14977 results for "cancer studies using rna-seq"

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• Low Density Genotyping from the Fragile Families and Child Wellbeing Study

  The Fragile Families and Child Wellbeing Study (FFCWS) is based on a stratified, multistage sample of 4898 children born in large U.S. cities (population over 200,000) between 1998 and 2000, where births to unmarried mothers were oversampled by a ratio of 3 to 1. This sampling strategy resulted in the inclusion of a large number of Black, Hispanic, and low-income families. The project “Low Density Genotyping from the Fragile Families & Child Wellbeing Study” uses saliva collected from a subset of FFCWS children at Year 9 and from teens at Year 15 to examine genetic variation using a genome-wide assay (Illumina PsychChip). In addition to the genotype data, a limited set of self reported and anthropomorphic measures are included.

  Study phs002417

• Tracing tumor evolution and heterogeneity of pleomorphic carcinoma of the lung

  Pulmonary pleomorphic carcinoma (PPC) is an aggressive and highly heterogeneous non-small-cell lung carcinoma whose underlying biology is still poorly understood. Forty-two tumor areas including 39 primary tumors and 3 metastases from 20 PPC patients were microdissected and the histologically distinct components were subjected to whole exome sequencing (WES) separately. Twenty non-tumor areas from matched PPC patients were also subjected to WES. Twist Human Core Exome + RefSeq + Mito-Panel kit (Twist Bioscience) was used for the whole exome capturing according to manufacturer’s guidelines. Paired-end 100-bp reads were generated on the Illumina NovaSeq 6000. After the sequencing, reads were aligned against the reference human genome GRCh38 using Burrows-Wheeler Aligner (BWA, v0.7.12)

  Study EGAS50000000314

• NGS-based monitoring of the T cell receptor repertoire in living donor kidney transplant patients undergoing combination cell therapy

  Tolerance induction through mixed chimerism is a promising approach to achieve long term graft patency without or only low dose maintenance immunosuppression. In this phase I/IIa academic trial kidney transplant recipients in the case group were co-transplanted with bone marrow from the same donor and received an autologous cell product. The control group did not receive donor bone marrow or an autologous cell product. Peripheral blood mononuclear cells were collected at multiple timepoints before and after transplantation and the recipients native T-cell receptor (TCR) repertoire at time points 0, 1, 3 and 6 months after transplantation as well as their donor reactive TCR repertoire were analysed using NGS-based TCR sequencing.

  Study EGAS50000000210

• Single cell RNAseq data of human neurons, Bouwen et al Nat Comm 2025

  Brain tumor patients often suffer from epilepsy, but the underlying neuronal changes leading to this neurological symptom are not well understood. This study examined the transcriptomic profiles of human cortical neurons collected from brain tumor patients with or without seizures. The goals of the study were to identify the molecular identities of the recorded neurons and confirm that they are layer 2/3 pyramidal neurons rather than neuron-like tumor cells; and to compare the differential gene expression profiles between patients with and without epilepsy. For each patient, cortical neurons were rapidly isolated from surgically resected tissue and prepared using the Smart-seq2 protocol. Samples from 12 patients were sequenced on an Illumina HiSeq 2500 platform to generate their transcriptomic profiles.

  Study EGAS50000001369

• Target resequencing of LQTS-related 100 genes in Japanese patients

  Some of the fatal familial arrhythmias cause sudden death in seemingly healthy individuals. It is not easy to make a pre-symptomatic diagnosis of these diseases using only clinical examinations such as an electrocardiogram. The purpose of this study is to elucidate the cause of lethal arrhythmia and/or the genetic factors that influence its susceptibility, thereby enabling early diagnosis and selection of effective treatment methods, and preventing sudden death. The most important purpose of this study is to identify the sites of genetic abnormalities and disease-related genes in pre-symptomatic cases. If a genetic abnormality is found, it is expected that the functional abnormality can be evaluated and appropriate measures to prevent sudden death can be taken.

  Study JGAS000579

• DPY30_ChIP_seq

  H9 human embryonic stem cells (hESCs) were cultured in feeder-free chemically-defined conditions in medium containing 10ng/ml Activin A and 12ng/ml FGF2 (Vallier L. 2011, Methods in Molecular Biology, 690: 57–66). Chromatin immunoprecipitation was performed as described in Brown S. et al. 2011. Stem Cells 29: 1176–85 by using 5ug of anti-DPY30 antibody (Sigma, cat. number HPA043761). This protocol was performed in control hESCs (expressing a scrambled shRNA) and in hESCs stably expressing an shRNA against DPY30 (Sigma, clone n. TRCN0000131112).This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001132

• Sampling of multi-centric lower grade glioma influences management and provides insight into gliomagenesis

  Rare multicentric lower-grade gliomas (LGG) represent a unique opportunity to study the heterogeneity between distinct tumor foci in a single patient, and to infer their origins and parallel patterns of evolution. In this study, we integrate clinical features, histology and immunohistochemistry for 4 patients with multicentric LGG, arising both synchronously and metachronously. For 3 patients we analyze the phylogeny of the lesions using exome sequencing, including one case with a total of 8 samples from the two lesions. We show that each tumor in multicentric LGG cases may arise independently or may diverge very early in their development, presenting as genetically and histologically distinct tumors suggesting that comprehensive sampling of these lesions can significantly alter diagnosis and management.

  Study EGAS00001002495

• Fixation effects on variant-calling in a clinical resequencing panel

  Formalin fixation is the standard method for preservation of tissue for diagnostic purposes, including pathological review and molecular assays. However, this method is known to cause artifacts that can affect the accuracy of molecular genetic tests. We assessed the applicability of alternative fixatives to determine whether these perform significantly better on next-generation sequencing assays, and whether adequate morphology is retained for primary diagnosis, in a prospective study using a clinical-grade laboratory-developed targeted resequencing assay. We examined and quantified several parameters relating to sequencing quality and variant-calling from tumor and normal colon epithelial tissues. We identify one alterative fixative, Sakura Universal Molecular Fixative that suppresses many formalin-related artifacts while retaining adequate morphology for pathological review.

  Study EGAS00001003507

• A Phase II Clinical Trial of the PARP Inhibitor Talazoparib in BRCA1 and BRCA2 Wild-Type Patients

  The Talazoparib Beyond BRCA (TBB), a phase II proof-of-concept clinical trial, was designed to test the efficacy of talazoparib, a potent, orally bioavailable PARP inhibitor, with an established Phase II recommended dose, in the treatment of advanced BRCA wildtype (WT), HER2-negative breast cancer and other solid tumors with homologous recombination (HR) deficiency. Eligible patients had advanced HER2-negative breast cancer or other solid tumors with a germline or somatic mutation in a gene linked to the HR pathway. Patients identified as harboring a deleterious or suspected deleterious germline or somatic mutation in the HR pathway were treated with single agent talazoparib until disease progression. Primary and metastatic tumor samples were sequenced with panel sequencing and exome sequencing to detect commonly mutated genes and those associated with DNA repair deficiency. In addition, ctDNA was sequenced at baseline and progression. Fastq files for tumor and ctDNA sequencing are deposited. 

  Study phs002803

• Hi-C Profiling of Solid Tumor Samples

  Structural variants, such as translocations, inversions, duplications, and deletions, have long been recognized as mutations that can contribute to the initiation and progression of cancer. This can occur through a variety of mechanisms, including changes in gene dosage and the generation of fusion genes. In recent years, it is becoming increasingly clear that these events also can alter how genomes are organized in 3D space in the nucleus and contribute to changes in enhancer-promoter regulation. However, it remains poorly understand how often 3D genome structural changes occur in cancers, what genes are affected by these events, and in which tumor types. This study is part of ongoing work to better characterize 3D genome structural changes across a variety of tumor types to better understand where and when 3D genome structure changes as the result of structural variation in cancer genomes and to characterize the genes and tumor types affected.

  Study phs003227

• Gut Microbiome and Types of Colorectal Polyps

  Colorectal cancer is a heterogeneous disease arising from at least two precursors-the conventional adenoma and the serrated polyp. This dataset was used to test the relationship of the gut microbiota to specific colorectal polyp types. We included samples from two independent study populations based at colonoscopy clinics: the Centers for Disease Control and Prevention (CDC) Study of In-home Tests for Colorectal Cancer (SIT), and the New York University (NYU) Human Microbiome and Colorectal Tumor study. Gut microbiota were assessed in 667 colonoscopy-screened adults by 16S rRNA gene sequencing of stool samples, of which 540 were included in our analysis. Participants were categorized as conventional adenoma cases, serrated polyp cases, or polyp-free controls. CA cases were further classified as proximal or distal and as non-advanced or advanced. Serrated polyp cases were further classified as hyperplastic polyp or sessile serrated adenoma. Our results show associations between gut microbiome composition and presence of conventional adenomas, including reduced diversity and alterations in taxon abundance.

  Study phs001381

• The Genomic Landscape of Interval Colorectal Cancers

  Interval colorectal cancers (CRC), cancers diagnosed after a screening/surveillance exam in which no cancer is detected, and before the date of the next recommended exam, reflect an unprecedented challenge in CRC detection and prevention. To better understand this poorly characterized CRC variant, we examined the clinical and mutational characteristics of interval CRCs in comparison to screen-detected CRCs. We used data from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer randomized controlled trial (RCT) of flexible sigmoidoscopy (FS) screening, which enrolled 154,900 men and women aged 55 to 74 years between 1993 and 2001. Whole Exome Sequencing (WES) was performed on 323 samples from 168 participants (data available through dbGaP). We found colorectal cancers diagnosed between screening exams (interval cancers) had significantly increased risk of death than screen detected cancers although no significant molecular differences were observed between screen-detected and interval CRC at the whole exome sequencing level.

  Study phs003093

• Whole-exome sequencing reveals the origin and evolution of Hepato-Cholangiocarcinoma

  Hepatocellular-cholangiocarcinoma (H-ChC) is a rare subtype of liver cancer with clinicopathological features of both hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). To date, molecular mechanisms underlying the co-existence of HCC and iCCA components in a single tumor remain elusive. Here we show that H-ChC samples contain substantial private mutations from WES analyses, ranging from 33.1% to 86.4%, indicative of substantive intratumor heterogeneity (ITH). However, on the other hand, numerous ubiquitous mutations shared by HCC and iCCA suggest the monoclonal origin of H-ChC. Mutated genes identify herein e.g. VCAN, ACVR2A and FCGBP are speculated to contribute to distinct differentiation of HCC and iCCA within H-ChC. Moreover, immunohistochemistry demonstrate that EpCAM is highly expressed in 80% of H-ChC implying the stemness of such liver cancer. In summary, our data highlight the monoclonal origin and stemness of H-ChC, as well as substantial intratumoral heterogeneity.

  Study EGAS00001002783

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• Germline Mutations and Developmental Mosaicism Underlying EGFR-Mutant Lung Cancer

  We designed this study to examine the etiology of multiple lung tumors in EGFR-mutant non-small cell lung cancer patients who undergo surgical resection. Participants were 52-85 years old never or former smokers, who had at least 2 surgical lung tumor pathology specimens available in the Massachusetts General Hospital biobank, at least one of which had an EGFR mutation by routine clinical genotyping. 10 patients were selected, along with two patients from a family with an inherited EGFR-T790M mutation leading to multiple lung tumors. These patient tumor samples and matched normal lung samples were microdissected to collect DNA.In addition to obtaining clinical histories, we performed whole exome sequencing to evaluate the genetic profile of matched tumor and normal samples. The WES data are available on dbGaP. Our data showed increasing mutational burden and copy number alteration with increasing invasiveness, as compared to normal tissues, for the subset of samples that were assessed for tumor invasiveness. We also observed a range of numbers of shared mutations between tumors in a single patient, indicating different degrees of relatedness between lung tumors. We hope that this resource will help researchers understand non-small cell lung cancer presentations with multiple lung tumors. These data may aid in clinical decision making for such patients.

  Study phs003379

• Emergence of oncofetal plasticity is ubiquitous in early colorectal cancers

  Metastasis formation is classically considered a late-stage event in colorectal cancer (CRC) evolution. Yet, the time and patterning by which metastatic competence is acquired remain poorly understood. Here, we show that metastasis-associated oncofetal cell states already emerge at the earliest stages of CRC, concurrent with invasive front formation. However, while necessary for metastasis, we detect them ubiquitously among early non-metastatic cancers, highlighting additional bottlenecks like immune evasion. To understand how oncofetal cells first emerge, we generated multiregional organoid models that reflect successive tumor progression stages within individual early-stage CRCs. Whole-genome sequencing and growth factor-dependency assays exclude tumor cell-intrinsic acquired traits. In contrast, single-cell spatial atlases of the tumor-microenvironment before and after malignant transformation revealed stereotypic patterning of fibroblast subtypes resembling normal tissue architecture, resulting in distinct regional microenvironments. At the onset of malignant growth into the submucosa, the first cancer-associated fibroblasts (CAFs) to appear strongly resemble submucosal trophocytes and colocalize with oncofetal cell states at invasive fronts. Functionally, fibroblast-organoid co-cultures confirm that these trophocyte-like CAFs induce plastic transitioning to oncofetal states. Thus, interactions between tumor and submucosal fibroblasts directly following malignant transformation dictate the timing and location at which oncofetal plasticity first occurs during CRC progression.

  Study EGAS50000001532