David Laplaud’s research while affiliated with Center for Research in Transplantation and Translational Immunology and other places

Background The clinical course of myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is variable. However, robust markers of poor outcome and/or relapse risk are still missing. Objective To evaluate the frequency of cerebrospinal fluid-restricted oligoclonal bands (CSF-OCB) in a national cohort of adult MOGAD patients and to assess their prognostic value for the risk of relapse and severity. Methods We included MOGAD adult patients fulfilling the MOGAD 2023 criteria who underwent CSF analysis at maximum 3 months from onset. Results Data from 190 patients were collected. We found the presence of CSF-OCB in 32 patients (16.8%). Positive and negative CSF-OCB patients were similar for median age at onset, sex, clinical presentation, severity at onset, and residual disability. Relapses were more frequent in the CSF-OCB+ group ( p = 0.049), particularly within the first year of follow-up ( p = 0.007). Although CSF-OCB+ was more frequently associated with imaging features suggestive of multiple sclerosis (MS) ( p = 0.014), 78% of these patients fulfilled the 2023 supportive features and 65% experienced lesion vanishing at follow-up magnetic resonance imaging (MRI). Conclusion We found a higher risk of relapse in MOGAD with CSF-OCB particularly during the first year. Close attention is recommended regarding the risk of misdiagnosis with MS.

Introduction Myeloid cells trafficking from the periphery to the central nervous system are key players in multiple sclerosis (MS) through antigen presentation, cytokine secretion and repair processes. Methods Combination of mass cytometry on blood cells from 60 MS patients at diagnosis and 29 healthy controls, along with single cell RNA sequencing on paired blood and cerebrospinal fluid (CSF) samples from 5 MS patients were used for myeloid cells detailing. Results Myeloid compartment study demonstrated an enrichment of a peculiar classical monocyte population in 22% of MS patients at the time of diagnosis. Notably, this patients’ subgroup exhibited a more aggressive disease phenotype two years post-diagnosis. This monocytic population, detected in both the CSF and blood, was characterized by CD206, CD209, CCR5 and CCR2 expression, and was found to be more frequent in MS patients carrying the HLA-DRB1*15:01 allele. Furthermore, pathways analysis predicted that these cells had antigen presentation capabilities coupled with pro-inflammatory phenotype. Discussion Altogether, these results point toward the amplification of a specific and pathogenic myeloid cell subset in MS patients with genetic susceptibilities.

Multiple sclerosis (MS) is the leading cause of severe non-traumatic disability in young adults and its incidence is increasing worldwide. The variability of gait impairment in MS necessitates the development of a non-invasive, sensitive, and cost-effective tool for quantitative gait evaluation. The eGait movement sensor, designed to characterize human gait through unit quaternion time series (QTS) representing hip rotations, is a promising approach. However, the small sample sizes typical of clinical studies pose challenges for the stability of gait data analysis tools. To address these challenges, this article presents two key scientific contributions. First, a comprehensive framework is proposed for transforming QTS data into a form that preserves the essential geometric properties of gait while enabling the use of any tabular synthetic data generation method. Second, a synthetic data generation method is introduced, based on nearest neighbors weighting, which produces high-fidelity synthetic QTS data suitable for small datasets and private data environments. The effectiveness of the proposed method, is demonstrated through its application to MS gait data, showing very good fidelity and respect of the initial geometry of the data. Thanks to this work, we are able to produce synthetic data sets and work on the stability of clustering methods.

Objective: The Projections In Multiple Sclerosis (PRIMUS) project aims to develop a precision medicine platform enabling neurologists to support therapeutic decisions in multiple sclerosis by visualizing similar patient data among a reference database. We present a data integration method to combine randomized clinical trials (RCTs) and observational studies data and optimize their informativeness. Methods: We developed an extract-transform-load data integration pipeline to combine 13 source databases with 31,786 patients: the mother and high-definition cohorts from the French MS registry and eleven industrial RCTs. We aimed to inform each treatment class initiation with at least 500 patients with 2-year clinical and MRI follow-up. Our data integration strategy used every patient visit as a potential baseline time point to inform a specific neurologist query to the platform, thus tailoring the actual analysis cohort to each patient. Results: The resulting PRIMUS database had 12,953 patients with at least one informative visit. It could inform 7/8 common treatment initiation scenarios with at least 500 patients (range: 485 for glatiramer acetate; 1,754 for natalizumab). The per-visit integration identified 696 more patients in the high-definition cohort than the classical epidemiological per-patient integration (+114 %). Although the mother cohort longitudinal data were deemed to be sparse, we identified 6,128 informative patients (yield: 27.8%; mean: 2.2 visits per patient). Interpretation: A data integration pipeline and per-visit integration enabled us to build a highly informative reference database to be queried by neurologists through a web application to support discussions with their patients and the selection of disease-modifying treatments.

Background and objectives: Although rituximab failed to demonstrate a significant effect on disability progression in primary progressive multiple sclerosis (PPMS), ocrelizumab succeeded. Our main objective was to analyze confirmed disability progression (CDP) in a cohort of patients with PPMS treated with anti-CD20 therapies compared with a weighted untreated control cohort. Methods: This was a retrospective study using data from the French MS registry (Observatoire Français de la Sclérose En Plaques). We included patients with PPMS treated or never treated with anti-CD20 therapies from 2016 to 2021, with an Expanded Disability Status Scale score of ≤6.5 at baseline. The primary outcome was time to first CDP. The secondary outcomes were time to first relapse, MRI activity at 2 years, identification of risk factors associated with CDP, and serious infection incidence rates (IIRs). Each outcome was studied using an inverse probability of treatment weighting method. The outcomes were modeled using a weighted proportional Cox model for the time-to-event outcomes and by a logistic regression regarding the MRI activity. Results: A total of 1,184 patients (426 treated and 758 untreated) fulfilled the inclusion criteria. Median age (Q1-Q3) was 56 years (49.3-63.8), and 52.7% were female. Among treated patients, 295 received rituximab, whereas 131 received ocrelizumab. At baseline, anti-CD20-treated patients were younger (median 51.9 vs 58.6 years, Cohen d = 0.683) and had more active disease (54.5 vs 27.8%, Cohen d = 0.562). 91.6% were drug-naive at inclusion. In time to first CDP analysis, no statistical significance was observed (hazard ratio [HR], 1.13; 95% CI 0.93-1.36, p = 0.2113). In time to first relapse analysis, a nonsignificant trend toward fewer patients relapsing in the treated group was observed (HR 0.83; 95% CI 0.48-1.28, p = 0.0809). For MRI activity, no significant difference was found between the 2 groups. Risk factors associated with CDP in the treated group were male sex and MS duration. IIR was 6.67 (95% CI 3.12-14.25) per 100 person-years in the treated group vs 2.67 (95% CI 0.80-8.86) in the untreated group. Discussion: Time to first CDP was not different between anti-CD20 treated and untreated patients with PPMS. Although our study is retrospective and mainly included patients treated by rituximab, our results indicate that there should be a constant evaluation of all available data to ascertain the best risk/benefit ratio for patients with PPMS. Classification of evidence: This study provides Class III evidence that anti-CD20 therapy of previously untreated patients with PPMS was not superior to no therapy in delaying time to first CDP.

Introduction Neurosarcoidosis (NS) is a systemic inflammatory granulomatous disease affecting of patients with sarcoidosis. Its diagnosis is difficult as there is no specific test for it. Because of its rarity, the management of NS has so far only been described in case series and short retrospective cohorts. The objective of this study is description of the clinical, paraclinical presentation and the therapeutic management of central nervous system (CNS) involvement in NS patients in France. Methods This multicenter, retrospective, observational study involved patients hospitalized between 2010 and 2019 with a diagnosis of sarcoidosis and CNS involvement. Results We included 118 patients (38 with isolated NS, 80 with NS associated with systemic sarcoidosis). NS was the initial presentation in 78% of patients, with cranial nerve involvement (36%), medullary symptoms (23%), and seizures (21%). Twenty‐one percent of the patients had already been diagnosed with systemic sarcoidosis. The most frequent biological abnormality was lymphopenia (62.5%), while angiotensin‐converting enzyme was increased in 21%. Meningitis was present in 45% and hyperproteinorachia in 69.5% of cases. MRI mainly revealed white matter abnormalities and leptomeningeal enhancement (34%). Corticosteroids were the most useful treatment, and immunosuppressive agents were used in steroid‐resistant patients and to limit side effects. Methotrexate, cyclophosphamide, and anti‐TNFα were also used, exhibiting good efficacy. Conclusions This cohort contributes to a better understanding of the clinical phenotype and associated imaging and biological abnormalities. Sharing of clinical, biological, and imaging data, as well as the therapeutic responses, of patients with NS helps to better understand and manage this disease that affects a small number of patients per center. A database project could be implemented in the future to enable this.

Sharing information provided by individual patient data is restricted by regulatory frameworks due to privacy concerns. Generative artificial intelligence could generate shareable virtual patient populations, as proxies of sensitive reference datasets. Explicit demonstration of privacy is demanded. Here, we determined whether a privacy-by-design technique called “avatars” can generate synthetic randomized clinical trials (RCTs). We generated 2160 synthetic datasets from two RCTs in multiple sclerosis (NCT00213135 and NCT00906399) with different configurations to select one synthetic dataset with optimal privacy and utility for each. Several privacy metrics were computed, including protection against distance-based membership inference attacks. We assessed utility by comparing variable distributions and checking that all of the endpoints reported in the publications had the same effect directions, were within the reported 95% confidence intervals, and had the same statistical significance. Protection against membership inference attacks was the hardest privacy metric to optimize, but the technique yielded robust privacy and replication of the primary endpoints. With optimized generation configurations, we could select one dataset from each RCT replicating all efficacy endpoints of the placebo and commercial treatment arms with a satisfying privacy. To show the potential to unlock health data sharing, we released both placebo arms as open datasets.

Sharing information provided by individual patient data is restricted by regulatory frameworks due to privacy concerns. Generative artificial intelligence could generate shareable virtual patient populations, as proxies of sensitive reference datasets. Explicit demonstration of privacy is demanded. Here, we determined whether a privacy-by-design technique called “avatars” can generate synthetic randomized clinical trials (RCTs). We generated 2160 synthetic datasets from two RCTs in multiple sclerosis (NCT00213135 and NCT00906399) with different configurations to select one synthetic dataset with optimal privacy and utility for each. Several privacy metrics were computed, including protection against distance-based membership inference attacks. We assessed utility by comparing variable distributions and checking that all of the endpoints reported in the publications had the same effect directions, were within the reported 95% confidence intervals, and had the same statistical significance. Protection against membership inference attacks was the hardest privacy metric to optimize, but the technique yielded robust privacy and replication of the primary endpoints. With optimized generation configurations, we could select one dataset from each RCT replicating all efficacy endpoints of the placebo and commercial treatment arms with a satisfying privacy. To show the potential to unlock health data sharing, we released both placebo arms as open datasets.