An algorithm using clinical data to predict the optimal individual glucocorticoid dosage to treat multiple sclerosis relapses

Predicting glucocorticoid resistance in multiple sclerosis relapse via a whole blood transcriptomic analysis

AIMS

Treatment of multiple sclerosis (MS) relapses consists of short-term administration of high-dose glucocorticoids (GCs). However, over 40% of patients show an insufficient response to GC treatment. We aimed to develop a predictive model for such GC resistance.

METHODS

We performed a receiver operating characteristic (ROC) curve analysis following the transcriptomic assay of whole blood samples from stable, relapsing GC-sensitive and relapsing GC-resistant patients with MS in two different European centers.

RESULTS

We identified 12 genes being regulated during a relapse and differentially expressed between GC-sensitive and GC-resistant patients with MS. Using these genes, we defined a statistical model to predict GC resistance with an area under the curve (AUC) of the ROC analysis of 0.913. Furthermore, we observed that relapsing GC-resistant patients with MS have decreased GR, DUSP1, and TSC22D3 mRNA levels compared with relapsing GC-sensitive patients with MS. Finally, we showed that the transcriptome of relapsing GC-resistant patients with MS resembles those of stable patients with MS.

CONCLUSION

Predicting GC resistance would allow patients to benefit from prompt initiation of an alternative relapse treatment leading to increased treatment efficacy. Thus, we think our model could contribute to reducing disability development in people with MS.

Multiple sclerosis: Immunopathological heterogeneity and its implications

MS is the most common autoimmune demyelinating disease of the CNS. For the past decades, several immunomodulatory disease-modifying treatments with multiple presumed mechanisms of action have been developed, but MS remains an incurable disease. Whereas high efficacy, at least in early disease, corroborates underlying immunopathophysiology, there is profound heterogeneity in clinical presentation as well as immunophenotypes that may also vary over time. In addition, functional plasticity in the immune system as well as in the inflamed CNS further contributes to disease heterogeneity. In this review, we will highlight immune-pathophysiological and associated clinical heterogeneity that may have an implication for more precise immunomodulatory therapeutic strategies in MS.