Background: Severe asthma is a heterogeneous disease, with a real unmet need in clinical and biological phenotyping. By combining clinical and various omics technologies, a phenotypic handprint of this disease can be produced. Objective: To integrate blood transcriptomics, serum proteomics and urine lipidomics data from 300 adult U-BIOPRED asthma patients in order to define a blood handprint. Methods: The omics datasets were fused using the Similarity Network Fusion method (Wang et al , Nature methods, 2014). Stable clusters were defined using spectral clustering and characterised using available clinical data. Results: Five stable clusters were defined. Cluster 1 (C1) was the most severe with the lowest FEV1% predicted, low FEV1/FVC and the highest proportion of patients on OCS. C2 was the mildest while C3, 4 and 5 had similar clinical characteristics. The clusters seemed mainly differentiated by the white blood cells percentages (WBC).
Conclusion: Various omics datasets of blood-related samples were successfully combined, defining five stable clusters of asthma patients mainly differentiated by the percentages of WBC. These results may help refining phenotypes of severe asthma. IMI grant n°115010 (U-BIOPRED).