How cytokines co-occur across asthma patients: from bipartite network analysis to a molecular-based classification.
Bhavnani-SK; Victor-S; Calhoun-WJ; Busse-WW; Bleecker-E; Castro-M; Ju-H; Pillai-R; Oezguen-N; Bellala-G; Brasier-AR
J Biomed Inform 2011 Dec; 44(Suppl 1):S24-S30
Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.
Respiratory-system-disorders; Bronchial-asthma; Corticoids; Medical-treatment; Diagnostic-techniques; Cytochemistry; Pulmonary-function; Lung-cells; Lung-function; Molecular-biology; Treatment; Airway-obstruction; Drug-therapy; Pharmacodynamics; Alveolar-cells; Analytical-instruments; Computer-models; Humans;
Author Keywords: Network analysis; Co-occurrence of cytokines; Molecular-based classification of asthma patients
Suresh K. Bhavnani, Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd., Galveston, TX 77555-0331, USA
Journal of Biomedical Informatics
University of Texas Medical Branch, Galveston