Comprehensive multimodal analysis of patients with neuroimmunological diseases

National Institute Of Allergy And Infectious Diseases

Investigators

Linked publications & trials

Abstract

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 2021-August 2022) we have generated following results: 1. Neurological examination takes 30-60 minutes to perform, but clinicians have much less time they can spend with patients. To facilitate reliable quantification of neurological functions in current healthcare system we have been developing smartphone-based collection of simple tests of neurological functions called Neurological Functions Test Suite (NeuFun-TS) that can be self-administered by patients. The goal of NeuFun-TS is to reproduce most aspects of clinician-performed neurological examination. To optimize NeuFun-TS tests and prove their clinical utility, we are mapping the different outcomes of the digitalized tests to the gold standard of full neurological examination and quantitative central nervous system (CNS) imaging. One of these tests measures cognitive functions such as reaction time using digital modification for the smartphone use of the traditional cognitive test, the Symbol Digit Modalities Test (SDMT). We made the SDMT randomized, which prevents the memorization of the test, and mapped all contributing neurological functions that affect the randomized SDMT (rSDMT) test results: these include motoric disability of the dominant hand, but also vision and eye-hand coordination. Because these functions are measured by other simple tests in NeuFun-TS, we can use the outcomes from these alternative tests as covariates in rSDMT outcome to isolate measured function, in this case cognitive disability. Resulting novel smartphone-computed outcome from this simple cognitive test explains 75% of CNS tissue damage measured by qMRI in the independent validation cohort, with extremely low p-value. We also used longitudinal data to derive algorithmic identification of the learning effect and to demonstrate that averaging repeated tests (e.g., 2-4 weekly tests) lowers the threshold for identifying true progression in cognitive decline, e.g. during clinical trials or in routine clinical practice. 2. Several animal models of neurodegenerative diseases and pathology studies of several human neurodegenerative diseases identified abnormal phenotype of astrocytes that instead of supporting neuronal functions, become neurotoxic. Previous year we showed that some of the cerebrospinal fluid (CSF) biomarkers that are most strongly associated with MS severity (i.e., that are strongly elevated in MS patients who accumulate irreversible disability faster), such as SERPINA3, are released by such neurotoxic astrocytes. This year we mapped the signaling pathways involved in the transformation of primary human astrocytes to neurotoxic phenotype by pro-inflammatory stimuli. We identified primary role of endoplasmic reticulum stress and linked mTOR/PI3K/AKT signaling. We also identified several drugs and drug categories that reproducibly inhibit transformation of astrocytes to neurotoxic phenotype in concentrations achievable with FDA-approved dosing, if they cross the blood brain barrier. Among these, dantrolene, an inhibitor of ryanodine receptor channels (RYR1 and RYR3) was selected for in-vivo validation of predicted intrathecal pharmacodynamic effects in our ongoing platform clinical trial. 3. Serum neurofilament light chain (sNFL) is emerging as the most useful noninvasive biomarker of the acute injury to neurons and axons, with potential clinical value in diagnosing and monitoring patients with neurological diseases. However, simultanously measured NFL concentrations in the CSF and serum show that, while these are correlated, linear models explain only 40-60% of variance and CSF NFL levels have stronger predictive value for measuring acute CNS damage than sNFL. Thus, to increase clinical value of sNFL, we used 1,138 matched CSF-serum samples to comprehensively map processes that influence NFL concentrations in the CSF and blood to derive (and validate) mathematical adjustment of sNFL to better approximate CSF NFL. This adjustment caused 36% improvement in the ability of sNFL to predict MS activity (measured by contrast enhancing lesions on brain and spinal cord MRI) in the independent validation cohort with very low p-value. However, only sNFL, but not CSF NFL weakly but significantly correlated with MS severity. We show that this sNFL advantage originates in its ability to reflect spinal cord damage resulting in NFL release from peripheral axons eventually to blood, bypassing the CSF. While careful evaluation of published literature on CSF/serum NFL levels in diseases of peripheral versus central nervous system support our conclusions, this aspect of axonal pathology has not been appreciated in MS. Our results also show that more research is required before sNFL can be used for making therapeutic decisions in MS on a patients level. 4. Development of effective treatments requires understanding of disease mechanisms. For CNS diseases like MS, human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow 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 technical rigor of study design, such as blinding, implementing controls, using large cohorts that encompass entire spectrum of disease phenotypes and, most importantly, validating models in independent cohort(s). To facilitate growth of this important research area, we performed a meta-analysis of publications that model MS clinical outcomes (n=302), extracting effect sizes, while also scoring technical quality of study design using pre-defined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of data using selective filtering. On average, published studies fulfilled only one out of seven criteria of study design rigor. Only 15.2% of 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 MRI and blood biomarker predictors, the median sample size was below 100 subjects. We observed inverse relationships between reported effect sizes and the numbers of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias over-estimate effect sizes. This meta-analysis represents a useful tool for researchers, reviewers, and funders to improve design of future modeling studies in MS and to easily compare new studies with published literature.