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• 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: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• EGA account management

  How to manage your EGA account Welcome to the documentation page for creating an EGA user. The European Genome-Phenome Archive (EGA) is a platform for archiving, managing, and sharing human genomic and phenotypic data. To submit, access, and manage requests for the data hosted on the EGA platform, users are required to register for an account. This documentation page provides step-by-step instructions for creating an EGA user account, as well as information on the different types of EGA user accounts and their associated privileges. You can check the EGA data flow for a better understanding. Manage EGA account step-by-step guide Contact Information Public Keys Password Authenticator Federated Identity Sessions Request an EGA account step-by-step guide Fill in this form.You cannot use a generic email account (e.g., Gmail). Make sure you use your institutional email account.Your email is by default your username.The criteria for a strong password is: It should contain between 8 and 32 charactersIt should contain at least one uppercase letterIt should contain at least one numberIt should contain at least one special characterYou will need to wait for the Helpdesk team to check your identity. Please allow two business days for this step.After the Helpdesk validation step, you will receive an email with a link to validate your institutional email.Click the link to verify your email. It will redirect you to the EGA website.As soon as your account has been validated, you will be able to start adding more information to your EGA profile. For that, you first need to log in using your credentials from step 1. Manage EGA account step-by-step guide As a logged-in user, go to your profile by clicking on Welcome > My Profile. To modify it, click the Edit button.You will see the following options: Contact Information In this section, you can modify the personal information of your EGA account. Public Keys Adding a public key is necessary for downloading data via Live Outbox distribution. If you don’t know how to generate your key, you can follow the instructions available on How do I create a key? Password In the Password tab, you can change your password anytime to ensure the security of your account. However, if you receive an email notifying you that your password was modified and you did not initiate the change, it could be a sign that your account has been compromised. In this case, you should immediately contact our Helpdesk team to report the issue and receive assistance in restoring your account security. Authenticator You can set your two-factor authentication (2FA) system for your EGA account as an identity and access management security method that requires two forms of identification to access resources and data. To obtain your code, please follow the instructions below, also available in the Authenticator tab in your profile. Sessions The Sessions tab displays a list of all devices where you are currently logged in as an EGA user. This includes any open sessions you have on your computer, phone, or other devices. By reviewing the list of active sessions, you can monitor the devices that have access to your account and ensure that there are no unauthorised logins. If you notice any suspicious activity, you can close the sessions from this page to immediately terminate access to your account from the unauthorised device.

  Documentation how-to-manage-your-account

• Processed microarray gene expression data from NPC samples_Cohort 1

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006275

• Raw microarray gene expression data from NPC samples_Cohort 1

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006273

• Single-Cell DNA Methylation Profiling via scTAMseq

  This dataset comprises single-cell DNA methylation and nuclear genotyping profiles of 7 total bone marrow aspirates (donors A.1-A.7) and 5 CD34+-enriched bone marrow samples (donors B.1-B.5) generated using the scTAMseq protocol. Sequencing was performed on the Mission Bio Tapestri platform, targeting a panel of 448 CpGs to assess epigenetic patterns and We also included 147 genomic regions commonly mutated in clonal hematopoiesis and 20 regions targeting chromosome Y. Samples B.1 & B.5, B.2 & B.4 and A.2 & A.5 were, in pairs, multiplexed into single tapestri lanes.

  Dataset EGAD00001015498

• Genome-Wide Association Study of Sporadic and Familial Testicular Germ Cell Tumors

  Case-Control Study:Testicular germ cell cancer has been increasing among men during most of the 20th century. Despite this increase, the etiology of testicular cancer is poorly understood. To better understand the molecular epidemiology of testicular cancer, the National Cancer Institute and the Department of Defense are conducting a case-control study of testicular cancer among military servicemen. The project includes obtaining biosamples and questionnaire data from all participants. Pre-diagnostic serum samples are available from the approximately 1,000 cases and 1,000 controls enrolled in the study. Multiple-Case Family Study:In a parallel project, we are also studying families in which 2 or more testicular cancers have occurred. Among those men diagnosed with testicular cancer, about 1-3 percent report a family history of the disease (FTGCT). Brothers of affected individuals are 8 to 10 times more likely to develop testicular cancer, and men whose fathers had testicular cancer are four times more likely to develop testicular cancer, when compared with the relatives of men who have never had testicular cancer. Large-scale genetic linkage studies have failed to identify one or more rare, highly-penetrant testicular susceptibility genes. Rather, it appears that these familial clusters are due to the combined effects of multiple more common genes with low penetrance (so-called "polygenic inheritance"). Our cohort of FTGCT families was assembled to characterize this syndrome's clinical phenotype, and to discover the full range of cancer susceptibility loci that influence TGCT pathogenesis.

  Study phs001303

• STAMPEED: Whole Genome Association Analysis of Hematopoietic Cell Transplant (HCT) Outcomes

  This retrospective cohort study was designed to identify single nucleotide polymorphisms (SNPs) that are associated with complications after allogeneic hematopoietic cell transplantation (HCT). Validated discoveries provide information to improve risk assessment, counseling and treatment planning and to direct future mechanistic studies of the genes and pathways that influence outcomes, thereby providing insight and rationale for new targeted therapies. The study was conducted with the use of 3 different approaches. (1) We used GWAS discovery and replication analyses to identify SNPs variants associated with outcomes after HCT. (2) We tested SNP alleles in HLA-matched sibling donors and recipients to determine whether mismatching in the recipient is associated with graft-versus-host disease (GVHD) or the risk of recurrent malignancy after HCT. Such associations would suggest that the peptide encoded by the SNP allele functions as a minor histocompatibility antigen. (3) We performed in silico candidate SNP studies to determine the validity of previously published results showing associations with outcomes after HCT. The study population represents a large cohort from the Fred Hutchinson Cancer Research Center and consists of 4,471 recipients and 4,628 donors containing 4,258 recipient-donor pairs. Recipients suffered from a hematologic malignancy or myelodysplastic syndrome and were treated in a highly structured clinical research environment with comprehensive monitoring and systematic data recording. We used proportional hazards analyses to test for associations with acute and chronic GVHD, acute kidney injury, gram-negative bacteremia, invasive fungal disease, CMV infection and disease, recurrent malignancy, death not attributable to recurrent malignancy, and death after recurrent or progressive malignancy.

  Study phs001918

• A Phase II Neoadjuvant Study of Palbociclib in Combination with Letrozole and Trastuzumab as Neoadjuvant Treatment of Stage II-III ER+ HER2+ Breast Cancer (PALTAN)

  Women with early stage ER+/HER2+ breast cancer (BC) are less likely to achieve pathological complete response (pCR) after chemotherapy with standard of care dual HER2 blockade than patients with ER-/HER2+ BC. Additionally, the toxicity burden of standard systemic therapy is high. Thus, this single arm phase 2 clinical trial investigated a novel de-escalation regimen with trastuzumab, letrozole, and palbociclib in those with early stage ER+/HER2+ BC. The aim was to determine if this treatment regimen will achieve similar pCR rates as conventional treatment and spare women the toxicity burden of regular treatments. The primary endpoint was pCR after 16 weeks of treatment with trastuzumab, letrozole, and palbociclib. Patients with newly diagnosed ER+ HER2+ invasive BC with clinical stages II and III were eligible for this protocol. Research biopsies were performed during the treatment course for whole exome and RNA sequencing, PAM50 molecular subtyping, and Ki67 assessment using immunohistochemistry for complete cell cycle arrest (CCCA: Ki67 ≤2.7%). Results showed a pCR of 7.7% and a rate of residual cancer burden at 0 or I of 38.5%. CCCA was observed in 85% at Cycle 1 day 15 post palbociclib, trastuzumab, and letrozole treatment (C1D15), compared to 27% at surgery after palbociclib was discontinued. PAM50 subtyping identified 31.2% HER2-E, 43.8% Luminal B, 25% Luminal A. 161 genes were differentially expressed comparing C1D15 to baseline. Whole exome and RNA sequencing data will be available through dbGaP.

  Study phs003147

• Breast Cancer Family Registry

  The Breast Cancer Family Registry (BCFR) is a multi-center prospective cohort, comprised of over 30,000 women and men from nearly 12,000 families from the United States, Canada, and Australia. Our BCFR resource has been used, and continues to be used, by breast cancer researchers around the world in order to find new ways to prevent, diagnose, and treat cancer. The BCFR provides an extensive and diverse range of resources, expertise, and specialized skills, and has several unique strengths: 1) the collection of a large number of individuals and families across a wide spectrum of breast cancer risk, including both affected and unaffected individuals; 2) the large collection of families with early-onset breast cancer; 3) the large collection of racial/ethnic minority families not replicated elsewhere; 4) the extensive molecular characterization performed to date; and 5) active follow-up of both probands and family members. Thus, the BCFR comprises a unique cohort of probands and family members at familial/genetic risk of breast cancer that will continue to facilitate a wide range of research studies, such as gene discovery, examination of cancer-related outcomes and risk factors in high-risk subjects, investigation of novel behavioral interventions, and cancer prevention trials among at-risk family members. Consequently, the BCFR Cohort, as one of the few cohorts available worldwide with biospecimens and extensive molecular and genetic characterization, combined with epidemiologic data and long-term follow up, will be an invaluable resource for translational research in the genetic epidemiology of breast cancer.

  Study phs002835

• Targeted and shallow whole genome sequencing identifies therapeutic opportunities in p53abn endometrial cancers

  Purpose: Shallow whole genome sequencing (sWGS) can detect copy number (CN) aberrations. In high-grade serous ovarian (HGSOC) sWGS identified CN signatures such as homologous recombination deficiency (HRD) to direct therapy. We applied sWGS with targeted sequencing to p53abn endometrial cancers (ECs) to identify additional prognostic stratification and therapeutic opportunities. Experimental Design: sWGS and targeted panel sequencing was performed on formalin-fixed paraffin-embedded p53abn ECs. CN alterations, mutational data and CN signatures were derived, and associations to clinicopathologic and outcomes data were assessed. Results: In 187 p53abn ECs, 5 distinct CN signatures were identified. Signature 5 was associated with BRCA1/2 CN loss with features similar to HGSOC HRD signature. 22% potential HRD cases were identified, 35 patients with signature 5, and 8 patients with BRCA1/2 somatic mutations. Signatures 3 and 4 were associated with whole genome duplication, and CCNE1, ERBB2 and MYC amplifications, with mutations in PIK3CA enriched in signature 3. We observed improved overall survival (OS) for patients with signature 2 and worse OS for signatures 1 and 3. 28% of patients had CCNE1 amplification and this subset was enriched with carcinosarcoma histotype. 34% of patients, across all histotypes, had ERBB2 amplification and/or HER2 overexpression on immunohistochemistry, which was associated with worse outcomes. Mutations in PPP2R1A (29%) and FBXW7 (16%) were among the top 5 most common mutations. Conclusions: sWGS and targeted sequencing identified therapeutic opportunities in 75% of p53abn EC patients. Further research is needed to determine the efficacy of treatments targeting these identified pathways within p53abn ECs.

  Study EGAS00001007661