8213 results for "canc*"

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• Cord blood DNA duplex sequencing libraries using deepDLP

  As part of the DeepClone protocols paper we used three cord blood samples to validate the error rate of deepDLP duplex library preparation method. The FASTQs shared below correspond to these three cord blood samples. With this data we concluded that the error rate of the technology is between 1 and 3x10-8 errors per sequence basepair.

  Study EGAS50000002002

• High Grade Serous Ovarian Cancer Whole Exome Sequencing

  Whole exome sequencing (WES) was performed on genomic DNA (50–200 ng) extracted from high-grade serous ovarian cancer (HGSOC) tumor tissue and matched buffy coat or normal tissue. DNA was sheared using a Covaris E220 system to generate fragments of approximately 150–200 bp. Sequencing libraries were prepared using the Agilent SureSelect XT Low Input Library Preparation Kit with unique dual indices. Tumor libraries were enriched using a custom SureSelect V6 + UTR + OneSeq LowRes capture probe set, while germline libraries were enriched using the Agilent SureSelect Human All Exon V6 capture kit supplemented with custom probes. Libraries underwent initial quality control sequencing on the Illumina MiSeq platform, followed by pooled sequencing on the Illumina NovaSeq 6000 using a 300-cycle kit. Archived here are the BAM files for each sample.

  Dataset EGAD50000002488

• Follicular Lymphoma Whole Genome and Transcriptome Sequencing

  Study EGAS00001006646

• Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

  Study EGAS00001007404

• METABRIC

  Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays. To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

  Study EGAS00000000098

• Efficacy of dual KRAS G12D–EGFR blockade versus triple combinations in patient-derived models of KRAS G12D-mutant colorectal cancer

  RNA sequencing was performed on patient-derived xenograft (PDX)-derived organoid models of metastatic colorectal cancer (mCRC). The aim of this study was to characterize the transcriptional landscape of KRAS-mutant mCRC cells in response to therapeutic strategies targeting the EGFR and KRAS signaling pathways. Transcriptomic profiling was conducted following drug treatments to investigate molecular changes associated with single and combination therapies. A total of 24 samples were analyzed.

  Study EGAS50000001700

• Genetic and evolutionary patterns of treatment resistance in relapsed B-cell lymphoma

  Study EGAS00001004469

• IDH1 Somatic Mutation Profile in Intrahepatic Cholangiocarcinoma

  The objective of this study is to identify somatic IDH1 mutations in intrahepatic cholangiocarcinoma samples.

  Study EGAS50000001638

• Whole-genome sequencing of cell-free DNA from pancreatic and breast cancer cohorts for fragmentomic and tumor fraction analysis

  This project provides whole-genome sequencing data from cell-free DNA collected from patients with pancreatic ductal adenocarcinoma and breast cancer, alongside healthy and benign controls. The study aims to identify novel liquid biopsy biomarkers by exploring the relationship between per-base quality scores and fragmentomic signals like end-motifs and fragment length. Furthermore, the data supports the development of Bayesian deep learning frameworks for the uncertainty-aware estimation of tumor fraction and ploidy in low-concentration clinical samples.

  Study EGAS50000001620

• High-grade B-cell lymphoma, not otherwise specified: an LLMPP study

  Molecular characterization of high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), is hindered by its rarity, evolving definition, and poor diagnostic reproducibility. To address this challenge, we analyzed 92 HGBCL-NOS tumors collected across Lymphoma/Leukemia Molecular Profiling Project sites. These tumors were profiled using whole-genome sequencing (WGS) or whole-exome sequencing (WES) to identify somatic mutations, WGS or low-pass WGS to detect copy number alterations, targeted capture sequencing to assess MYC and BCL2 structural variants, and RNA sequencing. The genetic features of HGBCL-NOS were compared to those of diffuse large B-cell lymphoma (DLBCL-NOS) and EBV-negative adult Burkitt lymphoma (BL). DLBCL-NOS tumors underwent targeted capture sequencing using a panel of 125 genes recurrently mutated in aggressive B-cell lymphomas. BL biopsies were drawn from the Burkitt Lymphoma Genome Sequencing Project and had available WGS (dbGaP Study Accession: phs000527.v18.p6).

  Study EGAS50000000932

• Patient-derived organoids as predictive models for drug testing and repurposing in glioblastoma therapy

  Glioblastoma multiforme (GBM) remains a highly aggressive brain tumor with limited treatment options and poor prognosis. Temozolomide (TMZ) is the only approved first-line therapy, but frequent resistance limits its efficacy, highlighting the urgent need for alternative treatments. Patient-derived glioblastoma organoids (GBOs) offer a promising preclinical model for personalized drug testing and therapy development.

  Study EGAS50000001221

• Tumoral immune microenvironment profiling in B-cell non-Hodgkin lymphoma stratifies patients in different prognostic groups

  Non-Hodgkin lymphomas (NHL) are heterogeneous B-cell malignancies in which the tumor immune microenvironment (TIM) influences disease progression and has emerging potential for prognostic patient stratification. This project investigates the TIM of NHL by combining multiparameter flow cytometry with targeted DNA sequencing of NHL biopsies. The study aimed to characterize the immune cell composition, immune checkpoint expression, and mutational landscape, and to evaluate their association with clinical and prognostic features across NHL subtypes. The classification of TIM profiles by flow cytometry predicts clinical outcomes in patients with follicular lymphoma (FL), highlighting its potential value for risk stratification and the identification of novel immunotherapeutic targets in NHL.

  Study EGAS50000001964

• Initial leukemic epigenomic state determines hypomethylating agent response

  Hypomethylating agents (HMAs) are a mainstay of therapy for myeloid cancers, but genetic biomarkers do not predict who will respond to treatment. Using a variety of single-cell sequencing approaches to define the epigenomic state of responder and nonresponder leukemic cells, we aim to demonstrate that leukemic stem cells (LSC) exist in at least two different epigenomic states: a hematopoietic stem cell (HSC)-or multipotent progenitor (MPP)-like state that is sensitive to HMAs, independent of genetic mutations, or a lymphoid-primed MPP (LMPP)-like nonresponder state. Additionally, we aim to demonstrate that hypomethylation and chromatin accessibility at ZNF143- and CTCF-binding sites result in activation of HOXB4, which defines the HSC/MPP-like state and HMA-sensitivity. Our study aims to provide evidence that the epigenomic state of the LSC is a major determinant of response to HMAs, and demonstrates that a routine clinical assay can identify patients who will respond.

  Study EGAS50000000936

• Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell-of-origin

  Study EGAS00001004660

• Mitochondrial peroxiredoxin 3 is a tractable cancer therapeutic target modulated by SLC7A11

  This study is aimed at elucidating the anti-tumor mechanism and therapeutic potential of the PRX3 inhibitor thiostrepton in mesothelioma by investigating its effects on mitochondrial redox homeostasis, oxidative stress–induced apoptosis, and tumor suppression in preclinical and clinical settings. Mesothelioma explants are treated with thiostrepton and subjected to RNA sequencing under baseline, untreated control, and thiostrepton-treated conditions to characterize treatment-induced transcriptional alterations and identify molecular pathways associated with therapeutic response and resistance.

  Study EGAS50000001827

• RAF1 extrachromosomal DNA amplification confers acquired erlotinib resistance in NSCLC cell model

  Despite their clinical significance, EGFR-directed therapies inevitably encounter drug resistance. This study demonstrates that ecDNA-mediated RAF1 amplification reactivates MAPK signaling independently of mutant EGFR, contributing to erlotinib resistance in EGFR-mutant NSCLC cell models. This finding identifies oncogenic amplification by ecDNA as a novel mechanism leading to erlotinib resistance, opening new avenues for therapeutic strategies in NSCLC.

  Study EGAS50000001832

• Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer

  The evolution of cancers from low grade to metastatic disease is a major determinant of cancer mortality. Cancer evolution involves a complex interplay between cell intrinsic genetics and transcriptional alterations and the extrinsic microenvironment. To define the key mechanisms underpinning metastatic development, we focused on the most lethal form of prostate cancer, metastatic castration-resistant prostate cancer, and employed single-cell multi-omics and whole-genome sequencing to deeply profile 34 metastatic lesions from 9 patients obtained from rapid autopsy. We found that intra-tumour heterogeneity is an indicator of key evolutionary processes, characterised by recurrent tumour populations acting as critical functional components of the tumour ecosystem, irrespective of clonal and microenvironmental backgrounds. Unexpectedly, microenvironments across metastatic sites played a limited role while clonal evolution primarily promoted transcriptional noise. Intra-patient functional convergence of tumour ecosystems was observed across metastatic sites, pointing towards system-level selection pressures that drive the heterogeneity landscape of mCRPC. Our findings reveal functional evolutionary convergence of metastatic disease into units of intra-tumour heterogeneity identifying critical determinants of metastatic progression for therapeutic targeting. Note: matched WGS for some tumour single-cell experiments, as well as germline WGS for patients CA0027, CA0034, CA0035, CA0043, and CA0076, can be found in EGAS00001006598.

  Study EGAS50000001312

• Single-cell profiling reveals mechanisms of response to anti-PD-L1 versus anti-PD-L1 combined with anti-CTLA4 in head and neck squamous cell carcinoma

  Cancer patients often receive a combination of PD-(L)1 and CTLA4 inhibitors, but the mechanisms underlying their concerted actions remain unclear. We conducted a neoadjuvant study in head and neck squamous cell carcinoma (HNSCC) involving anti-PD-L1 monotherapy versus anti-PD-L1/anti-CTLA4 combination. Single-cell profiling of on- versus pre-treatment biopsies confirmed T-cell expansion as response biomarker. We identified a type 1 immune response accompanying T-cell expansion on-treatment across treatment arms, and herein characterized co-localized IgG plasma cell expansion as important contributor. In pre-treatment biopsies, similar features correlated with expansion upon anti-PD-L1, but not anti-PD-L1/anti-CTLA4. By profiling tumor-draining lymph nodes, we found the addition of anti-CTLA4 to trigger activation and subsequent trafficking of CD4+ T-cells via the blood, to become T-helper 1 cells in the tumor. anti-PD-L1/anti-CTLA4 indeed facilitated co-localized expansion of CD4+ and CD8+ T-cells in tumors versus mostly CD8+ T-cells with anti-PD-L1 alone. Hence, we identify complimentary mechanisms underlying anti-PD-L1/anti-CTLA4 therapy in HNSCC.

  Study EGAS50000000037

• Targeted capture, whole genome sequencing, and RNAseq to identify rearrangements in B-cell lymphomas

  This study contains whole genome and custom targeted capture sequencing of mature B-cell lymphomas (DLBCL, FL, BL, HGBCL-DH-BCL2, HGBCL-DH-BCL6) to identify translocation breakpoints of common oncogene rearrangements (MYC, BCL2, BCL6). It is complemented by RNAseq data where available. Complete details are available in the publication Hilton et al, 2024, Blood. Capture sequencing: 357 samples; 364 unique libraries; cram file format aligned to hg38 Whole genome sequencing: 12 samples; 12 unique libraries; cram file format aligned to grch37 RNAseq: 257 samples; 257 unique libraries; fastq file format

  Study EGAS50000000328

• Uncovering the potential of circulating tumor DNA for pediatric precision oncology

  Pediatric solid high-risk malignancies mostly lack molecular biomarkers for early tumor detection, assessment of minimal residual disease or therapy monitoring. Small patient numbers, limited sample volumes, low tumor mutational burden, and few recurrent alterations in pediatric tumors pose further challenges to liquid biopsy analyses for this patient population. Within the prospective, multinational molecular profiling program INFORM, we collected liquid biopsies from 130 children patients followed by optimized isolation of cell-free DNA (cfDNA). CfDNA was subjected to whole-genome, whole-exome, and targeted panel sequencing applying liquid biopsy-adapted protocols. Integration with tissue-derived molecular information and orthogonal comparison revealed low-coverage whole genome sequencing (lcWGS) as a reliable method for tumor detection. In silico integration of fragment length with differentially weighted genome segment alterations into a ctDNA estimation score (CES) significantly increased sensitivity and specificity to 95% and allowed for plasma-based tumor detection in 93% of patients. Performance of whole exome sequencing (WES) and panel sequencing for detection of molecular targets being predictive for matched treatment responses varied between groups of high-risk entities and cumulative evidence for presence of cancer by all three methods was found in 92% of patients. In-depth analyses for selected patients also demonstrated great the potential of liquid biopsies to decipher temporal and spatial tumor evolution. This study establishes improved methods for liquid biopsy analyses in pediatric oncology and provides the rationale for well-informed implementation of these approaches in future clinical trials to improve the clinical management of high-risk pediatric cancer patients.

  Study EGAS50000000393

• Accelerated single cell seeding in relapsed multiple myeloma

  Study EGAS00001004404

• Anti-Cancer Therapies Induce Mutations in Adult Stem Cells in a Tissue-Specific Manner

  Study EGAS00001006042

• High-depth whole genome sequencing of 51 premalignant breast lesions

  Ductal carcinoma in situ (DCIS) is a non-obligate precursor lesion of breast cancer, representing 20-25% of newly diagnosed breast cancer lesions. It is currently not possible to predict which DCIS patients will progress to invasive breast cancer. The samples deposited here are part of a larger study using high-depth whole-genome sequencing (WGS) to investigate the somatic mutation genomic landscape of DCIS. Comprehensive evaluation of DCIS genomes aims to uncover biological insights into breast cancer progression and potential opportunities to stratify DCIS patients for personalised treatment options or active surveillance.

  Study EGAS50000001436

• DCIS fresh frozen fragmentomics dataset

  Ductal carcinoma in situ (DCIS) is a non-obligate precursor lesion of breast cancer (BC). Often detected by mammography, most cases are managed through surgical and/or radiotherapy approaches. Today, it is not possible to predict which patients will progress to invasive disease. Here, we evaluated high-depth whole-genome sequenced (WGS) DCIS, enriched for highgrade clinical lesions, to understand whether deep WGS could reveal biological insights and/or personalized therapeutic vulnerabilities that may be targetable. We find genomic locations that are likely susceptible to producing the initiating lesion for structural variations (SVs) prone to subsequent evolution, termed SHOREs (Susceptibility HOtspots for Rearrangements and ongoing Evolution). We additionally highlight individualized therapeutic potential that would otherwise not be appreciable without WGS. We posit that holistic WGS profiling could offer a more precise stratification approach, discerning higher-risk cases for prospective clinical studies on personalized therapies, from truly low-risk cases suitable for active monitoring. To substantiate our WGS findings, we also performed RNAseq on the same samples, this EGA study contains the RNAseq data.

  Study EGAS50000001475

• High-depth whole genome sequencing of 26 premalignant breast lesions

  Ductal carcinoma in situ (DCIS) is a non-obligate precursor lesion of breast cancer, representing 20-25% of newly diagnosed breast cancer lesions. It is currently not possible to predict which DCIS patients will progress to invasive breast cancer. The samples deposited here are part of a larger study using high-depth whole-genome sequencing (WGS) to investigate the somatic mutation genomic landscape of DCIS. Comprehensive evaluation of DCIS genomes aims to uncover biological insights into breast cancer progression and potential opportunities to stratify DCIS patients for personalised treatment options or active surveillance. Due to specific restrictions imposed by the ethical approval at sample collection, the use of the germline data is restricted to filtering of somatic mutation calls only and cannot be used outside this purpose.

  Study EGAS50000001559

• A mathematical model of cell-free DNA fragment size reveals cancer-specific fragmentomic patterns

  This study aim to test a mathematical model of cfDNA length distribution across multiple biodiy flulids. It try to investigate biological mechanisms of cfDNA fragmentation and release.

  Study EGAS50000001560

• Neo-CheckRay

  Patients with oestrogen receptor-positive (ER+), HER2-negative, early breast cancer (BC) have low pathological complete response (pCR) rates following neoadjuvant chemotherapy (NACT). Phase 3 trials showed that adding an anti-PD-1 immune checkpoint inhibitor (ICI) improves pCR, but there was less benefit in the PD-L1 negative tumours, characterised by an immune “cold” tumour microenvironment (TME). We hypothesized that immune-modulating stereotactic body radiation therapy (iSBRT) could enhance treatment response by reprogramming the immune deprived TME in early ER+/HER2- BCA. We conducted a phase 2, randomized, multicentre trial evaluating NACT plus iSBRT, with or without durvalumab (anti-PD-L1) and with or without oleclumab (anti-CD73) in 147 patients with high-risk, early, ER+/HER2- breast cancer patients.

  Study EGAS50000001777

• Spatial predictors of response to immunotherapy in microsatellite stable metastatic colorectal cancer

  Microsatellite stable (MSS) colorectal cancers (CRC) are largely unresponsive to immune checkpoint inhibition (ICI), prompting investigation into strategies to enhance sensitivity. The MAYA trial, which utilized temozolomide (TMZ) in MGMT-silenced MSS mCRC, hypothesized that TMZ-induced hypermutation could sensitize tumors to ICI. This phase II trial met its primary endpoint, demonstrating durable clinical responses with TMZ combined with ipilimumab and nivolumab. To elucidate factors influencing response heterogeneity, we conducted multi-omic spatial profiling of samples from patients who participated in the MAYA trial, including baseline and on-treatment tissue and blood specimens. While increased neoantigen load following TMZ exposure did not consistently predict for deep responses, spatial profiling revealed key determinants. Lymphocyte proportions, particularly CD8+KI67+ cells, within stromal and tumor compartments, along with macrophage composition (CD68+CD163+ cells) at the tumor-stromal interface, were predictive of response. Treatment pressures dynamically altered the tumor microenvironment composition and activated peripheral immune cells. This study is the first to identify spatial predictors of response to this promising novel treatment approach for MSS CRC.

  Study EGAS50000001567

• Tremelimumab +/- durvalumab in combination with paclitaxel as immune induction in metastatic urothelial cancer: clinical and translational results of the ICRA trial

  DESCRIPTION: New therapeutic strategies for therapy-refractory metastatic urothelial carcinoma (mUC) patients are still needed. In the phase I-II ICRA trial, we evaluated whether paclitaxel plus tremelimumab (T; anti-CTLA-4), with or without durvalumab (D; anti-PD-L1), could induce a tumor response in mUC patients who had already received platinum chemotherapy and immune checkpoint inhibition (ICI). After a dose-escalation safety phase, patients were randomized between three arms: A (n=20 after expansion) paclitaxel plus T750mg (cycles 2-8); B (n=12) paclitaxel plus T300mg (cycle 2) plus D1500mg (cycles 2-13); C (n=12) T750mg (cycles 1-7). The objective response rate in arm A was 26% (88% confidence interval (CI) = [14,36%]) versus 17% (88% CI = [3,42%]) in arm B and 8% (88% CI = [0.3,33%]) in arm C. Grade 3-4 treatment-related adverse events occurred in (A) 45%, (B) 75%, and (C) 25% of patients. Murine experiments showed attenuated tumor outgrowth for paclitaxel plus anti-CTLA-4 compared to monotherapy with either agent. Transcriptomic analyses showed upregulation of inflammation signatures in on-treatment tumor tissue biopsies. This study demonstrated encouraging antitumor activity in therapy-refractory mUC treated with paclitaxel plus high-dose anti-CTLA-4 and suggests immune induction may still be possible after failure to anti-PD-(L)1 therapy. PROJECT GOAL: We aimed to explore both potential biomarkers for response to combination ICB, as well as treatment dynamics to gain a better understanding of the effects of ICB on the urothelial cancer tumor microenvironment (TME).

  Study EGAS50000001574

• HSC_population_dynamics_KSP_samples

  My research project aims to use the clonal dynamics of spontaneously occurring somatic mutations to answer fundamental questions about human haematopoietic stem cell (HSC) biology. The four major questions I will address are: 1. How do age and aging affect normal human HSC dynamics in vivo? 2. How do in vivo perturbations, particularly chemotherapy and increased levels of reactive oxygen species, affect HSC population dynamics? 3. Is response to in vitro perturbation heritable and/or correlated with other features such as age of individual and contribution of the lineage to peripheral blood? 4. How are HSC dynamics altered in people with early driver mutations (clonal haematopoiesis)?

  Study EGAS00001002762

• Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

  Gene expression profiles of single cells from 26 chemo-naïve and IDS tumor tissue and ascites samples from 17 HGSC patients

  Study EGAS50000000607

• Dynamics of tumor ecosystems and microbiome in response to neoadjuvant atezolizumab, bevacizumab, and FOLFOX treatment in patients with unresectable colorectal cancer with liver metastasis

  This study aims to explore the effects of neoadjuvant atezolizumab, bevacizumab, leucovorin, 5-fluorouracil, and oxaliplatin (ABFOLFOX) in patients with unresectable colorectal liver metastases (CRLM), focusing on the molecular dynamics of tumor ecosystems (TE) of CRLM and their impact on treatment outcomes. The study comprises two cohorts with CRLM tissue samples analyzed with RNA sequencing and immunohistochemical staining: cross-sectional cohort A (n = 60, CRLM treated with or without neoadjuvant chemotherapy) and prospective cohort B (n = 20 with serial sampling and treated with ABFOLFOX). Shotgun metagenomic sequencing was done for stool samples from cohort B.

  Study EGAS50000000677

• Spatial Transcriptomics Uncovers Tumor Microenvironment-Based Subtypes in Invasive Lobular Carcinoma

  Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer (BC); however, little is known about its tumor microenvironment (TME). Here, we investigated the spatial architecture of 43 hormone-receptor-positive, HER2-negative (HR+/HER2-) primary ILC samples, using spatial transcriptomics and detailed morphological annotation. By integrating morphology, spatial, and single-cell data, we mapped TME-related cell types, exploring their proximity to tumor cells and their enrichment in specific compartments. Clustering of spatial transcriptomics spots revealed significant inter- and intra-tumor heterogeneity, identifying pathways and cell types linked with disease outcomes. Morphological and gene expression data were finally integrated with a multimodal approach, leading to the identification of 4 new ILC subtypes based on TME heterogeneity. By using derived, specific gene expression signatures, these subtypes were identified and validated in external bulk RNA-seq/microarray datasets (SCAN-B and METABRIC), where differences in prognosis emerged.

  Study EGAS50000001001

• Clonal evolution of metastatic colorectal cancer under anticancer therapies

  To investigate the clonal evolution of metastatic CRC at the single-cell level, we performed WGS in 58 clonal organoids and 18 fresh-frozen (FT) bulk-tissue samples from surgically resected primary and metastatic tumors before and after anticancer therapies in 6 patients. This approach enabled detailed phylogenetic reconstruction of individual clones. We discovered the timing and burden of treatment-related mutations as well as the heterogeneous evolution in driver mutations and genomic rearrangements in late-stage clonal evolution under anticancer therapies in metastatic CRC.

  Study EGAS50000001023

• Gene expression adaptation of metastases to their host tissue

  The adaptation of metastatic cells to their host tissue critically determines the pathogenicity of a cancer and therefore patient survival. Yet, it remains elusive to what extent the host environment drives gene expression programs in metastatic cells. Here we identify adaptive mechanisms that enable metastases to establish themselves in a novel tissue context. We performed single-cell RNA-sequencing on malignant and benign tissue samples from untreated donors with colorectal adenocarcinoma and liver metastasis to deduce tissue adaptive expression patterns. A novel computational approach identified genes and pathways that consistently adapted to the host tissue at the transition from the primary tumor to the paired metastasis across donors. This analysis revealed that the majority of expression changes in the metastasis reflect an expression signature reminiscent of benign liver epithelial cells. Cellular processes adapting to the liver environment include basic cellular functions such as energy metabolism, as well as tissue-specific pathways such as the regulation of lipid metabolism by PPAR-α. These adaptations potentially increase the pathogenicity of the metastatic cells and may provide new therapeutic strategies.

  Study EGAS50000000817

• Copy number profiling of circulating cell-free DNA from high grade serous ovarian cancer patients

  Study EGAS00001004670

• Human PMS1-dependent non-canonical mismatch repair converges with MBD4 to repair 5-methylcytosine deamination

  CpG dinucleotides are mutational hotspots due to spontaneous deamination of 5-methylcytosine (5mC), resulting in T:G mismatches that can lead to CpG>TpG transitions. These mutations are a hallmark of aging and cancer and play a central role in the evolution of vertebrate genomes. We have previously uncovered MBD4 as the primary Base Excision Repair (BER) glycosylase responsible for 5mC deamination repair. Here, we employ an APOBEC1 deaminase fused to a catalytically dead Cas9 to induce targeted 5mC deamination independently of DNA replication, and track its repair in human cells. This approach reveals that MBD4 elicits a coordinated repair response with a non-canonical branch of Mismatch Repair (MMR) involving complexes MutLβ (MLH1-PMS1) and MutSα (MSH2-MSH6). We demonstrate that human PMS1 loss phenocopies the CpG>TpG hypermutation associated with MBD4 deficiency, revealing 5mC deamination repair as one of its main functions. Structural predictions with functional validation reveal that MBD4 activity is dependent on its physical interaction with MutLβ. In alignment with our experimental data, we show that the CpG>TpG mutational burden of MMR-deficient tumors is partly explained by replication-independent processes. Altogether, we uncover a novel function of non-canonical MMR that underscores its interplay with BER in safeguarding genomic integrity against damage to methylated DNA.

  Study EGAS50000001662

• Screening for Prostate Cancer-Specific Differentially Methylated Regions Based on MeDIP-seq

  Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq) on tissue samples from prostate cancer, benign prostatic hyperplasia (BPH), and bladder cancer.

  Study EGAS50000001894

• Intrapatient tumor heterogeneity and clonal evolution in metastatic salivary gland cancer: an autopsy study

  This data contains DNA sequencing data of an autopsy study in patients with salivary gland cancer. Whole-genome sequencing was performed on multiple spatially separated tumor samples per patient (4-7 per patient) and healthy control samples.

  Study EGAS50000001420

• Base-excision repair pathway shapes 5-methylcytosine deamination signatures in pan-cancer genomes

  Transition of cytosine to thymine in CpG dinucleotides is the most frequent type of mutation in cancer. This increased mutability is commonly attributed to spontaneous deamination of 5-methylcytosine (5mC), which is normally repaired by the base-excision repair (BER) pathway. However, the contribution of 5mC deamination in the increasing diversity of cancer mutational signatures remains poorly explored. Here, we integrate mutational signatures analysis in a large series of tumor whole genomes with lineage-specific epigenomic data to draw a detailed view on 5mC deamination in cancer. We uncover tumor type-specific patterns of 5mC deamination signatures in CpG and non-CpG contexts. We demonstrate that the BER glycosylase MBD4 preferentially binds to open chromatin domains, which correlates with lower mutational burden in these domains. We validate our findings by modelling BER deficiencies in isogenic cell models. Overall, we establish MBD4 as the main actor responsible for 5mC deamination repair in humans.

  Study EGAS50000000536

• TransNEO neoadjuvant breast cancer study

  Study EGAS00001004582

• Fragmentation signatures in cancer patients are similar to those in patients with vascular and autoimmune diseases

  The analysis of fragmentation patterns in plasma cell-free DNA (cfDNA) has been shown to distinguish individuals with cancer from healthy controls in multiple case-controlled studies. However, there have been few studies that investigate various types cfDNA fragmentomics patterns in individuals with other diseases. For this purpose, we developed a comprehensive metric, called fragmentation signatures, that integrated the distributions of fragment positioning, fragment length, and fragment end-motifs. Using this metric, we found that individuals with venous thromboembolism, systemic lupus erythematosus, dermatomyositis, or scleroderma had cfDNA fragmentation signatures that closely mimicked those found in individuals with advanced cancers. Furthermore, these signatures were highly correlated with increases in inflammatory markers in the blood. Though these data put substantial limitations on the specificity of fragmentomics-based tests for cancer diagnostics, they also offer ways to improve the interpretability of such tests. Moreover, they should lead to a better understanding of the cells – most likely inflammatory cells - from which plasma cfDNA is derived.

  Study EGAS00001008004

• ProstOmics: spatial and bulk multi-omics of prostate cancer

  In this ERC-funded ProstOmics project we have used spatial and bulk multi-omics on fresh frozen prostate cancer samples to investigate cancer biology and find for biomarkers to improve patient treatment. All tissue material were collected from prostate cancer patients undergoing radical prostatectomy who had not received any prior cancer-specific treatment.Of the 498 prostate tissue samples (114 patients) included in our project, 176 samples (N=37 patients) have been analyzed with bulk transcriptomics (RNA-seq). Some of these 176 samples were also analyzed with spatial transcriptomics (n=32, N=8, Visium 10x RNA-seq) and DNA methylomics (n=96, N=24, array). All datasets include metadata for histopathological evaluation. Patient metadata include information of age at surgery, time (months) until reported relapse, pre-surgery PSA and post-surgery T-stage.

  Study EGAS50000000413

• an integrated molecular study of clear cell renal cell carcinoma (ccRCC) including whole-genome/exome and RNA sequencing as well as array-based gene expression/copy-number/methylation analyses

  an integrated molecular study of clear cell renal cell carcinoma (ccRCC) including whole-genome/exome and RNA sequencing as well as array-based gene expression/copy-number/methylation analyses

  Study EGAS00001000509

• Cell-free DNA fragmentome profiles predict immunotherapy response

  Mutation-based liquid biopsy approaches face challenges in accurately identifying the cellular origin of variants, which complicates the determination of circulating tumor DNA (ctDNA) molecular response. We hypothesized that these limitations could be addressed by utilizing genome-wide cell-free DNA (cfDNA) fragmentome methods. To this end, we performed whole-genome sequencing of 528 serial cfDNA samples and quantified the cfDNA tumor fraction (TF) using DELFI-TF, a tumor- and mutation-naive machine learning cfDNA fragmentome classifier, across two prospective clinical protocols for immunotherapy-treated non-small cell lung cancer (NSCLC). DELFI-TF values were validated independently by hybrid capture fixed gene panel next-generation sequencing, and the landmark cfDNA fragmentome molecular response (fMR) was determined within 3-9 weeks from the initiation of immunotherapy. Individuals with fMR attained significantly longer progression-free and overall survival than those without fMR (logrank p=2.7e-14 and p=3e-9), including in patients with mutation-based molecular progression (logrank p=1.2e-4 and p=2e-3). Our findings suggest that analyses of the cfDNA fragmentome can provide a clinically meaningful measure of circulating tumor fraction and validate the clinical sensitivity of plasma-only cfDNA fragmentome-based molecular response as an early indicator of immunotherapy response.

  Study EGAS50000001837

• Ouro-seq: Improved Recovery of Full-Length circRNAs from Samples with Limited RNA Content Using Short-Amplicon Suppression

  Circular RNAs (circRNAs) represent an emerging class of RNAs with potential roles in disease pathogenesis and promising biomarker applications. However, their detection in samples with limited RNA content, such as liquid biopsies, remains challenging due to extremely low abundance. Here, we introduce Ouro-seq, a novel long-read sequencing protocol optimized for full-length circRNA recovery. We applied Ouro-seq to urine and self-sampled cervical swab (CVS) samples from cervical cancer patients and plasma from lung cancer patients and healthy controls. Our approach significantly outperformed conventional methods, recovering 2-5 times more and substantially longer circRNA molecules. Unexpectedly, while plasma samples predominantly contained exonic circRNAs, urine and CVS were dominated by previously under characterized intergenic circRNA. We identified extensive alternative circularization and splicing events. Functional annotation revealed distinct specialization patterns among circRNA subtypes, with exonic circRNAs exhibiting enhanced miRNA sponging potential and circRNAs from unplaced genomic scaffolds showing greater peptide-coding capacity. This study significantly advances our understanding of circRNA biology in liquid biopsies and establishes Ouro-seq as a valuable tool for comprehensive circRNA characterization with potential applications in biomarker discovery and disease monitoring.

  Study EGAS50000001356

• UNICORN Data Access Committee

  The UNICORN Data Access Committee reviews and manages requests for controlled access to genomic, transcriptomic and associated clinical data generated within the UNICORN colorectal cancer study.

  Dac EGAC50000001116

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

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

  Study EGAS50000001910

• Arcagen - Gynecologic cancer domain

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

  Study EGAS50000000900

• Circulating cell-free DNA methylation profiling for detection, distinction, and estrogen receptor status classification of advanced breast cancer

  We developed cell-free DNA (cfDNA) methylation signatures for minimally invasive breast cancer detection, distinction, and estrogen receptor (ER) status classification with cell-free Methylated DNA Immunoprecipitation and Sequencing (cfMeDIP-seq). We profiled peripheral blood plasma from 87 patients with mBC spanning ER-positive/HER2-negative (ER+/HER2-), HER2-positive, and triple-negative breast cancer (TNBC).

  Study EGAS50000001675

• Single-cell transcriptome and TCR profiles of T cells from supercentenarians

  Our previous study identified CD4 cytotoxic T lymphocytes (CD4 CTLs) as a hallmark of supercentenarians. Using single-cell immune profiling, we found that CD4 CTLs begin expanding around age 100, characterized by sequential CD27/CD28 loss without exhaustion. These cells showed strong clonal expansion and shared CDR3beta sequences with tumor-infiltrating T cells, especially from lung cancer. These findings suggest that CD4 CTLs adapt to persistent antigens and may contribute to cancer surveillance in extreme aging.

  Study JGAS000815

• Whole Exome Sequencing and RNA Sequencing data

  Whole Exome Sequencing and RNA Sequencing data for SCLC samples used in the study "A targetable dependency on nonsense-mediated decay for cellular homeostasis and immune control in small cell lung cancer"

  Dataset EGAD50000002962

• Serafini et al., Nuclear localization of the glutaminyl-tRNA synthetase QARS reveals its role in DNA damage response in ovarian cancer

  Uncovering novel mechanisms controlling DNA damage response (DDR) is essential to better understand the molecular features of high-grade serous ovarian cancers (HGSOC), which remain a clinical challenge. Here, we demonstrate that the glutaminyl-tRNA-synthetase QARS is a new key player in DDR. We show that QARS is regulated at translational level by the MAPK pathway in HGSOC and controls homologous recombination (HR) through a translation-independent mechanism. Analyses combining RNA sequencing, immunohistochemistry, proteomic data and functional assays from HGSOC patient cohorts and relevant cellular models reveal that QARS promotes DNA repair via the HR pathway. Interestingly, we uncover that QARS is located in the nucleus and is enriched at chromatin upon DNA damage. Nuclear QARS controls HR by interacting with the nuclear autoantigen SP100, a major component of PML-nuclear bodies (PML-NB), that are regulatory hubs for DNA repair proteins. Our data highlight novel nuclear functions and mechanisms for QARS involving SP100, PML-NB and MRE11 that enhance HR in HGSOC.

  Study EGAS50000001968

• Fragmentomics Uncover Non-Mutational HRD Features

  Homologous recombination deficiency (HRD) is a key biomarker in prostate cancer (PCa), predicting response to PARP inhibitors and platinum-based therapies. However, current diagnostics often rely solely on BRCA1/2 mutations or genomic scars, missing cases driven by other mechanisms. Tissue-based testing is further limited by tumor heterogeneity and biopsy challenges in metastatic bone disease. Here, we applied a comprehensive multimodal approach to assess HRD in 106 advanced PCa patients using circulating tumor DNA (ctDNA). This included targeted sequencing of homologous recombination repair (HRR) genes, low-pass WGS for genomic instability (sHRD), WES for mutational signatures, and cfDNA fragmentomics and chromatin accessibility profiling. BRCA2 was the most frequently altered gene, often co-occurring with PTEN loss. High sHRD scores correlated with BRCA2/RB1 loss, somatic copy number alterations, and poor survival. HRD tumors showed enrichment of SBS3/ID6 signatures, altered fragment lengths, and reduced accessibility at zinc finger transcription factor sites. These results support ctDNA-based multimodal profiling as a non-invasive strategy for HRD detection and stratification.

  Study EGAS00001008190

• A targetable dependency on nonsense-mediated decay for cellular homeostasis and immune control in small cell lung cancer

  Small cell lung cancer (SCLC) is one of the most aggressive malignancies, characterized by rapid metastatic dissemination and poor overall survival. Despite harboring one of the highest tumor mutational burden (TMB), expectedly resulting in immunogenic neoantigens, patients with SCLC remain largely refractory to immunotherapy. We found abundant frameshift mutations in SCLC, regarded as highly immunogenic, counterbalanced by a hyperactive nonsense-mediated decay (NMD) pathway, responsible for frameshift-mRNA degradation. NMD activity correlated with tumor mutational burden (TMB) across cancers, suggesting that SCLC and other TMB-high cancers may depend on NMD to limit the accumulation of mutation-derived byproducts in order to maintain cellular homeostasis and evade immune recognition. In TMB-high SCLC models, inhibition of NMD impaired cell proliferation and induced ER stress-dependent apoptosis due to the accumulation of misfolded proteins. Genetic and pharmacological NMD inhibition in vivo effectively controlled TMB-high tumor growth without overt toxicity. By integrating genome and transcriptome sequencing with MHC-I immunopeptidomics and functional in vitro and in vivo assays, we identified that NMD inhibition boosted neoantigen expression and presentation by tumor cells and increased T cell recognition, thus enhancing overall tumor immunogenicity and further improving immunotherapy efficacy in vivo. Our work shows that SCLC – as a TMB-high cancer – relies on NMD for survival and immune escape, uncovering a novel TMB-dependent tractable vulnerability for this devastating disease.

  Study EGAS50000002061

• PTEN homozygous deletion is a negative prognostic factor in Tumor Treating Fields-treated glioblastoma, IDH wildtype patients

  This study investigates genomic determinants of outcome in glioblastoma, IDH-wildtype patients treated with Tumor Treating Fields (TTFields) in addition to standard radiochemotherapy. Tumor tissue from primary glioblastoma at initial diagnosis was profiled using the Illumina TruSight Oncology 500 (TSO500) targeted sequencing panel to characterise somatic alterations. The overarching goal is to provide exploration of genomic determinants of survival and treatment response in TTFields-treated glioblastoma, IDH-wildtype.

  Study EGAS50000001469

• Whole-exome sequencing and RNA-seq data of paired normal-tumour samples from MMR-proficient early-onset colorectal cancer patients

  This study aims to identify novel hereditary colorectal cancer predisposition genes by performing whole-exome sequencing and RNA-seq of paired normal colonic mucosa and colorectal cancer samples from 19 patients diagnosed with MMR-proficient colorectal cancer at an age of 50 years or younger. Both whole-exome and RNA sequencing were performed on the Illumina platform.

  Study EGAS50000001296

• E-cadherin inactivation shapes tumor microenvironment specificities in invasive lobular carcinoma

  Invasive lobular breast carcinoma (ILC) shows specific stromal features, T lymphocyte infiltration (TIL) being associated with poor prognosis. Here, we reveal the involved mechanism by performing single cell RNA sequencing, combined immunohistochemistry, deconvolution of bulk RNA sequencing from large retrospective ILC series, and functional assays using primary cells. We show that ILC accumulates FAP+ inflammatory cancer-associated fibroblasts (iCAF) through a previously undescribed mechanism mediated by E-cadherin/CDH1 on CAF plasticity. Indeed, CDH1 inactivation in ILC cancer cells prevents differentiation of iCAF into myofibroblastic CAF (myCAF), leading to iCAF accumulation. In turn, iCAF attract TILs into the tumor center and shape their spatial organization in ILC. Subsequently, CDH1-inactivated ILC cancer cells promote immune escape by lack of retention and activation of ITGAE-expressing resident memory CD8+ T lymphocytes (TRM). Hence, our study uncovers reciprocal interactions between CDH1-inactivated cancer cells, iCAF and CD8+ TRM, gaining insight into the underlying mechanism associated with the distinctive stromal reaction observed in ILC and revealing why and how TILs have a poor prognosis in ILC patients, a mechanism that can be generalized to other CDH1-inactivated cancer types.

  Study EGAS50000001760

• SCANDARE TNBC

  Prospective biobanking study in breast patients aiming at better understand the link between the molecular alterations of the tumor itself, its microenvironment and immune response.

  Study EGAS50000000970

• Clonotypic characterization defines B‐cell drivers of clonal expansion and intratumor heterogeneity in IgM monoclonal gammopathies

  Dataset accompanying the study entitled “Clonotypic characterization defines B‐cell drivers of clonal expansion and intratumor heterogeneity in IgM monoclonal gammopathies” (Moreno, Nadeu, et al. HemaSphere 2026), which includes genomic, epigenomic, and transcriptomic analyses of smoldering and symptomatic Waldenström macroglobulinemia (SWM and WM, respectively) as well as IgM monoclonal gammopathy of undetermined significance (MGUS).

  Study EGAS50000002033

• The UVA-induced single-base mutational signature of CX5461 in human cells

  Drug-related UVA-induced photoreactions have been reported for several therapeutic compounds, including fluoroquinolones. CX5461 is a clinically relevant quinolone-derived anti-cancer small molecule with documented UVA-sensitising activity. Here we compared, by bulk and clonal whole genome sequencing (WGS) under light-protected conditions, the mutational signatures in human retinal pigment epithelial cells (RPE1) exposed to UVA, CX5461, or co-exposed to UVA and CX5461. Treatment with CX5461 or UVA alone resulted in a low SNV burden and background-like mutational profiles. In contrast, bulk sequencing of human cells co-exposed to UVA and CX5461 had a markedly higher SNV burden characterized by T>A and T>C substitutions. Furthermore, single-cell clonal expansion and sequencing of CX5461 alone, UVA alone or CX5461+UVA treatments confirmed that the pattern was only observed when cells were exposed to both UVA and CX5461. The CX5461+UVA-associated SNV signature we report arises only when CX5461-treated cells are exposed to UVA, and is not observed when CX5461-treated cells are shielded from light. We do not observe strong single base mutagenic activity of CX5461 alone, under light protected conditions. Our data emphasise the need for appropriate controls and light-exposure precautions when studying base mutagenesis activity of known photosensitiser molecules.

  Study EGAS50000001145

• Single-nucleus profiling of postmortem diffuse midline gliomas identifies mitochondrial biogenesis as a resistance mechanism to imipridone therapy

  Diffuse midline glioma, H3 K27-altered (DMG), remains a lethal pediatric brain tumor with limited therapeutic options, and imipridone therapy produces only temporary responses before recurrence. To investigate resistance mechanisms, we performed single-nucleus RNA and chromatin accessibility profiling on 22 postmortem DMG specimens treated with imipridones and/or standard care, alongside functional assays of imipridone sensitivity and mitochondrial activity in DMG cell lines. Imipridone-treated tumors showed reduced mesenchymal transition, decreased myeloid-derived suppressor cells, and downregulation of aberrant enhancer activity associated with H3 K27 alterations. Mechanistically, treatment selected for increased mitochondrial density, turnover, and membrane potential. Together, these findings identify mitochondrial biogenesis as a key marker and driver of imipridone resistance, providing a rationale for combination strategies to improve therapeutic efficacy.

  Study EGAS50000001447

• Arcagen – thoracic malignancies

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from rare thoracic malignancies (n=102)

  Study EGAS50000000123

• 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

• Panel sequencing of endocrine-resistant breast cancer

  Patients with endocrine-resistant breast cancer in Stockholm Sweden. DNA obtained from patients primary and relapse tumors, and tumor-free lymph nodes used as germline control. DNA was extracted from formalin-fixed paraffin-embedded tissue and sequenced by 370-gene panel-based sequencing with Kapa HyperPlus library preparation and Twist Bioscience hybrid capture. Custom bait sets (panels) from Twist Bioscience. Paired end 2x150 bp using NovaSeq X. The data is presented as fastq-files.

  Study EGAS50000000236

• Transcriptomic profiling of prostate cancer metastasis xenograft models reveals conservation of bone microenvironment signatures

  Study EGAS00001004770

• Arcagen - Skin cancer domain

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

  Study EGAS50000000491

• Arcagen – Adrenal and Thyroid cancers domain

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

  Study EGAS50000000626

• Arcagen – Cancer of unknown Primary domain

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

  Study EGAS50000000177

• Arcagen - Head and Neck domain

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

  Study EGAS50000000625

• Arcagen - Sarcoma domain

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

  Study EGAS50000000639

• Arcagen - Relapsing germ cell tumors domain

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

  Study EGAS50000000645

• Arcagen - Extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3) domain

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files from patients with extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3) (n=85).

  Study EGAS50000000644

• Djerroudi et al., E-cadherin inactivation shapes tumor microenvironment specificities in invasive lobular carcinoma

  Invasive lobular breast carcinoma (ILC) shows specific stromal features, T lymphocyte infiltration (TIL) being associated with poor prognosis. Here, we reveal the involved mechanism by performing single cell RNA sequencing, combined immunohistochemistry, deconvolution of bulk RNA sequencing from large retrospective ILC series, and functional assays using primary cells. We show that ILC accumulate FAP+ inflammatory cancer-associated fibroblasts (iCAF) through a previously undescribed mechanism mediated by E-cadherin/CDH1 on CAF plasticity. Indeed, CDH1 inactivation in ILC cancer cells prevents differentiation of iCAF into myofibroblastic CAF (myCAF), leading to iCAF accumulation. In turn, iCAF increase TIL infiltration and shape their spatial organization in ILC. Subsequently, CDH1-inactivated ILC cancer cells promote immune escape by lack of retention and activation of ITGAE-expressing resident memory CD8+ T lymphocytes (TRM). Hence, our study uncovers reciprocal interactions between CDH1-inactivated cancer cells, iCAF and CD8+ TRM, revealing why and how TILs have a poor prognosis in ILC patients, a mechanism extrapolated to other CDH1-mutated cancer types.

  Study EGAS50000000761

• 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

• Arcagen - CNS domain

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

  Study EGAS50000001002

• Tumor Profiler Ovarian Study

  The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. Technologies used are digital pathology, targeted NGS, single-cell RNA, single-cell DNA, bulk RNA, bulk proteotyping, single-cell CyTOF, imaging mass cytometry, pharmacoscopy, 4i Drug Response Profiling, and cell-free DNA. These technologies were applied to 240 tumor samples collected over 3 years across three cancer indications: metastatic melanoma, metastatic epithelial ovarian cancer, and acute myeloid leukemia (AML). Here, we publish the scRNA and scDNA data of the ovarian cancer cohort.

  Study EGAS50000000885

• Precision tumor-on-chip for personalized assessment of drug efficacy and immune cell delivery

  Tumor heterogeneity and drug resistance limit the efficacy of cancer therapies and highlight the need for patient-specific preclinical models. We developed a microfluidic tumor-on-chip (TOC) platform that integrates patient-derived organoids with a functional endothelial barrier to recreate drug and immune cell transport from the vasculature into tumor tissue. This system enables evaluation of therapeutic efficacy, toxicity, and immune cell migration under physiologically relevant conditions. In a clinical pancreatic cancer case, the TOC model predicted treatment resistance and uncovered gene expression shifts and pathway rewiring at single-cell resolution. By simulating vascular drug transport, the platform provides a scalable and clinically relevant tool for studying treatment response mechanisms and tailoring cancer treatments.

  Study EGAS50000001605

• Lung Cancer Research Program Genomics Data Access Committee, University of California, Los Angeles

  The committee is responsible for reviewing and approving requests for access to genomic data generated by the Lung Cancer Research Program at the David Geffen School of Medicine at UCLA and ensuring compliance with applicable data-sharing policies, institutional requirements, and relevant regulations.

  Dac EGAC50000001154

• Combination of a CCL21-gene modified dendritic cell vaccine and pembrolizumab induces immune responses in non-small cell lung cancer.

  In a phase I study of advanced NSCLC, intratumoral CCL21-DC vaccination combined with pembrolizumab was safe and resulted in stable disease in 36.8% of patients. Analysis of paired pre- and post-treatment tumor biopsy WES data, together with immune profiling of tumor biopsies and peripheral blood, revealed that disease stability was associated with increased T cell receptor diversity, enhanced CD4+ T cell infiltration, and expansion of novel CD4+ memory T cell clones. These findings suggest that CCL21-DC vaccination can stimulate antitumor immunity; however, its efficacy may be limited by insufficient tumor-specific T cell responses and persistent immunosuppressive signaling within the tumor microenvironment.

  Study EGAS50000002127

• Phenomic and multiomic characterisation of microsatellite-stable colorectal cancer reveals novel biology and targets of vulnerability (COLOSSUS project): snRNASeq cohort

  This dataset contains single-nuclei RNA-sequencing (n = 18) samples. Primary tumour samples were obtained from a) colorectal cancer patients with stage 2 / 3 disease and b) colorectal cancer patients who developed metastatic colorectal cancer (stage 4), for whom tissue from the primary tumour was available.

  Study EGAS50000002087

• Phenomic and multiomic characterisation of microsatellite-stable colorectal cancer reveals novel biology and targets of vulnerability (COLOSSUS project): Stage (St) 2/3 CRC cohort

  This dataset contains bulk RNA sequencing (n = 184), low-coverage whole-genome sequencing (n = 182) and whole-exome sequencing (n = 88) data. Primary tumour samples were obtained from colorectal cancer patients with stage 2 / 3 disease.

  Study EGAS50000002088

• Phenomic and multiomic characterisation of microsatellite-stable colorectal cancer reveals novel biology and targets of vulnerability (COLOSSUS project): metastatic (m)CRC cohort

  This dataset contains bulk RNA sequencing samples (n = 111) and low-coverage whole-genome sequencing (n = 113). Primary tumour samples were retrospectively obtained from colorectal cancer patients who developed metastatic colorectal cancer (stage 4), for whom tissue from the primary tumour was available.

  Study EGAS50000002086

• 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

• After the completion of CINECA, EUCANCan, and euCanSHare. What's next?

  CINECA, EUCANCan, and euCanSHare were part of the EUCAN Cluster, made up of six projects that received funding under the same Horizon 2020 call. All EUCAN projects (CINECA, EUCANCan, EUCAN-Connect, euCanSHare, Receptor Plus, and ReCoDID) were aimed at facilitating data reuse and knowledge discovery by enhancing data exchange and long-term collaboration in the health field. Here are some highlights about EGA’s contribution to these projects. CINECA: advances in Federated discovery and infrastructure for cohorts CINECA (Common Infrastructure for National Cohorts in Europe, Canada, and Africa) developed a federated cloud-based infrastructure for making genomic and biomolecular data accessible. The project has assembled a virtual cohort of 1.4 million individuals from sources such as the EGA, CanDIG and H3Africa. The EGA–CRG co-leaded a work package Work Package 1 on Federated Data Discovery and Querying. Beacon v2, championed by Jordi Rambla and Lauren Fromont, has been one of the central elements for the discovery of human genetic and phenotypic data. The EGA-CRG contributed to the development of a model for cohort discovery inside the Beacon v2 model. The team also delivered a Discovery Portal was implemented to explore cohorts and individuals of synthetic data. It is a UI that gathers a network of Beacons, a service for query expansion, and a visualisation tool. Have a look at the Discovery Portal UI, gathering a network of Beacons. You will find entities such as the Barcelona Supercomputing Center (BSC), BioData.pt and the European Genomic Data Infrastructure (GDI), among others. euCanSHare: facilitation of data access management This joint EU-Canada project aimed to build a European and Canadian FAIR platform for cardiovascular data sharing and analysis. The EGA’s tasks contributed to the development of the data management plan and data flows, main web-portal and interoperability protocols. An important part of the EuCanShare platform is the data access manager, a tool for the data owners to control the access to their sensitive datasets. We built a user-friendly interface for data access committees (data owners) to easily manage their data requests and access credentials. The data access portal facilitates the creation and internal organization of the data access committees as well as the linkage of data usage conditions to specific datasets. The interface includes filters for browsing requests and page to visualize the request history helping with the handling of data access requests. EUCANCan: toward a federation of clinical institutions in oncology The EUropean-CAnadian CANcer Network worked towards building a federated network to advance personalized medicine in oncology, by promoting the standard analysis, management and sharing of harmonized genomic and phenotypic data. The EGA led tasks on defining data flows and preparing an adapted infrastructure for long-term data storage and sharing. One of the key contributions was the conception of the EGA communities, offering standard and interoperable solutions for data discovery, processing, and sharing to projects and institutions that would like to manage and share data in the context of the EGA ecosystem. What's Next? The participation in projects such as CINECA, EUCANCan, and euCanSHare provide us with experience for future projects. We are happy to enhance data sharing and reuse, vital for advancing clinical and genomic research. The Discovery Portal is a wonderful proof of concept for the Beacon network. Next, we will finalise both the network and the user interface, and make sure it can be applied to more clinically-centred settings like hospitals. Most data hosted at the EGA are about cancer research. Currently, we contribute to building tools and infrastructure to empower oncology research in several European projects such as EUCAIM, EUCANIMAGE and EOSC4Cancer. We also do not lose sight of initiatives in the field such as the International Cancer Genome Consortium (ICGC) with the project ARGO, whose data model was adopted in EUCanCan.

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• Beacon v2 and Federated EGA, part of the GDI project Starter Kit to enable access to genomic and phenotypic data across borders

  The European Genomic Data Infrastructure project has released a ‘Starter Kit’ based on the rare disease and cancer proof-of-concepts from the Beyond 1 Million Genomes (B1MG) project. The Starter Kit, freely available to any interested parties, contains a set of software applications and components co-developed by the 20 GDI nodes and based on open community standards from the Global Alliance for Genomics and Health (GA4GH). Products in the Starter Kit give all countries the technical capability to access synthetic genomic and phenotypic data across borders. The image above shows the full workflow of the GDI Starter Kit user journey. The dotted circles are not included in the Starter Kit yet The EGA’s work is represented in the Starter Kit with Beacon v2 and Federated EGA among the software applications and components. About Beacon v2 Beacon is a protocol for data discovery. This API allows the search of genomic variants and associated information without breaching the datasets privacy. The European Genome-phenome Archive (EGA) is a key contributor to the Beacon v2 Protocol, the Beacon v2 Reference Implementation (B2RI) and the EGA Beacon(s). About Federated EGA The Federated EGA network offers a tool for data storage and sharing. Officially launched in 2022, this network of connected resources enables transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. The Federated EGA Onboarding Website contains all the information related to the establishment of a Federated Node.

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• The EGA joins EUCAIM, the European project powering AI and imaging in cancer research

  EUCAIM (EUropean Federation for CAncer IMages) is the cornerstone of the European Cancer Imaging Initiative, one of the flagships of the Europe’s Beating Cancer Plan (EBCP). The European Genome-phenome Archive (EGA), as member of the EUCAIM consortium, attended the kick-off meeting organized this week in Brussels for the launch of the Initiative. The EGA will contribute to the project with the experience gained in the deployment of our federated network, the Federated EGA (FEGA), and the experience gained as partners of EuCanImage (AI4HI initiative) contributing to the definition of common data models, ontologies and data standards. The ultimate goal of the project is to unlock the power of imaging and Artificial Intelligence for the benefit of cancer patients, clinicians and researchers. The consortium will deploy a pan-European digital federated infrastructure hosting cancer-related images, and associated pathology, molecular and laboratory data from Real-World. This endeavour will provide a comprehensive dashboard for imaging data discovery, federated search, metadata harvesting, annotation and distributed data processing. EUCAIM joins 76 partners, gathering the experience and previous effort made by the different repositories of the AI4HI initiative, European Research Infrastructures and national/regional repositories. Find out more information about the EUCAIM launch here

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• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

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• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

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• How upcycled prostate cancer sequences enabled key findings on telomeres length

  The length of telomeres, the chromosomes’ end tails, as depicted in the figure below, physiologically shorten with aging. This means that younger cells have longer telomeres compared to their aged counterparts; one could say that the length of cells’ telomeres is a measure of their replicative power. Cancer cells are known to be able to turn this into their favour, and expand their replicative immortality through telomere lengthening and maintenance. Despite being a well-studied pan-cancer hallmark, the relation of the telomeres length to specific cancer genomic features is poorly understood. Julia Livingstone and her colleagues from P.C. Boutros' team in Los Angeles focused their work on prostate cancer. To investigate the relations between telomere length and the clinico-genomics of prostate tumours, they used whole genome sequencing (WGS) of 392 published tumour–reference pairs previously deposited at the EGA under accession numbers EGAS00001000400 and EGAS00001000900. Their work was published on Nature Communications in November 2021. They determined the telomere length of the sequencing sample, from both tumour and non-tumour adjacent tissues, and subsequently ran statistical analysis to investigate possible correlation between the telomeres length with several cancer features. The team discovered that telomeres length does not correlate with any specific driver gene mutation, but it does with the overall frequency of single nucleotides variants in a sample. This finding suggests that tumours with shorter telomeres accumulate more mutations without strong selective pressures for specific ones. While proliferation rate did not correlate directly with telomere length, methylation of almost half of all genes strongly did. The team’s results are massive and reveal a complicated and fascinating relation of different clinical and genomic features of prostate cancer with telomere length. In the paper discussion, they smartly consider different possibilities. As noted above, all these amazing results have been obtained analysing previously sequenced samples. Remarkably, the researchers who generated the samples did not focus at all on telomeres length. They indeed studied specific genes mutations and the features of early and late-onset prostate cancer. This proves, once again, the power of sequencing data re-use. This data is tremendously rich in information, and can hardly be exhausted with any number of analyses. Generating sequencing data, especially Whole Genome Sequencing, even better if tumor-reference paired, is like shopping every last article at the grocery store. You can then cook several recipes, or prepare a seven-course dinner, and still have ingredients left for someone else to borrow and cook something you have not thought about, a recipe that is just not in your cookbook. Noteworthy, the data retrieved from the EGA for this paper is associated with rich and complete metadata, meaning pheno-clinical information regarding the sample donors. As Julia Livingstone and her team wrote, using rich clinical annotations enabled them to assess the relationship between telomere length and patients outcome. This highlights once again the importance of reporting and annotating metadata correctly; It might feel like extra work for the researchers depositing their data, but it really has a huge value. Our team at the EGA is currently working to improve the metadata annotation process, with the aim to maximize the benefits for the scientific community.

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• EuCANCan: EUropean-CANadian Cancer network

  The EUropean-CANadian Cancer network (EuCANCan) is a project aiming to develop a federated network of aligned and interoperable infrastructures for the homogeneous analysis, management and sharing of genomic oncology data for Personalized Medicine. This initiative, funded by the European Commission and the Canadian Institutes of Health and coordinated by the Barcelona Supercomputing Center (BSC), is expected to process and provide the scientific community with around 30-35 thousand patient samples from different types of cancer, coming from different nodes involved in this project, during the four years of the project. Data generated as part of primary studies, often in small hospitals or research centers, is hardly reused by other projects or groups. EuCANCan is proposed to foster the homogenisation and discoverability of cancer samples from very different sources for the whole community. These efforts will help paving the way to future standardised and global genomics projects, specially for those participant countries that eventually integrate the methodology and infrastructure generated during the project. “The more data we have access to the more and better discoveries. We attempt to overcome the legal and technical limitations in order to generate, gather and exploit bigger datasets as well as answer more ambitious questions”. These were some of the words Dr. David Torrents, main coordinator of the project, shared with a regional radio during the kick-off meeting of the project, on February 11th, 2019. The EUCANCan network will be composed of reference nodes in Amsterdam, Barcelona, Berlin, Heidelberg, Paris and Toronto which have established strong research and clinical programs in the field of genomic oncology. The Centre for Genomic Regulation (CRG), specifically the European Genome-phenome Archive (EGA), is among the leading institutions. Its exhaustive experience in biomedical data archival and secure distribution is considered key for crucial developments within the project. It is also notable the recent inclusion of EUCANCan in the list of GA4GH driver projects. Further information and details can be found in the following links: InsideHPC BSC official description Techweek (Spanish)

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• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

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• Data Use Ontology (DUO) at EGA

  For those of you that follow the updates of GA4GH you will have seen the unanimous approval of the steering committee for DUO to be included in its suite of technical standards for the sharing of genomic and health related data. DUO has three main features: Each term has been generated by the community and includes a human readable definition that can be expanded where necessary. It is a machine-readable file that encodes how that data can be used and how a researcher intends to use the data. Can be implemented alongside an advanced search algorithm which would allow authenticated users to query and gain access to datasets pertaining to their research. e.g., an industry researcher working on cancer could potentially be matched to any dataset that is allowed for commercial use and for cancer research and offered the opportunity to fetch them automatically. So, what does this mean for EGA? EGA as a driver project has adopted this standard and will be utilizing it for two main purposes in the first instance. i) It will allow EGA users to instantly identify what terms they will need to agree to as part of any Data Access Agreements. This will save valuable time for both applicants and Data Access Committees alike - as you will be able to see if you are likely to be able to access the data based on your research intentions and working background. ii) In the future it will allow users to be able to search for data based on Data Use Ontology terms e.g., you could search for all data that can be used for General Research Use (GRU). As EGA moves forwards submitters will be asked to align their data access policies with DUO so that their dataset(s) can be tagged appropriately on our web pages. If you would like to add DUO to any of your existing datasets, or to any future datasets, do please just let us know.

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