Primary membranoproliferative glomerulonephritis represents a group of rare kidney disorders associated with complement activation. There is no specific therapy and the prognosis is unfavorable: about half of all patients, mostly children, develop end-stage renal disease and need dialysis within 10 years of onset. MPGN is heterogeneous and patients have abnormal activation at different levels of the complement system.
Several drugs targeting complement have been already approved or are investigated in trials for other conditions. However, trials to test new therapeutics will be heavily influenced by the heterogeneity MPGN. The current classification into C3 glomerulopathy (C3G) and immunecomplex- mediated MPGN (IC-MPGN) based on only histological data, does not reflect the etiology and is inefficient. Thus, there is an obligation in future trials to ensure that each patient is maximally characterized in order to receive the right drug. Previous studies identified four patient clusters, instrumental to define early and late complement activation.
DECODE will implement this strategy by combining omics approaches (i.e. WES, proteomics and metabolomics) and hierarchical clustering analysis to define a precise stratification of patients with C3G/IC-MPGN, and to identify specific biomarkers, which will be instrumental for diagnosis, predicting prognosis and tailoring the right therapeutic strategy for the right patients.
DECODE will offer also the unique opportunity to study a larger cohort of patients that will be in depth characterized for genetic and immune abnormalities in the various complement components, and for their clinical and biochemical characteristics.
Stratify patients into clusters and provide easy to handle platform to assign patients to each of them*
Identify novel biomarkers of each cluster
Identify cluster specific therapies
Design phase 2 clinical protocols
Address ethical, legal and social aspects related to trials based on personalized medicine and transition to clinical practice
*An innovative and easy-to handle clinical computational tool tailored to allow clinicians to assign their patients to a specific cluster, thus making a correct diagnosis and orienting among treatment options as to recruit the right patient into the right trial will be developed.
If proven effective, the approach proposed in DECODE could be applied for implementation of personalized medicine in other rare diseases as well as in more common heterogeneous diseases such as cardiovascular diseases, diabetes, autoimmune diseases.
