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Maggi P, Bulcke CV, Pedrini E, Bugli C, Sellimi A, Wynen M, Stölting A, Mullins WA, Kalaitzidis G, Lolli V, Perrotta G, El Sankari S, Duprez T, Li X, Calabresi PA, van Pesch V, Reich DS, Absinta M. B cell depletion therapy does not resolve chronic active multiple sclerosis lesions. EBioMedicine 2023; 94:104701. [PMID: 37437310 PMCID: PMC10436266 DOI: 10.1016/j.ebiom.2023.104701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 07/14/2023] Open
Abstract
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).
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Affiliation(s)
- Pietro Maggi
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium; Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium; Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland.
| | - Colin Vanden Bulcke
- Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
| | - Edoardo Pedrini
- Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital, Milan, Italy
| | - Céline Bugli
- Plateforme Technologique de Support en Méthodologie et Calcul Statistique, Université Catholique de Louvain, Brussels, Belgium
| | - Amina Sellimi
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Maxence Wynen
- Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
| | - Anna Stölting
- Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
| | - William A Mullins
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Grigorios Kalaitzidis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Valentina Lolli
- Hôpital Erasme, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Gaetano Perrotta
- Hôpital Erasme, Université Libre de Bruxelles, Bruxelles, Belgium
| | - Souraya El Sankari
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Thierry Duprez
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Xu Li
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Vincent van Pesch
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Martina Absinta
- Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital, Milan, Italy; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Barquero G, La Rosa F, Kebiri H, Lu PJ, Rahmanzadeh R, Weigel M, Fartaria MJ, Kober T, Théaudin M, Du Pasquier R, Sati P, Reich DS, Absinta M, Granziera C, Maggi P, Bach Cuadra M. RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis. Neuroimage Clin 2020; 28:102412. [PMID: 32961401 PMCID: PMC7509077 DOI: 10.1016/j.nicl.2020.102412] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/23/2020] [Accepted: 09/01/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVES In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts. MATERIALS AND METHODS Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal). RESULTS The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%. CONCLUSIONS The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS.
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Affiliation(s)
- Germán Barquero
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Hamza Kebiri
- Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Po-Jui Lu
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Reza Rahmanzadeh
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Mário João Fartaria
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tobias Kober
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Marie Théaudin
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Renaud Du Pasquier
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Martina Absinta
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Pietro Maggi
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Neurology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland.
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