Evaluation of frozen conditions on mRNA profiling by TaqMan assay (mRNA expression by Georgetown University). This study was to utilize gene expression profiling, using custom TaqMan arrays, to compare the molecular profiles of RNA from frozen tumor samples collected using two freezing methods (dry ice or LN2), two storage temperatures (-80°C or LN2 vapor), as well as Optimal Cutting Temperature (OCT) compound and non-OCT embedded. The study includes 100 samples from 20 donors. mRNA signature for stratification by cold ischemia time (mRNA expression by IBBL). The study was to determine the effects of cold ischemic time (delay-to-fixation) and formalin preservation (FFPE) on mRNA detection by Taqman assay using tumor tissue specimens from kidney, colon and ovarian cancer patients. There are160 samples from 40 donors. The Biospecimen PV cohort is utilized in the following dbGaP individual studies. To view molecular data, and derived variables collected in these individual studies, please click on the following individual studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001304 Biospecimen PV cohort. phs001634 CIT mRNA phs001635 CNV aCGH phs001636 Fixation Delay phs001637 Global Metabolite Profiling phs001638 mRNA TaqMan phs001639 NGS phs001640 Tumor Storage

  Study phs001304

• Gene Expression and Regulatory Networks in Human Leukocytes - Immunological Variation Consortium

  Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies. Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression. In another study we analyzed RNA-seq profiles from primary CD4+ T cells of two healthy donors at six time points from 0 to 72 hours after stimulation with bead-bound antibodies against CD3 and CD28.

  Study phs000815

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• Action to Control Cardiovascular Risk in Diabetes (ACCORD - Imaging)

  Available DataAction to Control Cardiovascular Risk in Diabetes (ACCORD-Imaging), provides access to ECG signals data from the ACCORD clinical trial. The clinical phenotyping and outcomes data from the trial are associated with ACCORD-BioLINCC, phs003551.ObjectivesThe purpose of this study was to determine if intensive glycemic control, multiple lipid management and intensive blood pressure control could prevent major cardiovascular events (myocardial infarction, stroke or cardiovascular death) in adults with type 2 diabetes mellitus. Secondary hypotheses included treatment differences in other cardiovascular outcomes, total mortality, microvascular outcomes, health-related quality of life and cost-effectiveness.BackgroundGlycemia Trial:Patients with type 2 diabetes mellitus die of cardiovascular disease (CVD) at rates two to four times higher than non-diabetic populations of similar demographic characteristics. They also experience increased rates of nonfatal myocardial infarction and stroke. With the growing prevalence of obesity in the United States, CVD associated with type 2 diabetes is expected to become an even greater public health challenge in the coming decades than it is now. Expected increases in event rates will be associated with a concomitant rise in suffering and resource utilization.The ACCORD study investigated whether intensive therapy to target normal glycated hemoglobin (HbA1c) levels would reduce cardiovascular events in patients with type 2 diabetes who had either established cardiovascular disease or additional cardiovascular risk factors when compared to standard therapy (HbA1c between 7.0% and 7.9%). A separate analysis investigated whether reduction of blood glucose concentration decreases the rate of microvascular complications in these patients.Lipid Therapy Trial:Patients with type 2 diabetes mellitus have an increased incidence of atherosclerotic cardiovascular disease attributable, in part, to associated risk factors such as dyslipidemia. This is characterized by elevated plasma triglyceride levels, low levels of high-density lipoprotein (HDL) cholesterol and small, dense low-density lipoprotein (LDL) particles. The ACCORD Lipid Therapy trial was designed to test the effect of a therapeutic strategy that uses a fibrate to raise HDL-C and lower triglyceride levels and uses a statin for treatment of LDL-C reduce the rate of CVD events compared to a strategy that only uses a statin for treatment of LDL-C on cardiovascular outcomes in patients with type 2 diabetes that were at high risk for cardiovascular disease.Blood Pressure Trial:Diabetes mellitus increases the risk of cardiovascular disease at every level of systolic blood pressure. Because cardiovascular risk in patients with diabetes is graded and continuous across the entire range of levels of systolic blood pressure, even at prehypertensive levels, the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) recommended beginning drug treatment in patients with diabetes who have systolic blood pressures of 130 mm Hg or higher, with a treatment goal of reducing systolic blood pressure to below 130 mm Hg. There is, however, a paucity of evidence from randomized clinical trials to support these recommendations. The ACCORD Blood Pressure trial tested the effect of a target systolic blood pressure below 120 mm Hg on major cardiovascular events among high-risk persons with type 2 diabetes compared to a strategy that targeted a SBP of EYE Substudy:Diabetic retinopathy, an important microvascular complication of diabetes, is a leading cause of blindness in the United States. Randomized, controlled clinical trials in cohorts of patients with type 1 diabetes and those with type 2 diabetes have shown the beneficial effects of intensive glycemic control and intensive treatment of elevated blood pressure on the progression of diabetic retinopathy. Elevated serum cholesterol and triglyceride levels have been implicated, in observational studies and small trials, as additional risk factors for the development of diabetic retinopathy and visual loss. The ACCORD EYE Substudy evaluated the effects of the ACCORD medical strategies on the progression of diabetic retinopathy in a subgroup of trial patients.MIND Substudy:Studies suggest that older persons with type 2 diabetes have at least twice the likelihood of developing late-life cognitive impairment or dementia compared to those without. The mechanisms underlying these cognitive disorders are increasingly thought to reflect a mixed pathology pattern with contributions from vascular, neurodegenerative and neurovascular processes. Pathophysiological mechanisms that have been described include inflammation, oxidative stress, energy imbalance, protein misfolding, glucocorticoid-mediated effects and differences in genetic susceptibilities. The ACCORD MIND substudy took as a premise that early intervention with the ACCORD therapeutic strategies to improve glycemic control could mitigate the adverse effects of type 2 diabetes on the brain.Subjects10,251 patients with type 2 diabetes and HbA1c concentrations of 7.5% or more participated in the trial. Of these patients, 5518 were assigned to the lipid therapy arm and 4733 to the blood pressure arm. EYE Substudy:A subgroup of 2856 patients was evaluated for the effects of the ACCORD interventions at 4 years on the progression of diabetic retinopathy. Patients who, at baseline, had a history of proliferative diabetic retinopathy that had been treated with laser photocoagulation or vitrectomy were excluded.MIND Substudy:A subgroup of 2977 patients was evaluated for cognitive function and brain volume. The ACCORD MIND substudy excluded patients aged DesignPatients were randomly assigned to undergo either intensive glycemic control (targeting a glycated hemoglobin level EYE Substudy:EYE Substudy patients were evaluated at two standardized and comprehensive eye examinations for the effects of the ACCORD interventions at 4 years on the progression of diabetic retinopathy by 3 or more steps on the Early Treatment Diabetic Retinopathy Study Severity Scale (as assessed from seven-field stereoscopic fundus photographs, with 17 possible steps and a higher number of steps indicating greater severity) or the development of diabetic retinopathy necessitating laser photocoagulation or vitrectomy.MIND Substudy:The cognitive primary outcome, the Digit Symbol Substitution Test (DSST) score, was assessed at baseline, 20 and 40 months. Total brain volume (TBV), the primary brain structure outcome, was assessed with MRI at baseline and 40 months in a sub-set of 632 patients. All patients with follow-up data were included in the primary analyses.ConclusionsGlycemia Trial:As compared with standard therapy, the use of intensive therapy to target normal glycated hemoglobin levels for 3.5 years increased mortality and did not significantly reduce major cardiovascular events. (NEJM. 2008; 358(24): 2545-59).Microvascular Outcomes of the Glycemia Trial:Intensive therapy did not reduce the risk of advanced measures of microvascular outcomes, but delayed the onset of albuminuria and some measures of eye complications and neuropathy. Microvascular benefits of intensive therapy should be weighed against the risk of increased total and cardiovascular disease-related mortality, increased weight gain, and higher risk for severe hypoglycemia. (Lancet. 2010; 376(9739): 419-30)Lipid Therapy Trial:The combination of fenofibrate and simvastatin did not reduce the rate of fatal cardiovascular events, nonfatal myocardial infarction or nonfatal stroke, as compared with simvastatin alone. These results do not support the routine use of combination therapy with fenofibrate and simvastatin to reduce cardiovascular risk in the majority of high-risk patients with type 2 diabetes (NEJM. 2010; 362(17): 1563–1574).Blood Pressure Trial:In patients with type 2 diabetes at high risk for cardiovascular events, targeting a systolic blood pressure of less than 120 mm Hg, as compared with less than 140 mm Hg, did not reduce the rate of a composite outcome of fatal and nonfatal major cardiovascular events (NEJM. 2010; 362: 1575-1585).EYE Substudy:Intensive glycemic control and intensive combination treatment of dyslipidemia, but not intensive blood-pressure control, reduced the rate of progression of diabetic retinopathy (NEJM. 2010; 363: 233-244).MIND Substudy:Although significant differences in TBV favored the intensive therapy, cognitive outcomes were not different. Combined with the unfavorable effects on other ACCORD outcomes, MIND findings do not support using intensive therapy to reduce the adverse effects of diabetes on the brain in patients similar to MIND patients (Lancet Neurol. 2011; 10(11): 969–977).

  Study phs003562

• Childhood Cancer Survivor Study (CCSS)

  Background and Rationale for the Childhood Cancer Survivor Study (CCSS) Over the last several decades, advances in treatments for childhood and adolescent cancer have substantially improved survival following diagnosis. These improvements gave rise to the responsibility for investigating long-term treatment-associated morbidity and mortality. Early efforts to describe late effects were largely conducted through single-institution and limited consortia studies. However, by the mid-1980s, it became increasingly clear that these approaches had inherent limitations, including small sample size, convenience sampling, incompletely characterized populations, and limited length of follow-up. To overcome these limitations, the CCSS was proposed and funded by the National Cancer Institute (NCI) as a U01 grant in 1994. Subsequently, the strengths of the CCSS, including an efficient and extensive infrastructure, plus expanding database and biorepository, were recognized and appreciated. Thus, in consultation with the NCI, the CCSS was converted to a U24 (resource grant) funding mechanism to serve the scientific community in 2000. The overarching goal of the CCSS resource is to increase the conduct of innovative and high impact research related to pediatric cancer survivorship. CCSS has been used extensively by researchers from a wide range of disciplines to address a broad spectrum of topics. Strengths of the resource include its large size, comprehensive annotation of treatment exposures, ongoing longitudinal follow-up with characterization of a wide array of participant characteristics and outcomes, and an established biorepository. Design of the Childhood Cancer Survivor Study The Childhood Cancer Survivor Study (CCSS) is a multi-institutional, multi-disciplinary collaborative research resource comprised of a retrospective hospital-based cohort of survivors of childhood cancer and a comparison sibling cohort. Eligible survivors from 31 participating institutions were diagnosed between 1970 and 1999, prior to age 21 years, with selected common pediatric cancers (leukemia, central nervous system tumors, Hodgkin lymphoma, non-Hodgkin lymphoma, kidney tumors, neuroblastoma, soft tissue sarcoma, or bone tumors). All patients who survived five years from the date of diagnosis were eligible, regardless of disease or treatment status. The baseline questionnaire was completed by 24,368 survivors and 5,039 siblings recruited to serve as a comparison group. To date, participants have completed three general follow-up surveys, as well as a number of specialized surveys on specific topics (e.g. health care, insurance, screening practices, men's and women's health issues, adolescent health, sleep and fatigue). In addition, biological samples (buccal cells, saliva and/or blood) have been collected for over 11,000 participants. Full descriptions of the design and characteristics of the CCSS have been previously published (Robison et al; Leisenring et al.), and available data and samples are described at https://ccss.stjude.org/develop-a-study/gwas-data-resource.html. Treatment Data in the Childhood Cancer Survivor Study A key feature of CCSS is the availability of detailed treatment data, which were collected by abstraction of medical records for each individual member of the cohort. Detailed abstraction included dates of therapy, protocol information, and specific details regarding surgery, chemotherapy and radiation. Quantitative dose details were collected for 22 specific chemotherapeutic agents, including alkylating agents, anthracyclines, platinum compounds and epipodophyllotoxins. In addition to individual agent doses, algorithms have been created to calculate cumulative doses of all drugs in a specific class, such as anthracyclines (doxorubicin, daunomycin and idarubicin) or platinum agents (cisplatinum and carboplatinum). Data abstracted for surgeries included dates and both the names and corresponding International Classification of Diseases (9th revision) code. For radiation treatment data, all relevant records were sent to the Radiation Physics Center at M.D. Anderson Cancer Center for detailed abstraction and dosimetry. Initial body region dosimetry was performed for all participants, followed by more detailed dosimetry as needed for specific studies. Genomics Data in the Childhood Cancer Survivor StudyThe NCI's Division of Cancer Epidemiology and Genetics and CCSS investigators collaborated to conduct genomics studies (SNP array genotyping and whole exome sequencing) using samples from the CCSS Biorepository. Studies included all cohort participants with available DNA regardless of sex or ancestry when the genomics studies were initiated. Phenotype Data in the Childhood Cancer Survivor Study Vital status and cause of death for both participants and non-participants is determined via linkage with the National Death Index (NDI). Identification of subsequent neoplasms is based on self-report, followed by validation using medical records, or via NDI. A wide array of additional health outcomes have been ascertained via a comprehensive set of questions on the CCSS questionnaires, covering potential adverse events across a range of organ systems (hearing/vision/speech, urinary, hormonal, heart and circulatory, respiratory, digestive, brain and nervous systems). In addition to health outcomes, longitudinal data have been collected on demographics, health behaviors, family history, screening practices, insurance status, and a range of psychosocial and neurocognitive factors. A full listing of available variables and copies of the CCSS questionnaires are available at http://ccss.stjude.org. Research Areas in the Childhood Cancer Survivor Study Extensive use by the research community has resulted in over 265 published manuscripts on a wide range of topics, including associations between treatment factors and mortality, subsequent neoplasms, chronic health conditions, cardiac events, neurocognitive sequelae, psychosocial factors, fertility, and health status. Additional topics have included health behaviors, screening practices, health care access and utilization, statistical and exposure assessment methodology, and development of risk prediction models. A full listing of published manuscripts using CCSS data is available on the CCSS website at https://ccss.stjude.org/published-research/publications.html. The Childhood Cancer Survivor Study as a Resource for Investigators The CCSS is an NCI-funded resource (U24 CA55727) to promote and facilitate research among long-term survivors of cancer diagnosed during childhood and adolescence. Interested investigators are encouraged to develop research ideas and propose projects within CCSS, whether or not they are from a participating CCSS institution. The CCSS is now accepting proposals to collaborate with CCSS and NCI investigators in the use of genomics data and corresponding outcomes-related data to address innovative research questions relating to potential genetic contributions to risk for treatment-related outcomes. Any researcher, or group of researchers, qualified to conduct genetic research can submit a proposal. There are no restrictions relative to country, institution, or prior involvement in CCSS. A full description of the process for developing a proposal for genetic research in CCSS can be found at https://ccss.stjude.org/develop-a-study/gwas-data-resource.html, along with listings of approved proposals.

  Study phs001327

• What is a DAC?

  What is a DAC? Given the complexity, scale, and diversity of global submitters and studies, the EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA. The European Commission defines a data controller, in the General Data Protection Regulation (GDPR), as the person that determines the purposes for which and the means by which personal data is processed. A Data Access Committees, commonly referred to as DACs, comprise on or more individuals (or data controllers) that review data access requests and make decisions on who can access personally identifiable genetic, phenotypic, and clinical data deposited at the EGA. Therefore, the members of a DAC should be individuals who have the authority to approve data access requests. The animation describes how you can authorise access to your sensitive data with the help of Data Access Committee and Authorisation tool. Acknowledgement to CSC - IT Center for Finland, Elixir Finland, Elixir Europe. Frequently Asked Questions How can I create a Data Access Committee? How can I create a Data Access Committee? The members of a DAC can come from different areas of expertise, such as data management, data analysis, information technology, legal and compliance, subject matter experts, privacy and security, and representatives from the organisations or individuals that provide data to the DAC. The specific members of a DAC can vary depending on the needs of the organisation and the type of data being managed. The EGA strongly suggests checking with your organisation to align with its regulations How should a DAC be named? The chosen name must be informative to the applicant.  For example, internal identifiers, such as grant numbers, should not be used. Individual PI names should also not be used. DAC's are often named after the organisation or department of the data source.  Browse the full list of DAC names currently in the EGA. How can I become an EGA DAC contact? To register a DAC at the EGA you must create first as an EGA user. Once your EGA user has been approved by the Helpdesk team, you will be able to log in to the DAC Portal. How can I register a DAC? To register a DAC, follow the DAC Portal instructions.  You will be required to provide a DAC name, name of the individual(s) that make up your DAC and contact details for your DAC including your Institutional email(s). Wherever possible, the DAC should make sure that all points of contact are readily available and able to answer any initial data requests/queries in < 2 weeks.  Once your DAC is registered, you will have to wait upon the validation from our Helpdesk team. As soon as all the validations have been completed, your DAC will be activated. Alternatively, you can also establish a DAC at the EGA during a programmatic submission through Webin API. Which are the possible roles of a DAC contact? There are two possible roles for DAC contacts: member and admin. An admin has additional privileges compared to a member: An “admin” can manage data requests, create and edit policies, edit the content of the DAC, add or remove contacts, and decide the role of each contact. A “member” can manage data requests and create policies. However, a member does not have permission to modify DAC details, edit information from policies where they are not admins, or add/remove contacts. There is no limit to the number of admins in a DAC, and each admin is responsible for deciding who should have editing privileges. This allows for a more decentralised and democratic approach to managing the DAC. How can I modify the information of a DAC? To modify a DAC, follow the instructions here. Keep in mind that only DAC contact with an admin role can modify the information of a DAC. If your DAC was registered before the lauch of the DAC Portal, and its ID is EGAC0 (not EGAC5), you must use the programmatic submission to modify it. Please, do not hesitate to contact our Helpdesk team if you need help with this! To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security. What’s the link between DAC, policy and dataset? A dataset is linked to one single policy. At the same time, one policy has a one to one relationship with a DAC. In this example, you can see that in this dataset page, we are only showing the information of one DAC (1 dataset - 1 DAC). However, the ratio of objects does not work the same in the other direction. One DAC can own multiple policy objects. And each policy object can be reused in several datasets. Thus, one DAC can manage one or more datasets. In this example, you can see that in this DAC page, we are showing all the datasets that are managed by one DAC (1 DAC - >400 datasets). EGA Data Access Committee Best Practices Which are the EGA DAC best practices? Refer to DAC Best Practices What happens if a DAC member changes institutions? EGA is committed to the protection and ownership of the data stored in our systems. We respect the institution's ownership of the data, and as such, if a DAC member changes institutions, the ownership of the data will not be transferred to the new institution. Therefore, before changing institutions, we request that the DAC contact add a new member who will replace them once they no longer work at the institution. This ensures that the data remains protected and is accessible to authorised personnel at the institution. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security. What happens if EGA detects an unresponsive DAC? EGA defines an unresponsive DAC as a DAC with one or more contacts who do not respond to data access requests. EGA has procedures in place to identify these types of DACs, escalate the issue, and attempt to reassign the DAC to a responsive contact. This is a crucial step in ensuring that data can be accessed and utilised by researchers. If EGA identifies an unresponsive DAC, the organisation will first try to resolve the issue by escalating it to the appropriate parties. This may involve attempting to reassign the DAC to a more responsive contact. Unfortunately, in situations where we cannot reassign the DAC, the dataset will be withdrawn from the public website and the files will be removed from our system. If an EGA ID is referenced in a publication, the EGA will take extra steps to ensure that the public is made aware of the data's unavailability. I don't want to receive an email notification for pending requests. How can I do that? If you are an EGA DAC with pending requests, you will always receive emails for new data access requests. However, the EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! How can I manage data access requests? What documentation does the DAC need to provide? Each dataset that is submitted to the EGA must be linked to a policy object. The policy is a Data Access Agreement (DAA), which defines the terms and conditions of using the dataset, such as how the data files should be stored once downloaded or details of publication embargoes that should be observed by the approved user. As part of the Data Access Agreement, information regarding the application can be captured to help inform the DAC when making its decision. For example, requestors could be asked to provide a proposed title for their research and a proposal of how the data will be used. By asking for provision of such information the DAC can be assured that the requestor fully understands any consents associated with the data. It is important that accounts created at the EGA, are created solely for those individuals that will be downloading the data from the EGA. As part of the data access request, we strongly encourage you to identify individuals that will need an account at the EGA in order to prevent sharing of login details, which is strictly prohibited under EGA user account policy. Such information can easily be captured in the DAA. NOTICE The data access agreement template below is provided for guidance only and should be adapted as you see fit to suit your own purpose. In the interest of promoting data sharing, we suggest that if an agreement cannot be met around clause 19 in this example that both parties should agree to remain silent, and that the clause should be removed from the agreement. Example DAA How can the DAC provide the DAA? The DAC should provide their own DAA when registering a policy. Data requestors will download this document and should fill it in and send it back to the DAC. Data access decisions should be based on such documentation. The DAA can be downloaded through the request data webpage. Once it has been filled in, the signed copy of the DAA can be uploaded back to the request data webpage and sent to the DAC for review. How can I grant access to the data? Once you receive a data access request, you can login to the DAC Portal. In this portal you will see all your pending requests and will be able to grant or decline access to the requestors. I am a member of the Data Access Committee. Could I approve somebody else to deal with the requests on my behalf? If you want to delegate data access decisions to someone else, make sure that the individual's account is officially registered as a member of the DAC. Remember that a DAC contact with an "admin" role can always add new members to an existing DAC, remove members, and modify contact details through the DAC Portal. Can I automatise the process of managing data access requests? The answer is yes! You can use a programmatic approach using our DAC API! Check out the DAC API specification! Data Breach What should a DAC do if they suspect a breach? If a DAC suspects a data breach of one or more of their datasets, they should immediately contact the EGA Helpdesk team at this link. The DAC must provide the following information when contacting the EGA Helpdesk team: A list of affected datasets An estimated date of the data breach (or interval of dates) A list of unauthorised users who accessed the data (if available). Otherwise, they can provide a list of authorised users for the affected datasets Any observations they would like to raise to the EGA team Once the DAC has contacted the EGA team, we will respond within 48 hours (please allow some leeway during peak times) and activate our data breach protocol. What can I expect from the EGA if they detect a breach? Once the EGA determines that a security incident has occurred, we will notify all DAC members that a data breach has been detected, and take steps to contain the incident. Containment approaches may include: Revoking a data provider's access to the EGA resources, such as by changing passwords. Removing affected EGA datasets from distribution, such as by withdrawing a dataset. Disabling certain functions or services, such as the EGA ingestion pipeline. Shutting down the system or disconnecting it from the network. After the incident has been contained, the EGA will determine whether it is necessary to eradicate components related to the incident. Finally, the EGA will enable recovery of the service to normal operation and confirm that all services are functioning normally.

  Documentation access/data-access-committee/what-is-dac

• Action to Control Cardiovascular Risk in Diabetes (ACCORD-BioLINCC)

  Related StudiesWhole genome and whole exome data is available on a subset of participants with phs001411. ECG signal data is available with phs003562.ObjectivesThe purpose of this study was to determine if intensive glycemic control, multiple lipid management and intensive blood pressure control could prevent major cardiovascular events (myocardial infarction, stroke or cardiovascular death) in adults with type 2 diabetes mellitus. Secondary hypotheses included treatment differences in other cardiovascular outcomes, total mortality, microvascular outcomes, health-related quality of life and cost-effectiveness. BackgroundGlycemia Trial:Patients with type 2 diabetes mellitus die of cardiovascular disease (CVD) at rates two to four times higher than non-diabetic populations of similar demographic characteristics. They also experience increased rates of nonfatal myocardial infarction and stroke. With the growing prevalence of obesity in the United States, CVD associated with type 2 diabetes is expected to become an even greater public health challenge in the coming decades than it is now. Expected increases in event rates will be associated with a concomitant rise in suffering and resource utilization.The ACCORD study investigated whether intensive therapy to target normal glycated hemoglobin (HbA1c) levels would reduce cardiovascular events in patients with type 2 diabetes who had either established cardiovascular disease or additional cardiovascular risk factors when compared to standard therapy (HbA1c between 7.0% and 7.9%). A separate analysis investigated whether reduction of blood glucose concentration decreases the rate of microvascular complications in these patients. Lipid Therapy Trial: Patients with type 2 diabetes mellitus have an increased incidence of atherosclerotic cardiovascular disease attributable, in part, to associated risk factors such as dyslipidemia. This is characterized by elevated plasma triglyceride levels, low levels of high-density lipoprotein (HDL) cholesterol and small, dense low-density lipoprotein (LDL) particles. The ACCORD Lipid Therapy trial was designed to test the effect of a therapeutic strategy that uses a fibrate to raise HDL-C and lower triglyceride levels and uses a statin for treatment of LDL-C reduce the rate of CVD events compared to a strategy that only uses a statin for treatment of LDL-C on cardiovascular outcomes in patients with type 2 diabetes that were at high risk for cardiovascular disease. Blood Pressure Trial: Diabetes mellitus increases the risk of cardiovascular disease at every level of systolic blood pressure. Because cardiovascular risk in patients with diabetes is graded and continuous across the entire range of levels of systolic blood pressure, even at prehypertensive levels, the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) recommended beginning drug treatment in patients with diabetes who have systolic blood pressures of 130 mm Hg or higher, with a treatment goal of reducing systolic blood pressure to below 130 mm Hg. There is, however, a paucity of evidence from randomized clinical trials to support these recommendations. The ACCORD Blood Pressure trial tested the effect of a target systolic blood pressure below 120 mm Hg on major cardiovascular events among high-risk persons with type 2 diabetes compared to a strategy that targeted a SBP of EYE Substudy: Diabetic retinopathy, an important microvascular complication of diabetes, is a leading cause of blindness in the United States. Randomized, controlled clinical trials in cohorts of patients with type 1 diabetes and those with type 2 diabetes have shown the beneficial effects of intensive glycemic control and intensive treatment of elevated blood pressure on the progression of diabetic retinopathy. Elevated serum cholesterol and triglyceride levels have been implicated, in observational studies and small trials, as additional risk factors for the development of diabetic retinopathy and visual loss. The ACCORD EYE Substudy evaluated the effects of the ACCORD medical strategies on the progression of diabetic retinopathy in a subgroup of trial patients. MIND Substudy: Studies suggest that older persons with type 2 diabetes have at least twice the likelihood of developing late-life cognitive impairment or dementia compared to those without. The mechanisms underlying these cognitive disorders are increasingly thought to reflect a mixed pathology pattern with contributions from vascular, neurodegenerative and neurovascular processes. Pathophysiological mechanisms that have been described include inflammation, oxidative stress, energy imbalance, protein misfolding, glucocorticoid-mediated effects and differences in genetic susceptibilities. The ACCORD MIND substudy took as a premise that early intervention with the ACCORD therapeutic strategies to improve glycemic control could mitigate the adverse effects of type 2 diabetes on the brain. Participants10,251 participants with type 2 diabetes and HbA1c concentrations of 7.5% or more participated in the trial. Of these patients, 5518 were assigned to the lipid therapy arm and 4733 to the blood pressure arm. EYE Substudy: A subgroup of 2856 participants was evaluated for the effects of the ACCORD interventions at 4 years on the progression of diabetic retinopathy. Participants who, at baseline, had a history of proliferative diabetic retinopathy that had been treated with laser photocoagulation or vitrectomy were excluded. MIND Substudy: A subgroup of 2977 participants was evaluated for cognitive function and brain volume. The ACCORD MIND substudy excluded participants aged Design Participants were randomly assigned to undergo either intensive glycemic control (targeting a glycated hemoglobin level EYE Substudy: EYE Substudy participants were evaluated at two standardized and comprehensive eye examinations for the effects of the ACCORD interventions at 4 years on the progression of diabetic retinopathy by 3 or more steps on the Early Treatment Diabetic Retinopathy Study Severity Scale (as assessed from seven-field stereoscopic fundus photographs, with 17 possible steps and a higher number of steps indicating greater severity) or the development of diabetic retinopathy necessitating laser photocoagulation or vitrectomy. MIND Substudy: The cognitive primary outcome, the Digit Symbol Substitution Test (DSST) score, was assessed at baseline, 20 and 40 months. Total brain volume (TBV), the primary brain structure outcome, was assessed with MRI at baseline and 40 months in a sub-set of 632 patients. All patients with follow-up data were included in the primary analyses. Conclusions Glycemia Trial: As compared with standard therapy, the use of intensive therapy to target normal glycated hemoglobin levels for 3.5 years increased mortality and did not significantly reduce major cardiovascular events. (Action to Control Cardiovascular Risk in Diabetes Study Group, et al.,2008, PMID:18539917). Microvascular Outcomes of the Glycemia Trial: Intensive therapy did not reduce the risk of advanced measures of microvascular outcomes, but delayed the onset of albuminuria and some measures of eye complications and neuropathy. Microvascular benefits of intensive therapy should be weighed against the risk of increased total and cardiovascular disease-related mortality, increased weight gain, and higher risk for severe hypoglycemia. (Ismail-Beigi et al., 2010, PMID: 20594588) Lipid Therapy Trial: The combination of fenofibrate and simvastatin did not reduce the rate of fatal cardiovascular events, nonfatal myocardial infarction or nonfatal stroke, as compared with simvastatin alone. These results do not support the routine use of combination therapy with fenofibrate and simvastatin to reduce cardiovascular risk in the majority of high-risk patients with type 2 diabetes (ACCORD Study Group, et al., 2010, PMID: 20228404). Blood Pressure Trial: In patients with type 2 diabetes at high risk for cardiovascular events, targeting a systolic blood pressure of less than 120 mm Hg, as compared with less than 140 mm Hg, did not reduce the rate of a composite outcome of fatal and nonfatal major cardiovascular events (ACCORD Study Group, et al., 2010, PMID: 20228401). EYE Substudy: Intensive glycemic control and intensive combination treatment of dyslipidemia, but not intensive blood-pressure control, reduced the rate of progression of diabetic retinopathy (ACCORD Study Group, et al., 2010, PMID: 20587587). MIND Substudy: Although significant differences in TBV favored the intensive therapy, cognitive outcomes were not different. Combined with the unfavorable effects on other ACCORD outcomes, MIND findings do not support using intensive therapy to reduce the adverse effects of diabetes on the brain in patients similar to MIND patients (Launer et al., 2011, PMID: 21958949).

  Study phs003551

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• All you need to know about our new DAC Portal

  With the DAC Portal, it is possible to streamline the process of managing access to sensitive data, ensuring that researchers have the necessary resources to advance scientific discovery while maintaining the highest standards of data protection and privacy. We know that, as a new tool, its first use can be complex. For this reason, in this article we will try to show all the elements of interest. Enjoy! Credentials: who needs to create a new user to access the DAC Portal and who doesn't? Only new users (since September the 12th) need to create an EGA account to access the DAC Portal. The EGA team went one step ahead by creating EGA accounts for former DAC members to login to the DAC Portal. These accounts are linked to the personal email used in the DAC structure: To check the linked email, DAC members can paste the DAC ID right after this link: https://metadata.ega-archive.org/dacs/ (e.g. https://metadata.ega-archive.org/dacs/EGAC00001002543) In case of password oblivion, it is possible to set a new one. All the process is channelled through the personal email. Some DAC members may be interested in updating the DAC email. This action can be done contacting our Helpdesk team. For the correct functioning of the DAC Portal, users must add the missing information of the DAC Profile. For a deeper dive, check out our documentation. The first step: knowing your workspace on the Portal Once logged in to the platform using the EGA User credentials, note the sections available: DACs and Policies. In the first one it is possible to check all user's registered DACs by default. It provides a comprehensive overview of each DAC, including its EGA accessions (EGAC), title, status, and the number of data access requests associated with each DAC. Whereas the Policies section is where users can manage and view all their registered policies, along with their associated EGA accessions, links, and titles. A drop-down menu is available with shortcuts to the most useful pages in the top right corner. It is possible to check DACs and policies, as well as creating new ones. In this menu, users can also contact the Helpdesk team using the “Need Help?” section to make inquiries about DACs and policies. Comprehending the DACs list & making the most of all the features Colours are crucial in the DAC's section as they will indicate the user role and the status in every committee, namely: Yellow: you are the administrator of the DAC and have full control to add other contacts, modify the content, and manage data access requests. Orange: you are a member of the DAC and have the authority to manage data access requests but cannot make any changes to the content. Blue: you have been invited to join the DAC and need to either accept or decline the invitation. Grey: the DAC is currently pending validation by the EGA Helpdesk team. Red: the DAC has been declined by the EGA Helpdesk team. Discover more information on how to view the message from Helpdesk containing the declination reason or how to validate or reject a DAC invitation by taking the tour of the DAC Portal. How to create, register and edit a DAC Creating a DAC is an easy process that only requires users to complete the necessary information. After that, the EGA Helpdesk team will review the proposal and validate the proposal. Once the DAC is validated it is possible for authorised users with admin role to edit it at any time, ensuring that all information and contacts associated with the committee is accurate and up to date. Membership management is controlled by searching for the EGA user looking for their username, email, or organisation. Please, note that for a person's details to appear in this search engine, they must have registered as a EGA user. Data Access Committee administrators manage two types of permissions that apply to contacts: Main contact and Role. Both can be modified at any time: Main contact refers to the primary person we firstly contact for any inquiry about the DAC (please, note that we can contact all DAC members, and this refers to the person we will contact first). Role defines the actions that can be taken by each contact. There are two possible status, admin and member, details of which can be found in the Take the Tour. Keep in mind that, to save the modifications, it is always necessary to click on “Update”. Moreover, it is also possible to delete any contact in a DAC whenever it is needed. The data request process: how to manage access requests The data request process is now channelled through the EGA website. Once a user sends a request access, the petition will go directly to the DAC Portal profile of the DAC members liked to the requested dataset. Whenever a request is received, a sand clock symbol will be displayed next to the DAC responsible for the dataset involved. By clicking on the DAC, it is possible to discover more information about requester(s). In this section, DAC Portal users will also be able to modify the display of the data access requests. It is possible to group requests by dataset accession or username, depending on the user preferences. Additionally, it is possible to isolate specific requests by user or dataset. To accept a request, users must slide the button to the right. Once the button turns green, it signifies that the request is accepted; the requester will be granted access to the requested dataset. To make lives easier, we have implemented a “Select all” button to easily accept or revoke multiple requests at once. To decline requests, it is necessary to slide the button to the left. A red button means the requester will not be granted access to the dataset. The DAC Portal will always ask to submit the reason why the data access was denied. Please, note that any decision, grant or denial, must be confirmed clicking the "Apply" button. This will update the status of the request and notify the requester accordingly. Taking control of the activity in the DAC Portal: checking the requests history The History button is a useful feature that allows users to access a list of all the requests that have been granted. This is especially useful for auditing purposes, as it enables to keep track of who has been granted access to the data and when. To facilitate control for those users with several requests we have included a filtering option. It allows to filter requests by user, mail, dataset, EGA ID, and date (these are combinable to boost the search). Don't forget about policies: creation, management, and update Similarly to the DACs section, in the Policies one users can manage and view all their registered policies. By clicking on any policy, it is possible to view its details as well as making any necessary updates or modifications. The sheets symbol and its adjacent number indicate the quantity of objects using a policy. By clicking in the location symbol users will go directly to the DAC page on the DAC Portal to see more information about that DAC. DAC Portal users can create new policies. The first step requires to link the policy to a registered DAC. After defining the title, users can either add a link to an external URL containing terms & conditions or write the policy content directly on the DAC Portal. Keep in mind that both options can be included. Data Use Ontology (DUO) codes can be added to new policies. These codes are used to specify the permitted and prohibited uses of the data. Please, note that some of them require a modifier to provide additional specificity. Find out more by checking out this specific content on the DAC Portal Tour. Policies can be modified at any given time by clicking on one registered policy, which will display the current information.

  Blog new-dac-portal

• Genetic Causes of Growth Disorders

  Description of the disorder: A very common disorder presenting to pediatricians/pediatric endocrinologists is childhood growth failure. Sometimes the cause is evident, for example, growth hormone deficiency. In other children, the etiology remains unknown despite extensive evaluation, resulting in the unhelpful diagnosis of severe idiopathic short stature (SISS). These conditions are quite heterogeneous, including children with isolated growth failure and others who also have other abnormalities such as developmental delay or a constellation of congenital anomalies (syndromic short stature). Sometimes, the disorder appears to primarily affect the growth plate, which drives skeletal growth and thereby determines overall body proportions, whereas in other children, the disorder affects skeletal and non-skeletal tissues equally. Some cases of SISS have a polygenic inheritance while others appear to follow a Mendelian inheritance model, recessive, dominant or X-linked. Very recently, genome-wide analysis for copy number variants (CNVs) and whole-exome sequencing have begun to identify some of the molecular etiologies of these disorders.1 Identifying the molecular etiology of growth disorders has clinical and scientific value. Clinically, identifying a molecular cause prevents extensive further testing and may direct anticipatory care for associated medical problems. For example, we recently studied aggrecan (ACAN) gene mutations in families with autosomal dominant short stature and accelerated skeletal maturation. These mutations affect both growth plate cartilage, causing linear growth failure, and also articular cartilage, causing osteochondritis dissecans and early-onset osteoarthritis.1 Etiological classification of idiopathic growth failure allows more precise characterization of prognosis and response to treatment, which are currently highly imprecise because of the locus heterogeneity. In some cases, finding the genetic etiology points to a novel treatment approach that targets the specific molecular pathway involved. The proposed project is central to the main focus of our group, the Section on Growth and Development, NICHD. Our primary goal is to investigate cellular and molecular mechanisms governing childhood growth and to gain insight into the many human genetic disorders causing childhood growth failure. The proposed project is well suited for the intramural program because it takes advantage of Clinical Center expertise to phenotype subjects with SISS. Study subjects: We will study subjects with SISS and nuclear family members. SISS will be defined by height SDS &lt; -2.5 for age without evident cause after routine evaluation including: growth hormone axis evaluation; thyroid function testing; celiac disease screening; urinalysis; CBC; chemistry; karyotype (girls, for Turner syndrome); and testing for single gene defects based on the clinical evaluation (for example, SHOX or Noonan-associated genes). Candidate families will include isolated growth disorders and growth disorders that are accompanied by congenital anomalies, developmental delay, or other syndromic short stature. Strong preference will be given to subjects with a severe phenotype and a pedigree that indicates a Mendelian inheritance. Multiple independent families with the same phenotype will have priority. The pool of applicants for recruitment is large, and we receive many inquiries by emails and phone consultations from pediatric endocrinologists for advice regarding diagnosis and management of unusual growth disorders, including familial disease. Often these families are seeking further evaluation and are willing to participate in a research study. From this pool, we will be selecting pedigrees with very favorable Mendelian characteristics, for example de novo dominant occurrences where two normal parents have a child with SISS, and the child grows up to be an adult who passes this phenotype on to multiple grandchildren in the next generation. Subjects and family members will be brought to the NIH Clinical Center (NIHCC) for outpatient evaluation. Participants will be evaluated by pediatric endocrinology fellows (as part of our training program) and by senior staff to establish the clinical findings and construct a pedigree. Subjects will receive additional biochemical and imaging studies at the Clinical Center to complete the phenotyping and assign affected status. The growth abnormality will be evaluated by assessing body proportions, relative organ size, and skeletal imaging as indicated. Associated clinical abnormalities beyond altered growth will be characterized with the help of other Clinical Center subspecialists. Subjects and family members will be evaluated by SNP microarray and whole-exome sequencing. We anticipate 4-5 persons for each of 16-20 families, for a total of 72-90 whole-exome sequences. Half of the families will be recruited and studied within the first year and half in the second year. We will use freshly collected peripheral blood as the DNA source for SNP array and NextGen Sequencing. Analytic approach: The candidate genes will be chosen based on 1) inheritance state consistency, 2) population frequency in the ESP and UDP databases, and 3) predictions of deleteriousness. We will use VarSifter and the B road Institute Integrated Genome Viewer to filter and visualize these data. The genetic model will be dependent on the family&#39;s pedigree. For a simple trio, we will explore variants using genetic models including autosomal recessive, de novo (dominant), compound heterozygous, deletion/point mutation recessive, and X-linked (male only). The candidate variants will be identified using Boolean logic sets in VarSifter following intramural NHGRI/UDP methods. We will also use SNP microarray data to identify copy number variations, complete/single copy deletions, duplications, non-paternity, consanguinity for homozygosity mapping, uniparental isodisomy, mosaicism, and segregation patterns (bed file generation for use in VarSifter filter work). After a list of candidate sequence variants has been generated, annotation will include using the Exome Variant Server, Polyphen-2, MutationTaster, Sift, and CADD predictions of deleteriousness. Biological laboratory data will be included to prioritize candidate variants. Our group has expertise in the molecular mechanisms regulating both skeletal growth2,3 and growth of other tissues4,5, which may be helpful in this phase of the analysis. The most promising candidate mutations will be confirmed by Sanger sequencing and studied functionally, in vitro and/or in vivo. For mutations that affect skeletal growth, we will use experimental systems related to growth plate cartilage. For in vitro studies, we have experience transfecting chondrocyte cell lines, such as ATDC5, and primary chondrocytes. We will determine whether the mutation alters protein and/or cell function. In vivo studies can be used to explore pathophysiology. We have recently successfully used a new approach, the CAS9/CRISPR system to knockout multiple loci in mice (unpublished), which can be used again in the future to create mouse models efficiently. 1J Clin Endocrinol Metab, 2014 (PMID: 24762113) 2J Mol Endocrinol, 2014 (PMID: 24740736) 3Hum Mol Genet, 2012 (PMID: 22914739) 4Proc Natl Acad Sci U S A, 2013 (PMID: 23530192) 5Endocr Rev, 2011 (PMID: 21441345)

  Study phs001617

• 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

• The Melbourne Urological Research Alliance (MURAL) Collection of Patient-Derived Models of Prostate Cancer

  1. Low-Dose Carboplatin Modifies the Tumor Microenvironment to Augment CAR T Cell Efficacy in Human Prostate Cancer Models.Single-cell RNA sequencing (scRNA-seq) was performed to analyze the transcriptional modifications in the tumor microenvironment of prostate cancer patient-derived xenografts (PDX). The analyzed PDX tumor was PDX-287R from the MURAL collection of PDXs (PMID: 34413304). Mice harboring PDX tumors were treated with the carboplatin chemotherapy, and tumors were harvested 3 weeks later. RNA sequencing (RNA-Seq) was used to define immune and non-immune cell populations and anti-tumor innate immune signaling within the tumor microenvironment.To investigate the response of tumor cells to carboplatin treatment in more detail, RNA-Seq was performed on FACs-isolated tumor cells from PDX-287R at one week post-treatment. Gene set enrichment analysis showed an enrichment for apoptotic pathways, with significantly increased expression of the pro-apoptotic genes FAS, BAX, BBC3, IFI6, and JUN. Previously, carboplatin has been shown to activate the cGAS/STING pathway, and here we also found an increase in STING signaling, with an enrichment for the cytosolic DNA sensing pathway and increased expression of STING1, STAT1 and STAT2. There was also a significant increase in the expression of genes involved in T cell chemotaxis, including CXCL10, CXCL11, and CCL20. Collectively, carboplatin-induced cell death in tumor cells likely initiated a pro-inflammatory phenotype in prostate cancer PDXs.2. Co-Targeting BET, CREBBP, and EP300 Inhibits Neuroendocrine Signaling in Androgen Receptor-Null Prostate Cancer.The landscape of castration-resistant prostate cancer (CRPC) is characterized by a multitude of phenotypes, among which neuroendocrine disease holds a prominent position, each displaying unique responses to therapeutic interventions. The efficacy of inhibitors targeting BET and CREBBP/EP300 in prostate cancer treatment is widely acknowledged, primarily due to their ability to attenuate androgen receptor (AR) signaling. However, the effectiveness of these inhibitors in prostate cancers devoid of AR remains uncertain.This investigation sought to elucidate the role of BRD4, CREBBP, and EP300, which are co-expressed in both AR-positive and AR-null prostate cancer. Our study revealed that a compound targeting all three proteins, NEO2734, exhibited efficacy in suppressing the growth of organoids from both AR-positive and AR-null tumors, as evidenced by alterations in viability, size, and cellular composition. Furthermore, NEO2734 treatment consistently led to transcriptional downregulation of cell cycle pathways across various models. In the context of neuroendocrine prostate cancer (NEPC), NEO2734 demonstrated notable efficacy. Treatment with NEO2734 resulted in decreased expression of ASCL1 and other neuroendocrine markers (including SYP) , accompanied by a reduction in tumor growth in vivo. These findings collectively underscore the potential of epigenome-targeted inhibitors in impeding the growth of neuroendocrine prostate cancer, by disrupting lineage regulators in a phenotype-dependent manner. Consistent with the NEPC organoids and explants, NKX3-1 was upregulated in NEO2734-treated PDXs.In this study, we examined the impact of NEO2734 treatment on 6 organoids (treated for 24 hrs), 3 explants treated with JQ1 (for 48 hrs) and 2 PDXs (at two time points) using bulk RNA sequencing (RNA-Seq). These results are available through this dbGaP submission. The implications of these results warrant further exploration and development of compounds possessing similar activities for clinical applications. The ability of NEO2734 to mitigate the growth of both AR-positive and AR-null prostate cancer underscores its potential as a versatile therapeutic agent. Moreover, its efficacy in targeting neuroendocrine phenotypes offers promising avenues for addressing a subtype of CRPC that poses significant clinical challenges. In conclusion, this study sheds light on the intricate interplay between epigenetic regulators and prostate cancer phenotypes. The observed effects of NEO2734 on growth inhibition and phenotype-specific disruption highlight the importance of continued research into compounds with similar epigenome-targeting mechanisms for advancing precision medicine approaches in the treatment of prostate cancer.3. Defining Focal Neuroendocrine Differentiation as a Transcriptionally Distinct Form of Prostate Cancer Pathology Characterized by the Expression of Androgen ReceptorsThe study aimed to understand the transcriptional differences among different neuroendocrine pathologies of prostate cancer. The study hypothesized that the diverse neuroendocrine pathology observed in prostate might be attributable to differences in transcriptional level that remain hidden in bulk RNA-Seq data. To investigate, we employed scRNA-seq to generate gene expression profiles of 18,632 individual tumor cells from 9 PDX models representing five distinct neuroendocrine pathologies of prostate cancer. Our analysis identified 3-8 transcriptionally distinct sub-populations per PDX. The expression of key oncogenic signaling pathways and master regulator activity varied across in neuroendocrine pathologies, with each type of pathology displaying a unique set of transcriptional sub-populations. We discovered that, like the amphicrine pathology, focal neuroendocrine differentiation (focal NED) cells maintain androgen receptor (AR) signaling. However, the expression profiles of focal NED cells differed from the other pathologies, indicating it should be considered as a distinct type of neuroendocrine pathology in prostate cancer. Analysis of copy number alterations suggested little clonal divergence between focal NED cells and neighboring adenocarcinoma cells, emphasizing the transcriptional distinctiveness of focal NED.Overall, the findings suggest potential differences in treatment approaches for tumors from prostate cancer patients showing neuroendocrine pathology, especially focal NED.4. Reprogramming of Androgen Receptor Activity in Castration-Resistant Prostate Cancer is Shaped by Truncated Variants.Under the influence of treatments that target the androgen receptor (AR) axis, prostate cancer cells undergo various changes to the AR gene. This includes the production of truncated AR variants that lack the ligand binding domain and are constitutively active. However, the exact role of these truncated variants in shaping the AR signaling axis and their importance in therapy resistance is not yet fully understood. By examining the AR cistrome in a group of patient-derived prostate cancer models with different mechanisms of castration resistance, we observed diverse changes in AR activity in metastatic prostate cancer, including castration-resistant models. Specifically, we identified a unique subset of tumors characterized by the expression of ARv567es, a variant resulting from structural rearrangements of the AR gene. Tumors positive for ARv567es showed a distinct AR binding profile and epigenome compared to those without ARv567es, which was linked to an altered transcriptional profile. The presence of ARv567es is associated with poor responses to treatments, such as castration and bipolar androgen therapy (BAT), regardless of the presence of full-length AR. This involves disruption of the autoregulatory loop that controls AR gene transcription and reduced transcriptional responses to treatment. Overall, this study demonstrates that ARv567es can lead to transcriptional reprogramming in advanced prostate cancer and drive therapy resistance. 5. The MURAL Collection of Prostate Cancer Patient-Derived Xenografts Enables Discovery Through Preclinical Models of Uro-Oncology.We created a comprehensive PDX resource for prostate cancer to facilitate the rapid and systematic evaluation of new therapies. This collection includes 59 tumors from 30 patients, gathered between 2012 and 2020, aligning with the availability of abiraterone and enzalutamide in the clinic. The PDXs cover the clinical, pathological, and genomic spectrum of prostate cancer, ranging from treatment-na&#239;ve primary tumors to castration-resistant metastases. We characterized heterogeneity in adenocarcinoma and neuroendocrine phenotypes using bulk and single-cell RNA sequencing. Additionally, organoids were cultured from PDXs, enhancing our preclinical study capabilities. Employing a 1x1x1 design, we quickly identified tumors with responses to combination therapies. To manage the distribution of PDXs, we established the Melbourne Urological Research Alliance (MURAL). This PDX collection significantly enhances our ability to test and prioritize effective treatments for future clinical trials in prostate cancer.6. Prostate Cancer Associated Fibroblasts Have Distinct Morpho-Mechanical Features That Are Associated With Patient Outcome.The tumor stroma, including cancer-associated fibroblasts (CAFs), has a crucial role in tumor progression. This study focused on the morphological and mechanical properties of CAFs and normal prostatic fibroblasts. The findings revealed that CAFs had distinct morphological and mechanical characteristics compared to normal fibroblasts, such as differences in nuclear size, shape, F-actin arrangement, cellular volumes, and elasticity. Additionally, the study correlated these biophysical properties with transcriptomic data, identifying pathways and cellular components linked to the observed changes. Overall, high-throughput assessments of biophysical properties in prostate cancer cells and stromal components may serve as predictive tools for patient outcomes and help identify new therapeutic approaches targeting the tumor stroma.

  Study phs003369

• GAW16 Framingham and Simulated Data

  Important links to apply for individual-level data Genetic Analysis Workshop Instructions to Request Authorized Access Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Research Use Statement Questions regarding GAW16 should be directed to Vanessa Olmo at vanessa@business-endeavors.com. Problem 2: Description of the Framingham Heart Study In GAW16, we use data drawn from the Framingham Heart Study. The Framingham Heart Study &#8212; under the direction of National Heart, Lung, and Blood Institute; NHLBI &#8212; began in 1948 with the recruitment of adults from the town of Framingham, Massachusetts. At the time, little was known about the general causes of heart disease and stroke, but the death rates for cardiovascular disease (CVD) had been increasing steadily since the beginning of the 20th century and had become an American epidemic. The Framingham Heart Study is now conducted in collaboration with Boston University. The objective of the Framingham Heart Study was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Between 1948 and 1953 the researchers recruited 5,209 subjects (2,336 men and 2,873 women) between the ages of 29 and 62 from the town of Framingham, Massachusetts and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Subjects were recruited from lists of addresses recorded for the town. Two out of every three households were approached for participation in the study. While there was no intention to recruit families for family studies, the plan was to recruit all household members in the ages 30-60 within each house that was selected for study. Hence, many biologically related individuals were recruited, including 1644 spouse pairs. Since 1948, these participants have returned to the study every two years for a detailed medical history, physical examination, and laboratory tests. Now in 2008 at 60 years of follow up, there remain about 500 participants from this cohort. Between 1971 and 1975 the study enrolled a second-generation group &#8212; 5,124 of the original participants&#39; children and the spouses of these children &#8212; to participate in similar examinations. 2,616 subjects are offspring of the original spouse pairs and 34 are stepchildren. A total of 898 offspring are children of cohort members where only one parent was a study participant and 1,576 are spouses of the offspring. The Offspring Cohort has been followed every four years through 2001 (except between Exams 1 and 2 with an intervening 8 years) using protocols similar to those used for study of the Original Cohort. Between 2002 and 2005 the study enrolled the third generation (Gen3) of the Framingham Heart Study - 4095 offspring of the second generation. None of their spouses were recruited. An additional 103 parents of this third generation, who were not recruited between 1971 and 1975, were also recruited at this time. The latter group is not included in the GAW16 data. With the recruitment of this third generation, the study has increasingly focused on genetic factors associated with the development of cardiovascular disease and its associated risk factors. To date, there is only one examination of this generation of participants. A description of the recruitment of this third generation and comparison with the earlier generations at their initial recruitment is presented in Splansky GL et al., 2007. Further information on the Study can be found at http://www.nhlbi.nih.gov/about/framingham/index.html. Genome-wide Dense SNP Scan in Framingham Heart Study Genetic studies did not begin in the FHS until the 1990s. In the late 1980s and through the 1990s DNA was extracted from blood samples of surviving FHS participants. In 2007, the FHS entered a new phase with the conduct of genotyping for the FHS SHARe (SNP Health Association Resource) project, for which dense SNP genotyping was performed using approximately 550,000 SNPs (GeneChip&#174; Human Mapping 500K Array Set and the 50K Human Gene Focused Panel) in 10,775 samples (some duplicates) from the three generations of subjects (including over 900 pedigrees). Affymetrix conducted all genotyping for the FHS SHARe project, using the 250K Sty, 250K Nsp, and the supplemental 50K platforms. Eighty-nine percent of the DNA samples were collected during the 1990s. To maximize the power of the study, we also extracted DNA from 1133 blood samples, drawn from subjects who had no DNA, to include in the SHARe project. These samples had been sitting in our refrigerators for some time, a few as far back as the 1970s. We refer to these DNA samples as the legacy samples. These samples had a higher failure rate in the genotyping process (40%) than the other eighty-nine percent (3%). Affymetrix invoked its own criteria for a sample to succeed in genotyping. All non-legacy samples must succeed on all three platforms, while legacy samples needed to pass on at least one platform. When a sample failed, additional attempts were made. Samples that repeatedly failed 2-4 times were called failures. Other samples failed due to issues of genotyped sex identification not matching our records or low SNP concordance among SNPs common across arrays or contamination. Eighty-nine percent of the legacy samples for which genotyping results are available passed all three platforms. The genotyping data from the 10,043 samples from 9354 subjects that passed the Affymetrix criteria were additionally checked for gender consistency and consistency with family structure, resulting in genotyping data for 9,274 participants in FHS SHARe. Genotype calls were made with the BRLMM algorithm. The SHARe database is housed at the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) and contains all ~550,000 SNPs. This genome-wide dense SNP scan and a subset of phenotypes from the Framingham Heart Study are the focus of the Genetic Analysis Workshop 16. Further information on the specific variables in the Problem 2 dataset can be found by clicking on the Documents tab at the top of the page. Problem 3: Description of the Simulated Data Set The focus of this simulation is gene discovery in genome-wide association scans (GWAS). The Framingham Heart Study data set (distributed as "Problem 2") is the basis for the FHS* simulated data. The pedigree structures are derived from the data distributed for Problem 2, and we distribute an accompanying triplet file (triplet_sim) containing person ID, father ID, mother ID, to ensure the identical subjects, pedigrees, and singletons are used in the simulated data analysis. Consistent with standard practice, founders and singletons are designated as subjects with both fshare and mshare equal to zero (missing). The simulated data includes a total of 6,479 subjects with both phenotype and genotype data, in 942 pedigrees distributed among 3 generations and 188 singletons. Data inclusion is consistent with the subjects&#39; consent for use by both for-profit and not-for-profit researchers. The genotypes for all Problem 3 replicates are fixed as measured and distributed for Problem 2 for both the genomewide scan and the additional candidate gene SNPs, for a total of approximately 550,000 SNPs (GeneChip&#174; Human Mapping 500K Array Set and the 50K Human Gene Focused Panel). Thus, to analyze the Problem 3 simulated data you also will need to download the Problem 2 genotypes. Note that there are slight discrepancies in counts between Problems 2 and 3 due to a change in consents between the two datasets. Several phenotypes that contribute to coronary heart disease (CHD) were simulated for all individuals with genotypes across three different time points, 10 years apart. All genotyped individuals have complete data; the effects of missing values can be investigated by user-specified missing value patterns. There are 200 longitudinal datasets created, based on the generating model, and each replication is found in a separate dataset. We suggest that if only one replication is to be analyzed, that it be replication 1 to enable more precise comparisons among analytical approaches. The &#39;shareid&#39; will allow you to merge the simulated phenotype data with the Problem 2 genotype data, and reconstruction of the pedigrees using the distributed &#39;triplet_sim&#39; file or for larger families&#39; relationships with the triplet distributed with the Problem 2. The simulated data problem is further described in the associated readme file, and a data dictionary is provided defining all the variables. For disclosure of the generating model for these data, please contact Jean MacCluer at jean@sfbrgenetics.org.

  Study phs000128

• How to upload Crypt4GH files

  Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool 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. [Optional] 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. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_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 inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs)  for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username.  If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days.  Specific to using keys to authenticate 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_INBOX_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 submission/data/uploading-files/inbox