Identification of predictive protein biomarkers for treatment efficacy and clinical relapses of multiple sclerosis (THER7P.950)

Abstract

Abstract Despite extensive research, MS remains a disease that lacks a definitive prognostic test to predict imminent disease relapses. Thus, patients may undergo years of unnecessary treatments. Additionally, current treatments for MS can produce dramatically different outcomes in different individuals and therefore there is a critical need to develop biomarkers for treatment efficacy and resistance. We have recently developed a novel quantitative proteomics method to measure changes in proteome expression over the course of experimental autoimmune encephalomyelitis (EAE). Our statistical analyses indicate a strong correlation to EAE severity and/or clinical-phase. Interestingly, we revealed characteristic CNS-specific protein expression waves prior to the onset of clinical symptoms. We are currently testing whether these protein expression waves allow us to predict the onset of clinical episodes and forecast the severity of the disease to guide treatment strategies. Additionally, we have identified changes in the CNS proteome that can be measured in serum during EAE that correlate with the therapeutic efficacy of glucocorticoid treatment. Our studies will provide proof-of-principle for developing homologous human biomarkers that may be useful to predict disease onset and treatment efficacy. Finally, the detected changes in the CNS proteome may provide insights into key mechanisms that contribute to the disease pathology and may be useful to develop new therapeutic targets for MS.