15160 results for "cancer studies using rna-seq"

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• bam files Targeted BS

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001667

• OT2_Illumina_WXS_MET_XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003381

• Solid_WXS_MET

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer metastasis sample

  Dataset EGAD00001002105

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases [MOSAIC] (2017-05-04)

  This dataset contains all the data available for this study on 2017-05-04.

  Dataset EGAD00001003321

• OT2_Illumina_RNA_T_XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample (OT2_cohort)

  Dataset EGAD00001003369

• OT2_Illumina_RNA_MET_CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003365

• Illumina_RNA_MET-XEN

  RNAseq of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002076

• Epi-Tax targeted sequencing

  Sequencing of 350 cancer genes in BC samples from patients treated with either Epirubicin or Paclitaxel monotherapy in the neoadjuvant setting.

  Dataset EGAD00001000812

• Illumina_WXS_T-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002102

• Lung Progression versus Regression Whole Genome Sequencing

  Around 50 samples of pre-invasive lung cancer lesions showing subsequent clinical and pathological progression or regression

  Dataset EGAD00001001271

• ICGC Korean Breast Cancer Project

  A comprehensive characterisation and analysis of human breast cancers through whole-genome sequencing.

  Dataset EGAD00001001322

• Illumina_WGS_T-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002091

• DNA sequencing for gastric cancer ascites

  Whole-exome sequencing (WES) was performed on a total of 34 PC specimens, with 15 cases having matched gDNAs extracted from blood.

  Dataset EGAD00001004364

• Sequencing .bam files

  Sequencing of untreated pancreatic cancer metastases and primary tumor sections.

  Dataset EGAD00001003153

• Kidney Cancer Heterogeneity and Evolution Revealed by Multi-Region Exome Sequencing

  Paired end Illumina sequencing of whole exomes of multiple tumour regions.

  Dataset EGAD00001000734

• Solid_WXS_T

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer primary tumor sample

  Dataset EGAD00001002107

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• RNA Transcriptomic and DNA Methylation Landscape in FTLD-TDP and Controls

  Frontotemporal lobar degeneration (FTLD) is one of the leading causes of dementia in individuals younger than 65 years, with the aggregation of TDP-43 as one of the most common forms of FTLD. FTLD-TDP is clinically, genetically and pathologically heterogeneous, with GRN and C9orf72 as the most common genetic forms (although more than 50% of the cases are genetically unexplained), and A, B and C as the most common pathological subtypes. To investigate the molecular differences between the different forms of FTLD-TDP, we performed bulk RNA sequencing (RNAseq) and reduced representation bisulfite sequencing (RRBS) from different brain regions of FTLD-TDP patients and controls. The RNAseq dataset includes samples from the frontal cortex of 149 individuals, comprising 127 FTLD-TDP cases (genetically unexplained types A (27), B (20) and C (22), 24 GRN mutation carriers, and 34 C9orf72 expansion carriers) and 22 controls. The RRBS dataset includes samples from the frontal cortex of 158 individuals —119 FTLD-TDP cases (genetically unexplained types A (25), B (24), and C (25), 20 GRN mutation carriers, and 25 C9orf72 expansion carriers) and 39 controls; and from the cerebellum of 151 individuals —113 FTLD-TDP cases (genetically unexplained types A (24), B (21), and C (23), 20 GRN mutation carriers, and 25 C9orf72 expansion carriers) and 38 controls. In total, the study consists of data from 183 unique individuals, with some overlap across the different datasets.

  Study phs004075

• Targeted DNA sequencing and mRNA sequencing data from patients with peritoneal metastasis from colorectal cancer

  Patients with peritoneal metastasis from colorectal cancer (PM-CRC) have inferior prognosis and respond particularly poorly to chemotherapy. This study aims to identify the molecular explanation for the observed clinical behavior and suggest novel treatment strategies in PM-CRC. Tumor samples (230) from a Norwegian national cohort undergoing surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) with mitomycin C (MMC) for PM-CRC were subjected to targeted DNA sequencing, and associations with clinical data were analyzed. mRNA sequencing was conducted on a subset of 30 samples to compare gene expression in tumors harboring BRAF or KRAS mutations and wild-type tumors.

  Dataset EGAD50000000593

• GeoMx DSP of NGS mRNA expression in pre-treatment biopsies from patients with metastatic triple-negative breast cancer treated with PARP inhibitors.

  This dataset contains GeoMx digital spatial profiling (DSP) data from 12 FFPE pre-treatment biopsies of metastatic HR-altered breast cancer patients subsequently treated with PARP inhibitors (Olaparib or Talazoparib). Samples were collected from primary breast tumors (n=7) and metastatic sites including lymph nodes (n=2), bone (n=2), and brain (n=1). Tissue sections were stained for pan-cytokeratin (PANCK) to selectively profile tumor cells, with NGS-based transcriptome data generated for both PANCK+ and PANCK- regions. The dataset includes raw data files (176 fastq files) along with clinical annotations.

  Dataset EGAD50000001932

• Identification of New Therapeutic Targets for Renal Medullary Carcinoma via Integrated Genomic and Transcriptomic Profiling

  Renal medullary carcinoma (RMC) is a rare, aggressive kidney cancer associated with sickle cell trait, characterized by SMARCB1 loss and resistance to conventional therapies used for other renal cell carcinomas (RCC). We conducted whole exome sequencing (WES) on RMC samples. This study provides a large molecular characterization of RMC, highlighting TROP2 and other cell surface markers as promising therapeutic targets.

  Study EGAS50000001280

• Whole-exome sequencing performed on a patient with chronic myelomonocytic leukemia and B cell acute lymphoblastic leukemia

  We studied the rare case of a patient who underwent transformation of myelodysplastic syndrome to chronic myelomonocytic leukemia and B cell acute lymphoblastic leukemia. We used fluorescence-activated cell sorting and whole-exome sequencing to identify driver mutations in the various clones. This allowed us to propose a model of branching clonal evolution with a possible germline predisposition to cancer.

  Study EGAS00001005117

• Diverse modes of genomic alterations in hepatocellular carcinoma

  Hepatocellular carcinoma is the most prevalent type of liver cancer. We intended to study the genomic landscape of this disease. We conducted whole-genome, exome and transcriptome sequencing for 42 patients. We show diverse modes in which the HCC genome is affected in these patients, including well-known and novel mutations, structural variations, and Hepatitis B-virus integration into the genome.

  Study EGAS00001000824

• Myelodysplastic_Syndrome_Exome_Sequnecing

  Cancer is driven by mutation. Illumina GA massively parallel sequencing technology will be applied to Agilent exome hybridisation capture libraries and total genomic paired-end libraries. Bespoke algorithms are being developed to identify the somatically acquired structural variants, point mutations, insertions and deletions in these samples. This project will give unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with Myelodysplastic Syndrome development.

  Study EGAS00001000089

• Developing somatic copy number and mutation calling tools for a bespoke sequencing platform

  We previously developed a linked-read sequencing platform (DigiPico) for analysing whole cancer genomes from picogram quantities of tumour DNA. The aim of this study was to develop and evaluate the specialised computational approaches required to obtain clinically useful results from the raw data. To this end, we performed matched DigiPico, bulk tumour and germline WGS on samples from four patients.

  Study EGAS00001007195

• Whole-exome sequencing of Fanconi anemia-like inherited bone marrow failure syndrome

  We identified three fibroblast cultures derived from aplastic anemia patients in the Japanese Cancer Research Resources Bank (JCRB) Cell Bank that showed high levels of sister chromatid exchanges (SCEs) in PHA-stimulated lymphoblasts. To elucidate the genetic basis of the high SCE, we carried out whole exome sequencing of these samples. Subsequently, germline variants were analysed for these patients.

  Study EGAS00001003809

• Clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint.

  Study EGAS00001004445

• Reconstruction of complex rearrangement patterns causing the initiation of clear cell renal cell carcinoma.

  The genomic hallmark of clear cell renal cell carcinoma is the loss of the short arm of chromosome three. This appears to be the earliest genomic event in the formation of these cancers. Often chromosome 3 is lost at the same time as part of chromosome 5 is duplicated via an unbalanced translocation, often with features consistent with focal chromothripsis. In this study, we sought to reconstruct the chromothriptic event that underlies the initiation of kidney cancer. We used long read sequencing (promethION, Oxford Nanopore Technologies) of patient tumour-derived DNA to elucidate how a single cell division error can generate cancer genome complexity.

  Dataset EGAD00001005495

• November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001007529

• WES and RNAseq of Simultaneous Bilateral Breast Cancer

  This dataset includes NGS profiling of 13 women with simultaneous bilateral breast cancer. Seven women have WES of untreated surgical resections and matched healthy tissue. The six other women have WES of healthy tissue, WES+RNAseq of pre-neoadjuvant tumor biopsies, and when residual disease was present (6 tumors in 4 patients), WES+RNAseq of residual disease from post-neoadjuvant therapy surgery. One patient from the WES+RNAseq cohort had multifocal bilateral disease at diagnosis so there are 2 pre-neoadjuvant biopsy samples from each breast.

  Dataset EGAD00001009987

• sWGS on cfDNA and matching tumor DNA in pediatric cancer

  This dataset contains shallow whole genome sequencing data from paired cfDNA - tumor DNA samples of various pediatric cancer entities (total 215 samples). Files are provided in fastq format. Samples were sequenced on an Ion Proton sequencer (Thermo Fisher Scientific) or a Hiseq3000 (Illumina). Data analysis is available at https://github.com/rmvpaeme/sWGS_pediatric_cancer.

  Dataset EGAD00001007508

• EPICC: Evolutionary Predictions in Colorectal Cancer

  661 bam files generated from high-throughput RNAseq of tumour biopsies from colorectal cancer patients

  Dataset EGAD00001007828

• March 2019 data update for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  March 2019 data update for cord blood CD34+CD38-, CMP, GMP, MEP, monocyte, erythroid precursor, B cell and primary AML total blasts reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium. This dataset contains data for samples: CEMT0158 CEMT0159 CEMT0160 CEMT0161 CEMT0162 CEMT0163 CEMT0164 CEMT0165 CEMT0166 CEMT0167 CEMT0168 CEMT0169 CEMT0170 CEMT0171 CEMT0172 CEMT0189

  Dataset EGAD00001004950

• Normal prostatectomy project analysis and leftovers

  Data collected as part of the Normal prostatectomy project analysis. Whole genome sequencing (WGS, targeted at 30X for normal tissue and 50X for tumour tissue) was performed on morphologically normal tissue samples from 30 patients with prostate cancer. In addition, seven prostate tissue samples were sequenced from 7 non-cancer patients: two collected after a cystoprostatectomy and five from samples collected at autopsy. Matched blood controls were included for all patients. An extra five samples were sequenced from the stroma of cell cultured fibroblasts. In addition a few tumour samples obtained at prostatectomy and their blood matched controls are included in this dataset from the main study that are not included elsewhere.

  Dataset EGAD00001004125

• ICR96 exon CNV validation series

  A resource for assessment of exon CNV calling methods in targeted NGS data, we here present the ICR96 exon CNV validation series. The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM and PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs the most difficult exon CNV to detect.

  Dataset EGAD00001003335

• EPICC: Evolutionary Predictions in Colorectal Cancer

  ATAC-seq profiling bam files from colorectal carcinoma and adenoma.

  Dataset EGAD00001008140

• Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT-BioLINCC)

  Data Access NOTEPlease refer to the "Authorized Access" section below for information about how access to the data from this accession. Access differs from many other dbGaP accessions. BiospecimensAccess to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from TOPCAT include Buffy Coat, DNA, Plasma, Serum, Urine, and Whole Blood. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives The TOPCAT trial evaluated the effectiveness of aldosterone antagonist therapy in reducing cardiovascular mortality, aborted cardiac arrest, and heart failure hospitalization in patients who have heart failure with preserved systolic function. Background Many patients with heart failure have a normal, or near-normal, left ventricular ejection fraction (LVEF). Such patients share similar signs and symptoms as patients who have heart failure and a reduced LVEF, as well as an impaired quality of life and a poor prognosis. However, at the time of TOPCAT, the benefit of medical therapies for heart failure were limited to those with a reduced LVEF. Due to a lack of favorable evidence from clinical trials, clinical guidelines offered no specific recommendations for the management of heart failure in patients with preserved LVEF, except for attention to coexisting conditions. Among patients with heart failure and a reduced LVEF and those with myocardial infarction complicated by heart failure and left ventricular dysfunction, mineralocorticoid receptor antagonists have been shown to be effective in reducing overall mortality and hospitalizations for heart failure. In small mechanistic studies involving patients with heart failure and preserved left ventricular function, mineralocorticoid receptor antagonists improved measures of diastolic function. However, rigorous testing was needed regarding their effect on clinical outcomes in patients with preserved LVEF. Therefore, the TOPCAT trial was initiated to determine whether treatment with spironolactone, an aldosterone antagonist, would improve clinical outcomes in patients with symptomatic heart failure and a relatively preserved LVEF. Participants A total of 3445 participants were enrolled, with 1722 assigned to the spironolactone group and 1723 assigned to the placebo group. Among these, 2464 participants were enrolled via the hospitalization stratum and 981 were enrolled via the BNP stratum. Design TOPCAT was a phase 3, multicenter, international, randomized, double-blind, and placebo controlled trial. Eligible participants were randomly assigned to receive either spironolactone or placebo in a 1:1 ratio. Randomization was stratified according to whether the patient met the criterion for previous hospitalization or BNP elevation. The baseline visit included assessment of socio-demographics, physical characteristics, medical history, lifestyle factors, laboratory measures, electrocardiography variables and health-related quality of life and functional status. Study drugs were initially administered at a dose of 15 mg once daily, which was increased as tolerated to a maximum of 45 mg daily during the first four months after randomization. Subsequent dose adjustments were made as required and subjects continued to receive other treatments for heart failure and co-existing illnesses. Measurement of potassium and creatinine levels was required within 1 week after a change in the study-drug dose and at each scheduled study visit. Follow-up visits to monitor symptoms, medications, and events and to dispense study drug were scheduled every four months during the subject's first year on the study, and every six months thereafter. The mean follow-up interval was 3.3 years in each study group. Repository blood and urine samples were collected at the baseline and 1 year visits from consenting subjects. The primary endpoint was a composite of cardiovascular mortality, aborted cardiac arrest or hospitalization for the management of heart failure. Secondary endpoints included all-cause mortality, hospitalization for heart failure management, new onset of diabetes mellitus or atrial fibrillation, and quality of life. A subset of subjects also participated in the Echocardiography or Echocardiography and Vascular Stiffness ancillary studies. Echocardiography, and additionally tonometry in the Echocardiography and Vascular Stiffness study, were performed at baseline and at either 12 or 18 months following randomization. If the subject was already enrolled in the TOPCAT trial at the time the ancillary study was initiated, but had not yet reached the 18 month visit, baseline was determined via a retrospective analysis performed on any echocardiographic images completed within 60 days prior to TOPCAT enrollment and no tonometry was performed if applicable. Conclusions In patients with heart failure and a preserved ejection fraction, treatment with spironolactone did not significantly reduce the incidence of the primary composite outcome of death from cardiovascular causes, aborted cardiac arrest, or hospitalization for the management of heart failure. However, the drug reduced the secondary endpoint of heart failure hospitalization incidence.

  Study phs003665

• TGF-β signaling mediates microglial resilience to spatiotemporally restricted myelin degeneration

  Microglia survey and regulate central nervous system myelination during embryonic development and adult homeostasis. However, whether microglia-myelin interactions are spatiotemporally regulated remains unexplored. By examining spinal cord white matter tracts, we found that myelin degeneration was particularly prominent in the dorsal column (DC) during normal aging. This was accompanied by molecular and functional changes in DC microglia as well as an upregulation of TGF-β signaling. Disrupting TGF-β signaling in microglia led to unchecked microglial responses and myelin loss in the DC, accompanied by neurological deficits exacerbated with aging. snRNA-seq revealed the emergence of a TGF-β signaling-sensitive microglial subset and a disease-associated oligodendrocyte subset, both spatially restricted to the DC. We further discovered that microglia rely on a TGFβ autocrine mechanism to prevent damage of myelin in the DC. These findings demonstrate that TGF-β signaling is indispensable for maintaining microglial resilience to myelin degeneration in the DC during the aging process. This highlights a previously unresolved checkpoint mechanism of TGF-β signaling with regional specificity and spatially restricted microglia-oligodendrocyte interactions.

  Study EGAS50000001413

• Genes associated with pancreas development and function maintain open chromatin in iPSCs generated from human pancreatic beta cells

  Current in-vitro islet differentiation protocols suffer from heterogeneity and low efficiency. Induced-pluripotent stem cells (iPSCs) derived from pancreatic beta-cells (BiPSCs) preferentially differentiate towards endocrine pancreas-like cells versus those from fibroblasts (FiPSCs). We interrogated genome-wide open chromatin in BiPSCs and FiPSCs via ATAC-seq, and identified ~8.3k significant, differential open chromatin sites (DOCS) between the two iPSC subtypes (FDR<0.05). DOCS where chromatin was more accessible in BiPSCs (Bi-DOCS) were significantly enriched for known regulators of endodermal development, including bivalent and weak enhancers, and FOXA2 binding sites (FDR<0.05). Bi-DOCS were associated with genes related to pancreas development and beta-cell function, including transcription factors mutated in monogenic diabetes (PDX1, NKX2-2, HNF1A; FDR<0.05). Moreover, Bi-DOCS correlated with enhanced gene expression in BiPSC-derived definitive endoderm and pancreatic progenitor cells. Bi-DOCS therefore highlight genes and pathways governing islet-lineage commitment, which can be exploited for differentiation protocol optimisation, diabetes disease modelling, and therapeutic purposes.

  Study EGAS00001002591

• Single Cell Genome Variation Induced by Mutational Processes in Cancer - HGSOC Trios Study

  Mutational signatures derived from whole genome sequencing have potential prognostic significance. Identification of high grade serous ovarian cancer patients with deficiency in homologous recombination repair pathways could identify a subset of patients that would benefit from homologous recombination deficiency-specific treatments independent of identification of a pathological BRCA1/2 gene mutation. Clinical protocols rely on a pathologist's assessment of formalin-fixed paraffin-embedded (FFPE) tissues, and thus FFPE tissue samples are readily available. However, the bulk of mutational signature analyses have been performed on fresh frozen tissues. In this study, we aimed to assess the reliability of whole genome sequencing, variant calling, and mutational signature analysis from FFPE tissue samples.

  Study phs003036

• Sequence Analysis of Mutations and Translocations Across Breast Cancer Subtypes

  This study combined whole exome (103 samples) and whole genome (22 samples) sequencing over a total of 108 breast tumors and matched normal DNA to identify novel mutations and translocations. Samples were subjected to paired-end Illumina sequencing with goal of 30x coverage of tumor/normal for whole genomes and 100x tumor/normal coverage for whole exomes. From these sequences, we used various computational techniques to identify somatic point mutations, insertion/deletions and structural rearrangements in these tumors. From these data, we identified new insights into the rates of background mutations in these cancers, novel recurrent mutated genes, and multiple gene rearrangements. One of these rearrangements appears to be a recurrent event in breast cancer.

  Study phs000369

• A Combined Omics and Tissue Biobank for Paediatric Cancers

  The Swedish Childhood Tumour Biobank (BTB) is a nonprofit national infrastructure for samples and genomic data collection from pediatric patients diagnosed with central nervous system and other solid tumours in Sweden. Fresh frozen tumour tissue and patient’s matched blood are collected from affected patients and genomic data is continuously generated and made available for approved medical research projects. We expect that the accessibility of biological samples and/or data to the research community will contribute to further advances in the understanding of tumour biology, which in turn will impact many aspects of clinical care of children with cancer, by refining diagnosis and/or identifying oncogenic drivers and therapeutic targets.

  Study EGAS50000000209

• Insights into BRCA1 and TP53 associated breast cancer development from integrated whole genome analysis of mouse model mammary tumors

  Germline mutations in BRCA1 are a leading cause of hereditary breast cancer. We generated Brca1;Trp53 double conditional knockout (CKO) and Trp53 single CKO mouse models, both with either one or two copies of Trp53 deleted by Cre. We used whole-genome sequencing and optical genome mapping to interrogate the genomes of the resulting mammary tumors. Our comprehensive genomic analyses show that the Brca1;Trp53-CKO tumors recapitulate major genomic features of the human BRCA1 breast samples indicative of conserved fundamental mutational processes. This study also provides new insights into the genetic mechanisms of tumor development with potential implications for the treatment of BRCA1 cancers.

  Study EGAS50000001402

• Elevated somatic mutation burdens in normal human cells due to defective DNA polymerases

  Mutation accumulation over time in normal somatic cells contributes to cancer development and is proposed as a cause of ageing. DNA polymerases POLE and POLD1 replicate DNA with high fidelity during normal cell divisions. However, in some cancers defective proofreading due to acquired mutations in the exonuclease domains of POLE or POLD1 causes markedly elevated somatic mutation burdens with distinctive mutational signatures. POLE and POLD1 exonuclease domain mutations also cause familial cancer predisposition when inherited through the germline. Here, we sequenced normal tissue DNA from individuals with germline POLE or POLD1 exonuclease domain mutations. Increased mutation burdens with characteristic mutational signatures were found to varying extents in all normal adult somatic cell types examined, during early embryogenesis and in sperm. Mutation burdens were further markedly elevated in neoplasms from these individuals. Thus human physiology is able to tolerate ubiquitously elevated mutation burdens. Indeed, with the exception of early onset cancer, individuals with germline POLE and POLD1 exonuclease domain mutations are not reported to show abnormal phenotypic features, including those of premature ageing. The results, therefore, do not support a simple model in which all features of ageing are attributable to widespread cell malfunction directly resulting from somatic mutation burdens accrued during life.

  Dataset EGAD00001006212

• The 5-hydroxymethylcytosine Landscape of Prostate Cancer

  This dataset consists of genome-wide 5hmC methylomes at various stages of prostate cancer, including not only 93 metastases from castration-resistant prostate cancer (mCRPC) patients, but also 5hmC patterns in cell-free DNA (cfDNA). There are 2000 runs in total as fastq files.

  Dataset EGAD00001008462

• Methylated DNA immunoprecipitation sequencing of untreated and castration resistant prostate cancer

  Methylated DNA immunoprecipitation sequencing of 28 untreated prostate cancers, 11 castration resistant prostate cancers, and 12 benign prostatic hyperplasias.

  Dataset EGAD00001000610

• Colorectal cancer methylation profiling

  4 AC samples, each with adenoma, carcinoma and normal colon tissue (12 samples in total) were analysed on the Infinium MethylationEPIC BeadChip for copy number alteration analyses.

  Dataset EGAD00010001691

• WES bam files associated with HNSCC patients in the LIONESS study

  This dataset contains 85 WES bam/bai files of HNSCC cancer patients as part of with the LIONESS study

  Dataset EGAD50000001487

• Res1_PC9_exp2_MC_01_04_22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Study EGAS00001006170

• Myeloma_Follow_up_Pilot

  Targeted capture of exonic and intronic regions of interest for the study of genomic alterations in multiple myeloma.

  Study EGAS00001000743