Schofield, James P R; Burg, Dominic; Nicholas, Ben; Strazzeri, Fabio; Brandsma, Joost; Staykova, Doroteya; Folisi, Caterina; Bansal, Aruna T; Xian, Yang; Guo, Yike; Rowe, Anthony; Corfield, Julie; Wilson, Susan; Ward, Jonathan; Lutter, Rene; Shaw, Dominick E; Bakke, Per S; Caruso, Massimo; Dahlen, Sven-Erik; Fowler, Stephen J; ... (2019). Stratification of asthma phenotypes by airway proteomic signatures. Journal of allergy and clinical immunology, 144(1), pp. 70-82. Elsevier 10.1016/j.jaci.2019.03.013
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stratification of asthma phenotypes by airway proteomic signatures_2019.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB) | Request a copy |
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.
CONCLUSION
This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies.
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