Background

Quantification of the activity and progression of multiple sclerosis (MS) is an important tool for research on MS as well as its clinical treatment. Currently, disease activity and progression assessments rely on qualitative clinical evaluations or the acquisition of radiographic data such as magnetic resonance imaging (MRI).

Objectives

Quantifying MS disease activity (DA) and progression (DP) instead through the use of blood biomarkers would provide a significant reduction in barriers to such testing (e.g. monetary cost, time and specialized personnel requirements, invasiveness, operational difficulty, etc.). The use of an ensemble of proteins representing various biological pathways involved in MS pathophysiology would also provide useful insights into this complex and heterogeneous disease.

Methods

We investigated proteomic biomarkers associated with different levels of MS DA and DP using 205 blood serum samples from 88 patients (University Hospital Basel), extracting protein levels using Proximity Extension Assays (PEA) from Olink^TM. We then conducted a focused statistical analysis on 21 proteins that were selected for a custom MS assay panel development project based on their association with endpoints in previous studies. We corrected these protein levels using clinical data, including: age, sex, disease duration, age of the bio-banked sample, and medication status. We then compared protein levels to five different radiographic and clinical endpoints.

– Primary Endpoint: Gadolinium (Gd) enhanced lesion count

– Secondary Endpoints: T2 lesion volume, Expanded Disability Status Scale (EDSS) score, Clinically Defined Relapse Status, and Annualized Relapse Rate (ARR)

Results

In this report, we examine the univariate performance of selected proteins on the prediction of all five endpoints, comparing it to that of several multivariate machine learning techniques. We draw distinctions between the highest performing models for each endpoint and draw connections to the underlying biology governing MS activity and progression.

Conclusions

We found significant improvements in predictive power from the use of multivariate models in comparison to even the highest performing univariate techniques. The samples analyzed in this study will be re-assayed for validation purposes alongside additional cohorts in the forthcoming 21-plex custom MS proteomic assay panel.