Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage

Chronic obstructive pulmonary disease (COPD) is a severe, progressive, and heterogeneous disease with a poor outcome. Inflammation plays a central role in disease pathogenesis; however, the interplay between immune changes and disease heterogeneity has been difficult to unravel. We performed a multilevel immunoinflammatory characterization of patients with COPD using flow cytometry, cytokine profiling, single-cell, or spatial transcriptomics in combination with machine learning algorithms. Our cross-cohort analysis demonstrated shared skewing of immune profiles in COPD lungs toward adaptive immune cells. We furthermore identified a subgroup of patients with COPD with a distinct immune profile, characterized by increased antigen-presenting cells, mast cells, and CD8<sup>+</sup> cells, and circulating IL-1β, IFN-β, and GM-CSF, that were associated with increased emphysema severity and decreased gas exchange parameters independent of their GOLD-stage. Our findings suggest that unbiased immune profiling can refine disease classification and reveal inflammation-driven disease subtypes with potential relevance for prognosis and treatment strategies.

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Publication Details

Journal
iScience
Published
2025-06-19
DOI
https://doi.org/10.1016/j.isci.2025.112966
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
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article

Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage

Diana Schnoegl, Robab Breyer‐Kohansal, Vasile Foris, Grażyna Kwapiszewska et al.
iScience
Chronic Obstructive Pulmonary Disease (COPD) Research
article

Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage

Diana Schnoegl, Robab Breyer‐Kohansal, Vasile Foris, Grażyna Kwapiszewska, Slaven Crnković, Leigh M. Marsh, Konrad Höetzenecker, Gregor Gorkiewicz, Katarina Zeder, Francesca Polverino, Katharina Jandl, Ayse Ceren Mutgan, Panja Maria Boehm, Ayu Hutami Syarif, Natalie Bordag, Juergen Gindlhuber
article en

Abstract

Chronic obstructive pulmonary disease (COPD) is a severe, progressive, and heterogeneous disease with a poor outcome. Inflammation plays a central role in disease pathogenesis; however, the interplay between immune changes and disease heterogeneity has been difficult to unravel. We performed a multilevel immunoinflammatory characterization of patients with COPD using flow cytometry, cytokine profiling, single-cell, or spatial transcriptomics in combination with machine learning algorithms. Our cross-cohort analysis demonstrated shared skewing of immune profiles in COPD lungs toward adaptive immune cells. We furthermore identified a subgroup of patients with COPD with a distinct immune profile, characterized by increased antigen-presenting cells, mast cells, and CD8<sup>+</sup> cells, and circulating IL-1β, IFN-β, and GM-CSF, that were associated with increased emphysema severity and decreased gas exchange parameters independent of their GOLD-stage. Our findings suggest that unbiased immune profiling can refine disease classification and reveal inflammation-driven disease subtypes with potential relevance for prognosis and treatment strategies.

iScienceVol. 28(7)
Brigham and Women's Hospital (US), Harvard University (US), Baylor College of Medicine (US), Medical University of Graz (AT), Klinik Hietzing (AT), Ludwig Boltzmann Institute for Lung Vascular Research (AT), Austrian Institute for Health Technology Assessment GmbH (AT), Nawi Graz (AT), Cardio-Pulmonary Institute (DE), University of Maryland, College Park (US), Medical University of Vienna (AT)
Openalex Percentile: Top 76%
Chronic Obstructive Pulmonary Disease (COPD) Research
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