Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

An 18-protein multiple sclerosis (MS) disease activity (DA) test was validated based on associations between algorithm scores and clinical/radiographic assessments (N = 614 serum samples; Train [n = 426; algorithm development] and Test [n = 188; evaluation] subsets). The multi-protein model was trained based on presence/absence of gadolinium-positive (Gd+) lesions and was also strongly associated with new/enlarging T2 lesions, and active versus stable disease (composite of radiographic and clinical evidence of DA) with improved performance (p < 0.05) compared to the neurofilament light single protein model. The odds of having ≥1 Gd+ lesions with a moderate/high DA score were 4.49 times that of a low DA score, and the odds of having ≥2 Gd+ lesions with a high DA score were 20.99 times that of a low/moderate DA score. The MSDA Test was clinically validated with improved performance compared to the top-performing single-protein model and can serve as a quantitative tool to enhance the care of MS patients.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis.

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of > 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis.

Analytical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

PURPOSE: To characterize and analytically validate the MSDA Test, a multi-protein, serum-based biomarker assay developed using Olink® PEA methodology. EXPERIMENTAL DESIGN: Two lots of the MSDA Test panel were manufactured and subjected to a comprehensive analytical characterization and validation protocol to detect biomarkers present in the serum of patients with multiple sclerosis (MS). Biomarker concentrations were incorporated into a final algorithm used for calculating four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score. RESULTS: Analytical characterization demonstrated that the multi-protein panel satisfied the criteria necessary for a fit-for-purpose validation considering the assay's intended clinical use. This panel met acceptability criteria for 18 biomarkers included in the final algorithm out of 21 biomarkers evaluated. VCAN was omitted based on factors outside of analytical validation; COL4A1 and GH were excluded based on imprecision and diurnal variability, respectively. Performance of the four Disease Pathway and overall Disease Activity scores met the established acceptability criteria. CONCLUSIONS AND CLINICAL RELEVANCE: Analytical validation of this multi-protein, serum-based assay is the first step in establishing its potential utility as a quantitative, minimally invasive, and scalable biomarker panel to enhance the standard of care for patients with MS.

Proteomics and relationship with axonal pathology in multiple sclerosis: 5-year diffusion tensor imaging study.

Blood-based biomarkers can be economic and easily accessible tools for monitoring and predicting disease activity in multiple sclerosis. The objective of this study was to determine the predictive value of a multivariate proteomic assay for concurrent and future microstructural/axonal brain pathology in a longitudinal study of a heterogeneous group of people with multiple sclerosis. A proteomic analysis was obtained on serum samples from 202 people with multiple sclerosis (148 relapsing-remitting and 54 progressive) at baseline and 5-year follow-up. The concentration of 21 proteins related to multiple pathways of multiple sclerosis pathophysiology was derived using Proximity Extension Assay on the Olink platform. Patients were imaged on the same 3T MRI scanner at both timepoints. Тhe rate of whole brain, white matter and grey matter atrophy over the 5-year follow-up was determined using the multi-timepoint Structural Image Evaluation, using Normalisation, of Atrophy algorithms. Lesion burden measures were also assessed. The severity of microstructural axonal brain pathology was quantified using diffusion tensor imaging. Fractional anisotropy and mean diffusivity of normal-appearing brain tissue, normal-appearing white matter, grey matter, T2 and T1 lesions were calculated. Age, sex and body mass index-adjusted step-wise regression models were used. Glial fibrillary acidic protein was the most common and highest-ranked proteomic biomarker associated with greater concurrent microstructural central nervous system alterations (P < 0.001). The rate of whole brain atrophy was associated with baseline levels of glial fibrillary acidic protein, protogenin precursor, neurofilament light chain and myelin oligodendrocyte (P < 0.009), whereas grey matter atrophy was associated with higher baseline neurofilament light chain, higher osteopontin and lower protogenin precursor levels (P < 0.016). Higher baseline glial fibrillary acidic protein level was a significant predictor of future severity of the microstructural CNS alterations as measured by normal-appearing brain tissue fractional anisotropy and mean diffusivity (standardized ß = -0.397/0.327, P < 0.001), normal-appearing white matter fractional anisotropy (standardized ß = -0.466, P < 0.0012), grey matter mean diffusivity (standardized ß = 0.346, P < 0.011) and T2 lesion mean diffusivity (standardized ß = 0.416, P < 0.001) at the 5-year follow-up. Serum levels of myelin-oligodendrocyte glycoprotein, neurofilament light chain, contactin-2 and osteopontin proteins were additionally and independently associated with worse concomitant and future axonal pathology. Higher glial fibrillary acidic protein levels were associated with future disability progression (Exp(B) = 8.65, P = 0.004). Multiple proteomic biomarkers are independently associated with greater severity of axonal brain pathology as measured by diffusion tensor imaging in multiple sclerosis. Baseline serum glial fibrillary acidic protein levels can predict future disability progression.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect disease activity in multiple sclerosis.

Background/Objectives: Serum proteomic analysis of deeply-phenotyped samples, biological pathway modeling and network analysis were performed to elucidate the inflammatory and neurodegenerative processes of multiple sclerosis (MS) and identify sensitive biomarkers of MS disease activity (DA). Methods: Over 1100 serum proteins were evaluated in >600 samples from three MS cohorts to identify biomarkers of clinical and radiographic (gadolinium-enhancing lesions) new MS DA. Protein levels were analyzed and associated with presence of gadolinium-enhancing lesions, clinical relapse status (CRS), and annualized relapse rate (ARR) to create a custom assay panel. Results: Twenty proteins were associated with increased clinical and radiographic MS DA. Serum neurofilament light chain (NfL) showed the strongest univariate correlation with radiographic and clinical DA measures. Multivariate modeling significantly outperformed univariate NfL to predict gadolinium lesion activity, CRS and ARR. Discussion: These findings provide insight regarding correlations between inflammatory and neurodegenerative biomarkers and clinical and radiographic MS DA. Funding: Octave Bioscience, Inc (Menlo Park, CA).