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Integrative multi-omic analyses identify major axes of heterogeneity in chronic obstructive pulmonary disease and uncover their molecular contributors - ECLIPSE replication cohort

ABSTRACT: Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, leading to classifications that only partially reflect disease heterogeneity. Here, we introduce a variational autoencoder-based subtyping pipeline that jointly embeds clinical and gene expression data into a single subject-level representation. We evaluate the framework in the COPDGene study, a large study of current and former smoking individuals with and without COPD. Prediction experiments show that the embeddings have predictive accuracy comparable to or better than other unsupervised embedding approaches. Using trajectory learning approaches, we identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and longitudinal outcomes. Finally, we show that our findings generalize to an external validation cohort. Overall, our approach enables a transition from isolated phenotypic or molecular subtyping toward an integrated and clinically meaningful understanding of COPD heterogeneity. This GEO dataset contains array-based blood gene expression and phenotype data from the ECLIPSE study, which served as a replication population for this study. COPDGene data are available through dbGaP.

ORGANISM(S): Homo sapiens

PROVIDER: GSE324136 | GEO | 2026/03/31

REPOSITORIES: GEO

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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-naÃ¯ve 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.

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.

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

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Integrative multi-omic analyses identify major axes of heterogeneity in chronic obstructive pulmonary disease and uncover their molecular contributors - ECLIPSE replication cohort

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