Methods for predicting the clinical outcome of patients suffering from chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with pulmonary and extra-pulmonary manifestations. COPD heterogeneity is poorly understood and insights into molecular mechanisms is required to provide better treatment with targeted therapies. The inventors collected serum samples from 241 COPD subjects in the COBRA cohort and measured the expression of 1305 proteins using SOMAscan proteomic platform. Modular analyses and clustering of the proteomics were applied to identify disease subtypes. Cluster discoveries were revalidated during a follow up visit, and confirmed in a different COPD cohort. Unsupervised clustering using protein modules identified two clusters within COPD subjects. One cluster presented a higher expression of proteins associated with enhanced immune and pro-survival activities, host defense and cell renewal. Subjects in this cluster had a lower incidence of exacerbations, unscheduled medical visits, emphysema and diabetes. These protein signatures were conserved during a follow up visit, and validated in another COPD cohort. Among the 96 proteins, the inventors identified a small specific signature consisting of 15 proteins that were able to accurately differentiate the two patient clusters. This signature would thus be useful for predicting the clinical outcome of patients suffering from COPD.

Keywords: COPD, Protein Signature, Prognosis, Disease exarcerbation prediction, ELISA
Patent Application number: European Procedure (Patents) (EPA) - 06 Mai 2022 - 22 305 676.3 and PCT/EP2023/062030 on 09/05/2023
Multicenter Study PLoS One 2022 Dec 8 Dagher et al. Proteomic profiling of serum identifies a molecular signature that correlates with clinical outcomes in COPD doi: 10.1371/journal.pone.0277357



    Business Developper
    Inserm Transfert
    Business Developer
    Patent filling date: 06-05-2022
    Rare disease: No
    Second indication: No

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