Search Results - EGA European Genome-Phenome Archive

single-cell RNA-seq Case-Control study of children progressing to Type1 diabetes

It was a single-cell RNA sequencing study on the PBMC samples from four Finnish children at risk of developing Type 1 diabetes and their gender age and HLA matched control children. All four Case children were positive for multiple islet specific autoantibodies and two of them also progressed to clinical disease during the follow up whereas the control children remain negative for all autoantibodies. Single-cell analysis confirmed some of the signatures obtained from the bulk data. It identified that high IL32 in case samples in the bulk RNA-seq was contributed mainly by activated T cells and NK cells. Trajectory analysis of the scRNA-seq data suggested that IL32 expression increased as the T cells moved towards activated state.

Dataset EGAD00001005768

Massive parallel RNA sequencing of highly purified mesenchymal cells derived from bone marrow specimens of 10 healthy donors

Highly purified mesenchymal cells (CD45-/7AAD-/CD235a-/CD31-/CD271+/CD105+) were prospectively FACS-isolated from bone marrow specimens of 10 healthy donors (HDs). This data set is used as a baseline control to observe the differences between gene expression profiles (GEPs) of pre-leukemia cases (45 low-risk myelodysplastic syndrome, 4 Shwachman-Diamond and 3 Diamond-Blackfan anemia patients) and gene expression patterns observed in a normal, healthy context. Through differential expression and gene set enrichment analysis we determined that inflammatory signaling pathways are significantly more active in mesenchymal cells of pre-leukemia cases compared to their healthy counterparts. Finally, we determined through statistical modelling of healthy donor's GEPs which pre-leukemia cases have significantly more active inflammatory signaling and demonstrated a strong relation to survival statistics.

Dataset EGAD00001002659

MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGBS)

B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the epigenetic landscape of this particular leukemia subtype, we performed DNA bisulfite-sequencing on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

Dataset EGAD00001005018

Complete DNA/RNA sequencing dataset for Australian ICGC ovarian cancer sequencing project 2014-07-07

Complete WGS and RNA-Seq dataset for Australian ICGC ovarian cancer sequencing project 2014-07-07, representing 93 donors. Sequencing was performed on Illumina HiSeq. Alignment of the lane-level fastq data was performed with bwa (WGS data) and RSEM (transcriptome data). For this dataset lane-level .bam files have been merged and de-duplicated to create a single bam file for each sample type (tumour/normal) for each donor. This dataset supersedes all previous datasets for this study. 2016-08-08 updated with 14 outstanding RNA-seq samples & corresponding RSEM bams 2016-12-07 updated with 7 outstanding RNA-seq controls and corresponding RSEM bams

Dataset EGAD00001000877

Identification of genes involved in congenital disorders of glycosylation and 3-methylglutaconic aciduria (2018-03-14)

Identification of genes involved in congenital disorders of glycosylation and 3-methylglutaconic aciduria. There are more than 100 genes known for congenital disorders of glycosylation and new disorders are discovered each year. WE included patients with a so far unsolved glycosylation disease. The diagnostic group 3-methyglutaconic aciduria is a heterogenous group of disorders mostly caused by abnormal phospholipid synthesis or in association with mitochondrial dysfunction. We included patients with a so far unsolved disease and 3-methylglutaconic aciduria. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2018-03-14.

Dataset EGAD00001004038

TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases (2015-07-02)[MOSAIC]

Systematic next generation sequencing efforts are beginning to define the genomic landscape across a range of primary tumours, but we know very little of the mutational evolution that contributes to disease progression. We therefore propose to obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in a cohort of matched primary and metastatic colorectal cancers, and additionally to explore the extent to which those mutations identified as recurrent in the metastatic setting are able to subvert normal biological processes using both genetically engineered mouse models and established cancer cell lines. This study will enable us to define to what extent primary tumour profiling can capture the biological processes operative in matched metastases as well as the significance of intratumoural heterogeneity. This dataset contains all the data available for this study on 2015-07-02.

Dataset EGAD00001001426

Genomic Signatures of Intestinal Metaplasia in Six Countries with Varying Incidence of Stomach Cancer

Intestinal metaplasia (IM) is a pre-cancerous condition associated with gastric cancer (GC), a deadly malignancy with varying geographic incidence. Performing a genomic analysis of over 1,500 IM samples from six countries, we identified 46 significantly mutated genes. We observed IM driver genes associated with high-risk populations and worse prognosis (ARID1A), members of the KRAS/MAPK signalling pathway (KRAS, BRAF, MAP2K1, MAP3K1, MAP2K4, NF1), and altered mucosal immunity (PIGR).

Study EGAS50000001056

Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals

Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals. Additional associated phenotype information is available for all individuals included in this study directly from CIBMTR. Data are available under controlled access release upon reasonable request and execution of a data use agreement. Requests should be submitted to CIBMTR at info-request@mcw.edu and include the study reference IB17-04.

Study EGAS00001006033

Relationship between low LDL cholesterol concentrations not due to statin therapy and risk of type 2 diabetes: a US-based cross-sectional observational study using electronic medical records

Lipid lowering therapy using HMG-CoA reductase inhibitors (statins) is associated with an approximately 9-12% increase in the risk of new-onset type 2 diabetes (T2DM). The risk of diabetes could be increased by statins directly; however, genetic approaches have also implicated low LDL cholesterol (LDL-C) concentrations as a risk factor for T2DM. Mendelian randomization studies using functional variants in both HMGCR (GeneID:3156) and PCSK9 (GeneID:255738) genes found a higher risk of T2DM in individuals with variants associated with lower LDL-C concentrations. Since PCSK9 and HMGCR are involved in lipid metabolism through distinct molecular pathways, the altered glycemic effect associated with variants in both genes is likely to be the result of their common effect on LDL-C concentrations. Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations with increased risk of T2DM. Individuals who have very low LDL-C concentrations not due to lipid lowering therapy can provide insights into the relationship between low LDL-C concentrations and T2DM. Here, we used de-identified electronic health records (EHRs) to test the hypothesis that low LDL-C concentrations are associated with T2DM.

Study phs001588

Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA)

The OPPERA project has two sub-studies investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD). OPPERA I baseline case-control study of chronic TMJD (dbGaP study accession phs000762). This sub-study uses a case-control study design to investigate characteristics associated with chronic TMD. The parent OPPERA phenotype file contains an indicator variable (PHASE1CASECONTROL) identifying participants in this sub-study. Data users should use the relevant case-classification variable (CHRNONICTMDCASE) and apply statistical methods appropriate for case-control studies when analyzing TMJD in this sub-study. OPPERA I prospective cohort for first onset of TMJD (dbGaP study accession phs000850). This sub-study uses a prospective cohort study design to investigate characteristics that predict risk of developing first-onset TMD. The parent OPPERA phenotype file contains an indicator variable (PROSPECTIVECOHORT) identifying participants in this sub-study. Data users should use the relevant case-classification variable (INCIDENTCASE) and the relevant time-in-study variable (FOLLOWUPYEARS) when analyzing TMJD in this sub-study. It is recommended that data users apply statistical methods appropriate for cohort studies of a dynamic population (for example, Cox regression models, where INCIDENTCASE is the censoring variable and FOLLOWUPYEARS is time in study).

Study phs000761

DEMENTIA-SEQ: WGS in Lewy Body Dementia and Frontotemporal Dementia

Lewy body dementia, amyotrophic lateral sclerosis/frontotemporal dementia, and multiple system atrophy are age-related, neurodegenerative syndromes that are poorly understood. Delineating the genetic risk that is driving the pathophysiology of these neurological diseases is fundamental for understanding disease mechanisms and for developing disease-modifying treatments. In version 1 of the study/dbGaP deposition, we performed a whole-genome sequencing study consisting of 7,403 total samples, including 2,633 genomes from patients with Lewy body dementia, 2,641 frontotemporal dementia patients, and 1,980 neurologically healthy controls. Of these, 6,907 were uploaded to dbGaP as the basis of the DementiaSeq, phs001963 dataset. The data relating to these samples are available on dbGaP. In version 2 of this study/dbGaP deposition, we made much of these data available on Anvil. More specifically, data for 6,254 of these samples were also uploaded to the ALS Compute platform on AnVIL. The data for the remaining 653 samples are only available on dbGaP. The dbGaP/AnVIL Table lists the availability of dbGaP and AnVIL for each individual sample: phd008475.In version 3 of the study/dbGaP deposition, we added whole-genome sequence data generated using DNA samples obtained from 683 patients diagnosed with multiple system atrophy.

Study phs001963

Phylogenetic analysis of paired breast carcinomas identifies genetic events associated with clonal recurrence and invasive progression

Development of ipsilateral breast carcinoma following diagnosis of breast ductal carcinoma in situ (DCIS) has been assumed to represent recurrence of the primary tumour. However, this may not be the case and it is important to know how often recurrences are new tumours. Ipsilateral primary-recurrence pairs (n=78) were sequenced to test their clonal relatedness. Shared genetic events were identified from whole exome sequencing (n=54 pairs) using haplotype-specific copy number and phylogenetic analysis. The remaining pairs were sequenced by a targeted panel or low-coverage whole genome sequencing. We included 32 non-recurrent DCIS to compare recurrent and non-recurrent disease. We found that 7% of DCIS recurrences were non-clonal by whole exome sequencing, indicative of a new breast carcinoma. Lower resolution methods detected a higher non-clonality rate (29%). Comparing primary DCIS with their recurrences found that evolution of DCIS to invasive disease was associated with increased ploidy and copy number events. TP53 mutations were enriched in DCIS with clonal recurrence compared with non-recurrent DCIS. Our results verify that de novo “recurrent tumours” of independent origin occur in patients who may be at high risk. (https://www.biorxiv.org/content/10.1101/2024.05.19.594731v2.full)

Study EGAS50000001298

Comparison of structural variations from 10X Genomics linked-reads and conventional Illumina short-reads sequencing

Structural variations (SVs) are large genomic rearrangements that can drive many diseases. Conventional short-reads whole genome sequencing (cWGS) allows their identification with base-pair resolution, but suffers from high false discovery rate. cWGS taps in short-range information from short-reads while linked-reads sequencing (10XWGS) utilizes long-range information. 10XWGS allows linkage of short-reads originating from the same large DNA molecule with a unique barcode captured in a gel bead in emulsion. This mitigates alignment-based artefacts from cWGS especially in repetitive regions. However, the false discovery rate of this technology is unclear. In this study, we performed a comprehensive analysis of different type and size of SVs predicted from these two technologies. The SVs common between both technologies were found to be highly specific by PCR and Sanger sequencing while validation rate dropped for uncommon events. Further, we propose a novel enrichment approach for filtering out false positive calls from both the technologies independently. To this end, we trained a machine learning model for respective technologies and used it to characterise SVs from MCF7 cell line and a primary breast cancer tumor with high precision. This approach would be valuable in understanding true mechanisms driven by SVs in various diseases.

Study EGAS00001004093

CancerLocator: Non-Invasive Cancer Diagnosis and Tissue-of-Origin Prediction Using Methylation Profiles of Cell-Free DNA

Background: The detection and characterization of cell-free DNA in plasma is one of the most promising new areas in cancer diagnosis. Liquid biopsy, unlike traditional tissue biopsy, has the potential to diagnose a variety of different malignancies. Results: Here we propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. We comprehensively evaluate CancerLocator with simulations and real data, and compare its performance with that of two established multi-class classification methods. We show that the predicted tumor burdens are highly consistent with the true values. In addition, when the proportion of tumor-derived DNAs in the cell-free DNAs is low, the two popular machine learning methods completely fail for cancer diagnosis, while CancerLocator successfully overcomes the challenge. CancerLocator also achieves promising results on patient plasma samples, despite the fact that the DNA methylation data from these samples has very low sequencing coverage. Conclusions: CfDNA methylation may be developed as an important approach for non-invasive early cancer diagnosis.

Study EGAS00001002211

Whole exome sequencing on primary retinoblastoma tissues and matching lymphocyte DNA.

Retinoblastoma is a rare childhood cancer initiated by RB1 mutation or MYCN amplification, while additional alterations may be required for tumor development. However, the view on single nucleotide variants is very limited. To better understand oncogenesis, we determined the genomic landscape of retinoblastoma. We performed exome sequencing of 71 retinoblastomas and matched blood DNA. Next, we determined the presence of single nucleotide variants, copy number alterations and viruses. Aside from RB1, recurrent gene mutations were very rare. Only a limited fraction of tumors showed BCOR (7/71, 10%) or CREBBP alterations (3/71, 4%). No evidence was found for the presence of viruses. Instead, specific somatic copy number alterations were more common, particularly in patients diagnosed at later age. Recurrent alterations of chromosomal arms often involved less than one copy, also in highly pure tumor samples, suggesting within-tumor heterogeneity. Our results show that retinoblastoma is among the least mutated cancers and signify the extreme sensitivity of the childhood retina for RB1 loss. We hypothesize that retinoblastomas arising later in retinal development benefit more from subclonal secondary alterations and therefore, these alterations are more selected for in these tumors. Targeted therapy based on these subclonal events might be insufficient for complete tumor control.

Study EGAS00001001690

Large-scale viral genome analysis identifies novel clinical associations between hepatitis B virus and chronically infected patients

Despite the high global prevalence of chronic hepatitis B (CHB) infection, datasets covering the whole hepatitis B viral genome from large patient cohorts are lacking, greatly limiting our understanding of the viral genetic factors involved in this deadly disease. We performed deep sequencing of viral samples from patients chronically infected with HBV to investigate the association between viral genome variation and patients’ clinical characteristics. We discovered novel viral variants strongly associated with viral load and HBeAg status. Patients with viral variants C1817T and A1838G had viral loads nearly three orders of magnitude lower than patients without those variants. These patients consequently experienced earlier viral suppression while on treatment. Furthermore, we identified novel variants that either independently or in combination with precore mutation G1896A were associated with the transition from HBeAg positive to negative phase of infection. These observations are consistent with the hypothesis that mutation of the HBeAg open reading frame is an important factor driving CHB patient’s HBeAg status. This analysis provides a detailed picture of HBV genetic variation in the largest patient cohort to date and highlights the diversity of plausible molecular mechanisms through which viral variation affects clinical phenotype.

Study EGAS00001003689

Longitudinal evaluation of serum microRNAs as biomarkers for neuroblastoma burden and therapeutic p53 reactivation

Accurate assessment of treatment response and residual disease is indispensable for the evaluation of cancer treatment efficacy. However, performing tissue biopsies for longitudinal follow-up poses a major challenge in the management of solid tumours like neuroblastoma. In the present study, we evaluated whether circulating miRNAs are suitable to monitor neuroblastoma tumour burden and whether treatment-induced changes of miRNA abundance in the tumour are detectable in serum. We performed small RNA sequencing on longitudinally collected serum samples from mice carrying orthotopic neuroblastoma xenografts that were exposed to treatment with idasanutlin or temsirolimus. We identified 57 serum miRNAs to be differentially expressed upon xenograft tumour manifestation, out of which 21 were also found specifically expressed in the serum of human high-risk neuroblastoma patients. The murine serum levels of these 57 miRNAs correlated with tumour tissue expression and tumour volume, suggesting potential utility for monitoring tumour burden. In addition, we describe serum miRNAs that dynamically respond to p53 activation following treatment of engrafted mice with idasanutlin. We identified idasanutlin-induced serum miRNA expression changes upon one day and 11 days of treatment. By limiting to miRNAs with a tumour-related induction, we put forward hsa-miR-34a-5p as a potential pharmacodynamic biomarker of p53 activation in serum.

Study EGAS00001006678

Knoll et al.: The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation

Following the demonstration of its life-saving effect in clinical trials, dexamethasone quickly became standard-of-care in the treatment of severe COVID-19. Beneficial effects were reported for patients requiring supplemental oxygen or invasive mechanical ventilation. Yet, a substantial proportion of patients still progressed to a critical condition or succumbed to the disease, despite timely initiation of glucocorticoid treatment. No molecular or cellular correlate of this beneficial treatment response has been defined. Here, we identify distinct cellular and molecular changes in circulating immune cells in patients with COVID-19 elicited in response to dexamethasone treatment. The most profound transcriptional changes in response to dexamethasone treatment were noted in monocytes and in B cells. In monocytes, we observed a reversal of hallmark signatures previously associated with COVID-19 severity, and the induction of a specific monocyte substate enriched in glucocorticoid response gene expression which we here refer to as dexamethasone response cluster. Molecular responses to dexamethasone treatment were directly linked to clinical outcome, since the reversal of pathogenic signatures and induction of glucocorticoid response genes were enriched in patients who survived the disease in response to dexamethasone treatment. Cellular responsiveness of circulating monocytes was thus identified as a correlate of clinical response to dexamethasone treatment. Changes in monocyte transcriptomes could also be linked to epigenetic alterations highlighting early differences in monocytes of dexamethasone responders and non-responders. Further, monocyte single-cell transcriptome-derived signatures were enriched in whole blood transcriptomes from patients with fatal outcome in two independent cohorts, highlighting the potential clinical value for early identification of non-responders who are refractory to dexamethasone treatment. Overall, our findings indicate that the life-saving effects of dexamethasone in COVID-19 patients are linked to a specific immunomodulatory effect based on the reversion of monocyte dysregulation. Our study elucidates the potential mechanism of action of one of the most efficient and cost-effective drugs for the treatment of COVID-19. It further highlights the potential of single-cell omics for monitoring target engagement of immunomodulatory drugs and for the stratification of patients for precision medicine approaches.

Study EGAS00001007461

The Familial Intracranial Aneurysm Linkage Study (FIA)

Our long-term objective is to identify susceptibility genes that are related to the formation of intracranial aneurysms (IA). Rupture of IAs occurs in 16,000 to 17,000 persons in the U.S. annually and nearly half of affected persons are dead within the first 30 days. An additional 6,000 to 7,000 persons with unruptured IAs are identified each year. Accumulated evidence indicates that a genetic component plays an important role in the development of IAs, but specific loci affecting the risk of IA have not been identified. The primary hypothesis of this study is that there are specific human chromosomal regions that are associated with an increased risk of IAs. Specific Aims of are: Recruitment of 400 (475) families with multiple individuals who have an IA through 23 (25) referral centers throughout North America, Australia, and New Zealand that represent 35 (40) recruitment sites. Ascertainment of interviews and blood samples from all affected family members as well as their first-degree relatives. White blood cells from living persons with an IA will be cryopreserved at Coriell Institute for Medical Research for future immortalization of cells lines as indicated. Identification of unruptured IAs by obtaining MRAs in selected asymptomatic siblings (of affected individuals). Completion of a 10 cM genome series in persons with IAs as well as the spouses and children of persons with an IA who are deceased. We will perform finer mapping of chromosomal regions with suggestive evidence of linkage in the genome screen. Performance of a nonparametric (allele sharing) linkage analysis, including relevant environmental factors such as smoking, to identify chromosomal regions linked to IA. Reconstruction of the genotypes of deceased affected family members will be performed. Identification of individuals who are genetically at high risk for the development of IAs would enable targeted and effective screening/prevention/treatment strategies to reduce the substantial mortality and morbidity associated with this devastating type of stroke. Only a multidisciplinary, collaborative effort to identify, accrue, and genotype FIA families will be successful in identifying sufficient high-risk families to characterize the genetic underpinnings of IA.

Study phs000293

National Institute of Mental Health (NIMH) Duke Cognition Cohort

The overall goal is to identify genetic variants influencing normal variation between humans, with a focus on cognitive and neurosensory traits. Because of the focus of funding agencies on disease, very few studies of the genetics of non-disease traits have been performed. The genetic variation underlying the vast majority of differences between humans therefore remains not only undiscovered, but also unexplored. We have assessed >1,200 healthy volunteers for >70 diverse, non-disease phenotypes, with a focus on cognitive and neurosensory traits that are understudied and seem to have a strong biological basis. Our study includes measurements of face recognition, night vision, cognitive performance, auditory pitch discrimination, contagious yawn susceptibility, time perception, sense of direction, taste preferences, personality, sexual behaviors, sleep habits, acne, headache locations, synesthesia, and teeth sensitivity. This method is very different from the focus of most studies on collecting cohorts of patients with disease and controls with minimal phenotypic information. Because we phenotype the same healthy volunteers for multiple highly variable yet heritable traits, we can perform genetic analyses on a large number of phenotypes by just investing once in sequencing the DNA of these participants. An additional >2,000 participants in our cohort have been measured just for cognitive performance instead of for the more extensive phenotyping. Most of the genetic studies in this cohort have been about cognitive perfromance. The number of participants with genetic data generated who have undergone the more extensive phenotyping is limited. Our studies of the more novel traits like contagious yawning have therefore focused more on correlations between different phenotypes until we have a sufficient sample size to truly attempt to make genetic discoveries. . The dataset also includes two small challenge studies. In one study, the cognitive performance of 150 healthy volunteers was measured before and after an acute dose of the antiepileptic drug topiramate. In the other study, the cognitive performance of 13 healthy volunteers was measured before and after 24 hours of sleep deprivation.

Study phs001406

National Institutes of Health The Cancer Genome Atlas (TCGA)

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

Study phs000178

ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA

Background Liquid biopsies, and the dynamic tracking of somatic mutations within circulating tumour DNA (ctDNA) can provide insight into the dynamics of cancer evolution and the intra-tumour heterogeneity that fuels treatment resistance. However, identifying and tracking dynamic changes in somatic copy-number alterations (SCNAs), which have been associated with poor outcome and metastasis, using ctDNA is challenging. Pancreatic adenocarcinoma is a disease which has been considered to harbour early punctuated events in its evolution, leading to an early fitness peak, with minimal further subclonal evolution. Methods To interrogate the role of SCNAs in pancreatic adenocarcinoma cancer evolution, we applied whole-exome sequencing of 55 longitudinal cell-free DNA (cfDNA) samples taken from 24 patients (including 8 from whom a patient-derived xenograft (PDX) was derived) with metastatic disease prospectively recruited into a clinical trial. We developed a method, Aneuploidy in Circulating Tumour DNA (ACT-Discover), that leverages haplotype phasing of paired tumour biopsies or PDXs to identify SCNAs in cfDNA with greater sensitivity. Results SCNAs were observed within 28 of 47 evaluable cfDNA samples. Of these events, 30% could only be identified by harnessing the haplotype-aware approach leveraged in ACT-Discover. The exceptional purity of PDX tumours enabled near-complete phasing of genomic regions in allelic imbalance, highlighting an important auxiliary function of PDXs. Finally, although the classical model of pancreatic cancer evolution emphasises the importance of early, homogenous somatic events as a key requirement for cancer development, ACT-Discover identified substantial heterogeneity of SCNAs, including parallel focal and arm-level events, affecting different parental alleles within individual tumours. Indeed, ongoing acquisition of SCNAs was identified within tumours throughout the disease course, including within an untreated metastatic tumour. Conclusions This work demonstrates the power of haplotype phasing to study genomic variation in cfDNA samples and reveals undiscovered intra-tumour heterogeneity with important scientific and clinical implications. Implementation of ACT-Discover could lead to important insights from existing cohorts, or underpin future prospective studies seeking to characterise the landscape of tumour evolution through liquid biopsy.

Study EGAS00001007077

Submitting array based metadata

Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

Documentation submission/metadata/submission/array

Array data for oesophageal and related samples â€“ sj_paper_methyl_normal_release

Dataset EGAD00010001834

TB-DAR Genotyping Study

Active TB patients (sputum smear-positive and GeneXpert-positive) recruited at the Temeke District Hospital in Dar es Salaam, Tanzania, as part of a prospective study that ran between November 2013 and June 2022.

Dataset EGAD00010002507

Genotype data for African Esophageal squamous cell cancer cases and controls from South Africa

This dataset contains the cleaned genotype data from 2173 African eosphageal squamous cell cancer cases and population controls. The genotype data was generated using the H3Africa Illumina Custom microarray.

Dataset EGAD00010002575

mCEL-Seq2 analysis of human brain tissues and border regions

mCEL-Seq2 analysis with reference mapping of human brain tissues and border regions

Dataset EGAD50000000046

Mutation detection of T follicular helper cell lymphomas

Whole-exome sequencing data for study of tumor heterogeneity and immune-evasive microenvironment in T follicular helper cell lymphomas

Study EGAS50000000276

African Demographic History Study Using Illumina 1M Array Data

These data were used as part of a study to characterize genomic diversity in Africa, population substructure, and evolutionary history.

Study phs001780

African Demographic History Study Based on WGS Data

These data were used as part of a study to characterize genomic diversity in Africa, population substructure, and evolutionary history.

Study phs003096

scATAC-seq of sorted CD45+ cells from blood and tissues

scATAC-seq by 10xGenomics Droplet Sequencing from two human donors. scATAC sequences are available for FACS-sorted CD45+ cells from blodd, skin and vat (visceral adipose tissue)

Dataset EGAD50000000510

Analysis of Multiparametric MR imaging and tumor tissue sampling to identify response and acquired resistance to HIF-2 inhibition in patients with advanced clear cell renal cell carcinoma enrolled in a phase 1, multiple-dose, dose-escalation trial of PT2385, a HIF-2alpha inhibitor

Study EGAS00001003506

DNAmet

DNAmet analysis of olfactory mucosa (OM) cells derived from cognitively healthy and individuals with AD exposed to traffic-related ultrafine particles (UFPs) for 72h in submerged cultures. The UFPs used for exposures were: A0 and A20. Exposures were compared to the corresponding blank samples.

Dataset EGAD50000001531

DynaTag for efficient profiling of transcription factors in small samples and single cells

The dataset included Dynatag data for the occupancy of transcription factors and single nuclei RNA-seq data. Corresponding metadata is provided for both experiments.

Dataset EGAD50000001562

WGS data of isogenic wild-type RPE1

Deposited here are WGS data generated from isogenic wild-type (WT) RPE1 untreated and treated with brequinar for 42 days (and other dNTP-pool disruptor compounds)

Dataset EGAD50000001602

Multiomics analysis of PBMCs from healthy individuals

We performed single-cell and bulk level multiomics analysis using PBMCs from healthy individuals to dipict the diversity of immune system.

Study JGAS000637

Meniere Disease Genomic Data Access Committee

This Data Access Committee is responsible for reviewing and managing data access requests related to genomic variant files from whole exome sequencing of patients diagnosed with Meniere disease. This includes data from both familial and sporadic forms of the disease.

Dac EGAC50000000708

16476 results for "sec+and+unspecified+malignant+neoplasm"

in 27.59 milliseconds.