Classification of high versus low annualized relapse rate status in subjects with relapsing-remitting multiple sclerosis using multivariate serum protein biomarker models
ECTRIMS Online Library. Sattarnezhad N. Sep 13, 2019; 278526; P1324
Neda Sattarnezhad
Neda Sattarnezhad
Contributions
Abstract

Abstract: P1324

Type: Poster Sessions

Abstract Category: Pathology and pathogenesis of MS - Biomarkers

N. Sattarnezhad1, S. Saxena1, C. Gonzalez1, H. Lokhande1, B. Glanz1, F. Qureshi2, M. Becich2, R. Osan2, H. Weiner1, T. Chitnis1

1Partners MS Center, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, 2Octave Bioscience, Menlo Park, CA, United States

Background: Annualized Relapse Rate (ARR) is a useful and quantifiable outcome measurement related to both disease activity and progression in relapsing forms of Multiple Sclerosis (MS). MS is a heterogeneous disease with various phenotypes and with symptoms that can evolve over time. Therefore, multivariate models reflecting multiple biological pathways that are involved in the complex pathophysiology of the disease including inflammation, immune modulation, and neurodegeneration are most likely to correlate strongly with clinical outcome measurements including ARR status.
Goal: To investigate the performance of multiple protein biomarker models to classify samples from Relapsing-Remitting (RR) MS subjects with High and Low ARR from the Comprehensive Longitudinal Investigation of Multiple Sclerosis at Brigham and Women's Hospital (CLIMB) study.
Methods: Serum samples from 30 subjects with Low ARR (≤0.2 relapses/year) were compared to 30 age, sex and treatment matched subjects with High ARR (≥1 relapses/year). All samples were measured for 1104 proteins, including serum levels of neurofilament light chain (sNfL), using Proximity Extension Assays (PEA) from Olink. Protein biomarker expression was quantified and univariate/multivariate machine learning-driven biostatistical techniques were applied to the data. Cross-validation was performed to minimize overfitting and ensure generalizability for predicting the relapse frequency status of new samples. Accuracy and area under the curve (AUC) were selected as the key metrics for evaluating model performance.
Results: Multivariate statistical approaches that included up to 21 biomarkers achieved performance with 0.917 ± 0.036 Accuracy and 0.918 ± 0.038AUC for classifying High vs. Low relapse rates. Multivariate models outperformed all univariate biomarkers including sNfL. Biomarkers that were identified as important features in multivariate models were investigated to establish causation using biological pathway and network modelsto corroborate their involvement in MS pathophysiology.
Conclusion: Multivariate protein biomarker models representing various biological pathways involved in the pathophysiology of MS effectively classified subjects with high versus low annualized relapse rates with stronger performance than any single biomarker, thereby warranting further investigation with larger sample numbers and from additional cohorts.
Disclosure: Neda Sattarnezhad has received research support from Serono and Verily.
Shrishti Saxena has received research support from Octave, Serono and Verily
Cindy Gonzalez has received research support from Serono and Verily
Hrishikesh Lokhande has received research support from Serono and Verily
Bonnie Glanz has received research support from Serono and Verily
Ferhan Qureshi, Michael Becich, and Remus Osan are employees of Octave Bioscience.
Howard Weiner reports grants from National Institutes of Health, grants from National Multiple Sclerosis Society, grants from Verily, grants from EMD Serono, grants from Biogen, grants from Teva Pharmaceuticals, grants from Sanofi, grants from Novartis, grants and personal fees from Genentech, Inc, grants and personal fees from Tilos Therapeutics, personal fees from Tiziana Life Sciences, personal fees from IM Therapeutics, personal fees from MedDay Pharmaceuticals, personal fees from vTv Therapeutics, outside the submitted work.
Tanuja Chitnis has served on advisory boards for Biogen, Novartis, and Sanofi-Genzyme; has participated in clinical trials sponsored by Sanofi-Genzyme and Novartis; has received research support from the Department of Defense, National MS Society, Guthy Jackson Charitable Foundation, Novartis, Octave, Serono and Verily
-Genzyme

By clicking “Accept Terms & all Cookies” or by continuing to browse, you agree to the storing of third-party cookies on your device to enhance your user experience and agree to the user terms and conditions of this learning management system (LMS).

Cookie Settings
Accept Terms & all Cookies