Molekulare Biomarker bei neuroinflammatorischen Erkrankungen

Biomarker gelten als Schlüsselelement für die Umsetzung von Präzisionsmedizin. Technische Fortschritte in der Quantifizierbarkeit von ZNS-Molekülen im peripheren Blut und im Design von klinischen Studien zur Identifizierung und Validierung von Biomarkern haben in den letzten Jahren zu einer starken Dynamik in der Biomarkerentwicklung sowohl bei entzündlichen als auch bei degenerativen Erkrankungen des Nervensystems geführt. Die vorliegende Übersicht fasst den aktuellen Stand und Entwicklungslinien zu molekularen Biomarkern bei entzündlichen neurologischen Erkrankungen zusammen.

Kernaussagen

• Biomarker sind insbesondere für die Weiterentwicklung der personalisierten Medizin wichtig.

• Die Aussagekraft des Biomarkers ist maßgeblich von der richtigen Auswahl des Biomarkers für die jeweilige Fragestellung abhängig.

• Klinische Biomarker benötigen mindestens 2 von 3 der folgenden Aspekte:

  • hohe Sensitivität,

  • hohe Spezifität,

  • leichte Materialgewinnung.

• Viele klinische Biomarker haben insbesondere einen diagnostischen Wert. Aktuell fehlen vor allem Biomarker für das Erkrankungs- und Therapiemonitoring sowie die Prognoseeinschätzung.

• Für die klinische Umsetzung und Validierung der Biomarker werden noch multizentrische Studien benötigt.

Schlüsselwörter

Diagnostik - Multiple Sklerose - Neuromyelitis optica (NMOSD) - Myelin-Oligodendrozyten-Glykoprotein-Antikörper-Krankheit (MOGAD - Autoimmunenzephalitis - Myasthenia gravis

Publikationsverlauf

Artikel online veröffentlicht:
05. März 2024

• Literatur

• 1 Griffith M, Spies NC, Krysiak K. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet 2017; 49: 170-4 DOI: 10.1038/ng.3774. (PMID: 28138153)
• 2 Thompson AJ, Baranzini SE, Geurts J. et al. Multiple sclerosis. Lancet 2018; 391: 1622-36 DOI: 10.1016/S0140-6736(18)30481-1. (PMID: 29576504)
• 3 van der Vuurst de Vries RM, Mescheriakova JY, Runia TF. et al. Soluble CD27 levels in cerebrospinal fluid as a prognostic biomarker in clinically isolated syndrome. JAMA Neurol 2017; 74: 286
• 4 Lebrun-Frénay C, Rollot F, Mondot L. et al. Risk factors and time to clinical symptoms of multiple sclerosis among patients with radiologically isolated syndrome. JAMA Netw Open 2021; 4: e2128271
• 5 Gill AJ, Schorr EM, Gadani SP. et al. Emerging imaging and liquid biomarkers in multiple sclerosis. Eur J Immunol 2023; 53: 2250228 DOI: 10.1002/eji.202250228. (PMID: 37194443)
• 6 Gaetani L, Di Carlo M, Brachelente G. et al. Cerebrospinal fluid free light chains compared to oligoclonal bands as biomarkers in multiple sclerosis. J Neuroimmunol 2020; 339: 577108 DOI: 10.1016/j.jneuroim.2019.577108. (PMID: 31743879)
• 7 Vecchio D, Bellomo G, Serino R. et al. Intrathecal kappa free light chains as markers for multiple sclerosis. Sci Rep 2020; 10: 20329 DOI: 10.1038/s41598-020-77029-7. (PMID: 33230241)
• 8 Disanto G, Barro C, Benkert P. et al. Serum neurofilament light: A biomarker of neuronal damage in multiple sclerosis: Serum NfL as a biomarker in MS. Ann Neurol 2017; 81: 857-70 DOI: 10.1002/ana.24954. (PMID: 28512753)
• 9 Cantó E, Barro C, Zhao C. et al. Association between serum neurofilament light chain levels and long-term disease course among patients with multiple sclerosis followed up for 12 years. JAMA Neurol 2019; 76: 1359
• 10 Novakova L, Zetterberg H, Sundström P. et al. Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology 2017; 89: 2230-7 DOI: 10.1212/WNL.0000000000004683. (PMID: 29079686)
• 11 Dalla Costa G, Martinelli V, Sangalli F. et al. Prognostic value of serum neurofilaments in patients with clinically isolated syndromes. Neurology 2019; 92: e733-41
• 12 Barro C, Benkert P, Disanto G. et al. Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 2018; 141: 2382-91 DOI: 10.1093/brain/awy154. (PMID: 29860296)
• 13 Williams T, Zetterberg H, Chataway J. Neurofilaments in progressive multiple sclerosis: a systematic review. J Neurol 268: 3212-22 DOI: 10.1007/s00415-020-09917-x. (PMID: 32447549)
• 14 Vermunt L, Otte M, Verberk IMW. et al. Age- and disease-specific reference values for neurofilament light presented in an online interactive support interface. Ann Clin Transl Neurol 2022; 9: 1832-7 DOI: 10.1002/acn3.51676. (PMID: 36196979)
• 15 Ayrignac X, Le Bars E, Duflos C. et al. Serum GFAP in multiple sclerosis: correlation with disease type and MRI markers of disease severity. Sci Rep 2020; 10: 10923
• 16 Abdelhak A, Huss A, Kassubek J. et al. Serum GFAP as a biomarker for disease severity in multiple sclerosis. Sci Rep 2018; 8: 14798 DOI: 10.1038/s41598-018-33158-8. (PMID: 30287870)
• 17 Lee E, Lim Y, Kim S. et al. Clinical implication of serum biomarkers and patient age in inflammatory demyelinating diseases. Ann Clin Transl Neurol 2020; 7: 992-1001 DOI: 10.1002/acn3.51070. (PMID: 32495489)
• 18 Tamam Y, Gunes B, Akbayir E. et al. CSF levels of HoxB3 and YKL-40 may predict conversion from clinically isolated syndrome to relapsing remitting multiple sclerosis. Mult Scler Relat Disord 2021; 48: 102697
• 19 Cantó E, Tintoré M, Villar LM. et al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain 2015; 138: 918-31 DOI: 10.1093/brain/awv017. (PMID: 25688078)
• 20 Floro S, Carandini T, Pietroboni AM. et al. Role of chitinase 3–like 1 as a biomarker in multiple sclerosis: A systematic review and meta-analysis. Neurol Neuroimmunol Neuroinflamm 2022; 9: e1164 DOI: 10.1212/NXI.0000000000001164. (PMID: 35534236)
• 21 Huss A, Otto M, Senel M. et al. A score based on NfL and glial markers may differentiate between relapsing–remitting and progressive MS course. Front Neurol 2020; 11: 608
• 22 Magliozzi R, Pitteri M, Ziccardi S. et al. CSF parvalbumin levels reflect interneuron loss linked with cortical pathology in multiple sclerosis. Ann Clin Transl Neurol 2021; 8: 534-47
• 23 Verderio C, Muzio L, Turola E. et al. Myeloid microvesicles are a marker and therapeutic target for neuroinflammation. Ann Neurol 2012; 72: 610-24 DOI: 10.1002/ana.23627. (PMID: 23109155)
• 24 Minagar A, Jy W, Jimenez JJ. et al. Elevated plasma endothelial microparticles in multiple sclerosis. Neurology 2001; 56: 1319-24 DOI: 10.1212/wnl.56.10.1319. (PMID: 11376181)
• 25 D’Anca M, Fenoglio C, Buccellato FR. et al. Extracellular vesicles in multiple sclerosis: role in the pathogenesis and potential usefulness as biomarkers and therapeutic tools. Cells 2021; 10: 1733 DOI: 10.3390/cells10071733. (PMID: 34359903)
• 26 Galazka G, Mycko MP, Selmaj I. et al. Multiple sclerosis: serum-derived exosomes express myelin proteins. Mult Scler 2018; 24: 449-58 DOI: 10.1177/1352458517696597. (PMID: 28273783)
• 27 Bhargava P, Nogueras-Ortiz C, Kim S. et al. Synaptic and complement markers in extracellular vesicles in multiple sclerosis. Mult Scler 2021; 27: 509-18 DOI: 10.1177/1352458520924590. (PMID: 32669030)
• 28 Alvarez JI, Katayama T, Prat A. Glial influence on the blood brain barrier. Glia 2013; 61: 1939-58 DOI: 10.1002/glia.22575. (PMID: 24123158)
• 29 Krumbholz M, Theil D, Cepok S. et al. Chemokines in multiple sclerosis: CXCL12 and CXCL13 up-regulation is differentially linked to CNS immune cell recruitment. Brain 2006; 129: 200-11 DOI: 10.1093/brain/awh680. (PMID: 16280350)
• 30 DiSano KD, Gilli F, Pachner AR. Intrathecally produced CXCL13: A predictive biomarker in multiple sclerosis. Mult Scler J Exp Transl Clin 2020; 6: 205521732098139 DOI: 10.1177/2055217320981396. (PMID: 33403120)
• 31 Lucchini M, De Arcangelis V, Piro G. et al. CSF CXCL13 and chitinase 3-like-1 levels predict disease course in relapsing multiple sclerosis. Mol Neurobiol 2023; 60: 36-50 DOI: 10.1007/s12035-022-03060-6. (PMID: 36215027)
• 32 Brettschneider J, Czerwoniak A, Senel M. et al. The chemokine CXCL13 is a prognostic marker in clinically isolated syndrome (CIS). PLoS ONE 2010; 5: e11986 DOI: 10.1371/journal.pone.0011986. (PMID: 20700489)
• 33 Alvarez E, Piccio L, Mikesell RJ. et al. Predicting optimal response to B-cell depletion with rituximab in multiple sclerosis using CXCL13 index, magnetic resonance imaging and clinical measures. Mult Scler J Exp Transl Clin 2015; 1: 205521731562380
• 34 Khademi M, Kockum I, Andersson ML. et al. Cerebrospinal fluid CXCL13 in multiple sclerosis: a suggestive prognostic marker for the disease course. Mult Scler 2011; 17: 335-43 DOI: 10.1177/1352458510389102. (PMID: 21135023)
• 35 DiSano KD, Gilli F, Pachner AR. Are CSF CXCL13 concentrations solely dependent on intrathecal production? A commentary on „Chemokine CXCL13 in serum, CSF, and blood–CSF barrier function. ” Fluids Barriers CNS 2021; 18: 9 DOI: 10.1186/s12987-021-00244-5. (PMID: 33632258)
• 36 Ngo C, Kothary R. MicroRNAs in oligodendrocyte development and remyelination. J Neurochem 2022; 162: 310-21 DOI: 10.1111/jnc.15618. (PMID: 35536759)
• 37 Singh RP, Massachi I, Manickavel S. et al. The role of miRNA in inflammation and autoimmunity. Autoimmun Rev 2013; 12: 1160-5 DOI: 10.1016/j.autrev.2013.07.003. (PMID: 23860189)
• 38 Gandhi R, Healy B, Gholipour T. et al. Circulating MicroRNAs as biomarkers for disease staging in multiple sclerosis. Ann Neurol 2013; 73: 729-40 DOI: 10.1002/ana.23880. (PMID: 23494648)
• 39 Hemond CC, Healy BC, Tauhid S. et al. MRI phenotypes in MS: Longitudinal changes and miRNA signatures. Neurol Neuroimmunol Neuroinflamm 2019; 6: e530 DOI: 10.1212/NXI.0000000000000530. (PMID: 30800720)
• 40 Gao Y, Han D, Feng J. MicroRNA in multiple sclerosis. Clinica Chimica Acta 2021; 516: 92-9 DOI: 10.1016/j.cca.2021.01.020. (PMID: 33545109)
• 41 Wang H. MicroRNAs, multiple sclerosis, and depression. IJMS 2021; 22: 7802 DOI: 10.3390/ijms22157802. (PMID: 34360568)
• 42 Jarius S, Aktas O, Ayzenberg I. et al. Update on the diagnosis and treatment of neuromyelits optica spectrum disorders (NMOSD) – revised recommendations of the Neuromyelitis Optica Study Group (NEMOS). Part I: Diagnosis and differential diagnosis. J Neurol 2023; 270: 3341-68 DOI: 10.1007/s00415-023-11634-0. (PMID: 37022481)
• 43 Graus F, Titulaer MJ, Balu R. et al. A clinical approach to diagnosis of autoimmune encephalitis. Lancet Neurol 2016; 15: 391-404 DOI: 10.1016/S1474-4422(15)00401-9. (PMID: 26906964)
• 44 Ciano-Petersen NL, Cabezudo-García P, Muñiz-Castrillo S. et al. Current status of biomarkers in Anti-N-Methyl-D-Aspartate receptor encephalitis. IJMS 2021; 22: 13127 DOI: 10.3390/ijms222313127. (PMID: 34884930)
• 45 Zhao J, Wang C, Zhang Y. et al. Elevated CHI3L1 and OPN levels in patients with anti-N-methyl-d-aspartate receptor encephalitis. J Neuroimmunol 2019; 334: 577005
• 46 Li J, Gu Y, An H. et al. Cerebrospinal fluid light and heavy neurofilament level increased in Anti-N-Methyl-D-Aspartate receptor encephalitis. Brain Behav 2019; 9: e01354 DOI: 10.1002/brb3.1354. (PMID: 31313506)
• 47 Goodfellow JA, Willison HJ. Antiganglioside, antiganglioside-complex, and antiglycolipid-complex antibodies in immune-mediated neuropathies. Curr Opin Neurol 2016; 29: 572-80 DOI: 10.1097/WCO.0000000000000361. (PMID: 27427992)
• 48 Petzold A, Brettschneider J, Jin K. et al. CSF protein biomarkers for proximal axonal damage improve prognostic accuracy in the acute phase of Guillain-Barré syndrome. Muscle Nerve 2009; 40: 42-9 DOI: 10.1002/mus.21239. (PMID: 19533642)
• 49 Wieske L, Smyth D, Lunn MP. et al. Fluid biomarkers for monitoring structural changes in polyneuropathies: Their use in clinical practice and trials. Neurotherapeutics 2021; 18: 2351-67
• 50 Heming M, Schulte-Mecklenbeck A, Brix T. et al. Immune cell profiling of the cerebrospinal fluid provides pathogenetic insights into inflammatory neuropathies. Front Immunol 2019; 10: 515
• 51 Meisel A, Baggi F, Behin A. et al. Role of autoantibody levels as biomarkers in the management of patients with myasthenia gravis: A systematic review and expert appraisal. Eur J Neurol 2023; 30: 266-82
• 52 Molin CJ, Sabre L, Weis CA. et al. Thymectomy lowers the myasthenia gravis biomarker miR-150–5p. Neurol Neuroimmunol Neuroinflamm 2018; 5: e450 DOI: 10.1212/NXI.0000000000000450. (PMID: 29511707)
• 53 Wolfe GI, Kaminski HJ, Aban IB. et al. Randomized trial of thymectomy in myasthenia gravis. N Engl J Med 2016; 375: 511-22 DOI: 10.1056/NEJMoa1602489. (PMID: 27509100)
• 54 Punga AR, Punga T. Circulating microRNAs as potential biomarkers in myasthenia gravis patients. Ann N Y Acad Sci 2018; 1412: 33-40 DOI: 10.1111/nyas.13510. (PMID: 29125182)
• 55 Sabre L, Maddison P, Wong SH. et al. miR-30e-5p as predictor of generalization in ocular myasthenia gravis. Ann Clin Transl Neurol 2019; 6: 243-51 DOI: 10.1002/acn3.692. (PMID: 30847357)