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• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002977

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002978

• Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient

  Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002979

• Genome and transcriptome sequence data from a metastatic fibrolamellar hepatocelluar carcinoma patient

  Genome and transcriptome sequence data from a metastatic fibrolamellar hepatocelluar carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002980

• Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient

  Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002981

• Genome and transcriptome sequence data from a metastatic rectosigmoid adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic rectosigmoid adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002982

• Genome and transcriptome sequence data from a metastatic carcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic carcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002983

• Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient

  Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002984

• Genome and transcriptome sequence data from a adenocarcinoma of the lung patient

  Genome and transcriptome sequence data from a adenocarcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002985

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002986

• Genome and transcriptome sequence data from a metastatic endocervical adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic endocervical adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002987

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002988

• Genome and transcriptome sequence data from a medullary thyroid cancer patient

  Genome and transcriptome sequence data from a medullary thyroid cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002989

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002990

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002992

• Genome and transcriptome sequence data from a metastatic carcinoma of primary unknown patient

  Genome and transcriptome sequence data from a metastatic carcinoma of primary unknown patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002993

• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of anus patient

  Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of anus patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002994

• Genome and transcriptome sequence data from a carcinosarcoma of the uterus patient

  Genome and transcriptome sequence data from a carcinosarcoma of the uterus patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002995

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002997

• Autozygosity pilot - Born in Bradford (2017-05-11)

  The offspring of first cousin marriages have ~6% of their genome autozygous, i.e. homozygous identical by descent, or even more if there was further consanguinity in their ancestry. In the UK there are large populations with very high first cousin marriage rates of 20-50%. Sequencing the exomes of a sample of these individuals has the potential both to support genetic health programmes in these populations, and to provide genetic research information about rare loss of function mutations. This pilot study based on existing cohort samples from the Born In Bradford study will identify homozygous individuals for almost all variants down to an allele frequency around 1%, plus individuals carrying hundreds of new homozygous rare loss-of-function variants, and will support development of community relations and ethics for a wider study currently being designed. The data deposited in the EGA consist of low coverage whole exome sequencing on these samples. This dataset contains all the data available for this study on 2017-05-11.

  Dataset EGAD00001003329

• Targeted resequencing of Cardiomyopathies associated genes

  In this study, we present the results of a custom “pan-cardiomyopathy panel” in a molecular screening of 38 unrelated patients, 16 affected by DCM, 14 by HCM, and 8 by ARVC. The panel was designed using the Design Studio Tool (Illumina, San Diego, CA,USA). Coding regions and intron–exon boundaries of 115 genes, known to be associated with 7 DCM, HCM, and ARVC as well as channelopathies, were selected for targeted gene enrichment. For genes with multiple transcripts, all exons included in transcripts expressed in cardiac muscle were considered in the gene panel design. Total DNA was extracted from peripheral blood samples using the Wizard Genomic DNA Purification Kit (Promega, Mannheim, Germany) according to the manufacturer’s instructions, quantified, and qualitatively checked using NanoDrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA). Custom targeted gene enrichment and DNA library preparation were performed using the Nextera Capture Custom Enrichment kit (Illumina) according to the manufacturer’s instructions. Targeted regions were sequenced using the Illumina MiSeq platform, generating approximately two millions of 150-bp paired-end reads for each sample (Q30 ≥90%).

  Dataset EGAD00001003359

• Leiomyosarcoma Cancer Transcriptome Sequencing

  RNA-Seq data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 75bp paired-end protocol to a minimum mean reads of 50 million.

  Dataset EGAD00001003192

• GoNL release 5 haplotype panel

  The samples in this panel come from 250 families: 248 parents-child trios and 2 parent-child duos. As the children do not provide additional haplotypes or population information, they were excluded from the panel. The samples present in the release are composed of 248 couples, 2 single individuals and 1 sample composed from the 2 haplotypes from the duo's children transmitted by their missing parent. The composed sample is named gonl-220c_223c.The files contain a total of 18.9M SNVs and 1.1M INDELs in autosomal chromosomes. They were generated by phasing/imputing the SNVs (a) and INDELs (b) using MVNCall. Only sites passing filters are reported. Sites filtered as part of the GoNL inaccessible genome were kept (but flagged as filtered) and still may contain true positive calls but should be used with care as they are located in parts of the genome that are less well captured (systematic under or over-covered or low-mapping quality)

  Dataset EGAD00001000744

• Integrated genomic characterization of adrenocortical carcinoma

  Adrenocortical carcinomas (ACC) are aggressive cancers originating in the cortex of the adrenal glands. Despite the overall poor prognosis, ACC outcome is heterogeneous. CTNNB1 and TP53 mutations are frequent in these tumors, but the complete spectrum of genetic changes remains undefined. Exome sequencing and SNP array analysis of 45 ACC revealed recurrent alterations in known drivers (CTNNB1, TP53, CDKN2A, RB1, MEN1) and genes not previously reported to be altered in ACC (ZNRF3, DAXX, TERT and MED12), which were validated in an independent cohort of 77 ACC. The cell-surface transmembrane E3 ubiquitin ligase ZNRF36 was the gene the most frequently altered (21%), and appears as a potential novel tumor suppressor gene related to the ß-catenin pathway.Our integrated genomic analyses led to the identification of two distinct molecular subgroups with opposite outcome. The C1A group of poor outcome ACC was characterized by numerous mutations and DNA methylation alterations, whereas the C1B group with good prognosis displayed a specific deregulation of two miRNA clusters. Thus, aggressive and indolent ACC correspond to two distinct molecular entities, driven by different oncogenic alterations.

  Dataset EGAD00001000764

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002048

• Genome and transcriptome sequence data from a liposarcoma patient

  Genome and transcriptome sequence data from a liposarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002046

• Genome and transcriptome sequence data from a lung cancer patient

  Genome and transcriptome sequence data from a lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002045

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002044

• Genome and transcriptome sequence data from a recurrent glioma patient

  Genome and transcriptome sequence data from a recurrent glioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002043

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002041

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002036

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002035

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002034

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002033

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002031

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002030

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002028

• Genome and transcriptome sequence data from a colorectal cancer patient

  Genome and transcriptome sequence data from a colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002027

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002026

• Genome and transcriptome sequence data from a colorectal cancer patient

  Genome and transcriptome sequence data from a colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002025

• Genome and transcriptome sequence data from a lung cancer patient

  Genome and transcriptome sequence data from a lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002023

• Genome and transcriptome sequence data from a colorectal cancer patient

  Genome and transcriptome sequence data from a colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002022

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002021

• Genome and transcriptome sequence data from a metastatic NPC patient

  Genome and transcriptome sequence data from a metastatic NPC patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002020

• RNA-seq FASTQ files from newborn screening dried blood spot samples

  Paired-end RNA-seq FASTQ files from 21 newborn screening dried blood spot (DBS) samples. These DBS samples were obtained from extremely low gestional age newborns, where 10 of them were affected by a fetal inflammatory response (FIR) before birth, and 11 were unaffected. Total RNA was sequenced using an Illumina NextSeq-500 instrument. The sample preparation protocol included the depletion of rRNA and globin mRNA using the Globin Zero Gold rRNA Removal Kit from Illumina. Libraries were prepared using the NebNext Ultra TM II Directionl RNA LIbrary Prep Kit (New England Biolabs). Each sample was sequenced in 4 lanes, leading to 8 FASTQ files per sample and a total of 21x8=168 FASTQ files. There is an additional number of 8 FASTQ files corresponding to sample BS13, which was downsampled to 1/4 of its original depth (see BS13_README file for details).

  Dataset EGAD00001004991

• UCSF Pediatric Bithalamic Glioma Genome Project

  Targeted next-generation sequencing of 13 pediatric bithalamic diffuse gliomas. BAM files of targeted next-generation DNA sequencing data of 13 pediatric gliomas, with multi-region sequencing data from 2 of these cases (17 total tumor samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from using the QIAamp DNA FFPE Tissue Kit (Qiagen). Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of 480 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

  Dataset EGAD00001005499

• MutWP5: CRUK Mutographs of Cancer: Breast: Cancer Mastectomy (Exome) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from 30 women who underwent mastectomies due to a breast cancer diagnosis. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who had cosmetic breast reduction surgeries and those with germline BRCA 1/2 mutations. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005924

• Genome and transcriptome sequence data from a colon cancer patient

  Genome and transcriptome sequence data from a colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005826

• Genome and transcriptome sequence data from a metastatic melanoma patient

  Genome and transcriptome sequence data from a metastatic melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005852

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005838

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005841

• Genome and transcriptome sequence data from a chondrosarcoma patient

  Genome and transcriptome sequence data from a chondrosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005848

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005849

• Genome and transcriptome sequence data from a metastatic melanoma patient

  Genome and transcriptome sequence data from a metastatic melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005851

• DRIP-seq data for Molecular Characterization of ETMRs

  This dataset contains DRIP-seq data from 2 patients. DNA–RNA hybrids were extracted from tissue derived from ETMR patient-derived xenograft (PDX) models (BT183) that were treated using topotecan or saline as described previously27. Tumours were subsequently frozen and pelleted using ultracentrifugation. DNA–RNA hybrids were extracted as described previously using the same protocol that is applied for cultured cells21. DNA was extracted using proteinase K followed by phenol–chloroform extraction and ethanol precipitation. Subsequently the DNA was fragmented using the restriction enzymes HindIII, EcoRI, BsrGI, XbaI and SspI (New England Biolabs). Digested DNA was subsequently incubated with the anti-DNA–RNA hybrid anti- body S9.6 (Merck, MABE1095) and immunoprecipitated using agarose beads. Bound DNA–RNA hybrids were eluted and incubated with pro- teinase K and cleaned with an additional phenol–chloroform–ethanol extraction. The DNA was subsequently sonicated and sequenced using a Hiseq 2000 machine with a 50-bp single-read protocol. Each treat- ment condition was performed in duplicate and both RNase H and the input was included as negative controls resulting in 10 Fastq files.

  Dataset EGAD00001006219

• Smart-seq 2 single cell sequencing of CD4 and CD8 T cells from the blood and synovial fluid of 4 psoriatic arthritis patients.

  This dataset was used to characterise T cell gene expression and clonality at sites of active inflammation within the joints of psoriatic arthritis (PsA) patients, and to compare these results with T cells from the peripheral blood of those same patients. Freshly sorted CD45RA negative CD3+CD4+ and CD3+CD8+ single cells from four patients were individually flow sorted into 96-well full-skirted plates (Eppendorf) containing 10µL of a 2% Dithiothreitol (DTT, 2M Sigma-Aldrich), RTL lysis buffer (Qiagen) solution. Cell lysates were sealed, mixed and spun down before storing at -80 ºC. Paired-end multiplexed sequencing libraries were prepared following the Smart-seq 2 protocol using the Nextera XT DNA library prep kit (Illumina). A pool of barcoded libraries from four different plates were sequenced across two lanes on the Illumina HiSeq 2500.

  Dataset EGAD00001006342

• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776

• WGS and RNA-Seq data from a GBM patient PT-SJ5453

  WGS and RNA-Seq data from a GBM patient PT-SJ5453

  Dataset EGAD00001008529

• WGS and RNA-Seq data from a GBM patient PT-PV2594

  WGS and RNA-Seq data from a GBM patient PT-PV2594

  Dataset EGAD00001008526

• WGS and RNA-Seq data from a GBM patient PT-SS3647

  WGS and RNA-Seq data from a GBM patient PT-SS3647

  Dataset EGAD00001008530

• Oesophageal adenocarcinoma RNAseq from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains bulk transcriptomes from the inoperable cohorts of the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19). Transcriptomes were prepared from pre-treatment oesophageal tumour biopsies using a ribodepletion approach in order to assess both previously reported (e.g. PD-L1 expression) and novel predictive expression-based biomarkers for immunochemotherapy treatment in this setting. On-treatment and post-treatment biopsies were generated as well to characterize response to therapy, and samples were also prepared from paired normal GI tissues for a subset of patients. scRNA-seq based deconvolution was also applied to bulk transcriptomes in this study to estimate the cell composition of tumour biopsies and assess the link between the presence of specific cell types with immunochemotherapy outcomes.

  Dataset EGAD00001009399

• RNA-sequencing of ex vivo exhausted human antigen-specific T cells

  Tumor-specific T cells are frequently exhausted by chronic antigenic stimulation. To explore new pathways for reinvigoration of anti-tumor immune functions, we developed a human ex vivo exhaustion model by repetitive antigenic stimulation of primary CD8 T cells. This results in T cells that resemble patient-derived T cells in tumors on a phenotypic and transcriptional level. Four human healhy donor CD8+ T cells were isolated, transduced with an NY-ESO-1 TCR lentivirus construct, stimulated in four different conditions (Trested, Ttumor, Tex, Teff) with T2 tumor cells and specific peptides over 12 days. Cells were then sorted for TCR Vbeta 13.1+ (NY-ESO-1 TCR) CD8+ CD3+ CD56- CD4- DAPI- cells. RNA-seq TruSeq libraries were generated from polyA-enriched mRNA isolated from the samples, and sequenced in paired-end mode (2x51bp) on 2 lanes of an Illumina NovaSeq 6000 flow-cell. FASTQ sequence files were generated with the Illumina RTA version 3.4.4 and Base-calling Version bcl2fastq-2.20.0.422.

  Dataset EGAD00001009754

• WGS of tumor and blood control samples of neuroblastoma

  high coverage whole genome sequencing of 38 samples was done on a patterned flowcell v.2.5 (150 bp paired end, HiSeq X Ten) with coverage of about 60x for the tumor and whole blood control samples. All tumors had a tumor cell content of ≥60%. Sequencing libraries were prepared using the Truseq DNA Nano kit (Illumina) according to the manufacturers’ instructions and size selected using SPRI beads (Beckman Coulter Genomics).

  Dataset EGAD00001009624

• Pediatric Patient-Derived-Xenograft development in MAPPYACTS – international pediatric cancer precision medicine trial in relapsed and refractory tumors

  Pediatric patients with recurrent and refractory cancers are in most need for new treatments. This study developed patient-derived-xenograft (PDX) models within the European MAPPYACTS cancer precision medicine trial (NCT02613962). To date, 131 PDX models were established following heterotopical and/or orthotopical implantation in immunocompromised mice: 76 sarcomas, 25 other solid tumors, 12 central nervous system tumors, 15 acute leukemias, and 3 lymphomas. PDX establishment rate was 43%. Histology, whole exome and RNA sequencing revealed a high concordance with the primary patient’s tumor profile, human leukocyte-antigen characteristics and specific metabolic pathway signatures. A detailed patient molecular characterization, including specific mutations prioritized in the clinical molecular tumor boards are provided. Ninety models were shared with the IMI2 ITCC Paediatric Preclinical Proof-of-concept Platform (IMI2 ITCC-P4) for further exploitation. This new PDX biobank of unique recurrent childhood cancers provides an essential support for basic and translational research and new treatments development in advanced pediatric malignancies.

  Dataset EGAD00001011048

• Familial breast cancer targeted sequencing with ONT

  A large cohort of 600 cases with familial breast cancer as classified by the Spanish Society of Medical Oncology (SEOM) Clinical Guidelines Update that were recruited during 6 years at Hospital Universitario Morales Meseguer (Murcia, Spain) were retrospectively evaluated to select 16 cases with no positive finding in NGS analysis of 20 genes implicated in this disease. These 16 cases were selected for further investigation using nanopore sequencing. This method involved the use of adaptive sampling enrichment, targeting a panel of 18 human genome regions, which contained the 20 genes (PTEN, ATM, BRCA2, PALB2, CDH1, TP53, NF1, RAD51D, BRCA1, RAD51C ,BRIP1, STK11, CHEK2, EPCAM, MSH2, MSH6, BARD1, MLH1, PMS2, NBN). In 5 samples (P1, P2, P4, P15 and P16) no selection of long reads was performed. Additionally, in 3 samples (P7, P9 and P10), both procedures were performed in two independent runs, and for the second run of P7, the DNA was previously fragmented using g-TUBE Covaris® (ref 520079) according to the protocol for 6 kb fragments.

  Dataset EGAD00001011106

• Genomic Advances in Sepsis (GAinS) genotyping

  This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 35 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected over the first five days of ICU admission to investigate how host genetics affects the individual repsonse to sepsis. DNA was extracted from buffy coat or whole blood samples using the Qiagen DNA extraction protocol, the automated Maxwell Blood purification kit (Promega), or the QIAamp Blood Midi kit protocol (Qiagen). Genotyping data were generated using the Illumina HumanOmniExpress BeadChip (295 patients), the Infinium CoreExome BeadChip (655 patients), and the Infinium Global Screening Array BeadChip (307 patients). Genotyping QC and imputation into the Haplotype Reference Consortium was perfomed within each batch. The datasets were combined and following post-imputation filtering data were available on 1168 samples.

  Dataset EGAD00001015369

• RNA-seq dataset: Single-cell spatial analysis of pediatric high-grade glioma reveals a novel population of immunosuppressive and tumor-promoting SPP1+/GPNMB+ myeloid cells

  Our study utilizes two novel techniques, cyclical immunofluorescence imaging and single-cell spatial transcriptomics, to spatially map the tumor microenvironment of a large sample set of pediatric high-grade gliomas (pHGG). Using these methods, we have identified an abundant immunosuppressive myeloid cell population that has not been described in the context of pediatric high-grade gliomas. We validate our findings using an in vitro assay, spatial analysis on additional pHGG biopsies, independent spatial transcriptomic data, bulk RNA sequencing data of an expanded cohort of in-house patients, and publicly available bulk RNA sequencing data of an even larger cohort of pHGG patients.

  Dataset EGAD00001015450

• Gene expression QTL mapping in stimulated iPSC-derived macrophages

  Immune cells sense and respond to external stimuli, initiating an inflammatory response, with genetic variants potentially altering these responses. Traditional QTL mapping often uses naïve cells, missing condition-specific variants detectable post-stimulation. Our study uses a high-throughput platform to map eQTLs across 24 conditions using iPSC-derived macrophages, identifying that 76% of eQTLs in stimulated conditions were also present in naïve cells. We found response eQTLs (reQTLs) vary widely across conditions, with rare single-condition reQTLs being overrepresented among disease-colocalizing eQTLs. This study nominates 21.7% more disease effector genes at GWAS loci through reQTL colocalization, with 38.6% not found in the GTEx catalogue, highlighting the importance of condition-specific regulatory variation in understanding disease risk alleles. Our findings underscore the value of condition-specific studies in elucidating the genetic mechanisms underlying complex diseases. 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 EGAD00001015380

• Understanding the development of resident memory T cells (Trm) in the human small intestine using integrative multiomic approaches: Adult RNA (2025-10-14)

  Animal studies have demonstrated that resident memory T (Trm) cells provide enhanced protective responses to a broad array of tissue-tropic pathogens, thus making Trm cells promising targets for novel vaccination strategies. However, the biological pathways that enable the long-term survival of Trm cells are poorly understood in humans. Here, we will employ a unique human intestinal transplantation setting that allows us to study the retention of persistent T cells in the grafts and the temporal development of resident T-cell populations from recruited recipient T cells. We will integrate high resolution transcriptomics, epigenetics, proteomics and immune repertoire single-cell data from purified intestinal T cells. These single-cell multiomics approaches will uncover the diversity and differentiation of the T cell populations in the human intestine, allowing us to temporally resolve the generation and maintenance of gut resident T cells. . This dataset contains all the data available for this study on 2025-10-14.

  Dataset EGAD00001015739

• Neuroblastoma and adrenal gland single-cell study

  Neuroblastoma is a pediatric tumor of the developing sympathetic nervous system. However, the cellular origin of neuroblastoma remains to be defined. Here, we study single-cell transcriptomes of neuroblastomas and normal human developing adrenal glands at various stages of embryonic and fetal development. We define normal differentiation trajectories during adrenal medullary development and identify the cellular origin of neuroblastoma. Importantly, adrenergic and mesenchymal neuroblastomas with varying clinical phenotypes match different temporal states along normal differentiation trajectories, with the degree of differentiation corresponding to clinical prognosis.

  Study EGAS00001004388

• Mapping genetic variants that underlie gene regulation in intestinal cell types to identify novel, validated, Crohn’s disease drug targets

  Biopsies from the terminal ileum and rectum of healthy individuals and terminal ileum and blood from Crohns disease patients are collected and processed single cells and processed for single-cell RNA-sequencing using 10X Genomics 3' v3 and v3.1 and Illumina sequencing. 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 EGAS00001003647

• Analyzing the expression profiles of genes in ureters with and without indwelling stents using RNAseq 

  The objective of the work is to explore the pathways and mechanisms driving inflammation and fibrosis in stented ureters. In total, 6 pigs underwent cystoscopic unilateral ureteral stent insertion for 14 days. Ureteral tissue was harvested in 3 pigs, while the remaining 3 pigs had their stents removed, and were recovered for 7 days. Three separate pigs served as controls. Stents cause significant inflammation and fibrosis of ureters. Gene set enrichment analysis confirmed fibrotic changes and tissue proliferation and suggests that epithelial-mesenchymal transition is a driver of fibrosis.

  Study EGAS00001006855

• SardiNIA Medical Sequencing Discovery Project

  The SardiNIA Medical Sequencing Discovery Project studies the genetics of blood lipid levels and personality in a Sardinian population cohort. The project has generated draft genome sequences for approximately 2,000 individuals using whole genome shotgun sequencing. The draft sequences will allow investigators to evaluate the contribution of common and rare single nucleotide polymorphisms, short insertions and deletions, large copy number polymorphisms and other structural variants to blood levels of low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and triglycerides (TG), all of which are key risk factors for cardiovascular disease, and to the 5 domains of personality as assessed by the NEO-PI-R questionnaire. The two traits represent different ends of the spectrum of medically interesting complex traits. Blood lipid levels are a risk factor for cardiovascular disease for which genetic studies have been very successful. In contrast, personality traits and other behavioral phenotypes represent a set of phenotypes that have proven more challenging to dissect using standard genetic tools. In both cases, we expect whole genome sequencing to improve our understanding of the underlying biology. The isolated Sardinian population is ideal for this type of study for several reasons, in particular because: (i) the bottleneck that occurred after colonization of the island attenuated natural selection against alleles with phenotypic consequences, increasing the odds that functional alleles will reach modest frequencies (0.5 - 5.0%) and will be detected in the present study and (ii) sharing of long haplotype stretches surrounding rare variants will facilitate imputation based analyses of shotgun sequence data, which improve the accuracy of individual genotype calls and thus increase power. This research helps advance NIH's mission by furthering our understanding of the genetic factors contributing to blood lipid levels and coronary heart disease and to personality, behavior and mental health. In addition, these data should facilitate development of analysis tools and strategies that can be used to study the genomes of hundreds to thousands of individuals and further our understanding of the genetics and biology of many different traits and conditions.

  Study phs000313

• Molecular defects in pseudohypoparathyroidism or related disorders

  Maintaining calcium levels within a narrow normal range is of critical importance for numerous different cellular functions. One of the most important regulators of blood calcium levels is parathyroid hormone (PTH), which mediates its actions through the PTH/PTHrP receptor, a Gαs-coupled receptor. Few inherited disorders are characterized by diminished blood calcium levels and elevated blood phosphate levels; some of these disorders are caused by too little PTH synthesis and/or secretion (hypoparathyroidism, HP), while others are caused by resistance towards PTH (pseudohypoparathyroidism, PHP). Only few of the inherited forms of HP (<10%) have been defined at the molecular level. In contrast, genetic mutations have been identified for several inherited forms of PHP. For example, PHP type Ia (PHP-Ia) is caused by maternally inherited mutations in those GNAS exons that encode Gαs, while autosomal dominant PHP type Ib (AD-PHP-Ib) is caused by maternal inherited deletions within or up-stream of GNAS, which are associated with abnormal GNAS methylation. However, a large number of patients with PTH-resistance and thus hypocalcemia show GNAS methylation changes, but their underlying genetic defects have not yet been defined at the DNA level. These "sporadic" patients may be affected by an autosomal recessive form of PHP-Ib (AR-PHP-Ib), which is most likely not linked to the GNAS locus. In our studies, we propose to search through exome sequence analyses for novel genetic mutations responsible for novel autosomal dominant forms of HP that are not caused by mutations in the known disease-causing genes; some of these families are large enough to perform genetic linkage studies. We furthermore propose to search for the genetic mutation(s) responsible for the autosomal recessive variant of PHP-Ib through the analysis of whole exome sequences; for these studies we focus particularly on patients, whose parents are likely to be consanguineous. The proposed efforts are expected to lead to the identification of novel genes that are involved in parathyroid development and function (HP) and genes that are involved in the establishment or maintenance of GNAS methylation (AR-PHP-Ib).

  Study phs000476

• Effect of Breast Feeding on Immunologic Priming in Young Infants

  The mechanisms generating immunoglobulin heavy chain (IgH) biodiversity result in over potentially unique immunoglobulin sequences within an individual. However, over the course of B cell development, B cell receptor (BCR) selection constrains the repertoire significantly. In newborn infants compared to adults, developmental regulation of terminal deoxynucleotidyl transferase (TdT) activity in the bone marrow, selective use of V, D and J gene segments in the IgH locus during fetal life, and immaturity of somatic hypermutation (SHM) and class switch recombination (CSR) mechanisms are likely to contribute to a less diverse IgH repertoire, although the extent of repertoire biodiversity in newborns has not been extensively examined. Furthermore, the fetal IgH repertoire is tightly controlled and for the most part independent of environmental antigenic influences. As a result, the neonatal repertoire, in contrast to older children and adults, is less impacted by the selective pressures of the microbiome, pathogen exposure, and environmental antigens. Evidence for developmental differences in TdT activity is seen in shorter CDR3 lengths and decreased junctional diversity in BCR in both term and preterm newborns. Next Generation Sequencing (NGS) is a sensitive approach to evaluate the neonatal IgH repertoire and assess the relative contribution of each aspect of IgH rearrangement and affinity maturation to overall IgH biodiversity prior to and following isotype class switch. NGS and bioinformatics accurately assess biodiversity by determining the number of unique sequences within the IgH repertoire. Combined with novel analytic tools, NGS provides an approach to compare the extent of somatic hypermutation (SHM), N insertions, V/D/J gene segment usage, and CDR3 length between newborns and adults. Taken together, these measures can be used to assess the earliest events in B cell development using the newborn Ig transcriptome to evaluate the IgM, IgG, and IgA IgH repertoires. The goal of this study was to define the extent of IgH biodiversity in newborns and examine the factors that contribute to the generation of biodiversity during early life. Establishment of the bioinformatics pipeline can be applied to examine the immune deficiency, response to vaccine and infection at molecular population level.

  Study phs002073

• NIDDK IBD Genetics Consortium Crohn's Disease Genome-Wide Association Study

  This dataset contains data from a genome-wide association study performed with 968 Inflammatory Bowel Disease (IBD) affected cases and 995 unrelated controls using the Illumina HumanHap300 Genotyping BeadChip. Cases were selected to have Crohn's disease with ileal involvement, and controls were matched to cases based on sex and year of birth. Subjects were drawn from two cohorts: (1) persons with non-Jewish, European ancestry (561 cases and 563 controls), and (2) persons with Jewish ancestry (407 cases and 432 controls). Genotyping was performed at the Feinstein Institute for Medical Research. Seven-hundred fifty-four of the samples (468 cases and 286 controls) were taken from the NIDDK IBD Genetics Consortium cell line repository. These samples are identified in the IBD_Sample file. The subject IDs for these individuals may be used to request corresponding samples for follow-up research through the repository. In addition, complete phenotype data for these individuals are available, together with the Consortium's phenotyping manual and the forms used to collect the data. The remaining 1,209 samples were obtained from pre-existing collections ascertained through Cedars-Sinai Medical Center, Johns Hopkins University, University of Chicago, University of Montreal, University of Pittsburgh, University of Toronto, and the New York Health project (controls only). For these samples, only sex, cohort (Jewish vs. non-Jewish), and age at diagnosis (cases only) are available. Two-hundred three individuals from among the pre-existing samples did not provide consent to release their genotype data (designated as consent group 2 in the file IBD_Subject). Thus, individual genotype data are only provided for 1,760 samples. To compensate for this, we have provided summary results for each SNP. These are based on a stratified analysis testing case/control association. Fifty-one samples had a call rate less than 93% and were therefore excluded from this analysis, leaving an overall sample size of 1,963 - 51 = 1,912. X Chromosome Heterozygosity Nine samples have X chromosome heterozygosity that is neither consistent nor inconsistent with their phenotypic sex. One of these samples was found to have Turner Syndrome. The remaining 8 samples have heterozygosity ranging from 35-76%.

  Study phs000130

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

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

  Study phs000178

• Type 1 Diabetes Genetics Consortium (T1DGC): ImmunoChip Study

  The Type 1 Diabetes Genetics Consortium (T1DGC) was formed to address issues of limited sample size and consistency of phenotyping that had limited genetic investigations on risk of type 1 diabetes (T1D). The T1DGC first collected affected sib pair (ASP) families from four geographic networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, T1D cases and controls were ascertained from existing and de novo collections. The T1DGC assembled 2,601 T1D ASP families and 69 Parent-T1D offspring trios, T1D cases from the UK Genetic Resource Investigating Diabetes (UKGRID, N=6,670), and controls from the British 1958 Birth Cohort (B58BC, N=6,523), the UK National Blood Services collection (NBS, N=2,893) and the NIHR Cambridge Biomedical Research Centre BioResource (CBR, N=2,846). All samples included in this series have reported or self-declared European ancestry. All DNA samples were collected after approval from relevant institutional research ethics committees. Genotyping was performed using a custom Illumina Infinium high-density genotyping array, ImmunoChip (Illumina, Inc; CA) according to manufacturer's protocols. The ImmunoChip was designed to densely genotype, using 1000 Genomes and any other available disease specific resequencing data, immune-mediated disease loci identified by common variant GWAS. The ImmunoChip Consortium selected 186 distinct loci containing markers meeting genome wide significance criteria (P<5x10-8) from twelve such diseases (autoimmune thyroid disease, ankylosing spondylitis, Crohn's disease, celiac disease, IgA deficiency, multiple sclerosis, primary biliary cirrhosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, T1D and ulcerative colitis). All 1000 Genomes Project pilot phase CEU population variants (Sept 2009 release) within 0.1cM (HapMap3 CEU) recombination blocks around each GWAS region lead marker were submitted for array design. No filtering on correlated variants (linkage disequilibrium) was applied. Additional content included regional resequencing data (submitted by several groups) as well as a small proportion of investigator-specific undisclosed content including intermediate GWAS results. After data cleaning and quality control, a total of 154,939 single nucleotide polymorphisms (SNPs) from 186 loci on ImmunoChip were scored. Case-control and family data were analyzed independently and combined by meta-analysis.

  Study phs000911

• Longitudinal Studies of Brain Structure and Function in MPS Disorders

  Mucopolysaccharidosis (MPS) types I, II, IV, VI and VII, are inherited diseases that result from the bodies inability to break down large sugar molecules that are by-products of metabolism. MPS affects most organs of the body and causes abnormalities in the liver, spleen, bones, and brain. We are studying the central nervous system (primarily the brain) so we can better understand the nature of the problems individuals with MPS I, II, IV, VI and VII have so we can find ways of better treating these problems. We would like to find out about the changes in the brain of individuals with MPS I, II, IV, VI and VII using data from MRI and neuropsychological tests. This is a longitudinal study so patients who enroll will need to be seen 3 times over 5 years. The longitudinal nature of the study allows us to make conclusions about how changes in some structures of the brain and changes in cognitive ability are related. The research objectives are: To identify abnormalities of brain (and central nervous system) structure and function in patients with MPS I, II, IV, VI and VII, regardless of how they are being treated or have been treated in the past; and to track disease progression over time. To examine the degree to which independent variables (e.g., age at first treatment, severity of disease, types of medical abnormalities, mutation, medical events, and sensory abnormalities) have an impact on both functional and structural outcomes as well as on quality-of-life. To identify, through longitudinal study, those measures that most accurately reflect the current disease state. This is a longitudinal study of 75-100 individuals with either MPS I, II, IV, VI and VII. Those participating in the study will be evaluated 3 times over 5 years. The primary site for this study is the University of Minnesota but there are an additional 6 centers in the United States and Canada that are also participating and will provide data for analysis. You will need to be able to travel to Minnesota or one of the participating centers in order to be a part of this study.

  Study phs001328

• Childhood Cancer Data Initiative (CCDI): Molecular Characterization across Pediatric Brain Tumors and Other Solid and Hematologic Malignancies for Research, Diagnostic, and Precision Medicine

  This study contains tumor and germline WGS, RNA-Seq, Clinical Panel Sequencing, and other omics and molecular data for patients with pediatric brain tumors and other solid and hematologic malignancies. This data has been collected from multiple organizations including the multi-institute Children's Brain Tumor Network (CBTN), the Pacific Pediatric Neuro-Oncology Consortium (PNOC), and the Children's Hospital of Philadelphia Division for Genomic Diagnostics (CHOP DGD). In addition to being available pre-publication and without embargo via dbGaP and the NCI Cancer Research Data Commons, this data is made available via Cavatica, PedcBioPortal and the Kids First Data Resource Portal. Collectively, this availability strives to accelerate the research process and enable members of the scientific and patient communities to partner and make discoveries faster than ever before.In 2019, the CBTN launched the Pediatric Brain Tumor Atlas, which now comprises the largest collection of childhood brain tumor data in the world. This resource is helping accelerate not only brain tumor research, but is also empowering discovery for other rare childhood conditions. Brain tumors are the most common form of cancer in children aged 0-19 in the United States, and are the largest cause of cancer-related deaths. The estimated number of new cases in 2019 is nearly 3,800, and thus brain tumors are a rare disease. Despite their relative rarity, the years of potential life lost due to brain tumors in 2009 was estimated at 47,631 years for children and adolescents aged 0-19 in the United States; this is a disproportionate amount of life lost compared to adult cancers and represents an unrecognized societal threat. There is an urgent need to improve therapies for these children. This sequencing cohort defines the largest, clinically annotated pediatric brain tumor cohort study to date and seeks to define the intersection of germline and somatic underpinnings of pediatric brain tumors across a shared developmental context of cancer and structural birth defects.

  Study phs002517

• Integrating Genomic and Transcriptomic Data to Identify Breast Cancer Susceptibility Genes

  Genetic factors play an important role in the etiology of both sporadic and familial breast cancer. Since 2007, common genetic variants in ~200 loci have been identified in genome-wide association studies (GWAS) in relation to breast cancer risk. However, it is often difficult to translate GWAS findings to disease prevention and treatment since causal genes in the large majority of GWAS-identified loci are unknown. Furthermore, a large fraction of breast cancer heritability remains unexplained. Recent studies suggest that nearly 80% of disease heritability can be explained by genetic variants regulating gene expression. Herein, we propose three well-powered transcriptome-wide association studies (TWAS) to systematically investigate the association of breast cancer risk with gene expression across the transcriptome of African, Asian and European descendants. In Aim 1, we will perform RNA sequencing and high-density genotyping assays using normal breast tissue samples, and build race-specific gene expression prediction models using data from 1000 women of African, Asian and European descent. These models will be applied to the GWAS data generated from approximately 320,000 breast cancer patients and controls to impute gene expression for association analyses of predicted gene expression with risk of breast cancer overall, and by estrogen receptor and HER2 status. In Aim 2, we will select the top 50 genes identified in Aim 1 for in vitro functional assays to assess their influence on major cell functions related to cancer biology. In Aim 3, we will evaluate whether TWAS-identified genes may express differently in normal breast tissues and breast cancer tissues collected from African, Asian, and European descendants to assess whether these genes may contribute to racial differences in breast cancer risk by molecular subtypes. With strong methodology and a large sample size, we believe that this proposed study should be able to identify and characterize a large number of novel genes related to breast cancer risk. Uncovering breast cancer susceptibility genes will greatly improve the understanding of the genetic and biological basis for breast cancer and accelerate the translation of genetic findings to disease prevention and patient care.

  Study phs003535

• Epi4K: Gene Discovery in 4,000 Epilepsy Genomes

  The Epi4K project began in 2011 as an international, multi-center study that seeks to identify and characterize the genetic bases of complex epilepsies. The Center without Walls Epi4K project includes three cores and four scientific projects, as well as a steering committee comprised of the primary study investigators and representatives from the National Institute of Neurological Disorders and Stroke (NINDS). The three cores include: (1) The Administrative Core which handles the overall coordination of Epi4K activities; (2) The Sequencing, Biostatistics and Bioinformatics Core which is responsible for generating the next-generation sequence data, inferring the genetic variation in each of the study participants, and performing the primary analyses to identify epilepsy genes; and (3) The Phenotyping and Clinical Informatics Core which verifies and archives all the phenotypic data from each study participant. The proposed number of patients to be sequenced and analyzed in the scientific projects is a minimum of 4,000; thus the Center was named "Epi4K: Gene Discovery in 4,000 Genomes". Project 1 addresses the genetics of rare and severe childhood epilepsies, including epileptic encephalopathies (infantile spasms and Lennox-Gastaut syndrome), and malformations of cortical development (periventricular nodular heterotopia and polymicrogyria). Exome and genome sequence data generated from DNA collected from patients will be screened for mutations (single nucleotide substitutions, small insertion-deletions, and copy number variations) that cause or contribute to the diseases. Project 2 is focused on genetic discovery in multiplex families. This study will use next-generation sequencing to identify genomic variation that influences risk for common subtypes of epilepsy including idiopathic generalized epilepsy and nonlesional focal epilepsy. Project 3 seeks to identify genetic determinants of prognosis in patients with a range of epilepsy disorders. This study will study established epilepsy cohorts with well-characterized data on seizure outcome to look for relationships between genetic variation and pharmacological control of seizures. Project 4 will use next-generation sequencing data (exome and genome) to screen for epilepsy-associated copy number variation across all Epi4K projects using novel computational algorithms.

  Study phs000653

• 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

• Profiling Genome-Wide Circulating ncRNAs for the Early Detection of Lung Cancer

  Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all cases. Although imaging techniques such as low-dose computed tomography (LDCT), combined with tissue biopsies, have improved early detection rates, current diagnostic approaches are often limited by high false-positive rates, invasiveness, and radiation exposure, which lead to misdiagnosis and increased patient burden. Therefore, there is an urgent need for minimally invasive, accurate, and safe diagnostic biomarkers, particularly those derived from liquid biopsies such as blood-based exosomal RNA.This dataset specifically focuses on plasma-derived exosomal small non-coding RNAs (sncRNAs).A total of 233 de-identified plasma samples were obtained from Rush University Medical Center, including 116 NSCLC patients and 117 non-cancer controls. Exosomes were isolated from plasma, RNA was extracted, and small RNA sequencing was performed using the Illumina NextSeq 500 platform. Data available through dbGaP include individual-level raw sequencing files (FASTQ) and basic phenotype annotations of the plasma cohort.A diagnostic model was initially developed using small RNA-seq data from 1,446 tissue samples obtained from TCGA and GEO, identifying a robust sncRNA signature that distinguished NSCLC from non-cancer samples with an AUC of 0.90 in hold-out tissue validation. This signature was subsequently validated in the plasma exosome cohort, achieving an AUC of 0.84 in independent validation. The model demonstrated consistent diagnostic performance across subgroups stratified by age, sex, smoking history, cancer stage, and NSCLC subtype. Survival and functional analyses further supported the clinical and biological relevance of the identified sncRNAs. It is hoped that this resource will contribute to a better understanding of the sncRNA expression landscape in NSCLC and promote the development of non-invasive biomarkers for early cancer detection. These data may be applied to the investigation of transcriptomic and other risk factors, provide insight into cancer screening, tumor progression mechanisms, and prognosis prediction, and ultimately support precision medicine for NSCLC.

  Study phs004166

• Psychophysiological Investigation of Myocardial Ischemia (PIMI-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 psychophysiological factors related to both symptomatic and asymptomatic cardiac ischemia.Background: An important hypothesis has been generated from current research in this area: manifestations and expressions of cardiac ischemia are influenced by specified psychophysiological mechanisms.Participants: The study population consisted of 196 participants recruited from four clinical units, all of which were clinical units for the Asymptomatic Cardiac Ischemia Pilot (ACIP). Participants eligible for PIMI were identified throughout the screening process established for the enrollment of participants in ACIP.Design: The primary goal of PIMI was to test the hypothesis, "Manifestations and expressions of cardiac ischemia are influenced by specific psychophysiological mechanisms." The specific relationships investigated were:Patients who are susceptible to mental stress ischemia (indexed by new left ventricular wall motion abnormalities) would display more ischemia during daily activities and would have ambulatory ischemia at lower heart rate and activity level thresholds than patients who are not susceptible. Cardiovascular and catecholamine reactivity to mental stress would be a predictive of severity of mental stress ischemia and of ambulatory ischemia. Β-edorphin responses to mental stress and to exercise would be predictive of asymptomatic ischemia. Patients with systematic ischemia and those with asymptomatic ischemia would show differences in each of the following at rest and after response to stresses (exercise and/or mental tests). Mental stress would produce different hormonal and perceptual responses than exercise. Asymptomatic ischemia would be associated with a lesser severity of ischemia than symptomatic ischemia. The location of the ischemic myocardium would be inferior in a higher proportion of patients with asymptomatic ischemia than of patients with symptomatic ischemia. The latency internal that would be the onset of ST segment depression and the onset of ischemic pain would be related to somatic sensory perception, autonomic nervous system reflux control of the heart, Β-endorphin responses, and psychosocial characteristics.

  Study phs004183

• Effects of 2DG, Galactose, or Oligomycin on the epigenome remodeling induce by T cells activation.

  T cell activation is known to require a metabolic shift towards glycolysis, triggered by TCR-engagement . To substantiate the role of glycolysis in transcriptional reprogramming during antigen-driven T cell activation, we utilized a well-established ex-vivo model to recapitulate the process of antigen stimulation without the requirement for additional antigen-presenting cells . We initially explored and validated the conditions for both metabolic and epigenetic reprogramming. Antigen-mediated CD4+ T cells activation initiates context-specific gene-expression programs that drive effector functions and cell fates through changes in the epigenetic landscape . To evaluate global changes in histone modifications, we isolated CD4+ T cells from peripheral blood (PB) of healthy control (HC) human donors and either maintained them in resting conditions or stimulated with anti-CD3/CD28 for 24h. Rapid changes in the CD4+ epigenome and transcriptome were observed within 24h as measured by RNA-sequencing (RNA-seq) and H3K27ac chromatin immunoprecipitation followed by DNA-sequencing (ChIP-seq).To explore the requirement for metabolic reprogramming in regulating transcriptional responses after initial TCR engagement, CD4+ T cells from PB of healthy donors were isolated and activated in either the presence or absence of oligomycin (inhibitor of ATP synthase blocking the production of ATP by OXPHOS), 2-deoxyglucose (2DG; competitively inhibiting production of glucose-6-phosphate from glucose), or replacement of glucose with galactose. Galactose is metabolized through glycolysis, utilizing the Leloir pathway which is energy neutral, forcing cells to use all the pyruvate production to sustain the TCA cycle for OXPHOS and ATP synthesis at the expense of lactate production. Employing ChIP-seq and RNA-seq, inhibition of OXPHOS-mediated ATP production by oligomycin had only a limited effect on H3K27-acetylation or activation-induced transcription during the first 24h of activation. In contrast, inhibition of glycolysis by 2DG treatment compromised both H3K27-acetylation and transcriptional changes. However, under conditions of galactose-metabolism where lactate production is abrogated, activated CD4+ T cells were still able to globally remodel H3K27 acetylation and reprogram the transcriptome.

  Study EGAS00001007115

• A study of the genetic basis of evation by Acute Myeloid Leukaemia of Graft vs Leukaemia effects after allogeneic bone marrow transplantation

  Acute myeloid leukaemia (AML) is an aggressive and molecularly diverse disease with a poor overall survival of 20-25%. With an annual incidence of 2.9 per 100,000, AML is currently the commonest myeloid malignancy in Europe, yet the two main therapeutic options for this disease, anthracyclines and purine analogues, have remained unchanged for over 20 years. Currently patients are stratified at diagnosis according to a series of clinicopathological parameters (e.g. age, white cell count and presence/absence of previous clonal haematological disease) and molecular markers (e.g. chromosomal translocations/deletions, aneuploidy and mutations in genes such as FLT3 and NPM1). Patients with adverse prognostic features, whose prognosis is particularly poor (e.g. <15% long-term survival) are offered treatment with allogeneic bone marrow transplantation (allo-BMT) if a sibling or unrelated donor is available. This can significantly improve survival (e.g. up to 40% long-term survival in some contexts), albeit at the expense of significant toxicity and transplant-related mortality (TRM). Allo-BMT is thought to work in part by allowing the delivery of large doses of chemotherapy followed by haemopoietic "rescue" with donor haemopoietic stem cells (haemopoietic failure would otherwise ensue). However, potentially the most potent effect of allo-BMT is the cytotoxic effect of donor lymphocytes against AML blasts, a phenomenon known as graft-vs-leukaemia (GVL) effect. Increasingly, transplants using reduced chemotherapy intensity (mini-allografts) are being used that partially circumvent the toxicity from chemotherapy and rely on GVL to effect cure. Nevertheless, AML relapse after allo-BMT still occurs at a significant rate of up to 80% depending on the type of transplant. There is accumulating evidence that genetic events in residual leukaemic cells enable them to evade immunodetection and therefore survive the GVL effect and expand to cause relapse. The most striking example of this is the loss of HLA antigens after transplants in which donor and recipient are not fully HLA-matched. In these cases, the leukaemia "deletes" the genomic region containing the disparate HLA antigen which was preferentially targeted as "foreign" by the GVL effect. However, the genetic basis of immune evasion in the majority of transplants, which are fully HLA matched, is not known. One possibility is that loss of genes coding for antigens outside the HLA locus but which are also targets of GVL may operate, alternatively genetic events that affect processes downstream of immunological cytotoxicity may be responsible. The identification of genetic events that mediate immune evasion would not only facilitate the understanding of this process but can help plan therapeutic interventions that improve the outcomes of allogeneic transplantation for AML and other disorders. We intend to study this by conducting exome sequencing on 6 cases of AMLs from patients that attend my clinic at Addenbrooke's hospital and have relapsed after allogeneic transplantation. Samples from AML diagnosis, remission/normal and AML relapse (total n=18) will be studied to identify somatic mutations in the primary AML and those acquired by the relapsed clone. The 18 samples will also be studied by array CGH to detect regions of genomic amplification or deletion.

  Dataset EGAD00001000404

• Lindsey E. Jones et al. Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neuro-Oncology Advances, 2020. We established robust, versatile, and well-characterized Patient Derived Cells (PDC) of an IDH1-mutant astrocytoma and an IDH1-mutant oligodendroglioma that represents defined evolutionary stages of chemotherapy-induced hypermutation. The PDCs retain tumor subtype defining features over time as well as classic hallmarks of cancer, including anchorage independent growth and cell immortality. The integrated phylogenies composed of PDCs,single-cell-derived PDCs, patient-derived xenografts, and corresponding spatiotemporal tumor tissue samples also provide new insight into the clonality, evolutionary pattern, and immense mutational load of hypermutated IDH mutant gliomas.

  IDH-mutant lower-grade gliomas can undergo malignant progression via temozolomide-driven hypermutation. Patient-derived cells (PDC) that model the genetically distinct hypermutated (HM) tumor subgroup are generally lacking, and few if any human brain tumor cell models are from defined evolutionary time points. Here, we characterize multiple PDC derived from independent surgical specimens of IDH1-mutant recurrences, including an ATRX and TP53-mutant astrocytoma and a 1p/19q co-deleted and TERT promoter-mutant oligodendroglioma. We determined the evolutionary time points represented by each PDC using exome sequencing and phylogenetic reconstruction, comparing the PDC and single cell clones of the PDC (scPDC) to multiple spatiotemporal tumor tissue samples, PDC-derived xenografts (PDX) and patient-matched blood. The tumor samples exhibited TMZ-induced mutagenesis and a branching pattern of evolution. We found clear evidence of two fully independent founder HM clones in the tumor tissue that are faithfully represented by independent PDC. The PDC, scPDC and PDX also shared the mutagenesis signature and represent the mid and later evolutionary time points of their corresponding tumors. The PDC maintained the tumor subtype-defining features over many passages, including heterozygosity of the IDH1 R132H mutation, production of 2-hydroxyglutarate (2-HG), and subtype-specific telomere maintenance mechanisms. The PDC from both tumors exhibited anchorage-independent growth in soft agar. The oligodendroglioma PDC formed infiltrative intracranial tumors with characteristic oligodendroglioma histology, initially with a long period to tumor formation. We conclude that the PDC, scPDC and PDX faithfully model the heterogeneous clonal origins of the corresponding tumor tissue. The multilevel analysis also provides new insight into the intratumoral heterogeneity and vast mutational load of HM glioma. The PDC from multiple evolutionary time points presented in the context of full clinical timelines may be useful to model evolution and intratumoral heterogeneity, important sources of therapeutic failure.

  Study EGAS00001003992

• The subclonal architecture of metastatic breast cancer: Results from a prospective community-based rapid autopsy program 'CASCADE'

  Background: Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. The majority of work in this area has targeted primary tumours however, and very few studies have performed comprehensive profiling of advanced disease. Evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease. Methods and Findings: Three patients with ER-positive, HER2-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program. Cases represented a range of management problems in breast cancer, including late relapse after early stage disease; de novo metastatic disease; discordant disease response and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing.Between cases, there were significant differences in mutational burden, driver mutations, mutational processes and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross seeding was found in each case and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases, and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the key role of mutational processes in subclonal evolution.Limitations of this study include the potential for bias introduced by joint analysis of formalin fixed archival specimens with fresh specimens, and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed. Conclusions: This study highlights the variety of mechanisms that shape the genome of metastatic breast cancer, and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.

  Study EGAS00001002153

• dbGaP Collection: NHLBI Heart Failure Related dbGaP Data (No IRB requirement)

  The National Heart, Lung, and Blood Institute (NHLBI) Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research has concluded. The NHLBI will continue to make this collection of data available to the research community.Past information regarding the Data Collection for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research is below.The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health (NIH), is inviting novel Solutions for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research. The goal of the challenge is to foster innovation in computational analysis and machine learning approaches utilizing large-scale NHLBI-funded datasets to identify new paradigms in heart failure research. The challenge aims to address the need for new open source disease models that can define sub-categorizations of adult heart failure to serve as a springboard for new research hypotheses and tool development in areas of heart failure research from basic to clinical settings. NHLBI has a history of making considerable investments in the creation of deep data resources including: long-standing, deeply-phenotyped epidemiological cohorts, innovative clinical trials, and large-scale precision medicine efforts that have generated whole genome sequencing and "other omics" data for more than one-hundred thousand individuals. To provide challenge participants with streamlined access to data across NHLBI's numerous studies containing heart failure data, the NHLBI has created this dbGaP study collection. Data access for this collection is controlled by NHLBI's Data Access Committee. Challenge participants are required to follow the use restrictions and acknowledgement instructions from the original dataset(s). Please note that the data in this collection are not harmonized across studies or otherwise altered from the original study. The NHLBI Big Data Analysis Challenge: Heart Failure Data Collection contains all NHLBI studies currently in dbGaP that contain data that may be relevant to research on heart failure. The studies in this collection are approved for General Research Use (GRU), Health/Medical/Biomedical Use (HMB), or Disease-Specific Use (DS) that permits research on heart failure. These studies span a variety of study designs, inclusion and exclusion criteria, sample sizes, and provide a wide breadth and depth of phenotype data on study participants. The available genomic data in these studies also varies, including genotyping arrays, sequencing (targeted, exome, whole genome), and additional -omic data (e.g., RNA or metabolite profiles). Please refer to each study's individual accession page to learn more about how study data were collected. Challenge participants are reminded that in addition to dbGaP, NHLBI's Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) contains other studies that have collected data relevant to heart failure and may wish to utilize BioLINCC data in the development of their Solution. Please refer to the individual accession page for each study in this collection to learn more about the history of each study.

  Study phs001991

• FHS-Net Social Networks

  Note: Data Use Restrictions: The data must only be used for genetic research projects solely in the area of health-related social-network research. This study compiles detailed information on family and social ties linked to participants in the offspring cohort of the Framingham Heart Study. The investigators took computerized information from self-report data used by the FHS for over 30 years to facilitate health exam scheduling. These tracking sheets asked participants to identify people close to them, which were assumed by the investigators to indicate a social tie. Individuals are listed as "egos" (if the ties are from their perspective) and/or "alters" (if they are linked as a friend or family members to one of the other participants). All egos were FHS offspring participants while alters could be FHS participants in any cohort or non-participants. This information was combined with internal FHS pedigree data on family ties to list family member alters (as well as the nature of their relationship). The resultant dataset therefore includes each ego-alter tie, the nature of their relationship and the start and end dates for their ties for each of seven waves for the offspring study. Due to privacy concerns, all exam dates for individuals are listed relative to the initial FHS exam date. The study investigators chose a random, confidential date, to which all initial exam dates are linked (by number of months from random date to initial exam date). With this information, researchers can have access to the temporal relationships among participants' exam dates while remaining unaware of the actual exam dates for individual participants. During each clinic exam cycle, the participants undergo a detailed examination including physical examination, medical history, laboratory testing, and electrocardiogram. Over the years, other tests (that may not be performed at every exam cycle) have included pulmonary function, lifestyle, physical function, cognitive function questionnaires, and various noninvasive cardiovascular tests including echocardiograms. This study involves phenotypic data from these exams including (when available) basic body measurements (height, weight, blood pressure), laboratory values (blood sugar levels, LDL levels), information on smoking and alcohol use, and tests of depression and cognitive functioning. Important links to apply for individual-level data Data Use Certification Requirements (DUC) Instructions to Request Authorized Access Apply here for controlled access to individual level data Participant Protection Policy FAQ In 1948, researchers recruited men and women from the town of Framingham, Massachusetts, beginning the first round of extensive physical examinations and lifestyle interviews that would later be analyzed for common patterns related to CVD development. Initially, the Framingham Heart Study enrolled 5,209 men and women from the Framingham area who were between the ages of 28 and 62 years. Beginning in 1971, the Framingham Heart Study enrolled 5,124 men and women, who were either offspring of the original cohort or spouses of those offspring. In 2002, 4,095 third generation participants (men and women) were enrolled.

  Study phs000153

• Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP): COVID-19 Testing and Vaccination Social Network Diffusion for Diverse Criminal Legal Involved Communities

  This RADx-UP Phase II proposal, "Social network diffusion of COVID-19 prevention for diverse Criminal Legal Involved Communities", will implement a situation appropriate COVID-19 testing and vaccination social network diffusion intervention - C3 - building upon RADx-UP Phase I lessons and successful social network prevention interventions developed previously by the research team. C3 Criminal Legal Involved (CLI) populations encompass those non-incarcerated who have experienced recent arrest, incarceration, probation, parole or diversion programs such as drug courts. While increases in COVID-19 testing have been observed among this group, there remain members with limited testing history as well as individuals who are vaccine hesitant. COVID-19 prevention messaging can no longer be simplified to "everyone test and/or everyone vaccinate" as testing and vaccination decisions among community members are sensitive to personal histories (i.e., prior infection), local infection rates (i.e., low rates) and testing/vaccination availability. As COVID-19 prevention efforts have become more complicated (i.e., test if exposed), people tend to focus on the messenger, and particularly those that are close to them. Personal connections and communications within existing personal network structures, such as families, friends and other trusted acquaintances represent the cornerstone to increase situation appropriate testing and overcoming COVID-19 vaccine hesitancy. C3 builds upon RADx-UP I, by using a network diffusion approach facilitated through motivational interviewing purposefully geared to mobilize one's own organic social network to increase context appropriate testing and vaccine uptake. Through this process we will maximize the primary benefit and impact of this type of intervention which also has the intended effect of increasing likelihood that the messenger themselves will undergo the same behavior change that they have been trained to promote. We will leverage infrastructure developed in RADx-UP Phase I, which includes 4 high-impact sites across the Central US from Phase I: Baton Rouge LA, Little Rock AR, Indianapolis IN, and Chicago IL. We will utilize established engagement efforts already in place and continue to fully integrate communities in the strategic application of the intervention. We will use the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework to guide implementation. C3 aims to: Aim 1a. Test the efficacy (3-month situation appropriate testing or vaccination) of a network diffusion intervention (C3) versus an existing COVID-19 testing and vaccine linkage to care intervention among: 1) primary study participants (primary outcome); and 2) secondary study participants connected to primary participants (secondary outcome) using a RCT design. Aim 1b. Explore the mechanisms for differential intervention effects at the individual and network-level that may increase situation appropriate testing and/or vaccination uptake. Aim 2. Examine key RE-AIM components in real time tied to the implementation of the network diffusion intervention (C3).

  Study phs003234

• The Finland-United States Investigation of NIDDM Genetics (FUSION) Study - Islet Expression and Regulation by RNAseq and ATACseq

  This study evaluates gene expression and its regulation in human pancreatic islets, a tissue relevant in the study of genetic risk factors contributing to diabetes. We obtained pancreatic tissue sections and purified pancreatic islets from deceased donors and processed these biological samples to generate various data types. We generated spatial transcriptomic data from pancreatic tissue samples using the 10X Genomics VISIUM technology with sequencing on the Illumina NovaSeq platform. We also generated multiple data types from the purified pancreatic islets using genome-wide SNP chips, bulk RNA-Seq, microRNA (miRNA)-Seq, whole genome sequence, DNA methylation (methyl)-Seq, cap analysis of gene expression (CAGE)-Seq, single cell RNA-seq, and single nuclei ATAC-seq approaches. These data include ATAC-seq of two islet subjects, RNA-seq of 31 additional subjects, genome-wide chip genotypes, and imputed genotypes of the 33 subjects released with phs001188.v1. For genotyping, 500-1000 islet equivalents (IEQ) were cultured as in Gershengorn (Science, 2004, PMID: 15564314); genomic DNA isolated from islet cultures. For RNA analyses, 2500-5000 IEQ from each islet source were used for bulk or single-cell RNA isolation. Messenger RNA was isolated with trizol extraction and 12-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol. Bulk RNA sequencing was performed on HiSeq2000/HiSeq2500/Hiseq4000/NovaSeq6000 sequencers using paired-end reads at the NIH Intramural Sequencing Center (NISC). miRNA libraries were prepared from total RNA from 68 samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. CAGE libraries were prepared from total RNA samples using the nAnT-iCAGE protocol at DNAFORM, Japan. CAGE libraries were sequenced at the NIH Intramural Sequencing Center (NISC) on the HiSeq2000 sequencer. Genotyping on the Illumina Omni2.5M array was performed at the NHGRI Genomics Core facility. Genotypes were imputed using the HRC.r1.1.2016 reference panel. To assess regions of open chromatin in islets, we performed bulk ATAC-seq on HiSeq2000 sequencers using paired-end reads at NISC. Single-nuclei ATAC-seq libraries were prepared using single-cell-combinatorial-indexing (sci-) ATAC-seq protocol and sequenced on Illumina NextSeq using paired-end reads. scRNA-seq libraries were generated using the 10X Genomics platform and sequenced on Illumina HiSeq3000 at the Genomics Technology Core of the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). Greater than 90% of the loci associated with T2D through genome-wide association studies occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Therefore, there is a critical need to understand the full spectrum of genetic variation and regulatory element usage in T2D-relevant tissues. To that end, this study contains multiple genomic and transcriptomic data sets from pancreas tissue sections and pancreatic islets, a resource that will contribute to our efforts to investigate the genetic risk factors for type 2 diabetes.

  Study phs001188

• Genome-Wide Association of Type 2 Diabetes in Africans: The AADM Study

  The Africa America Diabetes Mellitus (AADM) study is a genetic epidemiological study of type 2 diabetes in Sub-Saharan Africa. Study participants were enrolled through university medical centers in Nigeria, Ghana, and Kenya. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of each participating institution. All subjects provided written informed consent for the collection of samples and subsequent analysis. The case definition of type 2 diabetes was based on the American Diabetes Association (ADA) criteria. After providing informed consent, participants underwent the same enrollment procedures, which included collection of demographic information, medical history, clinical examination and a blood draw. Genome-wide SNP genotyping was done on either the Axiom™ PanAFR SNP array (n=1,808) or the Multi-Ethnic Global Array (MEGA) (n=3,423). After appropriate quality control, in silico imputation was done using the African Genome Resources Haplotype Reference Panel (at the Sanger Imputation Service). Imputed genotypes were filtered for variants with minor allele frequency (MAF)≥ 0.01 and information score (info) ≥ 0.3 for genetic association analysis. Genome-wide association analysis between type 2 diabetes and the imputed genotype dosages was done using a generalized linear mixed model, which adjusted for age, gender, body mass index, the genetic relatedness matrix and the first three principal components (PCs) of the genotypes.Metabolomics profiling of plasma samples of type 2 diabetes (T2D) cases and controls in Nigerians (West Africa) was done in the AADM Study. Plasma metabolites were measured in a total of 580 individuals (N=310 for the discovery phase and N=270 for the replication stage) using the global/untargeted approach on the Metabolon platform and following the manufacturer's standard operation protocols. The analytic methods are described in detail in Doumatey et al. [Genome Med 2024]. The measured metabolites level represented by peak areas are relative values. The peak area data were batch-normalized to remove the instrument batch effects (batch variability) and the batch-normalized data correspond to the median-scaled raw data. For each identified metabolite, the minimum value across all batches in the batch-normalized was imputed for the missing values. The batch-normalized and imputed data are natural log-transformed and consist of 1116 metabolites for the discovery cohort and 1071 metabolites for the replication. Welch's two-sample t-test on the log-transformed data was used to identify metabolites differentially expressed between T2D cases and controls . All other statistical analyses conducted on both the replication and discovery cohorts used the log-transformed data. To merge the discovery and replication, the same quality control samples (bridge samples) were run with each batch of the experimental samples in both cohorts and used to correct for additional variability and uniformize the procedures. The resulting merged data is the QC-normalized and imputed data that contains only metabolites that were common to both cohorts and successfully bridged for all batches (n= 891 metabolites).

  Study phs001844

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

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

  Study phs001028

• L1-Seq and Genome-Wide SNP Genotyping in a Multiethnic Asian Population

  Insertions of the human-specific subfamily of LINE-1 (L1) retrotransposon are highly polymorphic across individuals and can critically influence the human transcriptome. We hypothesized that L1 insertions could represent genetic variants determining important human phenotypic traits, and performed an integrated analysis of L1 elements and single nucleotide polymorphisms (SNPs) in several human populations. We found that a large fraction of L1s were in high linkage disequilibrium (LD) with their surrounding genomic regions and that they were well-tagged by SNPs. However, L1 variants were only partially captured by SNPs on standard SNP arrays, so that their potential phenotypic impact would be frequently missed by SNP array-based genome-wide association studies. We next identified potential phenotypic effects of L1s by looking for signatures of natural selection linked to L1 insertions; significant extended haplotype homozygosity (EHH) was detected around several L1 insertions. This suggests that some of these L1 insertions may have been the target of recent positive selection.

  Study phs000732

• A Phase III Randomized Study of Nivolumab Plus Ipilimumab Versus Nivolumab in Stage IV Squamous Cell Lung Cancer

  Lung-MAP (S1400, NCT02785952) was a multicenter, open-label, phase III randomized clinical trial. The Lung-MAP-I substudy (S1400I) was conducted from December 2015 to April 2018, through the National Clinical Trials Network and led by the SWOG Cancer Research Network. The trial compared nivolumab plus ipilimumab (nivo+ipi) with nivolumab monotherapy (nivo) in patients with chemotherapy-pretreated, immunotherapy-naïve, advanced squamous non-small cell lung carcinoma (sqNSCLC). Two hundred fifty-two patients were randomly assigned to receive nivo+ipi (n = 125) or nivo (n = 127). The clinical efficacy endpoints were overall survival, progression free survival, duration of response, and best objective response by RECIST 1.1. Multi-omic translational analysis was performed in collaboration with the the CIMAC–Cancer Immunologic Data Commons (CIDC) Network using available tumor tissue samples and blood samples (n = 160) submitted for Lung-MAP screening and provided by the SWOG tissue bank.

  Study phs003412

• EuCANCan: EUropean-CANadian Cancer network

  The EUropean-CANadian Cancer network (EuCANCan) is a project aiming to develop a federated network of aligned and interoperable infrastructures for the homogeneous analysis, management and sharing of genomic oncology data for Personalized Medicine. This initiative, funded by the European Commission and the Canadian Institutes of Health and coordinated by the Barcelona Supercomputing Center (BSC), is expected to process and provide the scientific community with around 30-35 thousand patient samples from different types of cancer, coming from different nodes involved in this project, during the four years of the project. Data generated as part of primary studies, often in small hospitals or research centers, is hardly reused by other projects or groups. EuCANCan is proposed to foster the homogenisation and discoverability of cancer samples from very different sources for the whole community. These efforts will help paving the way to future standardised and global genomics projects, specially for those participant countries that eventually integrate the methodology and infrastructure generated during the project. “The more data we have access to the more and better discoveries. We attempt to overcome the legal and technical limitations in order to generate, gather and exploit bigger datasets as well as answer more ambitious questions”. These were some of the words Dr. David Torrents, main coordinator of the project, shared with a regional radio during the kick-off meeting of the project, on February 11th, 2019. The EUCANCan network will be composed of reference nodes in Amsterdam, Barcelona, Berlin, Heidelberg, Paris and Toronto which have established strong research and clinical programs in the field of genomic oncology. The Centre for Genomic Regulation (CRG), specifically the European Genome-phenome Archive (EGA), is among the leading institutions. Its exhaustive experience in biomedical data archival and secure distribution is considered key for crucial developments within the project. It is also notable the recent inclusion of EUCANCan in the list of GA4GH driver projects. Further information and details can be found in the following links: InsideHPC BSC official description Techweek (Spanish)

  Blog eucancan

• Monosomy 7 delineates a primitive acute myeloid leukemia with adverse prognosis and responsiveness to epigenetic therapy

  The mechanistic basis for adverse risk associated with monosomy 7 remains undetermined in AML. In contrast to del(7q), monosomy 7 conferred adverse prognosis independent of TP53 and complex karyotype. Single-cell RNA and ATAC-seq revealed an epigenetically remodeled landscape and expansion of multipotent progenitor (MPP)-like cells. Monosomy 7 derived progenitors were distinguished by reduced chromatin accessibility and expression of STK17A and NT5C3A, which conferred resistance to cytotoxic and venetoclax-based therapy. To circumvent resistance mediated by monosomy 7 and deficient TP53, histone deacetylase inhibitors (HDACi) effectively restored apoptotic potential and augmented the activity of venetoclax. In a randomized clinical trial including older unfit patients with AML and monosomy 7, responses were observed in the majority that received low-dose cytarabine, venetoclax and the HDACi pracinostat, some with durable remissions exceeding 3 years. Longitudinal studies highlighted diminution of MPP-like cells, linking epigenetic remodeling to clinical response and therapeutic vulnerability of monosomy 7 AML.

  Study EGAS50000001641

• Trans-ethnic genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

  We carried out a trans-ethnic genome-wide association and replication study of blood pressure phenotypes amongst up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 novel loci to be associated with blood pressure (P=3.9x10-11 to P=5.0x10-21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that at some of the loci identified DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 novel loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7, TBX2) function. The novel and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, cardiovascular and all-cause mortality (P=0.04 to 8.6x10-6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.

  Study EGAS00001001427