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• Comprehensive characterization of pre- and post-treatment samples of breast cancer reveal potential mechanisms of chemotherapy resistance

  When locally advanced breast cancer is treated with neoadjuvant chemotherapy, the recurrence risk is significantly higher if no complete pathologic response is achieved. Identification of the underlying resistance mechanisms is essential to select treatments with maximal efficacy and minimal toxicity. Here we employed gene expression profiles derived from 317 HER2-negative treatment-naïve breast cancer biopsies of patients who underwent neoadjuvant chemotherapy, deep whole exome and RNA-sequencing profiles of 22 matched pre- and post-treatment tumors, and treatment outcome data to identify biomarkers of response and resistance mechanisms. Molecular profiling of treatment-naïve breast cancer samples revealed that expression levels of proliferation, immune response and extracellular matrix (ECM) organization combined predict response to chemotherapy. Triple negative patients with high proliferation, high immune response and low ECM expression had a significantly better treatment response and survival benefit (HR 0.29, 95% CI 0.10-0.85; p=0.02), while in ER+ patients the opposite was seen (HR 4.73, 95% CI 1.51-14.8; 0=0.008). The characterization of paired pre-and post-treatment samples revealed that aberrations of known cancer genes were either only present in the pre-treatment sample (CDKN1B) or in the post-treatment sample(TP53, APC, CTNNB1). Proliferation-associated genes were frequently down-regulated in post-treatment ER+ tumors, but not in triple negative tumors. Genes involved in ECM were upregulated in the majority of post-chemotherapy samples. Genomic and transcriptomic differences between pre- and post-chemotherapy samples are common and may reveal potential mechanisms of therapy resistance. Our results show a wide range of distinct, but related mechanisms, with a prominent role for proliferation- and ECM-related genes.

  Study EGAS00001005876

• Clinical data of liver cancer patients from EuCanImage - Use case 1 Synthetic

  This study is part of EuCanImage project. It contains the standardized clinical information following FHIR model. The data was collected together with MRI of patients diagnosed with liver cancer (HCC) and controls (non-HCC). All data is synthetic.

  Study EGAS50000001444

• Subtype specific studies of breast cancer progression. Milan cohort.

  The aim of this study is to compare DCIS and IBC in a subtype stratified manner in several genomic levels (gene expression, copy number, methylation).The data in this archive is supplementary to previously published data.

  Study EGAS00001004390

• Global Endometrial DNA Methylation Analysis Reveals Insights into mQTL Regulation and Associated Endometriosis Disease Risk and Endometrial Function

  Endometriosis is a common gynecological disorder affecting 11% of reproductive aged women and is a leading cause of pain and infertility. We investigated global DNA methylation (DNAm) profiles in endometrium associated with endometriosis, menstruation, and genetic variation. Endometrial samples were collected from 984 patients with confirmed endometriosis (n=637) and women without endometriosis (n=347). Patients were recruited through the University of California San Francisco, USA, University of Melbourne, Australia, Endometriosis CaRe Centre in Oxford, UK, and EXPPECT Centre, The University of Edinburgh, UK, using the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project (WERF EPHect) standardized protocols for tissue collection and processing, and participant characteristics and clinical annotation. DNA was extracted from samples and was use to generate DNAm data using the Illumina Infinium MethylationEPIC Beadchip and genotype data using the Axiom Precision Medicine Research array. Estimates from this study suggest that 15.4% of the variation in endometriosis is captured by DNAm. DNAm analysis identified significant differences in DNAm profiles and DNAm networks, associated with endometriosis, endometriosis sub-phenotypes, and menstrual cycle phases. Integration of DNAm and genetic data in a DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis highlighting target genes contributing to disease risk. This study identified novel factors affecting epigenetic regulation in endometrium associated with endometriosis risk and disease heterogeneity and provides an important data resource for reproductive medicine. Genotype data generated as part of this study are available on dbGaP and methylation data are available on GEO.

  Study phs003307

• Single-Cell Multiomics of the Immune Microenvironment in T-Cell Acute Lymphoblastic Leukemia

  We present single-cell transcriptome and surface protein expression (CITE-seq) data from four healthy donors and fifteen pediatric T cell acute lymphoblastic leukemia (T-ALL) patients at diagnosis and at different treatment timepoints (days 1 – 6 and at time of remission). Mononuclear cells from peripheral blood (PB; longitudinal) or bone marrow (BM; diagnosis only) were isolated and CD45high immune cells and CD45dimCD7+ malignant cells were sorted and subsequently pooled to enrich for immune cells. For comparison,CD45high immune cells from four healthy donors were also sorted. Single-cell sequencing of the transcriptome and surface protein expression (204 antibodies) was performed using the 10x Genomics platform.Multiomics analyses resulted in the identification of a T-ALL subgroup characterized by a remodeled immune microenvironment at initial diagnosis. This immune landscape is dominated by the presence of non-malignant CD4-CD8-TCRab T cells that interact with CXCL16 expressing non-classical monocytes. Leukemia cell intrinsic transcriptional rewiring in these patients is associated with activation of Rap1 signaling.

  Study phs004269

• Identification of genomic aberrations in low-grade serous ovarian cancer

  Low-grade serous ovarian cancer (LGSOC) is a rare disease that occurs more frequently in younger women compared with high-grade disease. The current treatment is suboptimal, and better understanding of molecular pathogenesis of this disease is needed. In this study, we compared the whole genome sequences of LGSOCs from short-term and long-term survivors (defined as <40 and >60 months, respectively). Our goal was to identify novel genomic aberrations in LGSOC, especially in short-term survivors.

  Study EGAS00001006679

• Single cell phenotypic profiling of 27 DLBCL cases reveals marked inter- and intra-tumoral heterogeneity

  Diffuse large B-cell lymphoma (DLBCL) is the most common histologic subtype of non-Hodgkin lymphoma and is notorious for its clinical heterogeneity. Patient outcomes can be predicted by cell-of-origin (COO) classification, demonstrating that the underlying transcriptional signature of malignant B-cells informs biological behavior in the context of standard combination chemotherapy regimens. In the current study, we used mass cytometry (CyTOF) to examine tumor phenotypes at the protein level with single cell resolution in a collection of 27 diagnostic DLBCL biopsy specimens from treatment naïve patients. We found that malignant B-cells from each patient occupied unique regions in 37-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Interestingly, variable MHC class II expression was found to be the greatest contributor to phenotypic diversity. Within individual tumors, a subset of cases showed multiple phenotypic subpopulations, and in one case we were able to demonstrate direct correspondence between protein-level phenotypic subsets and DNA mutation-defined subclones. In summary, CyTOF analysis can resolve both inter- and intra-tumoral heterogeneity among primary samples, and reveals that each case of DLBCL is unique and may be comprised of multiple, genetically distinct subclones.

  Study EGAS00001003860

• Whole Genome Sequencing of Skull-Based Chordoma

  Chordoma is a rare bone tumor, which is believed to originate from notochordal remnants. Based on the United States Surveillance Epidemiology and End Results (SEER) data, the incidence of chordoma varies by gender and race; however, little is known about the etiologic factors that predispose to it. Genomic profiling studies of chordoma are limited, particularly in the Chinese population. We therefore conducted a detailed molecular characterization of paired chordoma tumor/normal tissues using fresh frozen tissues and blood collected from skull-based Chinese chordoma patients. These analyses included whole exome sequencing and RNA sequencing analyses.

  Study phs002301

• Submitter Portal

  Submitter Portal The Submitter Portal does not support array submissions! Please refer to the array documentationAny new objects registered in the Submitter Portal will not be available for use in an array submission For further information please check our Submission FAQs, submission quickguide as well as submission terms! The metadata submission process can be difficult and time-consuming. For this reason, the EGA has developed the Submitter Portal, a tool that was created to offer a simplified and user-friendly method of registering metadata. Our portal provides features that are intended to make the input of metadata easier, ensuring that your data is registered correctly and effectively. Our page is divided into logical sections and includes a helpful video instruction to further assist you as you complete the submission process. With the Submitter Portal, you can rest assured that the submission of your data is in good hands. Submitter Portal Index Previous steps Access to Submitter Portal Start a Submission Take The Tour Tutorial Previous steps Create your EGA account To submit data, you first need to create your EGA user. Then, once your account has been verified by the Help Desk team, you will be able to request a submitter role and send the EGA Data Processing Agreement (DPA). In the case you already have an EGA account, or an ega-box (submission account), you can skip this step. Upload your files Please note that all your files must be encrypted using the Crypt4GH tool before upload them.As soon as you are assigned with a submitter role and added your public key to your profile, you will be able to connect to the EGA inbox and upload your files. Understand the EGA metadata schema It's crucial to comprehend the EGA metadata schema, a set of rules that specify how data is organised, described, and shared inside the EGA, in order to get the most out of this resource. Learn all about EGA metadata schema! Register your DAC and policy There are two objects in the EGA metadata schema that are registered in a separate portal. All DACs and policy objects are registered using the DAC Portal, a tool developed by EGA to help data controllers manage their data stored at the EGA. You can find relevant information in the DAC Portal Guide. Access to Submitter Portal 1. Request Submitter Role Once you have created your EGA user, request a Submitter Role and complete relevant information such as submission type and size. 2. Signature of the Data Processing Agreement The Data Processing Agreement (DPA) signed by CRG and EMBL-EBI (Joint Data Processors for the EGA) can be downloaded to the submitter for its signature. Further information related to the DPA can be in the Privacy Notice section. The DPA should be signed by a legal representative of the submitter with power of attorney/appointment duly authorised to sign contracts on behalf of the institution. The submitter will return a fully-executed copy of the DPA to us. Please note that due to the high volume of submissions to EGA the DPA is non-negotiable. Once you have completed all the information and attached the DPA signed, send your Submitter request and the EGA Helpdesk Team will validate it and send a confirmation to start submitting your metadata. Start a Submission The steps below will show you how to start a submission and are accompanied with a tutorial and the Take the tour of Submitter Portal. 1. Login Go to the Submitter Portal interface and log in using your EGA account credentials. 2. Create a submission The Submitter Portal interface will show you any already existing open submissions. To start a new submission, click on “Create a Submission”.In this phase, you have the option to add any collaborator(s) as well as their permissions (“Read only”, “Read & Write” or “Full Access”). Please note that to add a collaborator, they must also have a submitter role. When adding a collaborator, please enter the user’s full email address in the search field and select the user. For security reasons, the portal does not display partial matches, so the full email address must be entered.The collaborator option is available while the submission is open. In the event that the submission is closed, you will need to reopen it. To do this, you only need to make a small modification in any objects within the submission, for example, copy and paste a title or description of the submission. Once the submission is open, you can add a collaborator. Further information can be found in the Submission Tour 3. Create Study Click on Add Study and complete the form with the relevant information. The fields with an asterisk (*) are mandatory. Further information can be found in the Submission Tour4. Create Sample Sample registration can be done either individually or in batches by using a template. Batch upload: download the template and complete it with information about the sequencing samples. Upload the samples.csv, and the sample will be created.Add a single sample: complete the mandatory fields with the metadata information of your sample.  Further information can be found in the Submission Tour5. Create Experiment Register an experiment with ​information about the sequencing methods, protocols, and machines. Experiments generate the connection between the samples and the study. Further information can be found in the Submission Tour6. Create Run Samples, experiments, and files are linked through runs. To register a run, you will be asked to select the appropriate experiment and the file format. Run registration can be done either individually or in batches by using a template. Batch upload: download the template and complete it with the sample file linkage. Upload the run.csv, and the runs will register.As the INBOX is a directory, bear in mind to include the file path (/). Example: /filename.c4gh.Add single run: select the file format, sample, and experiment from the drop box. For re-using submitted samples, please use the EGA accession ID (EGANXXX). Please note that files must be uploaded to your inbox before linking your files and samples. Further information can be found in the Submission Tour7. Create Analysis Analysis should be only used for BAM/BAI or CRAM/CRAI pair, VCF, and phenotype linkage to samples. The analysis is an EGA-specific metadata object that links samples to files. This object also stores some metadata about your experiments, such as the experiment type, genome reference, or the platform used. **If only BAM or CRAM alignment files are submitted but not the original unaligned FASTQ files, then please make sure that the BAM or CRAM files also contain the unaligned reads. This is critical to enable primary re-analysis and re-alignment of the dataset using new tools or future genome assemblies.** Start by selecting the samples to be linked to the file, and then populate the required attribute fields. Please be aware of the existence of mandatory fields that must be populated.When populating the chromosome field (mandatory), please select the chromosomes from the checkbox. Please take in consideration that EGA allows for the re-use of any registered metadata. Therefore, a previously registered study or samples can be referenced for the analysis in the current data submission.Further information can be found in the Submission Tour 8. Create Dataset Dataset contains the collection of runs/analysis data files subjected to controlled access by being associated with a Policy. Complete the mandatory fields such as title, description, type and policy. Please, take in consideration that DAC and Policy must be registered in advance in the DAC Portal. Similar to other metadata objects, you can re-use previous runs or analyses, using their EGA accessions IDs (runs - EGAR, analyses - EGAZ). If you would like your dataset to be added to the cBioPortal instance, please review the cBioPortal documentation.Further information can be found in the Submission Tour 9. Finalise submission Once the files are ingested, please delete them from your SFTP INBOX after finalising your submission. This will prevent the files from reappearing in your Submitter Portal Inbox. Once you have completed your submission, click on the Finalise button and schedule an estimated release date. Afterwards, your submission will be sent to Helpdesk for approval. Please note that once the Helpdesk has approved your submission, you will receive the identifiers for your data. See the Frequently Asked Questions (FAQ) for more information. 10. Update closed submission If you want to append more files to a closed submission, you will need to reopen the submission where the dataset is registered.Please note that you can only modify datasets that begin with the ID EGAD5XXXXXXX.  Find the Submission: go to the Submitter Portal Menu -> Datasets section and find the submission ID (EGA0XXX). Reopen the submission where the dataset is located: to reopen a submission, simply register new metadata, such as runs or analyses, and the submission will automatically reopen and appear on the main page. You can also do a small edition, such as copying and pasting a title or description. Take The Tour The Submitter Portal Take The Tour is a visual guide of all the steps described previously about how to start a submission. Tutorial In this tutorial, we will show you how to use the Submitter Portal to register your metadata. Please note the video focuses on the run-based submission (for raw files - fastq - and aligned data - BAM/CRAM) and analysis-based submission (for your BAM/BAI pairs, variation -VCF - and phenotype files).

  Documentation submission/metadata/submission/sequencing-phenotype/submitter-portal

• Epi4K: Gene Discovery in 4,000 Epilepsy Genomes

  The Epi4K project began in 2011 as an international, multi-center study that seeks to identify and characterize the genetic bases of complex epilepsies. The Center without Walls Epi4K project includes three cores and four scientific projects, as well as a steering committee comprised of the primary study investigators and representatives from the National Institute of Neurological Disorders and Stroke (NINDS). The three cores include: (1) The Administrative Core which handles the overall coordination of Epi4K activities; (2) The Sequencing, Biostatistics and Bioinformatics Core which is responsible for generating the next-generation sequence data, inferring the genetic variation in each of the study participants, and performing the primary analyses to identify epilepsy genes; and (3) The Phenotyping and Clinical Informatics Core which verifies and archives all the phenotypic data from each study participant. The proposed number of patients to be sequenced and analyzed in the scientific projects is a minimum of 4,000; thus the Center was named "Epi4K: Gene Discovery in 4,000 Genomes". Project 1 addresses the genetics of rare and severe childhood epilepsies, including epileptic encephalopathies (infantile spasms and Lennox-Gastaut syndrome), and malformations of cortical development (periventricular nodular heterotopia and polymicrogyria). Exome and genome sequence data generated from DNA collected from patients will be screened for mutations (single nucleotide substitutions, small insertion-deletions, and copy number variations) that cause or contribute to the diseases. Project 2 is focused on genetic discovery in multiplex families. This study will use next-generation sequencing to identify genomic variation that influences risk for common subtypes of epilepsy including idiopathic generalized epilepsy and nonlesional focal epilepsy. Project 3 seeks to identify genetic determinants of prognosis in patients with a range of epilepsy disorders. This study will study established epilepsy cohorts with well-characterized data on seizure outcome to look for relationships between genetic variation and pharmacological control of seizures. Project 4 will use next-generation sequencing data (exome and genome) to screen for epilepsy-associated copy number variation across all Epi4K projects using novel computational algorithms.

  Study phs000653