14983 results for "cancer studies using rna-seq"

in 21.09 milliseconds.

• Low T cell diversity is associated with poor outcome in bladder cancer - Total RNAseq data

  T cells are crucial effector cells in the endogenous defense against cancer, yet the clinical impact of their quantity, diversity, and dynamics remains underexplored. In this study we performed total RNAseq on blood samples from 8 patients with muscle invasive bladder cancer. The goal was to compare blood T cell subtype composition between patients with low and high TCR diversity.

  Study EGAS50000000939

• Canadian prostate cancer samples (CPC-GENE) for PanProstate study

  CPC-GENE has sequenced the genomes of 350 intermediate risk prostate cancers to identify genetic signatures that differ in cancers that responded well to treatment compared with those that did not. CPC-GENE also has sequenced multiple regions of prostate cancer from the same gland to determine if and how the genetic make-up of prostate cancer varies within an individual man’s prostate

  Study EGAS00001003037

• Analysis of Loose Ends in Cancer Genome Structure

  Short-read sequencing (SRS) forms the basis of our understanding of cancer genome evolution, yet it is widely thought to be inadequate for detecting structural variants (SVs). To understand the nature of cancer SVs missed by SRS, we introduce the concept of "loose ends" - sites of missing rearrangements revealed by balancing copy number (CN) across the genomic intervals and adjacencies of a genome graph.

  Study EGAS00001007324

• B Cell Receptor Study From Metastatic Breast Cancer Tumour Samples

  B and T cells are key components of the adaptive immune system and play an important role in anti-cancer immunity. In this study, we performed an integrative analysis of the B cell and T cell repertoires across 27 metastatic breast tumours obtained from 8 donors with therapy-resistant lethal metastatic breast cancer at the time of a warm autopsy.

  Study EGAS00001006976

• Glioma_cell_lines_rearrangement_screen

  Genomic libraries (500 bps) will be generated from total genomic DNA derived from commomon epithelial cancer samples and subjected to total of eight lanes of 37 bp, paired end sequencing on the llumina GA. Paired reads will be mapped to build 37 of the human reference genome to facilitate the generation of genome wide copy number information, and the identification of novel rearranged cancer genes and gene fusions.

  Study EGAS00001000202

• Renal_Follow_Up_Series

  This study's purpose is to use data generated in Renal whole genome and exome Cancer sequencing to target specific areas in multiple other Renal Cancer samples. These areas will then be subjected to bespoke pull downs, tagged and then pool the resultant amplicons. These amplicons will then be sequenced. This is hoped to show the prevalence of previous findings in multiple individuals in a high throughput method.

  Study EGAS00001000095

• Non_Tumour_Renal_Cell_Line_Sequencing

  Genomic libraries (500 bps) will be generated from total genomic DNA derived from commomon epithelial cancer samples and subjected to total of eight lanes of 37 bp, paired end sequencing on the llumina GA. Paired reads will be mapped to build 37 of the human reference genome to facilitate the generation of genome wide copy number information, and the identification of novel rearranged cancer genes and gene fusions.

  Study EGAS00001000205

• Cancer Single Cell Sequencing

  This study is to ascertain whether it is feasible to extract a single cell form a Cancer cell line, amplify it and sequence it.

  Study EGAS00001000003

• Cancer Discovery Hub (CDH), National Cancer Center Singapore

  This is a DAC for data deposited into EGA generated by Cancer Discover Hub, NCCS. The use of datasets in this portal is solely for research and academic use only, uses other than the have to be approved by the relevant DAC members.

  Dac EGAC50000000039

• SCANDARE ovarian

  Prospective biobanking study in ovarian cancer patients aiming at better understand the link between the molecular alterations of the tumor itself, its microenvironment and immune response.

  Study EGAS50000001161

• scMultiome analysis of human tongue cancer organoids

  We performed single-cell Multiome analysis (10X Genomics) of a uniquely established human tongue cancer organoids, including both chemo-sensitive organoids and chemotherapy resistant organoids.

  Study JGAS000606

• Fluctuating DNA methylation tracks cancer evolution at clinical scale

  Long-read Nanopore data of fCpGs from Gabbutt & Duran-Ferrer (2025) for developing the EVOFLUx method to study cancer evolution via DNA methylation fluctuations.

  Study EGAS50000001192

• Genetic analysis of non-small cell lung cancer patients and PDX tumor harboring driver gene alteration

  Genetic analysis of non-small cell lung cancer patients and PDX tumors with EGFR mutation, ALK fusion, ROS1 fusion, etc.

  Study JGAS000413

• Metastatic Breast Cancer Whole Genome

  We propose to definitively characterise the somatic genetics of Metastatic breast cancer through generation of comprehensive catalogues of somatic mutations in Metastatic breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001000899

• 16S rRNA Rectal Mucus

  16S rRNA sequencing of rectal mucus samples from patients suspected to have colorectal cancer. This is 1 of 4 sequencing experiments on the same sample type.

  Study EGAS50000001262

• Molecular evolution and clinical trajectories of prostate cancer identifies novel markers for risk stratification - additional data

  Study EGAS00001004884

• Breast Cancer Exome Sequencing

  Breast Cancer Exome Sequencing

  Dataset EGAD00001000110

• HER2 positive Breast Cancer

  HER2 positive Breast Cancer

  Dataset EGAD00001000126

• Ultra-deep targeted sequencing for studying the accumulation of somatic mutations in cancer-free human skin

  Study EGAS00001004279

• Buccal sample methylation from breast cancer cases

  Buccal sample methylation from breast cancer cases

  Dataset EGAD00010002635

• Preliminary Results from the Initiative for Molecular Profiling and Advanced Cancer Therapy 2 (IMPACT 2) Study

  Study EGAS00001004964

• Evolutionary trajectories and clonal migration underlying tumor progression and lymph node metastasis in resectable lung cancer

  Study EGAS00001005242

• Homologous recombination DNA repair deficiency and activity of PARP inhibition in primary triple negative breast cancer

  Study EGAS00001004190

• Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)

  The Foundation Medicine adult cancer clinical dataset consists of 18,004 unique solid tumor samples that underwent genomic profiling on a single uniform platform as part of standard clinical care. The dataset is derived from the FoundationOne® genomic profiling assay version 2 that interrogates exonic regions of 287 cancer-related genes and selected introns from 19 genes known to undergo rearrangements in human cancer. Genomic DNA samples were sequenced to over 500x median coverage, and custom computational analyses identified all classes of genomic alterations (base substitutions, insertions and deletions, copy number alterations, and rearrangements). Since matched normal tissue was unavailable for analysis, these data underwent additional filtering to enrich for cancer-related events. The reported data includes genomic alterations that are known and suspected tumor drivers, as well as variants of unknown significance. To preserve patient anonymity, all known or suspected germline variants were removed from the data unless known to be associated with cancer development. The dataset contains genomic alteration profiles generated by FoundationOne version 2 testing for adult cancer patients (over 18 y.o.), and represents a vast diversity of tumor subtypes, including many rare diseases not profiled as part of large-scale profiling efforts. Cases are grouped into 16 broad disease categories containing tumors from 162 unique disease subtypes. Since specimens were profiled as part of clinical care, limited clinical parameters were available, including age, gender, tissue of origin, % of tumor nuclei, and diagnosis. Publication of this dataset is intended to allow the broad scientific community access to this unique cohort for use in scientific research projects of common and rare types of cancer, both for generating leads regarding causal mechanisms as well as cross-testing and confirming existing hypotheses. A pediatric cancer clinical dataset consisting of data from 1,215 patients under 18 y.o. is available separately at: FOUNDATION MEDICINE

  Study phs001179

• Linked Read Sequencing of Gastric Cancer Metastases for Complex SV Resolution

  In this study, linked read sequencing was performed on two ovarian metastases and matched normal tissue, from a patient with primary diffuse gastric cancer. Linked read sequencing is a DNA preparation technology whereby high molecular weight molecules of DNA are uniquely barcoded prior to fragmentation and sequencing, thus retaining information about genomic contiguity. This study performed an extended analysis of linked read sequencing data to resolve the complex structures of structural variants in the cancer genomes. Complex structural rearrangements were identified in the genomic region surrounding the known oncogene FGFR2, and the association between FGFR2 and gastric cancer metastasis was demonstrated in an organoid model.

  Study phs001362

• Early Detection of Malignant and Pre-Malignant Peripheral Nerve Tumors Using Cell-Free DNA Fragmentomics

  Early detection of neurofibromatosis type 1 (NF1) associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, potentially averting deadly outcomes. Here, we describe a cell-free DNA (cfDNA) fragmentomic approach which distinguishes non-malignant, pre-malignant and malignant forms of NF1 PNST. Using plasma samples from a novel cohort of 101 NF1 patients and 21 healthy controls, we validated that our previous cfDNA copy number alteration (CNA)-based approach identifies malignant peripheral nerve sheath tumor (MPNST) but cannot distinguish among benign and premalignant states. We therefore investigated the ability of fragment-based cfDNA features to differentiate NF1-associated tumors including binned genome-wide fragment length ratios, end motif analysis, and non-negative matrix factorization deconvolution of fragment lengths. Fragmentomic methods were able to differentiate pre-malignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management. Overall, this study pioneers the early detection of malignant and premalignant peripheral nerve sheath tumors in NF1 patients using plasma cfDNA fragmentomics. In addition to screening applications, this novel approach distinguishes atypical neurofibromas from benign plexiform neurofibromas and malignant peripheral nerve sheath tumors, enabling more precise clinical diagnosis and management.

  Study phs003712

• using-ega-account

  Using your EGA account The purpose of this page is to help you access and download the data you have been granted permission to view through your EGA account. To obtain an EGA account please register here. Once your account is created, you can find details on how to manage your EGA account here. EGA access policy (New account holders please read) Access to EGA services are only provided to EGA account holders. EGA accounts are created, updated and closed upon direct instruction from the relevant DAC contact, using the EGA account management tools or through EGA Helpdesk. As part of the EGA account creation process, the EGA account holder must reset their password when instructed. Password resets are checked and verified usually within 24 business hours, during which time the EGA account is inaccessible. EGA accounts are assigned on an individual basis for the exclusive use of the registered EGA account holder; log-in details must remain private and only be used by the EGA account holder. Controlled access data files are only provided, in accordance to the permissions set by the DAC, to EGA account holders. EGA controlled access data files are encrypted prior to download and must be decrypted on the EGA account holders side. EGA account holders are provided access to the EGA download streamer to download access approved datasets and files therein. The EGA download streamer represents the primary means for the EGA account holder to download access approved datasets and files therein. EGA account holders may be provided with an EGA download account for accessing approved datasets and files therein, using FTP or Aspera in response to clearly defined technical reasons, which may restrict the use of the EGA download streamer. It is the sole responsibility of the EGA account holder to ensure full compliance to all terms and conditions of the Data Access Agreement are met once data is downloaded from the EGA. Should you move institute you must contact all Data Access Committee's contacts to update your details and, if necessary, re-apply for data access. The Data Access Agreement (DAA) is a contract made between the DAC and the EGA account holder; the EGA is not responsible for enforcing the DAA. EGA reserves the right to place EGA accounts 'onhold', pending DAC approval to re-activate, should EGA account 'misuse' be suspected. Further information Introduction to the EGA Frequently Asked Questions Subscribe to the EGA mailing list

  Documentation using-ega-account

• Novel genes for Intellectual Disability identified using whole genome sequence and pathway analysis

  Intellectual Disability (ID) is one of the most common global disorders. However for ~30% of patients with ID no cause is identified, despite a clinical diagnostic odyssey that includes genome wide clinical microarray (CMA). We carefully selected 8 trios, each composed of a child with ID and brain structural defect, and both normal parents for whole genome sequencing (WGS). We conducted WGS on the trio and filtered out de novo single nucleotide variants (SNVs) and then used a pathway-based refinement to select candidates. We confirmed a de novo pathogenic SNV in ARID1B, and likely pathogenic SNVs in CACNB3, SPRY4, PHF6, SQSTM1 and UPF1 in 5 of the 8 children. All genes except ARID1B and PHF6 are previously unreported for ID. We analysed our WGS data using 4 independent algorthims for copy number and structural variation including using de novo whole genome assembly. We confirmed one likely contributory 165kb de novo CNV (missed by CMA). We conducted a validation study of over 2000 exomes for our novel candidate genes and also present a strategy to analyze the non-coding sequence space. This study provides an extensive analysis of WGS in the context of ID and yielded causative variants in >60% of cases.

  Study EGAS00001001386

• GR@ACE_StageI.v01

  Binary Plink files for pre-GWAS quality control in 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001654

• GR@ACE_StageI.CEL_files

  Intensity calculation on the pixel values of the DAT file for 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001655

• Average_5mC_Roadmap_histoneMarks

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002422

• Average_5hmC_Roadmap_histoneMarks

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002417

• Average_5hmC_Roadmap_chromatinStates

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002414

• Average_5mC_Roadmap_chromatinStates

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002413

• Guardant ctDNA variant analysis

  This table describes each variant detected using Guardant 360 for each sample included in this analysis.

  Dataset EGAD50000001340

• Validation of a genome-wide polygenic score for body mass index in South Asians

  Genotyping data of BMI model markers generated using Global Screening Array (Illumina Inc.)

  Dataset EGAD00001015700

• BCRC-ICGS (Bladder Cancer Research Centre, Institute of Cancer and Genomic Sciences, Univ. of Birmingham) Data Access Committee

  Dac EGAC00001001982

• OICR PANCREATIC CANCER DATASET

  OICR PANCREATIC CANCER DATASET

  Dataset EGAD00001000262

• Degradation of Cyclin K/CDK12 is a druggable vulnerability of colorectal cancer (H012)

  Study EGAS00001004517

• Secondary Resistance to Anti-EGFR Therapy by Transcriptional Reprogramming in Patient-Derived Colorectal Cancer Models (hipo_B012)

  Study EGAS00001005320

• Copy number profiling of circulating cell-free DNA from high grade serous ovarian cancer patients

  Study EGAS00001004670

• Residual ctDNA after treatment predicts early relapse in patients with early-stage non-small cell lung cancer

  Study EGAS00001005967

• OICR-DAC, Ontario Institute for Cancer Research; Biliary Tract Cancer

  Next Gen Sequencing data from Biliary Tract Cancers

  Dac EGAC50000000528

• Genentech Colon Cancer Screen

  Study EGAS00001000288

• Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

  Study EGAS00001008218

• Neoantigens in bladder cancer

  Detection of spontaneous anti-neoepitope T-cell responses in non-metastatic bladder cancer patients

  Dac EGAC50000000740

• Breast Cancer Association Consortium Data Access Coordinating Committee

  Data access committee for the Breast Cancer Association Consortium (BCAC).

  Dac EGAC50000000824

• Cancer Research UK Cambridge Institute

  A department of the University of Cambridge and Cancer Research UK funded Institute

  Dac EGAC50000000683

• Poly(A) RNA sequencing of hepatocellular carcinoma tumors and their matched noncancerous liver tissues

  Background & Aims: The differentiation of distinct multifocal hepatocellular carcinoma (HCC): multicentric disease vs. intrahepatic metastases, in which the management and prognosis varies substantively, remains problematic. We aim to stratify multifocal HCC and identify novel diagnostic and prognostic biomarkers by performing whole genome and transcriptome sequencing, as part of a multi-omics strategy Methods: A complete collection of tumour and somatic specimens (intrahepatic HCC lesions, matched non-cancerous liver tissue and blood) were obtained from representative patientswith multifocal HCC exhibiting two distinct postsurgical courses. Whole-genome and transcriptome sequencing with genotyping were performed for each tissue specimen to con-trast genomic alterations, including hepatitis B virus integrations, somatic mutations, copy number variations, and structural variations. We then constructed a phylogenetic tree to visualise individual tumour evolution and performed functional enrichment analyses on select differentially expressed genes to elucidate biological processes involved in multifocal HCC development. Multi-omics data were integrated with detailed clinicopathological information to identify HCC biomarkers, which were further validated using a large cohort of HCC patients (n = 174).Results: The multi-omics profiling and tumour biomarkers could successfully distinguish the two multifocal HCC types, while accurately predicting clonality and aggressiveness. The dual specificity protein kinase TTK, which is a key mitotic checkpoint regulator with links to p53 signaling, was further shown to be a promising overall prognostic marker for HCC in the large patient cohort.Conclusions: Comprehensive multi-omics characterisation of multifocal tumour evolution may improve clinical decision-making, facilitate personalised medicine, and expedite identification of novel biomarkers and therapeutic targets in HCC.

  Study EGAS00001002337

• Global Endometrial DNA Methylation Analysis Reveals Insights into mQTL Regulation and Associated Endometriosis Disease Risk and Endometrial Function

  Endometriosis is a common gynecological disorder affecting 11% of reproductive aged women and is a leading cause of pain and infertility. We investigated global DNA methylation (DNAm) profiles in endometrium associated with endometriosis, menstruation, and genetic variation. Endometrial samples were collected from 984 patients with confirmed endometriosis (n=637) and women without endometriosis (n=347). Patients were recruited through the University of California San Francisco, USA, University of Melbourne, Australia, Endometriosis CaRe Centre in Oxford, UK, and EXPPECT Centre, The University of Edinburgh, UK, using the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project (WERF EPHect) standardized protocols for tissue collection and processing, and participant characteristics and clinical annotation. DNA was extracted from samples and was use to generate DNAm data using the Illumina Infinium MethylationEPIC Beadchip and genotype data using the Axiom Precision Medicine Research array. Estimates from this study suggest that 15.4% of the variation in endometriosis is captured by DNAm. DNAm analysis identified significant differences in DNAm profiles and DNAm networks, associated with endometriosis, endometriosis sub-phenotypes, and menstrual cycle phases. Integration of DNAm and genetic data in a DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis highlighting target genes contributing to disease risk. This study identified novel factors affecting epigenetic regulation in endometrium associated with endometriosis risk and disease heterogeneity and provides an important data resource for reproductive medicine. Genotype data generated as part of this study are available on dbGaP and methylation data are available on GEO.

  Study phs003307