B cell depletion therapy does not resolve chronic active multiple sclerosis lesions

BACKGROUND

Chronic active lesions (CAL) in multiple sclerosis (MS) have been observed even in patients taking high-efficacy disease-modifying therapy, including B-cell depletion. Given that CAL are a major determinant of clinical progression, including progression independent of relapse activity (PIRA), understanding the predicted activity and real-world effects of targeting specific lymphocyte populations is critical for designing next-generation treatments to mitigate chronic inflammation in MS.

METHODS

We analyzed published lymphocyte single-cell transcriptomes from MS lesions and bioinformatically predicted the effects of depleting lymphocyte subpopulations (including CD20 B-cells) from CAL via gene-regulatory-network machine-learning analysis. Motivated by the results, we performed in vivo MRI assessment of PRL changes in 72 adults with MS, 46 treated with anti-CD20 antibodies and 26 untreated, over ∼2 years.

FINDINGS

Although only 4.3% of lymphocytes in CAL were CD20 B-cells, their depletion is predicted to affect microglial genes involved in iron/heme metabolism, hypoxia, and antigen presentation. In vivo, tracking 202 PRL (150 treated) and 175 non-PRL (124 treated), none of the treated paramagnetic rims disappeared at follow-up, nor was there a treatment effect on PRL for lesion volume, magnetic susceptibility, or T1 time. PIRA occurred in 20% of treated patients, more frequently in those with ≥4 PRL (p = 0.027).

INTERPRETATION

Despite predicted effects on microglia-mediated inflammatory networks in CAL and iron metabolism, anti-CD20 therapies do not fully resolve PRL after 2-year MRI follow up. Limited tissue turnover of B-cells, inefficient passage of anti-CD20 antibodies across the blood-brain-barrier, and a paucity of B-cells in CAL could explain our findings.

FUNDING

Intramural Research Program of NINDS, NIH; NINDS grants R01NS082347 and R01NS082347; Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; Cariplo Foundation (grant #1677), FRRB Early Career Award (grant #1750327); Fund for Scientific Research (FNRS).

BMAT: An open-source BIDS managing and analysis tool

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues

The current diagnostic criteria for multiple sclerosis (MS) lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progression. Recently, some MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis. Moreover, studies have shown that CL and PRL are potential prognostic biomarkers, the former correlating with cognitive impairments and the latter with early disability progression. As machine learning-based methods have achieved extraordinary performance in the assessment of conventional imaging biomarkers, such as white matter lesion segmentation, several automated or semi-automated methods have been proposed as well for CL, PRL, and CVS. In the present review, we first introduce these MS biomarkers and their imaging methods. Subsequently, we describe the corresponding machine learning-based methods that were proposed to tackle these clinical questions, putting them into context with respect to the challenges they are facing, including non-standardized MRI protocols, limited datasets, and moderate inter-rater variability. We conclude by presenting the current limitations that prevent their broader deployment and suggesting future research directions.