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Title: Airway Pathological Heterogeneity in Asthma: Visualisation of Disease Micro-Clusters using Topological Data Analysis
Authors: Siddiqui, Salman H.
Shikotra, Aarti
Richardson, Matthew
Doran, Emma
Choy, David
Bell, Alex
Austin, Cary D.
Eastham-Anderson, Jeffrey
Hargadon, Beverley
Arron, Joseph R.
Wardlaw, Andrew
Brightling, Christopher E.
Heaney, Liam G.
Bradding, Peter
First Published: 14-Mar-2018
Publisher: Elsevier
Citation: Journal of Allergy and Clinical Immunology, 2018, in press
Abstract: Background: Asthma is a complex chronic disease underpinned by pathological changes within the airway wall. How variations in structural airway pathology and cellular inflammation contribute to expression and severity of asthma are poorly understood. Objectives: We therefore evaluated pathological heterogeneity using topological data analysis (TDA) with the aim of visualizing disease clusters and microclusters. Methods: A discovery population of 202 adult patients [142 asthma, 60 healthy] and an external replication population [59 severe asthma] were evaluated. Pathology and gene expression were examined in bronchial biopsy samples. TDA was applied using pathological variables alone to create pathology-driven visual networks. Results: In the discovery cohort, TDA identified four groups/networks with multiple micro clusters/regions of interest that were masked by group level statistics. Specifically, TDA group 1 consisted of a high proportion of healthy subjects with a microcluster representing a topological continuum connecting healthy subjects to patients with mild-moderate asthma. Three additional moderate to severe asthma TDA groups (airway smooth muscleHIGH , reticular basement membraneHIGH and RemodellingLOW) were identified and contained numerous microclusters with varying pathological and clinical features. Mutually exclusive Th2 and Th17 tissue gene expression signatures were identified in all pathological groups. Discovery and external replication applied to the severe asthma subgroup only, identified highly similar 'pathological data shapes' via analyses of persistent homology. Conclusions: We have identified and replicated novel pathological phenotypes of asthma using topological data analysis. Our methodology is applicable to other complex chronic diseases
DOI Link: 10.1016/j.jaci.2017.12.982
ISSN: 0091-6749
Embargo on file until: 14-Mar-2019
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2018, Elsevier. Deposited with reference to the publisher’s open access archiving policy. (
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, College of Medicine, Biological Sciences and Psychology

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