Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.CC BY 4.0e-mail: mika.gustafsson@liu.seThe study was funded by the Swedish Foundation for Strategic Research (SB16-0011 [M.G., J.E.]), the Swedish Brain Foundation, Knut and Alice Wallenberg Foundation, and Margareth AF Ugglas Foundation, Swedish Research Council (2019-04193 [M.G.], 2018-02776 [J.E.], 2020-02700 [F.P.], 2020-00014 [Z.L.P.], 2021-03092 [J.E.]), the Medical Research Council of Southeast Sweden (FORSS-315121 [J.E.]), NEURO Sweden (F2018-0052 [J.E.]), ALF grants, Region Östergötland, the Swedish Foundation for MS Research and the European Union’s Marie Sklodowska-Curie (813863 [J.E.]). The authors would like to acknowledge support of the Clinical biomarker facility at SciLifeLab Sweden for providing assistance in protein analyses.Open access funding provided by Linköping University.