P49 Determining Response to Treatment With Rituximab in Relapsing Multiple Sclerosis

Saturday, June 1, 2013
Enrique Alvarez, MD/PhD , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Laura Piccio, MD/PhD , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Robert J Mikesell, BS , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Kim Trinkaus, PhD , Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO
Nhial Tutlam, MPH , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Michael J Ramsbottom, BS , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Neville Rapp, PhD , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Samantha Lancia, MS , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Becky J Parks, MD , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Robert T Naismith, MD , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO
Anne H Cross, MD , Department of Neurology, Division of Neuroimmunology, Washington University School of Medicine in St. Louis, St. Louis, MO


Background: As multiple sclerosis (MS) treatment options continue to increase, methods are needed to predict who will respond to specific treatments. Previously, we reported a phase II trial of rituximab as an add-on agent in relapsing MS (Naismith et al 2010) that showed significant decreases in contrast enhancing lesions (CEL). However, some subjects responded better than others.

Objectives: To identify baseline predictors of good response to B cell depletion in MS.

Methods: A subset of 24 subjects were studied who had cerebrospinal fluid (CSF) available pre- and post-treatment with rituximab. Prior to laboratory studies, subjects were categorized into ideal responders (n=6), intermediate responders (n=9), and non-responders (n=9) based on clinical measures and contrast enhancing lesions (CEL) after treatment. Clinical, laboratory, and imaging data were evaluated in these different subject groups. Additionally, levels of biomarkers of tissue destruction (CSF myelin basic protein [MBP], CSF phosphorylated heavy chain neurofilament [NF]), B cell activation (serum and CSF CXCL13 and BAFF), and antibodies to human recombinant myelin oligodendrocyte glycoprotein (MOG) were determined at weeks 0 and 24 using ELISA.  Nonparametric Kruskal-Wallis rank tests and Spearman correlation coefficients (rs) by rank were used for analyses.

Results: At baseline, ideal responders tended to be better clinically with lower 9-hole peg test times (p=0.025) and a trend for lower 25 foot walk times (p=0.050).  They also had lower Expanded Disability Status Scale (EDSS) and higher number of CEL at baseline although these were used to define treatment response.  Ideal responders had a higher CXCL13 index ([CSF CXCL13/serum CXCL13]/albumin index; p=0.020). Treatment with rituximab, as reported previously, decreased blood CD19+B cells, serum and CSF CXCL13 levels, and number of CEL.  Here we report that rituximab also decreased CSF MBP (p=0.031) with a trend for a decrease in CSF NF (p=0.062) in ideal responders but not in the other groups. Additionally, BAFF CSF levels only increased in patients who were non-responders (p=0.004), while serum BAFF increased in all groups.  CSF MBP levels correlated with the number of CEL (p=0.002), while CSF NF correlated with EDSS (p=0.018).

Conclusions: This study helps differentiate which relapsing MS patients respond best to B-cell depletion. A higher CSF CXCL13 index, along with less severe clinical disease at baseline, correlated with likelihood of being an ideal responder.  We also observed beneficial effects on CSF biomarkers of tissue destruction and inflammation in treatment responders.