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• Profiling the unique protective properties of intracranial arterial endothelial cells

  Cardiovascular disorders, like atherosclerosis and hypertension, are increasingly known to be associated with vascular cognitive impairment (VCI). In particular, intracranial atherosclerosis is one of the main causes of VCI, although plaque development occurs later in time and is structurally different compared to atherosclerosis in extracranial arteries. Recent data suggest that endothelial cells (ECs) that line the intracranial arteries may exert anti-atherosclerotic effects due to yet unidentified pathways. To gain insights into underlying mechanisms, we isolated post-mortem endothelial cells from both the intracranial basilar artery (BA) and the extracranial common carotid artery (CCA) from the same individual (total of 15 individuals) with laser capture microdissection. RNA sequencing revealed a distinct molecular signature of the two endothelial cell populations of which the most prominent ones were validated by means of qPCR. Our data reveal for the first time that intracranial artery ECs exert an immune quiescent phenotype. Secondly, genes known to be involved in the response of ECs to damage (inflammation, differentiation, adhesion, proliferation, permeability and oxidative stress) are differentially expressed in intracranial ECs compared to extracranial ECs. Finally, Desmoplakin (DSP) and Hop Homeobox (HOPX), two genes expressed at a higher level in intracranial ECs, and Sodium Voltage-Gated Channel Beta Subunit 3 (SCN3B), a gene expressed at a lower level in intracranial ECs compared to extracranial ECs, were shown to be responsive to shear stress and/or hypoxia. With our data we present a set of intracranial-specific endothelial genes that may contribute to its protective phenotype, thereby supporting proper perfusion and consequently may preserve cognitive function. Deciphering the molecular regulation of the vascular bed in the brain may lead to the identification of novel potential intervention strategies to halt vascular associated disorders, such as atherosclerosis and vascular cognitive dysfunction.

  Study EGAS00001004479

• Molecular and clinical diversity in primary central nervous system lymphoma: a LOC Network study

  Primary central nervous system lymphoma (PCNSL) is a distinct extranodal lymphoma presenting with limited stage disease but variable response rates to treatment despite homogenous pathological presentation. The likely underlying molecular heterogeneity and its clinical impact is poorly understood. We performed a comprehensive genome-wide analysis of 147 PCNSL from fresh-frozen tumor tissue from immunocompetent, treatment naïve PCNSL patients, employing whole-exome sequencing, assessment of somatic copy number alterations and DNA methylation, and RNA expression. These data were integrated and correlated with the clinico-radiological characteristics and outcomes of the patients. We validated our results in an independent series of 93 PCNSL formalin-fixed, paraffin-embedded (FFPE) samples. Consensus clustering of multi-omics data identified four robust, non-overlapping, prognostically significant clusters (CS) within PCNSL. The CS1 group, characterized by high proliferation and Polycomb Repressive Complex 2 (PRC2) complex activity had an intermediate outcome between CS2/CS3 and CS4. Patients who had PCNSL with an “immune-hot” (CS4) profile had the most favorable clinical outcome. In contrast, patients with the immune-cold hypermethylated CS2 and the heterogenous-immune CS3 groups had a poor prognosis. Nearly all PCNSL patients with meningeal infiltration harbored HIST1H1E mutations, enriched in the CS3 group. The integrated analysis suggests that the CS4 group may be more susceptible to immunotherapy. The integration of genome-wide data from multi-omics data revealed four molecular patterns in PCNSL with a distinctive prognostic impact that significantly improved the current clinical stratification. This molecular classification using FFPE samples facilitates routine use in clinical practice and provides potential precision-medicine strategies in PCNSL.

  Study EGAS00001006191

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• BCR Signaling in human BM PC

  The functions of bone marrow plasma cells (BMPC) beyond antibody production are not fully elucidated and distinct subsets of BMPC suggest potential different functions. Phenotypic differences were identified for human BMPC depending on CD19 expression. Since CD19 is a co-stimulatory molecule of the B-cell-receptor (BCR), and IgA+ and IgM+ BMPC express the BCR on their surface, we here studied whether CD19 expression affects cellular responses, such as BCR signaling and the expression of checkpoint molecules. We analyzed 132 BM samples from individuals undergoing routine total hip arthroplasty. We found that both CD19+ and CD19- BMPC expressed BCR signaling molecules. Notably, the BCR-associated kinase spleen tyrosine kinase (SYK) including pSYK was higher expressed in CD19+BMPC compared to CD19- BMPC. BCR stimulation also resulted in increased kinase phosphorylation downstream of the BCR while expression of CD19 remained stable afterwards. Interestingly, the BCR response was restricted to IgA+ BMPC independently of CD19 expression. With regard to the expression of checkpoint molecules, CD19- BMPC expressed higher levels of co-inhibitory molecule programmed cell death protein-1 (PD-1) than CD19+ BMPC. IgA+ BMPC characteristically upregulated PD-1 upon BCR stimulation in contrast to other PC subsets and inhibition of the kinase SYK abrogated PD-1 upregulation. In contrast, expression of PD-1 ligand, B and T lymphocyte attenuator (BTLA) and CD28 did not change upon BCR activation of IgA+ BMPC. Here, we identify a distinct functiotype of IgA+ BMPC that is independent of the phenotypic heterogeneity of the subsets according to their CD19 expression. The data suggest that IgA+ BMPC underlie different regulatory principles and/or exert distinct regulatory functions.

  Study EGAS00001004948

• Multi-omic analysis of the tumor microenvironment shows clinical correlations in Ph1 study of atezolizumab +/- SoC in MM

  Multiple myeloma (MM) remains incurable, and treatment of relapsed/refractory (R/R) disease is challenging. There is an unmet need for more targeted therapies in this setting; deep cellular and molecular phenotyping of the tumor and microenvironment in MM could help guide such therapies. This phase 1b study (NCT02431208) evaluated the safety and efficacy of the anti-programmed death-ligand 1 monoclonal antibody atezolizumab (Atezo) alone or in combination with the standard of care (SoC) treatments lenalidomide (Len) or pomalidomide (Pom) and/or daratumumab (Dara) in patients with R/R MM. Study endpoints included incidence of adverse events (AEs) and overall response rate (ORR). A novel unsupervised integrative multi-omic analysis was performed using RNA sequencing, mass cytometry immunophenotyping, and proteomic profiling of baseline and on-treatment bone marrow samples from patients receiving Atezo monotherapy or Atezo+Dara. A similarity network fusion (SNF) algorithm was applied to preprocessed data. Eighty-five patients were enrolled. Treatment-emergent deaths occurred in 2 patients; both deaths were considered unrelated to study treatment. ORRs ranged from 11.1% (Atezo+Len cohorts, n=18) to 83.3% (Atezo+Dara+Pom cohort, n=6). High-dimensional multi-omic profiling of the tumor microenvironment and integrative SNF analysis revealed novel correlations between cellular and molecular features of the tumor and immune microenvironment, patient selection criteria, and clinical outcome. Atezo monotherapy and SoC combinations were safe in this patient population and demonstrated some evidence of clinical efficacy. Integrative analysis of high dimensional genomics and immune data identified novel clinical correlations that may inform patient selection criteria and outcome assessment in future immunotherapy studies for myeloma.

  Study EGAS00001007286

• Ultra-fast deep-learned pediatric CNS tumor classification.

  The primary treatment of CNS tumors starts with a neurosurgical resection in order to obtain tumor tissue for diagnosis and to reduce tumor load and mass effect. The neurosurgeon has to decide between radical resection versus a more conservative strategy to prevent surgical morbidity. The prognostic impact of a radical resection varies between tumor types. However due to a lack of pre-operative tissue-based diagnostics, limited knowledge of the precise tumor type is available at the time of surgery. Current standard practice includes preoperative imaging and intraoperative histological analysis, but these are not always conclusive. After surgery, histopathological and molecular tests are performed to diagnose the precise tumor type. The results may indicate that an additional surgery is needed or that the initial surgery could have been less radical. Using rapid Nanopore sequencing, a sparse methylation profile can be directly obtained during surgery, making it ideally suited to enable intraoperative diagnostics. We developed a state-of-the-art neural-network approach called Sturgeon, to deliver trained models that are lightweight and universally applicable across patients and sequencing depths. We demonstrate our method to be accurate and fast enough to provide a correct diagnosis with as little as 20 to 40 minutes of sequencing data in 45 out of 49 pediatric samples, and inconclusive results in the other four. In four intraoperative cases we achieved a turnaround time of 60-90 minutes from sample biopsy to result; well in time to impact surgical decision making. We conclude that machine-learned diagnosis based on intraoperative sequencing can assist neurosurgical decision making, allowing neurological comorbidity to be avoided or preventing additional surgeries.

  Study EGAS00001007475

• Transcriptomics-driven classification of ALAL

  Acute leukemia of ambiguous lineage (ALAL) is a rare, poor-prognosis subtype of acute leukemia that cannot be assigned to a single hematopoietic lineage. Although ALAL patients are typically treated with acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) regimens, optimal treatment choice is hindered by their lineage ambiguity. Therefore, we investigated the added value of transcriptomics for improving lineage assignment, currently based mainly on surface markers. First, we used an in-house pipeline to comprehensively detect genetic lesions in RNA-sequencing data (n=23) with a sensitivity >90%, as confirmed by targeted DNA sequencing. Second, we compared ALAL gene expression profiles (GEPs) with representative AML (n=145), B-ALL (n=223) and T-ALL (n=85) cases. In a principal component analysis, ALALs did not form a separate group, but clustered with AML, B-ALL or T-ALL. Accordingly, a machine learning classifier trained with GEPs of acute leukemias segregated 20/23 ALALs into myeloid-, B- or T-lymphoid leukemia. These 20 cases harbored genetic abnormalities consistent with the classifier-assigned leukemia. Furthermore, ALAL GEPs were deconvoluted with single-cell transcriptional profiles of normal hematopoietic cells using CIBERSORTx, revealing enrichment for signatures of lineages corresponding to the leukemic type predicted by our algorithm. The classifier was validated on an external ALAL cohort (n=24), assigning 75% of the patients to a lineage matching their immunophenotypic and methylation profiles. In conclusion, integrative analysis of RNA-sequencing data can accurately classify most ALAL cases. The pipeline and classifier developed in our study are valuable tools to improve ALAL diagnosis and guide therapeutic decisions.

  Study EGAS00001007967

• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  Metformin reduces the incidence of breast cancer in patients with obesity and type 2 diabetes. However, our knowledge of the effects of metformin on breast cancer recurrence is limited. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin. To identify metformin-regulated signaling pathways, we integrated the transcriptomic, metabolomic and steroid hormone profiles using bivariate and functional analyses. We identified MS4A1, HBA2, MT-RNR1 and MT-RNR2 expression to be differentially expressed in breast tissues from metformin-treated women. We also found metformin-associated down-regulation of EGFL6 and FDCSP in postmenopausal women. The integration of transcriptomic and metabolomic profiles revealed down-regulation of immune response genes associated with reduced levels of arginine and citrulline in the metformin-treated group. The integration of transcriptomic and steroid hormone profiles showed an enrichment of steroid hormone biosynthesis and metabolism pathways with highly negatively correlated CYP11A1 and CYP1B1 expression in breast tissue from postmenopausal metformin-treated women. Our results indicate that breast cancer survivors treated with metformin have specific changes in breast tissue gene expression that may prevent the development of new tumors. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin using bulk RNAseq (total mRNA sequencing), that is deposited in Federated EGA. The steroid hormones, and metabolomics and the insulin sensitivity serum biomarker data underlying this article may be shared upon reasonable request to the PI of the MetBreCS study (Dr. Bernardo Bonanni), following approval by the Data and Safety Monitoring Board at IEO, Milan.

  Study EGAS50000000874

• 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