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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.

No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS-DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM-MSS). The GeM-MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis. GeM-MSS correlates with MS-DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression.

Therapeutic advance in progressive multiple sclerosis (MS) has been very slow. Based on the transformative role magnetic resonance imaging (MRI) contrast-enhancing lesions had on drug development for relapsing-remitting MS, we consider the lack of sensitive outcomes to be the greatest barrier for developing new treatments for progressive MS. The purpose of this study was to compare 58 prospectively-acquired candidate outcomes in the real-world situation of progressive MS trials, to select and validate the best-performing outcome. The one year pre-treatment period of adaptively-designed IPPoMS (ClinicalTrials.gov #NCT00950248) and RIVITaLISe (ClinicalTrials.gov #NCT01212094) Phase II trials served to determine the primary outcome for the subsequent blinded treatment phase by comparing eight clinical, one electrophysiological, one optical coherence tomography, seven MRI volumetric, nine quantitative T1 MRI, and 32 diffusion tensor imaging MRI outcomes. Fifteen outcomes demonstrated significant progression over one year (Δ) in the predetermined analysis and seven out of these were validated in two independent cohorts. Validated MRI outcomes had limited correlations with clinical scales, relatively poor signal-to-noise ratios (SNR) and recorded overlapping values between healthy subjects and MS patients with moderate-severe disability. Clinical measures correlated better, even though each reflects a somewhat different disability domain. Therefore, using machine-learning optimization, we developed a combinatorial weight-adjusted disability score (CombiWISE) that integrates four clinical scales: expanded disability status scale (EDSS), Scripps neurological rating scale, 25 foot walk and nine hole peg test. CombiWISE outperformed all clinical scales (Δ=9.10%; p=0.0003) and all MRI outcomes. CombiWISE recorded no overlapping values between healthy subjects and disabled MS patients, had high SNR and predicted changes in EDSS in a longitudinal assessment of 98 progressive MS patients and in a cross-sectional cohort of 303 untreated subjects. One point change in EDSS corresponds on average to 7.50 point change in CombiWISE with standard error of 0.10. A novel validated clinical outcome CombiWISE outperforms the current broadly utilized MRI brain atrophy outcome and more than doubles sensitivity in detecting clinical deterioration in progressive MS in comparison to the scale traditionally used for regulatory approval, EDSS.

Background While magnetic resonance imaging contrast-enhancing lesions represent an excellent screening tool for disease-modifying treatments in relapsing–remitting multiple sclerosis (RRMS), this biomarker is insensitive for testing therapies against compartmentalized inflammation in progressive multiple sclerosis (MS). Therefore, alternative sensitive outcomes are needed. Using machine learning, clinician-acquired disability scales can be combined with timed measures of neurological functions such as walking speed (e.g. 25-foot walk; 25FW) or fine finger movements (e.g. 9-hole peg test; 9HPT) into sensitive composite clinical scales, such as the recently developed combinatorial, weight-adjusted disability scale (CombiWISE). Ideally, these complementary simplified measurements of certain neurological functions could be performed regularly at patients’ homes using smartphones. Objectives We asked whether tests amenable to adaptation to smartphone technology, such as finger and foot tapping have comparable sensitivity and specificity to current non-clinician-acquired disability measures. Results We observed that finger and foot tapping can differentiate RRMS and progressive MS in a cross-sectional study and can also measure yearly and two-year disease progression in the latter, with better power (based on z-scores) in comparison to currently utilized 9HPT and 25FW. Conclusions Replacing the 9HPT and 25FW with simplified tests broadly adaptable to smartphone technology may enhance the power of composite scales for progressive MS.