Search Results - EGA European Genome-Phenome Archive

Single-cell analysis of matched FFPE and frozen tissue samples reveals comparable resolution of intratumoural heterogeneity

Testing the differences between FFPE and fresh frozen derived single cell results.

Dataset EGAD00001015781

Autosomal recessive CD70 deficiency is associated with combined immunodeficiency and EBV viremia

We describe four patients from two unrelated families of different ethnicities who had primary immunodeficiency predominantly manifesting as susceptibility to EBV-related diseases. We performed whole exome sequencing (P1 and P2 from family 1) or whole genome sequencing (P4 and both parents from family 2) in those two families and identified homozygous frameshift or in-frame deletions in CD70 in these patients which abolished either CD70 surface expression or binding to its counter structure CD27. Sanger sequencing identified the same homozygous CD70 mutation in P3, which is not included in the dbGaP submission. Autosomal recessive CD70 deficiency is a novel cause of combined immunodeficiency and EBV-associated diseases, reminiscent of CD27 deficiency.

Study phs001245

A Dormant TIL Phenotype Defines Non-Small Cell Lung Carcinomas Sensitive to Immune Checkpoint Blockers

The biological determinants of sensitivity and resistance to immune checkpoint inhibitors are not completely understood. To elucidate the role of intratumoral T-cells and its association with the tumor genomic landscape, we perform paired whole exome DNA sequencing and multiplexed quantitative immunofluorescence (QIF) in pre-treatment samples from non-small cell lung carcinoma patients treated with PD-1 axis blockers. QIF was used to simultaneously measure the level of CD3+ tumor infiltrating lymphocytes (TILs), in situ T-cell proliferation (Ki-67 in CD3) and effector capacity (Granzyme-B in CD3). Whole exome sequencing reported in the number/type of mutations and predicted class I and class II mutant neoantigens.

Study phs001618

Multi-Ethnic Study of Atherosclerosis (Electrocardiogram Tracing Repository)

The Multi-Ethnic Study of Atherosclerosis (MESA) is a study of the characteristics of subclinical cardiovascular disease (disease detected non-invasively before it has produced clinical signs and symptoms) and the risk factors that predict progression to clinically overt cardiovascular disease or progression of the subclinical disease. MESA researchers study a diverse, population-based sample of 6,814 asymptomatic men and women aged 45-84. Thirty-eight percent of the recruited participants are white, 28 percent African-American, 22 percent Hispanic, and 12 percent Asian, predominantly of Chinese descent. Electrocardiogram Tracing Repository includes ECG tracings performed during MESA Exam 1 conducted between 2000 and 2002 and during MESA Exam 5 conducted between 2010 and 2012.

Study phs003703

UQCCR/QCMG brain metastasis sequence analysis

The present study, conducted by the UQCCR and QCMG at the University of Queensland, investigated the genomic landscape of brain metastases from 36 patients (with matched normal samples) that originated from a range of primary tumour sites (including breast (11), lung (18), melanoma (6) and oesophageal (1)) utilizing Illumina 2500 HiSeq Exome sequencing. Somatic substitutions were identified using a dual qSNP/GATK calling strategy. The Pindel tool was used to determined short insertions and deletions. Key biological pathways were investigated using pathway analysis tools. It is envisaged that the current work may provide important biological insights into the genomic characterization and evolution of brain metastasis and additionally identify druggable targets of clinical relevance.

Study EGAS00001000722

BLUEPRINT Gene expression analysis during human B-cell differentiation using the Affymetrix Human Genome U219 Array

We performed gene expression analysis using the Affymetrix Human Genome U219 Array of 9 subpopulations covering the complete B-cell maturation program: hematopoietic progenitor cells (HPC); pre-B-I cells (preB1C); pre-B-II cells (preB2C); naive B cells from peripheral blood (naiBC); naive B cells from tonsil (t-naiBC); germinal center B cells (gcBC); plasma cells from tonsil (t-PC); memory B cells from peripheral blood (memBC) and plasma cells from bone marrow (bm-PC). All the samples were isolated from healthy donors and, with the exception of bm-PC (only 1 replicate), we used 3 to 11 biological replicates of each subtype.

Study EGAS00001001197

A Prospective Natural History Study of Diagnosis, Treatment and Outcomes of Children with SCID Disorders

Individuals with a recent or new diagnosis of severe combined immune deficiency (including infants who were identified by newborn screening) may be eligible to be enrolled on the PIDTC research study 6901. Speak to your doctor to determine if you or your child may be eligible. Protocol 6901 follows patients with SCID prospectively, meaning the 6901 study enrolls participants where there is a plan to receive a blood and marrow transplant, enzyme therapy, or gene therapy in the future. Patients are then followed according to a schedule set out by the study protocol after the procedure. The times the study requests follow up will be the same as when your doctor would want to be seeing you or your child as part of their regular ongoing medical care. Patients with "leaky SCID", reticular dysgenesis, and Omenn syndrome may also be eligible to participate in 6901. The 6901 research study does NOT dictate how your or your child's doctors should treat you or your child, as the PIDTC recognizes that there are many complex factors that go into this decision. The decision about how you or your child with SCID will be treated is made by your doctor. The 6901 study simply follows how you or your child do over time. There are no experimental therapies on this study. The 6901 study has been open since August 2010, and continues to be open and enrolling patients to the present day. The study plans to enroll approximately 250 patients with SCID. By studying new patients undergoing treatment for SCID, the goal is to learn more about: (1) outcomes from the treatment of SCID in the modern era of medicine, (2) what factors lead to the best long-term outcomes, such as best donor, conditioning regimen, timing of transplant, etc. and (3) what impact newborn screening and the early diagnosis of SCID has had on the long-term outcomes following BMT or gene therapy. A significant amount of information is also being gathered on how and when the immune system recovers after BMT, quality of life for long-term survivors, and about whether children develop normally after treatment. The 6901 study is the largest coordinated prospective study of patients with SCID ever performed. Information that we will learn, both now and in the future, will help doctors and other health professionals to better treat children with SCID. All hospitals within the PIDTC are enrolling patients with SCID on 6901, ensuring that the outcomes are reflective of what happens in the "real world" as opposed to at just one or two large centers.

Study phs001392

New England-Based Case-Control Study of Ovarian Cancer

The case-control data provided to dbGaP were collected under NCI grant number CA54419 which had 3 enrollment phases: I (1992-1997), II (1998-2002) and III (2003-2008). Altogether, CA54419 funded 15 years of data and specimen collection, resulting in one of the longest running population based case-control studies of ovarian cancer. The study goal was to examine reproductive factors, lifestyle exposures, and genetic background in relation to ovarian cancer risk. The project recruited cases from Eastern Massachusetts and New Hampshire, and became known as the New England-based Case-Control study (NECC) of ovarian cancer. 3957 ovarian cancer cases diagnosed at ages 18-80 and residing in Eastern Massachusetts and New Hampshire were identified through tumor boards and registries. Of these, 3083 (78%) were eligible and among those eligible, 2203 (71%) were enrolled. After excluding 127 non-epithelial and 35 mixed mesodermal tumors, 2041 cases with epithelial tumors of ovarian, primary peritoneal, and fallopian tube origin, including borderline malignancies were included. On average, cases were interviewed 10 months after their diagnosis. A pathologist reviewed pathology reports and recorded histologic subtype, grade, and stage. Mixed ovarian cancers described as predominantly one type were classified as that type. Undifferentiated, transitional cell tumors, or mixed serous/transitional cell tumors were counted as serous. Other mixed epithelial, malignant Brenner, and unspecified epithelial tumors were classified as other. 2100 controls were identified through random digit dialing (RDD), driver-license lists, and town-resident lists. During the first funding phase from 1992 to 1997, 420 (72%) controls identified through RDD and 102 (51%) through town-resident lists agreed to participate. From 1998 to 2008, only town-resident lists were used to identify potential controls. 4366 controls were identified, of whom 1426 (33%) were ineligible if they had died, moved, or were seriously ill or if they did not have a working telephone, speak English, or have at least one ovary. Of eligible controls, 1362 (46%) declined to participate by phone or via 'opt-out' postcard and 1578 (54%) were enrolled. Controls were frequency matched to cases by 5-year age groups and region of residence. In all phases, after written informed consent, demographic information, reproductive and medical history, and habits were assessed by in-person or telephone interview (in a few instances). All of the questions were framed with respect to a reference date defined as 1 year before the diagnosis date for women in the case group and the date of interview for those in the control group. This study was approved by institutional review boards at Dana Farber Harvard Cancer Center and Dartmouth Medical Center.

Study phs001034

A Clone's Genomic Stability as a Biomarker of Its DNA-Damage Resilience

A main problem in the treatment of advanced cancers, including gastric cancers and glioblastoma, is the incertitude at which we predict how individual patients will respond to DNA-damaging agents, especially on the long run. Knowing the mechanism behind a patient's response, or the lack thereof, will help us depart from the oversimplified “more-is-better” and “one-size-fits-all” principles according to which DNA-damaging agents are administered. This will improve clinical outcome by allowing us to pinpoint those who would respond better and longer to lower doses than to higher doses of DNA-damaging agents. Under the assumption that the success of DNA-damaging therapy increases with the proliferation rate of a relatively homogeneous tumor population, there was little reason to assume anything other than monotonic dose-response relations. With the recent paradigm shift that most cancers are in fact DNA mosaic products of ongoing evolution, comes the urgency to reconsider these fundamental principles behind DNA-damaging therapy administration. As the developers of one of the first DNA deconvolution methods and with access to technologies to profile the transcriptomes of up to 10,000 cells simultaneously, we are equipped to embark on first personalized dose-finding strategies for DNA-damaging therapies. We will test the potential of the very long-term legacy that DNA-damage entails on a cell “genomic instability” as new biomarker of DNA-damage response. Our preliminary studies showed that, for most cancer types, DNA-damaging agents change a clone's genomic instability and that clones succumb to a limit in the amount of genomic instability they can tolerate. In particular, our results showed that patients with intermediate genomic instability have a very poor outcome and that this relation is only evident among treatment-naive patients, but not among patients treated with DNA-damaging agents. Further they show that we can measure genomic instability per clone and that clones with extreme genomic instability typically don't grow large. Our hypothesis that genomic instability, rather than proliferation rate, determines how sensitive a tumor is to DNA-damaging agents on the long-term, is founded on two unexpected findings. Patients with extremely high genomic instability per tumor clone have an exceptionally good outcome. Aim 1 will integrate exome- and single cell RNA-Seq (scRNA-Seq) data to characterize clones and to measure how much genomic instability they can tolerate. Low genomic instability is associated with reduced benefit from DNA-damaging agents. Aim 2 will use comet assays and treatment history to quantify DNA damage per clone, relating it to the clones' ability to tolerate DNA damage and to changes in the genomic instability of therapy-surviving clones.

Study phs003762

How to upload Crypt4GH files

Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated, you will be able to request a submitter role. [Optional] Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g., ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs) for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username. If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days. Specific to using keys to authenticate Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

Documentation submission/data/uploading-files/inbox

Emirati T2T Assembly

This study generates a telomere-to-telomere (T2T) trio-based genome assembly for a female Emirati individual, creating a high-quality reference representative of an understudied population. We produced multi-platform sequencing for a single family trio: PacBio HiFi (>60× per parent, >120× offspring), ONT ultra-long reads (>110× offspring), and Illumina short-read WGS (>100× for all three). The offspring genome was assembled in a trio framework to leverage parental information for accurate phasing and error resolution. Scaffolding and finishing utilized NTLink for initial scaffolding, RagTag for reference-guided refinement, and Quartett for gap filling, resulting in a contiguous, population-relevant assembly suitable for downstream variant discovery and pangenome integration.

Study EGAS50000001235

Environmental Arsenic and Diabetes Mellitus (Chihuahua Cohort)

The Chihuahua Cohort was established as part of a larger NIEHS-funded project that used laboratory models (tissue culture and mice) and human subjects to examine the association between exposure to arsenic and diabetes, including identification of mechanisms underlying this association. All procedures involving human subjects were approved by institutional review boards at the University of North Carolina at Chapel Hill and Cinvestav-IPN (Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico). All participants provided signed informed consent. A total of 1,160 adults (≥ 18 years old) with a minimum 5-year uninterrupted residency in the Chihuahua study area were recruited between 2008 and 2012. Samples of drinking water were obtained from the participants' households. Data on residency, occupation, drinking-water sources and use, smoking, alcohol consumption, and medical history were recorded by a study questionnaire. Spot urine and fasting venous blood were collected during medical examination that included an oral glucose tolerance test (OGTT) with blood drawn 2 hr after a 75-g glucose dose. Plasma from both fasting and 2-hr blood samples was collected. Body mass index (BMI) of each participant was calculated from body weight and height. BMI cutoffs of ≥ 25.0, ≥ 30, and < 18.5 kg/m2 were used to define overweight, obese, and underweight individuals, respectively. Participants' waist circumference was also measured. Blood pressure was assessed using a manual sphygmomanometer. Three measurements were taken at intervals of at least 1 min, with a 5-min rest before obtaining the first reading; the mean of the last two measurements was used. The concentrations of arsenic in drinking water and arsenic species in urine were measured by hydride generation-atomic absorption spectrometry. The limit of detection for As in water as well as for As species in urine was 0.01 µg As/L. Creatinine concentration in urine was determined using an established colorimetric assay. DNA was isolated from blood for genotyping analysis.The following measures were taken: glucose concentration in fasting blood (FBG) and in blood collected 2 hours into OGTT (2HBG); concentrations of triglycerides, HDL and total cholesterol (TC) in fasting plasma; and LDL was calculated using the Friedewald equation. Diabetes was classified by FPG ≥ 126 mg/dL, 2HPG ≥ 200 mg/dL, or self-reported diabetes diagnosis or medication use (WHO/International Diabetes Federation 2006). Prediabetes was defined as the absence of diabetes with FPG ≥ 110 mg/ dL or 2HPG ≥ 140 mg/dL. Elevated fasting levels of each lipid were defined as plasma TG ≥ 150 mg/dL, TC ≥ 200 mg/dL, and LDL ≥ 130 mg/dL. Fasting HDL < 40 mg/dL in men and < 50 mg/dL in women were designated as low. Hypertension was defined by systolic blood pressure (SBP) > 140 mm Hg, diastolic blood pressure (DBP) > 90 mmHg, or self-reported use of anti-hypertensive medication. Genotyping analysis, which will be available in version 2 of this study, was carried out in a subset of ~600 subjects with focus on enzymes involved in the metabolism of arsenic, including AS3MT, GSTs.All data collected during the medical exams or generated by the above-described chemical and biochemical analysis of water, urine and blood/plasma, as well as results of the genotyping analysis are included in dbGaP. Data generated by metabolomics and epigenetic analyses in small subsets of the Chihuahua cohort are not included, but are available in published articles.

Study phs002139

Finding the way towards the eradication of therapy-related myeloid neoplasms

Therapy-related neoplasms are directly caused by the drugs used to eradicate a previous cancer. It is well known that a subset of drugs designed to kill cancer cells through DNA-damage-induced apoptosis can intrinsically, as secondary effect, cause DNA-damage in other cells, thus inducing a neoplasm. Those neoplasms have especially bad prognosis. Why are these drugs still in use? Because for many diseases we do not have anything better, at the moment. Approximately 0.5-1% of the kids treated for various types of cancers develop a therapy-related myeloid neoplasm (tMN) as a result of the treatment. Despite having the same origin, their prognostic and molecular features are different from the adult ones, and a complete understanding of the pathogenesis of tMN in children is still missing. A total of 84 paediatrics neoplasms have been profiled by either Whole Genome or Whole Exome sequencing by Jason R. Schwartz and his colleagues, in a collaborative effort guided at the St. Jude Children's hospital, in Memphis. They identified a mean of 28 somatic mutations per patient, a number which is significantly bigger than the number of mutations found in paediatrics primary neoplasms, as depicted in the original paper's figure 1b, shown here. Their sequencing data identified that Ras/MAPK pathway mutations, alterations in RUNX1 or TP53, and KMT2A rearrangements as the most frequent drivers of the disease. Clonal evolution analysis (the comparative study of the behaviour of cells harbouring different mutations) highlighted that, unlike in adults’ tMN, there were not pre-existing leukemic clones, and virtually all mutated cells were found arising as a direct consequence of cytotoxic therapy. This represents yet another important difference between adult and paediatric tMN. This important study have been published in Nature communications in February 2021. The sequencing data have been deposited at EGA under the study number EGAS00001004850. Thanks to this, other researchers around the world could re-use it to uncover more information, integrate and validate their studies. Revealing the molecular features underlying the development of cancers is the only way towards the development of new generation targeted therapies, which will allow us to finally drop the cytotoxic drugs eliciting such unwanted and catastrophic secondary effects as tMN. It is not an overstatement to say that, while improving the treatment of primary tumours, we could entirely eradicate therapy-related neoplasms right from their origin. Looking forward.

Blog eradication-of-therapy-related-myeloid-neoplasms

160 WES and 25 WGS for HBV related HCC, and 15 WES for ICC belongs LICA-CN.

Study EGAS00001002331

BCR-ABL is enriched in S- and G2-cell cycle phases

To address the unusually long duration of imatinib therapy for chronic myelogenous leukemia, we sought to understand the expression of BCR-ABL gene expression with different phases of the cell cycle. A precedent for dynamic fusion oncoprotein expression already exists for the pediatric solid tumor, rhabdomyosarcoma. Quantitative immunocytochemistry was conducted to compare the expression of BCR-ABL with markers of each cell cycle phase: CDK6 for G1, CDK2 for S, phospho-CDC2 for G2, and phospho-HH3 for M phase. BCR-ABL expression was most strongly correlated with the G2 and S cell cycle phases. Low BCR-ABL expression only coincided with high cell cycle marker expression for the G1. BCR-ABL was rarely if at all expressed in M. Our results suggest a possible explanation for the prolonged nature of imatinib therapy, which may be only effective on S- and G2-phase actively replicating leukemia cells.

Study EGAS00001006769

Single-Cell Analysis of CD19-Specific CAR T Cell Treatment of Relapsed/Refractory CD19+ Acute Lymphoblastic Leukemia

By leveraging single-cell transcriptome and T cell receptor (TCR) sequencing, we aimed to track the transcriptional signatures of chimeric antigen receptor (CAR) T cell clonotypes throughout the course of treatment and furthermore identify molecular patterns leading to potent CAR T cell cytotoxicity. The data presented in this study encompass blood and bone marrow samples from patients ≤ 21 years of age with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) participating in the SJCAR19 phase I/II clinical trial (NCT03573700). In brief, patients enrolled in the clinical trial received either 1 x 10^6 (dose level 1) or 3 x 10^6 (dose level 2) per kilogram of body weight following successful generation of autologous CAR T cell products and lymphodepleting chemotherapy. Peripheral blood was drawn from each participant every week until week 4 post-infusion, at week 6 or 8, and month 3 or 6 if feasible. At week 4 post-infusion, blood marrow was also collected from participants. Total T cells (CD3+) were sorted from each post-infusion sample, as well as the pre-infusion CAR T cell products, and processed through 10x Genomics’ single-cell gene expression and V(D)J sequencing platform using the standard protocol. We identified a unique and unexpected transcriptional signature in a subset of pre-infusion CAR T cells that shared TCRs with post-infusion cytotoxic effector CAR T cells. Functional validation of cells with even a subset of these pre-effector markers demonstrated their immediate cytotoxic potential and resistance to exhaustion. The data deposited into dbGaP include all raw single-cell expression and V(D)J sequencing generated from samples obtained from 16 study participants.

Study phs002966

Resuscitation Outcomes Consortium (ROC) Prehospital Resuscitation on Helicopter Study (PROHS) (ROC-PROHS-BioLINCC)

Accessing Data: Please refer to the "Authorized Access" section below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.Objective: To observe if patients with severe traumatic injuries evacuated to level 1 trauma centers on air ambulances who received prehospital red blood cells and/or plasma had decreased in-hospital mortality compared to patients who received only crystalloids.Background: Injury is the leading cause of death in adults and children between the ages of 1 and 44 years. However, approximately 40% of in-hospital deaths among injured patients involve massive truncal hemorrhage that is considered potentially salvageable. Multiple retrospective military and civilian studies have reported that transfusion involving blood component ratios approaching whole blood are associated with significant decreases in 24-hour and 30-day mortality among injured patients. Furthermore, prehospital transfusion (PHT) studies from military and civilian hospitals have shown that prehospital transfusion of plasma and red blood cells (RBCs) is not only feasible, but associated with improved coagulation status on arrival. Prehospital resuscitation practices in the US differ significantly in approach, with most systems using crystalloids while a few offer RBCs or a combination of plasma and RBCs. No large multicenter civilian studies have evaluated the use of prehospital plasma and RBCs in severely injured patients compared to crystalloids.Participants: A total of 25,118 trauma patients were admitted during the 10 month enrollment period to the nine participating centers, of which 2341 arrived by helicopter, and 1058 met the highest risk criteria. Of the high risk sub-set, 585 arrived on helicopters with blood products available and 473 patients arrived via helicopters without blood available. One hundred forty two patients (24%) of those transported on helicopters with blood products available actually received PHT and 916 patients did not.Design: PROHS was a multicenter, prospective pragmatic, observational study of prehospital resuscitation approaches. There were no study guidelines dictating resuscitation practice (i.e., use of blood products or end of resuscitation). Of the 9 participating Level I trauma centers, 5 had helicopter systems with plasma and/or RBCs while the other 4 had helicopter systems that used only crystalloid resuscitation. All patients arriving to the participating center's trauma emergency department via helicopter directly from the scene were screened and had initial data collected. Of that group, all patients who met the highest risk category or received blood during transport were followed by direct observation during the initial resuscitation period and then indirectly through medical chart review until hospital discharge or 30 days after admission (whichever occurred first).The primary outcome was in-hospital mortality at 3 hours, 24 hours, and 30 days. Other outcome measures included length of hospital stay, number of ICU days, number of ventilator days, blood product usage, GOSE score at discharge, number of patients with complications, and number of patients who required hemostatic devices. Conclusions:Because of the unexpected imbalance in injury severity between systems with and without blood products on helicopters, all analyses were inconclusive. With few units transfused to each patient and small outcome differences between groups, large randomized studies will be required to detect significant survival differences in this important population.

Study phs003826

dataset for esophageal cancer, 17 pairs for whole-genome sequencing and 71 pairs for whole-exome sequencing

Dataset EGAD00001000760

EORTC RP1335 SPECTA Lung cancer data

SPECTAlung has recruited patients with thoracic malignancies, collecting FFPE material for molecular analysis. Illumina TST170 NGS panel was used for sequencing. The goal was to identify patients for whom a personalized treatment was possible and build a clinically-annotated molecular database for thoracic patients.

Study EGAS00001004485

Childhood Cancer Data Initiative (CCDI): Texas Pediatric Patient Derived Xenograft

The DNA/RNA samples of patients for which PDX models were developed were from a population of children, adolescents, and young adults from South Texas. Consent was obtained for collection of malignant and non-malignant tissue samples for genetic analysis and for the development of models to study new ways to treat cancer. For additional details, please refer to these publications: PMID: 37915590; PMID: 37990009.

Study phs003215

Molecular Phenotyping to Accelerate Genomic Epidemiology (MolPAGE)

MolPAGE is an integrated EU project that aims to develop methods and tools for discovery of biomarkers associated with diabetes (see www.ocdem.ox.ac.uk for MolOBB and www.twinsuk.ac.uk for MolTWIN).

Study EGAS00000000102

Tumor Profiler Project - OV bulk transcriptomics data additional samples

The dataset contains bulk transcriptomics data from 4 samples from 3 different patients with ovarian cancer. 3 samples were collected from the tumor, 1 sample from ascites. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format. These samples are from patients not classified as as having high-grade serous adenocarcinoma.

Dataset EGAD50000001411

Indonesian Genome Diversity Project 3

Low coverage whole genome sequences (1-3x) were collected to study human genetic history across Wallacea archipelago and West Papuan Region of Indonesia . We collected 254 newly sequenced genomes mostly from previously undocumented populations including 9 communities in Wallacea and 3 in West Papuan regions. Out of 254, there are 8 high coverage whole genome sequences were produce in the study (L4).

Dataset EGAD50000000647

16SV4-sequence data

Raw 16SV4-sequence data from bronchial brushing DNA obtained from healthy volunteers prior to and four weeks after ICS treatment.

Dataset EGAD50000000527

Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression

Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycaemic traits. However, little is known about the function of the zinc finger transcription factor Ras-responsive element binding protein 1 (RREB1) in glucose homeostasis or how changes in its expression and/or function influence diabetes risk.

Dataset EGAD50000000515

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