Molecular-based diagnosis of multiple sclerosis and its progressive stage
Autor: | Tianxia Wu, Ruturaj R. Masvekar, Ronald Herbst, Keith Tan, Yue Wang, Panagiotis Douvaras, Mika Komori, Bibiana Bielekova, Mark C. Greenwood, Valentina Fossati, Christopher Barbour, Kory R. Johnson, Peter Kosa, Makoto Tanigawa |
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Rok vydání: | 2017 |
Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty Pathology Neurology business.industry Multiple sclerosis Case-control study Disease Precision medicine medicine.disease 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Cerebrospinal fluid Drug development Internal medicine Cohort medicine Neurology (clinical) business 030217 neurology & neurosurgery |
Zdroj: | Annals of Neurology. 82:795-812 |
ISSN: | 0364-5134 |
Popis: | 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. |
Databáze: | OpenAIRE |
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