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Ann Am Thorac Soc. 2014 Jul 1. [Epub ahead of print]

Cluster analysis and characterization of response to mepolizumab: A step closer to personalized medicine for patients with severe asthma.

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  • 1GlaxoSmithKline, Respiratory, RTP, North Carolina, United States ; hector.g.ortega@gsk.com.


Rationale: Detailed characterization of asthma phenotypes is essential for identification of responder populations to allow directed personalized medical intervention. Objectives: The aim of this study was to identify distinctive patient characteristics within subgroups of a well-characterized severe asthma population at risk for exacerbations and to determine the treatment response within each subgroup. Methods: A supervised cluster analysis with recursive partitioning approach was applied to data from the DREAM study to identify characteristics that maximized the differences across subgroups. Exacerbation rate ratios were calculated for each cluster comparing mepolizumab vs. placebo. Measurements and Main Results: Three predictors were identified in 4 primary clusters: blood eosinophils, airway reversibility, and BMI. The reduction in exacerbations was significantly greater in patients who received mepolizumab (Clusters 2, 3 and 4) with raised eosinophils (responder population). Cluster 2 with low airway reversibility (mean 11%) had a 53% reduction in exacerbations. These patients more frequently reported sinusitis and nasal polyposis. Those with higher airway reversibility (mean 28%) were further split by BMI. The non-obese vs. obese (Cluster 3 and 4) had a 35% and 67% reduction in exacerbations, respectively. Cluster 4 also had more patients with comorbidities including hypertension, weight gain and anxiety. Conclusions: Using supervised cluster analysis helped identify specific patient characteristics related to disease and therapeutic response. Patients with eosinophilic inflammation received significant therapeutic benefit with mepolizumab and responses differed within clusters.

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