Abstract
Personalized treatment is highly desirable in multiple sclerosis because it is an immensely heterogeneous disease. This heterogeneity is seen in both the disease course and the treatment responses. Currently, a combination of clinical features and imaging parameters in magnetic resonance imaging is used to classify active and non-active patients and treatment responders and non-responders. Although this classification works on a group level, individual patients often behave differently from the group. Therefore additional biomarkers are needed to provide better indicators for prognosis and treatment response. Basic and clinical research have discovered different promising targets. It is now essential to verify the utility and accuracy of these markers in large, prospectively sampled patient cohorts.
Highlights
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system
Expansion of CD56bright natural killer (NK) T-cells correlated with decreased magnetic resonance imaging (MRI) activity during daclizumab therapy in a phase 2 trial and may indicate a patient population that preferentially responds to this treatment [23]
A specific enzyme-linked immunosorbent assay (ELISA) for the causative JC virus was developed that indicates if a patient harbors latent JC virus [26]. Using these three parameters, a risk stratification algorithm has been established that can be used in clinical practice to counsel patients that are on current natalizumab treatment or that are suited to go on the treatment [25]
Summary
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. Does the patient need treatment at all, because he or she may have a benign disease Biomarkers that could predict disease course, treatment response, and risk of side effects would be highly appreciated.
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