Multiple sclerosis therapy monitoring based on gene expression

Abstract

Multiple sclerosis (MS) is the most prevalent chronic autoimmune, neurodegenerative disorder of the central nervous system (CNS). Despite substantial progress, treatment of MS and other autoimmune diseases is only moderately effective. It is anticipated that the treatment of autoimmune diseases with single drugs or biological approaches will in the future be complemented, or even replaced, by combination therapies, which include immunomodulation, elimination of infectious triggers and tissue repair. One proclaimed goal of biomedical research and clinical practice is the discovery of sets of genes with expression that correlates with successful outcomes of drug therapy, or with unfortunate side effects. Such information has direct consequences for selection, refinement or development of treatments and will soon be translated into clinical trials. The genome-wide RNA profile of an individual represents one complement to the comprehensive determination of disease- or drug response-related elements; comparable to a 'sentinel' method, it serves as a large-scale approach to MS biology. This work reviews the state of the art in MS research at the transcriptome level applying genomewide screening methods. It discusses implications in understanding disease pathogenicity, diagnostic markers, the identification of new therapeutic targets and a classification of patients towards the advent of tailored therapies.

Key Biomarkers

Symptom Clusters