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Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Bordag, N; Jandl, K; Syarif, AH; Gindlhuber, J; Schnoegl, D; Mutgan, AC; Foris, V; Hoetzenecker, K; Boehm, PM; Breyer-Kohansal, R; Zeder, K; Gorkiewicz, G; Polverino, F; Crnkovic, S; Kwapiszewska, G; Marsh, LM.
Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage.
iScience. 2025; 28(7): 112966
Doi: 10.1016/j.isci.2025.112966
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PubMed
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- Führende Autor*innen der Med Uni Graz
- Bordag Natalie
- Marsh Leigh
- Co-Autor*innen der Med Uni Graz
- Crnkovic Slaven
- Foris Vasile
- Gindlhuber Jürgen
- Gorkiewicz Gregor
- Jandl Katharina
- Kwapiszewska-Marsh Grazyna
- Mutgan Redolfi Ayse Ceren
- Schnögl Diana
- Zeder Katarina Eleonora
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- 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+ 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.