Highlights
- •To reduce the proportion of uncontrolled/unresponsive asthma patients, different strategies need to be identified to phenotype patients into subgroups with a more personalized approach to disease management.
- •To date, clinical trials investigating novel biologics and cluster analysis in asthma still heavily rely on clinically-defined phenotypes.
- •Bioprofiling patients with asthma with enhanced, machine learning, unbiased cluster analysis techniques could provide more targetable asthma phenotype groups for modern biologics.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Respiratory MedicineAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Machine learning in asthma research: moving toward a more integrated approach.Expert Rev Respir Med. 2021; 15: 609-621https://doi.org/10.1080/17476348.2021.1894133
- The global initiative for asthma (GINA): 25 years later.Eur. Respir. J. 2019; 541900598https://doi.org/10.1183/13993003.00598-2019
- Global strategy for asthma management and prevention.www.ginasthma.orgDate: 2022
- Assessing patient-reported outcomes in asthma and COPD patients: which can be recommended in clinical practice?.Curr. Opin. Pulm. Med. 2018; 24: 18-23https://doi.org/10.1097/mcp.0000000000000447
- An assessment of quality of life in patients with asthma through physical, emotional, social, and occupational aspects. A cross-sectional study.Front. Public Health. 2022; 10883784https://doi.org/10.3389/fpubh.2022.883784
- Understanding uncontrolled severe allergic asthma by integration of omic and clinical data.Allergy. 2022; 77: 1772-1785https://doi.org/10.1111/all.15192
- Inhibition of spleen tyrosine kinase restores glucocorticoid sensitivity to improve steroid-resistant asthma.Front. Pharmacol. 2022; 13885053https://doi.org/10.3389/fphar.2022.885053
- Leukocyte redistribution as immunological biomarker of corticosteroid resistance in severe asthma.Clin. Exp. Allergy. 2022; 52: 1183-1194https://doi.org/10.1111/cea.14128
- Real-life impact of uncontrolled severe asthma on mortality and healthcare use in adolescents and adults: findings from the retrospective, observational RESONANCE study in France.BMJ Open. 2022; 12e060160https://doi.org/10.1136/bmjopen-2021-060160
- Follow-up of patients with uncontrolled asthma: clinical features of asthma patients according to the level of control achieved (the COAS study).Eur. Respir. J. 2017; 491501885https://doi.org/10.1183/13993003.01885-2015
- Asthma: defining of the persistent adult phenotypes.Lancet. 2006; 368: 804-813https://doi.org/10.1016/S0140-6736(06)69290-8
- Treatment of allergic asthma with monoclonal anti-IgE antibody. rhuMAb-E25 Study Group.N. Engl. J. Med. 1999; 341: 1966-1973https://doi.org/10.1056/nejm199912233412603
- Mepolizumab for prednisone-dependent asthma with sputum eosinophilia.N. Engl. J. Med. 2009; 360: 985-993https://doi.org/10.1056/NEJMoa0805435
- Dupilumab improves lung function in patients with uncontrolled, moderate-to-severe asthma.ERJ Open Res. 2020; 6https://doi.org/10.1183/23120541.00204-2019
- Dupilumab efficacy and safety in moderate-to-severe uncontrolled asthma.N. Engl. J. Med. 2018; 378: 2486-2496https://doi.org/10.1056/NEJMoa1804092
- Biologic therapies for severe asthma.N. Engl. J. Med. 2022; 386: 157-171https://doi.org/10.1056/NEJMra2032506
- Effects of an interleukin-5 blocking monoclonal antibody on eosinophils, airway hyper-responsiveness, and the late asthmatic response.Lancet. 2000; 356: 2144-2148https://doi.org/10.1016/s0140-6736(00)03496-6
- Eosinophil's role remains uncertain as anti-interleukin-5 only partially depletes numbers in asthmatic airway.Am J of Respir Crit. 2003; 167: 199-204https://doi.org/10.1164/rccm.200208-789OC
- Biological therapies of severe asthma and their possible effects on airway remodeling.Front. Immunol. 2020; 11https://doi.org/10.3389/fimmu.2020.01134
- Mechanisms and therapeutic strategies for non-T2 asthma.Allergy. 2020; 75: 311-325https://doi.org/10.1111/all.13985
- Role of biologics targeting type 2 airway inflammation in asthma: what have we learned so far?.Curr. Opin. Pulm. Med. 2017; 23: 3-11https://doi.org/10.1097/mcp.0000000000000343
- Suboptimal treatment response to anti-IL-5 monoclonal antibodies in severe eosinophilic asthmatics with airway autoimmune phenomena.Eur. Respir. J. 2020; 56https://doi.org/10.1183/13993003.00117-2020
- Biologics in asthma: a molecular perspective to precision medicine.Front. Pharmacol. 2022; 12https://doi.org/10.3389/fphar.2021.793409
- Dual monoclonal antibody therapy for a severe asthma patient.Front. Pharmacol. 2020; 11587621https://doi.org/10.3389/fphar.2020.587621
- Which therapy for non-type(T)2/T2-Low asthma.J. Personalized Med. 2021; 12https://doi.org/10.3390/jpm12010010
- Tezepelumab in adults and adolescents with severe, uncontrolled asthma.N. Engl. J. Med. 2021; 384: 1800-1809https://doi.org/10.1056/NEJMoa2034975
- Composite type-2 biomarker strategy versus a symptom-risk-based algorithm to adjust corticosteroid dose in patients with severe asthma: a multicentre, single-blind, parallel group, randomised controlled trial.Lancet Respir. 2021; 9: 57-68https://doi.org/10.1016/S2213-2600(20)30397-0
- Distance-based clustering challenges for unbiased benchmarking studies.Sci. Rep. 2021; 1118988https://doi.org/10.1038/s41598-021-98126-1
- Severe asthma and personalized approach in the choice of biologic.Curr. Opin. Allergy Clin. Immunol. 2022; 22: 268-275https://doi.org/10.1097/aci.0000000000000829
- Asthma-prone areas modeling using a machine learning model.Sci. Rep. 2021; 11: 1912https://doi.org/10.1038/s41598-021-81147-1
- Behavioral and structural differences in migrating peripheral neutrophils from patients with chronic obstructive pulmonary disease.Am J of Respir Crit. 2011; 183: 1176-1186https://doi.org/10.1164/rccm.201008-1285OC
- Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: a cross-sectional analysis within a population-based birth cohort.PLoS Med. 2018; 15e1002691https://doi.org/10.1371/journal.pmed.1002691
- Connectivity patterns between multiple allergen specific IgE antibodies and their association with severe asthma.J. Allergy Clin. Immunol. 2020; 146: 821-830https://doi.org/10.1016/j.jaci.2020.02.031
- Phenotype clustering in health care: a narrative review for clinicians.Front Artif Intell. 2022; 5842306https://doi.org/10.3389/frai.2022.842306
- Two novel, severe asthma phenotypes identified during childhood using a clustering approach.Eur. Respir. J. 2012; 40: 55https://doi.org/10.1183/09031936.00123411
- Sputum neutrophil counts are associated with more severe asthma phenotypes using cluster analysis.J. Allergy Clin. Immunol. 2014; 133 (1557-63.e5)https://doi.org/10.1016/j.jaci.2013.10.011
- Clustering analysis of clinical variables in U-BIOPRED adult asthma cohort.Eur. Respir. J. 2014; 44: 225
- Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma.Am J of Respir Crit. 2019; 199: 1358-1367https://doi.org/10.1164/rccm.201808-1543OC
- Effectiveness of benralizumab in severe eosinophilic asthma: distinct sub-phenotypes of response identified by cluster analysis.Clin. Exp. Allergy. 2022; 52: 312-323https://doi.org/10.1111/cea.14026
- Endotypes identified by cluster analysis in asthmatics and non-asthmatics and their clinical characteristics at follow-up: the case-control EGEA study.BMJ Open Respir Res. 2020; 7https://doi.org/10.1136/bmjresp-2020-000632
- Cluster analysis of sputum cytokine-high profiles reveals diversity in T(h)2-high asthma patients.Respir. Res. 2017; 18: 39https://doi.org/10.1186/s12931-017-0524-y
- Novel blood-based transcriptional biomarker panels predict the late-phase asthmatic response.Am J of Respir Crit. 2018; 197: 450-462https://doi.org/10.1164/rccm.201701-0110OC
- iTRAQ-based proteomic analysis reveals potential serum biomarkers of allergic and non-allergic asthma.Allergy. 2020; https://doi.org/10.1111/all.14406
- The nasal methylome as a biomarker of asthma and airway inflammation in children.Nat. Commun. 2019; 10: 3095https://doi.org/10.1038/s41467-019-11058-3
- A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-biopred.Am J of Respir Crit. 2016; 195: 443-455https://doi.org/10.1164/rccm.201512-2452OC
- Neutrophil phenotypes in bronchial airways differentiate single from dual responding allergic asthmatics.Clin. Exp. Allergy. 2022; https://doi.org/10.1111/cea.14149
- Analysis of human neutrophil phenotypes as biomarker to monitor exercise-induced immune changes.J. Leukoc. Biol. 2021; 109: 833-842https://doi.org/10.1002/JLB.5A0820-436R
- Sputum T lymphocytes in asthma, COPD and healthy subjects have the phenotype of activated intraepithelial T cells (CD69+ CD103+).Thorax. 2003; 58: 23https://doi.org/10.1136/thorax.58.1.23
- Implication of cluster analysis in childhood asthma.Allergy Asthma Immunol Res. 2021; 13: 1-4https://doi.org/10.4168/aair.2021.13.1.1
Article info
Publication history
Accepted:
March 11,
2023
Received in revised form:
March 7,
2023
Received:
November 30,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2023 Published by Elsevier Ltd.
