15077 results for "cancer studies using rna-seq"

in 52.40 milliseconds.

• NHLBI TOPMed: Diabetes Heart Study (DHS) African American Coronary Artery Calcification (AA CAC)

  The Diabetes Heart Study (DHS) is a family-based study enriched for type 2 diabetes (T2D). The cohort included 1443 European American and African American participants from 564 families with multiple cases of type 2 diabetes (Bowden et al., 2010. Review of Diabetic Studies 7:188-201. PMID: 21409311). The cohort was recruited between 1998 and 2006. Participants were extensively phenotyped for measures of subclinical CVD and other known CVD risk factors. Primary outcomes were quantified burden of vascular calcified plaque in the coronary artery, carotid artery, and abdominal aorta all determined from non-contrast computed tomography scans.

  Study phs001412

• NHLBI TOPMed - NHGRI CCDG: Groningen Genetics of Atrial Fibrillation (GGAF) Study

  Atrial fibrillation (also called AFib or AF) is a quivering or irregular heartbeat (arrhythmia) that can lead to blood clots, stroke, heart failure and other heart-related complications. The Groningen Genetics of Atrial Fibrillation (GGAF) cohort is a cohort composed from 5 different sources of individuals with atrial fibrillation (AF) and age and sex-matched controls. Written informed consent was provided from all participating individuals, and all 5 studies were approved by the ethical committee at the University Medical Center (www.atrialfibrillationresearch.nl) and Maastricht University. All samples selected for TOPMed WGS are from individuals with atrial fibrillation.

  Study phs001725

• Epigenomic analysis of the human placenta and endometrium constituting the fetal-maternal interface

  This study aims to determine the reference epigenomes of the human placenta and endometrium, which constitute the fetal-meternal interface, for future use in studies of human diseases associated with reproduction and development. In the placenta, we focus on cytotrophoblast and syncytiotrophoblast cells (both at first trimester and at term) and, in the endometrium, we focus on epithelial cells (at the follicular and secretory phases) and stroma cells. These reference epigenomic data will be of valuable resource in studying disease-associated differences in epigenomic modification in the reproductive system. JGA submission accessions: JGA000074,JGA00000000074

  Study JGAS000073

• Epigenomic analysis of the human placenta and endometrium constituting the fetal-maternal interface

  This study aims to determine the reference epigenomes of the human placenta and endometrium, which constitute the fetal-meternal interface, for future use in studies of human diseases associated with reproduction and development. In the placenta, we focus on cytotrophoblast and syncytiotrophoblast cells (both at first trimester and at term) and, in the endometrium, we focus on epitherial cells (at the follicular and secretory phases) and stroma cells. These reference epigenomic data will be of valuable resource in studying disease-associated differences in epigenomic modification in the reproductive system. JGA submission accessions: JGA000117,JGA00000000117

  Study JGAS000107

• Illumina ExomeChip genotyping data from the Cretan Greek isolate collection HELIC MANOLIS

  Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies. Here we have genotyped individuals from a Greek population isolate on the Illumina HumanExome Beadchip. The HELIC (Hellenic Isolated Cohorts) MANOLIS (Minoan Isolates) collection focuses on Anogia and surrounding Mylopotamos villages. Recruitment of this population-based sample was primarily carried out at the village medical centres. All individuals were older than 17 years and had to have at least one parent from the Mylopotamos area. www.helic.org **The data may not be used for population genetics analyses.**

  Study EGAS00001000630

• HipSci___RNAseq___Rare_BBS

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS cells generated from skin biopsies or blood samples from rare disease patients diagnosed with Bardet-Biedl syndrome 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 EGAS00001001318

• Illumina Human OmniExpress genotyping data from the Cretan Greek isolate collection HELIC MANOLIS

  Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies. Here we have genotyped individuals from a Greek population isolate on the Illumina Human OmniExpress BeadChip array. The HELIC (Hellenic Isolated Cohorts) MANOLIS (Minoan Isolates) collection focuses on Anogia and surrounding Mylopotamos villages. Recruitment of this population-based sample was primarily carried out at the village medical centres. All individuals were older than 17 years and had to have at least one parent from the Mylopotamos area. www.helic.org **The data may not be used for population genetics analyses**

  Study EGAS00001000687

• Fetal hemoglobin in sickle cell disease patients from Tanzania

  Levels of fetal hemoglobin have been associated with differences in severity and outcome of sickle cell disease (SCD). While some genetic associations have been reported between SNPs and HbF level, little of the variance in the trait is explained, and most studies have focused on African American, rather than African samples. We have carried out a GWAS in a collection of SCD patients from Dar es Salaam, Tanzania. This sample represents a diverse, metropolitan collection of individuals from East Africa. Omni 2.5M genotypes and HbF levels (measured after 5 years of age) are available.

  Study EGAS00001000990

• HipSci___RNAseq___Rare_Monogenic Diabetese

  The HipSci project brings together diverse constituents in genomics, proteomics, cell biology and clinical genetics to create a UK national iPS cell resource and use it to carry out cellular genetic studies. In this sub-study we perform RNAseq on iPS cells generated from skin biopsies or blood samples from rare disease patients diagnosed with Monogenic Diabetes. 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 EGAS00001001137

• Whole Genome Sequencing of 317 individuals from the Pacific region

  Although genetic studies of modern Oceanians have revealed varying levels of admixture with populations of East Asian ancestry, attributed to the Austronesian expansion, the early peopling history of Oceania remains poorly characterized. Additionally, it is currently unknown whether and how the settlement of the Pacific was accompanied by genetic adaptation to island environments, and if archaic introgression facilitated this process in Oceanians, who present the highest worldwide levels of combined Neanderthal and Denisovan ancestry. This study reports the first whole genome-based survey that addresses a wide range of questions related to the demographic and adaptive history of Pacific populations.

  Study EGAS00001004540

• SARS-CoV-2 host genetics and COVID-19 outcomes in admixed Brazilians with extreme phenotypes

  COVID-19 comprises clinical outcomes of SARS-CoV-2 infection and is highly heterogeneous, ranging from asymptomatic individuals to deceased young adults without comorbidities. There is growing evidence that host genetics play an important role in COVID-19 severity, including inborn errors of immunity, age-related inflammation and immunosenescence. This study proposes a strategy of subject’s ascertainment based on phenotypic extremes to take part in genomic studies and elucidate intrinsic risk factors involved in COVID-19 severe outcomes. Different cohorts were collected and submitted to Next-Generation Sequencing (Whole exome and whole genome sequencing).

  Study EGAS00001006376

• RNA sequencing data of 142 samples from 142 patients with HER2+ breast cancer treated with letrozole or tamoxifen (SOLTI PAMELA trial)

  PAMELA (NCT01973660) is a non-randomized, open-label, multicentric, prospective translational research study in stage I-IIIA HER2+ breast cancer designed to evaluate the ability of the PAM50 intrinsic subtypes to predict pCR in the breast (pCRB) following 18 weeks of neoadjuvant lapatinib and trastuzumab (+/- hormonal therapy if hormone receptor [HR] positive). Patients with HR+ disease received letrozole (if postmenopausal) or tamoxifen (if pre-menopausal). The primary objective was to compare the pCRB rates of the HER2-E versus the non-HER2-E subtypes in the intent-to-treat population.

  Study EGAS00001006410

• Reduced Representation Bisulfite Sequencing (RRBS) in various tissues to discover DNA methylation markers for the diagnosis of breast and ovarian cancer.

  Despite a myriad of attempts in the last three decades to diagnose ovarian cancer (OC) earlier, this clinical aim still remains a significant challenge. In addition, monitoring treatment and early detection of fatal breast cancer (BC) remains a major unmet need. Aberrant methylation patterns of linked CpGs analyzed in DNA fragments shed by cancers into the bloodstream (i.e. cell-free DNA) can provide highly specific signals indicating cancer presence.In order to discover the most relevant CpG regions, we used RRBS and analysed several tissue samples: 11 prospectively collected invasive epithelial ovarian cancer samples (high grade serous n=8, low grade serous n=1, endometrioid n=1, mucinous n=1, mean age = 54.7 years), 8 prospectively collected invasive ductal breast cancer samples (2/8 triple negative; mean age = 56.6 years), one benign tumor (papillary serous cystadenoma, age = 86 years), 18 non-neoplastic tissue samples (breast n=7 and adnexal n=11, mean age = 60.2 years), two non-neoplastic endometrial tissues (mean age = 68 years) and twenty three white blood cell samples (mean age = 57.8 years) were assessed by RRBS. Genome wide methylation analysis was performed by Reduced Representation Bisulfite Sequencing (RRBS) at GATC Biotech. DNA was digested with MspI followed by size selection of the library, providing enhanced coverage for the CpG-rich regions. The digested DNA was adapter ligated, bisulfite modified and PCR amplified. The libraries were sequenced on Illumina’s HiSeq 2500. Analysis of the first samples sequenced with 100bp paired-end mode showed that the library insert size was small. Therefore, the remaining samples were sequenced with 50 bp paired-end mode. Using Genedata Expressionist® for Genomic Profiling v9.1, we established a bioinformatics pipeline for the detection of cancer specific differentially methylated regions (DMRs). The most promising DMRs were taken forward for the development and validation of serum based clinical assays.

  Study EGAS00001002609

• Mediator ChIP-Seq datasets in human islets

  6 ChIP-Seq datasets of Mediator in human pancreatic islets from 6 donors

  Dataset EGAD00001005202

• Reference epigenome Islet-derived_MSC08 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007907

• Reference epigenome Islet-derived_MSC08 input ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 input ChIP-Seq paired end data

  Dataset EGAD00001007908

• Reference epigenome Islet-derived_MSC06 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007897

• Reference epigenome Islet-derived_MSC08 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007904

• Reference epigenome Islet-derived_MSC06 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007898

• Reference epigenome Islet-derived_MSC08 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007905

• Reference epigenome Islet-derived_MSC08 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007906

• Reference epigenome Islet-derived_iPSC04 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007890

• Reference epigenome Islet-derived_iPSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007892

• Reference epigenome Islet-derived_iPSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007893

• Reference epigenome Islet-derived_MSC06 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007896

• Reference epigenome Islet-derived_MSC08 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007903

• Cohesin ChIP-Seq datasets in human islets

  3 ChIP-Seq datasets of Cohesin in human pancreatic islets from 3 donors

  Dataset EGAD00001005203

• Reference epigenome Islet-derived_MSC06 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007895

• Reference epigenome Islet-derived_iPSC04 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007889

• Reference epigenome Islet-derived_MSC06 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007900

• Reference epigenome Islet-derived_MSC06 input ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007901

• Reference epigenome Islet-derived_iPSC04 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007888

• Reference epigenome Islet-derived_iPSC04 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007891

• Reference epigenome Islet-derived_iPSC04 input ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007894

• Reference epigenome Islet-derived_MSC06 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007899

• Reference epigenome Islet-derived_MSC08 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007902

• STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  Dataset EGAD00001015361

• Chondrosarcoma Targeted Sequencing Study

  This Study uses a focused bespoke bait pull down library method to target findings of Chondrosarcoma whole genome and whole exome sequencing studies in order to validate findings. This method will also be used on a larger set of tumour only samples in order to find precedence of these findings in a larger set of patient samples.

  Dataset EGAD00001000265

• Osteosarcoma Targeted Sequencing Study

  This Study uses a focused bespoke bait pull down library method to target findings of Osteosarcoma whole genome and whole exome sequencing studies in order to validate findings. This method will also be used on a larger set of tumour only samples in order to find precedence of these findings in a larger set of patient samples.

  Dataset EGAD00001000266

• Chordoma Targeted Sequencing Study

  This Study uses a focused bespoke bait pull down library method to target findings of Chordoma whole genome and whole exome sequencing studies in order to validate findings. This method will also be used on a larger set of tumour only samples in order to find precedence of these findings in a larger set of patient samples.

  Dataset EGAD00001000267

• Meningioma Targeted Sequencing Study

  This Study uses a focused bespoke bait pull down library method to target findings of Meningioma whole genome and whole exome sequencing studies in order to validate findings. This method will also be used on a larger set of tumour only samples in order to find precedence of these findings in a larger set of patient samples.

  Dataset EGAD00001000273

• Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

  Advamced Visium Spatial Gene Expression assay for FFPE tissues with human and SARS-CoV-2 whole transcriptome (WT) information at a 55 µm resolution. The dataset consists of 13 tissue sections from 5 patient lung tissue samples, 3 from COVID-19 patients and 2 from control patients.

  Dataset EGAD00001008701

• Prognostic relevance of microenvironmental factors CD163 and CD8 combined with EZH2 and chromosome 18 gain in a validation cohort of follicular lymphoma patients of the Lunenburg Lymphoma Biomarker Consortium

  Background: In follicular lymphoma (FL), studies addressing the prognostic value of microenvironment-related immunohistochemical (IHC) markers and tumor cell-related genetic markers have yielded conflicting results, precluding implementation in practice. Therefore, the Lunenburg Lymphoma Biomarker Consortium (LLBC) performed a validation study for published markers. Methods: To maximize sensitivity, an end-of-spectrum design was applied for 122 uniformly immunochemotherapy-treated FL patients retrieved from international trials and registries; early failure (EF): progression or lymphoma-related death <2 years versus long remission: response duration of >5 years. IHC staining for T-cells and macrophages was performed on tissue microarrays from initial biopsy and scored with a validated computer-assisted protocol. Shallow whole-genome and deep targeted sequencing was performed on the same samples. Results: 96/122 cases with complete molecular and immunohistochemical data were included in the analysis. EZH2 wild-type (p=0.006), gain of chromosome 18 (p=0.002), low percentages of CD8+ cells (p=0.011) and CD163+ areas (p=0.038) were associated with EF. No significant differences in other markers were observed, thereby refuting previous claims on their prognostic significance. Conclusion: Using an optimized study design, this LLBC study validates wild-type EZH2 status, gain of chromosome 18, low percentages of CD8+ cells and CD163+ area as predictors of EF to immunochemotherapy in FL.

  Dataset EGAD00001002738

• BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  Study EGAS00001000753

• Immune Landscape of Cervical Lymph Nodes in Multiple Sclerosis

  This study aimed to investigate the cellular compartment of deep cervical lymph nodes (dcLNs) during early MS before the influence of immunosuppressive treatments. We used ultrasound-guided FNAs followed by scRNA-seq,CITE-seq, TCR and BCR seq to characterize the immune landscape in MS patients compared to controls within the dcLNs.

  Study EGAS50000000843

• Prediction of Trastuzumab Benefit in Adjuvant Breast Cancer: Gene Expression Profiling in NSABP B31

  Background National Surgical Adjuvant Breast and Bowel Project (NSABP) trial B-31 suggested the efficacy of adjuvant trastuzumab, even in HER2-negative breast cancer. This finding prompted us to develop a predictive model for degree of benefit from trastuzumab using archived tumor blocks from B-31. Methods Case subjects with tumor blocks were randomly divided into discovery (n = 588) and confirmation cohorts (n = 991). A predictive model was built from the discovery cohort through gene expression profiling of 462 genes with nCounter assay. A predefined cut point for the predictive model was tested in the confirmation cohort. Gene-by-treatment interaction was tested with Cox models, and correlations between variables were assessed with Spearman correlation. Principal component analysis was performed on the final set of selected genes. All statistical tests were two-sided. Results Eight predictive genes associated with HER2 (ERBB2, c17orf37, GRB7) or ER (ESR1, NAT1, GATA3, CA12, IGF1R) were selected for model building. Three-dimensional subset treatment effect pattern plot using two principal components of these genes was used to identify a subset with no benefit from trastuzumab, characterized by intermediate-level ERBB2 and high-level ESR1 mRNA expression. In the confirmation set, the predefined cut points for this model classified patients into three subsets with differential benefit from trastuzumab with hazard ratios of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100), 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449), and 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442; P interaction between the model and trastuzumab < .001). Conclusions We developed a gene expression-based predictive model for degree of benefit from trastuzumab and demostrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47). ** Reprinted from J Natl Cancer Inst;2013;105:1782-1788 with permission from Oxford University Press.

  Study phs000826

• Exome Sequencing to Define the Landscape of Plasma Cells in Systemic Light chain Amyloidosis

  Systemic light chain amyloidosis (AL) is characterized by the deposition of immunoglobulin light chains as amyloid fibrils in different organs, where they form toxic protein aggregates. The underlying disease is a plasma cell disorder, but limited whole exome data are available. We report the findings of an exome sequencing study in AL to define a plasma cell signature and compare this to monoclonal gammopathy of undefined significance (MGUS) and myeloma (MM). Twenty-four samples from unselected newly diagnosed untreated AL patients were analysed. CD138+ cells were isolated from bone marrow cells using MACSorting (Miltenyi Biotech, Bisley, UK). Cells were lysed in RLT+ buffer and DNA/RNA extracted using the AllPrep kit (Qiagen, Manchester, UK). Peripheral blood was isolated from patients, white blood cells purified by Ficoll-Pacque and DNA extracted using the QIAamp DNA mini kit (Qiagen). There were a median of 47 acquired nonsynonymous variants (range 4-253) per patient. Thirty percent of patients yielded a mutation in a myeloma driver gene. These mutations were either clonal or sub-clonal. There was evidence of MAPK activation with NRAS mutations, NF-κB activation and DNA repair pathway alterations. Exome data were used to determine the cytogenetic groups of AL samples and identified hyperdiploidy in 33% of cases and t(11;14) in 33% of cases. All translocations were generated via class switch recombination and breakpoints on chromosome 11 fell into the expected regions. A novel translocation, unlikely to be driving disease progression was identified involving RCC1. Finally, further analysis of the APCS gene encoding for the SAP protein did not show any biased genotype in the AL population in comparison to the general population. We conclude that exome sequencing identifies a genetic make-up of AL that is similar to other plasma cell disorders such as MM and MGUS. This not only includes copy number abnormalities and translocations but also nonsynonymous mutations.

  Study EGAS00001001418

• SF11964 snATAC Seq Low Grade Astrocytoma IDHR132H mutant

  Single Nuclei ATAC Seq IDHR132H mutant Astrocytoma . Male, 64. Single Nuclei ATAC seq data from low grade human glioma sample. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005414

• Gene Expression in an African American Schizophrenia Dataset

  Schizophrenia is a common and severe psychotic disorder. While some common SNPs and rare copy number variants have been identified as being significantly associated with disease risk, the biological mechanisms remain largely undefined. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using RNAseq on lymphoblastoid cell lines from schizophrenia cases and controls of African American ancestry. Genes related to immune processes were enriched in the genes differentially expressed in the schizophrenia cases. Approximately half of the differentially expressed genes in this African American dataset were also differentially expressed by affection status in our previously studied European ancestry dataset (PMID: 28418402).

  Study phs002842

• Reference epigenome SMC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC01_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003845