Search Results - EGA European Genome-Phenome Archive

Data upcycling, powered by EGA

In 2012 a collaborative effort headed by the teams of Carlos Caldas in Cambridge, UK and Samuel Aparicio, Canada, produced a pivotal study aimed at defining the mutational landscape of breast cancer. They gathered a collection of over 2000 breast cancer samples (METABRIC) associated with long-term clinical follow-up. They delineated an array of inherited and somatically acquired genetic variants and associated them to gene expression. This led to the identification of putative cancer-driving events and improved stratification of breast cancer patients. The study elicited huge attention and was tackled as herculean effort by fellow researchers. Cristina Curtis and her colleagues deposited the data in the EGA archive (EGAS00000000083). What they probably could not foresee is that their data would be reused more than 100 times, becoming one of the most downloaded datasets at EGA. In 2015, Frederik S. Varn and colleagues accessed the above-mentioned data from the New Hampshire, USA, to correlate it to transcriptional programs of hematopoietic cells infiltrated in breast tumours and found that those can influence patients' prognosis. Their findings, published in Nature Communications, improved the understanding of the interplay between immune system and cancer. Again, in 2017 the dataset was downloaded by an Italian/Polish collaboration and used to corroborate their mechanistic study that demonstrate how a long non-coding RNA (lncRNA) is aberrantly localized into the chromatin, where it modulates oncogenic isoforms expression, thus contributing to breast cancer. To date, the METABRIC dataset has been reused to contribute to 147 publications. Its impact on breast cancer research is incalculable, and it even spread beyond that, with recent contributions to the understanding of pan-cancer mechanisms and features. Some of the publications are translating into improvements in diagnosis and treatment of different types of breast cancer. This is just an example of how data upcycling can amplify the potential of any dataset, well past the scope of its creation, the imagination of its owners and any geographical border. At EGA, we are proud to empower such fruitful worldwide cycles of knowledge, providing a platform that enables safe sharing of sensitive genetic data.

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Gut microbiome dynamics unravelled with metagenomics sequencing

It has been already several years since we discovered that microbial therapeutics like Faecal Microbiota Transplantation can be used to treat Clostridium difficile infection (CDI) and Inflammatory bowel disease (IBD) with great success. The concept has challenged us to test the limits of our knowledge with the perspective to improve health in the most diverse conditions like Parkinson disease, depression, autism and obesity, just to mention a few. Note: the figure belongs to the original paper and is owned by the authors. Allyson L.Bird and colleagues from Genentech Inc. (San Francisco) recently published an interesting study building up on the key concept that “As microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome.” The authors of this work analysed 1359 gut microbiome samples using shotgun metagenomics sequencing. The results of the comparison done between microbiota composition across the different groups showed that biological sex is the strongest driver of diversity, while aging (age range studied: 20–69) appears to be the second factor. As smartly outlined in the authors’ own graphical abstract (figure to the left), Bacteroidota species consistently increased with donors' age while Actinobacteriota species, including Bifidobacterium, decreased. This study also integrated data from oncological patients to draw cancer-related evidences. After correcting the biases for technical and geographic differences, they found that cancer patients show significantly altered gut bacterial communities compared with their healthy counterparts. Eg. E.coli was found to be increased in cancer patients together with Bacteroidota/Firmicutes ratios. By comparing sequential samples taken from the same donors (413) within short-distance time points, the authors found that in the short term, intra-individual differences were less than the inter-individual ones, indicating a certain community stability being the norm. One of the pivotal points from this interesting study is that the data proceeds from extremely well-defined and selected donors. Specifically, the data for 949 selected healthy donors came from the Milieu Intérieur (MI) Consortium and extensive metadata, including demographic variables, serological measures, dietary information, and systemic immune profiles, are available. This precise stratification allowed for the robust detection of smaller changes, compared to former works. All sequencing data has been deposited in the EGA archive under the accession number EGAS00001004437. Enjoy this amazing open access paper published in the Experimental Journal of Medicine on January 2021.

Blog gut-microbiome-dynamics-unravelled

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.

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First datasets available in the Federated EGA Network

In February 2024, a great milestone was reached for the human omics data community: the first datasets are now available in the Federated European Genome-phenome Archive (FEGA). These datasets - hosted by national nodes in Poland, Norway, and Sweden - are discoverable in the EGA portal and accessible according to national laws. These datasets include: From FEGA Poland - The Genomic Map Of Poland From FEGA Norway - CRCbiome: Gut metagenome of Norwegian screening participants using FIT sampling Also from FEGA Norway - Single cell transcriptomics in expanded Tregs of APS-1 patients From FEGA Sweden - A Combined Omics and Tissue Biobank for Paediatric Cancers The establishment of these datasets represents a significant step towards achieving the broader objectives of the FEGA; to facilitate the sharing of high-quality human genomic and phenotypic data across borders. “The value for users is twofold: it becomes easier to make data available securely compared to storing data locally, and data becomes easier to find for other researchers. The possibility of reuse improves the chances of both verifying scientific discoveries and reaching groundbreaking conclusions, of benefit to the research community and later also patients,” said Anna Hagwall, project manager and head of human data at FEGA Sweden, NBIS. About Federated EGA FEGA was launched in 2022 as a network to store and lawfully share sensitive human omics data safely. It relies on the technology originally developed by the EGA and support from ongoing collaborations with ELIXIR - a European life sciences infrastructure. FEGA has contributed to and benefitted from the implementation of community standards for sensitive data management including those developed by the Global Alliance for Genomics and Health (GA4GH). “The FEGA concept and the development of the Nordic FEGA nodes have been made possible thanks to several projects coordinated by NeIC,” said Kjell Petersen, FEGA Norway technical lead. Managing human omics data is challenging due to its sheer size and privacy concerns, often resulting in restrictions on how and where sensitive data can be shared. The FEGA network follows a federated data access model whereby sensitive data is stored securely within a local or national jurisdiction, while discovery across the network for data of interest is enabled via a central portal. As of February 2024, the FEGA network comprises seven European national nodes with tens of additional national partners working towards establishing a FEGA node. As the FEGA network continues to onboard nodes from around the world, populated with more and more data, we move closer to a vision of a truly global resource for discovery of and access to sensitive human omics data to accelerate disease research and improve human health. "The Federated EGA network is entering a key phase where the first research datasets are now available across the network. In coming years, we look forward to expanding these datasets to cover key areas such as cancer and rare diseases for the benefit of patients" said Thomas Keane, Group Leader at EMBL-EBI.

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How mitochondrial DNA research can benefit from data reuse through EGA?

Mitochondria are the only cellular organelles harbouring their own DNA, which encodes for proteins essentials in its respiration process. Despite its discovery in 1960, Mitochondrial DNA is often overlooked compared to nuclear DNA. Indeed, not all sequencing technologies capture a good representation of mtDNA (mitochondrial DNA). Recently, Ed Reznik's team (The Ed Reznik Lab | Memorial Sloan Kettering Cancer Center) carried out a massive analysis and reanalysis of single cells WGS (whole genome sequencing) produced with an amplification-free protocol (Direct library prep, DLP). As they demonstrated in the paper Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes - PMC, DLP is more effective in catching mtDNA compared to classical single-cell sequencing. These data allowed the team to unravel mtDNA dynamics in tumoral and normal samples, highlighting how mtDNA and nuclear DNA are co-regulated and their ratio is critical for phenotype. Data reuse to achieve the greatest results For this project, Reznik's team re-used data from 3 studies stored at the European Genome-phenome Archive (EGAS00001006343, EGAS00001004448 and EGAS00001003190) that comprised a total of 602 datasets. All of them were initially produced by the teams of Sam Aparicio and Sohrab P. Shah at the Memorial Sloan Kettering Cancer Centre, where Ed Reznik is a promising group leader. This demonstrates, once again, that networking, collaboration and data sharing can achieve the greatest results when coupled with innovative and smart ideas. At the EGA we are proud to provide the scientific community with an infrastructure to enable all those very smart ideas out there to find the right data to be tested.

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Drug development for pediatric cancers: the importance of patient' s genomic data re-use

Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

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Highlights from 2025: Our top 10 requested studies

In 2025, as in previous years, EGA has facilitated the reuse of numerous datasets spanning a broad range of research and healthcare topics. To reflect on this activity, we would like to highlight the top 10 studies behind the most requested datasets of last year. Notably, the vast majority of these highly requested datasets are cancer-focused, particularly involving the study of immune checkpoint inhibitor therapies. Immune checkpoint inhibitors (ICIs) have become one of the most transformative advances in modern cancer therapy. These monoclonal antibodies enhance antitumor immune responses by targeting inhibitory immune checkpoint molecules. While ICIs have led to remarkably favorable and durable responses in some patients, treatment outcomes vary widely. This variability has fueled intense research efforts aimed at understanding resistance mechanisms, exploring combination therapies, and discovering predictive biomarkers to better stratify patients and guide treatment decisions. Study Title Disease 1 EGAS00001005503 Integrated genomic analyses reveal molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma Liver Cancer 2 EGAS00001005013 Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer Lung Cancer 3 EGAS00001004809 BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer Breast Cancer 4 EGAS50000000138 BIOKEY: Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade Lung Cancer 5 EGAS50000000497 Bladder cancer subtyping study across 4 atezo clinical trials Bladder Cancer 6 EGAS50000000689 MOSAIC - Multi-Omics Spatial Atlas In Cancer Cancer Atlas 7 EGAS00000000083 METABRIC (Breast cancer - genome and transcriptome) Breast Cancer 8 EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Synthetic data 9 EGAS00001002556 TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells Bladder Cancer 10 EGAS00001004353 Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade Renal Cancer The most requested dataset of the year was EGAD00001008128. It belongs to a study that analyzed 358 liver cancer patients enrolled in clinical trials and treated with the combination of atezolizumab (anti–PD-L1) and bevacizumab (anti-VEGF), atezolizumab alone, or sorafenib. The study highlights how bevacizumab enhances anti-PD-L1 effect and identifies candidate biomarkers to predict treatment response. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related death worldwide [1]. Because symptoms often appear late, many patients are diagnosed at advanced stages, when curative options are no longer viable. For many years, sorafenib—a multikinase inhibitor approved in 2007—was the only systemic treatment available. Although numerous clinical trials followed, it was not until a decade later that additional therapies showed meaningful clinical benefit. Among these advances, immunotherapy emerged as a promising first-line option [2]. In particular, the combination of atezolizumab and bevacizumab demonstrated improved survival in patients with advanced HCC, reshaping the treatment landscape. However, treatment efficacy remains limited by the complex and diverse hepatic microenvironments that influence immune responses, as well as by high intratumoral heterogeneity. Identifying robust predictors of treatment response is critical to enable effective patient stratification and maximize the benefits of these therapies [2]. The continued demand for these datasets highlights the interest and critical role of data reuse in deepening our understanding of cancer, accelerating the development of more effective therapies, and improving patient outcomes. By providing secure access to high-quality genomic and clinical data, EGA acts as a catalyst for biomedical progress. References Sung, Hyuna, et al. "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries."CA: a cancer journal for clinicians 71.3 (2021): 209-249. Ladd, Alexandra D, et al.“Mechanisms of drug resistance in HCC.” Hepatology 79(4) (2024): 926-940

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cBioportal lands at EGA: What this means for researchers

cBioPortal is a widely used platform designed to simplify the exploration of complex cancer genomic data. It brings together large-scale datasets—covering mutations, copy number alterations, gene expression, and clinical annotations—and presents them through an intuitive, interactive interface. Since its launch, it has become a cornerstone of cancer research, helping translate raw genomic data into meaningful biological insights. cBioPortal Is Now Available at EGA At the European Genome-phenome Archive (EGA), we host controlled-access genomic and phenoclinical datasets. By integrating cBioPortal within EGA, researchers could explore cancer datasets (once access has been granted by data owners) through the powerful and user-friendly cBioportal interface. We believe this marks an important step forward for the cancer research community, with several key benefits: Faster Insights Researchers can quickly visualize and explore datasets through the cBioPortal interface before downloading and analyzing complex raw data. This provides an immediate overview, saving time and enabling more informed decisions about which datasets to pursue. Broader Accessibility Working with complex genomic data often requires advanced computational and bioinformatic expertise. This integration lowers the technical barrier, allowing a wider range of scientists to engage with high-value datasets and, for example, accelerate hypothesis generation. Enhanced Data Integration cBioPortal enables the seamless combination of genomic and clinical data, as well as the comparison and exploration of multiple datasets within a single platform. This promotes more comprehensive analyses and supports deeper biological insights. The arrival of cBioPortal at EGA brings together powerful visualization and controlled data access, making it easier than ever for researchers to transform complex cancer genomics data into actionable knowledge. Data submitters: Would you like to make your cBioPortal data available? See our documentation page.

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Arcagen - Gynecologic cancer domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from patients with rare ovarian cancer and primary peritoneal cancer and/or fallopian-tube cancer, including low grade serous carcinoma, mucinous adenocarcinoma, clear cell adenocarcinoma; and other rare cancers such as sex cord tumors, germ cell tumor, yolk sac tumor, and malignant/immature teratoma (n=94).

Study EGAS50000000900

Arcagen - CNS domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset from patients with rare tumor from the central nervous system (n=61).

Study EGAS50000001002

SPECTA RP-1759-AYA CNS cohort

EORTC-SPECTA RP1759 study focuses on cancers in adolescent and young adult (AYA), age 12 to 29 years. This study explicitly recruited AYA patients and therefore collected empirical data to bridge the molecular gap between pediatric and adult oncology. The initial pilot study, activated in February 2019 across Europe, aimed to recruit 50 AYA patients (aged 12-29 years) with newly diagnosed or relapsed high-grade gliomas, and for which matching tumor and blood samples were collected.

Study EGAS50000000985

Arcagen - Gynecologic cancer (ovarian, peritoneal and/or fallopian-tube)

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files in .BAM format from 94 patients with rare ovarian cancer and primary peritoneal cancer and/or fallopian-tube cancer, including low grade serous carcinoma, mucinous adenocarcinoma, clear cell adenocarcinoma; and other rare cancers such as sex cord tumors, germ cell tumor, yolk sac tumor, and malignant/immature teratoma.

Dataset EGAD50000001315

MethylScan data from tissue samples

Raw data (fastq files) for tumor/normal paired samples (378 samples in total; 78, 58, 29, 24 pairs from liver, lung, ovary, and stomach cancer patients, respectively) profiled with the MethylScan assay, a low cost assay for profiling methylation. The MethylScan assay is designed for cfDNA methylome sequencing that enriches for the disease-associated DNA by depleting background signal. For solid tissues, the assay was performed without the MSRE digestion step.

Dataset EGAD00001015685

MethylScan data from plasma

Raw data (fastq files) from 1061 plasma samples profiled with the MethylScan assay, a highly cost-effective approach for cfDNA methylome sequencing. MethylScan is a cost-efficient cfDNA methylome sequencing method that enriches methylation-informative fragments while actively depleting abundant background DNA derived from blood cells. By reducing this background noise, it improves the detection sensitivity for disease-associated cfDNA, such as tumor-derived DNA.The plasma samples (n = 1,061) were from 460 cancer patients (covering 4 major cancers: liver, lung, ovarian, and stomach cancer) and 601 noncancer individuals. The noncancer individuals (n = 601) can be further categorized into three groups: noncancer patients at high-risk of liver cancer who have chronic liver diseases (n = 157), noncancer patients at high-risk of lung cancer who have benign lung nodules (n = 43), and general noncancer hospital visitors (n = 401).

Dataset EGAD00001015686

Arcagen - Relapsing germ cell tumors of any origin

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files in .BAM format from 23 patients with Relapsing germ cell tumors, of any origin and of any histological type.

Dataset EGAD50000000914

Arcagen – Cancer of unknown Primary domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files patients diagnosed with a locally advanced or metastatic Cancer of Unknown Primary (n=96).

Study EGAS50000000177

Arcagen - Skin cancer domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files patients diagnosed with adnexal carcinoma of the skin, Merkel cells cancer, and malignant melanoma of the uvea (n=49).

Study EGAS50000000491

Arcagen - Head and Neck domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files from patients with salivary gland tumors of major salivary gland; salivary gland type tumors of head and neck with a strong focus on acinic cell carcinoma, NUT carcinoma and sinus carcinoma (n=102).

Study EGAS50000000625

Arcagen – Adrenal and Thyroid cancers domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files from patients with adrenocortical carcinoma, pheochromocytoma, anaplastic thyroid cancer, medullary thyroid cancer and radioiodine refractory thyroid cancer (n=103).

Study EGAS50000000626

Arcagen - Sarcoma domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from patients with undifferentiated sarcoma of the bone; aggressive angiomyxoma; alveolar soft part sarcoma; small round cell sarcoma without EWSR1 and CIC translocations; undifferentiated pleomorphic sarcoma; undifferentiated sarcoma; myxofibrosarcoma; or sarcoma with an incidence of less than 1 per 10 million (n=62).

Study EGAS50000000639

Arcagen - Relapsing germ cell tumors domain

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from patients with Relapsing germ cell tumors, of any origin and of any histological type (n=23).

Study EGAS50000000645

Arcagen – Cancer of unknown Primary

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from 96 patients in .BAM format diagnosed with a locally advanced or metastatic Cancer of Unknown Primary.

Dataset EGAD50000000256

Arcagen - Skin cancer

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files in .BAM format from 49 patients diagnosed with adnexal carcinoma of the skin, Merkel cells cancer or malignant melanoma of the uvea.

Dataset EGAD50000000705

Arcagen - Head and Neck malignancies

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files in .BAM from 102 patients diagnosed with salivary gland tumors of major salivary gland; salivary gland type tumors of head and neck with a strong focus on acinic cell carcinoma (diagnostic based on morphology, but IHC can be performed for DOG1 and SOX10), NUT carcinoma (presence of NUT, cytokeratin and p40 by IHC) and sinus carcinoma.

Dataset EGAD50000000885

Arcagen – Adrenal and Thyroid cancers

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files in .BAM format from 103 patients with adrenocortical carcinoma, pheochromocytoma, anaplastic thyroid cancer, medullary thyroid cancer and radioiodine refractory thyroid cancer.

Dataset EGAD50000000886

Arcagen - Sarcoma

Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files in .BAM format from 62 patients with undifferentiated sarcoma of the bone; aggressive angiomyxoma; alveolar soft part sarcoma; small round cell sarcoma without EWSR1 and CIC translocations; undifferentiated pleomorphic sarcoma; undifferentiated sarcoma; myxofibrosarcoma; or sarcoma with an incidence of less than 1 per 10 million.

Dataset EGAD50000000902

Zanidatamab in Patients with Early Stage HER2-positive Breast Cancer: The NeoZanHER Phase 2 Single-Arm Open-Label Trial

Strategies are needed to avoid chemotherapy in early stage HER2-positive (HER2+) breast cancer (BC). We conducted a neoadjuvant therapy trial with zanidatamab, a dual HER2-directed bispecific antibody, in patients with 1-3 cm, node-negative HER2+ BC. Primary endpoint was pathologic complete response (pCR). Secondary endpoints were radiographic response by ultrasound and magnetic resonance imaging. US and MRI, pathologic response by residual cancer burden (RCB), rate of adverse events, feasibility of accrual and biomarkers of response. Fifteen patients with HER2 IHC 3+, and 5 with HER2 IHC 2+/ISH+ BC were enrolled. Patients received zanidatamab 20 mg/kg every 2 weeks for 6 (n=11) or 10 doses (n=9). Fourteen patients also received endocrine therapy. At 6 weeks, there was a significant decrease in tumor size and volume. At surgery, six patients (30%) had pCR, meeting the prespecified pCR target, and four had limited RCB (RCB-1; 20%). Treatment was well tolerated. All patients with pCR had HER2 IHC 3+ tumors, ERBB2 amplification on WES, PAM50 HER2-high subtype, and a trend towards higher HER2 mRNA expression (p=0.06). In conclusion, de-escalation to HER2-targeted therapy alone may be feasible. Further research is needed to refine patient selection.

Study EGAS50000001930

Whole-exome sequencing data for breast cancer patients

This datasets contains the FASTQ files sequenced by Illumina NovaSeqX Plus, and the variants called from the sequencing data.

Dataset EGAD50000002800

DAC for study of Zanidatamab in breast cancer patients

This is the DAC for manuscript "Zanidatamab in Patients with Early Stage HER2-positive Breast Cancer: The NeoZanHER Phase 2 Single-Arm Open-Label Trial"

Dac EGAC50000001059

Single-molecule nanopore sequencing of plasma and cerebrospinal fluid cell-free DNA from paediatric patients with brain and extracranial solid tumours.

Cell-free DNA (cfDNA) profiling enables minimally invasive cancer detection and monitoring. Here, we report single-molecule nanopore sequencing data generated using the SIMMA assay for 792 plasma and cerebrospinal fluid cfDNA samples from 277 paediatric patients with diverse brain and extracranial tumours. SIMMA is a low-input single-molecule sequencing approach that enables multimodal whole-genome and high-depth targeted sequencing of the same cfDNA sample for both tumour-agnostic and tumour-informed liquid biopsy analysis (Sauer et al. 2026).

Study EGAS50000001839

High-grade serous ovarian cancer is associated with increased TP53 mutation burden in uterine lavage

High grade serous ovarian cancer (HGSC) has low survival partly due to the lack of methods for detection, diagnosis, and risk prediction. TP53 mutations, which drive HGSC, are found in gynecological tissues as the result of somatic evolution, but it is unknown whether an excess of mutations is linked to ovarian cancer. Here we investigate if TP53 mutation burden measured in uterine lavage, a minimally invasive gynecological liquid biopsy, can discriminate between patients with and without HGSC. We used ultradeep TP53 duplex sequencing (>15,000x duplex depth) to detect TP53 mutations in uterine lavage collected pre-operatively in 278 patients undergoing gynecological surgery for pelvic masses (average risk) or cancer risk-reduction (high risk). All lavages contained multiple TP53 mutant clones, which were used to quantify TP53 mutation burden frequency (MBF). Average risk patients with HGSC had significantly higher TP53 MBF independently of age and other risk factors (77% sensitivity, 89% specificity, AUC =0.88). Excluding tumor TP53 clonal mutations from the lavage MBF calculation maintains this association, suggesting that it is the overall TP53 somatic mutation burden (rather than the discovery of the specific tumor driver mutation) that identifies HGSC. These results demonstrate that TP53 somatic mutations are common in uterine lavage but more abundant in patients with HGSC, highlighting a connection between TP53 somatic evolution and ovarian cancer. Uterine lavage offers a minimally invasive approach that could be valuable to identify patients with HGSC.

Study EGAS50000001910

Detection of high-grade serous ovarian cancer using TP53 mutation burden in uterine lavage

In total, 436 DNA samples from the 278 patients were processed for TP53 ultradeep duplex sequencing: 278 lavages, 86 tissue samples (some patients had multiple tissue samples), and 72 blood samples. Duplex sequencing was performed on an Illumina NovaSeq 6000 by TwinStrand Biosciences using a capture panel targeting the coding regions of the TP53 gene. Data were processed by TwinStrand Biosciences and .bam files were created for each sample.

Dataset EGAD50000002776

Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study

The Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study (NCT: NCT04750109) was sponsored by The Christie NHS Foundation Trust and funded by Innovate UK.

Study EGAS50000001821

Single-cell RNA sequencing of liver metastasis-derived colorectal cancer organoids profiling cellular heterogeneity and lineage dynamics across time

The objective of this analysis was to identify, at single-cell resolution, trasncriptionally distinct subpopulations in untreated metastatic colorectal cancer organoids with diverse genetic backgrounds and to investigate their temporal dynamics.

Study EGAS50000001927

Transcriptome profiling of liver metastasis-derived colorectal cancer organoids to dissect pro-differentiative effects of TGFB1 and IL-6

Bulk RNA sequencing was performed on patient-derived xenograft (PDX)-derived organoid models of metastatic colorectal cancer (mCRC). The aim of this study was to test the effect of IL-6 and TGFbeta1 on mCRC organoid cell differentiation. Transcriptomic profiling was conducted following cytokine treatments to investigate molecular changes associated with treatments. Three biological replicates were included for each experimental condition (control, IL-6, and TGF-beta1 treatments).

Study EGAS50000001914

Bulk RNA sequencing of metastatic colorectal cancer PDX-derived organoids - untreated and treated with IL-6 or TGF-beta1

Bulk RNA sequencing was performed on patient-derived xenograft (PDX)-derived organoid models of metastatic colorectal cancer (mCRC). The aim of this study was to test the effect of IL-6 and TGF-beta1 on mCRC organoid cell differentiation. Transcriptomic profiling was conducted following cytokine treatments to investigate molecular changes associated with treatments.

Dataset EGAD50000002782

Single-cell transcriptomic profiling of metastatic colorectal cancer organoids maintained in growth medium.

Single-cell RNA sequencing was performed on metastatic colorectal cancer (mCRC) organoids derived from patient-derived xenograft (PDX) models. Organoids were seeded in 12-well plates, embedded in 200 µL of Matrigel or BME diluted 1:1 with culture medium, and maintained with EGF-supplemented growth medium. After 7 days (or 11 days) of growth, organoids were harvested, dissociated, and live single cells were sorted for single-cell RNA sequencing using the 10x Genomics platform.

Dataset EGAD50000002797

IRCCS Candiolo - Quantitative Cancer Cell Biology (QCCB)

DAC for Quantitative Cancer Cell Biology (QCCB) datasets

Dac EGAC50000001019

Personalized ctDNA detection and genomic profiling in the NeoRHEA Study

In the management of early hormone receptor (HR)+, HER2-negative breast cancer, circulating tumor DNA (ctDNA) detection is an evolving yet promising diagnostic approach. We evaluated ctDNA detection using the personalized RaDaR assay in plasma samples from patients enrolled in the NeoRHEA clinical trial (NCT03065621), a single-arm, phase II study of neoadjuvant palbociclib and endocrine therapy administered over 4 months (four 28‑day cycles). Plasma samples were collected pre-treatment, after the first treatment cycle, prior to surgery, and one month post-surgery. At baseline, ctDNA positivity was detected in 55% of patients; however, this dropped to 5% during treatment and ctDNA became undetectable in all patients by one month post-surgery. CtDNA detection at baseline was higher in histological grade 3 tumors, lower in multifocal/multicentric tumors and higher in residual cancer burden (RCB) 3 tumors. Genomic analysis revealed MYC gene gains or amplifications were more frequent in ultrasound (US) responders, while PIK3CA gains were more common in US non-responders. Additionally, FAT1 losses were more frequently observed in patients with RCB 3. Our findings suggest that baseline ctDNA presence predicts poor response to neoadjuvant palbociclib and endocrine therapy, underscoring its potential as a biomarker to guide tailored therapeutic approaches in HR+/HER2-negative breast cancer.

Study EGAS50000001702

Personalized ctDNA Monitoring and Matched Tumor–Normal Whole-Exome Sequencing in Early HR+/HER2− Breast Cancer

The dataset comprises whole-exome sequencing (WES) data generated from 160 samples, corresponding to matched tumor–normal pairs from breast cancer patients. For each subject, one tumor biopsy sample and one matched white blood cell (WBC) sample (germline control) are included. Sequencing data are provided as aligned BAM files with corresponding BAI index files. The data were generated using Illumina paired-end sequencing technology following hybrid-capture exome enrichment. The matched tumor–normal design enables accurate identification of somatic variants by filtering germline alterations, supporting downstream analyses such as mutation profiling, biomarker discovery, and personalized circulating tumor DNA (ctDNA) assay development.

Dataset EGAD50000002446

Whole exome sequencing of small cell neuroendocrine cancer of the cervix

In order to improve treatment selection for high grade neuroendocrine carcinomas of the cervix (NECC), we performed a comparative genomic analysis between this rare tumor type and other cervical cancer types, as well as extra-cervical neuroendocrine small cell carcinomas of the lung and bladder. We performed whole exome sequencing on fresh-frozen tissue from 15 NECCs and matched normal tissue. We then identified mutations and copy number variants using standard analysis pipelines. Published mutation tables from cervical cancers and extra-cervical small cell carcinomas were used for comparative analysis. Descriptive statistical methods were used and a two-sided threshold of P < .05 was used for significance. In the NECC cohort, we detected a median of 1.7 somatic mutations per megabase (range 1.0-20.9). PIK3CA p.E545K mutations were the most frequency observed oncogenic mutation (4/15 tumors, 27%). Activating MAPK pathway mutations in KRAS (p.G12D) and GNAS (p.R201C) co-occurred in two tumors (13%). In total we identified PI3-kinase or MAPK pathway activating mutations in 67% of NECC. When compared to NECC, lung and bladder small cell carcinomas exhibited a statistically significant higher rate of coding mutations (P < .001 for lung; P = .001 for bladder). Mutation of TP53 was uncommon in NECC (13%) and was more frequent in both lung (103 of 110 tumors [94%], P < .001) and bladder (18 of 19 tumors [95%], P < .001) small cell carcinoma. These comparative genomics data suggest that NECC may be genetically more similar to common cervical cancer subtypes than to extra-cervical small cell neuroendocrine carcinomas of the lung and bladder. These results may have implications for the selection of cytotoxic and targeted therapy regimens for this rare disease.

Study EGAS00001003142

Persistent Mutation Burden Drives Sustained Anti-Tumor Immune Responses

Tumor mutation burden is an imperfect proxy of tumor foreignness and has therefore failed to consistently demonstrate clinical utility in predicting responses in the context of immunotherapy. We evaluated mutations in regions of the genome that are unlikely to undergo loss and discovered that clonal mutations in single-copy regions and those present in multiple copies per cell constitute a persistent tumor mutation burden (pTMB) that is linked with therapeutic response to immune checkpoint blockade. Persistent mutations were retained in the context of tumor evolution under selective pressure of immunotherapy and tumors with a high pTMB content were characterized by a more inflamed tumor microenvironment. pTMB imposes an evolutionary bottleneck that cancer cells cannot overcome and may thus drive sustained immunologic tumor control in the context of immunotherapy.

Study EGAS00001006660

molecular profiles of serum-derived extracellular vesicles in high-grade serous ovarian cancer

Patients with high-grade serous ovarian cancer (HGSC) who have no visible residual disease (R0) after primary surgery have the best clinical outcomes, followed by patients who undergo neoadjuvant chemotherapy (NACT) and have response enabling interval cytoreductive surgery. Clinically useful predictive biomarkers for such distinctions are still lacking. Extracellular vesicles (EVs) have been recognized as liquid biopsy-based biomarkers for early cancer detection and disease surveillance in other disease settings. In this study, we performed extensive molecular characterization of serum-derived EVs and correlated them with therapeutic outcomes in patients with HGSC.

Study EGAS00001006350

Combined targeting of BRD4-associated Promoter Activation and NFKB Immunomodulation in ARID1A-mutated Gastric Adenocarcinoma

Gastric cancer (GC) a leading causes of cancer-related deaths worldwide, with ARID1A identified as the second most frequently mutated driver gene in GC. This study involves comprehensive genomic profiling of a Singaporean cohort of over 200 GC patients to characterize the mutational signatures associated with ARID1A inactivation across various molecular subtypes of GC. The findings highlight a potential therapeutic approach for ARID1A-mutated GCs, targeting both tumor-intrinsic mechanisms (BRD4-associated promoter activation) and extrinsic immunomodulatory pathways (NFKB signaling).

Study EGAS00001006397

RNA sequencing data of 142 samples from 142 patients with HER2+ breast cancer treated with letrozole or tamoxifen (SOLTI PAMELA trial)

PAMELA (NCT01973660) is a non-randomized, open-label, multicentric, prospective translational research study in stage I-IIIA HER2+ breast cancer designed to evaluate the ability of the PAM50 intrinsic subtypes to predict pCR in the breast (pCRB) following 18 weeks of neoadjuvant lapatinib and trastuzumab (+/- hormonal therapy if hormone receptor [HR] positive). Patients with HR+ disease received letrozole (if postmenopausal) or tamoxifen (if pre-menopausal). The primary objective was to compare the pCRB rates of the HER2-E versus the non-HER2-E subtypes in the intent-to-treat population.

Study EGAS00001006410

Whole-Exome Sequencing analyses in tamoxifen-associated endometrial cancer

An increased incidence of endometrial cancer has been described for patients that have received tamoxifen to treat breast cancer. Using samples from endometrial tumors, isolated from surgivcal specimens of patients who previously received tamoxifen treatment for breast cancer, we aimed to identify whether there are specific somatic mutations enriched in this population, relative to endometrial tumors from the general population. For this, WES was performed on matched endometrial tumors and healthy tissue (n=21).

Study EGAS00001006453

Multimodal Epigenetic Sequencing Analysis (MESA) of Cell-free DNA for Non-invasive Cancer Detection

Multimodal characterization of cell-free DNA (cfDNA) in the blood can enable the sensitive and non-invasive detection of human cancers but remains technically challenging and costly. Here, we developed Multimodal Epigenetic Sequencing Analysis (MESA), a flexible and sensitive method of capturing and integrating multimodal epigenetic information of cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq (EM-seq) and TET-assisted pyridine borane sequencing (TAPS). MESA can simultaneously infer cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and fragmentation profile for regions surrounding the promoters and polyadenylation sites (PASs). MESA’s integrated analysis of multimodal epigenetic features significantly improved the performance of cancer detection models compared to the usage of any single modality alone. MESA captures additional and highly complementary epigenetic information from cfDNA without additional experimental assays, highlighting the importance and clinical prospect of using multimodal epigenetic features for non-invasive cancer detection

Study EGAS00001006462

Robust methylation-based classification of brain tumours using nanopore sequencing

Background: DNA methylation-based classification of cancer provides a comprehensive molecular approach to diagnose tumours. In fact, DNA methylation profiling of human brain tumours already profoundly impacts clinical neuro-oncology. However, current implementations using hybridization microarrays are time-consuming and costly. We recently reported on shallow nanopore whole-genome sequencing for rapid and cost-effective generation of genome-wide 5-methylcytosine profiles as input to supervised classification. Here, we demonstrate that this approach allows to discriminate a wide spectrum of primary brain tumours. Results: Using public reference data of 82 distinct tumour entities, we performed nanopore genome sequencing on N=382 tissue samples covering 46 brain tumour (sub)types. Using bootstrap sampling in a cohort of N = 56 cases, we find that a minimum set of 1,000 random CpG features is sufficient for high-confidence classification by ad hoc random forests. We implemented score recalibration as confidence measure for interpretation in a clinical context and empirically determined a platform-specific threshold in a randomly sampled discovery cohort (N = 185). Applying this cut-off to an independent validation series (N = 184) yielded 148 classifiable cases (sensitivity 80.4%) and demonstrated 100 % specificity. Cross-lab validation demonstrated robustness with concordant results across four laboratories in 10/11 (90.9%) cases. In a prospective benchmarking (N = 15), median time to results was 21.1 hours. Conclusions: In conclusion, nanopore sequencing allows robust and rapid methylation-based classification across the full spectrum of brain tumours. Platform-specific confidence scores facilitate clinical implementation for which prospective evaluation is warranted and ongoing.

Study EGAS00001006540

Neurodegenerative_TGS

An investigation of clonal haematopoiesis in patients with neurodegenerative disease.

Study EGAS00001002431

ER_HER2_PR__breast_Cancer_genome_sequencing

Agilent whole exome hybridisation capture will be performed on genomic DNA derived from X triple negative breast cancers and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting X exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

Study EGAS00001000197

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