8252 results for "canc*"

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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.

  Blog mitochondrial-dna-research

• 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

  Blog drug-development-for-pediatric-cancers

• 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

  Blog top-10-studies-2025

• 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.

  Blog cbioportal

• Epigenetic Heterogeneity in Breast Tumors with Implications for Disease Diagnosis and Monitoring

  This study investigates epigenetic heterogeneity in human breast cancer using complementary single-cell and bulk epigenomic approaches. Human breast tumor samples were profiled using whole-genome bisulfite sequencing, enzyme-mediated methylation sequencing of sorted tumor-cell populations, single-nucleus CUT&Tag, and single-cell RNA and chromatin-accessibility multiome profiling. These data were used to characterize recurrent epigenetic cell states and intra-tumor heterogeneity in breast cancer.

  Study EGAS50000002070

• NGS in FACSorted populations from AML patients

  FACS-enriched bone marrow cell populations from 22 AML patients were analyzed using a custom panel targeting 56 genes recurrently mutated in myeloid neoplasms. DNA was released from a median of 60,000 sorted cells by incubation with 2.5 μL proteinase K and 2 μL RNase A (Thermo Fisher Scientific) for 30 minutes at 37°C, purified using 2× AMPure XP beads (Beckman Coulter, Brea, CA), and stored in pH 8 elution buffer at −20°C. DNA quantity was measured using the Qubit dsDNA BR Assay on a Qubit 3.0 fluorometer (Thermo Fisher Scientific), and quality was assessed using the Genomic DNA ScreenTape assay on a 4200 TapeStation system (Agilent Technologies, Santa Clara, CA). Libraries were prepared from at least 20 ng of DNA according to the SOPHiA GENETICS protocol and sequenced on an Illumina MiSeq using 2 × 251-cycle paired-end reads. At least 95% of target bases achieved ≥1,000× coverage. FASTQ files were analyzed in SOPHiA DDM using the GRCh37/hg19 reference genome for alignment, variant calling, and annotation. Variants were retained when the variant allele frequency was ≥2%, coverage was ≥100 reads, and the alternate allele was supported on both strands. Synonymous, intronic, intergenic, 5′ untranslated-region, and recurrent panel-artifact calls were excluded. The remaining variants were classified according to Association for Molecular Pathology and American College of Medical Genetics and Genomics guidance using VarSome, ClinVar, COSMIC, and dbSNP. Only pathogenic or likely pathogenic variants were analyzed. All retained calls were visually confirmed in Integrative Genomics Viewer v2.11.3, and only variants previously reported as somatic in hematopoietic or myeloid neoplasms were retained.

  Study EGAS50000002132

• Single-Cell Multiomic and Whole-Genome Profiling of Primary and Metastatic Colorectal Cancer

  This study contains controlled-access single-cell multiomic sequencing (scRNA-seq and scATAC-seq) data generated from patients with colorectal cancer, including 33 primary tumours, 11 metastatic lesions, and 7 adjacent normal tissue samples. Whole-genome sequencing (WGS) data are available for the tumour samples. These data were generated to investigate the genomic and epigenomic landscape of colorectal cancer and to identify recurrent regulatory alterations associated with tumour progression and metastasis.

  Study EGAS50000002048

• Weizmann Breast Cancer Epigenetics Data Access Committee

  Data Access Committee responsible for reviewing requests for controlled access to human sequencing data generated in the study "Epigenetic Heterogeneity in Breast Tumors with Implications for Disease Diagnosis and Monitoring".

  Dac EGAC50000001136

• Committee of human data related to Livanova et al. paper submission

  This DAC controls access to single-cell multiomic (scRNA-seq and scATAC-seq) and matched whole-genome sequencing (WGS) data generated from colorectal cancer patient samples, including 33 primary tumours, 11 metastatic lesions, 7 adjacent normal tissues. The data were generated to investigate recurrent somatic chromatin accessibility alterations (SCAAs) associated with tumour progression and metastasis

  Dac EGAC50000001117

• UNICORN Visium HD spatial transcriptomics of colorectal cancer

  This controlled-access dataset contains raw paired-end sequencing data generated from 56 human colorectal cancer tissue samples using the 10x Genomics Visium HD spatial gene expression workflow. The dataset comprises 324 paired-end sequencing runs represented by 648 FASTQ files. Libraries were sequenced on an Illumina platform and are intended for spatial transcriptomic analysis of the colorectal cancer tissue microenvironment.

  Dataset EGAD50000002908

• Transcriptome Sequencing Data of High Grade Serous Ovarian Cancer

  Study EGAS00001004714

• Long-read whole genome sequencing of gastric cancer

  Gastric cancer (GC) remains a significant health challenge globally, with a particularly high incidence in Asia, including Singapore. Understanding the genomic landscape of normal gastric and the different GC subtypes along with its precursor lesions such as intestinal metaplasia (IM) as well as GC metastatic sites is crucial for advancing diagnostics, prognostics, and therapeutics. Long-read sequencing technologies offer an opportunity to delve deeper into the genomic and epigenetic alterations underlying these conditions and to complement existing short-read omics data. By integrating these data with previously generated in-house multi-omics data (such as RNA sequencing and whole exome sequencing), we hope to better elucidate the molecular mechanism underlying GC initiation and progression.

  Study EGAS50000001607

• NoSarC -- Norwegian Sarcoma Consortium. Towards individualized therapy for orphan cancer.

  This study is initiated and conducted by the Norwegian Sarcoma Consortium - a collaboration between 5 Norwegian hospitals and universities, coordinated by Oslo University Hospital. The NoSarC project was initiated in 2014 and aimed to perform molecular characterization of samples from a national, population-based cohort of sarcoma patients, to identify tumors which are sensitive to therapies already developed and approved for more common cancers. The NoSarC cohort in this study consists of Tumor-Normal pairs from 287 subjects that has been Whole Exome Sequenced.

  Study EGAS50000002163

• Recommended Text: Plasma cfChIP-seq profiling of cell-free nucleosomes in Small-Cell Lung Cancer and neuroendocrine carcinomas

  This dataset contains raw, encrypted Crypt4GH paired-end Fastq files from cell-free chromatin immunoprecipitation sequencing (cfChIP-seq) targeting histone modification H3K4me3 in plasma samples. The cohort includes small-cell lung cancer (SCLC) cases, neuroendocrine carcinomas (NEC) and non-small-cell lung cancer (NSCLC) samples. Access is restricted exclusively to non-commercial, duplicate-prevention SCLC research protocols approved by the Data Access Committee.

  Dataset EGAD50000002783

• Integrating transcriptional regulatory networks with chromatin accessibility as markers of survival outcome and drug response in triple-negative breast cancer

  Patients with triple-negative breast cancer (TNBC), a highly aggressive and heterogeneous breast cancer subtype, have limited effective targeted therapy options and frequently develop resistance to chemotherapy. Transcription regulators (TRs) guided precision medicine, namely anti-estrogen receptor therapy, has proven successful in hormone-dependent breast cancer, but remains a challenge in TNBC. Here, we identified key TRs underlying TNBC identity by combining a transcriptional interaction network with chromatin accessibility and defined TR activity score metrics. We show that the activity scores of these TRs independently discriminate patient outcome across different clinical endpoints. Matching TR activity scores to drug sensitivity identified drug-TR pairs across independent datasets, including NFE2L3 and CBFB activity associated with sensitivity to mTOR inhibition, which we validated using a cohort of TNBC patient-derived xenografts. Collectively, we present methodology to promote TR-guided precision medicine that captures inter-tumor heterogeneity in TNBC and nominates drug sensitivities according to TR activity scores inferred from expression and chromatin accessibility data.

  Study EGAS50000001691

• METABRIC miRNA landscape

  MicroRNAs (miRNAs) are key players in cancer pathogenesis1. They display differential expression across breast cancer subtypes, and play oncogenic and tumor-suppressive roles2-6. A systematic study of the global miRNA landscape in breast cancer and the factors shaping it has not been undertaken. Here we report the results of profiling miRNA expression in 1,302 breast tumors with detailed clinical annotation and long-term follow-up, and for which matching genomic and mRNA expression data were available7. This provides a comprehensive view of the quantity, distribution and variation of the miRNA population and dissects how much genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture. The key clinical parameters and cellular pathways related to the miRNA landscape are identified, exemplified by context-dependent interaction with cell adhesion and Wnt signaling. Strikingly, in breast cancer miRNAs appear to act as modulators of mRNA-mRNA interactions rather than molecular switches. We demonstrate an important modulatory role for miRNAs in the biology of breast tumors devoid of somatic copy number aberrations, a common subtype where immune response is prominent. Remarkably, prognostic miRNA signatures derived in this subtype are consistently prognostic across several other subtypes and validate in external cohorts. These findings represent a new framework to study the biology of miRNA in human cancer.

  Study EGAS00000000122

• SCANDARE HNSCC

  Prospective biobanking study in SCANDARE HNSCC (Head and Neck Squamous Cell Carcinoma) patients aiming at better understand the link between the molecular alterations of the tumor itself, its microenvironment and immune response.

  Study EGAS50000001158

• DAC for RNAseq data associated with transcriptomic profiling of ER alpha positive breast cancer patients before and after endocrine therapy

  This Data Access Committee (DAC) oversees access to RNAseq data collected as part of a study focussed on exploring transcriptomic profiles of ER alpha positive breast cancer patients who were treated with aromatase inhibitors for up to three months before surgical removal of the primary tumor. Sampling was performed at diagnosis and at surgery, and RNA-seq was performed. The DAC ensures that data access requests comply with the study's ethical and legal frameworks and that data is shared for non-commercial, academic research purposes under controlled access to protect participant privacy.

  Dac EGAC50000000499

• Transcriptomics profiling of paired pre- and post- neo-adjuvant endocrine therapy of ERa positive breast cancers

  Patients were treated with aromatase inhibitors for up to three months before surgical removal of the primary tumor. Sampling was performed at diagnosis and at surgery, and RNA-seq was performed.

  Study EGAS50000000832

• Colossus Project Single-nuclei RNA sequencing

  Single-nuclei RNA sequencing of human colorectal cancer samples. Nuclei were isolated on ice, followed by single-nuclei RNA-sequencing library preparation using the Chromium Single Cell 3’ reagent kit v3.1 (10x Genomics, Pleasanton, CA) Fastq files, N=18

  Dataset EGAD50000003002

• Colossus Project Whole exome sequencing

  Whole exome sequencing of colorectal cancer samples from stage 2/3 human patients. Whole-genome DNA libraries were prepared with the KAPA HyperPlus kit (Roche, Cape Town, South Africa), and exome fragments were captured using the SureSelect Human All Exon V7 kit (Agilent, Hangzhou, China) Fastq files, N=88

  Dataset EGAD50000003003

• Colossus Project Bulk Tumor RNA sequencing

  Bulk RNA sequencing of colorectal cancer samples from stage 2/3 human patients. RNA extraction using Qiagen RNeasy FFPE Extraction kit (Qiagen, UK), followed by RNA sequencing library generation with the QuantSeq 3' mRNA-Seq Library Prep Kit (FWD) (Illumina, USA). Fastq files, N=295

  Dataset EGAD50000003004

• Colossus Project Whole Genome Sequencing

  Low-coverage whole genome sequencing of colorectal cancer samples from stage 2/3 human patients. Tumor DNA from biopsies was extracted using the QIAamp circulating nucleic acid kit (Qiagen, UK), followed by generation of shot-gun whole genome libraries using the KAPA library preparation kit (KAPA Biosystems). Fastq files, N=295

  Dataset EGAD50000003005

• Expansion and cytotoxicity of mucosal T cells in enterocolitis associated with BCMA CAR-T cell therapy

  This study investigates immune effector cell enterocolitis (IEC-EC) associated with ciltacabtagene autoleucel (cilta-cel), a B-cell maturation antigen (BCMA)-targeting chimeric antigen receptor (CAR) T-cell therapy for relapsed or refractory multiple myeloma. IEC-EC was characterized by infiltration and expansion of clonal CAR-positive cytotoxic T-cell populations within the gastrointestinal tract. Molecular analyses were performed to characterize CAR T-cell clonality, persistence, vector integration, and genomic alterations associated with this complication. This dataset contains whole-genome sequencing (WGS) and whole-exome sequencing (WES) FASTQ files generated as part of the study and associated with the corresponding manuscript.

  Study EGAS50000002146

• Circulating tumour DNA in oligoprogressing metastatic castration-resistant prostate cancer treated with stereotactic body radiotherapy in the TRAP trial

  The TRAP trial (NCT03644303) reported that stereotactic body radiotherapy (SBRT) of up to two imaging-visible metastases in prostate cancer patients showing disease progression whilst continuing an androgen receptor pathway inhibitor (ARPI) improved outcomes. It is possible that the benefit from treatment is heterogeneous and patients with progressing sub-clinical metastases, not detected by imaging, derived no benefit from this intervention. The goal of this study was to determine if ctDNA levels detected before SBRT associates with worse outcome.

  Study EGAS50000002152

• Breast Cancer Atlas Research Consortium (BCARC)

  The Breast Cancer Atlas Research Consortium (BCARC) is an international translational research programme building a multi-modal atlas of breast cancer across subtypes, disease progression states, and treatment phases. Founding investigators span the Swarbrick Laboratory (Garvan Institute of Medical Research, Sydney), the Perou Laboratory (University of North Carolina at Chapel Hill), the Grimmond Laboratory (University of Melbourne Centre for Cancer Research), the Al'Khafaji Laboratory (Broad Institute of MIT and Harvard), and the Lundeberg Laboratory (KTH Royal Institute of Technology, Stockholm). Samples are drawn from primary and metastatic breast tumours together with matched adjacent tissues and where available, blood samples. All samples derive from participants consented under Garvan-approved research protocols. Access is controlled and governed by the BCARC data access agreement; requests are reviewed by the Data Access Committee named in the associated policy record

  Study EGAS00001008520

• Germline whole-genome sequencing (WGS) of colorectal cancer patients

  Whole-genome sequencing (WGS) data generated from matched germline blood DNA samples obtained from patients with primary or metastatic colorectal adenocarcinoma (n=37). These data were generated as part of a multiomic study integrating whole-genome sequencing with single-cell multiome profiling to investigate recurrent epigenetic alterations in colorectal cancer evolution and the tumour microenvironment.

  Dataset EGAD50000002942

• Whole Genome Sequencing (WGS) of Tumour Samples of Primary and Metastatic Colorectal Cancer

  WGS samples of primary (n=33) and metastatic (n=11) colorectal cancer tissues related to Livanova et al. manuscript 'Epigenetic evolution of colorectal cancer and its microenvironment reveals new vulnerabilities'

  Dataset EGAD50000002943

• Single-cell 10x Genomics Multiome ATAC-seq data from primary and metastatic colorectal cancers and normal colon tissues

  This dataset contains single-cell 10x Genomics Multiome ATAC-seq data from 33 primary colorectal cancer, 11 metastatic colorectal cancer, and 7 matched normal colon tissue samples generated as part of the study "Epigenetic evolution of colorectal cancer and its microenvironment reveals novel vulnerabilities."

  Dataset EGAD50000002944

• Single-cell 10x Genomics Multiome RNA-seq data from primary and metastatic colorectal cancers and normal colon tissues

  This dataset contains single-cell 10x Genomics Multiome RNA-seq data from 33 primary colorectal cancer, 11 metastatic colorectal cancer, and 7 matched normal colon tissue samples generated as part of the study "Epigenetic evolution of colorectal cancer and its microenvironment reveals novel vulnerabilities."

  Dataset EGAD50000002945

• Human breast cancer epigenetic sequencing data

  Controlled-access sequencing data generated from human breast tumor samples in the study "Epigenetic Heterogeneity in Breast Tumors with Implications for Disease Diagnosis and Monitoring". The dataset includes whole-genome DNA methylation sequencing, enzyme-mediated methylation sequencing of sorted tumor-cell populations, single-nucleus CUT&Tag, and single-cell RNA and chromatin-accessibility multiome sequencing. Raw sequencing data are provided as FASTQ files.

  Dataset EGAD50000002972

• ICGC PanCancer Analysis of Whole Genomes

  The Pancancer Analysis of Whole Genomes (PCAWG) study is an international collaboration to identify common patterns of mutation in more than 2,800 cancer whole genomes from the International Cancer Genome Consortium. Building upon previous work which examined cancer coding regions (Cancer Genome Atlas Research Network, The Cancer Genome Atlas Pan-Cancer analysis project, Nat. Genet. 2013 45:1113), this project is exploring the nature and consequences of somatic and germline variations in both coding and non-coding regions, with specific emphasis on cis-regulatory sites, non-coding RNAs, and large-scale structural alterations.

  Study EGAS00001001692

• CRCbiome: Gut metagenome of Norwegian screening participants using FIT sampling

  The study aims to characterize the gut metagenome of Norwegian screening participants and assess the ability of the gut metagenome to identify those at need for colonoscopy. Background: Colorectal cancer (CRC) screening reduces CRC incidence and mortality. However, current screening methods are either hampered by invasiveness or suboptimal performance, limiting their effectiveness as primary screening methods. To aid in the development of a non-invasive screening test with improved sensitivity and specificity, we have initiated a prospective biomarker study (CRCbiome), nested within a large randomized CRC screening trial in Norway. We aim to develop a microbiome-based classification algorithm to identify advanced colorectal lesions in screening participants testing positive for an immunochemical fecal occult blood test (FIT). We will also examine interactions with host factors, diet, lifestyle and prescription drugs. The prospective nature of the study also enables the analysis of changes in the gut microbiome following the removal of precancerous lesions. Methods: The CRCbiome study recruits participants enrolled in the Bowel Cancer Screening in Norway (BCSN) study, a randomized trial initiated in 2012 comparing once-only sigmoidoscopy to repeated biennial FIT, where women and men aged 50–74 years at study entry are invited to participate. Since 2017, participants randomized to FIT screening with a positive test result have been invited to join the CRCbiome study. Self-reported diet, lifestyle and demographic data are collected prior to colonoscopy after the positive FIT-test (baseline). Screening data, including colonoscopy findings are obtained from the BCSN database. Fecal samples for gut microbiome analyses are collected both before and 2 and 12 months after colonoscopy. Samples are analyzed using metagenome sequencing, with taxonomy profiles, and gene and pathway content as primary measures. CRCbiome data will also be linked to national registries to obtain information on prescription histories and cancer relevant outcomes occurring during the 10 year follow-up period. Discussion: The CRCbiome study will increase our understanding of how the gut microbiome, in combination with lifestyle and environmental factors, influences the early stages of colorectal carcinogenesis. This knowledge will be crucial to develop microbiome-based screening tools for CRC. By evaluating biomarker performance in a screening setting, using samples from the target population, the generalizability of the findings to future screening cohorts is likely to be high.

  Study EGAS50000000170

• Non-small cell lung cancer sequencing

  Study EGAS00001005499

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001005827

• WES of breast cancer patients and controls

  This study assessed the variant profiles of 408 samples from 126 breast cancer patients with various survival outcomes, including paired uninvolved mammary gland samples collected at proximal distances from primary lesions. As a reference, control samples from 15 mammoplasty individuals without cancer history were studied. Whole exome sequencing was performed to identify post-zygotic and germline variants present in the studied samples. Variant annotation in known databases, interpretation, and statistical analyses followed to discern the clinical relevance of called variants.

  Study EGAS50000000539

• 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

• Oslo University Hospital Data Access Committee for I-BCT study datasets submitted to FEGA Norway

  This is the Data Access Committee appointed by Oslo University Hospital for controlled access to datasets from the Improved Breast Cancer Treatment (I-BCT) study under the Ethical approval reference EU CT 2024-515087-31-00 (Originally approved by REK: 2014/167/REK sør-øst A) .

  Dac EGAC50000001170

• I-BCT-1 (Improved Breast Cancer Therapy) - WXS

  A prospective, randomized neoadjuvant study that evaluates DNA-based biomarkers to identify breast cancer patients most likely to benefit from carboplatin (Cb). While Cb improves outcomes in triple-negative breast cancer (TNBC), responses vary, and its role in aggressive hormone receptor–positive breast cancer (HR+BC) remains uncertain. Patients (N = 187) with early, high-risk, HER2-negative cT2-4 tumors received 12 weekly paclitaxel with or without Cb, followed by four cycles of epirubicin and cyclophosphamide. Whole Exome Sequencing data for normal-tumor pairs from the same subjects is available in the matching study titled I-BCT-1 (Improved Breast Cancer Therapy) - WES

  Study EGAS50000002154

• I-BCT-1 (Improved Breast Cancer Therapy) - WES

  A prospective, randomized neoadjuvant study that evaluates DNA-based biomarkers to identify breast cancer patients most likely to benefit from carboplatin (Cb). While Cb improves outcomes in triple-negative breast cancer (TNBC), responses vary, and its role in aggressive hormone receptor–positive breast cancer (HR+BC) remains uncertain. Patients (N = 187) with early, high-risk, HER2-negative cT2-4 tumors received 12 weekly paclitaxel with or without Cb, followed by four cycles of epirubicin and cyclophosphamide. Transcriptomic analysis data for the tumor samples in this study is available in the matching Study titled I-BCT-1 (Improved Breast Cancer Therapy) - WXS.

  Study EGAS50000002155

• Genomic gain of EBV's LMP-1 in NKTCL

  The study found a high recurrence of genomic gain of LMP1 locus within EBV-associated NKTCL. The pilot data set and extended dataset of 77 WGS NKTCLs found 18/77 tumoral genomes to harbour genomic gain of LMP-1 locus within the EBV viral genomes within NKTCL tumoral sequencing data. The study also found 1/10 NKTCL cell lines to be positive for LMP-1 locus gain within EBV genomes too. This dataset includes solely the confirmatory WGS data of LMP-1 gain from NKYS cell lines using Oxford Nanopore long-read sequencing technology.

  Study EGAS50000000260

• Nanopore whole-genome sequencing of human sarcomas

  We performed single and multi-region nanopore whole-genome sequencing on human sarcoma samples from adult and paediatric patients.

  Study EGAS50000000651

• Bladder cancer subtyping study across 4 atezo clinical trials

  Checkpoint inhibitors targeting the PD-1/PD-L1 interaction have revolutionized cancer therapy across many indications. In urothelial carcinoma (UC), several PD-1/PD-L1 inhibitors have been approved for treatment in localized and metastatic disease. While these agents provide durable clinical benefit in a number of patients, the majority do not respond. A better understanding of the molecular mechanisms underlying response and resistance is needed to tailor existing treatments and develop new therapeutics in UC. We profiled tumors from 2803 patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab in metastatic UC and muscle-invasive bladder cancer by RNA-seq, a targeted DNA panel, immunohistochemistry, and digital pathology. A machine learning algorithm identified four UC transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care was observed in immune and basal tumors (45% of patients). Digital pathology showed enrichment of human interpretable features in specific molecular subtypes, and a self-supervised approach could classify molecular subtypes from H&E slides with high accuracy, suggesting that machine-based image interpretation could accelerate tumor molecular profiling. Somatic alterations showed limited capacity to identify responders. Finally, mechanisms of benefit with PD-L1 blockade appeared different in immune and basal tumors, with immune tumors benefiting from B/plasma cell enrichment, while basal tumors benefited from granulocyte enrichment. This study represents the largest integration of UC molecular and curated clinical data in randomized clinical trials to date, paving the way for prospective clinical studies tailoring treatment regimens to specific tumor molecular subtypes in UC and other indications.

  Study EGAS50000000497

• Plasma Nucleosome Profiling Reports on Tumor Burden and Molecular Subtypes in Small Cell Lung Cancer

  Small cell lung cancer (SCLC) is an aggressive malignancy with dismal prognosis and marked transcriptional heterogeneity that drives distinct therapeutic vulnerabilities. The clinical translation of these molecular insights is limited by limited access to tumor biopsies. Here, we apply chromatin immunoprecipitation of cell-free nucleosomes carrying active histone modifications followed by sequencing (cfChIP-seq) to 441 plasma samples from individuals with advanced SCLC, other neuroendocrine carcinomas (NEC), non-SCLC cancers, and healthy controls. cfChIP-seq faithfully captured the epigenetic and transcriptional landscape of tumor-derived cfDNA, including SCLC tissue/cell-of-origin signatures, and yielded a quantitative score of tumor fraction that tracked radiographic burden and prognosis. Integrating plasma cfChIP-seq with matched tumor transcriptomes (n = 73, including 41 time-matched pairs), we show that cfChIP-seq levels at promoters of lineage-defining transcription factors (ASCL1, NEUROD1, POU2F3, and ATOH1) correlate strongly with tumor RNA and support noninvasive inference of SCLC transcriptional subtypes directly from plasma. These findings establish cfChIP-seq as a practical liquid-biopsy platform that reports real-time tumor state and subtype, enabling precision monitoring and laying the groundwork for subtype-informed therapeutic stratification in SCLC.

  Study EGAS50000001916

• Liquid Biopsy-informed Precision Oncology Study to Evaluate the Clinical Utility of Non-invasive Genomic Profiling for Cancer Treatment Selection

  DAC for EGA study: "Liquid Biopsy-informed Precision Oncology Study to Evaluate the Clinical Utility of  Non-invasive Genomic Profiling for Cancer Treatment Selection".

  Dac EGAC50000001165

• Nanopore Amplicon Sequencing data of 33 patient tumor and one primary cell line samples - all uveal melanoma

  Nanopore Amplicon Sequencing data of 4 amplicons in the GNA11, GNAQ, SF3B1 and SRSF2 genes in 33 uveal melanoma patient tumor samples and one primary cell line sample. Sequencing was performed on a MinION platform (Mk1B) using the SQK-RBK114.24 kit multiplexing 24 samples per flow cell. Basecalling was performed using the dna_r10.4.1_e8.2_400bps_sup@v4.3.0 model and dorado v.0.6.2.

  Study EGAS50000002137

• Liquid Biopsy-informed Precision Oncology Study to Evaluate the Clinical Utility of Non-invasive Genomic Profiling for Cancer Treatment Selection

  This dataset pertains to the first planned analysis of an observational trial assessing the clinical utility of serial LB, combined with precision oncology recommendations by a molecular tumor board (MTB). Serial LBs utilized a clinically validated 33-gene panel next-generation sequencing (NGS) assay. Key eligibility criteria included age≥18years, ECOG performance status 0-1, and advanced/metastatic solid tumors with progression on ≥1 lines of therapy. 50 patients were enrolled between March 2023 and July 2024.

  Study EGAS50000002145

• Evolutionary dynamics in the decades preceding acute myeloid leukaemia

  Somatic evolution in ageing tissues underlies many cancers, yet the quantitative rules governing pre-cancerous evolution remain incompletely understood. This study exploits a collection of serial blood serum samples from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), collected annually from as early as 15 years before a diagnosis of acute myeloid leukaemia (AML), to provide a quantitative description of pre-cancerous evolutionary dynamics. Longitudinal samples from women who subsequently developed AML, alongside timepoint-matched samples from age-matched cancer-free controls, were sequenced using TETRIS-seq, a deep duplex error-corrected sequencing approach covering a custom panel of single-nucleotide variants and indels, mosaic chromosomal alterations, and chromosomal rearrangements associated with clonal haematopoiesis and AML. Combined with evolutionary modelling, these data quantify the acquisition ages and fitness effects of key driver events in AML development, characterise the clonal dynamics that precede diagnosis, and show that pre-leukaemic evolution is consistent with a small set of evolutionary rules balancing chance and determinism. The deposited data comprise raw paired-end FASTQ files for all sequenced samples.

  Study EGAS50000002168

• Alterations of whole genome and extrachromosomal circular cell-free DNA fragmentomes across the cancer care continuum

  The origins and properties of cancer-associated genetic changes in cell-free DNA (cfDNA) are not fully understood. Through genome-wide analyses of 1,807 samples from 1,064 patients across eight common cancer types, we identified extrachromosomal circular DNA (eccDNA) as a source of cfDNA fragments. Characterization of eccDNA-derived cfDNA fragments along with single base substitutions, small insertions and deletions, rearrangements, and phased variants in single cfDNA molecules, revealed differences between tumor tissues and cfDNA, with an increase in tumor-derived cfDNA alterations at epigenetically repressed regions and a decrease at active promoters. Combination of variant characteristics with individual error rates and machine learning analyses enabled tumor-informed and tumor-independent measures of circulating tumor DNA for early detection, detection of minimal residual disease, and for monitoring patients during therapy. The universal nature of eccDNA and other tumor-derived characteristics of cfDNA may be broadly useful for cancer detection.

  Study EGAS50000001331

• Cryptic mRNA Splicing in FLT3 Internal Tandem Duplications

  NGS analyses of genetic material from Acute Myeloid Leukemia cases harboring FLT3 Internal Tandem Duplications to investigate the occurrence of cryptic mRNA splicing.

  Study EGAS50000002156

• Standardized Molecular Detection of RET and Other Gene Fusions in Thyroid Cancer: A Two-Site Validation Study

  This study validated methods for detecting RET gene alterations in thyroid cancer using molecular testing across two institutions. It found that next-generation sequencing (NGS) reliably identified RET fusions, other gene fusions, and key mutations, with confirmation by FISH and RT-PCR. Results showed a high level of agreement between both institutions, demonstrating the accuracy and reproducibility of the testing methods. These findings support the use of standardized NGS-based protocols to identify patients who may benefit from RET-targeted therapies. 60 DNA and 60 RNA has been sequenced by targeted NGS

  Study EGAS50000001977

• VHIR Data Access Committee 2

  VHIR "Data Access CommitteeStandardized Molecular Detection of RET and Other Gene Fusions in Thyroid Cancer: A Two-Site Validation Study" Data Access Committee

  Dac EGAC50000001089

• Trascriptional profile of primary human NK cells after culture with A549 ß2m-/-

  This dataset comprises the transcriptional profiles of human Natural Killer (NK) cells obtained via bulk RNA sequencing approach. The NK cells were isolated from the peripheral blood mononuclear cells (PBMCs) of six healthy donors and subsequently cultured in the presence of A549 ß2m-/- cancer cells for a duration of 9 days. After culture, RNA was extracted using the QiagenRNeasy Plus Mini Kit (250) (Qiagen, 74136) according to manufacturer's instructions and sequencing was performed on two lanes of the Illumina NovaSeq 6000 instrument resulting in 51nt-long paired-end reads.

  Dataset EGAD50000000819

• RNA-seq fastq files

  RNA-seq fastq files for ER+ breast cancer patients before and after neo-adjuvant endocrine therapy

  Dataset EGAD50000001221

• ctDNA detection in preSBRT samples in oligoprogressing metastatic castration-resistant prostate cancer patients

  Targeted sequencing using the PCF-SELECT capture panel and the KAPA Hypercap workflow on DNA extracted from liquid biopsies to test if ctDNA detection in pretreatment samples associates with worse outcome to stereotactic body radiotherapy in oligoprogressing metastatic castration-resistant prostate cancer patients.

  Dataset EGAD50000003089

• 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

• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  Nodal B-cell non-Hodgkin lymphomas (B-NHL) are a clinically diverse set of cancers thought to originate from distinct stages of B-cell maturation. To characterize intratumor heterogeneity across B-NHL entities, we performed CITE-Seq (cellular indexing of transcriptomes and epitopes) on 51 lymph node samples including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL, n = 11) tumors, in addition to non-malignant reactive lymph nodes (rLN, n = 8). We also performed 5 prime scRNA-seq with immune receptor profiling for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL, and 2 MZL).

  Study EGAS50000000342

• IBCT-1 (Improved Breast Cancer Therapy) - WXS

  Study Purpose and Design This RNA-seq dataset is derived from the same prospective, randomized neoadjuvant breast cancer trial (IBCT-1) and complements the WXS analysis. RNA sequencing was performed at basline to characterize transcriptomic biomarkers associated with carboplatin response prediction. The cohort includes 187 patients breast cancer tumors across multiple subtypes. Sample Composition - Total samples: 187 (tumor tissue only) Tumor samples: 187 - Sample type: RNA from primary tumor tissue biopsies Technical Specifications - Sequencing platform: Illumina HiSeq4000 - Read type: Paired-end - Read length: 2 × 76 bp - Library preparation: KAPA RNA HyperPrep kit with Twist Bioscience Human Comprehensive Exome Plus RefSeq v1 hybrid selection - RNA input: 100 ng per sample - Sequencing depth: Mean of 35 million read pairs per sample

  Dataset EGAD50000003092

• IBCT-1 (Improved Breast Cancer Therapy) - WES

  This dataset comprises DNA whole-exome sequencing from a prospective, randomized neoadjuvant trial designed to evaluate DNA-based biomarkers for predicting carboplatin response in patients with early, high-risk breast cancer. The study enrolled 196 patients with with high-risk features, including triple-negative breast cancer (TNBC) and hormone receptor-positive (HR+) breast cancer subtypes. The final analysis included 187 patients, for which WES data is being made available for controlled access. Sample Composition Total samples: 373 (186 blood/normal + 187 tumor) - Blood samples: 186 (germline/reference) - Tumor samples: 187 (tumor tissue biopsies) Technical Specifications - Sequencing platform: Illumina HiSeq4000 - Read type: Paired-end - Read length: 2 × 76 bp - Library preparation: Twist Bioscience Human Comprehensive Exome Plus RefSeq v1 hybrid selection - DNA input: 100 ng per sample - Mean sequencing coverage: 46× for normal samples; 106× for tumor samples

  Dataset EGAD50000003091

• Genomic correlates with survival after treatment of oesophageal adenocarcinoma by surgery alone

  A 451 cancer-associated gene panel, described in McCabe et al. 2019, was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients who underwent surgical resection without perioperative treatment. Primary tumour tissues and matched normal squamous oesophagus tissues were collected from each individual. Laboratory studies were performed at the Centre for Applied Medical Research (AMR) and sequencing was performed at the Kinghorn Cancer Centre. 64 of 65 cancer samples and 61 matched squamous oesophageal mucosa control tissues were successfully sequenced and passed minimal quality metrics.

  Study EGAS50000002190

• Window-of-opportunity trial of olaparib and durvalumab before standard neoadjuvant chemotherapy for stage II/III triple-negative or ER-low breast cancer (MEDIOLA WOO): whole-exome and transcriptome profiling of serial tumor biopsies

  The MEDIOLA window-of-opportunity (WOO) trial (ESR-16-11923; NCT03594396) was a single-arm, non-randomized, open-label study conducted at Seoul National University Hospital in 54 female patients with stage II/III triple-negative or estrogen receptor (ER)-low breast cancer. Patients received olaparib 300 mg twice daily for 4 weeks with a single 1500 mg dose of durvalumab on day 15, followed by standard neoadjuvant chemotherapy. The primary objective was to characterize changes in tumor biology through serial biopsies obtained at baseline, day 14 (on-treatment 1; OT1, after olaparib alone and before durvalumab), and day 29 (on-treatment 2; OT2, after olaparib plus durvalumab). This study comprises two sequencing datasets. Whole-exome sequencing was performed on baseline tumor biopsies with matched peripheral-blood germline DNA from all 54 patients (54 tumor-normal pairs). RNA sequencing was performed on fresh-frozen serial tumor biopsies, yielding 160 libraries (baseline n = 54, OT1 n = 54, OT2 n = 52; two patients did not undergo OT2 biopsy). All libraries were sequenced on an Illumina NovaSeq 6000 platform. Raw sequence data are provided here under controlled access; processed RNA-seq quantification files are available through the Gene Expression Omnibus.

  Study EGAS50000002019

• MEDIOLA WOO Data Access Committee

  The MEDIOLA WOO Data Access Committee oversees access to controlled-access human genomic data generated by the MEDIOLA window-of-opportunity trial of olaparib and durvalumab in triple-negative or ER-low breast cancer. The Committee reviews and approves data access requests in accordance with the conditions of the original participant consent and institutional ethical approvals. The Committee is administered by Seoul National University Hospital and comprises the corresponding author and the data custodian.

  Dac EGAC50000001066

• This is the dataset for the study "Liquid Biopsy-informed Precision Oncology Study to Evaluate the Clinical Utility of Non-invasive Genomic Profiling for Cancer Treatment Selection"

  This dataset pertains to the study of the first planned analysis of an observational trial assessing the clinical utility of serial LB, combined with precision oncology recommendations by a molecular tumor board (MTB). 50 patients were enrolled between March 2023 and July 2024. The dataset includes 129 BAM files from the targeted capture. 84 samples are from cfDNA, and 45 are from matched buffy coat.

  Dataset EGAD50000003077

• Whole genome sequencing of CheckPAC cfDNA replicates

  188 cfDNA samples from individuals with pancreatic cancer from the CheckPAC trial sequenced to ~2x coverage using 150 bp reads on the Illumina NovaSeq 6000 instrument.

  Dataset EGAD50000001905

• Whole genome sequencing of Endoscopy III Project cfDNA replicates

  181 cfDNA samples from individuals without cancer from the Endoscopy III Project sequenced to ~10x coverage using 100 bp reads on the Illumina NovaSeq 6000 instrument.

  Dataset EGAD50000001906

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an AND operator when a space is inserted between two words. Include OR (capitalised) between the keywords in your search in order to show pages containing either term or both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• EGA synthetic data

  Synthetic Data One of the limitations in genomics research is that human genomics data is not openly available; access must be controlled according to participant consent agreements and data protection regulations such as GDPR. Obtaining authorization to access such data can sometimes take a long time, resulting in delays to important research work. In this context, synthetic genomic and phenotype data can be useful resources for researchers to avoid these delays. Synthetic data are artificially generated datasets, often created with algorithms, which can be used without the need for authorization to test new products and tools, build technical demonstrators, validate data models, and train AI models. The EGA provides access to synthetic cohort datasets augmented with rich synthetic metadata that overcomes these real data usage restrictions. Whilst synthetic datasets are not included in the general EGA mandate and services, we can consider such submissions and evaluate their acceptance on the basis of their unique use cases not already covered by existing synthetic datasets. All synthetic data studies are open and permission will be granted automatically, however it is still required to request access. Requests are handled by the responsible DAC at Central EGA or the relevant Federated EGA Node. Study ID Title Located in EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples Central EGA EGAS00001005591 Synthetic data - Genome in a Bottle Central EGA EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 Central EGA EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Central EGA EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data Central EGA EGAS50000001932 Synthetic longitudinal breast cancer whole-genome sequencing dataset Central EGA EGAS50000000086 Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023 Federated EGA Sweden EGAS50000000678 Synthetic - GDI synthetic data Federated EGA Spain

  Documentation synthetic-data

• What is the cBioPortal

  EGA cBioPortal What is the EGA cBioPortal? EGA hosts a dedicated cBioPortal instance that enables authorized researchers to explore and visualize selected cancer datasets archived at EGA. Data submitters can also contribute cancer datasets to be made available through the portal. The platform provides interactive tools to analyse genomic and clinical data once access has been granted by the relevant Data Access Committee (DAC). How to find and access an EGA cBioPortal Dataset 1. Register as an EGA user 2. Go to the dataset page of interest. You can use the Search function to find datasets available in cBioPortal. If the dataset is available on the EGA cBioPortal Instance, the cBioPortal icon will be displayed on the dataset page, as shown below: On the dataset page, you will find information about access requirements. For more details, please visit how to request access. 3. Once access has been granted Go to the EGA cBioPortal instance and log in with your EGA user accountThe approved dataset will be listed in cBioPortalSelect the dataset to start exploring the data What you can do in cBioPortal The EGA cBioPortal is designed for interactive exploration and visualization of controlled-access cancer datasets, including: Visualising genomic alterationsAnalysing mutation patternsFiltering by clinical attributesComparing molecular profilesGenerating plots and summaries Important Notes Datasets are only visible in cBioPortal after DAC approvalAccess requests must always be submitted through the EGA website If you encounter any issues gaining access to a dataset, please contact EGA Support.If your question is related to the cBioPortal features, please reach out to: cbioportal@googlegroups.com. The EGA Helpdesk does not support this platform. How to link a dataset to the EGA cBioPortal instance during a submission STEP 0 – Verify the cBioPortal metadataFollow the instructions at https://github.com/EGA-archive/ega-cbioportal to verify that your cBioPortal metadata can be successfully imported into the EGA.STEP 1 – cBioPortal File Upload to EGA Files must be uploaded to the SFTP INBOX. The supported cBioPortal file types are: Raw data (e.g., VCF files)cBioPortal-specific formats (e.g., MAF files) STEP 2 – Register the cBioPortal dataset in the Submitter Portal Register the cBioPortal files in an analysis objectRegister a dataset with the title “cBioPortal Metadata” and include the analysis and nothing else Please note that cBioPortal is not directly supported by the EGA Helpdesk. For questions about the platform, please visit the cBioPortal website or contact the cBioPortal support team at cbioportal@googlegroups.com. The EGA does not support this platform.

  Documentation submission/metadata/submission/cbioportal

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? The most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. How can I add my dataset to the cBioPortal instance? In order to link your data to the cBioPortal, you must register it as a normal dataset in the Submitter Portal. For more information, please refer to the cBioPortal documentation. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : 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 at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• Federated EGA

  Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please send an email to fega-info [at] lists [dot] ega-archive [dot] org. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about joining the Federated EGA Network in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Federated EGA Node Operations Guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior to signing through its official channels. Federated EGA Affiliate Collaboration Agreement 1.0 Affiliates are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior to signing through its official channels.

  Documentation about/projects-and-funders/federated-ega

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Projects

  Projects Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, the EGA provides an invaluable service to the worldwide biomedical research community. The teams leading the EGA are involved in several international partnerships and consortia in numerous scientific fields, where they contribute to ambitious projects. In addition to the project listed below, the EGA is in a long-standing partnership with the Global Alliance for Genomics and Health (GA4GH), as described on the dedicated page. On-going projects Project Duration Domain Funder Tags CANDLE | CANDLE project aims to conceptualise and advance the development of National Cancer Data Nodes (NCDNs) in European countries. These NCDNs will boost the reuse of cancer data for research, innovation and policy making, in order to improve diagnostics and treatment for cancer patients, as well as prevention and early detection. 2025-2028 Cancer Horizon Europe DOCUMENTATION EASIGEN-DS | The EASIGEN-DS project aims to conduct a design study to establish a new European Research Infrastructure on Advanced Genomics Technologies, EASIGEN. To develop an excellent scientific, technological and operational design, we will conduct landscape studies, stakeholder consultations, and community surveying. 2025-2028 Genomic and health data Horizon Europe DATA MANAGEMENT DOCUMENTATION INFRASTRUCTURE Go-IMPaCT | Go-IMPaCT will contribute sequenced genomes and provide infrastructure as part of IMPaCT-Cohort, one of the three fundamental pillars of the Precision Medicine Infrastructure associated with Science and Technology (IMPaCT) program in Spain. Along with the Genome of Europe (GoE) project, around 18.000 people will have their genomes sequenced, also contributing to Spain's commitments in 1+MG. Go-IMPaCT will fund the development of an EGA node to manage and share this genomic and phenoclinic data, laying the foundations for regional and ethnic genomic variability in Spain to be available for research purposes. The IMPaCT cohort is created with the spirit of being an open research tool, compatible with the rest of the health research ecosystem, and other international initiatives. 2025-2027 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS FAIR-FEGA | This project seeks to accelerate data depositions into FEGA, significantly increasing the data flow in and from FEGA nodes. It will build capacity within the FEGA nodes and increase awareness in a wide range of stakeholders, thus altogether achieving the ultimate goal of enhancing data reuse. The project will be carried out by a strategic consortium comprising seven ELIXIR nodes and two ELIXIR communities. 2025-2026 Not applicable ELIXIR ACCESS DISCOVERY DOCUMENTATION INFRASTRUCTURE METADATA STANDARDS FEGA-Connect | A consortium of six ELIXIR nodes plus the Polish FEGA node (in-kind contribution) joining forces to build a solid base to develop solutions for effective multi-omic sensitive data integration between FEGA nodes and other infrastructures and specialised Data repositories. We aim to promote a more coherent data deposition, discoverability and retrieval of multi-omics datasets, providing FAIRer data and consequently accelerating research. 2025-2026 Multi-omics data ELIXIR ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE METADATA STANDARDS IMPaCT-Data 2 | IMPaCT-Data 2 will develop a digital platform for the integration and modelling of biomedical data associated with IMPaCT (Precision Medicine Infrastructure associated with Science and Technology) projects in Spain. It will deploy a sustainable infrastructure that facilitates the integration, standardisation, interoperability and analysis of clinical, genomic, molecular and medical imaging data. This platform will be aligned with European projects such as Genome of Europe (GoE), the first project to make use of the European Genomic Data Infrastructure (GDI), and EUCAIM. IMPaCT-Data 2 will benefit from advanced Artificial Intelligence and High Computing Capacity Systems capabilities, offering robust and accessible tools for researchers from the National Health System in Spain. 2025-2026 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS SenSec | This project aims to establish a mechanism for orchestrating secure access to sensitive data hosted by the EGA, whether in Central EGA or any Federated Node, from Galaxy, a popular open-source, community-driven VRE (Virtual Research Environment) for bioinformatics analysis. Building on a previous prototype that enabled Galaxy users within Trusted Research Environments (TREs) to decrypt sensitive data for workflow execution without sharing private encryption keys, SenSec will expand this prototype into a comprehensive solution for secure data analysis in Galaxy, facilitating encrypted data access and transfer from FEGA/EGA repositories to designated TREs. 2025-2026 Genomic and health data; trusted research environment ELIXIR ACCESS DATA ANALYSIS ERDERA | The European Rare Disease Research Alliance (ERDERA) takes over EJPRD to deliver concrete health benefits to rare disease patients in the next decade by advancing prevention, diagnosis and treatment research. To leave no one behind, over 170 organisations championed by the European Union and member states are working hand in hand to make Europe a world leader in rare diseases research and innovation. 2024-2034 Rare diseases Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution ACCESS DATA ANALYSIS DISCOVERY INFRASTRUCTURE SYNTHIA | The aim of SYNTHIA is to deliver validated, reliable tools and methods for synthetic data generation (SDG). The tools will cover multiple data types including lab results, clinical notes, genomics, imaging and m-health data. SYNTHIA also hopes to make possible the generation of longitudinal data. 2024-2029 Genomic and health data; multi-omics; AI solutions Innovative Health Initiative (IHI) DATA ANALYSIS DATA MANAGEMENT INFRASTRUCTURE GoE | The Genome of Europe initiative aims to build a European network of national genomic reference cohorts of at least 500.000 citizens. These reference cohorts will be selected to be representative of the European population. 2024-2028 Large-scale genomic and health data Horizon Europe ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS HEREDITARY | HEREDITARY aims to transform the way we approach disease detection, prepare treatment response, and explore medical knowledge by building a robust, interoperable, trustworthy, and secure framework that integrates multimodal health data (including genetic data) while ensuring compliance with cross-national privacy-preserving policies. 2024-2027 Neurodegenerative disorders, gut-brain interplay Horizon Europe DATA MANAGEMENT DATA ANALYSIS EOSC-ENTRUST | The mission of EOSC-ENTRUST is to create a European network of trusted research environments for sensitive data and to drive European interoperability by joint development of a common blueprint for federated data access and analysis. 2024-2026 Trusted Research Environment Horizon Europe INFRASTRUCTURE EBV-MS | "Targeting Epstein-Barr Virus Infection for Treatment and Prevention of Multiple Sclerosis". The ambitious goals of the project are to answer the questions why only a few EBV infected persons develop MS, and define the underlying mechanism of this process, as well as clarify if targeting the EBV infection can prevent MS or improve the disease course. 2023-2028 Viral-host genetics; immune response; disease modelling; disease prevention; AI/ML solutions Horizon Europe DATA MANAGEMENT DATA ANALYSIS WISDOM | WELL-BEING IMPROVEMENT THROUGH THE INTEGRATION OF HEALTHCARE AND RESEARCH DATA AND MODELS WITHOUT BORDER FOR CHRONIC IMMUNE-MEDIATED DISEASES aims to deploy novel approaches for data processing, harmonisation, management, and secure data sharing and federated access for diseases like multiple sclerosis. Using an end-user guided approach, it will facilitate responsible and critical assessment of the use of AI in healthcare. 2023-2028 Chronic immune-mediated diseases Horizon Europe DATA MANAGEMENT INFRASTRUCTURE EUCAIM | EUropean Federation for CAncer IMages is a project that will build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of Artificial Intelligence in oncology. 2023-2027 Cancer Digital Europe Programme (DIGITAL) DISCOVERY CONTAGIO | CONTAGIO (COhorts Network To be Activated Globally In Outbreaks) aims to create coordination mechanisms to rapidly react to infectious disease (re-)emergence in low- and middle-income countries (LMICs). 2023-2026 Infectious Diseases European Commission - Horizon Europe ACCESS DATA MANAGEMENT DISCOVERY Youth-GEMs | Youth-GEMS (Gene Environment Interactions in Mental Health TrajectorieS of Youth) will conduct research into the genetic and environmental factors of mental health in young European people. 2022-2027 Mental health European Commission - Horizon Europe DATA MANAGEMENT DISCOVERY GDI | The European Genomics Data Infrastructure project is enabling access to genomic and related phenotypic and clinical data across Europe. It is doing this by establishing a federated, sustainable and secure infrastructure to access the data. 2022-2026 Genomic and health data European Commission - Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution DISCOVERY DOCUMENTATION INFRASTRUCTURE IMPaCT-T2D | The IMPaCT-T2D project aims at studying the complete genomes of a large cohort of patients with Type 2 Diabetes mellitus (T2D), using modern sequencing technologies and artificial intelligence (AI) in order to improve the stratification and pharmacological treatment in the context of precision medicine. 2022-2025 Cardiovascular and Complex Diseases Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE Completed projects Project Duration Domain Funder Tags EOSC4Cancer | EOSC4Cancer builds on existing projects, research outcomes and established community solutions to create the federated FAIR data, analysis and services infrastructure needed for European Cancer research programmes. 2022-2025 Cancer European Commission - Horizon Europe DISCOVERY EuCanImage | A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology. 2020-2025 AI Solutions in Oncology European Commission - H2020 Programme; "La Caixa" Foundation cofunds CRG's contribution DATA MANAGEMENT METADATA STANDARDS GenoMed4ALL | A consortium built to empower personalised medicine in the field of haematological diseases through the use of AI and the pooling of genomic and clinical data. 2020-2025 Hematological diseases European Commission - H2020 Programme DISCOVERY METADATA STANDARDS BY-COVID | The BeYond-COVID project aims to make COVID-19 data accessible to scientists in laboratories but also to anyone who can use it, such as medical staff in hospitals or government officials. Going beyond SARS-CoV-2 data, the project will provide a framework for making data from other infectious diseases open and accessible to everyone. 2021-2024 Infectious diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE IMPaCT-Data | IMPaCT-Data aims to create the infrastructure for secondary use of data from Spanish healthcare systems - electronic health records, medical imaging and genomic repositories - and contribute with the knowledge and methodology produced to the healthcare system. 2021-2024 Large-scale genomics and health dataSpanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE LaMarato | It is a project aimed at creating and developing a catalan interhospitalary network to interrogate genetic variants from thousands of genetic tests carried out in patients with rare diseases from the main catalan hospitals. 2021-2024 Genomic and health data Fundacio La Marato de TV3 (catalan foundation) DISCOVERY HealthyCloud | This consortium will contribute a Strategic Agenda towards the European Health Research and Innovation Cloud. The project will work in collaboration with a broad range of stakeholders to ensure that all voices are included and that the results are technically and ethically sound. 2021-2023 Not Applicable European Commission - H2020 Programme DOCUMENTATION B1MG | Beyond 1 Million Genomes aims to create a network of genetic and clinical data across Europe. The project provides coordination and support to the 1+ Million Genomes Initiative (1+MG). This initiative is a commitment of 24 EU countries, the UK and Norway to give cross-border access to one million sequenced genomes by 2022. 2020-2023 Not applicable European Commission - Horizon Europe DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS ELIXIR-CONVERGE | An alliance with the goal of Connecting and aligning ELIXIR Nodes to deliver sustainable FAIR life-science data management services. 2020-2023 Data Management and Infectious Diseases European Commission - H2020 Programme DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS IHCC | The International HundredK+ Cohorts Consortium aims to create a global platform for translational research ? informing the biological and genetic basis for disease and improving clinical care and population health. 2020-2022 Translational research NIH; The Wellcome Trust; CZI INFRASTRUCTURE METADATA STANDARDS PPCG | The Pan Prostate Cancer Group aims to harmonise and interrogate Whole Genome DNA Sequence data generated around the world from over 2000 men with prostate cancer, with associated transcriptome and methylome data to include men from different clinical categories, and ethnicities. This project is about providing breakthrough advances through analysis of a very large series of Whole Genome DNA data from prostate cancer contributed by many of the leading scientists and clinicians working in prostate cancer genomics. 2019-2024 Cancer Cancer Research UK DATA MANAGEMENT CINECA | Consortium providing a Federated solution enabling population-scale genomic and biomolecular data accessible across international borders accelerating research and improving the health of individuals resident across continents. 2019-2023 Large-scale Genomics and Health Data European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE EASI-Genomics | A project designed to provide easy access to cutting-edge DNA sequencing technologies to researchers from academia and industry, within a framework that ensures compliance with ethical and legal requirements, as well as FAIR and secure data management. 2019-2023 Next Generation Sequencing European Commission - H2020 Programme ACCESS EJP-RD | An European consortium built to create a comprehensive, sustainable ecosystem allowing a virtuous circle between research, care, and medical innovation. 2019-2023 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DOCUMENTATION METADATA STANDARDS EOSC-Life | EOSC-Life brings together the 13 Life Science research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research. The project will publish 'FAIR' data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). 2019-2023 Not applicable European Commission - H2020 Programme DOCUMENTATION EUCANCan | A federated network aiming at implementing a cultural, technological and legal integrated framework across Europe and Canada, to enable and facilitate the efficient sharing of cancer genomic data. 2019-2023 Cancer European Commission - H2020 Programme DATA MANAGEMENT METADATA STANDARDS The Federated EGA framework: supporting sensitive data management across the ELIXIR Nodes | This project is a direct continuation of the FHD IS with the goal to position the FEGA framework as the core infrastructure driver to support human data sharing for research. 2019-2023 Human genomic data ELIXIR INFRASTRUCTURE UK Biobank | UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. This project is to archive whole genome sequencing and other genetic data for UK Biobank participants. 2019-2023 Large-scale Genomics and Health Data The Wellcome Trust; UKRI; Amgen; AstraZeneca; GSK; Johnson & Johnson DATA MANAGEMENT INFRASTRUCTURE VEIS | The core mission of VEIS is to create an open ecosystem of technologies that will address and adapt to the requirements of the systems used to analyse and interpret -omics and clinical data in research and application environments in biomedicine. The aim of the project is to leverage the value of the EGA for both industry and society. 2019-2022 Oncology and Rare diseases Generalitat de Catalunya and European Regional Development Fund (ERDF) ACCESS DISCOVERY ELIXIR BEACON IS | This study follows on from a number of earlier activities that have established the ELIXIR Beacon Project. The main aim is to extend the Beacon protocol, developed at EGA, to become the reference ELIXIR Data Discovery product 2019-2021 Not applicable ELIXIR DISCOVERY ELIXIR FHD IS | This project coordinates the delivery of FAIR compliant metadata standards, interfaces, and reference implementation to support the federated ELIXIR network of human data resources. 2019-2021 Human genomic data ELIXIR INFRASTRUCTURE ELIXIR Rare Disease | The Rare Disease Community extends and generalises the system of access authorisation and high volume secure data transfer developed within the EGA. The goal of the Community is to create a federated infrastructure that will enable researchers to discover, access and analyse different rare disease repositories across Europe. It is doing this in partnership with other European infrastructure projects, namely RD-CONNECT, BBMRI-ERIC and E-Rare.2019-2021 Rare diseases ELIXIR INFRASTRUCTURE Solve-RD | Solve-RD - solving the unsolved rare diseases - is a research project funded by the European Commission. It echoes the ambitious goals set out by the International Rare Diseases Research Consortium (IRDiRC) to deliver diagnostic tests for most rare diseases by 2020. The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory for a large proportion of rare disease patients - the unsolved RD cases. For these unsolved rare diseases, we are unable to explain the etiology responsible for the disease phenotype, predict the individual disease risk and/or rate of disease progression, and/or quantitate the risk of relatives to develop the same disorder. 2018-2024 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT METADATA STANDARDS EuCanShare | An EU-Canada joint infrastructure for next-generation multi-Study Heart research. 2018-2022 Cardiovascular Diseases European Commission - H2020 Programme ACCESS METADATA STANDARDS

  Documentation about/projects-and-funders/projects

• Catalogue Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue What is in the EGA? The European Genome-phenome Archive (EGA) overview The EGA archives and distributes the results of several types of studies. Such studies include genome-wide association studies (GWAS), different purpose sequencing and molecular diagnosis assays among others. Moreover, these studies target many different kind of diseases from cancer to neurodegenerative alterations. We summarised all this data in the charts below. The statistics are computed daily based on an updated list of studies. Each pie chart depicts a different aspect of the studies: which disease type was studied, which type of sampling method was used and what was the scope of the genomic analysis. Number of studies per disease type Studies in the EGA by disease barHorizontalChart('disease-graph', 'https://stats.ega-archive.org/catalog/by_disease') Number of studies per technology type Studies in the EGA by technology barHorizontalChart('technology-graph', '/stats/studies/technology', 'Include dbGaP') Number of studies per sample type Studies in the EGA by samples barChart('samples-graph', '/stats/studies/sample-types', 'Include dbGaP')

  Documentation about/statistics/catalogue

• Visium Spatial transcriptomic profiling of IOKIN samples

  This study aimed to define the spatial tumor microenvironment features associated with response to immune checkpoint blockade in recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC). Spatial transcriptomic profiling was performed on tumor biopsies from ICB-naive patients to characterize malignant, stromal, and immune cell states and their spatial interactions. The study investigated how intratumoral heterogeneity, cancer-associated fibroblast programs, immune contexture, and tumor-microenvironment signaling relate to subsequent clinical response and progression-free survival following immunotherapy.

  Study EGAS50000002196

• Bladder cancer genomic sequencing data from exclusive waterpipe tobacco smokers - AUBMC cohort

  Raw sequencing data from 13 bladder cancer patients at the American University of Beirut Medical Center (AUBMC, Lebanon) with exclusive waterpipe tobacco use, profiled by MSK-IMPACT® targeted sequencing and whole-exome sequencing (TEMPO pipeline). Data were generated as part of a cross-sectional genomic study comparing bladder cancer in waterpipe smokers, cigarette smokers, and never-smokers to characterize mutational signatures, tumor mutational burden, and oncogenic pathways.

  Study EGAS50000002183

• AUBMC Waterpipe Bladder Cancer Data Access Committee

  This Data Access Committee (DAC) oversees controlled access to raw genomic sequencing data generated from the collaborative study on bladder cancer in exclusive waterpipe smokers conducted by the American University of Beirut Medical Center (AUBMC) and Memorial Sloan Kettering Cancer Center (MSK). The committee evaluates Data Access Requests from qualified scientific researchers to ensure proposed research aligns with patient consent, ethical approvals (IRB ID: IM.AS.34 / BIO-2017-0464), and international data protection standards.

  Dac EGAC50000001155

• Whole-Exome Sequencing of Tumor Biopsies from a Colorectal Cancer Patient with Two Distinct Primary Tumors and Matched Metastases

  Colorectal cancer (CRC) is a heterogeneous disease in which rare genetic alterations, such as neurotrophic receptor tyrosine kinase (NTRK) fusions, have emerged as clinically relevant therapeutic targets. Here, we report the case of a 69-year-old man with an advanced colorectal carcinoma comprising two intermixed but histologically distinct components: a predominant (>90%) poorly differentiated solid carcinoma and a conventional adenocarcinoma with mucinous features. Whole-exome sequencing (WES) of microdissected tumor regions (two primary tumors and two matched metastases) along with germline DNA analysis revealed profound genetic divergence between the two tumor components. This case illustrates the coexistence of two genetically independent yet spatially inseparable CRCs following distinct evolutionary trajectories—one driven by an NTRK1 fusion and microsatellite instability (MSI), and the other by canonical KRAS and SMAD4 alterations.

  Study EGAS50000001385

• Genomic correlates with survival after treatment of oesophageal adenocarcinoma by surgery alone

  A 451 cancer-associated gene panel, described in McCabe et al. 2019, was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients who underwent surgical resection without perioperative treatment. This DAC controls access to sequencing data.

  Dac EGAC50000001177

• Bladder Cancer Genomics Data Access Committee – Erlangen–Bonn Collaboration

  The Bladder Cancer Genomics Data Access Committee (DAC) represents the collaborative bladder cancer research program between the Institute of Pathology, Universitätsklinikum Erlangen; the Department of Urology and the Institute of Experimental Oncology, Universitätsklinikum Bonn. The DAC comprises representatives from each participating institution, including Prof. Markus Eckstein (Universitätsklinikum Erlangen) and Prof. Niklas Klümper (Universitätsklinikum Bonn). The committee is responsible for reviewing and approving requests for access to controlled-access bladder cancer genomic and associated clinical datasets generated through this collaboration deposited in the European Genome-phenome Archive (EGA). The committee reviews requests from qualified researchers to determine whether the proposed use of the data is consistent with the participants' informed consent, applicable ethical approvals, institutional data access policies, and relevant data protection regulations before granting access.

  Dac EGAC50000001118

• Aligned BAM files from targeted amplicon sequencing

  This dataset contains aligned BAM files generated from targeted amplicon sequencing, using Oncomine Focus assay (Thermo Fisher Scientific). The dataset includes 120 samples from 60 patients of Thyroid cancer. Reads were aligned to the reference genome and are provided as BAM files for downstream analysis. No processed variant calls are included. We uploaded 60 BAM from DNA samples and the 60 BAM of the RNA samples of these patients

  Dataset EGAD50000002861

• Multiomic Analysis of Treatment Naive MYD88 Mutated Patients with Waldenstrom’s Macroglobulinemia

  This study consists of RNASeq with additional whole exome sequencing and enhanced reduced representational bisulfite sequencing for a subset of study participants. Samples were derived from CD19 selected bone marrow biopsies. Deidentified clinical annotation of these samples is also included. Samples range from patients at the earliest stages of disease development to those who are about to initiate therapy. The aim of this study is to look for clinical/molecular subtypes of WM and study the biology of disease evolution/progression.

  Study EGAS50000002182

• Multi-omics analysis revealed clonal evolution of the sarcoma patient-derived xenograft

  We performed whole-exome sequencing and RNA-seq of sarcoma PDXs, along with their matched original tumors when available.

  Study EGAS50000001860

• Genomic sequencing in an oesophageal adenocarcinoma cohort

  Genome sequencing was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients. Primary tumour tissues and matched normal squamous oesophagus tissues were sequenced using a 451 cancer-associated gene panel. This dataset consists of sequence data in fastq format.

  Dataset EGAD50000003139

• Spatial transcriptomic profiling of IOKIN samples

  This study aimed t o define the spatial tumor microenvironment features associated with response to immune checkpoint blockade in recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC). Spatial transcriptomic profiling was performed on tumor biopsies from ICB-naive patients to characterize malignant, stromal, and immune cell states and their spatial interactions. The study investigated how intratumoral heterogeneity, cancer-associated fibroblast programs, immune contexture, and tumor-microenvironment signaling relate to subsequent clinical response and progression-free survival following immunotherapy.

  Dataset EGAD50000003147

• MEDIOLA WOO serial tumor RNA sequencing (raw reads)

  Poly(A)-selected RNA sequencing of 160 fresh-frozen serial tumor biopsies from 54 patients with stage II/III triple-negative or ER-low breast cancer enrolled in the MEDIOLA window-of-opportunity trial (NCT03594396). Biopsies were obtained at baseline (n = 54), on day 14 after olaparib alone and before durvalumab (n = 54), and on day 29 after olaparib plus a single dose of durvalumab (n = 52). Libraries were prepared with the TruSeq Stranded mRNA LT kit and sequenced as paired-end reads on an Illumina NovaSeq 6000. The dataset comprises 320 paired-end FASTQ files (160 runs), encrypted with Crypt4GH.

  Dataset EGAD50000002906

• MEDIOLA WOO baseline tumor and germline whole-exome sequencing (raw reads)

  Whole-exome sequencing of baseline primary breast tumor biopsies and matched peripheral-blood germline DNA from 54 patients with stage II/III triple-negative or ER-low breast cancer enrolled in the MEDIOLA window-of-opportunity trial (NCT03594396), comprising 54 tumor-normal pairs. Exome libraries were prepared with the SureSelect V6 Post Capture Exome Kit and sequenced as 101-bp paired-end reads on an Illumina NovaSeq 6000. The dataset comprises 216 paired-end FASTQ files (108 runs), encrypted with Crypt4GH.

  Dataset EGAD50000002907

• Whole-Exome Sequencing Analysis of Tumor Biopsies from a Colorectal Cancer Patient

  After the sequencing, reads were aligned against the reference human genome GRCh38 using Burrows-Wheeler Aligner (BWA, v0.7.12). SNVs and indels were called using MuTect2 (GATK 4.1.4.1). SNVs or indels with a VAF < 5% or those that were covered by fewer than three reads were discarded., iIf the SNVs were found in both components of one sample, a cut-off of two reads was applied. We further excluded variants identified in at least two of a panel of 210 non-tumor samples, including the non-tumor samples included in the current study. Variant annotation was performed by SnpEff software v.4.1.

  Dataset EGAD50000002011

• PEGASUS cohort transcriptomic profiling

  This study characterizes the transcriptomic landscape of FFPE tumor tissue samples from the PEGASUS colorectal cancer cohort using Digital mRNA-seq (NEGEDIA, Next Generation Diagnostic srl). Libraries were sequenced single-end on an Illumina NovaSeq 6000.

  Study EGAS50000002062

• Plasma CTCF ChIP-seq Reveals Cancer-Associated cfDNA Binding Profiles

  Cell-free DNA (cfDNA) in plasma predominantly exists as nucleoprotein complexes. Although circulating CTCF-bound cfDNA (cfCTCF-DNA) has previously been reported, the occupancy of individual CTCF binding sites in plasma has not been systematically characterised. This study describes the isolation of endogenous plasma cfCTCF-DNA by chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) in samples obtained from cancer patients and healthy individuals. A two-step enrichment strategy was employed, combining physical removal of background cfDNA by immunoprecipitation with bioinformatic filtering to remove residual non-CTCF-derived sequences. Sequencing reads were aligned to the UCSC hg38 reference genome using BWA-MEM v0.7.17 and processed to generate CRAM files containing alignments restricted to the primary chromosomes (chr1–chr22, chrX, chrY and chrM). CTCF-associated cfDNA fragments were classified according to their similarity to a consensus CTCF binding motif, enabling discrimination between low-affinity and high-affinity CTCF binding sites. Comparative analyses identified quantitative and qualitative differences in plasma cfCTCF-DNA profiles between healthy individuals and patients with cancer. The deposited dataset comprises plasma cfCTCF-DNA ChIP-seq data from four clinical cohorts and includes both CTCF-enriched and total cfDNA samples where applicable. These data support the investigation of CTCF occupancy patterns as a novel class of liquid biopsy biomarkers for cancer detection and monitoring.

  Study EGAS50000001937

• METABRIC

  The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (CNVs, SNPs) and acquired somatic copy number aberrations (CNAs) were associated with expression in 40% of genes, although the landscape was dominated by cis and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP, and MAP2K4. Unsupervised analysis of paired DNA/RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, ER-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration 0152hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 0152CNA-devoid sub-group and a Basal-specific chromosome 5 deletion-driven mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic copy number aberrations on the transcriptome.

  Study EGAS00000000083

• Machine Learning Uncovers Previously Undetectable Genetic Deletions in Cancer Panel Sequencing

  We developed a novel machine-learning pipeline for detection of genetic deletions and zygosity evaluation within MASTERsg, a precision oncology program using the TSO500 DNA gene panel to identify targetable alterations in advanced cancers. The TSO500 pipeline detects copy number variations (CNVs) through bin count averaging but may miss focal CNVs not meeting thresholds. As a proof-of-concept we used deletions in PTEN, which confer sensitivity to mTOR and Akt inhibition. An Extreme Gradient Boosting model trained on tumor samples with PTEN deletions and PTEN wild-type status enabled the correct identification of missed focal deletions, which were subsequently confirmed by immunohistochemistry. Additional cases of PTEN loss could be identified in 34 of 204 previously profiled, seemingly uneventful samples, which were validated in 75% of stainable cases. The findings underscore the substantial clinical potential of this method to uncover therapeutically relevant alterations beyond established diagnostic pipelines.

  Study EGAS00001008324

• Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade

  Following the pivotal Phase III IMpower133 study, atezolizumab (anti–PD-L1), combined with carboplatin and etoposide (CE), was the first immune checkpoint inhibitor approved for first-line treatment of extensive-stage small cell lung cancer (ES-SCLC) and is now a standard of care. A clearer understanding of therapeutically relevant SCLC subsets is needed to improve outcomes and to identify rational combination strategies. Transcriptomic analyses and nonnegative matrix factorization were conducted on 271 pre-treatment patient tumor samples from IMpower133 and four subsets with general concordance to previously reported SCLC subtypes were identified. Deeper investigation into the immune heterogeneity relevant to clinical outcomes uncover two subsets with differing neuroendocrine (NE) versus non-neuroendocrine (non-NE) phenotypes that demonstrated hallmarks of immune cell infiltration. The balance of tumor-associated macrophage (TAM) to T-effector signals distinguished these inflamed subsets. Tumors with low TAM but high T-effector signals had a NE phenotype and demonstrated longer overall survival with PD-L1 blockade and CE versus CE alone than did tumors with high TAM and high T-effector signal, which had a non-NE phenotype. The delineation of SCLC immune heterogeneity offers a clinically relevant approach to discriminate SCLC patients likely to benefit most from immunotherapeutic approaches and highlights the complex mechanisms underlying response and resistance to immune checkpoint blockade. 

  Study EGAS50000000138

• Gene expression profiling in pregnancy-associated breast cancer

  Study EGAS00001008013

• Somatic mutations of non-malignant T cells

  Somatic mutations in T cells can cause cancer but also have implications for immunological diseases and cell therapies. The mutation spectrum in non-malignant T cells is unclear. Here, we examined somatic mutations in CD4+ and CD8+ T cells from 90 patients with hematological and immunological disorders and used T cell receptor (TCR) and single-cell sequencing to link mutations with T cell expansions and phenotypes. CD8+ cells had higher mutation burden than CD4+ cells. Notably, the biggest variant allele frequency (VAF) of non-synonymous variants was higher than synonymous variants in CD8+ T cells, indicating non-random occurrence. The non-synonymous VAF in CD8+ T cells strongly correlated with the TCR frequency, but not age. We identified mutations in pathways essential for T cell function and often affected in lymphoid neoplasia. Single-cell sequencing revealed cytotoxic Temra phenotypes of mutated T cells. Our findings suggest that somatic mutations contribute to CD8+ T cell expansions without malignant transformation.

  Study EGAS50000000237

• Spatial transcriptomic profiling of INSPIRE samples

  This study aimed t o define the spatial tumor microenvironment features associated with response to immune checkpoint blockade in recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC). Spatial transcriptomic profiling was performed on tumor biopsies from ICB-naive patients to characterize malignant, stromal, and immune cell states and their spatial interactions. The study investigated how intratumoral heterogeneity, cancer-associated fibroblast programs, immune contexture, and tumor-microenvironment signaling relate to subsequent clinical response and progression-free survival following immunotherapy.

  Dataset EGAD50000003130

• Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities

  Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data, we applied non-negative matrix factorization, which identified four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed signature (SCLC-A, N, P and I, respectively). The presence of these subtypes were then validated in tumor gene expression data from the IMpower133 clinical trial.

  Study EGAS00001004888

• MutWPX__CRUK_Grand_Challenge_Mutographs_of_Cancer__Effects_of_Chemotherapy_on_the_Somatic_Mutational_Landscape_in_Normal_Human_Tissue___Matched_Normals

  This study is investigating the effects of chemotherapy drugs used in cancer treatment on the somatic mutational landscape in normal human tissues. The samples used in this study have been taken from rapid autopsies of patients who have undergone chemotherapy using drugs thought to cause mutations in the DNA. Structures from a variety of tissues will be dissected using LCM and whole genome sequences will be produced in order to assess mutational burdens and signatures. This study contains the samples used as matched normals for each patient to remove germline mutations.

  Study EGAS00001004957

• SCLC ctDNA sequencing

  In this study we have sequenced the ctDNA of 28 Small Cell Lung Cancer patients in the TP53 gene, each sample has been sequenced twice.

  Study EGAS00001003984

• Integrated Genomic, Epigenetic, and Expression Analyses of Ovarian Cancer Cell Lines

  Epithelial ovarian carcinoma of all subtypes has one of the worst outcomes of any human cancer. To improve our understanding of underlying biology and therapeutic possibilities in this disease, we performed whole genome sequencing, methylation, and expression analyses of a comprehensive set of 47 ovarian cancer cell lines representing different histologic tumor subtypes. Given the challenges of genomic analyses in tumors without matched normal samples, we developed novel approaches for detection of somatic alterations and used these to identify sequence, copy number, and rearrangement changes and integrated these with genome-wide epigenetic and expression alterations. Ovarian cancer cell lines had molecular changes in genes involved in cell cycle, PI3K, RAS, BRCA, chromatin regulating, and p53 pathways, and displayed global mutation and methylation changes similar to primary ovarian carcinoma. We identified alterations not previously implicated in ovarian cancer including amplification or overexpression of ASXL1 and H3F3B, deletion or underexpression of CDC73 and members of the TGF beta receptor pathway, including TGFBR2, SMAD3 and SMAD4, in-frame rearrangements of YAP1-MAML2 and IKZF2-ERBB4, and fusions involving MET, NF1, FBXW7 and CCND1. Integrating cell line alterations with dose-response data using three targeted therapies revealed novel molecular dependencies, including increased sensitivity of tumors with PIK3CA and PPP2R1A alterations to PI3K inhibitor GNE-493, those with MYC amplifications to PARP inhibitor BMN673, and SMAD4 alterations to MEK inhibitor MEK162. Genome-wide analyses revealed intrachromosomal rearrangements as an improved measure of sensitivity to PARP inhibition compared to the homologous recombination defect (HRD) score. This study provides a comprehensive resource of molecular information for ovarian cancer cell line models and a pharmacogenomic platform for developing rational cancer therapeutic strategies.

  Study EGAS00001002998

• Epigenetic profiling of colorectal cancer initiating cells (CC-ICs) to identify bivalently marked genes (H3K4me3 and H3K27me3 ChIP-seq), and investigation of changes in transcriptome following EZH2 inhibition using RNA-seq.

  Epigenetic regulation of transcription plays a crucial role in lineage commitment of embryonic stem cells. Promoters of key lineage-specific differentiation genes are found in a repressed bivalent state, having both activating H3K4me3 and repressive H3K27me3 histone marks, making them poised for transcription upon loss of H3K27me3 in response to environmental cues. Whether the tumour-initiating, self-renewing, cancer-initiating cells (C-ICs) have similar epigenetic regulatory mechanism that prevent lineage commitment is unknown. In order to investigate bivalently marked and repressed promoters, we used a patient-derived CC-IC enriched model to identify the changes in transcriptome following inhibition of EZH2, the H3K27 methyltransferase. We also performed ChIP-seq for H3K27me3 and H3K4me3 at baseline in order to identify repressed and bivalently marked promoters.

  Study EGAS00001003003