Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.

U-BIOPRED cohort n=91 epithelial brushings or biopsies IL-17 High Clinical phenotype Nasal polyps Smoking Antibiotic use Epithelial Gene Expression Profile Clinical phenotype FeNO Exacerbations Gene expression shared with psoriasis IDO1 IL1B DEFB4B S100A8, S100A9 PI3 CXCL3, CXCL8 CXCL10, CCL20 Gene signature SERPINB2 POSTN CLCA1 IL-13 High T cell infiltration Neutrophilia Eosinophilia IL-17-high asthma with features of a psoriasis immunophenotype From a the Respiratory,

Background: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. Objective: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. Methods: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. Results: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms.

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