Towards harmonization of autoantibody detection in relation to paraneoplastic neurological syndromes (PNS): a European Survey on laboratory practices
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Matthijs Oyaert
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Marco Schreurs
Abstract
Objectives
Detection of paraneoplastic anti-neuronal antibodies (PNS autoantibodies) aids in diagnosing the particular syndrome and guides the search for an underlying tumor. To identify opportunities for harmonization that could enhance diagnostic value, we assessed variability in autoimmune serological testing for PNS across laboratories.
Methods
The European Autoimmunity Standardisation Initiative (EASI) developed a questionnaire addressing testing methods, digital tools, interpretation, clinical context, and quality assurance. This was distributed through two external quality assessment (EQA) providers (UK NEQAS and IfQ Lübeck) to clinical labs routinely performing PNS autoantibody assays.
Results
Among 139 responding laboratories, 78 % perform both cerebellum indirect immunofluorescence (IIF) and line/dot blot assays on serum and cerebrospinal fluid (76 %). Alignment on sample dilution (61 %) and conjugate type (70 %) is variable. Differences in antigen composition and reporting strategies contribute to variability in line/dot blot testing. Digitization is widespread for line/dot blot data (87 %) but limited for cerebellum IIF (31 %). About 53 % use testing algorithms that vary by sample type and requested antibodies. Internal quality control is performed by 89 % (IIF) and 48 % (line/dot blot), mainly using commercial controls. Nearly half (43 %) participate in multiple EQA programs; 46 % (IIF) and 42 % (line/dot blot) have ISO15189 certification, with 20 % planning certification. Positivity rates are low (<6 % in 73 % of labs), indicating low pre-test probability. While only 46 % restrict testing by discipline, most labs access clinical context (84 %) and interact with clinicians (63 %).
Conclusions
Considerable variability exists in PNS autoantibody assay execution, reporting, and quality control. Introducing lab-specific recommendations may harmonize practices and improve diagnostic quality.
Acknowledgments
We are grateful to all the participating laboratories for completing the survey.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The data that support the findings of this study are available from the corresponding author, MO, upon reasonable request.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-1030).
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