Table_1_Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis.DOCX

Development of effective treatments requires understanding of disease mechanisms. For diseases of the central nervous system (CNS), such as multiple sclerosis (MS), human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link the identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow the generation of thousands of biomarkers in living human subjects, derived from genes, transcripts, medical images, and proteins or metabolites in biological fluids. These biomarkers can be assembled into computational models of clinical value, provided such models are generalizable. Reproducibility of models increases with the technical rigor of the study design, such as blinding, control implementation, the use of large cohorts that encompass the entire spectrum of disease phenotypes and, most importantly, model validation in independent cohort(s). To facilitate the growth of this important research area, we performed a meta-analysis of publications (n = 302) that model MS clinical outcomes extracting effect sizes, while also scoring the technical quality of the study design using predefined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of the data by selective filtering. On average, the published studies fulfilled only one of the seven criteria of study design rigor. Only 15.2% of the studies used any validation strategy, and only 8% used the gold standard of independent cohort validation. Many studies also used small cohorts, e.g., for magnetic resonance imaging (MRI) and blood biomarker predictors, the median sample size was <100 subjects. We observed inverse relationships between reported effect sizes and the number of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias overestimate effect sizes. In conclusion, the presented meta-analysis represents a useful tool for researchers, reviewers, and funders to improve the design of future modeling studies in MS and to easily compare new studies with the published literature. We expect that this will accelerate research in this important area, leading to the development of robust models with proven clinical value.</p

Data_Sheet_1_Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis.XLSX

Development of effective treatments requires understanding of disease mechanisms. For diseases of the central nervous system (CNS), such as multiple sclerosis (MS), human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link the identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow the generation of thousands of biomarkers in living human subjects, derived from genes, transcripts, medical images, and proteins or metabolites in biological fluids. These biomarkers can be assembled into computational models of clinical value, provided such models are generalizable. Reproducibility of models increases with the technical rigor of the study design, such as blinding, control implementation, the use of large cohorts that encompass the entire spectrum of disease phenotypes and, most importantly, model validation in independent cohort(s). To facilitate the growth of this important research area, we performed a meta-analysis of publications (n = 302) that model MS clinical outcomes extracting effect sizes, while also scoring the technical quality of the study design using predefined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of the data by selective filtering. On average, the published studies fulfilled only one of the seven criteria of study design rigor. Only 15.2% of the studies used any validation strategy, and only 8% used the gold standard of independent cohort validation. Many studies also used small cohorts, e.g., for magnetic resonance imaging (MRI) and blood biomarker predictors, the median sample size was <100 subjects. We observed inverse relationships between reported effect sizes and the number of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias overestimate effect sizes. In conclusion, the presented meta-analysis represents a useful tool for researchers, reviewers, and funders to improve the design of future modeling studies in MS and to easily compare new studies with the published literature. We expect that this will accelerate research in this important area, leading to the development of robust models with proven clinical value.</p

Presentation_1_Confounder-adjusted MRI-based predictors of multiple sclerosis disability.zip

IntroductionBoth aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.MethodsStandardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.ResultsConfounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.ConclusionGBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.</p

Data_Sheet_2_Confounder-adjusted MRI-based predictors of multiple sclerosis disability.xlsx

IntroductionBoth aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.MethodsStandardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.ResultsConfounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.ConclusionGBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.</p

Data_Sheet_1_Confounder-adjusted MRI-based predictors of multiple sclerosis disability.pdf

IntroductionBoth aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.MethodsStandardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.ResultsConfounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.ConclusionGBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.</p

Table_1_Quantifications of CSF Apoptotic Bodies Do Not Provide Clinical Value in Multiple Sclerosis.XLSX

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) that leads to the death of neurons and oligodendrocytes, which cannot be measured in living subjects. Physiological cellular death, otherwise known as apoptosis, progresses through a series of stages which culminates in the discharge of cellular contents into vesicles known as apoptotic bodies (ABs) or apoptosomes. These ABs can be detected in bodily fluids as Annexin-V-positive vesicles of 0.5–4.0 μm in size. In addition, the origin of these ABs might be detected by staining for cell-specific surface markers. Thus, we investigated whether quantifications of the total and CNS cell-specific ABs in the cerebrospinal fluid (CSF) of patients provided any clinical value in MS. Extracellular vesicles, from CSF of 64 prospectively-acquired subjects, were collected in a blinded fashion using ultra-centrifugation. ABs were detected by flow cytometry using bead-enabled size-gating and Annexin-V-staining. The origin of these ABs was further classified by staining the vesicles for cell-specific surface markers. Upon unblinding, we evaluated the differences between diagnostic categories and correlations with clinical measures. There were no statistically significant differences in the numbers of total or any cell-specific ABs across different disease diagnostic subgroups and no significant correlations with any of the tested clinical measures of CNS tissue destruction, disability, MS activity, and severity (i.e., rates of disability accumulation). Overlap of cell surface markers suggests inability to reliably determine origin of ABs using antibody-based flow cytometry. These negative data suggest that CNS cells in MS either die by non-apoptotic mechanisms or die in frequencies indistinguishable by current assays from apoptosis of other cells, such as immune cells performing immunosurveillance in healthy conditions.</p

Correlations between CSF IL-12p40, CXCL13 and IL-8 (all measured in pg/ml) and traditional clinical measures of intrathecal inflammation: CSF biomarkers measured by NIH clinical laboratory: WBC count (# of cells per mm³ measured in unspun CSF), IgG index (normal range 0.26–0.62), OCB and total protein (g/dl) and CEL measured as described in detail in method section.

<p>Correlation coefficients and p values are detailed in each panel. The data originate from the confirmatory (NIB) cohort only.</p

Methodological details of biomarker measurements.

a<p>If indicated, CSF was concentrated using Millipore Amicon Ultra 3 kDa filters.</p>b<p>Only linear part of standard curve was used for quantification of protein; when concentrated CSF was used, detection limit is recalculated to reflect utilized concentration factor.</p>c<p>Intra-assay coefficient of variance could not be calculated because all data were below the detection limit of the assay.</p

ROC curves for all three CSF biomarkers and their PCA1 for both WMS and NIB cohorts.

<p>ROC curves for all three CSF biomarkers and their PCA1 for both WMS and NIB cohorts.</p

Clinical utility of CSF biomarkers.

*<p>AUC is the percentage of randomly drawn pairs for which the test is correct (i.e. it correctly classifies the two patients in the pair).</p