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Review
. 2023 Mar:89:104456.
doi: 10.1016/j.ebiom.2023.104456. Epub 2023 Feb 4.

A unified classification approach rating clinical utility of protein biomarkers across neurologic diseases

Alexander M Bernhardt  1 , Steffen Tiedt  2 , Daniel Teupser  3 , Martin Dichgans  2 , Bernhard Meyer  4 , Jens Gempt  4 , Peer-Hendrik Kuhn  5 , Mikael Simons  6 , Carla Palleis  7 , Endy Weidinger  7 , Georg Nübling  1 , Lesca Holdt  3 , Lisa Hönikl  4 , Christiane Gasperi  8 , Pieter Giesbertz  9 , Stephan A Müller  9 , Stephan Breimann  10 , Stefan F Lichtenthaler  11 , Bernhard Kuster  12 , Matthias Mann  13 , Axel Imhof  14 , Teresa Barth  14 , Stefanie M Hauck  15 , Henrik Zetterberg  16 , Markus Otto  17 , Wilko Weichert  5 , Bernhard Hemmer  18 , Johannes Levin  19
Affiliations
Review

A unified classification approach rating clinical utility of protein biomarkers across neurologic diseases

Alexander M Bernhardt et al. EBioMedicine. 2023 Mar.

Abstract

A major evolution from purely clinical diagnoses to biomarker supported clinical diagnosing has been occurring over the past years in neurology. High-throughput methods, such as next-generation sequencing and mass spectrometry-based proteomics along with improved neuroimaging methods, are accelerating this development. This calls for a consensus framework that is broadly applicable and provides a spot-on overview of the clinical validity of novel biomarkers. We propose a harmonized terminology and a uniform concept that stratifies biomarkers according to clinical context of use and evidence levels, adapted from existing frameworks in oncology with a strong focus on (epi)genetic markers and treatment context. We demonstrate that this framework allows for a consistent assessment of clinical validity across disease entities and that sufficient evidence for many clinical applications of protein biomarkers is lacking. Our framework may help to identify promising biomarker candidates and classify their applications by clinical context, aiming for routine clinical use of (protein) biomarkers in neurology.

Keywords: Analytical validity; Biomarker; Clinical utility; Neurology; Protein; Proteomics.

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Conflict of interest statement

Declaration of interests Johannes Levin reports part-time employment by MODAG GmbH and a grant of the Michael J Fox Foundation for Parkinson's Research. In addition, he reports speaker fees from Bayer Vital, Biogen and Roche, consulting fees from Axon Neuroscience and Biogen, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers, all outside the submitted work. He is a member of the advisory board of Biogen and a member of the Data Safety Monitoring Board of Axon Neuroscience. He is beneficiary of the phantom share program of MODAG GmbH. In addition, he is inventor in a patent “Pharmaceutical Composition and Methods of Use” (EP 22 159 408.8) filed by MODAG GmbH. Bernhard Hemmer received funding by the European Union's Horizon 2020 Research and Innovation Program and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology and Roche. He holds part of two patents: one for the detection of antibodies against KIR4.1 in a subpopulation of patients with multiple sclerosis and one for genetic determinants of neutralizing antibodies to interferon. Wilko Weichert reports research funding from Roche, MSD, BMS and AstraZeneca. He has attended and given talks at Advisory Boards, gave advice to and served as speaker on national and international conferences for Roche, MSD, BMS, AstraZeneca, Pfizer, Merck, Lilly, Boehringer, Novartis, Takeda, Bayer, Amgen, Astellas, Eisai, Johnson and Johnson, Janssen, Illumina, Siemens, Agilent, ADC, GSK and Molecular Health. Stefan F. Lichtenthaler reports research funding from Shionogi and Novartis. Steffen Tiedt reports consulting fees from Alpha Apollo Inc. Christiane Gasperi reports funding from the Hertie Foundation, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) and the Hans and Klementia Langmatz Stiftung. Carla Palleis reports funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology. No other disclosures were reported.

Figures

Fig. 1
Fig. 1
Unifying classification concept for protein biomarkers across neurologic disease entities. According to the unified classification, a biomarker is first classified by its clinical application by grouping it into one of the seven categories and by precisely defining the associated clinical endpoint. In a second step, the available evidence is summarized in one of the levels A–E. Rating clinical utility in a third step is difficult to operationalise. Depending on the individual patient case and disease, the one or other approach may be more suitable. It is important to note whether a gold-standard for the measurement of the clinical endpoint already exists or whether there is a general lack of appropriate biomarkers.
Fig. 2
Fig. 2
Selected genomic biomarkers of neurooncologic (A) and selected protein biomarkers of neurooncologic (B), neurodegenerative (C), neurovascular (D) and neuroinflammatory (E) diseases. Biomarkers were grouped into one of the seven categories of clinical applications and the corresponding evidence level is indicated by both bar length and color. Biomarkers with level A and B evidence form the basis, few biomarkers of lower evidence levels are shown as well. Disease entity is depicted by the signs within the bars. Abs (antibodies), AIS (Acute Ischemic Stroke), ALP (Alkaline Phosphatase), ALS (Amyotrophic Lateral Sclerosis), APS (Atypical Parkinsonian Syndromes), C1s (Complement component C1s), C5 (Complement component 5), CJD (Creutzfeldt-Jakob Disease), DLB (Dementia with Lewy Bodies), DMG (Diffuse Midline Glioma), FTD (Frontotemporal Dementia), GBM (Glioblastoma), HD (Huntington's Disease), ICH (Intracerebral Hemorrhage), LGG (Lower Grade Glioma), MNG (Meningeoma), MOGAD (Myelin Oligodendrocyte Glycoprotein antibody associated Autoimmune Disease), MS (Multiple Sclerosis), MSA (Multiple System Atrophy), MTx (brain metastases), AD (Alzheimer's Disease), Nabs (Neutralizing antibodies), NfL (Neurofilament light chain), NMOSD (Neuromyelitis Optica Spectrum Disorder), OCBs (Oligoclonal Bands), OGD (Oligodendroglioma), OPN (Osteopontin), PCNSL (Primary Central Nervous System Lymphoma), pNfH (phosphorylated Neurofilament heavy chain), PSP (Progressive Supranuclear Palsy), RT-QuIC (Real-Time Quaking-Induced Conversion).
Fig. 2
Fig. 2
Selected genomic biomarkers of neurooncologic (A) and selected protein biomarkers of neurooncologic (B), neurodegenerative (C), neurovascular (D) and neuroinflammatory (E) diseases. Biomarkers were grouped into one of the seven categories of clinical applications and the corresponding evidence level is indicated by both bar length and color. Biomarkers with level A and B evidence form the basis, few biomarkers of lower evidence levels are shown as well. Disease entity is depicted by the signs within the bars. Abs (antibodies), AIS (Acute Ischemic Stroke), ALP (Alkaline Phosphatase), ALS (Amyotrophic Lateral Sclerosis), APS (Atypical Parkinsonian Syndromes), C1s (Complement component C1s), C5 (Complement component 5), CJD (Creutzfeldt-Jakob Disease), DLB (Dementia with Lewy Bodies), DMG (Diffuse Midline Glioma), FTD (Frontotemporal Dementia), GBM (Glioblastoma), HD (Huntington's Disease), ICH (Intracerebral Hemorrhage), LGG (Lower Grade Glioma), MNG (Meningeoma), MOGAD (Myelin Oligodendrocyte Glycoprotein antibody associated Autoimmune Disease), MS (Multiple Sclerosis), MSA (Multiple System Atrophy), MTx (brain metastases), AD (Alzheimer's Disease), Nabs (Neutralizing antibodies), NfL (Neurofilament light chain), NMOSD (Neuromyelitis Optica Spectrum Disorder), OCBs (Oligoclonal Bands), OGD (Oligodendroglioma), OPN (Osteopontin), PCNSL (Primary Central Nervous System Lymphoma), pNfH (phosphorylated Neurofilament heavy chain), PSP (Progressive Supranuclear Palsy), RT-QuIC (Real-Time Quaking-Induced Conversion).
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References

    1. Feigin V.L., Abajobir A.A., Abate K.H., et al. Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol. 2017;16:877–897. doi: 10.1016/S1474-4422(17)30299-5. - DOI - PMC - PubMed
    1. Dubois B., Villain N., Frisoni G.B., et al. Clinical diagnosis of Alzheimer's disease: recommendations of the international working group. Lancet Neurol. 2021;20:484–496. doi: 10.1016/S1474-4422(21)00066-1. - DOI - PMC - PubMed
    1. Thompson A.J., Banwell B.L., Barkhof F., et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162–173. doi: 10.1016/S1474-4422(17)30470-2. - DOI - PubMed
    1. Method of the year 2012. Nat Methods. 2013;10:1. doi: 10.1038/nmeth.2329. - DOI - PubMed
    1. Petrera A., von Toerne C., Behler J., et al. Multiplatform approach for plasma proteomics: complementarity of olink proximity extension assay technology to mass spectrometry-based protein profiling. J Proteome Res. 2021;20:751–762. doi: 10.1021/acs.jproteome.0c00641. - DOI - PubMed