Dataset Information

Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder. COPD patients may have different clinical features, imaging characteristics and natural history. Multiple studies have investigated heterogeneity using statistical methods such as unsupervised clustering to define different subgroups of COPD based largely on clinical phenotypes. Some studies have performed clustering using genetic data or limited numbers of blood biomarkers. Our primary goal was to use proteomic data to find subtypes of COPD within clinically similar individuals. In the Treatment of Emphysema with a gamma-Selective Retinoid Agonist (TESRA) study, multiplex biomarker panels were run in serum samples collected prior to randomization. After implementing an algorithm to minimize missing values, the dataset included 396 COPD individuals and 87 biomarkers. Using hierarchical clustering, we identified 3 COPD subgroups, containing 267 (67.4%), 104 (26.3%), and 25 (6.3%) individuals, respectively. The third cluster had less emphysema on quantitative analysis of chest computed tomography scans (p=0.03) and worse disease-related quality of life based on the St. George's Respiratory Questionnaire (total score cluster 1: 45.6, cluster 2: 45.4, cluster 3: 56.6; p=0.01), despite similar levels of lung function impairment (forced expiratory volume in 1 second (49.2%, 49.2%, 48.2 % predicted, respectively). Enrichment analysis showed the biomarkers distinguishing cluster 3 mapped to platelet alpha granule and cell chemotaxis pathways. Thus, we identified a subgroup which has less emphysema but may have greater inflammation, which could be potentially targeted with anti-inflammatory therapies.

SUBMITTER: Zarei S

PROVIDER: S-EPMC5559108 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics.

Zarei Sara S Mirtar Ali A Morrow Jarrett D JD Castaldi Peter J PJ Belloni Paula P Hersh Craig P CP

Chronic obstructive pulmonary diseases (Miami, Fla.) 20170208 2

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder. COPD patients may have different clinical features, imaging characteristics and natural history. Multiple studies have investigated heterogeneity using statistical methods such as unsupervised clustering to define different subgroups of COPD based largely on clinical phenotypes. Some studies have performed clustering using genetic data or limited numbers of blood biomarkers. Our primary goal was to use proteomic data to fi ...[more]

PMID: 28848918

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Project description:Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Dataset Information

Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics.

Publications

Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics.

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