Background: Atopic dermatitis (AD) is a frequent and heterogeneous inflammatory skin disease, for which personalized medicine remains a challenge. High-throughput approaches have improved understanding of the complex pathophysiology of AD. However, a purely data-driven AD classification is still lacking.
Methods: To address this question, we applied an original unsupervised approach on the largest available transcriptome dataset of AD lesional (n = 82) and healthy (n = 213) skin biopsies.
Results: Taking into account pathological and physiological state, a variance-based filtering revealed 222 AD-specific hyper-variable genes that efficiently classified the AD samples into 4 clusters that turned out to be clinically and biologically distinct. Comparison of gene expressions between clusters identified 3 sets of upregulated genes used to derive metagenes (MGs): MG-I (19 genes) was associated with IL-1 family signaling (including IL-36A and 36G) and skin remodeling, MG-II (23 genes) with negative immune regulation (including IL-34 and 37) and skin architecture, and MG-III (17 genes) with B lymphocyte immunity. Sample clusters differed in terms of disease severity (p = .02) and S. aureus (SA) colonization (p = .02). Cluster 1 contained the most severe AD, highest SA colonization, and overexpressed MG-I. Cluster 2 was characterized by less severe AD, low SA colonization, and high MG-II expression. Cluster 3 included mild AD, mild SA colonization, and mild expression of all MGs. Cluster 4 had the same clinical features as cluster 3 but had hyper-expression of MG-III. Last, we successfully validated our method and results in an independent cohort.
Conclusion: Our study revealed unrecognized AD endotypes with specific underlying biological pathways, highlighting novel pathophysiological mechanisms. These data could provide new insights into personalized treatment strategies.
Keywords: atopic dermatitis; clustering; endotype; transcriptome.
© 2021 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.