Rationale: Although asthma is recognized as a heterogeneous disease associated with clinical phenotypes, the molecular basis of these phenotypes remains poorly understood. Although genomic studies have successfully broadened our understanding in diseases such as cancer, they have not been widely used in asthma studies.
Objectives: To link gene expression patterns to clinical asthma phenotypes.
Methods: We used a microarray platform to analyze bronchial airway epithelial cell gene expression in relation to the asthma biomarker fractional exhaled nitric oxide (FeNO) in 155 subjects with asthma and healthy control subjects from the Severe Asthma Research Program (SARP).
Measurements and main results: We first identified a diverse set of 549 genes whose expression correlated with FeNO. We used k-means to cluster the patient samples according to the expression of these genes, identifying five asthma clusters/phenotypes with distinct clinical, physiological, cellular, and gene transcription characteristics-termed "subject clusters" (SCs). To then investigate differences in gene expression between SCs, a total of 1,384 genes were identified that highly differentiated the SCs at an unadjusted P value < 10(-6). Hierarchical clustering of these 1,384 genes identified nine gene clusters or "biclusters," whose coexpression suggested biological characteristics unique to each SC. Although genes related to type 2 inflammation were present, novel pathways, including those related to neuronal function, WNT pathways, and actin cytoskeleton, were noted.
Conclusions: These findings show that bronchial epithelial cell gene expression, as related to the asthma biomarker FeNO, can identify distinct asthma phenotypes, while also suggesting the presence of underlying novel gene pathways relevant to these phenotypes.
Keywords: clustering; exhaled nitric oxide; severe asthma.