Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA354105 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:NHLBI TOPMed: Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
| PRJNA414681 | ENA
Project description:NHLBI TOPMed: Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
| PRJNA414680 | ENA
Project description:Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinson’s disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drug’s effect on cholesterol homeostasis. Study subjects were a subset of those with COPD from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (Vestbo et al.), funded by GlaxoSmithKline (GSK Study No. SCO104960, NCT00292552). ECLIPSE is a non-interventional, observational, multicentre, three-year study in people with COPD. Blood was collected in PAXGene tubes and frozen at -80oC. In this work we have looked at the effect of statins on gene expression in 620 subjects of whom 168 were statin users. ECLIPSE study was described in: Vestbo J, Anderson W, Coxson HO, et al.: Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J. 2008;31(4):869-73
2015-07-23 | E-GEOD-71220 | biostudies-arrayexpress
Project description:Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)
| PRJNA354106 | ENA
Project description:<p>ECLIPSE was a longitudinal observational study of 2164 COPD subjects and a smaller number of smoking controls (337) and nonsmoking controls (245) followed regularly for three years, with three chest CT scans (at baseline, one year, and three years) (Vestbo, European Respiratory Journal 2008; 31: 869). Inclusion criteria included age 40-75, at least 10 pack-years of smoking, and spirometry in GOLD grades 2-4 (COPD cases) or normal spirometry with post-bronchodilator FEV1 >85% predicted and FEV1/FVC>0.7 (controls). Study visits were performed at enrollment, three months, and every six months thereafter with spirometry, questionnaires, and other clinical evaluations. The ECLIPSE CT scans have been analyzed with the VIDA software for emphysema and airway phenotypes. ECLIPSE has provided key insights into the clinical epidemiology of COPD, including COPD exacerbations (Hurst, NEJM 2010; 363: 1128) and lung function decline in COPD (Vestbo, NEJM 2011; 365: 1184). ECLIPSE has been used in a number of genetic studies of COPD susceptibility and protein biomarkers(Faner, Thorax 2014; 69: 666). Genome-wide gene expression microarray data are available in 147 induced sputum samples from COPD subjects and 248 peripheral blood samples from COPD and control subjects.</p>
| phs001252 | dbGaP
Project description:Background: COPD is currently the fourth leading cause of death worldwide and predicted to rank third by 2020. Statins are commonly used lipid lowering agents with documented benefits on cardiovascular morbidity and mortality, and have also been shown to have pleiotropic effects including anti-inflammatory and anti-oxidant activity. Objective: Identify a gene signature associated with statin use in the blood of COPD patients, and identify molecular mechanisms and pathways underpinning this signature that could explain any potential benefits in COPD. Methods: Whole blood gene expression was measured on 168 statin users and 452 non-users from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. Gene expression was measured using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data and to filter out non-informative probe sets. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and employing a surrogate variable analysis. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results: 18 genes were differentially expressed between statin users and non-users at a false discovery rate of 10%. Top genes included LDLR, ABCA1, ABCG1, MYLIP, SC4MOL, and DHCR24. The 18 genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, and were enriched for transcription factor binding sites for sterol regulatory element binding protein 2 (SREBP-2). The resulting gene signature showed correlation with Huntington disease, Parkinson’s disease and acute myeloid leukemia. Conclusion: Statins gene signature was not enriched in any pathways related to respiratory diseases, beyond the drug’s effect on cholesterol homeostasis.
2015-07-23 | GSE71220 | GEO
Project description:Chronic Obstructive Pulmonary Disease (COPD) is a respiratory disorder that is the result of extended exposure of the airways to noxious stimuli, principally cigarette smoke (CS). The mechanisms through which COPD evolves are not fully understood though it is believed that the disease process includes a genetic component since not all smokers develop COPD. To investigate the mechanism leading to the development of COPD/emphysema, we performed an experiment in which whole genome gene expression and several COPD-relevant biological endpoints (MMP-9, MMP activity, TIMP-1 and lung weight) were measured in lung tissue after exposure to two doses of CS for various periods of time. A novel and powerful method, known as reverse engineering and forward simulation (REFS(TM)), was employed to identify key molecular drivers by integrating gene expression data and 4 measured COPD-relevant endpoints. An ensemble of molecular networks was generated using REFS(TM). Simulations showed that this ensemble could successfully recover the measured experimental data for gene expression and measured COPD-relevant endpoints. This ensemble of networks was then further employed to simulate thousands of in silico gene knockdown experiments. Based on the in silico gene knockdown, thirty-three molecular key drivers for the above four COPD-relevant endpoints were identified, with the majority of them being enriched in inflammation, emphysema and COPD.
2014-05-14 | E-MTAB-1426 | biostudies-arrayexpress
Project description:Induced sputum is used to sample inflammatory cells, predominantly neutrophils and macrophages, from the airways of COPD patients. Our aim was to identify candidate genes associated with the degree of airflow obstruction and the extent of emphysema by expression profiling, and then to confirm these findings for selected candidates using specific PCR and protein analysis. Two sputum studies were performed in GOLD stage 2 -4 COPD ex-smokers from the ECLIPSE cohort. First, gene array profiling at baseline in 1480 patients was performed. At year 1, samples from a separate population of 176 patients were used for real-time PCR. The gene expression findings for IL-18R were further analysed using immunohistochemistry in lung tissue and induced sputum samples from patients outside the ECLIPSE cohort.
2010-06-16 | E-GEOD-22148 | biostudies-arrayexpress
Project description:One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Briefly, gene expression was derived from whole blood samples in ECLIPSE subjects and peripheral blood mononuclear cells (PBMCs) for the COPDGene subjects. Gene expression profiling was performed using the Affymetrix Human U133 Plus2 array. Gene expression data were log-transformed, and background correction and normalization were performed for the merged ECLIPSE and COPDGene samples using robust multi-array averaging and quantile normalization as implemented in the affy Bioconductor package[27]. Of the 136 COPDGene subjects reported in a previous publication[13], one self-reported African-American subject was removed from analysis, which was conducted on the remaining 135 non-Hispanic white subjects. To identify a set of genes associated with COPD, we performed differential expression analysis for 38,519 probesets in ECLIPSE that passed quality control measures. Normalized probeset intensities were related to measures indicative of two primary dimensions of pulmonary impairment in COPD airway obstruction as indicated by two measures of spirometric lung function (FEV1 (% of predicted) and FEV1/FVC) and lung parenchymal destruction, i.e., emphysema (as quantified by the percentage of low attenuation area less than -950 Hounsfield units on lung computed tomography, %LAA-950). The analysis was conducted using the limma Bioconductor package, and the false discovery rate was controlled at 5%. The following covariates were included in the differential expression analysis age, pack-years of cigarette smoke exposure, and gender. After standardizing gene expression data from 229 ECLIPSE subjects by the variance of each probe set, we applied NMF[29] and NBS[6] to identify meta-patients (i.e. subtypes or subject clusters) and meta-genes (i.e. representative subtype expression profiles). Cross-sectional study of smokers. 229 subjects from the ECLIPSE study were analyzed in the model discovery phase. 135 subjects from the COPDGene Study (GSE42057) were used for replication. Please note that the entire data set for total 364 samples including the re-analyzed samples is provided in the *364samples.txt files.
2016-01-12 | E-GEOD-76705 | biostudies-arrayexpress
Project description:One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Briefly, gene expression was derived from whole blood samples in ECLIPSE subjects and peripheral blood mononuclear cells (PBMCs) for the COPDGene subjects. Gene expression profiling was performed using the Affymetrix Human U133 Plus2 array. Gene expression data were log-transformed, and background correction and normalization were performed for the merged ECLIPSE and COPDGene samples using robust multi-array averaging and quantile normalization as implemented in the affy Bioconductor package[27]. Of the 136 COPDGene subjects reported in a previous publication[13], one self-reported African-American subject was removed from analysis, which was conducted on the remaining 135 non-Hispanic white subjects. To identify a set of genes associated with COPD, we performed differential expression analysis for 38,519 probesets in ECLIPSE that passed quality control measures. Normalized probeset intensities were related to measures indicative of two primary dimensions of pulmonary impairment in COPD airway obstruction as indicated by two measures of spirometric lung function (FEV1 (% of predicted) and FEV1/FVC) and lung parenchymal destruction, i.e., emphysema (as quantified by the percentage of low attenuation area less than -950 Hounsfield units on lung computed tomography, %LAA-950). The analysis was conducted using the limma Bioconductor package, and the false discovery rate was controlled at 5%. The following covariates were included in the differential expression analysis age, pack-years of cigarette smoke exposure, and gender.
2016-01-12 | GSE76705 | GEO