Title: Endotyping Asthma for the Benefit of Patients
Funder: Asthma UK

Abstract: Asthma is a common disorder accounting for significant morbidity and mortality. Approximately 10% of patients have asthma resistant to current therapies reflecting a considerable unmet clinical need. Until recently asthma was considered a single disease entity caused by Th2-driven eosinophilic airway inflammation, and a &ldquo;one size fits all&rdquo; approach to therapy and drug development has dominated the field. With this model, development of effective new treatments has met with limited success. We and others have identified several reproducible clinical phenotypes of severe asthma suggesting the presence of different underlying pathophysiological processes. We hypothesise that asthma is not a single disease, but a syndrome resulting from several distinct underlying pathological processes known as endotypes. While a phenotype describes the clinical features of a disease, distinct underlying pathological processes leading to disease expression represent endotypes<sup>1</sup>. Our preliminary work undertaken using whole genome expression profiling and immunohistology on bronchial biopsies has identified 3 mutually exclusive distinct molecular endotypes of severe asthma: Th2-high; Th17-high; Th2-low/Th17-low. We have also identified a novel gene signature related to carcinoembryonic antigen-related cell adhesion molecule (CEACAM)-5 and CEACAM-6 in severe asthma. Our aims are to delineate asthma endotypes via assessments of structural, cellular, and molecular components using the following complementary approaches: <span style="text-decoration: underline;">AIM 1</span>: Perform detailed structural and cellular pathological analysis of bronchial tissue in extensively characterised asthmatic patients (n=153 [85 severe]) using unbiased bio-statistical tools including cluster analysis on. The following clustering analyses to identify putative immunopathological asthma endotypes will be performed: (i) Clustering based upon immunopathology alone (ii) Clustering based upon previous clinical clustering (iii) Clustering based upon both clinical and immunopathological parameters Immunopathological clusters will be examined with respect to clinically important asthma outcome measures. <span style="text-decoration: underline;">AIM 2</span>: Identify and cross-validate molecular correlates of asthma endotypes using genome-wide transcriptomic analyses of airway samples (n=51-71 asthma from Aim1 cohort, n=20 healthy controls). Aim 2.1. To investigate disease mechanisms in Th2-low/Th17-low severe asthma using transcriptomics, and detailed immunopathological and clinical characterisation To interrogate the factors driving Th2-low/Th17-low severe asthma, detailed pathway analysis on gene array data from the asthma gene array cohort (n=51 &ndash; with a further 20 severe asthma biopsies pending gene array) will be undertaken using standard biostatistical techniques e.g. linear models and hierarchical clustering. This will be validated using qRT-PCR and coupled with detailed clinical and immunopathological analysis. Further gene array dataset mining will be undertaken to assess the relationships of gene expression metrics with pathological and physiological parameters. Aim 2.2. Validation of novel gene clusters CEACAM-5 and -6. Using unbiased bronchial biopsy gene microarrays we have identified CEACAM-5 and CEACAM-6 as tissue metaplasia genes associated with severe asthma. We will examine the tissue expression of CEACAM-5 and CEACAM-6 within the airway wall in our asthma cohorts and their relationship to the immunopathology and clinical characteristics. In addition we will measure their concentration in serum and induced sputum. Summary We anticipate that these approaches will identify potential therapeutic targets in subsets of asthma patients, leading to endotype-specific personalised therapies.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: RESPIRATORY