Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.

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...

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Autores principales:	Lucas A Gillenwater, Shahab Helmi, Evan Stene, Katherine A Pratte, Yonghua Zhuang, Ronald P Schuyler, Leslie Lange, Peter J Castaldi, Craig P Hersh, Farnoush Banaei-Kashani, Russell P Bowler, Katerina J Kechris
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/720ad2983d46440eb08aaa313bff75a2
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spelling	oai:doaj.org-article:720ad2983d46440eb08aaa313bff75a22021-12-02T20:17:36ZMulti-omics subtyping pipeline for chronic obstructive pulmonary disease.1932-620310.1371/journal.pone.0255337https://doaj.org/article/720ad2983d46440eb08aaa313bff75a22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255337https://doaj.org/toc/1932-6203Chronic 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.Lucas A GillenwaterShahab HelmiEvan SteneKatherine A PratteYonghua ZhuangRonald P SchuylerLeslie LangePeter J CastaldiCraig P HershFarnoush Banaei-KashaniRussell P BowlerKaterina J KechrisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255337 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lucas A Gillenwater Shahab Helmi Evan Stene Katherine A Pratte Yonghua Zhuang Ronald P Schuyler Leslie Lange Peter J Castaldi Craig P Hersh Farnoush Banaei-Kashani Russell P Bowler Katerina J Kechris Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
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.
format	article
author	Lucas A Gillenwater Shahab Helmi Evan Stene Katherine A Pratte Yonghua Zhuang Ronald P Schuyler Leslie Lange Peter J Castaldi Craig P Hersh Farnoush Banaei-Kashani Russell P Bowler Katerina J Kechris
author_facet	Lucas A Gillenwater Shahab Helmi Evan Stene Katherine A Pratte Yonghua Zhuang Ronald P Schuyler Leslie Lange Peter J Castaldi Craig P Hersh Farnoush Banaei-Kashani Russell P Bowler Katerina J Kechris
author_sort	Lucas A Gillenwater
title	Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
title_short	Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
title_full	Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
title_fullStr	Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
title_full_unstemmed	Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
title_sort	multi-omics subtyping pipeline for chronic obstructive pulmonary disease.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/720ad2983d46440eb08aaa313bff75a2
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Multi-omics subtyping pipeline for chronic obstructive pulmonary disease.

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