Search Results - EGA European Genome-Phenome Archive

WES data for Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma

Neuroblastoma (NB) is one of the most lethal childhood cancers due its propensity to treatment resistance. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicates these two contrasting scenarios and shows branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones, emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

Dataset EGAD00001015413

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

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

Dataset EGAD00001015450

CcRCC_metabolic_heterogeneity

Heterogeneity is a hallmark of clear cell renal cell carcinoma (ccRCC). In this study, hyperpolarized [1-13C]pyruvate MRI (HP-13C-MRI) was used to probe metabolic heterogeneity non-invasively in 6 ccRCC patients from which 58 tumor and healthy tissues biopsies were acquired postoperatively. MRI parameters were correlated with 146 metabolite concentrations and the expression of 2523 metabolic genes across 34 metabolic pathways. Using metabologram projections and pathway consensus scoring, several metabolic pathways were correlated with the metabolic MRI, including glycolysis, pentose phosphate pathway, and the TCA cycle. A simple thresholding approach applied to the imaging metrics was sufficient to provide meaningful metabolic pathway information. We also show that tissue metabolic heterogeneity increases in regions with higher pyruvate-to-lactate conversion on imaging. These results provide validation of the biological relevance of HP-13C-MRI and support the possibility of its clinical use to improve disease characterization, stratification, targeting of biopsies, and providing novel methods to monitor treatment response.

Dataset EGAD00001015780

Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

Dataset EGAD00001015713

Cancer-independent, second somatic NF1 mutation of normal tissues in neurofibromatosis type 1

Cancer predisposition syndromes mediated by recessive cancer genes generate tumours via somatic variants (second hits) in the unaffected allele. Second hits may or may not be sufficient for neoplastic transformation. Here, we performed whole genome and exome sequencing on 479 tissue biopsies from a child with neurofibromatosis type 1, a multi-system cancer-predisposing syndrome mediated by constitutive monoallelic NF1 inactivation. We identified multiple independent NF1 driver variants in histologically normal tissues, but not in 610 biopsies from two non-predisposed children. We corroborated this finding using targeted duplex sequencing, including a further nine adults with the same syndrome. Overall, truncating NF1 mutations were under positive selection in normal tissues from individuals with neurofibromatosis type 1. We demonstrate that normal tissues in neurofibromatosis type 1 commonly harbour second hits in NF1, the extent and pattern of which may underpin the syndrome's cancer phenotype.

Dataset EGAD00001015398

scDNA-seq for 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

Neuroblastoma (NB) is one of the most lethal childhood cancers due its propensity to treatment resistance. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicates these two contrasting scenarios and shows branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones, emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

Dataset EGAD00001015414

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

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

A comprehensive clinical and multi-omic profiling for 199 patients with acute coronary syndrome (ACS), an acute subcategory of CAD, that we recruited in two major Israeli hospitals. We demonstrate that ACS has distinct serum metabolome and gut microbial signatures, as compared to a control cohort. Metabolic aberrations linked with microbiome and diet show a gradual trend with significant metabolite deviations in control participants with metabolic impairment suggesting their involvement in earlier dysmetabolic phases preceding clinically overt CAD.

Study EGAS00001005342

H3Africa - Consortium WGS

To address gaps in sequence data for some of the African populations being studied in H3Africa projects, additional sequencing has been carried out through various projects and the data are being made available. The first dataset includes a high coverage whole genome sequence dataset generated at the Baylor College of Medicine with samples provided by H3Africa PIs and collaborators, funded by the National Institutes of Health. The sequence data files are accompanied by minimal metadata, including country, ethnic group (where available) and sex.

Study EGAS00001005972

Khoe-San Genome Project

The dataset contains BAM files of whole genome sequencing data of African Khoe-San population (n=169, 30X coverage) recruited from South Africa and Namibia. The population captured the true extent of human genetic diversity as represent the earliest diverged human population. The project described a detailed divergence of human population and identified positive selections associated forager lifestyles in Khoe-San population. These findings emphasise an inclusive catalogue with better representation of the earliest human genomic diversity to better advance biomedical research.

Study EGAS50000001408

Privacy Notice for Helpdesk service

Privacy Notice for EGA Helpdesk service This Privacy Notice explains what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. Note that this service collects personal data directly provided by the user, and also collects personal data from users that is provided by other organisations. 1. Who controls your personal data and how to contact us? European Genome- Phenome Archive - EGA offers a service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects, jointly managed by European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI) and Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG). EMBL-EBI and CRG represent joint Data Controllers’ of processing of your personal data. They and their Data protection officers may be contacted for data protection queries and for exercising your rights under Section 8. You may contact EMBL-EBI, represented by dr. Thomas Keane, by: email at: tk2@ebi.ac.uk or post at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: telephone at +49 6221 387-8590, email at dpo@embl.org , or post at EMBL Heidelberg, Data protection officer, Meyerhofstraße 1, 69117 Heidelberg, Germany. You may contact CRG, whose EGA team is represented by dr. Jordi Rambla de Argila, by: email at jordi.rambla@crg.eu, or post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), Dr.Aiguader 88, PRBB Building, 08003 Barcelona, Spain. CRG Data protection officer may be contacted by: email at dpo@crg.eu post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain. 2. 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Such duration serves the purpose of enabling scientific research and ensures legal compliance and facilitates internal and external audits if they arise. By contrast, the log files for the data categories related to anonymous usage statistics (raw web service logs) are processed only for 30 days and thereafter erased. 7. The joint Data Controllers provide these rights regarding your personal data You have the right to: Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration. Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period. Request information to understand data processing activities when the results of these activities are applied to you. It must be clarified that rights under points 4 and 5 are only available whenever you need support whilst using our website. For other processing based on the grounds of important public interest you cannot exercise your rights to object, rectify or erase your personal data according to the Article 13(2)(a)(b) of IP 68 (equivalent to Article 17(3)(b)(d) and Article 21(6) of the GDPR). 8. Supervisory authority If you wish to complain against the processing of your personal data, you may do so by post at: EMBL Heidelberg, Data Protection Committee, Meyerhofstraße 1, 69117 Heidelberg, Germany, or Autoritat Catalana de Protecció de Dades (Catalan Data Protection Authority), C/Rosselló 214, Esc A, 1r 1a, Barcelona 08008, Spain. Published at: February 6, 2019

Documentation data-protection/privacy-notice/ega-helpdesk

Profiling RNA Translation in Pediatric Medulloblastoma

This study provides RNA-sequencing and ribosome profiling data for patient-derived cell lines and patient tissue samples for children with medulloblastoma. Ribosome profiling is a variant protocol of RNA-sequencing that directly sequences ribosome-bound RNA fragments only. Associated RNA-seq and Ribo-seq data obtained separately for some cancer cell lines can be found on the NCBI SRA as PRJNA957428. Samples were processed for poly-A mRNA sequencing using the Roche Kapa Kit. Ribosome profiling was performed as described in the manuscript referenced below (Hofman et al.) and based on the article by McGlincy et al., Methods (2017). Ribo-seq data were analyzed for sample quality using RiboseQC (Calviello, Nat Struct Mol Biol, 2020). These data were used to quantify P-sites of open reading frames. RNAseq and ribo-seq data were integrated and compared to determine translational efficiency values using TPM (Ribo-seq) over TPM (RNA-seq) as the metric. Ribo-seq data on 14 samples and RNAseq data on 21 samples derived from 16 fresh-frozen surgical samples for medulloblastoma and 5 autopsy samples are available through dbGaP. A second cohort of 4 medulloblastoma samples (RNAseq, Ribo-seq) related to this study can be found on the EGA at EGAS00001007426.

Study phs003446

Shedding light over COVID-19 susceptibility and severity

“One of the most remarkable features of SARS-CoV-2 infection is the variation in consequence ranging from asymptomatic to life-threatening” state the authors of the majestic Nature paper released in accelerated pre-view on July 2021. This variation in consequences represent not only an issue of burning medical relevance, but also a scientific and intellectual challenge for scientists around the globe. Researchers are known to be easily triggered by a challenge, and they recently demonstrated to be even better at double their efforts to focus on an urgent societal need. In this work led and coordinated by the COVID-19 Host Genetics Initiative (COVID-19 HGI), researchers gathered data from 49,562 cases and two million controls from 46 studies performed worldwide. The majority of the data has been deposited at the EGA, where 29 datasets have been collected under the study number EGAS00001005304. Data gathered from diverse sources conferred a huge robustness to the results. Genetic data from different ancestry origin were included in the analyses, even though data from patients with European origin was highly predominant (approx. 75%). Separated analysis of the different ancestries gave rise to relevant differences in the results, highlighting how important it is to take this aspect in consideration for future studies. Data from the individual studies was compiled into three categories according to COVID-19 severity for a meta-analysis: (a) critically ill cases (b) moderate or severe cases (c) all cases. They were paired with non-COVID-19-reported controls. All three analysis resulted in a total of 13 genetic loci associated with COVID-19 severity. Out of those, 9 loci associated specifically with critical illness, while 6 (a few overlapped) associated with moderated illness. Some genomic position matched previous findings. Interestingly, working with such an enormous amount of data allowed the depth to discriminate association with disease susceptibility vs severity, an issue of large medical relevance. Several loci mapped into biological relevant genes, like TYK2, PPP1R15A and FOXP4, previously implicated in immune response and/or lung functions. Researchers participating in the COVID-19 Host Genetics Initiative have worked in an unprecedented time-tight collaboration, prioritizing data sharing and collective results to tackle the pandemic affecting the world. The scientific community won’t miss any of the lessons to learn from this, and the EGA is ready to keep working to enable safe data sharing and fruitful collaborations.

Blog covid-19-susceptibility-and-severity

Discover Cancer Image Europe, the first release of the EUCAIM platform to fuel cancer research and data sharing

The EUCAIM consortium and the European Commission have announced the release of Cancer Image Europe, a platform to fuel collaborative innovation and data sharing across Europe. This constitutes a major milestone in EUCAIM's development and an exciting step towards achieving the project's vision and goals. The platform aims to accelerate the pace of AI development and other data-intensive cancer research activities, constituting the basis for a fully-fledged infrastructure for sharing, reusing, and exploiting cancer imaging data, especially using AI techniques. A platform paving the way for the future of cancer diagnosis and treatment Cancer Image Europe brings benefits to researchers, clinicians, and AI innovators across Europe as it addresses the fragmentation of existing cancer image repositories. The platform gathers a public catalogue of cancer imaging datasets from the repositories of the EU-funded AI for Health Imaging projects. A distributed Atlas of Cancer Imaging with over 60 million anonymised cancer image data from over 100,000 patients will be established as part of future updates. The first version of Cancer Image Europe also includes a public catalogue of cancer imaging datasets, a federated searching tool to understand the information available in the federated providers, and full integration with Life Science Login. Moreover, it reuses and adds value to key components of EU-funded research projects and infrastructure in the field of cancer. The Public Catalogue When we talk about data re-use, data discovery is key. The EUCAIM public catalogue is the result of a systematic and collaborative process to which the EGA-CRG has actively contributed. This browsing interface gathers the metadata of the available cancer imaging datasets. This descriptive information will help researchers in their data retrieval. Our contribution to the Public Catalogue comes from our experience gained in the deployment of our federated network, the Federated EGA (FEGA), and as partners in projects such as EuCanImage (AI4HI initiative), in which we worked on the definition of common data models, ontologies and data standards. The EUCAIM Project EUCAIM is a cornerstone of the European Commission-initiated European Cancer Imaging Initiative, a flagship of the Europe's Beating Cancer Plan, which aims to foster innovation and deployment of digital technologies in cancer treatment and care, to achieve more precise and faster clinical decision-making, diagnostics, treatments, and predictive medicine for cancer patients. For more information about the first release of the Cancer Image Europe platform and the EUCAIM project, please visit cancerimage.eu.

Blog discover-cancer-image-europe

The Dutch Microbiome Project Batch 2

The Dutch Microbiome Project (DMP) data includes shotgun metagenomic sequencing of faecal samples 8,208 Dutch individuals. Paired-end sequencing was performed using Illumina HiSeq 2000 platform. Data is archived in two batches to facilitate easier data access and upload to EGA. Batch 2 of DMP includes ~400 samples.

Dataset EGAD00001007501

ARID1A Mutations in Endometriosis-Associated Ovarian Carcinomas

We have sequenced the whole transcriptomes of 18 ovarian clear-cell carcinomas and 1 ovarian clear-cell carcinoma cell line and found somatic mutations in ARID1A (the AT-rich interactive domain 1A [SWI-like] gene) in 6 of the samples. ARID1A encodes BAF250a, a key component of the SWIâ€“SNF chromatin remodeling complex. We sequenced ARID1A in an additional 210 ovarian carcinomas and a second ovarian clear-cell carcinoma cell line and measured BAF250a expression by means of immunohistochemical analysis in an additional 455 ovarian carcinomas

Study EGAS00000000075

DO NOT USE - Whole genome sequencing of SI-NETs from six patients

Small intestine neuroendocrine tumor (SI-NET), the most common cancer of the small bowel, often displays a curious multifocal phenotype with several intestinal tumors centered around a regional lymph node metastasis, yet the typical path of evolution of these lesions remains unclear. Here, we determined the complete genome sequences of 24 tumor and 3 adjacent normal tissue samples with their paired normal blood samples (totally 33 whole genomes) from 6 patients with multifocal SI-NETs, allowing elucidation of phylogenetic relationships between multiple intestinal tumors and metastases in individual patients.

Study EGAS00001005009

St. Jude Children’s Research Hospital – Washington University Pediatric Cancer Genome Project Steering Committee

Dac EGAC00001000044

Genetic mechanisms of resistance to chemotherapy in breast cancer

Resistance towards chemotherapy is one of the main causes of treatment failure and deathamong breast cancer patients.The main objective of this project is toidentify genetic mechanisms causing some breast cancer patients not torespond to a particluar type of chemotherapy (epirubicin) while otherpatients respond very well to the same treatment. In the project wewill perform genome / exome sequencing of a selection of breast cancerpatients (n=30). These patients are drawn from a cohort where allpatients have recieved treatment with epirubicin monotherapy before surgical removal of alocally advanced breast tumour, and where all patients have beensubjected to objective evaluation of the response to thetherapy. Subsequent to sequencing, we will analyse the data andcompare with the clinical data for each patient (object response totherapy). The main aim being to identify mutations that are associatedwith resistance to epirubicin. Identification of mutations with strongpredictive value, may have a direct impact on cancer treatment sinceit opens the possibility for genetic testing of a tumour, and desicionon which drug is likely to work best, prior to treatment start.

Dataset EGAD00001000264

Somatic mutations, clonal architecture and genomic evolution in multiple myeloma

Multiple myeloma is an incurable plasma cell malignancy whose molecular pathogenesis is incompletely understood. We used whole exome sequencing, copy number profiling and cytogenetic to analyses 84 samples from 67 patients with myeloma. In addition to known myeloma genes, we identify new candidate genes, including truncations of SP140, ROBO1 and FAT3 and clustered missense mutations in EGR1. We find oncogenic mutations in cancer genes not previously implicated in myeloma, including SF3B1, PI3KCA and PTEN. We define diverse processes contributing to the mutational repertoire, including kataegis and somatic hypermutation. Most cases have at least one cluster of subclonal variants, including subclonal driver mutations, implying on-going tumor evolution. Serial samples revealed diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Our findings reveal the myeloma genome to be heterogeneous across patients and, within individual patients, to exhibit diversity in clonal admixture and dynamics in response to therapy.

Dataset EGAD00001000339

ICGC Prostate Cancer Whole Genome Sequencing

To characterize the subclonal genomic architecture of androgen-deprived metastatic prostate cancer, we performed whole-genome sequencing (WGS) of 51 tumours from 10 patients to an average sequencing depth of 55x, including multiple metastases from different anatomic sites in each patient and, in five cases, the prostate tumour. Noncancerous DNA from blood or other tissue is used as reference comparison for each patient. The patients are part of PELICAN (Project to ELIminate Lethal Cancer) rapid autopsy study led by G. Steven Bova at Johns Hopkins University (USA) and Tampere University (Finland). As of September 2020, some of the studies using these data include: Gundem et al, Nature 2015 (PMID: 25830880). Additional EGAD00001000891 sample metadata is contained in Supplementary Information in this report.Tubio et al, Science 2014 (PMID: 25082706) Behjati et al, Nature Comm 2015 (PMID: 27615322) Wedge et al, Nature Genetics 2018 (PMID: 29662167)Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)Rodriguez-Martin et al, Nature Genetics 2020 (PMID: 32024998)Woodcock et al, Nature Comm 2020 (In Press)

Dataset EGAD00001000891

Shallow Whole Genome Sequencing of Patient Derived Xenografts

Chemotherapy is the standard-of-care treatment for metastatic colorectal cancer (mCRC) and benefits some patients, but what distinguishes responders from non-responders is unclear. In this study, we leveraged a comprehensive collection of 27 molecularly annotated patient-derived xenografts to uncover functional predictors of response to 5-FU and irinotecan combination therapy (FOLFIRI) in mCRC. Genetic analyses revealed that treatment sensitivity was marked by genomic scars indicative of BRCAness, suggesting homologous recombination (HR) deficiency as a key determinant. Accordingly, we surveyed a manually curated panel of 44 genes with a documented role in HR for the potential presence of pathogenic mutations. We did not observe a specific enrichment of HR gene mutations based on response to FOLFIRI. This result, combined with the absence of widespread biallelic inactivation of the analyzed genes and the predominance of mutations categorized as variants of unknown significance, suggests that FOLFIRI sensitivity is not primarily governed by underlying mutations in HR genes responsible for mitigating the genotoxic effects of this therapeutic regimen.

Dataset EGAD50000000277

SNP Genotype Data: Subpopulations of Filipino, Malaysian, and Papua New Guinea

In the context of the study 'Exploring Adaptive Phenotypes for the Human Calcium-Sensing Receptor Polymorphism R990G,' this repository contains 11 VCF files. Each file includes genetic data from 22 autosomes, representing distinct subpopulations. The subpopulations are as follows: Malaysian: Aeta (15 samples) Agta (11 samples) Batak (10 samples) Mamanwa (6 samples) Filipino: Jehai (13 samples) Kintaq (6 samples) Temiar (5 samples) Papua New Guinea: Highlands (2 samples) Koinambe (10 samples) Kosipe (10 samples) Sepik (2 samples) The numbers in parentheses indicate the sample size for each respective subpopulation.

Study EGAS50000000044

A tumor focused approach to resolving the etiology of DNA mismatch repair deficient tumors classified as suspected Lynch syndrome

Routine screening of tumors for DNA mismatch repair (MMR) deficiency (dMMR) in colorectal (CRC), endometrial (EC) and sebaceous skin (SST) tumors leads to a significant proportion of unresolved cases classified as suspected Lynch syndrome (SLS). A tumor-focused testing approach reclassified 86.9% of SLS into Lynch syndrome, sporadic dMMR or MMR-proficient cases. These findings support the incorporation of tumor sequencing and alternate MLH1 methylation assays into clinical diagnostics to reduce the number of SLS patients and provide more appropriate surveillance and screening recommendations.

Study EGAS50000000021

Nurminen et al ("GP2Men") Study Primary and Metastatic Prostate Cancer Whole Genome Sequence Data

We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

Study EGAS50000000005

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