15174 results for "cancer studies using rna-seq"

in 15.90 milliseconds.

• 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

• 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

• Genomic Analysis of Prostate Tumor Heterogeneity in Metastasis

  To investigate the molecular changes, mutations, and signaling pathways that characterize the initial steps of prostate cancer metastasis to the lymph nodes, we analyzed 19 discrete lymph node (LN) metastases, 97 primary tumor foci, 39 benign lymph nodes, and 10 normal adjacent prostate tissue samples from 43 patients enrolled in a clinical trial (NCT01808222) of extended lymphadenectomy and metastasis excision guided by [18F]-fluciclovine PET imaging for high risk, newly diagnosed prostate cancer conducted at Emory University. These patients fell within high or very high-risk groups (T3a, Gleason score 8-10, or PSA greater than 20 ng/ml). Of these patients, 16 had LN metastases at least 4 mm in diameter, and 20 patients had no metastases. For metastatic patients, we performed RNA and Whole Exome Sequencing (WES) on three primary tumor foci, one benign LN, and all LN metastases greater than 4mm in diameter. For non-metastatic patients, we sequenced two primary tumor foci and one benign LN. In total, RNAseq was performed on 165 tissue samples from 43 patients, and WES was performed on 137 samples from 35 patients.

  Study phs003404

• Impact of Respiratory Virus Infections and Bacterial Microbiome Shifts on Lymphocyte and Respiratory Function in Infants Born Prematurely or Full Term

  Public health importance: Babies born preterm, approximately 1 out of every 9 live births in the United States, have significant respiratory morbidity over the first two years of life, exacerbated by respiratory viral infections. Many (<50%) return to pediatricians, emergency rooms and pulmonologists with symptoms of respiratory dysfunction (SRD): intermittent or chronic wheezing, poor growth and an excess of upper and lower respiratory tract infections (LRTI). SRD correlate inversely with gestational age and weight at birth and is more common in those with chronic lung disease of prematurity, yet its incidence and severity varies widely among both the prematurely born and those born at term. There is evidence from clinical studies and animal models that risks of LRTI and recurrent wheezing is influenced by gut and respiratory flora and by T cell responses to infection. Information gained from this study will be used to identify characteristics, risk factors and potential mechanisms for early and persistent lung disease in children born at term and born preterm. This Clinical Research Study will investigate the relationships between sequential respiratory viral infections, patterns of intestinal and respiratory bacterial colonization, and adaptive cellular immune phenotypes which are associated with increased susceptibility to respiratory infections and long term respiratory morbidity in preterm and full term infants. We hypothesize that the timing and acquisition of specific viral infections and bacterial species are directly related to respiratory morbidity in the first year of life as defined by SRD and by measures of pulmonary function. We hypothesize that cellular and molecular immuno-maturity are altered due to factors presented by premature birth in such a way as to promote chronic inflammatory and cytotoxic damage to the lung, with subsequent enhanced, damaging responses to infectious agents and environmental irritants. Our preliminary studies demonstrate both feasibility and expertise in mutiparameter immunophenotyping of small volume peripheral blood samples obtained from premature infants including gene expression arrays of flow cytometry sorted cells. We will use new technologies for known viral identification, as well as high-throughput metagenome sequencing of RNA and DNA virus like particles (VLP) to be used for viral discovery in infant respiratory sample and use of high-throughput pyrosequencing (454T) of bacterial 16S rRNA to determine shifts in bacterial community structure, occurring in pre-term (PT) as compared to full term (FT) infants, over the first year of life. Finally, we present statistical approaches to stratify disease risk predictors using multivariate logistic regression modeling approaches. We propose to evaluate T cell phenotypic and functional profiles relative to viral and predominant bacterial exposures according to highly complementary, but independent, Specific Objectives. Objective 1: To determine if viral respiratory infections and patterns of respiratory and gut bacterial community structure (microbiome) in prematurely born babies predict the rate and degree of immunologic maturation, and pulmonary dysfunction, measured from birth to 36 weeks corrected gestational age (CGA). Objective 2: To determine the relationship between respiratory viral infections and disease severity up to one year CGA, and the lymphocyte (Lc) phenotypes documented at term gestation (birth for term infants and 36 wks/NICU discharge in preterm infants) and at one year CGA. Three secondary outcomes of this objective will be to a) relate the quantity, type and severity of viral infections with pulmonary function at one and three years of life, b) relate the viral community structure to severity of viral infections and c) to seek evidence of modulation of viral susceptibility by bacterial respiratory and gut community structure (microbiome). The relationship of colonization with known and non-identified bacterial species in both the respiratory tract and the gut will be evaluated. Flow cytometry data corresponding to this study can be found within Immport study SDY1302. Positive and negative controls for microbiome samples are uploaded under SRA bioproject PRJNA474485. Microbiome samples corresponding to PRISM2 are distinguished from PRISM1 via "_PRISM2" appended to the sample name. Within the positive and negative controls, PRISM1 controls are uploaded as bam files and PRISM2 controls are uploaded as paired fastq. Samples ending in -08 correspond to TLDA qPCR results for a given sample. There is a column for each pathogen tested and a column to indicate where that pathogen was bacteria or virus.

  Study phs001347

• Single cell and plasma RNA sequencing

  Single cell and plasma RNA sequencing for RNA liquid biopsy for hepatocellular carcinoma

  Study EGAS00001005194

• Data upcycling, powered by EGA

  In 2012 a collaborative effort headed by the teams of Carlos Caldas in Cambridge, UK and Samuel Aparicio, Canada, produced a pivotal study aimed at defining the mutational landscape of breast cancer. They gathered a collection of over 2000 breast cancer samples (METABRIC) associated with long-term clinical follow-up. They delineated an array of inherited and somatically acquired genetic variants and associated them to gene expression. This led to the identification of putative cancer-driving events and improved stratification of breast cancer patients. The study elicited huge attention and was tackled as herculean effort by fellow researchers. Cristina Curtis and her colleagues deposited the data in the EGA archive (EGAS00000000083). What they probably could not foresee is that their data would be reused more than 100 times, becoming one of the most downloaded datasets at EGA. In 2015, Frederik S. Varn and colleagues accessed the above-mentioned data from the New Hampshire, USA, to correlate it to transcriptional programs of hematopoietic cells infiltrated in breast tumours and found that those can influence patients' prognosis. Their findings, published in Nature Communications, improved the understanding of the interplay between immune system and cancer. Again, in 2017 the dataset was downloaded by an Italian/Polish collaboration and used to corroborate their mechanistic study that demonstrate how a long non-coding RNA (lncRNA) is aberrantly localized into the chromatin, where it modulates oncogenic isoforms expression, thus contributing to breast cancer. To date, the METABRIC dataset has been reused to contribute to 147 publications. Its impact on breast cancer research is incalculable, and it even spread beyond that, with recent contributions to the understanding of pan-cancer mechanisms and features. Some of the publications are translating into improvements in diagnosis and treatment of different types of breast cancer. This is just an example of how data upcycling can amplify the potential of any dataset, well past the scope of its creation, the imagination of its owners and any geographical border. At EGA, we are proud to empower such fruitful worldwide cycles of knowledge, providing a platform that enables safe sharing of sensitive genetic data.

  Blog data-upcycling-powered-by-ega

• H3K27ac ChIP-seq in Insulinomas

  H3K27ac ChIP-seq datasets in human insulinoma samples

  Dataset EGAD50000000461

• BLUEPRINT EpiVar ChIP-seq of Monocytes & Neutrophils

  BLUEPRINT EpiVar ChIP-seq of Monocytes & Neutrophils

  Study EGAS00001000870

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases (2015-07-02)[MOSAIC]

  Systematic next generation sequencing efforts are beginning to define the genomic landscape across a range of primary tumours, but we know very little of the mutational evolution that contributes to disease progression. We therefore propose to obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in a cohort of matched primary and metastatic colorectal cancers, and additionally to explore the extent to which those mutations identified as recurrent in the metastatic setting are able to subvert normal biological processes using both genetically engineered mouse models and established cancer cell lines. This study will enable us to define to what extent primary tumour profiling can capture the biological processes operative in matched metastases as well as the significance of intratumoural heterogeneity. This dataset contains all the data available for this study on 2015-07-02.

  Dataset EGAD00001001426

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

  Islet-derived_MSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007907