14977 results for "cancer studies using rna-seq"

in 64.13 milliseconds.

• SNParray copy number dataset

  4 oesophageal cancer derived organoid lines and 2 ovarian cancer derived organoid lines

  Dataset EGAD00010002002

• Amsterdam UMC Head and Neck Cancer Biology and Immunology

  This data access committee oversees data and assesses requests for the Head and Neck Cancer Biology and Immunology lab of Amsterdam UMC, location VUmc.

  Dac EGAC50000000350

• SPECTA Lung cancer DNA FASTQ files

  SPECTA Lung cancer DNA FASTQ files (Illumina TST170 targeted analysis)

  Dataset EGAD00001006223

• Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Dataset EGAD00001006237

• SNP data for Breast cancer PRS

  SNP data for 313 loci required for calculation of the Breast cancer PRS

  Dataset EGAD00001008144

• Ulcerative colitis study - WES data

  Whole Exome Sequencing data of Single crypts, bulk crypt samples, and colitis-associated cancer.

  Study EGAS00001003801

• 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

• EGAD00010000632

  WTCCC2 People of the British Isles (POBI) samples using Illumina 1.2M array

  Dataset EGAD00010000632

• Oncotrack_450K_metastatic

  Oncotrack metastatic samples using 450K. The shared AF analysis files oncotrackDNAmAnalysis.R and oncotrackDNAmBetaScores.txt which were applied for both Oncotrack_450K_tumor (EGAD00010001162) and Oncotrack_450K_metastatic (EGAD00010001161) datasets are included on Oncotrack_450K_tumor (EGAD00010001162) dataset.

  Dataset EGAD00010001161

• CCA methylation data (12 CCA, 7 normal)

  Raw idat files for DNA methylation profiling for 12 CCAs and 7 normal bile duct tissues. DNA methylation profiling was performed using Infinium MethylationEPIC v2.0 Kit.

  Dataset EGAD00010002613

• RISE-UP Cohort

  The RISE-UP study contains raw metagenomic sequencing data of Crohn's disease patients. Paired-end sequencing was performed using the Illumina HiSeq 2000 platform.

  Dataset EGAD50000001436

• PBAT sequencing of human embryonic stem cells

  To assess the DNA methylation landscape of naïve human embryonic stem cells derived from ERKi or control blastocysts using PXGL or UXGL media.

  Dataset EGAD50000001475

• HV31 - MGI standard short-read sequencing

  MGI standard short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007044

• HV31 - MGI CoolMPS short-read sequencing

  MGI CoolMPS short-read sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~57×.

  Dataset EGAD00001007048

• Comparison of HCC cell lines and primary HCCs

  Whole genome sequencing (30X) using Hiseq X TEN on 4 HCC cell lines, primary HCCs and early-passage PDCs

  Dataset EGAD00001001995

• Amplicon–Based Metagenomic Analysis.

  Amplicon-based fungal metagenomic sequencing for the identification of fungal species in brain tissue from Alzheimer's disease. The study consists in 14 samples, sequenced using Illumina's paired-end technology.

  Dataset EGAD00001003196

• Exome sequencing of FFPE sample

  Bam file from exome sequencing of FFPE sample from Ly3 using SeqCap EZ Human Exome Library (Roche Nimblegen, Inc., WI, USA) and Illumina HiSeq2000 sequencer.

  Dataset EGAD00001004782

• Bulk transcriptomic analyses of monocyte-derived dendritic cells treated with CES1i

  The data published here contains bulk RNA-sequencing (RNAseq) data as obtainedfrom monocyte-derived dendritic cells in treated with/without LPS and with/without CESi (WWL113). Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000344

• Small RNA sequencing of human oocytes and early embryos

  This dataset contains single-end fastq files for human oocytes (n=12), zygotes (n=5), and early embryos (n=10) sequenced with Illumina NextSeq500. The sequencing libraries were prepared from single oocytes, zygotes, and embryos in two batches.

  Dataset EGAD50000000227

• Single-nucleus RNA-sequencing of normal adrenal cortex and adrenocortical tumors

  This study reports a single-nucleus transcriptome atlas of steroid, stromal and immune cells in 38 human normal adrenals and adrenocortical tumors.

  Study EGAS50000000585

• Integrated Multiomics Uncovers Distinct Macrophage Alterations in Human Metabolic dysfunction-Associated Steatohepatitis Progression

  We report single-nuclei RNA sequencing data (n=18) from human livers in metabolic dysfunction-associated steatotic liver disease (MASLD) patients. there are 5 MASH, 4 MASL, 5 control obese and 4 control lean samples.

  Dataset EGAD50000001129

• Kidney tumour_DNA (2018-09-19)

  This is a bulk DNA and RNA sequencing study of human renal tumours . This dataset contains all the data available for this study on 2018-09-19.

  Dataset EGAD00001004346

• Data set for pan.met study

  The dataset referenced by EGA Study ID EGAS00001004208 includes 20 human exome sequencing data and 11 human RNA sequencing data from tumor or normal tissues. Each sequencing data includes two pair-end short read files in fastq format.

  Dataset EGAD00001005957

• Sequencing data for oesophageal / related samples - Foley, Shorthouse et al (RNA)

  Data supporting: "SMAD4 and KCNQ3 Alterations are Associated with Lymph Node Metastases in Oesophageal Adenocarcinoma" RNAseq (FASTQ files) 6 samples

  Dataset EGAD00001011065

• Comparative transcriptome of CD34+ hematopoietic progenitors from myeloproliferative patients and control donors

  Comparative transcriptome of CD34+ hematopoietic progenitors from 4 myeloproliferative patients (MPN) and 4 control donors performed by RNA-Sequencing.

  Dataset EGAD00001007531

• Transcriptome Analysis of CLL patient samples

  This dataset includes Illumina RNA Sequencing Data for 59 chronic lymphocytic leukemia patient samples. 57 samples are single end, 2 samples are paired end sequencing.

  Dataset EGAD00001008320

• RNASeq read files of glioblastoma PDX samples

  This dataset contains 46 fastq files of paired-end RNA sequencing of an Illumina®️ TrueSeq stranded mRNA library of 23 glioblasoma PDX samples.

  Dataset EGAD00001010196

• Gene expression profile of mesothelial cells from peritoneal adhesion biopsies

  RNA-sequencing analysis was carried out on mesothelial cells isolated from peritoneal adhesion biopsies and compared to control human peritoneal mesothelial cells to identify a mesothelial-to-mesenchymal transition (MMT)-related gene signature in patients.

  Dataset EGAD00001008324

• Sequencing data for oesophageal / related samples - Kazachenka et al (RNA)

  Data supporting: "The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes" RNAseq for 279 samples

  Dataset EGAD00001011076

• Skin Fibroblast Transcriptome of Type 2 Diabetic Patients

  Comparative analysis of transcriptomes of skin fibroblasts in 17 Type 2 Diabetic individuals by RNA sequencing.

  Dataset EGAD00001004980

• The Primary Open-Angle Glaucoma Genes and Environment (GLAUGEN) Study

  Background Information: Primary open-angle glaucoma (POAG) is an age-related, intraocular pressure (IOP)-dependent progressive optic neuropathy that ultimately leads to irreversible blindness. Vision loss from POAG is a condition of public health significance. Current evidence suggests that POAG is a polygenetic disease modified by environmental influences. Despite the fact that a positive family history of disease is an important risk factor for POAG, conventional linkage and candidate gene approaches have revealed less than 5% of the genetic component of the disease. Furthermore, there is no consensus on environment risk factors for POAG. Elevated IOP is the only modifiable risk factor for POAG; yet, lowering IOP slows, but does not halt the disease process. Study Objectives: The overall goal of our research is to elucidate the pathogenesis of POAG so that cost-effective disease detection and primary prevention strategies can be implemented. The primary aim of the Glaucoma Gene Environment Initiative (GLAUGEN), funded by the Human Genome Research Institute (NHGRI), is to discover genetic loci associated with POAG. The secondary aim of GLAUGEN, funded by NHGRI and the National Eye Institute, is to discover gene environment interactions in POAG. Methods and study populations: For this study, we have assembled cases and controls from three studies: the Nurses' Health Study (NHS), the Health Professionals Follow-up Study (HPFS) and the Genetic Etiologies of POAG (GEP) project based at Massachusetts Eye and Ear Infirmary (MEEI). This case-control group includes 1057 unrelated cases and 1272 controls. Members of the NHS and HPFS also have repeated environmental exposure data collected prior to a diagnosis of POAG. NHS - The Nurses Health Study started in 1976 under the direction of Dr. Frank E. Speizer. With funding from the NIH, registered nurses from 11 US states were invited to complete a detailed questionnaire regarding lifestyle and health biennially. Initially, 121,000 women responded to the baseline questionnaire. Currently, Dr. Susan Hankinson serves as the program director for the NHS. HPFS - The Health Professionals Follow-up Study began in 1986 under the direction of Drs. Walter Willett and Meir Stampfer. Under the auspices of the NIH, they enlisted 51,529 male health professionals from throughout the US to complete similarly designed biennial questionnaires. Beginning in 1990, questions regarding ocular health were added to biennial questionaires completed by health professionals participating in the NHS and HPFS. This allowed us to formulate (PI: S. Hankinson; NEI) and maintain (PI: L. Pasquale; NEI) a cohort at risk for POAG derived from the respective general cohorts who were under ophthalmic care. We then developed a definition of POAG that allowed us to identify cases from a population that was geographically dispersed. The centerpiece of this definition is the presence of reproducible visual field loss consistent with nerve fiber layer (NFL) dropout (the NFL contains the axons that comprise the optic nerve) on reliable tests. Reproducible visual field loss occurred in the context of anterior segment findings that did not suggest a secondary cause of elevated IOP and posterior segment findings that did not suggest a secondary cause of visual field loss. We selected controls from the cohort at risk for POAG on the basis of age, gender and time period when cases were identified. GEP - The Genetic Etiologies of POAG was initiated in 1996 with funding from the National Eye Institute under the direction of Dr. Janey Wiggs. The purpose of this work was to discover novel genetic loci associated with POAG. In the GEP, cases were derived predominantly from the Glaucoma Service at MEEI. The majority of cases had an examination by a glaucoma specialist and met the definition for POAG used in NHS and HPFS. Cases with only one reliable visual field consistent with NFL dropout were included if there was a cup-disc ratio of 0.7 or more. The majority of controls were patients who presented to the MEEI comprehensive ophthalmology service for routine eye examination or from spouses of MEEI patients with secondary forms of glaucoma. Other controls were identified from regional glaucoma screenings held throughout Massachusetts. Members of GEP have detailed ocular phenotype data but limited information on environmental exposures. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to primary open-angle glaucoma through large-scale genome-wide association studies of three well-characterized cohorts of cases and controls, some in matched pairs. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000308

• Genomic and Genetic Characterization of Prostate Tumors Treated with Neoadjuvant Intense Androgen Deprivation Therapy

  The purpose of this study was to develop better ways of detecting prostate cancer before and after pre-operative treatment, and to test the feasibility of multi parametric magnetic resonance imaging (mpMRI) for the localization and detection of focal prostate cancer both before and after pre-operative treatment with ADT and enzalutamide. In this study, patients with intermediate and high risk prostate cancer received six months of neoadjuvant intense androgen deprivation therapy prior to radical prostatectomy. Biopsies were acquired prior to treatment. Tissue from biopsy and radical prostatectomy were subjected to sequencing and analysis for determination of features associated with response or resistance to treatment.

  Study phs001938

• The EGA joins EUCAIM, the European project powering AI and imaging in cancer research

  EUCAIM (EUropean Federation for CAncer IMages) is the cornerstone of the European Cancer Imaging Initiative, one of the flagships of the Europe’s Beating Cancer Plan (EBCP). The European Genome-Phenome Archive (EGA), as member of the EUCAIM consortium, attended the kick-off meeting organized this week in Brussels for the launch of the Initiative. The EGA will contribute to the project with the experience gained in the deployment of our federated network, the Federated EGA (FEGA), and the experience gained as partners of EuCanImage (AI4HI initiative) contributing to the definition of common data models, ontologies and data standards. The ultimate goal of the project is to unlock the power of imaging and Artificial Intelligence for the benefit of cancer patients, clinicians and researchers. The consortium will deploy a pan-European digital federated infrastructure hosting cancer-related images, and associated pathology, molecular and laboratory data from Real-World. This endeavour will provide a comprehensive dashboard for imaging data discovery, federated search, metadata harvesting, annotation and distributed data processing. EUCAIM joins 76 partners, gathering the experience and previous effort made by the different repositories of the AI4HI initiative, European Research Infrastructures and national/regional repositories. Find out more information about the EUCAIM launch here

  Blog the-ega-joins-eucaim

• 2014_Lung_sq_WES

  Purpose Lung squamous cell carcinoma (SqCC) is the second most prevalent type of lung cancer. Currently, no targeted-therapeutics are approved for treatment of this cancer, largely due to lack of systematic understanding of the molecular parthenogenesis of the disease. To identify therapeutic targets and comparatively analyze them in lung SqCC, we probed somatic alterations of Korean lung SqCC. Patients and Methods We performed massively parallel whole-exome sequencing of 104 Korean lung SqCC patients’ samples with matched normal samples. In addition, copy number analysis and transcriptome analysis were conducted for a subset of these samples. Clinical association with cancer-specific somatic alterations was investigated.

  Study EGAS00001002844

• MutWP5__CRUK_Mutographs_of_Cancer__Lung__PD43291_Novaseq__WG_

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from lung tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will deeply sample from individuals with smoking-related lung diseases (COPD, pulmonary fibrosis, lung cancer) and those without smoking history. This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumor development.

  Study EGAS00001003502

• PD-L1 blockade immunotherapy rewires cancer-induced emergency myelopoiesis

  Introduction Immune checkpoint blockade (ICB) immunotherapy has revolutionized cancer treatment, demonstrating exceptional clinical responses in a wide range of cancers. Despite the success, a significant proportion of patients still fail to respond, highlighting the existence of unappreciated mechanisms of immunotherapy resistance. Delineating such mechanisms is paramount to minimize immunotherapy failures and optimize the clinical benefit. Methods In this study, we treated tumour-bearing mice with PD-L1 blockage antibody (aPD-L1) immunotherapy, to investigate its effects on cancer-induced emergency myelopoiesis, focusing on bone marrow HSPCs. We examined the impact of aPD-L1 treatment on HSPC quiescence, proliferation, transcriptomic profile, and functionality. Results Herein, we reveal that aPD-L1 in tumour-bearing mice targets the hematopoietic stem and progenitor cells (HSPCs) in the bone marrow (ΒΜ), mediating their exit from quiescence and promoting their proliferation. Notably, disruption of the PDL1/PD1 axis induces transcriptomic reprogramming in HSPCs, observed in both individuals with Hodgkin lymphoma (HL) and tumour-bearing mice, shifting towards an inflammatory state. Furthermore, HSPCs from aPDL1-treated mice demonstrated resistance to cancer-induced emergency myelopoiesis, evidenced by a lower generation of MDSCs compared to control-treated mice. Discussion: Our findings shed light on unrecognized mechanisms of action of ICB immunotherapy in cancer, which involves targeting of BM-driven HSPCs and reprogramming of cancer-induced emergency myelopoiesis.

  Study EGAS00001007873

• Deep multi-region whole-genome sequencing reveals heterogeneity and gene-by-environment interactions in treatment-naive, metastatic lung cancer

  Our understanding of genomic heterogeneity in lung cancer is largely based on the analysis of early-stage surgical specimens. Here we used endoscopic sampling of paired primary and intrathoracic metastatic tumors from 11 lung cancer patients to map genomic heterogeneity inoperable lung cancer with deep whole-genome sequencing. Intra-patient heterogeneity in driver or targetable mutations was predominantly in the form of copy number gain. Private mutation signatures, including patterns consistent with defects in homologous recombination, were highly variable both within and between patients. Irrespective of histotype, we observed a smaller than expected number of private mutations, suggesting that ancestral clones accumulated large mutation burdens immediately prior to metastasis. Single-region whole-genome sequencing of from 20 patients showed that tumors in ever-smokers with the strongest tobacco signatures were associated with germline variants in genes implicated in the repair of cigarette-induced DNA damage. Our results suggest that lung cancer precursors in ever-smokers accumulate large numbers of mutations prior to the formation of frank malignancy followed by rapid metastatic spread. In advanced lung cancer, germline variants in DNA repair genes may interact with the airway environment to influence the pattern of founder mutations, whereas similar interactions with the tumor microenvironment may play a role in the acquisition of mutations following metastasis.

  Study EGAS00001003830

• Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT-BioLINCC)

  Accessing Data: Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.Objective: To determine whether an amino-terminal pro-B-type natriuretic peptide (NT-pro-BNP)-guided treatment strategy improves clinical outcomes vs usual care in high-risk patients with heart failure (HF) and reduced ejection fraction (HFrEF).Background: Heart failure is a common disorder. Standard treatment for HF includes diuretics to control fluid, and drugs called "neurohormonal antagonists" (such as beta-blockers and ACE-inhibitors) to help the heart work more efficiently. The natriuretic peptides, specifically NT-pro-BNP, are biomarkers that reflect HF severity and are significantly associated with adverse outcomes in HF. Smaller studies have evaluated adjusting HF therapy based on natriuretic peptide levels (“guided therapy”) with inconsistent results.Participants: A total of 894 participants were enrolled at 45 sites in the United States and Canada, with 446 randomized to the NT-pro-BNP-guided strategy treatment group and 448 randomized to the usual care treatment group.Design: GUIDE-IT was a multicenter randomized clinical trial with participants randomized to either the NT-pro-BNP-guided therapy strategy or usual care. Given the nature of the study intervention, treatment assignment was not blinded. For participants randomized to the NT-pro-BNP-guided strategy, clinicians were instructed to titrate HF therapy to target an NT-pro-BNP level After an initial visit at 2 and 6 weeks, visits occurred every 3 months throughout the remainder of the study. After therapy adjustment for HF (whether driven by NT-pro-BNP levels or clinical reasons), participants had a 2-week follow-up visit for reassessment. All participants in either group also had blinded NT-pro-BNP concentrations measured in a core laboratory at each study visit. The primary end point was the composite of time-to-first HF hospitalization or cardiovascular mortality. Secondary end points included all-cause mortality, total hospitalizations for HF, days alive and not hospitalized for cardiovascular reasons, the individual components on the primary end point, and adverse events. Conclusions: The guided strategy intervention was stopped in July of 2016 (median follow-up of 15 months) after the study met prespecified inefficacy criteria. Compared to usual care, a strategy of NT-pro-BNP-guided therapy was not more effective in improving time-to-first HF hospitalization or cardiovascular mortality in high-risk participants with HFrEF.

  Study phs003621

• PARADIGM: Combined ctDNA and serum PSA for dynamic monitoring of metastatic prostate cancer starting first-line treatment

  The prognosis of newly-diagnosed metastatic prostate cancer is highly variable. The PARADIGM prospective cohort study evaluated predictors of survival in blood collected at the start of each of the first six treatment cycles from 114 high-volume metastatic patients starting androgen deprivation therapy combined with docetaxel or an androgen receptor pathway inhibitor.

  Study EGAS50000001357

• Cancer_Genome_Libraries_Tests

  Testing library production.Genomic libraries (500 bps) will be generated from total genomic DNA derived from commomon epithelial cancer samples and subjected to paired end sequencing on the llumina plateforms. 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 EGAS00001000208

• High intensity sequencing of plasma cfDNA and WBC gDNA

  We sought to define the technical feasibility of a high-intensity sequencing assay of cfDNA and matched white-blood cell (WBC) DNA covering a large genomic region (508 genes, 2Mb, 60,000x raw-depth) in a prospective study of 124 metastatic cancer patients, with contemporaneous matched metastatic tumor tissue biopsies, and age-matched 47 non-cancer controls.

  Study EGAS00001003755

• Analysis of RAD51C promoter methylation using targeted bisulfite sequencing (amplicon sequencing) in ovarian cancer pre-clinical models and patient samples.

  In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in the DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 11 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic 'scarring', indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after 6 lines of therapy (4 platinum-based), whilst another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neo-adjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy.

  Study EGAS00001005395

• A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  Dataset EGAD00001000149

• Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Dataset EGAD50000000099

• APOBEC Breast Cancer Whole Genome

  Five tumor-normal human breast cancer cases driven by APOBEC mutagenesis, including normal tissue, primary, and metastatic samples for each patient.

  Dataset EGAD50000001275

• SNP data for Ovarian cancer PRS (controls)

  SNP data of 28 sites required for the Ovarian cancer PRS (controls)

  Dataset EGAD00001008145

• Single Cell RNAseq of blood and tumor from renal cancer patients

  Single Cell RNAseq of blood and tumor from renal cancer patients

  Dataset EGAD00001005977

• Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Dataset EGAD00001001421

• Single_cell_RNA_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008271

• SCLC tumor sequencing

  In this study we have sequenced the tumor of 28 Small Cell Lung Cancer patients.

  Study EGAS00001003985

• PEACE (Posthumous Evaluation of Advanced Cancer Environment) whole exome sequencing data

  PEACE (Posthumous Evaluation of Advanced Cancer Environment) whole exome sequencing data

  Dataset EGAD00001015763

• The genetic evolution of precursor lesions in pancreatic cancer

  Mapping genetic evolution of pancreatic cancer precursor lesions such as IPMNs and PanINs.

  Dataset EGAD00001002232

• WES data of serially passaged TIC-enriched spheres of colorectal cancer

  WES data of serially passaged TIC-enriched spheres of colorectal cancer (CRC)

  Dataset EGAD00001006266

• Primary Lung Cancer whole genome study

  Genomic libraries (500 bps) will be generated from total genomic DNA derived from lung cancer patients and subjected to short paired end sequencing on the llumina platform. 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.

  Dataset EGAD00001000388

• Comprehensive genomic characterization of early stage bladder cancer - whole exome sequencing data

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed WES data from 438 patients. The dataset is composed of 876 BAM files: 438 tumor samples and 438 paired blood samples.

  Study EGAS50000000511

• Exome Recapture and Sequencing of Prospectively Characterized Clinical Specimens From Cancer Patients

  The study cohort comprises samples from patients whose tumors have been prospectively characterized as part of their care at Memorial Sloan Kettering Cancer Center. This includes a variety of distinct tumor type and molecular subtypes.

  Study phs001783

• The dataset for Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer

  The dataset for “Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer” includes 496 cram files from whole genome next-generation sequencing on the Illumina NovaSeq 6000. The samples analyzed include tumor tissue, matched buffy coat and longitudinal plasma samples from individuals with pancreatic cancer.

  Dataset EGAD50000001353

• Tumor gene project

  The study focuses on human tumor susceptibility as well as somatic cancer genomics. We use high-throughput sequencing approaches to achieve a comprehensive molecular characterization of tumors. Our goal is to better understand their genesis and management opportunities.

  Study EGAS50000000984

• Breast Cancer PDTX Encyclopaedia

  Molecular data from the collection of PDTX breast cancer models described in Bruna et al, 2016. Cell. It includes whole exome sequencing, shallow whole genome sequencing, expression arrays and reduced bisulfite representation sequencing (RRBS).

  Study EGAS00001001913

• BASIS_RNAseq

  We propose to definitively characterise the somatic genetics of ER+ve, HER2-ve breast cancer through generation of comprehensive catalogues of somatic mutations in 500 cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000707

• Lung_Multi_site_Targeted_Sequence_Capture

  DNA extracted from multiple biopsies taken from different areas of primary lung tumours will be subjected to targeted re-sequencing and analysed in order to assess intra-tumour heterogeneity with respect to mutations in a selection of cancer related genes.

  Study EGAS00001000436

• Breast_Cancer_Whole_Genome_Sequencing

  We propose to definitively characterise the somatic genetics of ER+ve, HER2-ve breast cancer through generation of comprehensive catalogues of somatic mutations in 500 cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000210

• Harnessing_transposons_for_drug_resistance_gene_discovery_in_cancer

  This study involves a forward genetic screen to identify common insertion sites in drug resistant clones. We will be utilising piggybac transposon systems in order to generate multiple drug resistant clones in a range of human cancer cell lines.

  Study EGAS00001000468

• Multisite_Primary_Breast_Cancer

  DNA extracted from multiple biopsies taken from different areas of primary breast tumours will be subjected to targeted re-sequencing and analysed in order to assess intra-tumour heterogeneity with respect to mutations in a selection of cancer related genes.

  Study EGAS00001000891

• Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004909

• Genome and transcriptome sequence data from a rectosigmoid adenocarcinoma (colorectal cancer) patient

  Genome and transcriptome sequence data from a rectosigmoid adenocarcinoma (colorectal cancer) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002567

• Genome and transcriptome sequence data from an adenocarcinoma of primary unknown cancer patient

  Genome and transcriptome sequence data from an adenocarcinoma of primary unknown cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002577

• Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient

  Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002979

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004923

• Genome and transcriptome sequence data from a large intestine-colon cancer patient

  Genome and transcriptome sequence data from a large intestine-colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005885

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001969

• Genome and transcriptome sequence data from a metastatic medullary thyroid cancer patient

  Genome and transcriptome sequence data from a metastatic medullary thyroid cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003006

• Genome and transcriptome sequence data from a low grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003680

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003713

• Genome and transcriptome sequence data from a metastatic collecting duct kidney cancer patient

  Genome and transcriptome sequence data from a metastatic collecting duct kidney cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003031

• Genome and transcriptome sequence data from an adenocarcinoma of unknown primary cancer patient

  Genome and transcriptome sequence data from an adenocarcinoma of unknown primary cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002611

• Genome and transcriptome sequence data from a metastatic unknown primary cancer patient

  Genome and transcriptome sequence data from a metastatic unknown primary cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005845

• Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003609

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003639

• Genome and transcriptome sequence data from a bilateral breast lobular cancer patient

  Genome and transcriptome sequence data from a bilateral breast lobular cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003711

• Genome and transcriptome sequence data from a large-cell neuroendocrine lung cancer patient

  Genome and transcriptome sequence data from a large-cell neuroendocrine lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003726

• Genome and transcriptome sequence data from a primary unknown cancer patient

  Genome and transcriptome sequence data from a primary unknown cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001308

• Genome and transcriptome sequence data from a locally advanced right breast cancer patient

  Genome and transcriptome sequence data from a locally advanced right breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003698

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001964

• Genome and transcriptome sequence data from a pancreatic cancer (likely PNET) patient

  Genome and transcriptome sequence data from a pancreatic cancer (likely PNET) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002607

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003041

• Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003672

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003682

• Genome and transcriptome sequence data from a locally advanced breast cancer patient

  Genome and transcriptome sequence data from a locally advanced breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004897

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003624

• Genome and transcriptome sequence data from a metastatic rectosigmoid cancer patient

  Genome and transcriptome sequence data from a metastatic rectosigmoid cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004622

• Genome and transcriptome sequence data from a high grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a high grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004702

• Genome and transcriptome sequence data from a metastatic breast cancer ER+ patient

  Genome and transcriptome sequence data from a metastatic breast cancer ER+ patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004631

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004697

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003047

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004646

• Genome and transcriptome sequence data from a high grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a high grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004638

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003054

• Genome and transcriptome sequence data from a non small cell lung cancer patient

  Genome and transcriptome sequence data from a non small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001963

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002990