Identification of neuropathology-based subgroups in multiple sclerosis using a data-driven approach

Multiple sclerosis (MS) is a heterogeneous disorder with regards to clinical presentation and pathophysiology. Stratification into biologically distinct subgroups could enhance prognostication and efficacious allocation to disease-modifying therapies. In this study, we identified MS subgroups by performing a clustering analysis on neuropathology data collected for MS donors in the Netherlands Brain Bank (NBB) autopsy cohort. The input dataset contained detailed information on white matter lesion load, the proportion of active, mixed active/inactive, inactive and remyelinating lesions, microglia morphology in these lesions, and the presence of microglial nodules, perivascular cuffs and cortical lesions for 228 donors. A factor analysis was performed to reduce noise and redundancy prior to hierarchical clustering with K-means consolidation. Four subgroups with distinct patterns of white matter lesions were identified. These were subsequently validated with additional clinical, neuropathological and genetic data. The subgroups differed with regards to disease progression and duration, the timing of motor, sensory and other relevant signs and symptoms, patterns of cortical lesions and the presence of B cells. Age at MS onset and sex, previously associated with milder forms of MS, did not differ between the subgroups; the subgroups could also not be distinguished based on the manifestation of clinical signs and symptoms. The available genetic data was used to calculate MS polygenic risk scores (PRSs) for donors included in the NBB cohort. The MS PRS did not differ between the subgroups, but was significantly correlated with the first and second dimension of the factor analysis, the latter lending genetic support to our subdivision. Taken together, these findings suggest a complex relationship between neuropathological subgroups and clinical characteristics, indicating that post-mortem cohort studies are critical to better stratify patients and understand underlying neuropathophysiological mechanisms, in order to ultimately achieve personalised medicine in MS.

[1]  Jeffrey A. Cohen,et al.  Multiple sclerosis progression: time for a new mechanism-driven framework , 2022, The Lancet Neurology.

[2]  E. J. Hoekstra,et al.  Natural language processing and modeling of clinical disease trajectories across brain disorders , 2022, medRxiv.

[3]  M. Battaglini,et al.  Association of Brain Atrophy With Disease Progression Independent of Relapse Activity in Patients With Relapsing Multiple Sclerosis. , 2022, JAMA neurology.

[4]  M. Magyari,et al.  Quantitative effect of sex on disease activity and disability accumulation in multiple sclerosis , 2022, Journal of Neurology, Neurosurgery, and Psychiatry.

[5]  J. Duyn,et al.  Cortical lesion hotspots and association of subpial lesions with disability in multiple sclerosis , 2022, Multiple sclerosis.

[6]  M. Esiri,et al.  The influence of HLA‐DRB1*15 on the relationship between microglia and neurons in multiple sclerosis normal appearing cortical grey matter , 2021, Brain pathology.

[7]  O. Ciccarelli,et al.  B Cells in the CNS at Postmortem Are Associated With Worse Outcome and Cell Types in Multiple Sclerosis , 2021, Neurology: Neuroimmunology & Neuroinflammation.

[8]  M. Magyari,et al.  Apparent changes in the epidemiology and severity of multiple sclerosis , 2021, Nature Reviews Neurology.

[9]  S. Gauthier,et al.  Lesion features on magnetic resonance imaging discriminate multiple sclerosis patients , 2021, European journal of neurology.

[10]  D. Arnold,et al.  Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data , 2021, Nature Communications.

[11]  T. Kuhlmann,et al.  Absence of B Cells in Brainstem and White Matter Lesions Associates With Less Severe Disease and Absence of Oligoclonal Bands in MS , 2021, Neurology: Neuroimmunology & Neuroinflammation.

[12]  M. Filippi,et al.  Identifying the Distinct Cognitive Phenotypes in Multiple Sclerosis. , 2021, JAMA neurology.

[13]  M. Filippi,et al.  Involvement of Genetic Factors in Multiple Sclerosis , 2020, Frontiers in Cellular Neuroscience.

[14]  T. Olsson,et al.  Microglial autophagy–associated phagocytosis is essential for recovery from neuroinflammation , 2020, Science Immunology.

[15]  J. Kira,et al.  Genetic factors for susceptibility to and manifestations of neuromyelitis optica , 2020, Annals of clinical and translational neurology.

[16]  B. Vilhjálmsson,et al.  Improved genetic prediction of complex traits from individual-level data or summary statistics , 2020, Nature Communications.

[17]  T. Kuhlmann,et al.  Lesion stage-dependent causes for impaired remyelination in MS , 2020, Acta Neuropathologica.

[18]  Daniel C. Factor,et al.  Cell Type-Specific Intralocus Interactions Reveal Oligodendrocyte Mechanisms in MS , 2020, Cell.

[19]  Simon C. Potter,et al.  Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility , 2019, Science.

[20]  Robert C Reiner,et al.  Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 , 2019, The Lancet Neurology.

[21]  T. Olsson,et al.  Factors associated with and long-term outcome of benign multiple sclerosis: a nationwide cohort study , 2019, Journal of Neurology, Neurosurgery, and Psychiatry.

[22]  Joanne Rich,et al.  Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups , 2018, bioRxiv.

[23]  X. Montalban,et al.  Multiple sclerosis: clinical aspects , 2018, Current opinion in neurology.

[24]  C. V. van Eden,et al.  Progressive multiple sclerosis patients show substantial lesion activity that correlates with clinical disease severity and sex: a retrospective autopsy cohort analysis , 2018, Acta Neuropathologica.

[25]  Pierre Grammond,et al.  Towards personalized therapy for multiple sclerosis: prediction of individual treatment response , 2017, Brain : a journal of neurology.

[26]  Martin Krzywinski,et al.  Points of Significance: Clustering , 2017, Nature Methods.

[27]  Alan M. Kwong,et al.  Next-generation genotype imputation service and methods , 2016, Nature Genetics.

[28]  Nick C Fox,et al.  Analysis of shared heritability in common disorders of the brain , 2018, Science.

[29]  J. Josse,et al.  missMDA: A Package for Handling Missing Values in Multivariate Data Analysis , 2016 .

[30]  Mitchell J. Machiela,et al.  LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants , 2015, Bioinform..

[31]  Istvan Pirko,et al.  Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque , 2015, Annals of neurology.

[32]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[33]  Peter K. Stys,et al.  Inefficient clearance of myelin debris by microglia impairs remyelinating processes , 2015, The Journal of experimental medicine.

[34]  Jacqueline Palace,et al.  The influence of HLA‐DRB1*15 on motor cortical pathology in multiple sclerosis , 2015, Neuropathology and applied neurobiology.

[35]  F. Jacques Defining the clinical course of multiple sclerosis: The 2013 revisions , 2015, Neurology.

[36]  S. Weigand,et al.  Pathologic heterogeneity persists in early active multiple sclerosis lesions , 2014, Annals of neurology.

[37]  M. Calabrese,et al.  Low degree of cortical pathology is associated with benign course of multiple sclerosis , 2013, Multiple sclerosis.

[38]  W. Brück,et al.  Microglial nodules in early multiple sclerosis white matter are associated with degenerating axons , 2013, Acta Neuropathologica.

[39]  Patrick F. Sullivan,et al.  zCall: a rare variant caller for array-based genotyping: Genetics and population analysis , 2012, Bioinform..

[40]  J. Geurts,et al.  Clusters of activated microglia in normal-appearing white matter show signs of innate immune activation , 2012, Journal of Neuroinflammation.

[41]  P. D. Jager,et al.  Genome-wide association study of severity in multiple sclerosis , 2011, Genes and Immunity.

[42]  Sébastien Lê,et al.  FactoMineR: An R Package for Multivariate Analysis , 2008 .

[43]  L. Bö,et al.  Homogeneity of active demyelinating lesions in established multiple sclerosis , 2008, Annals of neurology.

[44]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[45]  S. Gabriel,et al.  Risk alleles for multiple sclerosis identified by a genomewide study. , 2007, The New England journal of medicine.

[46]  Hans Lassmann,et al.  Remyelination is extensive in a subset of multiple sclerosis patients. , 2006, Brain : a journal of neurology.

[47]  Pardis C Sabeti,et al.  A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC , 2006, Nature Genetics.

[48]  V. Pawlowsky-Glahn,et al.  Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation , 2003 .

[49]  F Barkhof,et al.  Post-mortem MRI-guided sampling of multiple sclerosis brain lesions: increased yield of active demyelinating and (p)reactive lesions. , 2001, Brain : a journal of neurology.

[50]  J. Parisi,et al.  Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination , 2000, Annals of neurology.

[51]  Hans Lassmann,et al.  An updated histological classification system for multiple sclerosis lesions , 2016, Acta Neuropathologica.

[52]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[53]  Julie Josse,et al.  Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data? , 2010 .

[54]  V. Pawlowsky-Glahn,et al.  on Compositional Data Analysis , 2007 .

[55]  W. L. Benedict,et al.  Multiple Sclerosis , 2007, Journal - Michigan State Medical Society.