Gene signatures in U-BIOPRED severe asthma for molecular phenotyping and precision medicine: time for clinical use
Author(s)
Type
Journal Article
Abstract
INTRODUCTION: The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype. AREAS COVERED: Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset. There has also been the identification of the role of mast cell activation and of macrophage dysfunction in various phenotypes of severe asthma. EXPERT OPINION: Phenotyping on the basis of clinical treatable traits is not sufficient for understanding of mechanisms driving the disease in severe asthma. It is time to consider whether certain patients with severe asthma, such as those non-responsive to current therapies, including Type 2 biologics, would be better served using an approach of molecular endotyping using gene signatures for management purposes rather than the current sole reliance on blood eosinophil counts or exhaled nitric oxide measurements.
Date Issued
2023
Date Acceptance
2023-10-30
Citation
Expert Review of Respiratory Medicine, 2023, 17 (11), pp.965-971
ISSN
1747-6348
Publisher
Taylor and Francis Group
Start Page
965
End Page
971
Journal / Book Title
Expert Review of Respiratory Medicine
Volume
17
Issue
11
Copyright Statement
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/37997709
Subjects
Asthma
Biomarkers
Eosinophils
Humans
Phenotype
Precision Medicine
differential gene expression
endotypes
gene set variation analysis
gene signatures
molecular phenotypes
Severe asthma
topological data analysis
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2023-12-26