Chen, John J.

Abstract B-cell non-Hodgkin lymphoma cell survival depends on poorly understood immune evasion mechanisms. In melanoma, the composition of the gut microbiota (GMB) is associated with immune system regulation and response to immunotherapy. We investigated the association of GMB composition and diversity with lymphoma biology and treatment outcome. Patients with diffuse large B-cell lymphoma (DLBCL), marginal zone (MZL), and follicular lymphoma (FL) were recruited at Mayo Clinic, Minnesota, and Perlmutter Cancer Center, NYU Langone Health. The pretreatment GMB was analyzed using 16S ribosomal RNA gene sequencing. We examined GMB compositions in 3 contexts: lymphoma patients (51) compared with healthy controls (58), aggressive (DLBCL) (8) compared with indolent (FL, MZL) (18), and the association of GMB with immunochemotherapy treatment outcomes (8 responders, 6 nonresponders). Respectively, we found that the pretreatment GMB in lymphoma patients had a distinct composition compared with healthy controls (P < .001); GMB compositions in DLBCL patients were significantly different than indolent patients (P = .01) with a trend toward reduced microbial diversity in DLBCL patients (P = .08); and pretreatment GMB diversity and composition were significant predictors of treatment responses (P = .01). The impact of these pilot results is limited by our small sample size, and should be considered a proof of principle. If validated, our results could lead toward improved treatment outcomes by improving medication stewardship and informing which GMB-targeted therapies should be tested to improve patient outcomes.

Background: The presence of co-existent neuronal antibodies (neuronal-IgG) in patients with myelin oligodendrocyte glycoprotein immunoglobulin G (MOG-IgG1) is not yet well understood. Objectives: The aim of this study was to investigate the co-existence of a broad range of neuronal-IgG in MOG-IgG1+ patients. Methods: MOG-IgG1+ patients were tested for 17 neuronal-IgGs in cerebrospinal fluid (CSF) and serum including NMDA-R-IgG, AMPA-R-IgG, GABAB-R-IgG, LGI1-IgG, CASPR2-IgG, GABAA-R-IgG, GAD65-IgG, mGLUR1-IgG, DPPX-IgG, CRMP5-IgG, amphiphysin-IgG, PCA1,2,Tr, and ANNA1,2,3. Clinical and radiological features of MOG-IgG1+ with NMDA-R-IgG in CSF were compared to a control cohort of MOG-IgG1+ patients without NMDA-R-IgG. Results: A total of 376 MOG-IgG1+ patients underwent testing for neuronal-IgGs. Serum testing for neuronal-IgGs (113 adults, 142 children) identified one child with NMDA-R-IgG (0.7%), one child with CASPR2-IgG (0.7%), one adult with LGI1-IgG (0.9%) and one adult with GABAA-R-IgG (0.9%). CSF testing for neuronal-IgGs (97 adults, 169 children) identified seven children (4%) and seven adults (7%) with NMDA-R-IgG, and one adult with GABAA-R-IgG (1%). The MOG-IgG1+/NMDA-R-IgG+ patients had a median age of 17 (range: 2–39) years. Features associated with MOG-IgG1+/NMDA-R-IgG+ included encephalopathy ( p = 0.001), seizures ( p = 0.045), and leptomeningeal enhancement ( p = 0.045). Conclusion: NMDA-R-IgG was the most frequently detected neuronal-IgG to co-exist with MOG-IgG1. MOG-IgG1+/NMDA-R-IgG+ patients most often presented with encephalopathy and seizures. Testing for MOG-IgG1 and NMDA-R-IgG may be warranted in patients with encephalopathy and inflammatory demyelinating syndromes.

Aquaporin-4 (AQP4) neuromyelitis optica spectrum disorder (NMOSD) has been demonstrated to be associated with non-organ and organ-specific autoantibodies (antinuclear antibody, extractable nuclear antibody, double-stranded DNA, muscle acetylcholine receptor antibody) and systemic autoimmune diseases. In this study, we evaluated whether a similar association with non-organ and organ-specifc autoantibodies occurs in patients with MOG-IgG1-associated disorders. We determined that MOG-IgG1 was not strongly associated with these organ and non-organ-specific autoantibodies. Systemic lupus erythematous (SLE) was significantly associated with AQP4-IgG+ NMOSD and not with MOGAD ( p = 0.037). These findings suggest differences in co-existing systemic and organ-specific autoimmunity between MOGAD and AQP4-IgG+ NMOSD.

ObjectiveTo compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro‐ophthalmologists in classifying optic disc appearance.MethodsThe deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro‐ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated.ResultsThe system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96–0.98), 0.96 (95% CI = 0.94–0.97), and 0.89 (95% CI = 0.87–0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67–0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68–0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61–0.70) between the system and Expert 2.InterpretationThe performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro‐ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785–795