Effectiveness of benralizumab in severe eosinophilic asthma: Distinct sub‐phenotypes of response identified by cluster analysis

Background Benralizumab is effective in severe eosinophilic asthma (SEA), but suboptimal responses are observed in some patients. Although several factors have been associated with benralizumab response, no cluster analysis has yet been undertaken to identify different responsiveness sub‐phenotypes....

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Published in:Clinical and experimental allergy 2022-02, Vol.52 (2), p.312-323
Main Authors: Di Bona, Danilo, Crimi, Claudia, D’Uggento, Angela Maria, Benfante, Alida, Caiaffa, Maria Filomena, Calabrese, Cecilia, Campisi, Raffaele, Carpagnano, Giovanna Elisiana, Ciotta, Domenico, D'Amato, Maria, Pelaia, Corrado, Pelaia, Girolamo, Pellegrino, Simona, Scichilone, Nicola, Scioscia, Giulia, Ribecco, Nunziata, Spadaro, Giuseppe, Valenti, Giuseppe, Vatrella, Alessandro, Crimi, Nunzio, Macchia, Luigi
Format: Article
Language:English
Subjects:
Allergens
Anti-Asthmatic Agents - adverse effects
Antibodies, Monoclonal, Humanized
Asthma
Asthma - diagnosis
Asthma - drug therapy
biologicals
Biomarkers
Bronchiectasis
Cluster Analysis
Corticosteroids
Disease Progression
Eosinophils
Humans
Immunoglobulin E
Immunosuppressive agents
Leukocytes (eosinophilic)
Monoclonal antibodies
Observational studies
Original
Patients
Phenotype
Phenotypes
Polyposis
precision medicine
real‐life
Respiratory function
Steroids
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Summary:Background Benralizumab is effective in severe eosinophilic asthma (SEA), but suboptimal responses are observed in some patients. Although several factors have been associated with benralizumab response, no cluster analysis has yet been undertaken to identify different responsiveness sub‐phenotypes. Objective To identify SEA sub‐phenotypes with differential responsiveness to benralizumab. Methods One hundred and five patients diagnosed with SEA who had completed 6 months of benralizumab treatment were included in a hierarchical cluster analysis based on a set of clinical variables that can be easily collected in routine practice (age, age at disease onset, disease length, allergen sensitization status, blood eosinophil count, IgE levels, FEV1% predicted, nasal polyposis, bronchiectasis). Results Four clusters were identified: Clusters 2 and 3 included patients with high levels of both IgE and eosinophils (type‐2 biomarkers high), whereas Clusters 1 and 4 included patients with only one type‐2 biomarker at a high level: IgE in Cluster 1 and eosinophils in Cluster 4. Clusters 2 and 3 (both type‐2 biomarkers high) showed the highest response rate to benralizumab in terms of elimination of exacerbations (79% and 80% respectively) compared to Clusters 1 and 4 (52% and 60% respectively). When super‐response (the absence of exacerbation without oral corticosteroid use) was assessed, Cluster 2, including patients with more preserved lung function than the other clusters, but comparable exacerbation rate, oral corticosteroid use and symptom severity, was the most responsive cluster (87.5% of patients). Conclusions Our cluster analysis identified benralizumab differential response sub‐phenotypes in SEA, with the potential of improving disease treatment and precision management. Cluster analysis revealed four different benralizumab responsiveness sub‐phenotypes within the apparently homogeneous severe eosinophilic asthma type‐2 phenotype. Clusters 2 and 3, with both eosinophils and IgE at high level, and distinct clinical features, showed the highest response rate. This approach may potentially identify biomarkers useful to select the right target population.
ISSN:0954-7894
1365-2222
DOI:10.1111/cea.14026