14970 results for "cancer studies using rna-seq"

in 34.04 milliseconds.

• 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

• 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

• A somatic reference standard for cancer genome sequencing with COLO829

  Identification of somatic alterations in cancer has become feasible with the massive adoption of next generation sequencing. However, due to variability in sequencing and informatics pipelines, a common somatic reference is needed. We thus performed paired whole genome sequencing of a metastatic melanoma cell line (COLO829) and a matched lymphoblastoid line (COLO829BL) across three institutions (TGen, Illumina, Genome Sciences Centre at the British Columbia Cancer Agency). We performed a meta-analysis of all data, in combination with the originally reported analysis of these cell lines by Pleasance et al (PMID: 20016485), and report a somatic reference standard based on consensus events. DNA extractions, library preparation, sequencing, and analysis were separately performed at each site. Results were compiled with sequence data previously generated for the cell lines to identify true positive variants, collectively representing a somatic reference standard. Overall, common somatic events include point mutations, small insertion/deletions, and genes affected by concurrent copy number changes. We additionally show divergence of somatic mutations in COLO829 associated with differences in cell line lineage. We present this reference to the community as a standard for enabling interpretation and identification of somatic mutations across institutions and analytical pipelines.

  Study EGAS00001001385

• Nanopore_and_Illumina_sequencing_of_human_glioblastomas

  Liquid biopsy analysis refers to methods designed to detect tumour-specific material (e.g., circulating tumour cells or tumour DNA) in body fluids, such as urine or blood samples. A widely-used liquid biopsy approach consists of genotyping the circulating tumour DNA (ctDNA) through sequencing of plasma/serum DNA. Although useful in the context of metastatic disease (where the concentration of ctDNA is high), current liquid biopsy technologies show limited sensitivity of detection for the early detection of cancer, and low specificity, as cancer-related mutations accumulate in healthy tissues as part of the ageing process, thus making it difficult to distinguish these from tumour mutations, and because sequencing errors and true mutations show overlapping profiles. Therefore, developing liquid biopsy protocols with increased sensitivity and specificity represents an urgent clinical need. Here we harness extrachromosomal circular DNA (eccDNA) elements, which are circular DNA structures physically separated from the chromosomes of up to several Mbp long pervasive in human cancers, for liquid biopsy analysis. In this pilot study we will focus on the analysis of glioblastomas, because there is strong evidence for the presence of eccDNA in these tumour types, and because developing liquid biopsy approaches for brain tumours to reduce the invasiveness of brain tumour biopsies remains an unmet clinical need.

  Study EGAS00001005812

• 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

• Durable clinical impacts and mechanisms of action and resistance in histone K27 methylation-targeting epigenetic therapy

  Epigenomes allow the rectification of disordered cancer gene expressions, thereby providing new targets for pharmacological interventions in cancer therapy. The clinical utility of targeting histone H3 lysine tri-methylation (H3K27me3) as an epigenetic hallmark has been demonstrated. However, in actual therapeutic settings, the mechanism by which H3K27me3-targeting therapies exert their effects and the response of tumor cells remain unclear. Here we show the potency and mechanisms of action and resistance of a newly developed EZH1/2 dual inhibitor, valemetostat, in the clinical trials of patients with adult T-cell leukemia/lymphoma (ATL). Valemetostat administration reduced the tumor size and demonstrated durable clinical benefits. Integrative single-cell analyses of patients before and after the administration of valemetostat showed that valemetostat abolishes the highly condensed chromatin structure formed by the plastic H3K27me3 and neutralizes multiple gene loci, such as tumor suppressor genes. Nevertheless, subsequent long-term treatment encounters the emergence of resistant clones with reconstructed aggregate chromatin that closely resemble the pre-dose state. The acquired mutations at the EZH2???compound interface result in the propagation of the clones with re-increased H3K27me3. In patients free of this mutation, TET2 mutation or elevated DNMT3A expression causes similar chromatin recondensation through de novo DNA methylation at the H3K27me3-associated regions. We identified subpopulations with distinct metabolic and gene translation characteristics implicated in primary susceptibility until the acquisition of the heritable (epi)mutations. Targeting chromatin homeostasis might provide opportunities for further sustained epigenetic cancer therapies.

  Study JGAS000553

• Organoid cultures of early-onset colorectal cancers reveal distinct and rare genetic profiles

  Sporadic early-onset colorectal cancer (EOCRC) has bad prognosis, yet is poorly represented by cell line models. We examine the key mutational and transcriptomic alterations in an organoid biobank enriched in EOCRCs. We established paired cancer (n=32) and normal organoids (n=18) from 20 patients enriched in microsatellite-stable EOCRC. Exome and transcriptome analysis was performed. We observed a striking diversity of molecular phenotypes, including PTPRK-RSPO3 fusions. Transcriptionally, RSPO fusion organoids resembled normal colon organoids and were distinct from APC mutant organoids, with high BMP2 and low PTK7 expression. Single cell transcriptome analysis confirmed the similarity between RSPO fusion organoids and normal organoids, with a propensity for maturation on Wnt withdrawal, whereas the APC mutant organoids were locked in progenitor stages. CRISPR/Cas9 engineered mutation of APC in normal human colon organoids led to upregulation of PTK7 protein and suppression of BMP2, but less so with an engineered RNF43 mutation. The frequent co-occurrence of RSPO fusions with SMAD4 or BMPR1A mutation was confirmed in TCGA database searches. RNF43 mutation was found in organoid from a leukaemia survivor with a novel mutational signature; and organoids with POLE proofreading mutation displayed ultramutation. The cancer organoid genomes were stable over long culture periods, while normal human colon organoids tended to be subject to clonal dominance over time. These organoid models enriched in EOCRCs with linked genomic data fill a gap in existing CRC models and reveal distinct genetic profiles and novel pathway cooperativity.

  Study EGAS00001004063

• BASIS_450k

  BASIS breast cancer DNA methylation Illumina 450k

  Dataset EGAD00010000916

• The evolution of ovarian high grade serous carcinoma from STIC lesions

  We performed Ampliseq on 11 samples on a targeted panel of 17 genes.

  Study EGAS50000000361

• cfDNA dataset with whole genome sequencing for cfDNA cohort

  Cell free DNA sequencing data for 13samples with whole genome sequencing across multiple cancer types for cfDNA cohort

  Dataset EGAD50000000666

• Data Access Committee for Translational analyses from a phase II study of pembrolizumab and epigenetic modification with azacitidine in platinum-resistant epithelial ovarian cancer

  This DAC is responsible for request review and handling related to the study above.

  Dac EGAC50000000697

• CRC and UC WES samples

  This is the dataset used to produce results for the report "NOUS-209 off-the-shelf immunotherapy has the potential to hit primary and metachronous colorectal and urothelial cancer in Lynch syndrome"

  Dac EGAC50000000782

• IDH1 Somatic Mutation Profile in Intrahepatic Cholangiocarcinoma

  The objective of this study is to identify somatic IDH1 mutations in intrahepatic cholangiocarcinoma samples.

  Study EGAS50000001638

• Use_of_deep_sequencing_to_detect_clonal_mutations_in_sun_exposed_human_epidermis___whole_genome

  Deep sequencing of two skin biopsies to study the landscape of somatic mutations in human adult tissues.

  Study EGAS00001000860

• Psoriatic_arthritis

  This is a pilot single cell mRNA sequencing experiment to study immune cells in psoriatic arthritis.

  Study EGAS00001002104

• FWO_project_G_0687_12_X10_WGS

  Profiling subclonal architecture and phylogeny in tumors by whole-genome sequence data mining and single-cell genome sequencing

  Study EGAS00001001145

• Solid_WXS_MET-XEN

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002106

• bam files Targeted BS

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001667

• Exome and Transcriptome for gynecologic cancer gene-drug analysis

  Total of 180 gynecologic tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing.

  Dataset EGAD00001005331

• Exome and Transcriptome for gastric cancer gene-drug analysis

  Newly generated 52 gastric tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing

  Dataset EGAD00001005766

• Sequence of breast cancer bone metastases PDX from WES

  Exome sequencing analyses obtained from 11 samples of PDX engrafted with bone metastases, match primary tumors and/or metastases and normal tissus.

  Dataset EGAD00001006071

• OT2_Illumina_WXS_MET_XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003381

• Lung cancers from admixed Latin American populations (1)

  Raw data from cancer panel sequencing of lung adenocarcinomas from admixed Latin American populations. Predominantly samples carrying known oncogene mutations (n=581).

  Dataset EGAD00001006552

• Solid_WXS_MET

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer metastasis sample

  Dataset EGAD00001002105

• OT2_Illumina_RNA_T_XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample (OT2_cohort)

  Dataset EGAD00001003369

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases [MOSAIC] (2017-05-04)

  This dataset contains all the data available for this study on 2017-05-04.

  Dataset EGAD00001003321

• OT2_Illumina_RNA_MET_CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003365

• Illumina_RNA_MET-XEN

  RNAseq of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002076

• Epi-Tax targeted sequencing

  Sequencing of 350 cancer genes in BC samples from patients treated with either Epirubicin or Paclitaxel monotherapy in the neoadjuvant setting.

  Dataset EGAD00001000812

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: kidney (2019-03-26)

  This dataset contains all the data available for this study on 2019-03-26.

  Dataset EGAD00001004867

• Lung Progression versus Regression Whole Genome Sequencing

  Around 50 samples of pre-invasive lung cancer lesions showing subsequent clinical and pathological progression or regression

  Dataset EGAD00001001271

• Illumina_WXS_T-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002102

• ICGC Korean Breast Cancer Project

  A comprehensive characterisation and analysis of human breast cancers through whole-genome sequencing.

  Dataset EGAD00001001322

• Illumina_WGS_T-XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002091

• DNA sequencing for gastric cancer ascites

  Whole-exome sequencing (WES) was performed on a total of 34 PC specimens, with 15 cases having matched gDNAs extracted from blood.

  Dataset EGAD00001004364

• Sequencing .bam files

  Sequencing of untreated pancreatic cancer metastases and primary tumor sections.

  Dataset EGAD00001003153

• Benchmarking_CRISPR_Whole_genome_Drop_out_Screen___B_S

  This study is a benchmarking exercise to explore potential source of variation between different CRISPR drop out libraries.

  Study EGAS00001002931

• Normal sample for patient SA221

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009362

• Tumour sample for patient SA425

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009356

• Tumour sample for patient SA720

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009585

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015302

• Tumour sample for patient SA601

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA601

  Dataset EGAD00001009205

• Normal sample for patient SA604

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009217

• Tumour sample for patient SA590

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA590

  Dataset EGAD00001009196

• Tumour sample for patient SA588

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA588

  Dataset EGAD00001009194

• Normal sample for patient SA398

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009231

• Tumour sample for patient SA654

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA654

  Dataset EGAD00001009206

• Tumour sample for patient SA226

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA226

  Dataset EGAD00001009532

• Tumour sample for patient SA231

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA231

  Dataset EGAD00001009535

• Tumour sample for patient SA234

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA234

  Dataset EGAD00001009525

• Normal sample for patient SA591

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA591

  Dataset EGAD00001009241

• Tumour sample for patient SA591

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA591

  Dataset EGAD00001009197

• Tumour sample for patient SA676

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009343

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA600

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA600

  Dataset EGAD00001009204

• Tumour sample for patient SA277

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA277

  Dataset EGAD00001009539

• Normal sample for patient SA237

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA237

  Dataset EGAD00001009558

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_002 for Triple negative breast cancer sample SA609X7XB03510

  Dataset EGAD00001011218

• Normal sample for patient SA495

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009372

• Tumour sample for patient SA668

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA668

  Dataset EGAD00001009211

• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Tumour sample for patient SA101

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA101

  Dataset EGAD00001009527

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA530

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA530

  Dataset EGAD00001009234

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_001 for Triple negative breast cancer sample SA609X6XB03447

  Dataset EGAD00001011217

• 10x Single Cell Gene Expression Library TENX048

  10x Single Cell Gene Expression library TENX048 for Triple negative breast cancer sample SA535X5XB02895

  Dataset EGAD00001011222

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_001 for Triple negative breast cancer sample SA535X10XB03693

  Dataset EGAD00001011236

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0076_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0076_000 for Triple negative breast cancer sample SA1035X5XB03021

  Dataset EGAD00001011240

• Tumour sample for patient SA276

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA276

  Dataset EGAD00001009182

• Tumour sample for patient SA599

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009203

• Tumour sample for patient SA655

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009207

• Normal sample for patient SA402

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA402

  Dataset EGAD00001009232

• Tumour sample for patient SA283

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA283

  Dataset EGAD00001009184

• Tumour sample for patient SA592

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA592

  Dataset EGAD00001009198

• Tumour sample for patient SA595

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009200

• Normal sample for patient SA404

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA404

  Dataset EGAD00001009233

• Normal sample for patient SA535

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA535

  Dataset EGAD00001009235

• Normal sample for patient SA665

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA665

  Dataset EGAD00001009252

• Tumour sample for patient SA279

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA279

  Dataset EGAD00001009183

• Tumour sample for patient SA398

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009188

• Normal sample for patient SA671

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA671

  Dataset EGAD00001009256

• Tumour sample for patient SA593

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA593

  Dataset EGAD00001009199

• Tumour sample for patient SA609

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009262

• Normal sample for patient SA605

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009218

• Normal sample for patient SA609

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009219

• Normal sample for patient SA595

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009244

• Normal sample for patient SA599

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009247

• Normal sample for patient SA655

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009251

• Tumour sample for patient SA994

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009259

• Tumour sample for patient SA605

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009261

• Normal sample for patient SA394

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA394

  Dataset EGAD00001009229

• Tumour sample for patient SA300

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009354

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0164_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0164_002 for Triple negative breast cancer sample SA530X3XB03295

  Dataset EGAD00001011250

• Normal sample for patient SA286

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009366

• Normal sample for patient SA669

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA669

  Dataset EGAD00001009255

• Tumour sample for patient SA673

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009340

• Tumour sample for patient SA677

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009344

• Tumour sample for patient SA674

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009341

• Tumour sample for patient SA675

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009342

• Normal sample for patient SA423

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009370

• Tumour sample for patient SA679

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009346

• Tumour sample for patient SA678

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009345

• Tumour sample for patient SA239

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009353

• Tumour sample for patient SA683

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009350

• Tumour sample for patient SA238

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009352

• Normal sample for patient SA291

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009368

• Tumour sample for patient SA423

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009355

• Normal sample for patient SA238

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009363

• Normal sample for patient SA239

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009364

• Genome and transcriptome sequence data from a ependymoma tumor patient

  Genome and transcriptome sequence data from a ependymoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015287

• Normal sample for patient SA425

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009371

• Normal sample for patient SA675

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009375

• Normal sample for patient SA674

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009374

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015294

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015300

• Tumour sample for patient SA232

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA232

  Dataset EGAD00001009522

• Tumour sample for patient SA230

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA230

  Dataset EGAD00001009524

• Tumour sample for patient SA278

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA278

  Dataset EGAD00001009526

• Normal sample for patient SA224

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA224

  Dataset EGAD00001009548

• Tumour sample for patient SA596

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA596

  Dataset EGAD00001009201

• Tumour sample for patient SA229

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA229

  Dataset EGAD00001009534

• Normal sample for patient SA212

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA212

  Dataset EGAD00001009546

• Normal sample for patient SA225

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA225

  Dataset EGAD00001009549

• Normal sample for patient SA230

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA230

  Dataset EGAD00001009553

• Normal sample for patient SA294

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA294

  Dataset EGAD00001009565