Literature DB >> 30728305

A multicenter comparison of MOG-IgG cell-based assays.

Patrick J Waters1, Lars Komorowski1, Mark Woodhall1, Sabine Lederer1, Masoud Majed1, Jim Fryer1, John Mills1, Eoin P Flanagan1, Sarosh R Irani1, Amy C Kunchok1, Andrew McKeon1, Sean J Pittock2.   

Abstract

OBJECTIVES: To compares 3 different myelin oligodendrocyte glycoprotein-immunoglobulin G (IgG) cell-based assays (CBAs) from 3 international centers.
METHODS: Serum samples from 394 patients were as follows: acute disseminated encephalomyelitis (28), seronegative neuromyelitis optica (27), optic neuritis (21 single, 2 relapsing), and longitudinally extensive (10 single, 3 recurrent). The control samples were from patients with multiple sclerosis (244), hypergammaglobulinemia (42), and other (17). Seropositivity was determined by visual observation on a fluorescence microscope (Euroimmun fixed CBA, Oxford live cell CBA) or flow cytometry (Mayo live cell fluorescence-activated cell sorting assay).
RESULTS: Of 25 samples positive by any methodology, 21 were concordant on all 3 assays, 2 were positive at Oxford and Euroimmun, and 2 were positive only at Oxford. Euroimmun, Mayo, and Oxford results were as follows: clinical specificity 98.1%, 99.6%, and 100%; positive predictive values (PPVs) 82.1%, 95.5%, and 100%; and negative predictive values 79.0%, 78.8%, and 79.8%. Of 5 false-positives, 1 was positive at both Euroimmun and Mayo and 4 were positive at Euroimmun alone.
CONCLUSIONS: Overall, a high degree of agreement was observed across 3 different MOG-IgG CBAs. Both live cell-based methodologies had superior PPVs to the fixed cell assays, indicating that positive results in these assays are more reliable indicators of MOG autoimmune spectrum disorders.
© 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

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Year:  2019        PMID: 30728305      PMCID: PMC6511109          DOI: 10.1212/WNL.0000000000007096

Source DB:  PubMed          Journal:  Neurology        ISSN: 0028-3878            Impact factor:   9.910


There is accumulating evidence that CNS inflammatory demyelinating disorders (IDDs), including forms of neuromyelitis optica (NMO) spectrum disorders, acute disseminated encephalomyelitis (ADEM), optic neuritis (recurrent more than single episode), and transverse myelitis are commonly associated with immunoglobulin G (IgG) targeting aquaporin-4 (AQP4) or myelin oligodendrocyte glycoprotein (MOG).[1-5] Until their relatively recent discovery, patients with these disorders were commonly misdiagnosed as having multiple sclerosis (MS), yet contemporary findings show that MS, MOG-IgG, and AQP4-IgG–associated IDDs have clinical, radiologic, pathologic, and prognostic differences.[5,6] MOG-IgG–associated IDDs may have a higher prevalence in children and are often relapsing, commonly manifesting as optic neuritis. Attacks may be associated with accumulating neuronal injury and functional impairment. MOG-IgG may be transient or persistent, and its role as a predictor of relapse remains a focus of ongoing study. While MOG antibody has had a checkered past as a biomarker because of a lack of any specific disease association, contemporary methodologies using cell-based assays (CBAs) now define an autoimmune oligodendroglyopathy with a preferential response to immunosuppressants rather than disease-modifying agents (DMA) commonly used in MS.[4-8] Early initiation and prolonged administration of such drugs may prevent relapses and reduce disability accrual, although randomized clinical trials have not yet been undertaken. MOG-IgG also provides important prognostic information. Hence, accurate serologic diagnosis is imperative to optimize clinical care. A recent review article published in 2017 by key opinion leaders in the field stated that “methods for detecting MOG antibodies have improved substantially, with cell based assays (CBAs) being state of the art.”[1] In this blinded study, 3 different MOG-IgG CBAs from 3 international centers were compared.

Methods

Standard protocol approvals, registrations, and patient consents

All patients in our study consented to the use of their medical records for research purposes. The study was approved by the Institutional Review Board of Mayo Clinic, Rochester, MN (No. 08-007846). Serum samples from 394 patients and controls were tested: 91 patients were classified as having a MOG-IgG–like clinical phenotype and included ADEM (28), AQP4-IgG seronegative NMO (27, fulfilling Wingerchuk diagnostic criteria for NMO, either 1999 or 2006 [excluding antibody status]), optic neuritis (21 single, 2 relapsing), or longitudinally extensive transverse myelitis (10 single, 3 recurrent). The control samples were collected from patients with MS (244, selected from the Olmsted County MS population-based cohort), hypergammaglobulinemia (42), and other (17, encephalitis, glioma, Creutzfeldt-Jakob disease, glaucoma). Sensitivity was calculated as the percentage of positives cases within the MOG-IgG–like clinical phenotype cohort. Specificity was calculated as the percentage of positive cases in the MS cohort and those with other neurologic presentations inconsistent with an MOG-related clinical phenotype. Positive predictive value (PPV) was calculated as the percentage of positive test results in patients with MOG-IgG–like clinical phenotypes of all positive test results and estimates the reliability of a positive test result. In contrast, the negative predictive value is the percentage of negative test results in patients without an MOG-IgG–like clinical phenotype of all negative test results and is an estimate of how reliably a negative test result rules out the disease. This study was approved by the Mayo Clinic Institutional Review Board. All samples were stored at −80°C at the Mayo Clinic central laboratory. They were divided into aliquots and provided frozen as coded samples to the 3 neuroimmunology laboratories: Mayo Clinic; Oxford, UK; and Euroimmun, Germany. All samples were tested by investigators blinded to the clinical information. Methodologies of the 3 assays are shown in table 1, and staining of cells considered positive and negative by all 3 assays is illustrated in the figure
Table 1

CBA methodologies

Figure

Comparison of positive and negative controls for Oxford, Euroimmun, and Mayo Clinic MOG-IgG assays

Oxford cell-based assay (CBA) (A.a) negative and (A.b) positive result; Euroimmun CBA (B.a) negative and (B.b) positive result; and Mayo fluorescence-activated cell sorting assay (FACS) (C.a) negative and (C.b) positive result. For the Mayo FACS assay, 2 cell populations are used to obtain a median fluorescent intensity. The green fluorescent protein (GFP)–negative population represents nontransfected cells, and the GFP-positive population represents cells that express both acetylated GFP and myelin oligodendrocyte glycoprotein (MOG) protein. The AlexaFluor-647 median intensity is an indicator of bound human serum antibodies. As shown in panel (C.b), the positive control has a median AlexaFluor-647 intensity of 40,593 for the GFP-positive population, and the GFP-negative population is 477. The negative control (C.a) has a median 647 intensity of 162 for the GFP-positive population, and the GFP-negative population is similar at 147. These statistical values are used to calculate the immunoglobulin G (IgG) binding index, which is a ratio of the GFP-positive value over the GFP-negative value.

CBA methodologies

Comparison of positive and negative controls for Oxford, Euroimmun, and Mayo Clinic MOG-IgG assays

Oxford cell-based assay (CBA) (A.a) negative and (A.b) positive result; Euroimmun CBA (B.a) negative and (B.b) positive result; and Mayo fluorescence-activated cell sorting assay (FACS) (C.a) negative and (C.b) positive result. For the Mayo FACS assay, 2 cell populations are used to obtain a median fluorescent intensity. The green fluorescent protein (GFP)–negative population represents nontransfected cells, and the GFP-positive population represents cells that express both acetylated GFP and myelin oligodendrocyte glycoprotein (MOG) protein. The AlexaFluor-647 median intensity is an indicator of bound human serum antibodies. As shown in panel (C.b), the positive control has a median AlexaFluor-647 intensity of 40,593 for the GFP-positive population, and the GFP-negative population is 477. The negative control (C.a) has a median 647 intensity of 162 for the GFP-positive population, and the GFP-negative population is similar at 147. These statistical values are used to calculate the immunoglobulin G (IgG) binding index, which is a ratio of the GFP-positive value over the GFP-negative value.

Data availability statement

The dataset used and analyzed during the current study is available from the corresponding author on reasonable request.

Results

Of the 25 case samples positive by any methodology, 21 were concordant on all 3 assays, 2 were positive by the Oxford assay and Euroimmun assays, and 2 were positive only by the Oxford assay. Clinical specificity, as measured using a cohort of 244 patients with MS and 17 patients with disorders clearly outside of the autoimmune MOG spectrum, was 98.1% for Euroimmun, 99.6% for Mayo, and 100% for Oxford. The corresponding PPVs were 82.1%, 95.5%, and 100%, respectively. Negative predictive values were 79.0%, 78.8%, and 79.8%. Of the 5 false-positive findings in this cohort, 1 was positive by both the Euroimmun and Mayo assays (table 2). The additional 4 false-positive results were limited to the Euroimmun CBA. Analytical specificity was high for all 3 assays; no false-positives were identified in a cohort of 42 patients with hypergammaglobulinemia. The results of this multicenter method comparison study of MOG-IgG testing are summarized in table 2. All pairwise comparisons revealed good interassay reliability with κ values >0.8 indicating a high degree of agreement across methods (Cohen κ statistic). Therefore, despite different methodologies and testing locations, the majority of samples achieved the same results across platforms. This is critical for the initial deployment of MOG-IgG–based assays because it provides confidence in the reliability of a positive result but also indicates that detection of MOG-IgG antibodies is robust and that these assays are inherently well standardized.
Table 2

Distribution of MOG-IgG result by 3 assays in serum samples of cases and controls

Distribution of MOG-IgG result by 3 assays in serum samples of cases and controls

Discussion

Both live cell–based methodologies, distinct assays performed at different centers, had superior PPVs to the fixed assays, indicating that positive results in these assays are more reliable indicators of MOG spectrum disorders. ELISA is not a reliable methodology for MOG-IgG detection. MOG-IgG–related diseases may benefit from early and ongoing immunotherapies. Often, inflammatory idiopathic CNS disease such as ADEM, optic neuritis, and transverse myelitis are treated similarly to those with glial antibodies in the acute setting (steroids or plasmapheresis). However, for maintenance immunotherapy, patients without a glial antibody may be less likely to be treated with longer-term immunosuppressants, and longer treatment regimens are associated with fewer relapses in MOG-IgG–related diseases. A false-negative result would often result in a misdiagnosis of MS and consequent treatment with DMAs, which have been reported to worsen AQP4-IgG–positive IDDs, although some are effective for both disorders (anti-CD20 treatments). Data on DMAs exacerbating MOG-IgG disease are currently lacking. Another concerning consequence of diagnostic inaccuracies is the detection of a false-positive result. Because MOG-IgGs will likely be commonly ordered in the clinical evaluation of a suspected demyelinating event, a false-positive result in a patient with a clinical diagnosis of MS might result in the selection of an immunosuppressant drug (e.g., mycophenolate mofetil, cellcept) rather than a Food and Drug Administration–approved DMA. In this study, 5 of 27 (18.5%) positive results in the commercial test were in control samples, giving a relatively poor PPV (82.1% vs 95.5%–100%). The test is simpler to run in routine diagnostic laboratories, but it has to be fixed to allow transport and storage. The fixation may generate cryptic epitopes, which could explain the clearly positive binding. These discrepancies have also been described in AQP4 assay comparisons. Future studies should address this issue in their design, which may help with a better understanding of this kind of discrepancy.
  10 in total

1.  Defining secondary progressive multiple sclerosis.

Authors:  Johannes Lorscheider; Katherine Buzzard; Vilija Jokubaitis; Tim Spelman; Eva Havrdova; Dana Horakova; Maria Trojano; Guillermo Izquierdo; Marc Girard; Pierre Duquette; Alexandre Prat; Alessandra Lugaresi; François Grand'Maison; Pierre Grammond; Raymond Hupperts; Raed Alroughani; Patrizia Sola; Cavit Boz; Eugenio Pucci; Jeanette Lechner-Scott; Roberto Bergamaschi; Celia Oreja-Guevara; Gerardo Iuliano; Vincent Van Pesch; Franco Granella; Cristina Ramo-Tello; Daniele Spitaleri; Thor Petersen; Mark Slee; Freek Verheul; Radek Ampapa; Maria Pia Amato; Pamela McCombe; Steve Vucic; José Luis Sánchez Menoyo; Edgardo Cristiano; Michael H Barnett; Suzanne Hodgkinson; Javier Olascoaga; Maria Laura Saladino; Orla Gray; Cameron Shaw; Fraser Moore; Helmut Butzkueven; Tomas Kalincik
Journal:  Brain       Date:  2016-07-07       Impact factor: 13.501

2.  Distinct brain imaging characteristics of autoantibody-mediated CNS conditions and multiple sclerosis.

Authors:  Maciej Jurynczyk; Ruth Geraldes; Fay Probert; Mark R Woodhall; Patrick Waters; George Tackley; Gabriele DeLuca; Saleel Chandratre; Maria I Leite; Angela Vincent; Jacqueline Palace
Journal:  Brain       Date:  2017-03-01       Impact factor: 13.501

Review 3.  Myelin oligodendrocyte glycoprotein antibodies: How clinically useful are they?

Authors:  Markus Reindl; Sven Jarius; Kevin Rostasy; Thomas Berger
Journal:  Curr Opin Neurol       Date:  2017-06       Impact factor: 5.710

4.  Self-antigen tetramers discriminate between myelin autoantibodies to native or denatured protein.

Authors:  Kevin C O'Connor; Katherine A McLaughlin; Philip L De Jager; Tanuja Chitnis; Estelle Bettelli; Chenqi Xu; William H Robinson; Sunil V Cherry; Amit Bar-Or; Brenda Banwell; Hikoaki Fukaura; Toshiyuki Fukazawa; Silvia Tenembaum; Susan J Wong; Norma P Tavakoli; Zhannat Idrissova; Vissia Viglietta; Kevin Rostasy; Daniela Pohl; Russell C Dale; Mark Freedman; Lawrence Steinman; Guy J Buckle; Vijay K Kuchroo; David A Hafler; Kai W Wucherpfennig
Journal:  Nat Med       Date:  2007-01-12       Impact factor: 53.440

5.  Prevalence of Myelin Oligodendrocyte Glycoprotein and Aquaporin-4-IgG in Patients in the Optic Neuritis Treatment Trial.

Authors:  John J Chen; W Oliver Tobin; Masoud Majed; Jiraporn Jitprapaikulsan; James P Fryer; Jacqueline A Leavitt; Eoin P Flanagan; Andrew McKeon; Sean J Pittock
Journal:  JAMA Ophthalmol       Date:  2018-04-01       Impact factor: 7.389

6.  Aquaporin-4 and Myelin Oligodendrocyte Glycoprotein Autoantibody Status Predict Outcome of Recurrent Optic Neuritis.

Authors:  Jiraporn Jitprapaikulsan; John J Chen; Eoin P Flanagan; W Oliver Tobin; Jim P Fryer; Brian G Weinshenker; Andrew McKeon; Vanda A Lennon; Jacqueline A Leavitt; Jan-Mendelt Tillema; Claudia Lucchinetti; B Mark Keegan; Orhun Kantarci; Cheryl Khanna; Sarah M Jenkins; Grant M Spears; Jessica Sagan; Sean J Pittock
Journal:  Ophthalmology       Date:  2018-04-30       Impact factor: 12.079

Review 7.  Does MOG Ig-positive AQP4-seronegative opticospinal inflammatory disease justify a diagnosis of NMO spectrum disorder?

Authors:  Scott S Zamvil; Anthony J Slavin
Journal:  Neurol Neuroimmunol Neuroinflamm       Date:  2015-01-22

8.  MOG cell-based assay detects non-MS patients with inflammatory neurologic disease.

Authors:  Patrick Waters; Mark Woodhall; Kevin C O'Connor; Markus Reindl; Bethan Lang; Douglas K Sato; Maciej Juryńczyk; George Tackley; Joao Rocha; Toshiyuki Takahashi; Tatsuro Misu; Ichiro Nakashima; Jacqueline Palace; Kazuo Fujihara; M Isabel Leite; Angela Vincent
Journal:  Neurol Neuroimmunol Neuroinflamm       Date:  2015-03-19

9.  Multicentre comparison of a diagnostic assay: aquaporin-4 antibodies in neuromyelitis optica.

Authors:  Patrick Waters; Markus Reindl; Albert Saiz; Kathrin Schanda; Friederike Tuller; Vlastimil Kral; Petra Nytrova; Ondrej Sobek; Helle Hvilsted Nielsen; Torben Barington; Søren T Lillevang; Zsolt Illes; Kristin Rentzsch; Achim Berthele; Tímea Berki; Letizia Granieri; Antonio Bertolotto; Bruno Giometto; Luigi Zuliani; Dörte Hamann; E Daniëlle van Pelt; Rogier Hintzen; Romana Höftberger; Carme Costa; Manuel Comabella; Xavier Montalban; Mar Tintoré; Aksel Siva; Ayse Altintas; Günnur Deniz; Mark Woodhall; Jacqueline Palace; Friedemann Paul; Hans-Peter Hartung; Orhan Aktas; Sven Jarius; Brigitte Wildemann; Christian Vedeler; Anne Ruiz; M Isabel Leite; Peter Trillenberg; Monika Probst; Sandra Saschenbrecker; Angela Vincent; Romain Marignier
Journal:  J Neurol Neurosurg Psychiatry       Date:  2016-04-25       Impact factor: 10.154

Review 10.  MOG-IgG in primary and secondary chronic progressive multiple sclerosis: a multicenter study of 200 patients and review of the literature.

Authors:  S Jarius; K Ruprecht; J P Stellmann; A Huss; I Ayzenberg; A Willing; C Trebst; M Pawlitzki; A Abdelhak; T Grüter; F Leypoldt; J Haas; I Kleiter; H Tumani; K Fechner; M Reindl; F Paul; B Wildemann
Journal:  J Neuroinflammation       Date:  2018-03-19       Impact factor: 8.322

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  49 in total

Review 1.  Detection of MOG-IgG by cell-based assay: moving from discovery to clinical practice.

Authors:  Amanda Marchionatti; Mark Woodhall; Patrick Joseph Waters; Douglas Kazutoshi Sato
Journal:  Neurol Sci       Date:  2020-10-15       Impact factor: 3.307

Review 2.  Clinical Characteristics and Treatment of MOG-IgG-Associated Optic Neuritis.

Authors:  Deena A Tajfirouz; M Tariq Bhatti; John J Chen
Journal:  Curr Neurol Neurosci Rep       Date:  2019-11-26       Impact factor: 5.081

3.  Antibodies against neural antigens in patients with acute stroke: joint results of three independent cohort studies.

Authors:  Georg Royl; Tsafack Judicael Fokou; Rittika Chunder; Rakad Isa; Thomas F Münte; Klaus-Peter Wandinger; Markus Schwaninger; Oliver Herrmann; José Manuel Valdueza; Jan Brocke; Martin Willkomm; Dietrich Willemsen; Gerd U Auffarth; Swantje Mindorf; Britta Brix; Angel Chamorro; Anna Planas; Xabier Urra
Journal:  J Neurol       Date:  2019-07-29       Impact factor: 4.849

4.  OCT retinal nerve fiber layer thickness differentiates acute optic neuritis from MOG antibody-associated disease and Multiple Sclerosis: RNFL thickening in acute optic neuritis from MOGAD vs MS.

Authors:  John J Chen; Elias S Sotirchos; Amanda D Henderson; Eleni S Vasileiou; Eoin P Flanagan; M Tariq Bhatti; Sepideh Jamali; Eric R Eggenberger; Marie Dinome; Larry P Frohman; Anthony C Arnold; Laura Bonelli; Nicolas Seleme; Alvaro J Mejia-Vergara; Heather E Moss; Tanyatuth Padungkiatsagul; Hadas Stiebel-Kalish; Itay Lotan; Mark A Hellmann; Dave Hodge; Frederike Cosima Oertel; Friedemann Paul; Shiv Saidha; Peter A Calabresi; Sean J Pittock
Journal:  Mult Scler Relat Disord       Date:  2022-01-11       Impact factor: 4.339

5.  Possible clinical role of MOG antibody testing in children presenting with acute neurological symptoms.

Authors:  Giulia Musso; Margherita Nosadini; Nicoletta Gallo; Stefano Sartori; Mara Seguso; Mario Plebani
Journal:  Neurol Sci       Date:  2020-04-03       Impact factor: 3.307

6.  MOG-IgG1 and co-existence of neuronal autoantibodies.

Authors:  Amy Kunchok; Eoin P Flanagan; Karl N Krecke; John J Chen; J Alfredo Caceres; Justin Dominick; Ian Ferguson; Revere Kinkel; John C Probasco; Miguel Ruvalcaba; Jonathan D Santoro; Kurt Sieloff; Jeremy Timothy; Brian G Weinshenker; Andrew McKeon; Sean J Pittock
Journal:  Mult Scler       Date:  2020-09-10       Impact factor: 6.312

Review 7.  Acute flaccid myelitis: cause, diagnosis, and management.

Authors:  Olwen C Murphy; Kevin Messacar; Leslie Benson; Riley Bove; Jessica L Carpenter; Thomas Crawford; Janet Dean; Roberta DeBiasi; Jay Desai; Matthew J Elrick; Raquel Farias-Moeller; Grace Y Gombolay; Benjamin Greenberg; Matthew Harmelink; Sue Hong; Sarah E Hopkins; Joyce Oleszek; Catherine Otten; Cristina L Sadowsky; Teri L Schreiner; Kiran T Thakur; Keith Van Haren; Carolina M Carballo; Pin Fee Chong; Amary Fall; Vykuntaraju K Gowda; Jelte Helfferich; Ryutaro Kira; Ming Lim; Eduardo L Lopez; Elizabeth M Wells; E Ann Yeh; Carlos A Pardo
Journal:  Lancet       Date:  2020-12-23       Impact factor: 79.321

8.  Seropositive anti-MOG antibody-associated acute disseminated encephalomyelitis (ADEM): a sequelae of Mycoplasma pneumoniae infection.

Authors:  Pranay Bonagiri; Daniel Park; Joanna Ingebritsen; Laura J Christie
Journal:  BMJ Case Rep       Date:  2020-05-19

9.  Population-Based Incidence of Optic Neuritis in the Era of Aquaporin-4 and Myelin Oligodendrocyte Glycoprotein Antibodies.

Authors:  Mohamed B Hassan; Caroline Stern; Eoin P Flanagan; Sean J Pittock; Amy Kunchok; Robert C Foster; Jiraporn Jitprapaikulsan; David O Hodge; M Tariq Bhatti; John J Chen
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10.  Differential Binding of Autoantibodies to MOG Isoforms in Inflammatory Demyelinating Diseases.

Authors:  Kathrin Schanda; Patrick Peschl; Magdalena Lerch; Barbara Seebacher; Swantje Mindorf; Nora Ritter; Monika Probst; Harald Hegen; Franziska Di Pauli; Eva-Maria Wendel; Christian Lechner; Matthias Baumann; Sara Mariotto; Sergio Ferrari; Albert Saiz; Michael Farrell; Maria Isabel S Leite; Sarosh R Irani; Jacqueline Palace; Andreas Lutterotti; Tania Kümpfel; Sandra Vukusic; Romain Marignier; Patrick Waters; Kevin Rostasy; Thomas Berger; Christian Probst; Romana Höftberger; Markus Reindl
Journal:  Neurol Neuroimmunol Neuroinflamm       Date:  2021-06-15
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