Sputum gene expression signature of 6 biomarkers discriminates asthma inflammatory phenotypes

Airway inflammation is associated with asthma exacerbation risk, treatment response, and disease mechanisms.

This study aimed to identify and validate a sputum gene expression signature that discriminates asthma inflammatory phenotypes.

An asthma phenotype biomarker discovery study generated gene expression profiles from induced sputum of 47 asthmatic patients. A clinical validation study (n = 59 asthmatic patients) confirmed differential expression of key genes. A 6-gene signature was identified and evaluated for reproducibility (n = 30 asthmatic patients and n = 20 control subjects) and prediction of inhaled corticosteroid (ICS) response (n = 71 asthmatic patients). Receiver operating characteristic curves were calculated, and area under the curve (AUC) values were reported.

From 277 differentially expressed genes between asthma inflammatory phenotypes, we identified 23 genes that showed highly significant differential expression in both the discovery and validation populations. A signature of 6 genes, including Charcot-Leydon crystal protein (CLC); carboxypeptidase A3 (CPA3); deoxyribonuclease I-like 3 (DNASE1L3); IL-1β (IL1B); alkaline phosphatase, tissue-nonspecific isozyme (ALPL); and chemokine (C-X-C motif) receptor 2 (CXCR2), was reproducible and could significantly (P < .0001) discriminate eosinophilic asthma from other phenotypes, including patients with noneosinophilic asthma (AUC, 89.6%), paucigranulocytic asthma (AUC, 92.6%), or neutrophilic asthma (AUC, 91.4%) and healthy control subjects (AUC, 97.6%), as well as discriminating patients with neutrophilic asthma from those with paucigranulocytic asthma (AUC, 85.7%) and healthy control subjects (AUC, 90.8). The 6-gene signature predicted ICS response (>12% change in FEV1; AUC, 91.5%). ICS treatment reduced the expression of CLC, CPA3, and DNASE1L3 in patients with eosinophilic asthma.

A sputum gene expression signature of 6 biomarkers reproducibly and significantly discriminates inflammatory phenotypes of asthma and predicts ICS treatment response. This signature has the potential to become a useful diagnostic tool to assist in the clinical diagnosis and management of asthma.