@article{Schofield:2019:10.1016/j.jaci.2019.03.013,
author = {Schofield, JPR and Burg, D and Nicholas, B and Strazzeri, F and Brandsma, J and Staykova, D and Folisi, C and Bansal, AT and Xian, Y and Guo, Y and Rowe, A and Corfield, J and Wilson, S and Ward, J and Lutter, R and Shaw, DE and Bakke, PS and Caruso, M and Dahlen, S-E and Fowler, SJ and Horváth, I and Howarth, P and Krug, N and Montuschi, P and Sanak, M and Sandström, T and Sun, K and Pandis, I and Riley, J and Auffray, C and De, Meulder B and Lefaudeux, D and Sousa, AR and Adcock, IM and Chung, KF and Sterk, PJ and Skipp, PJ and Djukanovi, R and U-BIOPRED, Study Group},
doi = {10.1016/j.jaci.2019.03.013},
journal = {Journal of Allergy and Clinical Immunology},
pages = {70--82},
title = {Stratification of asthma phenotypes by airway proteomic signatures},
url = {http://dx.doi.org/10.1016/j.jaci.2019.03.013},
volume = {144},
year = {2019}
}

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TY  - JOUR
AB  - 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 to predict treatment responses and a need for better understanding of the underlying mechanisms. OBJECTIVE: Identify molecular sub-phenotypes of asthma defined by proteomic signatures for improved stratification. METHODS: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyse the proteomes of sputum supernatants from 246 participants (206 asthmatics) 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, proteotypes, based on similarity in proteomics features, representing discrete molecular sub-phenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined three of these as highly eosinophilic, three as highly neutrophilic, and two as highly atopic with relatively low granulocytic inflammation. For each of these three 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 by quantifying granulocytic inflammation and gives additional insight into their underlying mechanisms which could become targets for novel therapies.
AU  - Schofield,JPR
AU  - Burg,D
AU  - Nicholas,B
AU  - Strazzeri,F
AU  - Brandsma,J
AU  - Staykova,D
AU  - Folisi,C
AU  - Bansal,AT
AU  - Xian,Y
AU  - Guo,Y
AU  - Rowe,A
AU  - Corfield,J
AU  - Wilson,S
AU  - Ward,J
AU  - Lutter,R
AU  - Shaw,DE
AU  - Bakke,PS
AU  - Caruso,M
AU  - Dahlen,S-E
AU  - Fowler,SJ
AU  - Horváth,I
AU  - Howarth,P
AU  - Krug,N
AU  - Montuschi,P
AU  - Sanak,M
AU  - Sandström,T
AU  - Sun,K
AU  - Pandis,I
AU  - Riley,J
AU  - Auffray,C
AU  - De,Meulder B
AU  - Lefaudeux,D
AU  - Sousa,AR
AU  - Adcock,IM
AU  - Chung,KF
AU  - Sterk,PJ
AU  - Skipp,PJ
AU  - Djukanovi,R
AU  - U-BIOPRED,Study Group
DO  - 10.1016/j.jaci.2019.03.013
EP  - 82
PY  - 2019///
SN  - 0091-6749
SP  - 70
TI  - Stratification of asthma phenotypes by airway proteomic signatures
T2  - Journal of Allergy and Clinical Immunology
UR  - http://dx.doi.org/10.1016/j.jaci.2019.03.013
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30928653
UR  - http://hdl.handle.net/10044/1/69041
VL  - 144
ER  - 

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