Search Results - EGA European Genome-Phenome Archive

Single-cell genome and transcriptome profiling of high-grade serous ovarian cancer

This dataset contains joint single-cell whole-genome sequencing (scWGS) and mRNA sequencing data generated using DNTR-seq from 4 high-grade serous ovarian cancer samples. Approximately 500 quality-controlled single cells per patient were profiled, with DNA and RNA libraries sequenced separately but derived from the same individual cells. Raw sequencing files are provided in FASTQ format and enable analysis of subclonal copy number alterations, gene dosage effects, and transcriptional heterogeneity.

Dataset EGAD50000002361

RNA-seq TPMs quantification for baseline tumor samples originating from IMpower150, and relevant clinical metadata

This dataset includes full tumor transcriptomes from 558 advanced NSCLC tumors. These data originate from pre-treatment samples from a clinical trial for first-line non-small cell lung cancer (IMpower150). The patients in this trial were treated with the PD-L1 inhibitor atezolizumab +bevacizumab + chemotherapy or bevacizumab + chemotherapy or atezolizumab + chemotherapy.

Dataset EGAD50000001814

Brazilian GWAS Axiom850K Cancer Study

Genome-wide genotyping data generated using the Affymetrix Axiom 850K PMDA Array from 2,844 Brazilian individuals enrolled in a cancer-focused biorepository. The dataset includes colorectal, breast, and cervical cancer cases as well as cancer-free controls. The data support genome-wide association studies (GWAS), replication analyses, and polygenic risk score evaluation in an admixed Brazilian population.

Dataset EGAD00010002841

Intratumoral Ploidy Dynamics in HCC

Hepatocellular carcinoma (HCC) ranks as the fourth leading cause of cancer-related deaths. Effective therapies are limited, highlighting the critical need for stratification biomarkers to improve prognosis and predict treatment response. Polyploidization or hyperploidization, an early event observed in many solid cancers, may offer such potential. Here, we developed a new high throughput in situ quantitative approach to assess HCC ploidy profiles. This novel method highlights the presence of both Diploid (DP-) and Hyperploid (HP-) HCCs. RNA-seq profiling classified hyperploid HCCs as belonging to the proliferation subclass, predicting unfavourable clinical outcomes, and revealing a gene expression signature indicative of stemness. Spatially reconstructing nuclear ploidy also revealed DP/HP-HCCs with hyperploid nuclei forming discrete clusters within a predominantly diploid background. Remarkably, these emerging hyperploid foci shared a portion of their transcriptomic programme with fully developed hyperploid HCCs and displayed a signature of genomic instability, including upregulation of multiple DNA repair pathways in response to replication-associated DNA damage. Importantly, spatial transcriptomics of DP/HP HCC demonstrated that hyperploid tumor hepatocytes arise through whole-genome doubling of diploid tumor hepatocytes. Collectively, our findings underscore the prognostic value of nuclear ploidy profiling in HCC and suggest that hyperploid HCCs evolve clonally from intratumor hyperploid foci during tumor progression.

Study EGAS00001008084

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 BSAC

Bisulfite Amplicon Sequencing on tissue samples from prostate cancer, benign prostatic hyperplasia (BPH), and bladder cancer.

Study EGAS50000001895

PROMETEO

The SOLTI-1503 PROMETEO trial was a single-arm, preoperative, window-of-opportunity phase II study conducted at four sites in Spain between December 2018 and February 2022. It enrolled female patients with operable HER2-negative breast cancer (triple-negative or Luminal B-like) and residual disease after neoadjuvant chemotherapy. Tumor biopsies were collected at five time points (baseline, post-NAC screening, C2D1, C3D1, and surgery) for translational research. This dataset includes raw paired-end RNA sequencing data (~110M reads per library) generated from the 113 biopy samples, archived in FASTQ format.

Study EGAS50000001499

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

FASTQ Files from Breast Tumor Biopsies Collected Across Five Timepoints in the PROMETEO Phase II Trial

The dataset contains RNA sequencing raw data from tumor biopsies collected at five time points during the SOLTI-1503 PROMETEO trial: baseline (pre-NAC), post-NAC screening, Cycle 2 Day 1, Cycle 3 Day 1, and surgery. The study intervention consisted of intratumoral injections of T-VEC followed by intravenous administrations of atezolizumab prior to surgery. The data is obtained from tumor samples of patients included in the study: female breast cancer patients with residual disease after neoadjuvant chemotherapy, including both triple-negative and Luminal B-like subtypes. Each sample includes raw paired-end RNAseq reads (~110 million reads per library) aligned to the GRCh38/hg38 reference genome

Dataset EGAD50000002165

Xiangya Third Hospital Prostate Cancer Methylation Study

This DAC manages the controlled access data for the prostate cancer research project from Xiangya Third Hospital. The dataset includes raw high-throughput sequencing data (PE150, MeDIP-seq & BSAS) from 19 Prostate Cancer (PCA) samples, 29 Benign Prostatic Hyperplasia (BPH) samples, and 19 Bladder Cancer tissue samples.

Dac EGAC50000001034

Duplex sequencing of selected breast cancer patients

This study assessed the variant profiles of 22 samples from 11 breast cancer patients with adverse survival outcomes, including paired uninvolved mammary gland samples collected at various distances from primary lesions. Ultra-deep sequencing was performed to identify the presence of low-frequency pathogenic post-zygotic variants in PIK3CA and TP53 genes.

Study EGAS50000000538

WES of breast cancer patients and controls

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

Study EGAS50000000539

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

LySeqST: A targeted sequencing assay for robust genomic classification of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease. Two genomic classification systems, LymphGen and DLBclass, are capable of reproducibly classifying single tumors into subtypes with prognostic relevance in the context of standard-of-care R-CHOP chemoimmunotherapy. Preliminary data from subgroup analyses of clinical trials suggest that the distinct biological features are targetable with drugs that exploit subtype-specific vulnerabilities, and large clinical trials are being designed to test this hypothesis. Although whole exome sequencing (WES) remains the gold standard for each of these classification systems, many laboratories may opt for smaller targeted sequencing panels that reduce costs associated with sequencing, data storage, and processing. Here, we describe the design and validation of a targeted sequencing panel (LySeqST) that captures the genomic features required specifically for LymphGen classification. We perform in silico and real-world validation vs. WES data and demonstrate that LySeqST can be used to accurately classify DLBCL tumors. In an unselected population-based cohort, we determine the real-world proportions and validate the prognostic relevance of LymphGen subtypes, confirming that only tumors expressing the dark zone gene expression signature are consistently associated with inferior outcomes. Our results support the use of LySeqST for accurate genomic classification of DLBCL.

Study EGAS50000001601

Oropharynx cancers sequencing study (RNA-seq from FFPE)

This is a study of transcriptomics in HPV-associated oropharynx tumors. The goals of this study were to characterize HPV+ oropharynx tumor subtypes and to examine HPV integration events and their downstream effects. HPV-host fusion transcripts and recurrent HPV integration genes were identified. Various alternative isoforms and splicing events of HPV genes were also studied. Multiple potential biomarkers of prognosis were associated with clinical outcomes.

Study EGAS50000000893

Liquid biopsy sequencing

Liquid biopsy samples were collected from patients. Cell free DNA was extracted and profiled by NGS. Samples were analyzed by low pass whole genome sequencing, targeted panel sequencing using a custom hybrid capture panel, or both.

Study EGAS50000001528

Vaccine-Expanded Plasmablast-like B Cells Are Associated with Response to Dendritic Cell Therapy in Metastatic Melanoma

Dendritic cells (DCs), either monocyte-derived (moDCs) or naturally circulating, have been employed in cancer treatment across both metastatic and adjuvant settings. DC vaccines (DC Vax) are capable of inducing tumor-specific immune responses, yet clinical outcomes have thus far fallen short of expectations. Here, we performed a multi-omics analysis of the apheresis product, including CD14⁺ monocytes, and the final DCVax formulation. Among all the components analyzed, the most striking finding was the unexpected presence of CD19⁺ B cells within the DC Vax formulation, which distinguished Responders from Non-Responders to moDC vaccination in metastatic melanoma. The abundance of B cells in DC Vax correlated with their peripheral frequency; however, within the vaccine, these cells acquired a CD20lowCD27hiCD38hi plasmablast-like phenotype, distinct from their autologous circulating B-cell precursors. Notably, a parallel observation was made in Responders, whose pre-treatment lesions were enriched in B cells organized as Tertiary Lymphoid Structures. These results underscore the importance of B cells in both baseline immunity and DCVax formulation, hinting at unexplored mechanisms by which they may influence vaccine effectiveness.

Study EGAS50000001177

METABRIC

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

Study EGAS00000000083

Spatial transcriptomics of human meningioma samples.

We performed spatial transcriptomic profiling of human meningioma samples to map gene expression patterns within their native tissue architecture.

Study EGAS50000001556

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

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

Study EGAS50000001691

ATAC-seq of 38 TNBC Patient Derived Xenograft Models

Chromatin accessibility profiles from 38 triple‑negative breast cancer (TNBC) patient‑derived xenograft (PDX) tumor samples generated by ATAC‑seq. Fastq files and aligned bam files are uploaded.

Dataset EGAD50000002430

Long-read single-cell genome, transcriptome and open chromatin profiling links genotype to phenotypes

Current single-cell multiomics methods typically provide limited genomic information, constraining genotype-phenotype studies. To address this gap, we developed SPLONGGET (Single-cell Profiling of LONG-read Genome, Epigenome, and Transcriptome), which integrates 10X Genomics barcoding with Oxford Nanopore sequencing to simultaneously profile genome, chromatin accessibility, and full-length transcriptomes in thousands of single cells. By retaining all tagmentation fragments during library preparation, SPLONGGET delivers whole-genome coverage, supports target enrichment for effective single-cell genotyping, and remains backwards compatible with existing short-read workflows. We apply SPLONGGET to longitudinal samples from a case of pediatric B-cell acute lymphoblastic leukemia (B-ALL) to reveal clonal dynamics during therapy and the phenotypic effects of somatic variation, providing valuable insights into disease progression and relapse. In conclusion, SPLONGGET enables integrated high-throughput analysis of genetic variation and molecular phenotypes using off-the-shelf kits, offering a timely and powerful tool to study genetically heterogeneous samples, such as tumours but also ageing normal tissues.

Study EGAS50000001278

cell-free Methylated DNA by Immunoprecipitation and Sequencing (cfMeDIP) of human meningioma samples.

Raw data for cell-free Methylated DNA by Immunoprecipitation and Sequencing (cfMeDIP) of human meningioma samples.

Study EGAS50000001539

Proteomics of human cancer-associated T cells identifies regulators of T cell functionality

CD8⁺ T cells in solid tumors progressively lose anti-tumor activity, yet the cell-intrinsic mechanisms driving this loss of function remain incompletely defined. Here, we performed matched proteomic and transcriptomic profiling of dysfunctional and bystander CD8⁺ tumor-infiltrating T cells isolated from primary tumors of treatment-naïve non-small cell lung cancer patients. Proteomic analysis revealed widespread discordance with mRNA expression, with 8% of differentially expressed proteins detected exclusively at the protein level. Genetic perturbation of such differentially expressed proteins identified the chromatin remodeler CHD4 and fatty acid synthase (FASN) as cell-intrinsic regulators of T cell function. CHD4 deletion promoted effector differentiation and enhanced cytokine production, whereas FASN deletion preserved mitochondrial fitness and sustained T cell functionality under chronic T cell receptor stimulation. Together, these findings demonstrate that proteomic profiling uncovers regulators of T cell functionality that are not apparent from transcriptomic analyses alone, highlighting an additional layer of regulatory control.

Study EGAS50000001886

scRNAseq of tumor infiltrating CD8+ T cells

single cell mRNA sequencing of tumor infiltrating CD8+ T cells. CD8+ T cells were FACS sorted from tumor tissue obtained from 8 non-small cell lung cancer patients. Gene expression and TCR library preparation was performed using the Chromium Next GEM Single Cell 5' Kit v2, Chromium 5' Feature Barcode Kit, and Chromium Single Cell Human TCR Amplification Kit from 10x genomics. After paired-end sequencing this resulted in fastq files containing TCR sequences, gene expression, and feature barcode levels.

Dataset EGAD50000002739

Bulk RNAseq of cancer-associated CD8 T cell populations

Bulk mRNA sequencing of cancer-associated CD8 T cell populations isolated from tumor and tumor-adjacent tissue from non-small cell lung cancer patients. CD8 T cell populations isolated were CD103+CD39- and CD103+CD39+ from tumor tissue, and CD103+CD39- from adjacent tissue. These populations were isolated from 5 patients, totaling 15 samples. bulk poly-A RNA was isolated and libraries were prepped using the Smart-Seq2 protocol, followed by paired-end sequencing (Illumina). Raw data are in fastq format.

Dataset EGAD50000002740

Multimodal analysis of rare BARD1 missense variant

This project analyzes whole-exome sequencing data from a patient with breast cancer recurrence and endometrial cancer, as well as from the patient’s father’s colon tumor, all harboring a rare germline BARD1 missense variant (p.Gly753Val). The data reveal loss of heterozygosity and homologous recombination deficiency specific to the triple-negative breast cancer recurrence, supporting the variant’s conditional pathogenicity linked to prior treatment.

Study EGAS50000001682

BIOMEDE : genomic data analysis from clinical trial

Genomics landscapte of DIPG

Study EGAS50000001164

Genomic Data from Patients with Advanced Rare Cancers Treated with Pembrolizumab

Rare cancers lack effective evidence-based treatments despite contributing substantially to cancer-related mortality. The goal of this study is to identify molecular biomarkers associated with response to pembrolizumab in patients with advanced rare cancers.This dataset includes genomic profiling generated from patients enrolled in an investigator-initiated phase II basket trial of pembrolizumab.

Study EGAS50000001508

Transcriptomic Data from Patients with Advanced Rare Cancers Treated with Pembrolizumab

Rare cancers lack effective evidence-based treatments despite contributing substantially to cancer-related mortality. The goal of this study is to identify molecular biomarkers associated with response to pembrolizumab in patients with advanced rare cancers.This dataset includes transcriptomic profiling generated from patients enrolled in an investigator-initiated phase II basket trial of pembrolizumab.

Study EGAS50000001509

Epigenomic profiling of neuroendocrine lung cancer

Heterogeneity of neuroendocrine (NE) differentiation in pulmonary high-grade NE carcinoma, mainly small-cell lung cancer (SCLC), has been recently explored based on the expression of lineage transcription factors such as ASCL1, NEUROD1 and POU2F3. However, molecular classification determined by these factors is not settled and accumulating evidence suggest that NE differentiation among lung cancers is more heterogeneous. Here, we investigated super-enhancer profiles of formalin-fixed paraffin-embedded tumor tissues from patients with a wide range of lung cancer with NE components, mainly large cell neuroendocrine carcinoma (LCNEC).

Study JGAS000903

Plasma cell-free small RNA sequencing comparison of Illumina and Ultima Genomics in pancreatic cancer

This study evaluates plasma-derived cell-free small RNA sequencing in a case-control cohort of 39 pancreatic ductal adenocarcinoma patients and 39 control subjects. Cell-free nucleic acids were isolated from plasma, small RNA libraries were prepared using the SMARTer smRNA-Seq workflow, and the same libraries were sequenced on both Illumina NovaSeq 6000 and Ultima Genomics UG 100 platforms. The project aims to compare platform performance for circulating small RNA and mature microRNA profiling, including read yield, alignment, RNA biotype composition, mature miRNA detection, expression concordance, and pancreatic cancer-associated differential expression. The dataset supports benchmarking of cross-platform reproducibility and assessment of biologically relevant plasma microRNA signals in pancreatic cancer.

Study EGAS50000001907

Excised DNA Circles from V(D)J Recombination Promote Relapsed Leukaemia

In this study, we show that ecDNAs generated from V(D)J recombination replicate and persist through many cell generations. High ecDNA copies at diagnosis of B-cell precursor acute lymphoblastic leukaemia strongly correlate with subsequent relapse.

Study EGAS50000000401

Plasma cell-free small RNA sequencing of pancreatic cancer and control samples

This dataset contains plasma-derived cell-free small RNA sequencing data from 78 human samples, comprising 39 pancreatic ductal adenocarcinoma patients and 39 control subjects. Each sample was sequenced as a single-end FASTQ library on both Illumina NovaSeq 6000 and Ultima Genomics UG 100 platforms, yielding 156 submitted sequencing runs. The data were generated from SMARTer smRNA-Seq libraries to support comparative analysis of sequencing performance, mature microRNA detection, and pancreatic cancer-associated cell-free small RNA expression across the two platforms.

Dataset EGAD50000002771

Frequency and prognostic significance of genetic abnormalities in a subgroup of intermediate risk neuroblastoma patients: A SIOPEN study

Intermediate risk neuroblastoma includes a rare subgroup of patients over the age of 18 months at diagnosis with unresectable, localised tumours with unfavourable histology without MYCN amplification. This group is treated differently around the world with some patients receiving upfront myeloablative therapy (MAT) in addition to standard treatment with chemotherapy, surgery, radiotherapy and 13 cis retinoic acid, but the overall 5-year patient survival with or without MAT is similar (75%). This study aimed to understand the neuroblastoma biology of this rare patient subgroup.

Study EGAS50000001908

DFCI Gynecological Oncology Data Access Committee

This Data Access Committee is responsible for reviewing requests to access controlled human genomic data generated as part of the RESOLVE clinical trial at DFCI. The committee ensures that access complies with participant consent, institutional policies, and applicable data protection regulations (e.g., GDPR). Data access decisions are made by a multidisciplinary panel including clinical researchers, bioinformaticians, and data governance officers from Dana-Farber Cancer Institute. The committee reviews each access request to determine whether the proposed research aligns with the study’s consent terms and the data access policy. If approved, access is granted via the European Genome-phenome Archive (EGA) in accordance with EGA’s data use and security guidelines.

Dac EGAC50000000712

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.

Blog cineca-eucancan-and-eucanshare-were-concluded-in-june

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.

Blog beacon-v2-and-federated-ega-part-of-the-gdi-project-starter-kit

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

Blog the-ega-joins-eucaim

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

Blog upcycling-and-merging-data-to-challenge-glioblastoma

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

Blog safe-access-to-sensitive-human-data-federated-ega

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.

Blog prostate-cancer-sequences-enabled-key-findings-on-telomeres-length

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)

Blog eucancan

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!

Blog the-last-addition-to-the-list-of-clonal-evolution-studies-in-the-ega

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.

Blog data-use-ontology

Advancing fast in the analysis of circulating tumour DNA

In cancer patients, solid tumours also release DNA fragments into the blood, and they are called Circulating Tumours DNA (ctDNA). Nowadays, the standard procedure for cancer monitoring is analysis of surgically obtained biopsies. They generate a big amount of material, but the operation is highly invasive and provide medical doctors with a single-time snapshot of the tumours, which are often highly changeable. Analysis of ctDNA, on the other hand, can provide continuous information regarding the tumour itself without the need of invasive procedures. This is why interest in this method is growing, and with it, the need for its technological optimization. Guanhua Zhu and colleagues, at the Genome Institute of Singapore, developed a methodology to quantify ctDNA burden, which means to estimate the amount of tumours derived circulating DNA among all cell-free DNA in the blood of individuals. Their work, published in Nature Communications in april 2021 and summarized in a sketch below, uses state-of-the art methods to analyse cfDNA degradation patterns at promoters and exon regions, to discern it from the DNA coming from the tumours. The bright point of their approach is that simultaneously enable for profiling of actionable cancer mutations and quantitative estimation of ctDNA burden. Their sequencing data of ctDNA has been deposited in the EGA under the accession number EGAS00001004657. This study sums up to a total of 37 of cfDNA and 36 about ctDNA present up to date in the EGA archive. The existence of cfDNA was firstly described back in 2002, while the use of ctDNA analysis in the oncological field exploded only after 2016. The approach is rapidly evolving, moving in parallel with the ever-improving sequencing technologies. Especially, the possibility to obtain high quality sequences from minimal amount of material is a crucial factor. Being able to alleviate cancer patients of disturbing and often slow-recovery procedures, while at the same time improving the quality of diagnosis and disease monitoring seems like a lot to ask. Thankfully, we are blessed with brilliant and dedicated scientists from all around the world who collaborate to make this a reality, sharing their results, knowledge and data.

Blog advancing-fast-in-the-analysis-of-circulating-tumour-dna

Finding the way towards the eradication of therapy-related myeloid neoplasms

Therapy-related neoplasms are directly caused by the drugs used to eradicate a previous cancer. It is well known that a subset of drugs designed to kill cancer cells through DNA-damage-induced apoptosis can intrinsically, as secondary effect, cause DNA-damage in other cells, thus inducing a neoplasm. Those neoplasms have especially bad prognosis. Why are these drugs still in use? Because for many diseases we do not have anything better, at the moment. Approximately 0.5-1% of the kids treated for various types of cancers develop a therapy-related myeloid neoplasm (tMN) as a result of the treatment. Despite having the same origin, their prognostic and molecular features are different from the adult ones, and a complete understanding of the pathogenesis of tMN in children is still missing. A total of 84 paediatrics neoplasms have been profiled by either Whole Genome or Whole Exome sequencing by Jason R. Schwartz and his colleagues, in a collaborative effort guided at the St. Jude Children's hospital, in Memphis. They identified a mean of 28 somatic mutations per patient, a number which is significantly bigger than the number of mutations found in paediatrics primary neoplasms, as depicted in the original paper's figure 1b, shown here. Their sequencing data identified that Ras/MAPK pathway mutations, alterations in RUNX1 or TP53, and KMT2A rearrangements as the most frequent drivers of the disease. Clonal evolution analysis (the comparative study of the behaviour of cells harbouring different mutations) highlighted that, unlike in adults’ tMN, there were not pre-existing leukemic clones, and virtually all mutated cells were found arising as a direct consequence of cytotoxic therapy. This represents yet another important difference between adult and paediatric tMN. This important study have been published in Nature communications in February 2021. The sequencing data have been deposited at EGA under the study number EGAS00001004850. Thanks to this, other researchers around the world could re-use it to uncover more information, integrate and validate their studies. Revealing the molecular features underlying the development of cancers is the only way towards the development of new generation targeted therapies, which will allow us to finally drop the cytotoxic drugs eliciting such unwanted and catastrophic secondary effects as tMN. It is not an overstatement to say that, while improving the treatment of primary tumours, we could entirely eradicate therapy-related neoplasms right from their origin. Looking forward.

Blog eradication-of-therapy-related-myeloid-neoplasms

Data upcycling, powered by EGA

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

Blog data-upcycling-powered-by-ega

Gut microbiome dynamics unravelled with metagenomics sequencing

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

Blog gut-microbiome-dynamics-unravelled

7825 results for "canc*"

in 36.02 milliseconds.