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Rationale: Unbiased discovery of gene signatures in sputum in the U-BIOPRED cohort revealed a possible role for mast cell gene expression in adult-onset severe asthma [Hekking et al , ERS 2015]. Therefore, we aimed to investigate whether enrichment scores of a mast cell gene signature (ESM) associate with phenotypic characteristics of adult-onset severe asthma. Methods: Patients with adult-onset severe asthma (onset ≥18yr) were selected from the U-BIOPRED cohort. ESM were calculated with Gene Set Variation Analysis (GSVA) based on expression of a set of 6 genes associated with mast cells. Clinical and inflammatory characteristics were compared between patients with high and low ESM (Fig.), using student t-tests, chi-square or Mann-Whitney U tests. Results: Patients with high ESM (n=33) were significantly older when asthma was first diagnosed (mean±SD; 43.2±14.3 vs 34.2±9.7 yr; P=0.02) and had higher % eosinophils in blood (median(IQR); 20.7(4.5-42.0) vs 1.2 (0.2-2.5); P e.g. age, gender and BMI) were not significantly different between the groups. Conclusion: These results reveal two distinct subphenotypes of adult-onset severe asthma based on high and low mast cell gene signature enrichment. Their association with age-of-onset and eosinophilia may have implications for clinical management and identification of endotypes. U-BIOPRED is funded by IMI (grant 115010)

Rationale: Severe asthma is a heterogeneous clinical condition including various pathophysiological pathways. Metabolomics of exhaled air is associated with airways inflammation in patients with asthma and COPD. Aim: To reveal severe asthma phenotypes by unbiased cluster analysis based on metabolomic fingerprints from exhaled breath by gas-chromatography/mass-spectrometry (GCMS) and to link results to electronic nose (eNose) data. Methods: This was a cross-sectional analysis in the U-BIOPRED cohort. Severe asthma was defined by IMI-criteria [Bel Thorax 2011]. Exhaled volatile organic compounds (VOCs) were trapped on two adsorption tubes per subject: one for GCMS and one for the U-BIOPRED eNose platform (Owlstone Lonestar, Cyranose C320, Comon Invent, Tor Vergata TEN). Data cleaning on both omic datasets included: normalization and data reduction by principal component analysis (PCA). Statistical analysis of GCMS data was performed using Ward clustering, followed by Similarity Profile Analysis. The between-cluster comparison of baseline variables and eNose PCs was done by ANOVA, Kruskal-Wallis or χ2 tests. Results: Shared GCMS-eNose data were available for 35 patients (age 50±15yr, 45% male, 46% (ex-)smokers). Four clusters of GCMS data were delineated, that differed significantly regarding: SNOT questionnaire (p=0.02), smoking pack years (p=0.04), sputum neutrophils (p=0.04), serum periostin (p=0.03) and 7 out of 12 eNose PCs (p Conclusions: Unbiased fingerprinting of exhaled air by GCMS provides clusters of severe asthma patients that differ regarding clinical and inflammatory parameters. Breath analysis by eNose technology is resembling, but not identical to GCMS in severe asthma.

For more than a century, clinicians have attempted to subdivide asthma into different phenotypes based on triggers that cause asthma attacks, the course of the disease, or the prognosis. The first phenotypes that were described included allergic asthma, intrinsic or nonallergic asthma, infectious asthma, and aspirin-exacerbated asthma. These phenotypes are being reviewed elsewhere in this issue of the journal. The present article focuses on developing and emerging clinical asthma phenotypes. First, asthma phenotypes that are associated with environmental exposures (occupational agents, cigarette smoke, air pollution, cold dry air); second, asthma phenotypes that are associated with specific symptoms or clinical characteristics (cough, obesity, adult onset of disease); and third, asthma phenotypes that are based on biomarkers. This latter approach is the most promising because it attempts to identify asthma phenotypes with different underlying mechanisms so that therapies can be better targeted toward disease-specific features and disease outcomes can be improved.