Predicting future exacerbations in uncontrolled asthma: a discovery/validation approach using large-scale clinical proteomics.

Abstract

Background: Exacerbations contribute significantly to the burden of asthma. Some individuals are predisposed to recurring exacerbations however, the underlying mechanisms are not well understood.

Objective: To generate a sputum protein signature associated with future exacerbations.

Method: 22 baseline sputum samples from the control (placebo control) arm of the AMAZES study were analysed using an optimised high-throughput mass spectrometry method.

Results: Using a log-fold change of ≥ 1.5 and a p-value of 0.05 as cut-offs, univariate analysis identified 533 differentially abundant sputum proteins in participants with and without future exacerbations over the ensuring 48 weeks. A multivariate signature of 260 proteins for predicting future exacerbations was developed using sparse partial least squares data analysis, that was partially able to predict those who would likely experience an exacerbation with an area under the receiver-operating curve of 0.95 and an error rate of 0.41. Next, the 20 most influential proteins were selected for validation and quantification in sputum from an additional 123 participants. Upon validation, 9 proteins were found to be linked to future exacerbation risk. The final model could predict future exacerbations with an area under the receiver-operating curve of 0.77 with an error rate of 0.40. Pathway analysis revealed major themes associated with exacerbations including inflammation, recruitment, and proliferation of immune cells.

Conclusion: This study has identified for the first time a sputum proteomic signature and pathways associated with future exacerbations, which will facilitate the discovery of new biomarkers and novel therapeutic targets in uncontrolled persistent asthma.