15080 results for "cancer studies using rna-seq"

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• Reference epigenome ADMSC06 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC06 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007381

• Reference epigenome ADMSC06 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007384

• Reference epigenome ADMSC07 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC07 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007388

• Reference epigenome ADMSC07 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC07 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007389

• Reference epigenome ADMSC06 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007383

• Reference epigenome KNIH009 mRNA-seq data generated from KEP study

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175

• Reference epigenome SMC07_smRNA-Seq data generated from KEP study

  A SMC07_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003864

• Reference epigenome SMC09_smRNA-Seq data generated from KEP study

  A SMC09_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003866

• Reference epigenome SMC03_smRNA-Seq data generated from KEP study

  A SMC03_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003860

• Reference epigenome SMC08_smRNA-Seq data generated from KEP study

  A SMC08_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003865

• Reference epigenome KNIH007 mRNA-seq data generated from KEP study

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

• Reference epigenome SMC01_smRNA-Seq data generated from KEP study

  A SMC01_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003858

• Reference epigenome SMC06_smRNA-Seq data generated from KEP study

  A SMC06_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003863

• Reference epigenome SMC04_smRNA-Seq data generated from KEP study

  A SMC04_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003861

• Reference epigenome SMC05_smRNA-Seq data generated from KEP study

  A SMC05_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003862

• Reference epigenome KNIH008 mRNA-seq data generated from KEP study

  A KNIH008 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002174

• Reference epigenome SMC02_smRNA-Seq data generated from KEP study

  A SMC02_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003859

• Reference epigenome KNIH007 miRNA-seq data generated from KEP study

  A KNIH007 miRNA-seq single end data for adipocytes

  Dataset EGAD00001002766

• Reference epigenome KNIH008 miRNA-seq data generated from KEP study

  A KNIH008 miRNA-seq single end data for adipocytes

  Dataset EGAD00001002767

• Reference epigenome ADMSC03 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007368

• Reference epigenome SMC08 h3k4me1 ChIP-Seq data generated from KEP study

  SMC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007346

• Reference epigenome Islet-derived-MSC04 miRNA-Seq data generated from KEP study

  Islet-derived-MSC04 miRNA-Seq single end data

  Dataset EGAD00001007184

• Reference epigenome SMC03 input ChIP-Seq data generated from KEP study

  SMC03 input ChIP-Seq paired end data

  Dataset EGAD00001007325

• Reference epigenome ADMSC02 input ChIP-Seq data generated from KEP study

  ADMSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007367

• Reference epigenome ADMSC03 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007369

• Reference epigenome ADMSC03 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007370

• Reference epigenome ADMSC03 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007371

• Reference epigenome ADMSC03 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007372

• Reference epigenome ADMSC03 input ChIP-Seq data generated from KEP study

  ADMSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007373

• Reference epigenome ADMSC05 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC05 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007374

• Single-cell RNA-sequencing of a multi-region pleural mesothelioma case

  Single-cell RNA-sequencing from three distant regions of an epithelioid pleural mesothelioma case.

  Study EGAS00001008062

• Quantitative analysis of a novel DNA hypermethylation panel for lung cancer diagnosis using bronchial specimens

  Non-diagnostic findings in Transbronchial Lung Biopsy (TBLB) and Endobronchial Ultrasound-guided Transbronchial Lung Biopsy (EBUS-TBLB) are not uncommon. A challenge is to improve lung cancer detection by TBLB/EBUS-TBLB. In order to improve the diagnostic yield of bronchoscopy, we used 850K methylation chip to identify methylation sites that distinguish malignant from benign lung nodules.We found that the combination of HOXA7, SHOX2,and SCT methylation analysis has the best diagnostic yield in 123 bronchial washing (sensitivity: 74·1%; AUC: 0·851) and 172 brushing samples (sensitivity: 86·1%; AUC: 0·915). We developed a kit comprising these three genes and validated the kit in 329 unique bronchial washing samples, 397 unique brushing samples, and 179 unique patients with both washing and brushing samples. The diagnostic accuracy of the panel alone in lung cancer diagnosis was 86·9%, 91·2% and 95% in bronchial washing, brushing, and washing+brushing samples, respectively. The panel's sensitivity in combination with cytology, rapid on-site evaluation (ROSE), and histology in lung cancer diagnosis was 90·8% and 95.8% in bronchial washing and brushing samples, respectively, and 100% in washing+brushing samples. Quantitative analysis of the three-gene panel improves lung cancer diagnosis by bronchoscopy.

  Study EGAS00001007089

• Implementation, Adoption, and Utility of Family History in Diverse Care Settings

  The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

  Study phs001641

• RNA-sequencing

  RNA-sequencing for a cohort of B-ALL samples : 5 Down Syndrome (DS), 16 Hyperdiploid (HeH), 6 iAMP21, 9 other. RNA-sequencing for B-cell progenitors from 3 healthy. It also contains RNA-sequencing datasets of Patient-Derived Xenografts (X) developed from the B-ALL samples : 4 Down Syndrome (DS), 4 Hyperdiploid (HeH), 1 iAMP21, 3 other.

  Dataset EGAD00001005426

• Spatiotemporal immune atlas of the first clinical-grade gene-edited pig-to-human kidney xenotransplant

  Pig-to-human xenotransplantation is rapidly approaching the clinical arena; however, it is unclear which immunomodulatory regimens will effectively control human immune responses to pig xenografts. We transplanted a gene-edited pig kidney into a brain-dead human recipient on pharmacologic immunosuppression and studied the human immune response to the xenograft using spatial transcriptomics and single-cell RNA sequencing. Human immune cells were uncommon in the porcine kidney cortex early after xenotransplantation and consisted of primarily myeloid cells. Both the porcine resident macrophages and human infiltrating macrophages expressed genes consistent with an alternatively activated, anti-inflammatory phenotype. No significant infiltration of human B or T cells into the porcine kidney xenograft was detected. Altogether, these findings provide proof of concept that conventional pharmacologic immunosuppression is sufficient to restrict infiltration of human immune cells into the xenograft early after compatible pig-to-human kidney xenotransplantation.

  Dataset EGAD50000000359

• RNAseq data from the study - Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML

  This study assessed molecular determinants of response in a cohort of patients with AML that were treated with venetoclax in combination with either DNA methyltransferase inhibitors or low dose cytarabine. RNA sequencing was performed on 31 patients from three different response classes [10 Group A - Durable remission (n=10), Group B - Relapsed (n=10) and Group C - Refractory (n=11)]. Library preparation and sequencing was performed at the Australian Genome Research Facility, using the Truseq Stranded mRNA library kit. Technical and batch replicate samples are included. Gene count data are provided with the original publication. The use of the sequencing data is subject to a data transfer agreement and is restricted to ethically approved research into blood cell malignancies and cannot be used to assess germline variants.

  Dataset EGAD00001005949

• Atypical teratoid/rhabdoid tumors (ATRT) are comprised of three epigenetic subgroups with distinct enhancer landscapes

  Atypical teratoid/rhabdoid tumor (ATRT) is one of the most common brain tumors in infants and young children. Although the prognosis of ATRT patients is poor, some patients respond very well to current treatments, suggesting inter-tumor molecular heterogeneity. To investigate this further, we genetically and epigenetically analyzed a large cohort of ATRTs (n = 170). Three distinct molecular subgroups of ATRTs, associated with differences in demographics, tumor location and type of SMARCB1 alterations, were identified using DNA-methylation or gene expression analyses. Whole genome DNA- and RNA-sequencing found no other recurrent mutations explaining the differences between subgroups. However, whole genome bisulfite-sequencing and H3K27Ac ChIP-sequencing of primary tumors revealed clear differences in methylation patterns and enhancer landscapes, leading to the identification of subgroup-specific regulatory networks.

  Dataset EGAD00001001444

• Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer

  Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitors. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. Here we evaluate bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples. In CheckMate 026, TMB was retrospectively assessed in 312 patients with non-small cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne CDx™ assay. TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. Parameter variation can therefore impact TMB calculations, highlighting the need for standardization. Encouragingly, WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90) and differences could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms and centers is achievable.

  Study EGAS00001003661

• ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  Dataset EGAD00001000356

• ATAC-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ATAC-seq (42 samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001004829

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• Reference epigenome Islet-derived-iPSC02 mRNA-Seq data generated from KEP study

  Islet-derived-iPSC02 mRNA-Seq paired end data

  Dataset EGAD00001007165

• Reference epigenome Pancreas-Islet02 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007195

• Reference epigenome IPS-Fibroblast02 input ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 input ChIP-Seq paired end data

  Dataset EGAD00001007276

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• Reference epigenome KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• Reference epigenome Islet-derived-iPSC01 mRNA-Seq data generated from KEP study

  Islet-derived-iPSC01 mRNA-Seq paired end data

  Dataset EGAD00001007164

• Reference epigenome Islet-derived-MSC01 mRNA-Seq data generated from KEP study

  Islet-derived-MSC01 mRNA-Seq paired end data

  Dataset EGAD00001007160