CD19-Directed CAR T-Cells in a Patient With Refractory MOGAD: Clinical and Immunologic Follow-Up for 1 Year.

OBJECTIVES: In MOG antibody-associated disease (MOGAD), relapse prevention and the treatment approach to refractory symptoms are unknown. We report a patient with refractory MOGAD treated with CD19-directed CAR T-cells. METHODS: CD19-directed CAR T-cells (ARI-0001) were produced in-house by lentiviral transduction of autologous fresh leukapheresis and infused after a conventional lymphodepleting regimen. RESULTS: A 18-year-old man developed 2 episodes of myelitis associated with serum MOG-IgG, which were followed by 6 episodes of left optic neuritis (ON) and sustained the presence of MOG-IgG over 6 years despite multiple immunotherapies. After the sixth episode of ON, accompanied by severe residual visual deficits, CAR T-cell treatment was provided without complications. Follow-up of cell counts showed complete depletion of CD19+ B cells at day +7; reconstituted B cells at day +141 showing a naïve B-cell phenotype, and low or absent memory B cells and plasmablasts for 1 year. MOG-IgG titers have remained undetectable since CAR T-cell infusion. The patient had an early episode of left ON at day +29, when MOG-IgG was already negative, and since then he has remained free of relapses without immunotherapy for 1 year. DISCUSSION: This clinical case shows that CD19-directed CAR T-cell therapy is well-tolerated and is a potential treatment for patients with refractory MOGAD. CLASSIFICATION OF EVIDENCE: This provides Class IV evidence. It is a single observational study without controls.

Serum biomarkers at disease onset for personalized therapy in multiple sclerosis.

The potential of combining serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) levels to predict disability worsening in multiple sclerosis (MS) remains underexplored. We aimed to investigate whether sNfL and sGFAP values identify distinct subgroups of patients according to the risk of disability worsening and their response to disease-modifying treatments (DMTs). This multicentre study, conducted across thirteen European hospitals, spanned from July 15, 1994, to August 18, 2022, with follow-up until September 26, 2023. We enrolled MS patients who had serum samples collected within 12 months from disease onset and before initiating DMTs. Multivariable regression models were used to estimate the risk of relapse-associated worsening (RAW), progression independent of relapse activity (PIRA), and Expanded Disability Status Scale (EDSS) score of 3. Of the 725 patients included, median age was 34.2 years (IQR, 27.6-42.4), and 509 patients (70.2%) were female. Median follow-up duration was 6.43 years (IQR, 4.65-9.81). Higher sNfL values associated with an elevated risk of RAW (HR of 1.45; 95% CI 1.19-1.76; P < 0.001), PIRA (HR of 1.43; 95% CI 1.13-1.81; P = 0.003), and reaching an EDSS of 3 (HR of 1.55; 95% CI 1.29-1.85; P < 0.001). Moreover, higher sGFAP levels were linked to a higher risk of achieving an EDSS score of 3 (HR of 1.36; 95% CI 1.06-1.74; P = 0.02) and, in patients with low sNfL values, to PIRA (HR of 1.86; 95% CI 1.01-3.45; P = 0.04). We further examined the combined effect of sNfL and sGFAP levels. Patients with low sNfL and sGFAP values (NLGL) exhibited a low risk of all outcomes and served as reference. Untreated patients with high sNfL levels showed a higher risk of RAW, PIRA, and reaching an EDSS of 3. Injectable or oral DMTs reduced the risk of RAW in these patients but failed to mitigate the risk of PIRA and reaching an EDSS of 3. Conversely, high-efficacy DMTs counteracted the heightened risk of these outcomes, except for the risk of PIRA in patients with high sNfL and sGFAP levels. Patients with low sNfL and high sGFAP values (NLGH) showed an increased risk of PIRA and achieving an EDSS of 3, which remained unchanged with either high-efficacy or other DMTs. In conclusion, evaluating sNfL and sGFAP levels at disease onset in MS may identify distinct phenotypes associated with diverse immunological pathways of disability acquisition and therapeutic response.

MOG Antibodies Restricted to CSF in Children With Inflammatory CNS Disorders.

OBJECTIVES: To assess the clinical significance of myelin oligodendrocyte glycoprotein antibodies (MOG-abs) restricted to CSF in children with inflammatory CNS disorders. METHODS: Patients included 760 children (younger than 18 years) from 3 multicenter prospective cohort studies: (A) acquired demyelinating syndromes, including acute disseminated encephalomyelitis (ADEM); (B) non-ADEM encephalitis; and (C) noninflammatory neurologic disorders. For all cases, paired serum/CSF samples were systematically examined using brain immunohistochemistry and live cell-based assays. RESULTS: A total of 109 patients (14%) had MOG-abs in serum or CSF: 79 from cohort A, 30 from B, and none from C. Of these, 63 (58%) had antibodies in both samples, 37 (34%) only in serum, and 9 (8%) only in CSF. Children with MOG-abs only in CSF were older than those with MOG-abs only in serum or in both samples (median 12 vs 6 vs 5 years, p = 0.0002) and were more likely to have CSF oligoclonal bands (86% vs 12% vs 7%, p = 0.0001) and be diagnosed with multiple sclerosis (6/9 [67%] vs 0/37 [0%] vs 1/63 [2%], p < 0.0001). DISCUSSION: Detection of MOG-abs in serum or CSF is associated with CNS inflammatory disorders. Children with MOG-abs restricted to CSF are more likely to have CSF oligoclonal bands and multiple sclerosis than those with MOG-abs detectable in serum.

Neuronal antibodies in nonparaneoplastic autoimmune cerebellar ataxias.

PURPOSE OF REVIEW: To describe relevant advances in nonparaneoplastic autoimmune cerebellar ataxias (ACA) with neuronal antibodies. RECENT FINDINGS: Apart from metabotropic glutamate receptor 1(mGluR1) antibodies, in recent years, the number of neuronal antibodies against surface antigens in ACA has increased with the description of glutamate kainate receptor subunit 2 (GluK2) antibodies in young patients with cerebellitis. Around 20% of patients with contactin-associated protein-like 2 (CASPR2) encephalitis also present prominent cerebellar ataxia. However, isolate cerebellar ataxia is unusual (<4%). Outcome in patients with neuronal antibodies against surface antigens remains suboptimal despite the cerebellar ataxia probably is antibody-mediated.Concerning neuronal antibodies against intracellular antigens, up to 25% of patients with glutamic acid decarboxylase (GAD) antibodies present transient episodes of vertigo or diplopia that antedate the development of the ACA. There is in-vitro evidence that septin-5 is partially exposed to the membrane and the antibodies may interfere with septin-5 function. The clinical significance of the remaining antibodies against intracellular antigens remains unclear. SUMMARY: The number of antibodies against surface antigens is increasing in ACA, but the response to the immunotherapy remains suboptimal. More studies are needed to clarify the role of most of the antibodies against intracellular antigens described in these patients.

Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

BACKGROUND: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.