Predictive Signature of Benralizumab Response. BENRAPRED Study.

Severe asthma

We will evaluate an early blood gene expression signature of Benralizumab response through a clinically relevant reduction of the number of exacerbations at M12 assessed in: - Three patient response groups (responders, intermediates responders, non-responders), - Gene expression signature. A 3 categories Pi-PLS-DA analysis will identify the major molecular discriminants of the responders groups.

At M0 a composite blood molecular signature predictive of reduction of the exacerbation rate at M12 in severe asthmatic patients treated by Benralizumab will be assessed as mentioned above. The definition of stable class of patients (low category) is used as a target for the prediction. Methods used for the primary objective (PO) applies to SO1 using similar input data on a different 3-class prediction target., The significance of center and the relevance of time dependent modelling will be evaluated using Generalised Mixed Models on independently established molecular response signature. It is expected a robust and reproducible gene expression to assess the inter and intra-individual trajectories of the signature over time and across centers (from early at M0 and M3, to late prediction at M6 and M9)., Correlations network between blood gene expression of Benralizumab significant response will be assessed thanks to weighted gene correlation network analysis (gene co-expression network analysis (WGCNA)) with an expected increase in FEV1 + AQLQ + peak-flow values and expected decrease of ACQ-7, ACQ-6 scores, Correlations network between blood gene expression at M0 and clinical characteristics of frequent exacerbations will be assessed thanks to WGCNA., Correlation network between stratification value of gene expressions in severe asthma and its correlation with clinical subgroups will be assessed thanks to WGCNA. This analysis is based on pairwise correlations between genetic variables and clinical variables underlying the amount of overall variance captured by high dimensional gene expression datasets., Concerning cost-utility analysis, two strategies of treatment with Benralizumab will be compared: the first one will consider a strategy not using an early blood gene expression signature of Benralizumab response and the second will consider a simulated strategy using an early blood gene expression signature of Benralizumab response

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