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• Circulating cell-free DNA analysis in Small Cell Lung Cancer

  Small cell lung cancer (SCLC) accounts for ~250,000 deaths worldwide each year. Acquisition of adequate tumour biopsies is challenging and liquid biopsies present the best option for patient stratification and response monitoring. We present genome-wide and targeted plasma DNA sequencing data that identifies tumour related changes in 91% of limited (LS-SCLC) and 100% of extensive stage (ES-SCLC) patients. Parallel analysis of circulating tumour cells (CTCs, by CellSearch) based on ≥ 1 CTC/7.5ml blood increased tumour detection frequencies to 98% patients (95% for LS-SCLC) where CTC count and plasma DNA readouts correlated with disease stage and overall survival (OS). Targeted plasma DNA sequencing identified potential therapeutic targets in >50% of patients. This approach will support trials of targeted therapies where improved patients outcomes are urgently required.

  Study EGAS00001003110

• Bulk RNASeq of metastatic colorectal cancer organoids treated with crenigacestat alone or in combination with cetuximab

  Bulk RNA sequencing was performed on mCRC organoids cultured under untreated conditions or treated with crenigacestat alone or in combination with cetuximab. This dataset was generated to investigate the transcriptional changes associated with combination therapy and to compare these profiles with the adaptive responses observed following cetuximab monotherapy in the corresponding ATOH1 wild-type and knockout organoid models.

  Study EGAS50000001422

• RNA sequencing data of pediatric BCR::ABL1 acute lymphoblastic leukemia (set 2)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one BCR::ABL1 ALL patient (ALLT-399). TruSeq Stranded Total RNA Library Prep (Illumina) kit was used in library preparation, and Novogene's internal method was utilized in rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq X Plus, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002580

• RNA sequencing data of pediatric KMT2A-rearranged acute lymphoblastic leukemia (set 2)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one KMT2A-r ALL patient (ALLT-355). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and Ribo-Zero Magnetic Kit (Illumina) was utilized in rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002583

• RNA sequencing data of pediatric ETV6::RUNX1 acute lymphoblastic leukemia (set 3)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one ETV6::RUNX1 ALL patient (ALLT-397). TruSeq Stranded Total RNA Library Prep (Illumina) kit was used in library preparation, and Novogene's internal method was utilized in rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq X Plus, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002587

• RNA sequencing data of pediatric hyperdiploid acute lymphoblastic leukemia (set 2)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one hyperdiploid ALL patient (ALLT-356). NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and Ribo-Zero Magnetic Kit (Illumina) was utilized in rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002593

• RNA sequencing data of pediatric B-other acute lymphoblastic leukemia (set 5)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one B-other ALL patient (ALLT-396). TruSeq Stranded Total RNA Library Prep (Illumina) kit was used in library preparation, and Novogene's internal method was utilized in rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq X Plus, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002601

• RNA sequencing data from glioblastoma primary cell lines treated with indisulam data access committee

  Four glioblastoma primary cell lines (BT592/pt1, BT1007/pt2, BT1009/pt3; BT1012/pt4) were derived from glioblastoma Cavitron Ultrasonic Aspirator (CUSA) material and cultured as neurospheres (GB-NS) in DMEM/F12+Glutamax medium containing B27 supplement (Thermofisher, Waltham, MA, USA) and the mitogenic factors epidermal growth factor (EGF) and fibroblast growth factor b (bFGF) (Peprotech, Rocky Hill, NJ, USA). GB-NS were confirmed mycoplasma-free by PCR Test. Cells were seeded in triplicate in 6-well plates, treated with 5 μM indisulam (Sigma Aldrich, Merk), or vehicle control, and collected after 6 and 16 hours for RNA extraction. Total RNA was extracted using TRIzol Reagent (Life Technologies, Thermo Fisher, cat n. 15596026), RNeasy Mini Kit, and RNase-Free DNase Set (Qiagen, cat n. 74104 and 79254). Sample QC, RNA library preparations, and sequencing reactions were conducted at GENEWIZ (Germany GmbH). The concentration of RNA was quantified using a Qubit Fluorometer (Life Technologies, Carlsbad, CA) and RNA integrity was assayed using a TapeStation. Sequencing libraries were prepared with RNA with PolyA selection and ERCC spike-in with standard ERCC kit (ERCC RNA Spike-In Mix,Thermo Fisher 4456740). Final libraries (Illumina, RNA with PolyA selection) were sequenced on a NovaSeq platform, producing 2×150 base paired-end reads.

  Dac EGAC50000000407

• Pacbio_methylation_cases

  The PacBio cases dataset for differential methylation analysis included 10 case samples with known KMT2A loss-of-function variants, 2 VUS samples. All samples were sequenced using PacBio Revio® HiFi reads at a matched coverage of 15X, and methylation was called using pb-cpg-tools on aligned BAM files.

  Dataset EGAD00010002807

• 16S data from IBD patients

  Bacterial 16S V4 rDNA was amplified using two differently barcoded V4 fusion primers. Pooled PCR samples were purified and paired-end sequenced on MiSeq instrument for 250 cycles. The steps from DNA quantification to sequencing were conducted at Second Genome Inc.

  Dataset EGAD00001005482

• Additional RNAseq data of metastatic breast cancer samples

  Paired-end RNA sequencing data from 33 metastatic breast cancer samples. Sequencing was performed on the Illumina NovSeq 6000 using NEBNext Single Cell and Low Input RNA Library Prep Kit for Illumina.

  Dataset EGAD00001015678

• Combined – whole blood and skin fibroblasts - transcriptomic analysis in Psoriatic arthritis.

  Whole blood RNA sequencing was performed longitudinally in 27 subjects with PsA at the beginning, one month and six months after treatment and response to therapy was defined at 6 months. As control groups 7 healthy individuals and 9 subjects with rheumatoid arthritis were recruited combined with public RNA-sequencing datasets from patients with psoriasis and systemic lupus erythematous. Differential expression analysis and weighted gene co-expression network analysis were performed to identify gene expression signatures, while deconvolution and flow cytometry to characterize the peripheral blood immune cell profile. RNA sequencing of skin fibroblasts was performed in a subset of affected (n=3) and healthy individuals (n=3) combined with CellChat to identify the blood-skin fibroblasts interaction network.

  Study EGAS00001006288

• 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

• NIH RECOVER: A Multi-Site Pathology Study of Post-Acute Sequelae of SARS-CoV-2 Infection

  The RECOVER tissue pathology study is a cross-sectional study designed to define and characterize the epidemiology, natural history, clinical spectrum, and underlying mechanisms of post-acute effects of SARS-CoV-2 infection in a diverse population representative of the general COVID-19 population in the US. The autopsy study will characterize the pathology of PASC in decedents who die more than 60 days after initial onset or after hospital discharge, whether in-hospital or out-of-hospital at time of death, and who meet the working definition of PASC as defined by the recent World Health Organization publication given in Section 5.4. The autopsy study will also explore the pathology of acute SARS-CoV-2 infection in a smaller subset of decedents who died between15-60 days from symptom onset. This protocol defines the common set of clinical data elements, autopsy procedures for tissue collection, core measures, pathology protocols, shared pathology tissues, data elements, and methodology. Each investigator site is expected to perform autopsies on the decedents to address the pathophysiology of the potential long-term effects of SARS-CoV-2 infection on human health. The Consortium analysis plan aims to address research questions by incorporating: 1) tissue obtained from autopsies performed at each Phase II participant's site; and 2) tissue available from other pathology investigators/autopsy sites within the Consortium.

  Study phs003768

• Exploring the heterogeneity of sarcoma using single cell sequencing

  Multi region samples are collected from patients, with consent, immediately after resection of the tumour. Samples are digested and sorted using FACS as single cells into lysis buffer. Cells are then stored until further processing for G&T-seq. After sequencing, we will explore intra-tumour heterogeneity using computational approaches to integrate RNA and DNA data onto the tumour phylogeny 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 EGAD00001006786

• Genetic Discovery and Application in a Clinical Setting: Continuing a Partnership (eMERGE Phase II)

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines a biobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics. The eMERGE Network is comprised of 9 sites and one coordinating center. Each site maintains its own biorepository where DNA specimens are linked to phenotypic data contained within EMRs. Children's Hospital of Pennsylvania, PI: Hakon Hakonarson, MD, PhD Cincinnati Children's Medical Center with Boston Children's Hospital, PI: John Harley, MD, PhD - CCHMC and Ingrid Holm, MD, MPH - BCH Geisinger Health System, PI: David Carey, PhD and Marc Williams, MD Group Health Cooperative with University of Washington, PI: Eric Larson, MD, MPH - GHC and Gail Jarvik, MD, PhD - UW Marshfield Clinic with Essentia Institute of Rural Health, PI: Catherine McCarty, PhD, MPH - Essentia and Murray Brilliant, PhD - Marshfield Mayo Clinic, PI: Christopher Chute, MD, DrPH and Iftikhar Kullo, MD Icahn School of Medicine at Mount Sinai, PI: Erwin Bottinger, MD Northwestern University, PI: Rex Chisholm, PhD and Maureen Smith, MS Vanderbilt University, PI: Dan Roden, MD eMERGE Coordinating Center - Vanderbilt University, PI: Paul Harris, PhD Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes.

  Study phs000948

• Aligned FOS RNA sequencing data

  Total RNA-sequencing Paired-End was performed by GenomeScan (Leiden, the Netherlands) on the NovaSeq6000 platform for full-length FOS (FOS FL), truncated FOS (FOSΔ), a FOS isoform with an L376N mutation (FOS mut), untransduced fMSC (unt) and fMSC with empty pLV (pLV) at week 0, 1, 2, and 3 in triplicate (total of 60 samples). Raw RNA-seq data were processed by the RNA-seq BioWDL pipeline pipeline version 5.0.0 developed by the SASC team at LUMC, LUMC, Leiden, the Netherlands).

  Dataset EGAD50000001839

• Arcagen - Extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3) domain

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files from patients with extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3) (n=85).

  Study EGAS50000000644

• Genetic Underpinnings of Ethnic Disparities in Bone Toxicities Between Hispanic and Non-Hispanic Children Treated for Acute Lymphoblastic Leukemia

  Acute lymphoblastic leukemia (ALL) is the most common malignancy among children and young adolescents. More than 90% of children are cured with a combination of multiple chemotherapeutic drugs. Some may suffer from debilitating toxicities due to the cytotoxic drugs, necessitating pharmacogenetic research to search for genetic markers predictive of treatment toxicities, so that treatment can be better tailored based on patients' genetic makeup. Bone toxicities, including osteonecrosis and fractures, most often due to glucocorticoids, are the most common complications in ALL, which have long-lasting detrimental impact on the still developing skeletons in children. In DFCI ALL Consortium Protocol 05-001, a multi-center clinical trial for childhood ALL conducted in Canada and the US, we found that Hispanic children had less drug toxicities to their bones than non-Hispanic children. Although reasons for the observed ethnic differences are largely unknown, patients' inherited genetic background may be at play. This study seeks to perform a novel pharmacogenomic study based on multi-site clinical trials DFCI ALL Consortium Protocol 05-001 and 11-001 to identify genetic underpinnings of ethnic disparities in bone toxicities.

  Study phs002317

• CTCF/cohesin-binding sites are frequently mutated in cancer

  Cohesin is present in almost all active enhancer regions, where it is associated with transcription factors. Cohesin frequently colocalizes with CTCF (CCCTC-binding factor), affecting genomic stability, expression and epigenetic homeostasis. Cohesin subunits are mutated in cancer, but CTCF/cohesin-binding sites (CBSs) in DNA have not been examined for mutations. Here we report frequent mutations at CBSs in cancers displaying a mutational signature where mutations in A•T base pairs predominate. Integration of whole-genome sequencing data from 213 colorectal cancer (CRC) samples and chromatin immunoprecipitation sequencing (ChIP-exo) data identified frequent point mutations at CBSs. In contrast, CRCs showing an ultramutator phenotype caused by defects in the exonuclease domain of DNA polymerase ɛ (POLE) displayed significantly fewer mutations at and adjacent to CBSs. Analysis of public data showed that multiple cancer types accumulate CBS mutations. CBSs are a major mutational hotspot in the noncoding cancer genome.

  Study EGAS00001003010

• A_systems_biology_approach_to_understand_immunity_and_pathogenesis_of_malaria_in_children_exposed_to_endemic_Plasmodium_falciparum_transmission

  From long-term studies of children in Kenya, we know that some children have numerous malaria episodes through to older age, while other children become immune more rapidly. Our hypothesis is that these children with frequent malaria episodes are caught in a vicious circle, whereby malaria episodes lead to impaired immunity to malaria, which in turn leads to further episodes of malaria. We will therefore investigate immune responses in children with different life histories of malaria episodes. We will also include a group of children living in similar environmental conditions but who are completely unexposed to malaria. Aims: Using RNAseq we will characterise the transcriptome of a group of children with frequent and febrile malaria or infrequent/asymptomatic. These data will be combined with cytokine profiling data (NIMR) and used to build predictive models (Exeter). The predictions will be validated against a similar second group. A single snap shot of immune responses may represent only the endpoint of many immune processes and may not reflect those that are causally related to differences in malaria outcome. Therefore, we will collect and store samples from a larger group of children who will then be followed up for over 3-4 years. This extended follow up will allow us to identify the same groups as described above, and then examine samples taken before the development of those outcomes. 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 EGAS00001002978

• The Genomic and Transcriptomic Landscape of a HeLa Cell Line

  HeLa is the most widely used model cell line for studying human cellular and molecular biology. To date, no genomic reference for this cell line has been released, and experiments have relied on the human reference genome. Effective design and interpretation of molecular genetic studies performed using HeLa cells require accurate genomic information. Here we present a detailed genomic and transcriptomic characterization of a HeLa cell line. We performed DNA and RNA sequencing of a HeLa Kyoto cell line and analyzed its mutational portfolio and gene expression profile. Segmentation of the genome according to copy number revealed a remarkably high level of aneuploidy and numerous large structural variants at unprecedented resolution. Some of the extensive genomic rearrangements are indicative of catastrophic chromosome shattering, known as chromothripsis. Our analysis of the HeLa gene expression profile revealed that several pathways, including cell cycle and DNA repair, exhibit significantly different expression patterns from those in normal human tissues. Our results provide the first detailed account of genomic variants in the HeLa genome, yielding insight into their impact on gene expression and cellular function as well as their origins. This study underscores the importance of accounting for the strikingly aberrant characteristics of HeLa cells when designing and interpreting experiments, and has implications for the use of HeLa as a model of human biology. Copyright Landry et al., "The Genomic and Transcriptomic Landscape of a HeLa Cell Line," G3: Genes | Genomes | Genetics (2013).

  Study phs000643

• A Genome-Wide Association Study for Post-bronchodilator Lung Function in Children with Asthma

  In this study, we conducted a genome-wide association study (GWAS) of post-bronchodilator (BD) FEV1 (forced expiratory volume in 1 second) and FEV1/FVC (forced vital capacity) in Puerto Rican children with asthma. From September 2003 to June 2010, we recruited 618 Puerto Rican children with asthma, which was defined as physician diagnosed asthma and at least one episode of wheezing in the previous year. The recruited children include 267 from public schools in Hartford, Connecticut and 351 from randomly selected households in San Juan, Puerto Rico.

  Study phs001216

• The T cell receptor repertoire of tumor infiltrating T cells is predictive and prognostic for cancer survival

  Transcriptomic sequencing on pre-immunotherapy melanoma patients.

  Dataset EGAD00001007710

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

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

  Study phs000815