Search Results - EGA European Genome-Phenome Archive

Integrated genomic analysis identifies driver genes and cisplatin-resistant progenitor phenotype in pediatric liver cancer

Dataset EGAD00001007858

Altered enhancer-promoter interaction leads to MNX1 expression in pediatric acute myeloid leukemia with t(7;12)(q36;p13)

Acute myeloid leukemia (AML) with the t(7;12)(q36;p13) translocation occurs only in very young children and has a poor clinical outcome. The expected oncofusion between breakpoint partners (MNX1 and ETV6) has only been reported in a subset of cases. However, a universal feature is the strong transcript and protein expression of MNX1, a homeobox transcription factor that is normally not expressed in hematopoietic cells. Here, we map the translocation breakpoints on chromosomes 7 and 12 in affected patients to a region proximal to MNX1 and either introns 1 or 2 of ETV6. The frequency of MNX1 overexpression in pediatric AML (n=1556, own and published data) is 2.4% and occurs predominantly in t(7;12)(q36;p13) AML. Chromatin interaction assays in a t(7;12)(q36;p13) iPSC cell line model unravel an enhancer-hijacking event that explains MNX1 overexpression in hematopoietic cells. Our data suggest that enhancer-hijacking is a more common and overlooked mechanism for structural rearrangement-mediated gene activation in AML.

Study EGAS50000000130

Copy number variations and fragmentation features in cell-free DNA predict response in advanced non-small cell lung cancer patients under anti-PD-(L)1 therapy

Anti-PD-(L)1 therapy is routinely used in metastatic NSCLC. However, response rates vary and biomarkers predicting treatment efficacy are lacking. Liquid biopsies allow monitoring of residual disease during therapy. Residual circulating tumor DNA (ctDNA) after therapy represents a predictive marker for adverse outcome. Here, we assessed copy number variations (CNVs) in serial plasma samples of NSCLC patients receiving PD-(L)1 blockade. We profiled CNV and fragmentation features in 118 plasma samples of advanced NSCLC patients by low-coverage whole genome sequencing taken at baseline, after four therapy cycles and at disease progression. CNVs and fragment features (i.e., fragment length, end motifs, position) were significantly altered in patients compared to healthy donors. Furthermore, fragment length changes at baseline and residual CNVs after four therapy cycles correlated with short progression-free survival. Our results highlight the suitability of plasma-based CNV and fragment analysis for disease monitoring and as a predictive marker for response duration in NSCLC patients under anti-PD-(L)1 therapy.

Study EGAS50000000441

Development of a fully human glioblastoma-in-brain-spheroid model for accelerated translational research

In glioblastoma (GBM) the intricate interplay between tumor cells and the surrounding microenvironment plays a crucial role in tumor progression, invasion, and therapeutic resistance. So far, studying these interactions in a controlled and representative model system has been challenging. Here, we report the development of hGliCS, a human glioma-cortical spheroid model that allows the elucidation of the biology of GBM cells and their interactions with a human-specific brain-like microenvironment and neurons. GBM cells efficiently invade the cortical spheroids, forming a well-connected network of communicating cells. The heterogeneous cellular states of the GBM cells within this model closely resembled findings previously observed in glioblastoma patients and in mouse xenografts. We observed a transition from predominantly mesenchymal-like cells to heterotopic states with a high diversity. In contrast to the substantial changes observed in the tumor cell population, the impact of the GBM cells on the neurons was minimal. We further demonstrate the suitability of hGliCS to test compounds targeting tumor-specific neurobiological features.

Study EGAS50000000757

KAT6A and KAT7 Histone Acetyltransferase Complexes Are Molecular Dependencies and Therapeutic Targets in NUP98-Rearranged Acute Myeloid Leukemia

NUP98 fusion oncoproteins (FOs) are a hallmark of childhood acute myeloid leukemia (AML). NUP98 FOs drive leukemogenesis through phase-separated condensate formation and maintenance of an active chromatin landscape at stem cell-associated genes in cooperation with epigenetic regulators. Here we show that MYST family histone acetyltransferase (HAT) complex proteins including KAT6A/MOZ, KAT7/HBO1, and the common KAT6A/7 complex subunit BRPF1 associate with NUP98 FOs on chromatin and within condensates. MYST HATs are molecular dependencies in NUP98-rearranged (NUP98-r) leukemia, and genetic inactivation or pharmacologic inhibition of Kat6a and Kat7 impairs NUP98-r cell fitness. KAT6A/7 inhibition decreased global H3K23ac levels, displaced NUP98::HOXA9 from chromatin at the Meis1 locus, and led to myeloid cell differentiation. Additionally, KAT6A/7 inhibition decreased leukemic burden in multiple NUP98-r leukemia xenograft mouse models, synergized with Menin inhibitor treatment, and was efficacious in Menin inhibitor-resistant cells. In summary, we show that MYST family HATs are therapeutically actionable dependencies in NUP98-r AML.

Study EGAS50000001075

Spatial predictors of response to immunotherapy in microsatellite stable metastatic colorectal cancer

Microsatellite stable (MSS) colorectal cancers (CRC) are largely unresponsive to immune checkpoint inhibition (ICI), prompting investigation into strategies to enhance sensitivity. The MAYA trial, which utilized temozolomide (TMZ) in MGMT-silenced MSS mCRC, hypothesized that TMZ-induced hypermutation could sensitize tumors to ICI. This phase II trial met its primary endpoint, demonstrating durable clinical responses with TMZ combined with ipilimumab and nivolumab. To elucidate factors influencing response heterogeneity, we conducted multi-omic spatial profiling of samples from patients who participated in the MAYA trial, including baseline and on-treatment tissue and blood specimens. While increased neoantigen load following TMZ exposure did not consistently predict for deep responses, spatial profiling revealed key determinants. Lymphocyte proportions, particularly CD8+KI67+ cells, within stromal and tumor compartments, along with macrophage composition (CD68+CD163+ cells) at the tumor-stromal interface, were predictive of response. Treatment pressures dynamically altered the tumor microenvironment composition and activated peripheral immune cells. This study is the first to identify spatial predictors of response to this promising novel treatment approach for MSS CRC.

Study EGAS50000001567

Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade

We conducted integrated multi-omics evaluation of 823 tumors from advanced renal cell carcinoma (RCC) patients and identified molecular subsets associated with differential clinical outcomes to angiogenesis blockade alone or with a checkpoint inhibitor. Unsupervised transcriptomic analysis revealed six molecular subsets with distinct angiogenesis, immune, cell cycle, metabolism, and stromal programs. While sunitinib was effective in subsets with high angiogenesis, atezolizumab+bevacizumab improved clinical benefit in tumors with high T-effector and/or cell cycle transcription. Somatic mutations in PBRM1 and KDM5C associated with high angiogenesis and AMPK/Fatty acid oxidation gene expression, while CDKN2A/B and TP53 alterations were more prevalent in tumors with increased cell cycle and anabolic metabolism. Sarcomatoid tumors exhibited low prevalence of PBRM1 mutations and angiogenesis markers, frequent CDKN2A/B, BAP1 and TP53 alterations, and increased PD-L1 expression. These findings can be applied to molecularly stratify advanced RCC patients, explain improved outcomes of sarcomatoid tumors to checkpoint blockade, and develop personalized therapies.

Study EGAS00001004353

The Precision Medicine in Liver Cancer across an Asia-pacific NETwork (PLANET) - Intratumoural immune heterogeneity

The clinical relevance of immune landscape intratumoural heterogeneity (immune-ITH) and its role in tumour evolution remain largely unexplored. Here, we uncover significant spatial and phenotypic immune–ITH from multiple tumour sectors and decipher its relationship with tumour evolution and disease progression in hepatocellular carcinomas (HCC). Immune–ITH is associated with tumour transcriptomic-ITH, mutational burden, and distinct immune microenvironments. Tumours with low immune–ITH experience higher immunoselective pressure and escape via loss of heterozygosity in human leukocyte antigens and immunoediting. Instead, the tumours with high immune-ITH evolve to a more immunosuppressive/exhausted microenvironment. This gradient of immune pressure along with immune-ITH represents a hallmark of tumour evolution, which is closely linked to the transcriptome-immune networks that contributes to disease progression and immune inactivation. Remarkably, high immune-ITH and its transcriptomic signature are predictive for worse clinical outcome in HCC patients. This in-depth investigation of ITH provides evidence on tumour-immune co-evolution along HCC progression.

Study EGAS00001003814

33 patients with Monoclonal Gammopathy of Undetermined Significance (MGUS) had whole exome sequencing performed. Cells were sorted by FACSAria using CD138, CD19 and CD56 to obtain a pure abnormal plasma cell population. Generated somatic variants were compared to a previous study of 463 multiple myeloma patients.

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant precursor of multiple myeloma (MM) with a 1% risk of progression per year. Although targeted analyses have shown the presence of specific genetic abnormalities such as IGH translocations, RB1 deletion, 1q gain, hyperdiploidy or RAS genes mutations, little is known about molecular mechanism of malignant transformation. We have performed whole exome sequencing together with SNP array analysis in 33 flow-cytometry separated abnormal PC samples of MGUS patients to describe somatic gene mutations and chromosome changes at the genome-wide level. Non-synonymous mutations (NS-SNVs) and copy number alterations (CNAs) were present in 97.0% and in 63.6% of cases, respectively. Importantly, the number of somatic mutations was significantly lower in MGUS compared to MM (p<10-4) and we have identified 6 myeloma significantly mutated genes which are KRAS, NRAS, DIS3, HIST1H1E, EGR1 and LTB in the MGUS dataset. We also found a positive correlation with increasing chromosome changes and somatic mutations. IGH translocations were present in 27.3% of cases comprising t(4;14), t(11;14), t(14;16) or t(14;20) and were in a similar frequency to MM, which corresponded with primary lesion hypothesis. Data from this study showed MGUS is a genetically comprehensive disease, however overall genetic instability is significantly lower compared to MM.

Study EGAS00001001658

DAC Portal documentation

DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

Documentation access/data-access-committee/dac-portal

Treatment of Pulmonary Hypertension and Sickle Cell Disease with Sildenafil Therapy (Walk-PHaSST)

Pulmonary arterial hypertension (PAH) is a progressive condition characterized by narrowing or stiffening pulmonary arterioles resulting in increased pulmonary blood pressure and reduced delivery of oxygenated blood to the body. It is a common complication of sickle cell disease and initially presents with the symptom of shortness of breath (dyspnea) on exertion. As the condition worsens, other symptoms such as dizziness, lower extremity edema, and chest pain can develop. The drug, sildenafil, works by relaxing blood vessels in the lungs which reduces pulmonary blood pressure and allows more oxygenated blood to circulate. Increased levels of oxygenated blood allows individuals with PAH to tolerate more activity, but guidelines for using sildenafil in patients with PAH and sickle cell disease were unavailable at the time of the Walk-PHaSST trial.Participants were screened for the existence of pulmonary hypertension with a six minute walk test and a Doppler echocardiogram that assessed TRV, diastolic function, and valvular and systolic function. Subjects with TRV ≥ 2.7 m/s received further clinical evaluation for possible causes of pulmonary hypertension. Other screening data included medical history, a physical exam, and standard laboratory testing. For individuals with moderate to severe pulmonary hypertension (TRV ≥ 3.0), a cardiac catheterization was done at the baseline and week 16 data collection periods.Subjects eligible for the main intervention trial based on screening results were randomized in a 1:1 double blind fashion to receive sildenafil or placebo for 16 weeks. Subjects received 20 mg of oral sildenafil or matching placebo 3 times daily for 6 weeks, followed by 40 mg 3 times daily for 4 weeks, followed by 80 mg 3 times daily for 6 weeks, as tolerated. Participants could also receive other therapies as needed to manage sickle cell and related complications. The primary outcome measure of the trial was change in the six minute walk test, a standard indicator of a person's heart and lung function and exercise capacity, from baseline to week 16. After completing the study treatment (or placebo), participants could choose to be part of the open-label follow-up phase of the study and continue to be assessed for up to one year.The study was intended to screen about 1000 subjects and randomize 132 subjects, however it was terminated early due to the unforeseen increase in adverse events in participants treated with sildenafil as compared to placebo. When the study was stopped, 33 participants had completed the trial. Subjects continued to be monitored, but were instructed to taper sildenafil treatment over three to seven days.There was no evidence that treatment with sildenafil impacted the six minute walk distance from baseline to week 16. In addition, treatment with sildenafil appeared to increase rates of hospitalization due to sickle cell disease pain.Due to in part to the early termination of the trial, the majority of subject data was collected from the screening phase of the study (n=720), as opposed to the main intervention trial (n=74).Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

Study phs002383

Genome-Wide Analysis of Diffuse Large B-Cell Lymphoma (De Novo and Derived from the High Grade Transformation of Follicular Lymphoma)

Version 1. Diffuse Large B-cell Lymphoma (DLBCL) represents the most common form of B-cell non-Hodgkin Lymphoma (B-NHL), accounting for ~30% of the de novo diagnoses and also arising as a frequent clinical evolution of Follicular Lymphoma (FL). The molecular pathogenesis of DLBCL is associated with multiple genetic lesions that in part distinctly segregate with individual phenotypic subtypes, suggesting the involvement of distinct oncogenic pathways. However, the lesions identified so far likely represent only a fraction of those necessary for malignant transformation. In order to characterize the entire set of structural alterations present in the DLBCL genome, we have integrated next generation whole exome sequencing analysis of 6 DLBCL cases and genome-wide high-density SNP array analysis of 72 DLBCL cases. We report here that FL and DLBCL harbor frequent structural alterations inactivating CREBBP and, more rarely, EP300, two highly related histone and non-histone acetyltransferases (HATs) that act as transcriptional co-activators in multiple signaling pathways. Overall, ~37% of DLBCL and 36% of FL cases display genomic deletions and/or somatic point mutations that remove or inactivate the HAT coding domain of these two genes. These lesions commonly affect a single allele, suggesting that reduction in HAT dosage is important for lymphomagenesis. We demonstrate specific defects in the acetylation-mediated inactivation of the BCL6 oncoprotein and activation of the p53 tumor suppressor. These results identify CREBBP/EP300 mutations as a major pathogenic mechanism shared by common forms of B-NHL, and have direct implications for the use of drugs targeting acetylation/deacetylation mechanisms. Version 2. Follicular lymphoma (FL) is an indolent, but incurable disease that, in 30-40% of cases, undergoes transformation to an aggressive diffuse large B cell lymphoma (DLBCL). The history of clonal evolution and the mechanisms that underlie transformation to DLBCL (tFL) remain largely unknown. Using whole exome sequencing and copy number analysis of 39 tFL patients, including 12 with paired sequential FL/tFL biopsies, we show that, in most cases, FL and tFL arise by divergent evolution from a common mutated precursor cell through the acquisition of distinct genetic lesions. Mutations in epigenetic modifiers (e.g., MLL2, CREBBP, EZH2, ARID1A) and anti-apoptotic genes (BCL2, FAS) were observed in 93% (36/39) and 78% (30/39) of cases, respectively, and were invariably shared between the two disease phases, suggesting an early acquisition in the common mutated precursor. Conversely, the development of tFL is associated with deregulation of genes involved in the control of cell proliferation, cell cycle progression and DNA damage responses (CDKN2A/2B, MYC, TP53), as well as with an aberrant activity of the somatic hypermutation mechanism. Finally, we show that the genomic profile of tFL shares significant similarities with that of germinal center B-cell type de novo DLBCL, but also displays unique combinations of altered genes that may explain the dismal clinical course of tFL.Version 3. This version includes thirty-five additional de novo DLBCL samples, newly diagnosed, and their paired normal DNA from previously untreated patients, which were analyzed by whole exome sequencing (n=27T and 25N), whole genome sequencing (8T and 10N), and/or targeted HLA-next generation sequencing (n=26T/N pairs) in order to identify genetic alterations associated with loss of surface MHC-II expression.

Study phs000328

Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Version 3 (ROC-Cardiac Epistry 3-BioLINCC)

Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data The ROC Cardiac Epistry version 3 include all cardiac arrest cases entered into the ROC database from April 1, 2011 to June 30, 2015. This is separate from versions 1 and 2 because of the major changes in how and which data were collected. Version 3 represents the following major changes to the structure and data collection: separate ED Arrival and Hospital Admits forms, mandatory ED and Hospital elements and a revamped CPR Process form. For ROC traumatic injury Epistry data please see: ROC-Trauma Epistry Objectives To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest; to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes. Background Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes. Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants There were 120,306 participants. Design ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

Study phs003726

Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Versions 1 and 2 (ROC-Cardiac Epistry 1 and 2-BioLINCC)

Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data: The ROC Cardiac Epistry versions 1 and 2 include all cardiac arrest cases entered into the ROC database from December 1, 2005 to April 1, 2011. ROC Cardiac Epistry 3 includes cardiac arrest cases captured between 2011 and 2015, and introduced significant changes in how and which data were collected.For ROC traumatic injury Epistry data, please see: ROC-Trauma Epistry Objectives: To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest;to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes.Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 109,326 cardiac arrest events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

Study phs003803

Prognostic and therapeutic significance of leukemia subtypes and minimal residual disease measurements in pediatric acute lymphoblastic leukemia treated with contemporary risk-directed trial: a cohort study

Background Genomic analyses to identify leukemia subtypes and refinement of minimal residual disease (MRD) determination are two recent advances that would have substantial impact on the management of children with acute lymphoblastic leukemia (ALL). However, the combined application of these two approaches to determine the prognostic and therapeutic significance of MRD measurement and full spectrum of leukemia subtypes in the context of contemporary risk-directed therapy has not been studied comprehensively.MethodsBetween October 29, 2007 and March 26, 2017, 598 consecutive children with newly diagnosed ALL were enrolled in a clinical trial at St. Jude Children’s Research Hospital. Patients were provisional classified according to the presenting clinical and laboratory features including immunophenotype and cytogenetics. Patients with ETV6-RUNX1 and high hyperdiploid B-progenitor ALL were provisionally included in the low-risk groups; those with TCF3-PBX1, hypodiploidy and T-cell ALL in the standard (intermediate)-risk group; and patients with BCR-ABL1, KMT2A and early T-cell precursor ALL in the high-risk group. Final risk assignment was based primarily on MRD levels measured by flow cytometry on Day 15 (≥1%) and Day 42 (<0.01% or ≥1%) of remission induction. Using genomic classification based on cytogenetics and genomic analysis (single nucleotide polymorphism array analysis and transcriptome, exome, and genome sequencing), patients were classified into 17 subtypes. The primary aim was to determine the event-free survival, overall survival and cumulative risk of relapse according to specific leukemia subtypes and MRD levels on Days 8, 15 and 42 of remission induction. This clinical trial was registered at ClinicalTrials.gov, number NCT00549848.FindingsPatients with ETV6-RUNX1, high hyperdiploidy and DUX4-rearranged B-ALL all received low- or standard-risk therapy, and had the highest 5-year event-free survival rates: 98.4% (95% CI, 95.9-100), 95.3% (91.2-99.4), and 95.0% (84.2-100), respectively. All 142 patients with Day 8 MRD<0.01%, except two with KMT2A-rearranged ALL and one with TCF3-PBX1, were in continuous complete remission. The TCF3-PBX1, PAX5alt, T-cell, early T-cell precursor, iAMP21 and hypodiploid ALL had intermediate outcome with 5-year event-free survival rates ranging from 80.0% (39.4-100) to 88.2% (71.7-100). Patients with KMT2A-rearranged, BCR-ABL1, BCR-ABL1-like and ETV6-RUNX1-like ALL had the worst outcomes, with 5-year event-free survival rates between 64.1% (43.9-84.3) and 76.2% (51.9-100), despite most patients receiving standard- or high-risk therapy. Treatment intensification based on MRD and genotype improved outcome. Relapse did not occur in any of the patients with ETV6-RUNX1, DUX4-rearranged, TCF3-PBX1, iAMP21, ZNF384-rearranged, hypodiploid, or BCR-ABL1-like B-ALL who had day 15 MRD≥1% and received standard-risk therapy. However, day 42 MRD<0.01% did not preclude relapse in patients with intermediate-risk or unfavorable subtypes including TCF3-PBX1, PAX5alt, T-cell, iAMP21, BCR-ABL1, BCR-ABL1-like, ETV6-RUNX1-like, KMT2A-rearranged, and MEF2D-rearranged ALL, despite intensified treatment in the standard- or high-risk arms. InterpretationContemporary risk-directed treatment based on genotypes and MRD can cure virtually all patients with ETV6-RUNX1, hyperdiploid>50 and DUX4-rearranged ALL. While intensified treatment improve outcome for patients with high MRD during induction, substantial proportions of patients with intermediate-risk or unfavorable subtypes would still relapse even achieving negative MRD status at the end of induction and require novel therapeutics to improve outcome.

Study EGAS00001004739

African American Multiple Myeloma GWAS

The case set is from a case-control study designed to identify common genetic risk factors for multiple myeloma in African Americans, the population with the highest risk for this cancer. We conducted two GWAS and combined each of these with convenience controls consisting of unaffected African American participants in cohorts with existing GWAS data. We then conducted a meta-analysis of the two sets. Cases were persons of African ancestry with smoldering or active multiple myeloma identified at participating oncology clinics or through SEER registries, as part of the African American Multiple Myeloma Study (AAMMS) diagnosed from Jan 1, 1988 through July 31, 2016. The majority of the samples were collected from incident and prevalent cases diagnosed since Jan 1, 2008 (80.9%), with a minority obtained from biobanks from cases diagnosed prior to 2008 (19.1%). Additional samples were obtained from the Multiethnic Cohort (USC and University of Hawaii) (n=40), the University of California at San Francisco Multiple Myeloma Study (n=27), and from the Multiple Myeloma Research Consortium for secondary analysis (samples originally provided to MMRC by 8 additional sites (n=84)). We have identified the phenotype (smoldering myeloma, plasma cell multiple myeloma, or myeloma not otherwise specified (myeloma NOS) when myeloma phenotype was not known), sex and age at diagnosis in this data set. The initial GWAS set consisted of 1308 (1,305 passed QC) cases with DNA samples, with a GWAS performed on the Illumina Human Core. Controls consisted of 7,078 unaffected African American subjects who were participants in the African American Prostate and Breast Consortium, with existing GWAS data from the Illumina1M Duo BeadChip. The second GWAS set consisted of 529 African American multiple myeloma patients with samples (406 from University of Arkansas, results not contained in this dataset because NCI funds were not used for the collection and genotyping) with GWAS data resulting from the Illumina Mega-BeadChip v1.1. Controls were 2,390 unaffected African American participants in the Multiethnic Cohort with existing GWAS data from the same array. After QC and removal of duplicates within sets, sex mismatches, and removal of plasmacytoma cases (ICD-0 code 9731), this deposited data set contains the typed GWAS data for the Illumina Human Core (set 1) (n=1298), and for the MegaBead Chip v1.1 (set 2) (n=123), with the University of Arkansas samples removed, for a total of 1,421 case genotypes. Note that 13 samples that overlap set 1 and set 2 were included.

Study phs001632

14970 results for "cancer studies using rna-seq"

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