Childhood asthma in the new omics era: challenges and perspectives. Issue 2 (April 2020)

• Record Type: Journal Article
• Authors: Golebski, Korneliusz; Kabesch, Michael; Melén, Erik; Potočnik, Uroš; van Drunen, Cornelis M.; Reinarts, Susanne; Maitland-van der Zee, Anke H.; Vijverberg, Susanne J.H.
• Is Part Of: Current opinion in allergy and clinical immunology. Volume 20:Issue 2(2020:Apr.)
• Publication Date: 2020-04
• Subjects: biologicals -- childhood asthma -- endotypes -- omics -- precision medicine; Allergy -- Periodicals; Clinical immunology -- Periodicals; Hypersensitivity; Immunity; Immune System Diseases; Allergy and Immunology; 616.079
• DOI: 10.1097/ACI.0000000000000626
• Languages: English
• ISSNs: 1528-4050

Abstract

Abstract : Purpose of review: Childhood asthma is a heterogeneous inflammatory disease comprising different phenotypes and endotypes and, particularly in its severe forms, has a large impact on the quality-of-life of patients and caregivers. The application of advanced omics technologies provides useful insights into underlying asthma endotypes and may provide potential clinical biomarkers to guide treatment and move towards a precision medicine approach. Recent findings: The current article addresses how novel omics approaches have shaped our current understanding of childhood asthma and highlights recent findings from (pharmaco)genomics, epigenomics, transcriptomics, and metabolomics studies on childhood asthma and their potential clinical implications to guide treatment in severe asthmatics. Summary: Until now, omics studies have largely expanded our view on asthma heterogeneity, helped understand cellular processes underlying asthma, and brought us closer towards identifying (bio)markers that will allow the prediction of treatment responsiveness and disease progression. There is a clinical need for biomarkers that will guide treatment at the individual level, particularly in the field of biologicals. The integration of multiomics data together with clinical data could be the next promising step towards development individual risk prediction models to guide treatment. However, this requires large-scale collaboration in a multidisciplinary setting.