3413 results for "*geneti*"

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• Genetics of Antinuclear Antibodies

  Antinuclear antibodies (ANA) are antibodies that react against self-antigens in the cell. While it has been reported that 12 to 20% of the general population have a positive ANA test, the genetics of ANA positivity is unknown. We performed a genome wide-association study for positive ANA tests among individuals without an ANA related autoimmune disease in the Vanderbilt University Medical Center (VUMC) DNA biobank linked to the de-identified electronic health record (EHR) system. The study is funded by the National Institute of Arthritis and Musculoskeletal and Skin diseases (NIAMS) grant R01AR076516.

  Study phs003189

• The Contribution of De Novo Coding Mutations to Meningomyelocele

  Meningomyelocele (MM) is considered a genetically complex disease resulting from the failure of the neural tube to close; a neural tube defect (NTD). Patients display neuromotor disability and frequent hydrocephalus requiring ventricular shunting. A few genes have been proposed to contribute to disease susceptibility, but most risk remains unexplained. 851 MM trios were recruited and we found 187 likely gene disrupting or damaging missense de novo mutations (DNMs) that are estimated to contribute to disease risk. These DNMs collectively define networks including actin cytoskeleton and microtubule-based processes, axon guidance, and histone modification. Gene validation demonstrates partial or complete loss of function, impaired signaling and defective neural tube closure in Xenopus embryos. Our results suggest DNMs make key contributions to MM risk, and highlight critical pathways required for neural tube closure in human embryogenesis. Data for 245 WES trios and 1 quad are available through dbGaP.

  Study phs003746

• NHLBI TOPMed: Lung Tissue Research Consortium (LTRC)

  Chronic obstructive pulmonary disease (COPD), a disease state characterized by airflow limitation that is not fully reversible, is the third leading cause of death in the U.S. COPD is a heterogeneous syndrome, with affected individuals demonstrating marked differences in lung structure (emphysema vs. airway disease); physiology (airflow obstruction); and other clinical features (e.g., exacerbations, co-morbid illnesses). Multiple genomic regions influencing COPD susceptibility have been identified by genome-wide association studies (GWAS), and rare coding variants can also influence risk for COPD. However, only a small percentage of the estimated heritability for COPD risk can be explained by known genetic loci. Like most complex diseases, COPD is influenced by multiple genetic determinants (each with modest individual effects). Emerging evidence supports the paradigm that complex disease genetic determinants are part of a network of interacting genes and proteins; perturbations of this network can increase disease risk. To identify this network, multiple Omics data will need to be analyzed with methods to account for nonlinear relationships and interactions between key genes and proteins. Our overall hypothesis is that integrated network analysis of genetic, transcriptomic, proteomic, and epigenetic data from biospecimens ranging from lung tissue to nasal epithelial cells to blood in highly phenotyped subjects will provide insights into COPD pathogenesis and heterogeneity. We will leverage the well-phenotyped, NHLBI-funded Lung Tissue Research Consortium (LTRC) to address these questions. We will perform multi-omics analysis in 1548 lung tissue and blood samples from the LTRC. With these multi-omics data, we will utilize a systems biology approach to understand relationships between multiple genetic determinants and multiple types of Omics data. We will begin by performing single Omics analyses in COPD vs. control lung, nasal, and blood samples. Next, we will integrate single Omics data with genetic variants identified by WGS to assist in fine mapping genetic determinants of COPD. We will then perform integrated network analysis of COPD with genetic and multiple Omics data using correlation-based, gene regulatory, and Bayesian networks. Subjects were recruited from Mayo Clinic, Universities of Colorado, Michigan, and Pittsburgh, and Temple University.

  Study phs001662

• Human Autism Genetics

  The goal of this study is to bring together the power of 1) whole exome sequencing, 2) homozygosity mapping in consanguineous families, and 3) transcriptional analysis in brain tissue to identify genetic causes of autism spectrum disorders (ASD).

  Study phs000639

• Trisomy 21 Dosage Compensation Map (T21DoCoMap)

  Excess gene dosage from human chromosome 21 (HSA21), due to trisomy or translocation of HSA21 material, causes Down syndrome (DS). We derived trisomy 21 (T21) induced pluripotent stem cells (iPSCs) alongside otherwise isogenic euploid controls from mosaic DS fibroblasts (GM00260, first described in Lubiniecki et al., PMID: 225951) of the NIGMS genetic cell repository, to enable analysis of whole-chromosomal and individual gene dosage imbalance in the context of in vitro models of DS development. Virtual karyotyping (by Infinium CytoSNP-850k), and phased genotyping (by linked-read whole-genome sequencing) of the resulting iPSC lines provide a deeply-characterized genomic platform for dissection of HSA21 gene dosage. We apply this highly unique resource to allelic analysis of mRNA sequencing of iPSCs and differentiated neural lineages, and demonstrate its utility in the context of inducible HSA21 dosage compensation. To this end, we equipped one distinct HSA21 copy with a doxycycline-inducible XIST transgene, which endogenously silences one X in 46,XX females. Standard and allele-specific RNA-seq analysis confirms mono-allelic T21 silencing to be virtually complete and irreversible, further supported by DNA methylation data (Infinium methylEPIC). This full-length HSA21 map of phased single-nucleotide and insertion/deletion variants enables allele-specific applications for gene-level dissection T21 dosage effects. Because our XIST remains inducible in post-mitotic T21 neurons and astrocytes, we perform single-nuclei RNA-seq to demonstrate XIST efficiently represses genes even after terminal differentiation.

  Study phs002397

• Immunophenotyping in a COVID-19 Cohort (IMPACC) Transcriptomics and Genotyping Assays

  IMPACC (IMmunoPhenotyping Assessment in a COVID-19 Cohort) is a prospective longitudinal cohort study (NCT04378777) launched in May 2020 in response to the worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was funded by the National Institute of Allergy and Infectious Diseases (NIAID) National Institute of Health (NIH). Patient enrollment and sample analysis involved 20 hospitals associated with 15 U.S. biomedical research centers and 11 centralized Core immunoassay laboratories. The study aimed to enable the identification of potential biomarkers and inform future therapeutic interventions using a longitudinal approach encompassing the whole disease course for a comprehensive understanding of the contributions of the pathogen and host immune response in modulating the manifestations, severity, and post-acute sequelae of the infection. IMPACC enrolled close to 1200 adult symptomatic, molecularly confirmed hospitalized COVID-19 patients in the study within 48 hours of their hospital admission. Clinical data and biological samples (blood and mid-turbinate nasal swabs) were collected according to minimal risk guidelines at enrollment and on days 4, 7, 14, 21, and 28. Biological samples were collected at 3-month intervals up to 12 months after hospital discharge to assess functional and immunologic recovery measures. Sample collection, processing, and storage procedures were standardized across sites, and samples were transported to centralized assay-specific core laboratories (Core Labs) in batches for testing and analysis. The description of outcome severity was not limited to survival versus death but encompassed a clinical trajectory approach using 5 groups leveraging longitudinal data based on time in hospital, disease severity by ordinal scale based on the degree of respiratory illness, and presence or absence of limitations at discharge. Disease severity was assessed based on the degree of respiratory illness using a 7-point ordinal scale adapted from the World Health Organization COVID-19 and NIAID disease ordinal severity scales.DNA for genetic analyses was collected from whole blood at a single time point. To identify any genomic determinants of severe COVID-19 disease, single-nucleotide polymorphism (SNP) genotyping was conducted to assess genetic variants associated with individual susceptibility to severe disease. In addition, IMPACC included bulk transcriptomic analysis of upper airway (nasal swaps) samples and peripheral blood mononuclear cells (PBMCs) to evaluate both dynamic changes in cellular composition and cellular response during the course of the disease. Reads were aligned to the GRCh38 human reference genome.The data submitted here represents an initially analyzed dataset encompassing samples from the first 3 of 5 study phases. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Dr. Steven Kleinstein (steven.kleinstein@yale.edu).

  Study phs002686

• Wnt Activity Reveals Context-Dependent Genetic Effects on Gene Regulation in Neural Progenitors

  In this study we aimed to identify context-dependent regulatory functions of common genetic variation after stimulation of the canonical Wnt pathway. Fetal cortical tissue between 14 and 21 gestation weeks was subjected to RNA-Sequencing (242 samples) and ATAC-Sequencing (222 samples) to profile expression and chromatin accessibility, respectively. Considering donor genotypes, we were able to identify eQTLs, caQTLs, as well as response QTLs of both categories. The dataset provided here includes 242 RNA-sequencing files in bam format and 222 ATAC-sequencing files in bam format. We also provide 83 donor genotypes in VCF format (hg38).

  Study phs003642

• Clinical and Genetic Evaluation of Individuals with Undiagnosed Disorders through the Undiagnosed Diseases Network (UDN)

  The National Human Genome Research Institute and the NIH Clinical Center established the intramural Undiagnosed Diseases Program (UDP) in 2008 to make progress in uncovering, understanding, and treating rare disorders. Based on the success of the UDP, the NIH Common Fund established the Undiagnosed Diseases Network (UDN) in FY 2013 (Phase I, 9/16/2015-8/31/2018) to achieve this type of cross-disciplinary approach to disease diagnosis in academic medical centers around the United States. The NIH Common Fund expanded the network in 2018 (Phase II, 9/1/2018-6/20/2023) to increase the availability of diagnostic services, foster opportunities for collaboration between laboratory and clinical investigators, provide resulting data and protocols to the broader community, and assess development of a sustainable national resource after Common Fund support ends. The UDN transitioned from Common Fund in 2023. The National Institute of Neurological Disorders and Stroke (NINDS) will oversee the Network in Phase III, which launched in July 2023, with help from 17 different NIH Institutes and Centers along with the NIH Office of the Director. With help from patients, family members, patient advocacy groups, a Data Management and Coordinating Center (DMCC), several Clinical Sites, and other stakeholders, the NIH envisions the UDN evolving into a larger, diverse, and self-sustained network that fosters scientific discovery and provides expert diagnostic services for undiagnosed patients across the nation. The overarching goals of the UDN in Phase III include: Continuing the proven success of the UDN in improving the clinical evaluation of difficult-to-diagnose patients using a collaborative team approach and investigating and validating promising new diagnostic technologies. Facilitating research into the etiology of undiagnosed diseases by collecting and sharing standardized, high-quality clinical and laboratory data including genotyping, phenotyping, and documentation of environmental exposures. Promoting a broader integrated and collaborative community across multiple Clinical Sites and among laboratory and clinical investigators prepared to investigate the genetic, pathophysiologic, cell biologic, and molecular mechanisms underpinning these difficult-to-diagnose conditions.

  Study phs001232

• The Genetic Evolution of Acral Melanoma

  Acral melanoma is a rare melanoma subtype arising on the sole, palm, or nail apparatus. We performed whole-exome sequencing (WES) for a cohort of 35 acral melanoma patients. For each patient, we sequenced multiple tumor areas representing different tumor stages, including the in situ and invasive portions of the primary tumor and available regional or distant metastases. Normal areas were included for all except one patient. We identified distinct evolutional trajectory compared to melanomas arising on the sun-exposed skin. Whole-genome sequencing and targeted sequencing were additionally performed for a subset of the samples. Particularly, in acral melanomas, complex genomic aberrations, characterized by clustered copy number transitions and focal amplifications, and telomerase reverse transcriptase (TERT) activating alterations occur very early during evolution, while MAP-kinase pathway mutations, such as BRAF V600E and NRAS hotspot mutations, can occur late and often result in striking intra-tumor heterogeneity.

  Study phs003451

• UCSF Database for the Advancement of JMML - Integration of Metadata with Omic Data

  Juvenile myelomonocytic leukemia (JMML) is a rare and frequently fatal myeloproliferative/myelodysplastic disorder of early childhood with an estimated incidence of 1.2 cases per million. It is associated with a spectrum of diverse outcomes ranging from spontaneous resolution in rare patients to transformation to acute myeloid leukemia in others. The overwhelming majority of JMML patients (~95%) will harbor mutations in canonical Ras pathway genes, including NF1, NRAS, KRAS, PTPN11, and CBL. As Ras proteins are mutated in more than 30% of human cancers, the information gleaned from the study of JMML has provided insights into Ras signaling in cancer as well as a group of congenital diseases with tumor predispositions known as the “Rasopathies”. While oncogenic Ras is one of the most common mutations in human cancer, it remains one of the most vexing targets for efficacious therapy. To better understand this disease, a JMML tissue bank was established at UCSF that allows for consenting of subjects, biobanking of tissue and collection of follow up data from patients both locally and at other institutions. Through the collection and analysis of specimens, knowledge about the genetic, epigenetic, and biochemical basis of JMML has been gained. This integrated database will enable future JMML research, and importantly, additional Ras/MAPK pathway research by depositing linked, processed data to well annotated anonymized patients in an easily accessible online database

  Study phs002504

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

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

  Study phs001203

• 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

• NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)

  Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, and the only leading cause of death that is steadily increasing in frequency. This project established a racially diverse cohort that is sufficiently large and appropriately designed for genome-wide association analysis of COPD. A total of 10,720 subjects were recruited, including control smokers and nonsmokers, definite COPD cases (GOLD Stage 2 to 4), and subjects not included in either group (GOLD 1 and PRISm). This cohort is being used for cross-sectional analysis, and long-term longitudinal follow-up visits after five years and after ten years are also being performed. The primary focus of the study is to identify the genetic risk factors that determine susceptibility for COPD and COPD-related phenotypes. Detailed phenotyping of both cases and controls, including chest CT scan assessment of emphysema and airway disease, will allow identification of genetic determinants for the heterogeneous components of the COPD syndrome. The aims for this study are: Precise phenotypic characterization of COPD subjects using computed tomography, as well as clinical and physiological measures, that will provide data to enable the broad COPD syndrome to be decomposed into clinically significant subtype; Genome-wide association studies will identify genetic determinants for COPD susceptibility that will provide insight into clinically relevant COPD subtypes; Distinct genetic determinants influence the development of emphysema and airway disease. The TOPMed analysis will include approximately 10,500 subjects with whole genome sequencing after quality control is completed. Comprehensive phenotypic data for COPDGene subjects is available through dbGaP study phs000179.

  Study phs000951

• Multiome_HB

  Hepatoblastoma (HB) cells display strong phenotypic heterogeneity with a major impact on drug response, but the underlying mechanisms are poorly understood. Here, we use a single-cell multi-omic strategy to unravel the molecular determinants of this plasticity. HB display a continuum of single-cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activation underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation poles. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.

  Study EGAS00001006932

• Mapping Genotypes to Chromatin Accessibility Profiles in Single Cells

  In somatic tissue differentiation, chromatin accessibility changes govern priming and precursor commitment towards cellular fates1–3. Therefore, somatic mutations likely alter chromatin accessibility patterns, as they disrupt differentiation topologies leading to abnormal clonal outgrowth. However, defining the impact of somatic mutations on the epigenome in human samples is challenging due to admixed mutated and wild-type cells. To chart how somatic mutations disrupt epigenetic landscapes in human clonal outgrowths, we developed Genotyping of Targeted loci with single-cell Chromatin Accessibility (GoT-ChA). This high-throughput platform links genotypes to chromatin accessibility at single-cell resolution, across thousands of cells within a single assay. We applied GoT-ChA to CD34+ cells from myeloproliferative neoplasm (MPN) patients with JAK2V617F-mutated hematopoiesis.

  Study EGAS50000000164

• Single cell chromatin accessibility profiles from myelofibrosis patients

  To explore how JAK2V617F disrupts cell-fate and the regulatory chromatin landscape of HSCs, we applied GoT-ChA to CD34+ sorted progenitor cells from 21 human primary samples, comprising 18 patients with JAK2V617F-mutated MF (no additional mutations, except for Pt-08), who either had no treatment (n = 12; including three longitudinal samples from a PV patient who progressed to MF) or who were treated with ruxolitinib (n = 6), a JAK1/2 inhibitor. We included a JAK2V617F CH sample (Pt-19) to explore early epigenetic changes, before the onset of overt hematological abnormality.

  Dataset EGAD50000000234

• UK10K_OBESITY_SCOOP

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches.The SCOOP samples are part of the Obesity group and will undergo exome sequencing. Severe Childhood Onset Obesity Project (SCOOP) is a sub-cohort of the Genetics Of Obesity Study (GOOS) cohort established by Sadaf Farooqi and Steve O’Rahilly at the University of Cambridge over the last 12 years. The GOOS cohort contains >4,000 patients of diverse geographic origin, many of whom have monogenic and syndromic forms of obesity, and includes patients that are offspring of consanguineous union. SCOOP is a subset of >1500 UK Caucasian patients with severe, early onset obesity (all patients have a BMI Standard Deviation Score (SDS) > 3 and obesity onset before the age of 10 years), in whom all known monogenic causes of obesity have been excluded. GWAS data on the SCOOP cohort will be available (WTCCC2 independent study) at the time of the start of this study. Data from this cohort has demonstrated that the prevalence of the common obesity risk alleles in FTO, MC4R and NEGR1 are amongst the highest within SCOOP, demonstrating its value also in the study of genetic variants with an impact on more common obesity. For further information with regard to this cohort please contact Sadaf Farooqi (isf20@medschl.cam.ac.uk).

  Study EGAS00001000124

• RNAseq data of polyA+ RNA from Leukocytes from 624 individuals of the SardiNIA cohort.

  We sequenced the polyA+ of the RNA of the leukocytes from 624 sardinian individuals with RNAseq (Illumina 2000, an average of round 60M reads per sample, 51 + 51 nt). Combining the genotype information of the same individuals (already available from the ProgeNIA project) with the transcriptome data we identified 21,183 independent expression quantitative trait loci (eQTLs) and 6,768 independent splicing quantitative trait loci (sQTLs), including 619 novel QTLs that exhibit population-specific trait associations. Using family relationships, we identified 809 segregating expression outliers and provide a new approach to study large effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

  Study EGAS00001002105

• Sequencing of an organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity.

  Kidney tumors are among the most common solid tumors in children, comprising several distinct subtypes differing in many aspects, including cell-of-origin, genetics, and pathology. Pre-clinical cell models capturing the disease heterogeneity are currently lacking. Here, we describe the first pediatric cancer organoid biobank. It contains tumor and matching normal organoids from over 50 children with different subtypes of kidney cancer, including Wilms tumors, malignant rhabdoid tumors, renal cell carcinomas, and congenital mesoblastic nephromas. The malignant rhabdoid tumor organoids represent the first organoid model for tumors of non-epithelial origin. The tumor organoids retain key properties of native tumors, useful for revealing patient specific drug vulnerabilities. We further demonstrate that organoid cultures derived from Wilms tumors consist of multiple different cell types, including epithelial, stromal and blastemal-like. Our organoid biobank captures the cellular heterogeneity of pediatric kidney tumors, providing a representative collection of well-characterized models for basic cancer research, drug-screening, and personalized medicine.

  Study EGAS00001003853

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Study EGAS00001007767

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Dataset EGAD00001015357

• Sanger sequencing of MV4-11 cell lines targeting the TP53 R284 locus

  Sanger sequencing data of cell lines derived from bulk MV4-11 cells, targeting the R248 locus of TP53, consisting of either wildtype or homozygous mutant lines.

  Dataset EGAD50000000447

• Genetic landscape of Extranodal NK/T-cell lymphoma

  Targeted-capture sequencing data from 168 ENKTCL patients

  Dataset EGAD50000000448

• Cancer Genomics, ICR, cell line data

  DAC for cell line (non patient) data from the Cancer Genomics group, Division of Genetics and Epidemiology at the Institute of Cancer Research, Sutton, UK.

  Dac EGAC50000000023

• Timing the Philadelphia chromosome and trajectory to chronic myeloid leukaemia

  We sequence >1000 whole genomes from 9 patients with CML, providing the largest sequencing dataset for this cancer. We reconstruct phylogenetic trees using somatic mutations and infer BCR::ABL1 timing and tumour growth rates. We correlate mutation landscapes and clonal trajectories with clinical features.

  Dataset EGAD00001015353