Coelewij, Leda Maria Giuseppina;
(2023)
Using metabolomic and transcriptomic profiles to predict development of anti-drug antibodies in people with relapsing remitting multiple sclerosis.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Background: Anti-drug antibodies (ADA) can greatly reduce the efficacy of immunotherapies in people affected by multiple sclerosis (MS). Immunogenicity risk factors are poorly characterised, creating an unmet need for biomarkers predicting ADA development to biopharmaceuticals. Up to 35% of MS patients treated with beta interferons (IFNβ) develop ADA making them an ideal cohort to investigate predictive ADA biomarkers. Methods: Peripheral blood was collected from 82 MS patients as part of the ABIRISK consortium from before (M0), three months (M3), and 12 months (M12) after starting IFNβ treatment. ADA status was determined after 12 months on IFNβ, where 30 patients were ADA positive (ADApos). These samples were investigated using metabolomics and whole blood transcriptomics analysis. Machine learning was used to predict ADA status using metabolomic data (n=82), while whole blood transcriptomics were examined in a subset of patients (n=11) using differential gene expression and pathway enrichment analysis. Results: Metabolomic and transcriptomic analysis identified dyslipidaemia amongst ADApos patients at M0, with “Metabolism of Lipids” amongst the top enriched pathways and lipid-focused metabolites able to classify MS patients by ADA status with an accuracy of 85%. Significant correlations between metabolite concentrations and genes associated with lipid metabolism were also observed. Additionally, ADApos patients demonstrated baseline signatures of immune response activation, evidenced by upregulated membrane trafficking, and signal transduction. At M3, ADApos patients had a dampened response to IFNβ-treatment, with a downregulation of 18 IFN response genes and 45 metabolites differentially regulated. Conclusion: Metabolite concentration and gene expression signatures are promising tools for prediction of ADA development in MS patients treated with IFNβ and could provide novel insights into mechanisms of immunogenicity, which may apply to other treatments. Additionally, transcriptomic analysis reveals that ADApos patients have increased immune activation at baseline, but a comparatively lower immune response to IFNβ at M3, suggesting a dysregulated immune process.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Using metabolomic and transcriptomic profiles to predict development of anti-drug antibodies in people with relapsing remitting multiple sclerosis |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inflammation |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166534 |
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