Gene signatures in U-BIOPRED severe asthma for molecular phenotyping and precision medicine: time for clinical use

ABSTRACT

Introduction

The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype.

Areas covered

Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset. There has also been the identification of the role of mast cell activation and of macrophage dysfunction in various phenotypes of severe asthma.

Expert opinion

Phenotyping on the basis of clinical treatable traits is not sufficient for understanding of mechanisms driving the disease in severe asthma. It is time to consider whether certain patients with severe asthma, such as those non-responsive to current therapies, including Type 2 biologics, would be better served using an approach of molecular endotyping using gene signatures for management purposes rather than the current sole reliance on blood eosinophil counts or exhaled nitric oxide measurements.

KEYWORDS:

• Severe asthma
• gene signatures
• differential gene expression
• molecular phenotypes
• endotypes
• gene set variation analysis
• topological data analysis

Article highlights

• The advent of high-throughput sequencing, enabling the simultaneous characterization of the whole gene expression of a given clinical sample, is leading to the identification of disease mechanisms.

• Gene signatures can be used to examine coordinated gene expression patterns or alterations associated with a disease state or biological process or in response to drug treatment by assessing their enrichment using differentially expressed gene analysis and GSVA.

• A semi-biased transcriptomic analysis together with unsupervised machine learning analysis of sputum samples identified a Type 2 high severe eosinophilic asthma endotype and two other distinct low Type 2 molecular phenotypes.

• Analysis of gene expression data from bronchial biopsies and epithelial brushings identified two subtypes of patients with Type 2 eosinophilic inflammation and relative corticosteroid insensitivity.

• Supervised approaches using non-T2 signatures have identified patients expressing IL-6 trans-signaling, Th17, and IL-22-associated pathways identified within the neutrophilic inflammatory phenotype.

• The use of gene signatures in the clinical setting of the management of a patient with severe asthma would enhance our understanding of the pathways that may be driving the pathophysiology of severe asthma.

Declaration of interest

KF Chung has received honoraria for participating in Advisory Board meetings of GSK, AZ, Roche, Novartis, Merck, BI, and Shionogi regarding treatments for asthma, chronic obstructive pulmonary disease, and chronic cough and have also been renumerated for speaking engagements for AZ, Novartis, Sanofi, and GSK. KF Chung’s institution has received research grants that they are an Investigator on from GSK and Merck and from UKRI. CoI for Dr Ian M. Adcock: IMA has received honoraria for consulting and participating in Advisory Board meetings of Chiesi, GSK, Kinaset and Sanofi; has been renumerated for travel and speaking engagements for AZ, Eurodrug and Sanofi and has been funded by Institutional grants from EU-IMI, GSK, Sanofi and UKRI. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgments

The U-BIOPRED project referred to in this review was supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) Innovative Medicines Initiative Joint Undertaking (grant 115010). U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes) was also supported by European Federation of Pharmaceutical Industries and Associations companies’ in-kind contribution (www.imi.europa.eu).

Additional information

Funding

This paper was not funded.