Stratification of asthma phenotypes by airway proteomic signatures.
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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, Horváth Ildikó, Howarth Peter, Krug Norbert, Montuschi Paolo, Sanak Marek, Sandström Thomas, Sun Kai, Pandis Ioannis, Riley John, Auffray Charles, De Meulder Bertrand, Lefaudeux Diane, Sousa Ana R, Adcock Ian M, Chung Kian Fan, Sterk Peter J, Skipp Paul J, Djukanović Ratko, U-BIOPRED Study Group
Аннотация
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.