RGC-32, FasL, and SIRT1 As Potential Biomarkers of Relapse and Response to Treatment with Glatiramer Acetate in Multiple Sclerosis

Background:  Due to the heterogeneous nature of Relapsing-Remitting Multiple Sclerosis (RRMS), it is difficult to predict patient response to treatment. Currently there is critical need for the development of reliable biomarkers to aid clinicians in the management of RRMS patients. Previously we have shown that the Response Gene to Complement (RGC)-32 is expressed by CD3⁺ as well as CD4⁺T-cells in peripheral blood mononuclear cells (PBMCs) and in brain tissue from RRMS patients. RGC-32 regulates cell cycle kinase CDC2 activity. In turn, CDC2 modulates the expression of FasL and SIRT1, key regulators of T-cell survival. Previous, separate studies have shown that RGC-32, FasL, and SIRT1 mRNA expression is significantly lower within PBMCs of RRMS patients during relapses compared to remission. 

Objectives:  Presently, for the first time we longitudinally investigated the combined roles of RGC-32, FasL, SIRT1, and CDC2 as possible biomarkers of relapse and predictors of response to glatiramer acetate (GA) treatment in RRMS patients.

Methods:  Over the course of 2 years, a cohort of 15 GA-treated RRMS patients was clinically monitored using the Expanded Disability Status Scale and blood samples were collected at 0, 3, 6, 12, and 24 months. Target gene mRNA expression was measured in patients’ isolated PBMCs using two-step real-time quantitative reverse transcription PCR. 

Results:  Relapsing MS patients had decreased expression of RGC-32 (P<0.0001), FasL (P<0.0005), and SIRT1 (P<0.003) but no change in CDC2 compared to stable MS patients. Non-responders to GA treatment were defined as patients who exhibited at least 2 relapse events following the initiation of GA treatment. Responders to GA treatment had significantly higher levels of RGC-32 (P<0.0001), FasL (P<0.003), and SIRT1 (P<0.009) but no change in CDC2 compared to patients who were considered non-responders. Receiver operating characteristic analysis was used to assess the predictive accuracy of each putative biomarker. Predictive probabilities for relapse were 90% using RGC-32, 84% using FasL, and 73% using SIRT1. Predictive probabilities for responsiveness to GA treatment were 85% using RGC-32, 85% using FasL, and 71% using SIRT1.

Conclusions:  The data suggest that RGC-32, FasL, and SIRT1 could serve as potential biomarkers for the prediction of MS relapses and the evaluation of patient response to GA therapy. Such information could help guide treatment decisions and improve MS patient outcomes.