Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.

Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis, i.e. define pathological phenotypes, and facilitate the monitoring of disease and therapy, and also, unravel underlying molecular pathways. Biomarker studies can either select predefined biomarker(s) measured by specific methods or apply an ''unbiased'' approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the 'omics field. The contributions of the individual 'omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease. @ERSpublications Summary of the application of 'omics-based analytical platforms for biomarker discovery in inflammatory lung diseases

U-BIOPRED cohort n=91 epithelial brushings or biopsies IL-17 High Clinical phenotype Nasal polyps Smoking Antibiotic use Epithelial Gene Expression Profile Clinical phenotype FeNO Exacerbations Gene expression shared with psoriasis IDO1 IL1B DEFB4B S100A8, S100A9 PI3 CXCL3, CXCL8 CXCL10, CCL20 Gene signature SERPINB2 POSTN CLCA1 IL-13 High T cell infiltration Neutrophilia Eosinophilia IL-17-high asthma with features of a psoriasis immunophenotype From a the Respiratory,