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.
(© 2023 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd.)