6115 results for "canc*"

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• Res1_PC9_exp1_MC_29_03_22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Study EGAS00001006169

• Res1_PC9_exp2_MC_01_04_22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Study EGAS00001006170

• Res1_HT29_exp2_MC_03_03_22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006093

• Genome-wide genetic and epigenetic dataset of pancreatic acinar cell carcinomas

  Pancreatic acinar cell carcinoma (ACC) is an aggressive exocrine tumor with largely unknown biology. To identify potential targets for personalized treatment, integrative genome- and epigenome-wide analyses were performed. The results show frequently aberrant DNA methylation, abundant chromosomal amplifications and deletions, and mutational signatures suggesting defective DNA repair. In contrast to pancreatic ductal adenocarcinoma, no recurrent point mutations are known. The tumor suppressors ID3, ARID1A, APC, and CDKN2A are frequently impaired also on the protein level and thus potentially affect ACC tumorigenesis. Consequently, this work identifies potential novel therapeutic targets in ACC for drugs recently approved for precision cancer therapy.

  Study EGAS00001002533

• WES data from primary CRCs tissues in ctDNA positive patients

  Whole exome data from primary colorectal cancer tissue samples were analysed in patients where mutations where found in circulating tumour DNA. DNA was extracted from FFPE blocks and exome libraries were prepared using the KAPA HyperPlus Kit (Roche diagnostics)

  Study EGAS50000000650

• WES data from primary CRC

  Whole exome sequencing from ffpe samples DNA was extracted from FFPE tissue samples from colorectal cancer biopsies. Exome libraries where prepared using the KAPA Hyper Plus Kit (Roche Diagnostics) followed by target enrichment by hybridisation capture using the Roche Hyper Exome Kit. Sequenced on NovaSeq6000 to generated 2x 151 paired-end reads.

  Dataset EGAD50000000924

• Stratified Medicine Paediatrics (SMPaeds): molecular profiling of relapsed paediatric cancer

  We profiled a large heterogenous cohort of matched diagnostic-relapse tumour tissue and plasma-derived cell free DNA (cfDNA) from patients with relapsed and progressive solid tumours of childhood. Tumour DNA and cfDNA were analysed by targeted panel sequencing and low coverage whole genome sequencing.

  Study EGAS50000000549

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• SMPaeds PanelSeq of tumour tissue

  Deep sequencing of a targeted panel of 92 paediatric cancer related genes or 233 pan-cancer related genes for both relapse/archival disgnostic tumour tissue and germline DNA from whole blood cell pellet. Tumour tissue was sequenced to approximately 400X coverage and germline DNA was sequences to aproximately 100X coverage.

  Dataset EGAD50000000783

• Transplant Outcomes in Aplastic Anemia (TOAA): GWAS and Whole Exome Sequence Data

  The TOAA study is a research collaboration between the National Cancer Institute and the Center for International Blood and Marrow Transplant Research. The study aims to identify molecular predictors of outcomes after hematopoietic cell transplant in patients with severe aplastic anemia.

  Study phs001710

• Low-coverage whole-genome sequencing of cancer and healthy plasma circulating DNA

  Study EGAS00001007593

• RNAseq analysis on metastatic Colorectal Cancer xenografts samples

  Study EGAS00001006492

• Genome-wide cell-free DNA fragmentation in patients with cancer

  Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

  Study EGAS00001003611

• Comprehensive genomic and transcriptomic analysis of three synchronous primary tumours and a recurrence from a head and neck cancer patient

  Synchronous primary malignancies occur in a small proportion of head and neck squamous cell carcinoma patients. Here, we analysed three synchronous primaries and a recurrence from one patient, by comparing the genomic and transcriptomic profiles among the tumour samples and determining the recurrence origin. We found remarkable levels of heterogeneity among the primary tumours, and through the patterns of shared mutations, we traced the origin of the recurrence. Interestingly, the patient carried germline variants which might have predisposed him to carcinogenesis, together with a history of alcohol and tobacco consumption. Mutational signature analysis confirmed the impact of alcohol exposure, with Signature 16 present in all tumour samples. Characterisation of immune cell infiltration highlighted an immunosuppressive environment in all samples, which exceeded the potential activity of T cells. Studies such as the one described here have important clinical value and contribute to personalised treatment decisions for patients with synchronous primaries and matched recurrences.

  Study EGAS00001004857

• Plasma whole genome sequencing from patients with stage IV colorectal cancer and microsatellite instability

  This study aims to explore genomic features of cancer in plasma WGS data from patients with advanced colorectal cancer. Cell-free DNA (cfDNA) was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit. Libraries were prepared with 5 to 250 ng of cfDNA using the NEBNext DNA Library Prep Kit.

  Study EGAS00001006377

• Single-cell RNA sequencing of human skin tumors (BCC, SCC and Melanoma)

  Cancer-associated fibroblasts (CAFs) play a key role in cancer progression and treatment outcome. Here, we elucidate the yet unresolved intra-tumoral CAF variety in three skin cancer types at molecular and spatial single-cell resolution in a large cohort. We show that two out of three CAF subtypes contribute to tumor immune surveillance with distinct mechanisms. Matrix CAFs (mCAFs) ensheath tumor nests and synthesize extracellular-matrix to prevent T cell invasion. Immunomodulatory CAFs (iCAFs), which express proinflammatory and immunomodulatory factors, are only detected in high abundance in aggressive tumors. Strikingly, iCAFs but not tumor cells are (apart from immune cells) the exclusive cell type producing chemokines and, thus, play a key role in immune cell recruitment and activation. Mechanistically, we show that cancer cells transform adjacent healthy fibroblasts into cytokine-expressing iCAFs, which subsequently recruit immune cells and modulate the immune response. In conclusion, targeting CAF variants holds promise for improved efficacy of immunotherapy.

  Study EGAS50000000365

• Genome and transcriptome sequencing of cancer of unknown primary tumours

  Genome and transcriptome sequencing of cancer of unknown primary tumours was used to determine yield of clinical biomarkers for a molecular guided trial or for resolving cancer type of origin. This study includes profiling of germline DNA and tumour DNA by whole genome sequencing of tissue and cfDNA biopsies, as well as targeted genome sequencing using two panels, including Comprehensive cancer panel (CCP) and TruSight Oncology 500 (TSO500). This study also includes profiling of tumour RNA by whole transcriptome sequencing.

  Study EGAS50000000452

• 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

• Neoadjuvant nivolumab or nivolumab plus ipilimumab in early-stage triple negative breast cancer: phase 2 adaptive BELLINI trial. WES

  The adaptive BELLINI trial explored whether short combination ICI induces immune activation (primary endpoint: two-fold increase in CD8+ T cells or IFNG), providing rationale for neoadjuvant ICI without chemotherapy. In window of opportunity cohorts A (4 weeks anti-PD1) and B (4 weeks anti-PD1 + anti-CTLA4), we observed immune activation in 53% (8/15) and 60% (9/15) of patients, respectively. High tumor-infiltrating lymphocytes (TILs) correlated with response. Based on these data, we opened cohort C for patients with high TILs (≥50%) who received 6 weeks neoadjuvant anti-PD1 + anti-CTLA4 followed by surgery (primary endpoint: pathological complete response, pCR). 53% (8/15) of patients had major pathological response (< 10% viable tumor) at resection, with 33% (5/15) having pCR. All cohorts met Simon’s two-stage threshold for expansion to stage II. In conclusion, neoadjuvant immunotherapy without chemotherapy demonstrates potential efficacy and warrants further investigation in patients with early TNBC.

  Study EGAS50000000568

• Whole-genome sequencing of BRCA-mutant breast cancer patient samples from tumour, germline tissue and plasma

  The use of circulating tumor DNA (ctDNA) to profile mutational signatures presents a non-invasive opportunity for understanding cancer mutational processes. In this study we developed MisMatchFinder, a liquid biopsy approach for mutational signature detection using low- coverage whole-genome sequencing of ctDNA. Through analysis of plasma samples across multiple different cancers, we demonstrated that MisMatchFinder accurately infers single-base and doublet-base substitutions as well as indels to enhance detection of ctDNA and clinically relevant mutational signatures. This study contains fastq files from whole-genome sequencing of temporally matched tumour (fresh frozen biopsies), blood germline and plasma samples collected from a BRCA1-mutant breast cancer patient to directly compare mutation signature analysis using gold-standard tumour-germline paired variant calling with our novel ctDNA-based method (MisMatchFinder).

  Study EGAS50000000569

• Spatiotemporal evolution and inter-patient heterogeneity in primary and recurrent/metastatic head and neck squamous cell carcinoma

  Head and neck cancer squamous cell carcinomas (HNSCCs) are heterogeneous in terms of origin and aetiology. In addition, there is uncertainty about the spatiotemporal genetic evolution from initial diagnosis to recurrence/metastasis (R/M) after primary treatments and further disease progression following systemic treatment. Changes in the genetic profile have implications on the selection of appropriate treatments for patients, especially in the era of targeted therapies and immunotherapies. Here, we analysed a cohort of 11 HNSCC patients with metachronous R/M, among whom nine had paired primary and R/M samples suitable for next-generation sequencing (multiple R/M samples collected at different time points were available for some of the patients). At the genomic level, the R/M samples shared a fraction of the somatic single nucleotide variants (SNVs) with the index primary tumours, but they also acquired many additional mutations, while losing only a few others. A similar behaviour was also observed when examining the changes of mutational signatures between primary and R/M samples. Overall, R/M appeared thus more genetically diverse than the respective primary tumours. The transcriptomic analysis showed that R/M samples had lower immune cell infiltration and several genes related to immune response were significantly downregulated compared to the primary samples. Our results underline the importance of analysing multiple samples per patient to obtain a more complete picture of the patient’s tumour and advocates a re-biopsy in the event of R/M and treatment failure, in order to select the most appropriate therapeutic strategy.

  Study EGAS00001007464

• Neoadjuvant nivolumab or nivolumab plus ipilimumab in early-stage triple negative breast cancer: phase 2 adaptive BELLINI trial. RNA-Seq

  The adaptive BELLINI trial explored whether short combination ICI induces immune activation (primary endpoint: two-fold increase in CD8+ T cells or IFNG), providing rationale for neoadjuvant ICI without chemotherapy. In window of opportunity cohorts A (4 weeks anti-PD1) and B (4 weeks anti-PD1 + anti-CTLA4), we observed immune activation in 53% (8/15) and 60% (9/15) of patients, respectively. High tumor-infiltrating lymphocytes (TILs) correlated with response. Based on these data, we opened cohort C for patients with high TILs (≥50%) who received 6 weeks neoadjuvant anti-PD1 + anti-CTLA4 followed by surgery (primary endpoint: pathological complete response, pCR). 53% (8/15) of patients had major pathological response (< 10% viable tumor) at resection, with 33% (5/15) having pCR. All cohorts met Simon’s two-stage threshold for expansion to stage II. In conclusion, neoadjuvant immunotherapy without chemotherapy demonstrates potential efficacy and warrants further investigation in patients with early TNBC.

  Study EGAS50000000567

• Copy numbers in resectable gastric cancer treated with surgery alone

  Purpose: Gastric cancer is a heterogeneous disease requiring stratification to recognize its underlying biology and variable clinical outcome. The Cancer Genome Atlas (TCGA) recognizes four molecular subgroups: Epstein-Barr virus positive (EBV+) and microsatellite instable (MSI) with superior survival compared to genomically stable (GS) and chromosomal instable (CIN). Since classification criteria to distinguish GS from CIN were not defined, alternative markers such as Lauren’s histopathology or CDH1/p53 immunohistochemistry are commonly applied. Here we compared survival of gastric cancer subgroups determined by four methods. Experimental Design: EBV- and microsatellite stable tumors from a total of 309 patients treated with surgery alone were assigned to subgroups by: (i) specific copy number aberrations as determined by TCGA (GS or CIN), (ii) genome instability index (GII; high or low), (iii) Lauren’s classification (diffuse or intestinal), and (iv) CDH1/p53 immunohistochemistry (IHC; aberrant CDH1, normal-, or aberrant p53 expression). Subgroups were associated with cancer-related survival (CRS). Results: Five-year CRS was 42.0% for diffuse and 49.5% for intestinal type tumors, and 57.8% for GS and 41.6% for CIN tumors. Classification by GII or CDH1/p53 IHC did not correlate with CRS. The combination of TCGA and Lauren classifications resulted in four distinct subgroups. Five-year CRS for GS-intestinal (n=24), GS-diffuse (n=57), CIN-intestinal (n=142) and CIN-diffuse (n=86) was 61.4%, 56.5%, 47.6%, and 31.5%, respectively. Conclusions: TCGA’s GS and CIN have additional prognostic value to Lauren’s diffuse and intestinal subgroups in gastric cancer. GS-intestinal, GS-diffuse, CIN-intestinal and CIN-diffuse constitute biologically distinct entities and are suggested as stratification variables for future studies.

  Study EGAS00001007394

• Single cell RNA sequencing of colorectal cancer patients (CRC-SG1)

  Fresh single-cell suspensions were loaded into the Chromium system (10x Genomics) targeting 5,000 cells per well. Barcoded sequencing libraries were generated using 3’v3 Reagent kit.

  Study EGAS00001006013

• Single cell RNA sequencing of colorectal cancer patients (KUL3/KUL5)

  Libraries for scRNA-seq were generated using the Chromium Single Cell 3′ or 5′ library and Gel Bead &amp; Multiplex Kit from 10x Genomics. Aimed to profile 5000 cells per library (if sufficient cells were retained during dissociation).

  Study EGAS00001006049

• 5' Single cell RNA sequencing of pre-treatment tissues from lung cancer patients receiving immunotherapy

  Study EGAS00001006188

• Whole genome sequencing of primary and metastatic Melanoma cases in an Australian cohort.

  Melanoma is the fourth most common cancer in Australia and the leading cause of cancer death in young adults. The Australian Melanoma Genome Project (AMGP) is analysing whole genomes from melanomas. We include the results of whole genome sequencing (WGS) for a number of datasets that include cutaneous, acral and mucosal melanoma subtypes.

  Study EGAS00001001552

• RNA sequencing data from patient derived colorectal cancer organoids

  This study aimed to develop NovumRNA, a bioinformatics pipeline designed to predict non-canonical tumor-specific antigens (ncTSAs) from human tumor RNA-sequencing data, potentially broadening the target pool for cancer neo-epitope-based immunotherapies. The pipeline was utilized to analyze RNA-seq data from ten patient-derived colorectal cancer (CRC) organoids, representing seven microsatellite stable (MSS) and three microsatellite instable (MSI) tumors. The goal was to explore the ncTSA landscape in CRC, particularly in MSS cases, which typically exhibit low tumor mutational burden and limited response to current immunotherapies. The study identified comparable ncTSA burdens across MSI and MSS tumors, suggesting new avenues for immunotherapy targeting non-canonical antigens in CRC. NovumRNA is available at: https://github.com/ComputationalBiomedicineGroup/NovumRNA.

  Study EGAS50000000685

• RNA sequencing data from 10 patient derived colorectal cancer organoids

  This dataset comprises RNA-sequencing data from ten colorectal cancer (CRC) organoids, derived from histologically verified primary tumor tissues of patients undergoing surgical resection. Paired-end sequencing produced 20 FASTQ.gz files. The organoids were cultured in Geltrex-based droplets using PDO culture medium and harvested for RNA extraction. Total RNA was isolated and subjected to full-length mRNA sequencing, producing high-quality RNA-seq data. The dataset includes transcriptomic profiles of seven microsatellite stable (MSS) and three microsatellite instable (MSI) tumors, providing a valuable resource for the analysis of non-canonical tumor-specific antigens (ncTSAs) using the NovumRNA pipeline. This dataset aids in uncovering potential immunotherapy targets in CRC, particularly in hard-to-treat low-TMB MSS cases.

  Dataset EGAD50000000962

• CRC organoid RNA-seq data access committee

  This DAC is responsible for granting access to RNA-seq data (paired-end FASTQ files) from 10 patient-derived colorectal cancer organoid patients. The data was ad hoc generated as part of the development of NovumRNA, a non-canonical tumor-specific antigen prediction pipeline.

  Dac EGAC50000000406

• Mutational_Analysis_of_Colorectal_PDX_models

  Targeted exome sequencing of patient derived xenografts from primary colorectal tumours and liver metastases.

  Study EGAS00001001171

• RNA sequencing data from glioblastoma primary cell lines treated with indisulam

  This study aimed to develop NovumRNA, a bioinformatics pipeline designed to predict non-canonical tumor-specific antigens (ncTSAs) from human tumor RNA-sequencing data, potentially broadening the target pool for cancer neo-epitope-based immunotherapies. The splicing-modulating drug indisulam was investigated for its potential to induce aberrant transcriptomes, potentially increasing ncTSAs, subsequently predicted by NovumRNA. Four glioblastoma primary cell lines were derived from patient Cavitron Ultrasonic Aspirator (CUSA) material, cultured as neurospheres, and treated with indisulam or vehicle control. RNA was extracted at 6 and 16 hours post-treatment and sequenced using the NovaSeq platform. The resulting RNA-seq data was analyzed with NovumRNA, using healthy brain GTEx samples as controls, to identify and validate ncTSAs induced by indisulam treatment. NovumRNA is available at: https://github.com/ComputationalBiomedicineGroup/NovumRNA.

  Study EGAS50000000680

• Characterization of copy number quiet oral cancer

  Copy number quiet head and neck squamous cell carcinoma are a distinct subset with relatively good prognosis. Previous work has indicated this subset is most commonly found in the oral cavity and is highly enriched for HRAS and CASP8 mutations. TP53 mutations are much less common than in CNA-quiet oral cancers. In this study we corroborate the validity of CNA-quiet tumors as a distinct subset in a large OSCC cohort and further chart clinical, genomic, and immunological characteristics of this subset.

  Study EGAS50000000558

• Genomic characterization of CNA-quiet oral cancer

  Low-coverage whole genome sequencing and targeted (30 gene panel) deep sequencing of oral cancer. Note that the targeted deep sequencing is not actually amplicon sequencing but hybrid capture sequencing, which was not available as on option.

  Dataset EGAD50000000790

• A comprehensive DNA methylation landscape of human and mouse cell lines derived from hematological malignancies

  Human cancer cell lines constitute useful models to study the primary disease and many relevant findings in tumor biology have been originated from them since the first immortalized cell line (HeLa) was obtained. The easy experimental intervention, the purity of the transformed cells and their versatility to undergo high-throughput screenings represent advantageous features of the established cancer cell lines. In recent years, major efforts have characterized in detail the multiomics make-up of hundreds of cancer cell lines and studied their association with sensitivity to anticancer drugs. Most of these studies have been genetic-centric and have not characterized in detail the epigenetic profiles underlying the characteristics of these cells or their impact on the efficacy of antitumoral compounds. In this regard, a past version of a DNA methylation microarray or a less accessible and more time-consuming such as reduced representation bisulfite sequencing have been used in those attempts to interrogate the epigenetic setting. Herein, we have obtained the DNA methylation profiles of 210 cell lines derived from hematological malignancies utilizing comprehensive DNA methylation microarrays that interrogates more than 850,000 and 285,000 CpG sites from human and mouse genomes, respectively. Importantly, we also provide a pharmacoepigenetic example of the potential use of this resource by showing how DNA methylation can predict response to nucleoside analogues.

  Study EGAS50000000627

• Melanoma Genome Sequencing Project

  Melanoma is the most prevalent cause of skin cancer morbidity and mortality. In order to characterize the full range of somatic mutations that may drive the growth of melanoma, we are sequencing tumor and normal DNA from a set of roughly 150 melanomas. For the majority of samples (approximately 90% of the cases), mutations in protein coding genes will be assessed in the exonic DNA of tumor-normal pairs using hybrid capture and paired-end DNA sequencing. For a few of DNA samples (approximately 10% of the cases), the entire genomes will be analyzed to assess the possible contribution of complex structural rearrangements contributing to oncogenesis. The sequencing data are supplemented by copy number profiling on the same tumors using high-density SNP arrays. Integration of these approaches will enable the unbiased and comprehensive characterization of both known and novel recurrent DNA alterations that arise in melanoma.

  Study phs000452

• The Melbourne Urological Research Alliance (MURAL) Collection of Patient-Derived Models of Prostate Cancer

  1. Low-Dose Carboplatin Modifies the Tumor Microenvironment to Augment CAR T Cell Efficacy in Human Prostate Cancer Models.Single-cell RNA sequencing (scRNA-seq) was performed to analyze the transcriptional modifications in the tumor microenvironment of prostate cancer patient-derived xenografts (PDX). The analyzed PDX tumor was PDX-287R from the MURAL collection of PDXs (PMID: 34413304). Mice harboring PDX tumors were treated with the carboplatin chemotherapy, and tumors were harvested 3 weeks later. RNA sequencing (RNA-Seq) was used to define immune and non-immune cell populations and anti-tumor innate immune signaling within the tumor microenvironment.To investigate the response of tumor cells to carboplatin treatment in more detail, RNA-Seq was performed on FACs-isolated tumor cells from PDX-287R at one week post-treatment. Gene set enrichment analysis showed an enrichment for apoptotic pathways, with significantly increased expression of the pro-apoptotic genes FAS, BAX, BBC3, IFI6, and JUN. Previously, carboplatin has been shown to activate the cGAS/STING pathway, and here we also found an increase in STING signaling, with an enrichment for the cytosolic DNA sensing pathway and increased expression of STING1, STAT1 and STAT2. There was also a significant increase in the expression of genes involved in T cell chemotaxis, including CXCL10, CXCL11, and CCL20. Collectively, carboplatin-induced cell death in tumor cells likely initiated a pro-inflammatory phenotype in prostate cancer PDXs.2. Co-Targeting BET, CREBBP, and EP300 Inhibits Neuroendocrine Signaling in Androgen Receptor-Null Prostate Cancer.The landscape of castration-resistant prostate cancer (CRPC) is characterized by a multitude of phenotypes, among which neuroendocrine disease holds a prominent position, each displaying unique responses to therapeutic interventions. The efficacy of inhibitors targeting BET and CREBBP/EP300 in prostate cancer treatment is widely acknowledged, primarily due to their ability to attenuate androgen receptor (AR) signaling. However, the effectiveness of these inhibitors in prostate cancers devoid of AR remains uncertain.This investigation sought to elucidate the role of BRD4, CREBBP, and EP300, which are co-expressed in both AR-positive and AR-null prostate cancer. Our study revealed that a compound targeting all three proteins, NEO2734, exhibited efficacy in suppressing the growth of organoids from both AR-positive and AR-null tumors, as evidenced by alterations in viability, size, and cellular composition. Furthermore, NEO2734 treatment consistently led to transcriptional downregulation of cell cycle pathways across various models. In the context of neuroendocrine prostate cancer (NEPC), NEO2734 demonstrated notable efficacy. Treatment with NEO2734 resulted in decreased expression of ASCL1 and other neuroendocrine markers (including SYP) , accompanied by a reduction in tumor growth in vivo. These findings collectively underscore the potential of epigenome-targeted inhibitors in impeding the growth of neuroendocrine prostate cancer, by disrupting lineage regulators in a phenotype-dependent manner. Consistent with the NEPC organoids and explants, NKX3-1 was upregulated in NEO2734-treated PDXs.In this study, we examined the impact of NEO2734 treatment on 6 organoids (treated for 24 hrs), 3 explants treated with JQ1 (for 48 hrs) and 2 PDXs (at two time points) using bulk RNA sequencing (RNA-Seq). These results are available through this dbGaP submission. The implications of these results warrant further exploration and development of compounds possessing similar activities for clinical applications. The ability of NEO2734 to mitigate the growth of both AR-positive and AR-null prostate cancer underscores its potential as a versatile therapeutic agent. Moreover, its efficacy in targeting neuroendocrine phenotypes offers promising avenues for addressing a subtype of CRPC that poses significant clinical challenges. In conclusion, this study sheds light on the intricate interplay between epigenetic regulators and prostate cancer phenotypes. The observed effects of NEO2734 on growth inhibition and phenotype-specific disruption highlight the importance of continued research into compounds with similar epigenome-targeting mechanisms for advancing precision medicine approaches in the treatment of prostate cancer.3. Defining Focal Neuroendocrine Differentiation as a Transcriptionally Distinct Form of Prostate Cancer Pathology Characterized by the Expression of Androgen ReceptorsThe study aimed to understand the transcriptional differences among different neuroendocrine pathologies of prostate cancer. The study hypothesized that the diverse neuroendocrine pathology observed in prostate might be attributable to differences in transcriptional level that remain hidden in bulk RNA-Seq data. To investigate, we employed scRNA-seq to generate gene expression profiles of 18,632 individual tumor cells from 9 PDX models representing five distinct neuroendocrine pathologies of prostate cancer. Our analysis identified 3-8 transcriptionally distinct sub-populations per PDX. The expression of key oncogenic signaling pathways and master regulator activity varied across in neuroendocrine pathologies, with each type of pathology displaying a unique set of transcriptional sub-populations. We discovered that, like the amphicrine pathology, focal neuroendocrine differentiation (focal NED) cells maintain androgen receptor (AR) signaling. However, the expression profiles of focal NED cells differed from the other pathologies, indicating it should be considered as a distinct type of neuroendocrine pathology in prostate cancer. Analysis of copy number alterations suggested little clonal divergence between focal NED cells and neighboring adenocarcinoma cells, emphasizing the transcriptional distinctiveness of focal NED.Overall, the findings suggest potential differences in treatment approaches for tumors from prostate cancer patients showing neuroendocrine pathology, especially focal NED.4. Reprogramming of Androgen Receptor Activity in Castration-Resistant Prostate Cancer is Shaped by Truncated Variants.Under the influence of treatments that target the androgen receptor (AR) axis, prostate cancer cells undergo various changes to the AR gene. This includes the production of truncated AR variants that lack the ligand binding domain and are constitutively active. However, the exact role of these truncated variants in shaping the AR signaling axis and their importance in therapy resistance is not yet fully understood. By examining the AR cistrome in a group of patient-derived prostate cancer models with different mechanisms of castration resistance, we observed diverse changes in AR activity in metastatic prostate cancer, including castration-resistant models. Specifically, we identified a unique subset of tumors characterized by the expression of ARv567es, a variant resulting from structural rearrangements of the AR gene. Tumors positive for ARv567es showed a distinct AR binding profile and epigenome compared to those without ARv567es, which was linked to an altered transcriptional profile. The presence of ARv567es is associated with poor responses to treatments, such as castration and bipolar androgen therapy (BAT), regardless of the presence of full-length AR. This involves disruption of the autoregulatory loop that controls AR gene transcription and reduced transcriptional responses to treatment. Overall, this study demonstrates that ARv567es can lead to transcriptional reprogramming in advanced prostate cancer and drive therapy resistance. 5. The MURAL Collection of Prostate Cancer Patient-Derived Xenografts Enables Discovery Through Preclinical Models of Uro-Oncology.We created a comprehensive PDX resource for prostate cancer to facilitate the rapid and systematic evaluation of new therapies. This collection includes 59 tumors from 30 patients, gathered between 2012 and 2020, aligning with the availability of abiraterone and enzalutamide in the clinic. The PDXs cover the clinical, pathological, and genomic spectrum of prostate cancer, ranging from treatment-na&#239;ve primary tumors to castration-resistant metastases. We characterized heterogeneity in adenocarcinoma and neuroendocrine phenotypes using bulk and single-cell RNA sequencing. Additionally, organoids were cultured from PDXs, enhancing our preclinical study capabilities. Employing a 1x1x1 design, we quickly identified tumors with responses to combination therapies. To manage the distribution of PDXs, we established the Melbourne Urological Research Alliance (MURAL). This PDX collection significantly enhances our ability to test and prioritize effective treatments for future clinical trials in prostate cancer.6. Prostate Cancer Associated Fibroblasts Have Distinct Morpho-Mechanical Features That Are Associated With Patient Outcome.The tumor stroma, including cancer-associated fibroblasts (CAFs), has a crucial role in tumor progression. This study focused on the morphological and mechanical properties of CAFs and normal prostatic fibroblasts. The findings revealed that CAFs had distinct morphological and mechanical characteristics compared to normal fibroblasts, such as differences in nuclear size, shape, F-actin arrangement, cellular volumes, and elasticity. Additionally, the study correlated these biophysical properties with transcriptomic data, identifying pathways and cellular components linked to the observed changes. Overall, high-throughput assessments of biophysical properties in prostate cancer cells and stromal components may serve as predictive tools for patient outcomes and help identify new therapeutic approaches targeting the tumor stroma.

  Study phs003369

• Epigenomics of Prostate Cancer from Cell Free Plasma

  We sought to perform epigenomic characterization of prostate cancers by profiling circulating nucleosomes isolated from patient plasma using cell free chromatin immunoprecipitation followed by sequencing (cfChIP-seq). Our major findings include identification of prostate-cancer specific regulatory elements using a novel statistical method accounting for variable tumor DNA fraction in plasma, inferring transcription factor activity based on binding motif enrichment within regulatory elements, and epigenetically phenotyping patients based on clinical and genomic features. Data available in dbGaP will include cfChIP-seq and targeted sequencing from approximately 50 patients with a known diagnosis of cancer and healthy patient controls.

  Study phs003482

• Integrative Molecular Characterization of Breast Cancer

  Version 1: We have performed integrative analyses of breast cancer tumors, including whole exome sequencing (WES) of tumor and normal pairs, whole transcriptome sequencing, and single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq), to determine molecular correlates of response to novel and immune therapies. We have focused on dissecting the role of anticancer immunity in response to these therapies and understanding biological mechanisms underlying therapeutic resistance. The goal of this work is to advance a precision medicine approach to breast cancer treatment by identifying molecular biomarkers to improve therapy selection and novel therapeutic targets to overcome resistance. Version 2: We performed bulk whole exome sequencing (WES) of fresh frozen tissue from primary hormone receptor-positive breast cancer. Additionally, where available, we performed bulk whole transcriptome sequencing for the same individuals (tumor only). Additionally, we performed single cell sequencing on fresh frozen tissue from primary hormone receptor-positive breast cancer.

  Study phs002419

• Personalized Breast Cancer Vaccines Based on Genome Sequencing

  This study is from a phase I clinical trial of neoantigen DNA vaccines in triple negative breast cancer patients with persistent disease following neoadjuvant chemotherapy. The exome and RNA sequences used to identify somatic mutations, predict neoantigens, and design vaccines is within.

  Study phs002787

• Colorectal cancer – unmapped reads (Mutographs)

  Colorectal cancer – unmapped reads (Mutographs)

  Dataset EGAD00001015364

• Geographic and age-related variations in mutational processes in colorectal cancer - patient metadata files (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - patient metadata files (Mutographs)

  Dataset EGAD00001015437

• 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

• Division of Brain Tumor Translational Research, National Cancer Center Japan Data Access Committee

  Dac EGAC50000000366

• Childhood B-cell acute lymphoblastic leukemia genomic and transcriptomic data

  The aim of the two studies was: 1) to genetically characterise childhood leukemia patients that lacked established cytogenetic abnormalities (termed B-other leukemia) and assess the clinical features of recurrent genetic abnormalities. 2) Assess the accuracy of whole genome sequencing as a diagnostic genetic test in childhood leukemia, relative to other traditional methods (karyotyping, FISH, MLPA) and whole transcriptome sequencing. As a result, this study includes targeted DNA-sequencing, whole genome sequencing and whole transcriptome sequencing data on childhood leukemia patients enrolled on UK clinical trials (UKALL2003 and UKALL2011).

  Study EGAS00001006863

• 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 OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing 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: type, repository, dataset type, dataset technology, file format, email domain, and whether it is in the EGA Beacon or not. 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 lung word 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. Access to synthetic data studies is managed by the EGA Helpdesk Data Access Committee. Study ID Title EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples EGAS00001005591 Synthetic data - Genome in a Bottle EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data

  Documentation synthetic-data

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? TThe 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. 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 Federated EGA Vision Statement Map of FEGA status across the world Current status of the FEGA Network Documentation Federated EGA Vision Statement The Federated EGA is the primary global resource for discovery and access of sensitive human omics and associated data consented for secondary use, through a network of national human data repositories to accelerate disease research and improve human health. Overview In the era of rapidly expanding genomics research and technologies, data reuse is more important than ever. This shift prompted the establishment of federated networks for genomic data sharing in Europe. The 1+ Million Genomes (1+MG) Initiative, launched in 2018 to enable access to 1 million sequenced human genomes, provided a cohesive vision to support diverse jurisdictional data sharing requirements. This catalysed the development of multiple projects, including the development and implementation of national-level EGA nodes, coordinated by the Central EGA, and together constituting the Federated EGA Network. Officially launched in 2022, the current status of the FEGA Network is described in the table below. In a rapidly evolving data sharing landscape, FEGA 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. Check out the Federated EGA 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 Documentation Title Version Description Structure and Organisation Federated EGA Structure and Organisation 1.1 The structure of an Federated EGA network and service expectations. We organise the EGA into three types of nodes: Central EGA, Federated EGA nodes and EGA Community nodes; we outline the goals of such an organisation, and summarise the commitments and services provided by the nodes. Strategic Committee Federated EGA Strategic Committee 1.1 In the Federated EGA Strategic Committee terms of reference document we describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the federated EGA project. The committee receives input from the EGA Strategic Committee and provides feedback for the EGA strategic roadmap. Operations Committee Federated EGA Operations Committee 1.1 The Federated EGA Operations Committee terms of reference describes the purpose and objectives of the operations committee, which is to review operational performance and coordinate technical implementation roadmaps of EGA Federated and Community nodes. The committee receives advice from the EGA Federated Strategic Committee, and provides operational reporting to the EGA Federated Strategic Committee Guidelines Node Operations guidelines 2.1 The Federated EGA Node Operations gives an overview of the operational areas which require resources in order to create a federated EGA node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial experiences of the inaugural Federated EGA nodes. The operational areas of responsibility are inspired by the Federated EGA Maturity Model which guides establishment and operation of nodes as part of the Federated EGA network of human data resources. Collaboration Federated EGA Collaboration Agreement 1.0 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 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