Research Presented at the Joint ACTRIMS-ECTRIMS Meeting
MS Virtual 2020 — September 11-13, 2020
Tanuja Chitnis¹, Michael Becich², Riley Bove³, Bruce A.C. Cree³, Victor Gehman², Refugia Gomez³, Stephen L. Hauser³, Roland Henry³, Amal Katrib², Hrishikesh Lokhande¹, Jorge R. Oksenberg³, Anu Paul¹, Ferhan Qureshi², Adam Santaniello³, Neda Sattarnezhad4, Shrishti Saxena¹, Howard Weiner¹, Michael Wilson³, Hajime Yano¹, Sergio E. Baranzini³
¹Brigham and Women’s Hospital, Boston, MA, USA, ²Octave Bioscience, Menlo Park, CA, USA, ³University of California San Francisco, CA, USA, 4University of Illinois at Chicago, IL, USA
Introduction:A blood based, 21-plex proteomic custom assay has been developed to effectively classify several radiographic and clinical endpoints including the presence of gadolinium-enhanced (Gd+) lesions, Annualized Relapse Rate (ARR) and clinically defined relapse status (active versus stable). Samples from multiple deeply-phenotyped cohorts (ACP, CLIMB and EPIC) were tested using two immunoassay platforms. The 21 proteins were selected for inclusion in the custom assay based on their performance in univariate and multivariate statistical models, replication across the independent cohorts, and biological pathway modeling to ensure comprehensive representation of MS neurophysiology.
Jens Kuhle¹, David Leppert¹, Pascal Benkert¹, Johanna Oechtering¹, Annette Orleth¹, Özgür Yaldizli¹, Michael Becich², Victor Gehman², Amal Katrib², Fatima Rubio da Costa², Ferhan Qureshi², Cristina Granziera¹
¹University Hospital Basel, Switzerland, ²Octave Bioscience, Menlo Park, CA, United States of America
Introduction: Quantification of Disease Activity and Disease Progression are important tools for MS research and can also be utilized to enhance clinical treatment. Association with several MS endpoints (Gad lesions, ARR, and clinical relapse status) in earlier feasibility studies alongside computational biology modeling led to the development of a custom 21-plex proteomic assay panel. Expression levels of these 21 proteins were analyzed relative to five clinical and radiographic endpoints in a cohort of samples from the University Hospital Basel.
Amal Katrib¹, Ferhan Qureshi¹, Michael Becich¹, Victor Gehman¹, Susan Goelz²
¹Octave Bioscience, Menlo Park, CA, USA, ²Myelin Repair Foundation, Saratoga, CA, USA
Introduction:Multiple sclerosis (MS) is a multifaceted disease with an intricate pathophysiology that lies at the intersection of autoimmunity, inflammation, redox imbalance, demyelination, and neurodegeneration. The varying interplay of distinct and converging mechanistic profiles in MS is believed to contribute to the heterogeneity observed in disease course and outcomes, clinical presentation, and therapeutic response. With this high degree of undeciphered molecular complexity, identifying biomolecular markers that are reproducible as well as specific to even the major subclasses of MS has been problematic. These difficulties have hindered the clinical translation of biomarkers and their use to aid in disease assessment and treatment strategies for individual MS patients. We posit that a biocentric framework can be leveraged to augment the prognostic capacity of MS biomarkers.
Annalise Miner¹, Michael Becich², Victor Gehman PhD², Jennifer Graves MD¹
¹University of California San Diego, CA, USA, ²Octave Bioscience, Menlo Park, CA, USA,
Introduction: When standard of care neurology visits are 6 months apart, there can often be gaps in care and missed opportunities to improve patient function; the result is poor follow-up for care management or low medication adherence. MS care can be comprehensively approached with a human-tech platform that supports symptom and medication tracking, nursing interventions, laboratory monitoring for subclinical disease activity and curated MRI reports to ensure accurate data at return visits.
Kelly Leyden MRes¹, Anisha Keshavan PhD¹, Michael Iv MD1,2
¹Octave Bioscience, Menlo Park, CA, USA, ²Stanford University, CA, USA
Introduction: Quantitative metrics such as lesion count and brain volume can provide objective data of disease progression in Multiple Sclerosis (MS). However, implementing FDA-approved quantitative software in clinical practice requires significant effort and investment. It has not yet been established if quantitative software can consistently improve the detection of clinically relevant MRI findings in MS-specific radiology reports. In this study, we characterize clinically relevant findings in MS-specific radiology reports generated by a neuroradiologist after visual interpretation of images alone, and then supplemented with FDA-approved software.
David Hughes BSN¹, Weidong Yang PhD², Kelly Leyden MRes¹, Michael Iv MD1,3, Anisha Keshavan PhD¹
¹Octave Bioscience, Menlo Park, CA, USA, ²Kineviz, San Francisco, CA, USA, ³Stanford University, CA, USA
Introduction: Novel visualization of neuroimaging data can lead to clinical insights and ultimately new imaging analysis capabilities. Graph models of magnetic resonance imaging (MRI) data can reveal the topology and temporal nature of multiple sclerosis disease progression, by exposing novel structural features of the brain through representation of data as interactive 3D projections. Existing standards and evolving approaches to neuroimaging can benefit from an integration of graph analytics and visualization.
Anisha Keshavan PhD¹, Kelly M. Leyden¹, Barbara Dappert MD¹, Michael Iv MD1,2, Michael Becich¹, Michael Towbin¹, David Hughes BSN¹, Annalise Miner¹, Revere Kinkel MD³, Jennifer Graves MD³
¹Octave Bioscience, Menlo Park, CA, USA, ²Stanford University, CA, USA, ³University of California, San Diego, CA, USA
Introduction: Automated multiple sclerosis lesion counts and volumes are poised to be salient clinical biomarkers of disease progression; however, algorithmic variability and low expert agreement prevents widespread adoption in clinical practice. Because every method has a non-negligible error rate, visual quality control (QC) is required before a clinical decision can be made. QC is a bottleneck to the use of automated lesion count and volume metrics in the clinic. A method is needed to 1) quickly evaluate experts and non-experts to understand and resolve disagreements, and 2) quickly QC the output of automated lesion segmentation methods.
Research Presented at ECTRIMS 2019
Stockholm, Sweden — September 11-13, 2019
Multivariate protein biomarker models more accurately predict multiple sclerosis MRI disease activity compared to serum levels of neurofilament light chain alone
T. Chitnis1, H. Yano1, S. Saxena1, H. Lokhande1, N. Sattarnezhad1, M.C. Manieri1, A. Paul1, F. Saleh1, M. Collins1, B. Glanz1, C. Guttmann1, R. Bakshi1, F. Qureshi2, M. Becich2, R. Osan2, V. Gehman2, H. Weiner1
1Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, 2Octave Bioscience, Menlo Park, CA, United States
Introduction: Serum levels of neurofilament light chain (sNfL) are associated with neurodegeneration in Multiple Sclerosis (MS) and correlate with measurements of disease activity (DA), including the presence of gadolinium enhancing (GAD+) lesions. MS is a complex disease. Many inflammatory and immune-modulated biological pathways associated with neurodegeneration may impact MS pathophysiology. Investigating these pathways, as represented by protein biomarker expression, can provide deeper insights and reveal stronger correlations to radiographic DA than sNfL alone.
Classification of high versus low annualized relapse rate status in subjects with relapsing-remitting multiple sclerosis using multivariate serum protein biomarker models
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.
Clinical disease activity status (exacerbation versus quiescence) in subjects with relapsing-remitting multiple sclerosis is accurately classified using multivariate serum protein biomarker models
R. Osan1, F. Qureshi2, M. Becich1, W. Hagstrom3
1Data Science, 2Assay Development, 3Octave Bioscience, Menlo Park, CA, United States
Introduction: Relapsing-Remitting Multiple Sclerosis (RRMS) is a complex and heterogeneous disease, and multiple biological pathways, including inflammation, immune modulation and neurodegeneration are involved in MS pathophysiology. Investigating these pathways, as represented by differential protein biomarker expression in serum, can help inform the development of tools to accurately track disease activity, identify early evidence of relapse, and monitor treatment response.