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J Allergy Clin Immunol. 2018 Nov;142(5):1457-1468. doi: 10.1016/j.jaci.2017.12.982. Epub 2018 Mar 14.

Airway pathological heterogeneity in asthma: Visualization of disease microclusters using topological data analysis.

Author information

1
Department of Infection Immunity and Inflammation, Institute for Lung Health, University of Leicester, Glenfield Hospital, Leicester, United Kingdom. Electronic address: ss338@le.ac.uk.
2
Department of Infection Immunity and Inflammation, Institute for Lung Health, University of Leicester, Glenfield Hospital, Leicester, United Kingdom.
3
Genentech, South San Francisco, Calif.
4
Department of Infection Immunity and Inflammation, Institute for Lung Health, University of Leicester, Glenfield Hospital, Leicester, United Kingdom; Department of Mathematics, University of Leicester, Leicester, United Kingdom.
5
Centre for Infection and Immunity, Health Sciences Building, Queens University Belfast, Belfast, United Kingdom.

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 the expression and severity of asthma are poorly understood.

OBJECTIVES:

Therefore we 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 asthmatic patients and 60 healthy subjects) and an external replication population (59 patients with severe asthma) were evaluated. Pathology and gene expression were examined in bronchial biopsy samples. TDA was applied by using pathological variables alone to create pathology-driven visual networks.

RESULTS:

In the discovery cohort TDA identified 4 groups/networks with multiple microclusters/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-to-moderate asthma. Three additional TDA groups with moderate-to-severe asthma (Airway Smooth MuscleHigh, Reticular Basement MembraneHigh, and RemodelingLow groups) 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 identified only highly similar "pathological data shapes" through analyses of persistent homology.

CONCLUSIONS:

We have identified and replicated novel pathological phenotypes of asthma using TDA. Our methodology is applicable to other complex chronic diseases.

KEYWORDS:

Severe asthma; airway inflammation; phenotyping; remodeling; topological data analysis

PMID:
29550052
DOI:
10.1016/j.jaci.2017.12.982

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