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Objective: Biomarkers aid diagnosis, allow inexpensive screening of therapies and guide selection of patient-specific therapeutic regimens in most internal medicine disciplines. In contrast, neurology lacks validated measurements of the physiological status, or dysfunction(s) of cells of the central nervous system (CNS). Accordingly, patients with chronic neurological diseases are often treated with a single disease-modifying therapy without understanding patient-specific drivers of disability. Therefore, using multiple sclerosis (MS) as an example of a complex polygenic neurological disease, we sought to determine if cerebrospinal fluid (CSF) biomarkers are intra-individually stable, cell type-, disease- and/or process-specific and responsive to therapeutic intervention. Methods: We used statistical learning in a modeling cohort (n=225) to develop diagnostic classifiers from DNA-aptamer-based measurements of 1128 CSF proteins. An independent validation cohort (n=85) assessed the reliability of derived classifiers. The biological interpretation resulted from in-vitro modeling of primary or stem cell-derived human CNS cells and cell lines. Results: The classifier that differentiates MS from CNS diseases that mimic MS clinically, pathophysiologically and on imaging, achieved a validated area under receiver-operator characteristic curve (AUROC) of 0.98, while the classifier that differentiates relapsing-remitting from progressive MS achieved a validated AUROC of 0.91. No classifiers could differentiate primary- from secondary-progressive MS better than random guessing. Treatment-induced changes in biomarkers greatly exceeded intra-individual- and technical variabilities of the assay. Interpretation: CNS biological processes reflected by CSF biomarkers are robust, stable, and disease- or even disease-stage specific. This opens opportunities for broad utilization of CSF biomarkers in drug development and precision medicine for CNS disorders. This article is protected by copyright. All rights reserved.

Biomarkers aid diagnosis, allow inexpensive screening of therapies, and guide selection of patient-specific therapeutic regimens in most internal medicine disciplines. In contrast, neurology lacks validated measurements of the physiological status, or dysfunction(s) of cells of the central nervous system (CNS). Accordingly, patients with chronic neurological diseases are often treated with a single disease-modifying therapy without understanding patient-specific drivers of disability. Therefore, using multiple sclerosis (MS) as an example of a complex polygenic neurological disease, we sought to determine whether cerebrospinal fluid (CSF) biomarkers are intraindividually stable, cell type-, disease- and/or process-specific, and responsive to therapeutic intervention.We used statistical learning in a modeling cohort (n = 225) to develop diagnostic classifiers from DNA-aptamer-based measurements of 1,128 CSF proteins. An independent validation cohort (n = 85) assessed the reliability of derived classifiers. The biological interpretation resulted from in vitro modeling of primary or stem cell-derived human CNS cells and cell lines.The classifier that differentiates MS from CNS diseases that mimic MS clinically, pathophysiologically, and on imaging achieved a validated area under the receiver operating characteristic curve (AUROC) of 0.98, whereas the classifier that differentiates relapsing-remitting from progressive MS achieved a validated AUROC of 0.91. No classifiers could differentiate primary progressive from secondary progressive MS better than random guessing. Treatment-induced changes in biomarkers greatly exceeded intraindividual and technical variabilities of the assay.CNS biological processes reflected by CSF biomarkers are robust, stable, disease specific, or even disease stage specific. This opens opportunities for broad utilization of CSF biomarkers in drug development and precision medicine for CNS disorders. Ann Neurol 2017;82:795-812.