Can renal problems be solved by mathematics – let’s start with MPGN.

Nephrologists are used to  classifying glomerulonephritis on the basis of histological patterns rather than understanding of pathophysiology (maybe because we do not know a lot). Other findings (clinical/lab) inform severity assessment and therapeutic decision making, but the diagnostic label is set by looking under the microscope.

This started to change for mesangiocapillary or membranoproliferative GN some years ago when it seemed like we were coming close to pathogenesis-based reclassification of conditions that looked similar on light microscopy.

2003 saw the birth of a new entity – C3 glomerulopathy. The conditions was identified by the presence of glomerular C3 in the absence of substantial Ig and without deposition of the early components of the classic or lectin pathways of complement activation.

Soon it became apparent, however, that not all cases of C3GN looked similar on LM or IF also and the idea that a clear understanding of MPGN by classification had been achieved seemed obsolete.

What next?

We have mathematics coming to rescue. A group from researchers based in Italy (first author Iatropoulos) have a new publication out. They took 178 cases of DDD, C3GN, or idiopathic MPGN from Membranoproliferative Glomerulonephritis/C3 Glomerulopathy Registry, identified 34 characteristics: 7 clinical, 17 pathologic (LM, IF, EM) and the rest complement measurements and genetic variants. Then they through them in a blender and performed a kind of differential centrifugation. Statistical geeks call it unsupervised hierarchical clustering, a technique widely used in gene expression analysis. The analysis yielded four distinct clusters, each of which included patients with C3GN and MPGN. Cook and Pickering, who wrote the editorial accompanying the article thus summarise their findings –

Patients with DDD fell predominantly into cluster 3. Clusters 1 and 2 had C3 and C5 activation in the circulation with high levels of circulating C5b-9 and were distinguished by more Ig deposition in cluster 2. In cluster 3, C3 convertase activity seemed to predominate over C5 convertase activity, and many patients had very dense deposits on EM. Cluster 4 represented a group that had activation of C3 in glomeruli but did not have activation of C3 in the circulation and did not have either genetic variants or C3 nephritic factor.

Interestingly, cluster 4 had poorer survival. Iatropoulos and colleagues note –

The newly identified clusters may be useful for better defining the multifaceted molecular mechanisms underlying C3G/IC-MPGN and to predict the risk of ESRD and the response to anticomplement therapies. Patients from clusters 1 and 2, characterized by intense C5 convertase activation, may be more likely to respond to anti-C5 blockade, which is consistent with published data showing that a high level of plasma SC5b-9 was potentially a marker of responsiveness.20,28 Patients from cluster 3 might benefit from new molecules under clinical development, such as factor D or CFB inhibitors that target the C3 convertase of the alternative pathway of complement.29 Finally, emerging complement inhibitors targeting C3 activation products on cell surfaces, such as TT30,30 might be helpful for blocking solid-phase restricted complement activation in patients in cluster 4. The latter may lack the side effects of unselective C3 inhibitors, such as infections and autoimmunity.

Pickering and Cook are appreciative of the novelty of the approach, but recognise that this modelling is constrained the baseline variables that go into the initial model, and the need of validation in a distinct cohort needs to happen. They say

….. ultimate test of the utility of this approach in the clinic will be if it can also identify groups of patients who will benefit from distinct complement-modulating therapies.

Stay Tuned…

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