Comprehensive multimodal analysis of patients with neuroimmunological diseases

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Neuroimmunological diseases of the central nervous system (CNS) represent a growing spectrum of diagnoses, most of which are considered rare disorders. The pathophysiology of these diseases is poorly understood, and effective therapies are sporadic. The most common immune-mediated CNS disease is multiple sclerosis (MS). The initial stage of MS, relapsing-remitting MS (RRMS) can be effectively treated by immunomodulatory treatments, if these are initiated at young age, before the substantial CNS damage occurred. Although there are currently more than 20 Food and Drug Administration (FDA)-approved treatments of MS, their efficacy on disability progression strongly declines with advancing age of patients, so that after age of 54 years, no efficacy on disability progression is seen on a group level. This protocol is advancing knowledge about disease mechanisms that are not targeted by current FDA-approved treatments and is also developing and validating tools of clinical utility. This review period (October 2022-August 2023) we have generated following results: 1. While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1,305 proteins, measured blindly in the training dataset of untreated MS patients (N=129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N=24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p<0.0001) in an independent longitudinal cohort (N=98), and uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making it hopefully more efficient and successful. 2. Composite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm. The prospectively acquired MS patients, divided into training (n=172) and validation (n=83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx App that automatically computes disability scales. qMRI features were computed by lesion-TOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort. COMRISv2 models validated moderate correlation with cognitive disability (Rho = 0.674; Linhs concordance coefficient CCC = 0.458; p<0.001) and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p<0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy. COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data. 3. Both 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. Standardized brain MRI and neurological examination were acquired prospectively in 649 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by 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=160). MS patients were randomly split into training (n=277) and validation (n=132) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Confounder 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 outperforms published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. GBM 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. 4. Our work this period also contributed to meta-analyses that demonstrated value of serum neurofilament light chain (NFL) biomarker in clinical care of patients with severe COVID19, to paper that describes clinical value of combining exome sequencing with copy number variants genetic evaluation of unknown pediatric disorder and to work that defined value of NFL as biomarker of severity and therapeutic response in Niemann-Pick Disease, Type C1.