Anti-CD20 therapy depletes activated myelin-specific CD8+ T cells in multiple sclerosis.

CD8+ T cells are believed to play an important role in multiple sclerosis (MS), yet their role in MS pathogenesis remains poorly defined. Although myelin proteins are considered potential autoantigenic targets, prior studies of myelin-reactive CD8+ T cells in MS have relied on in vitro stimulation, thereby limiting accurate measurement of their ex vivo precursor frequencies and phenotypes. Peptide:MHC I tetramers were used to identify and validate 5 myelin CD8+ T cell epitopes, including 2 newly described determinants in humans. The validated tetramers were used to measure the ex vivo precursor frequencies and phenotypes of myelin-specific CD8+ T cells in the peripheral blood of untreated MS patients and HLA allele-matched healthy controls. In parallel, CD8+ T cell responses against immunodominant influenza epitopes were also measured. There were no differences in ex vivo frequencies of tetramer-positive myelin-specific CD8+ T cells between MS patients and control subjects. An increased proportion of myelin-specific CD8+ T cells in MS patients exhibited a memory phenotype and expressed CD20 compared to control subjects, while there were no phenotypic differences observed among influenza-specific CD8+ T cells. Longitudinal assessments were also measured in a subset of MS patients subsequently treated with anti-CD20 monoclonal antibody therapy. The proportion of memory and CD20+ CD8+ T cells specific for certain myelin but not influenza epitopes was significantly reduced following anti-CD20 treatment. This study, representing a characterization of unmanipulated myelin-reactive CD8+ T cells in MS, indicates these cells may be attractive targets in MS therapy

Optical coherence tomography in multiple sclerosis: A 3-year prospective multicenter study

Prospective multicenter study; Multiple sclerosis; TomographyEstudi prospectiu multicèntric; Esclerosi múltiple; TomografiaEstudio multicéntrico prospectivo; Esclerosis múltiple; TomografíaObjective To evaluate changes over 3 years in the thickness of inner retinal layers including the peripapillary retinal nerve fiber layer (pRNFL), and combined macular ganglion cell and inner plexiform layers (mGCIPL), in individuals with relapsing-remitting multiple sclerosis (RRMS) versus healthy controls; to determine whether optical coherence tomography (OCT) is sufficiently sensitive and reproducible to detect small degrees of neuroaxonal loss over time that correlate with changes in brain volume and disability progression as measured by the Expanded Disability Status Scale (EDSS). Methods Individuals with RRMS from 28 centers (n = 333) were matched with 64 healthy participants. OCT scans were performed on Heidelberg Spectralis machines (at baseline; 1 month; 6 months; 6-monthly thereafter). Results OCT measurements were highly reproducible between baseline and 1 month (intraclass correlation coefficient >0.98). Significant inner retinal layer thinning was observed in individuals with multiple sclerosis (MS) compared with controls regardless of previous MS-associated optic neuritis––group differences (95% CI) over 3 years: pRNFL: −1.86 (−2.54, −1.17) µm; mGCIPL: −2.03 (−2.78, −1.28) µm (both p 5 years (pRNFL: p < 0.05; mGCIPL: p < 0.01). Brain volume decreased by 1.3% in individuals with MS over 3 years compared to 0.5% in control subjects (effect size 0.76). mGCIPL atrophy correlated with brain atrophy (p < 0.0001). There was no correlation of OCT data with disability progression. Interpretation OCT has potential to estimate rates of neurodegeneration in the retina and brain. The effect size for OCT, smaller than for magnetic resonance imaging based on Heidelberg Spectralis data acquired in this study, was increased in early disease.The authors wish to thank Carolyn M. Ervin for her substantial contribution in the data analyses, as well as Mark Kirby, Aisling Towell, and Marie-Catherine Mousseau (Novartis Ireland Ltd.) for their writing support, funded by Novartis Pharma AG, Basel, Switzerland. FB is supported by the NIHR biomedical research center at UCLH

Do the current MS clinical course descriptors need to change and if so how? A survey of the MS community

Multiple sclerosis; Clinical course; ProgressionEsclerosis múltiple; Curso clínico; ProgresiónEsclerosi múltiple; Curs clínic; ProgressióBackground and Objectives: The current clinical course descriptors of multiple sclerosis (MS) include a combination of clinical and magnetic resonance imaging (MRI) features. Recently there has been a growing call to base these descriptors more firmly on biological mechanisms. We investigated the implications of proposing a new mechanism-driven framework for describing MS. Methods: In a web-based survey, multiple stakeholders rated the need to change current MS clinical course descriptors, the definitions of disease course and their value in clinical practice and related topics. Results: We received 502 responses across 49 countries. In all, 77% of the survey respondents supported changing the current MS clinical course descriptors. They preferred a framework that informs treatment decisions, aids the design and conduct of clinical trials, allows patients to understand their disease, and links disease mechanisms and clinical expression of disease. Clinical validation before dissemination and ease of communication to patients were rated as the most important aspects to consider when developing any new framework for describing MS. Conclusion: A majority of MS stakeholders agreed that the current MS clinical course descriptors need to change. Any change process will need to engage a wide range of affected stakeholders and be guided by foundational principles.This work and the International Advisory Committee on Clinical Trials in MS are funded by the European Committee for Treatment and Research in Multiple Sclerosis and the National Multiple Sclerosis Society

Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images

Peginterferon beta-1a improves MRI measures and increases the proportion of patients with no evidence of disease activity in relapsing-remitting multiple sclerosis: 2-year results from the ADVANCE randomized controlled trial

Summary of MRI endpoints over 2 years by original randomisation group [6]. (DOC 53kb

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers