U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics

BACKGROUND: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalised management approach can be provided.OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinico-physiological parameters and performed an -omics analysis of sputum.METHODS: Partition-around-medoid clustering was applied to a training set of 266 asthma participants from the European U-BIOPRED adult cohort using 8 pre-specified clinic-physiological variables. This was repeated in a separate validation set of 152 asthmatics. The clusters were compared based on sputum proteomic and transcriptomic data.RESULTS: Four reproducible and stable clusters of asthmatics were identified. The training set cluster T1 consists of well-controlled moderate-to-severe asthmatics, while cluster T2 is a group of late-onset severe asthmatics with history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of non-smokers. Cluster T4 is predominantly composed of obese female uncontrolled severe asthmatics with increased exacerbations, but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters, T2, T3 and T4, had higher sputum eosinophilia than T1 with no differences in sputum neutrophil counts, exhaled nitric oxide and serum IgE levels.CONCLUSION: Clustering based on clinico-physiological parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.CLINICAL IMPLICATIONS: The definition of four distinct clusters of asthma linked to different pathobiological pathways provides a better template for the phenotyping and personalised treatment of severe asthma, where high unmet needs remain.CAPSULE SUMMARY: Unsupervised clustering of asthma on clinical features alone has led to the definition of four phenotypes. Sputum 'omics' analysis has revealed different biological pathways pointing towards potential new treatments.</p

Stratification of asthma phenotypes by airway proteomic signatures

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 analysisidentified 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.</p