Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ
Background: Neutralizing anti-drug antibodies (ADA) can greatly reduce the efficacy of biopharmaceuticals used to treat patients with multiple sclerosis (MS). However, the biological factors pre-disposing an individual to develop ADA are poorly characterized. Thus, there is an unmet clinical need fo...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-07-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fimmu.2020.01527/full |
| _version_ | 1811225423534620672 |
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| author | Kirsty E. Waddington Kirsty E. Waddington Artemis Papadaki Leda Coelewij Leda Coelewij Marsilio Adriani Petra Nytrova Eva Kubala Havrdova Anna Fogdell-Hahn Rachel Farrell Pierre Dönnes Pierre Dönnes Inés Pineda-Torra Elizabeth C. Jury |
| author_facet | Kirsty E. Waddington Kirsty E. Waddington Artemis Papadaki Leda Coelewij Leda Coelewij Marsilio Adriani Petra Nytrova Eva Kubala Havrdova Anna Fogdell-Hahn Rachel Farrell Pierre Dönnes Pierre Dönnes Inés Pineda-Torra Elizabeth C. Jury |
| author_sort | Kirsty E. Waddington |
| collection | DOAJ |
| description | Background: Neutralizing anti-drug antibodies (ADA) can greatly reduce the efficacy of biopharmaceuticals used to treat patients with multiple sclerosis (MS). However, the biological factors pre-disposing an individual to develop ADA are poorly characterized. Thus, there is an unmet clinical need for biomarkers to predict the development of immunogenicity, and subsequent treatment failure. Up to 35% of MS patients treated with beta interferons (IFNβ) develop ADA. Here we use machine learning to predict immunogenicity against IFNβ utilizing serum metabolomics data.Methods: Serum samples were collected from 89 MS patients as part of the ABIRISK consortium—a multi-center prospective study of ADA development. Metabolites and ADA were quantified prior to and after IFNβ treatment. Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.Results: We were able to predict future immunogenicity from baseline metabolomics data. Lasso logistic regression with/without interactions and support vector machines were the most successful at identifying ADA+ or ADA– cases, respectively. Furthermore, patients who become ADA+ had a distinct metabolic response to IFNβ in the first 3 months, with 29 differentially regulated metabolites. Machine learning algorithms could also predict ADA status based on metabolite concentrations at 3 months. Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. Finally, we hypothesized that serum lipids could contribute to ADA development by altering immune-cell lipid rafts. This was supported by experimental evidence demonstrating that, prior to IFNβ exposure, lipid raft-associated lipids were differentially expressed between MS patients who became ADA+ or remained ADA–.Conclusion: Serum metabolites are a promising biomarker for prediction of ADA development in MS patients treated with IFNβ, and could provide novel insight into mechanisms of immunogenicity. |
| first_indexed | 2024-04-12T09:07:01Z |
| format | Article |
| id | doaj.art-3f18697f0ea04704bfe363826743d56e |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T09:07:01Z |
| publishDate | 2020-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-3f18697f0ea04704bfe363826743d56e2022-12-22T03:39:04ZengFrontiers Media S.A.Frontiers in Immunology1664-32242020-07-011110.3389/fimmu.2020.01527544281Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβKirsty E. Waddington0Kirsty E. Waddington1Artemis Papadaki2Leda Coelewij3Leda Coelewij4Marsilio Adriani5Petra Nytrova6Eva Kubala Havrdova7Anna Fogdell-Hahn8Rachel Farrell9Pierre Dönnes10Pierre Dönnes11Inés Pineda-Torra12Elizabeth C. Jury13Centre for Rheumatology, University College London, London, United KingdomCentre for Cardiometabolic and Vascular Medicine, University College London, London, United KingdomCentre for Rheumatology, University College London, London, United KingdomCentre for Rheumatology, University College London, London, United KingdomCentre for Cardiometabolic and Vascular Medicine, University College London, London, United KingdomCentre for Rheumatology, University College London, London, United KingdomDepartment of Neurology and Centre of Clinical Neuroscience, General University Hospital and First Faculty of Medicine, Charles University in Prague, Prague, CzechiaDepartment of Neurology and Centre of Clinical Neuroscience, General University Hospital and First Faculty of Medicine, Charles University in Prague, Prague, CzechiaDepartment of Clinical Neuroscience, Center for Molecular Medicine (CMM), Karolinska Institutet, Karolinska University Hospital, Huddinge, SwedenDepartment of Neuroinflammation, University College London, Institute of Neurology and National Hospital of Neurology and Neurosurgery, London, United KingdomCentre for Rheumatology, University College London, London, United KingdomScicross AB, Skövde, SwedenCentre for Cardiometabolic and Vascular Medicine, University College London, London, United KingdomCentre for Rheumatology, University College London, London, United KingdomBackground: Neutralizing anti-drug antibodies (ADA) can greatly reduce the efficacy of biopharmaceuticals used to treat patients with multiple sclerosis (MS). However, the biological factors pre-disposing an individual to develop ADA are poorly characterized. Thus, there is an unmet clinical need for biomarkers to predict the development of immunogenicity, and subsequent treatment failure. Up to 35% of MS patients treated with beta interferons (IFNβ) develop ADA. Here we use machine learning to predict immunogenicity against IFNβ utilizing serum metabolomics data.Methods: Serum samples were collected from 89 MS patients as part of the ABIRISK consortium—a multi-center prospective study of ADA development. Metabolites and ADA were quantified prior to and after IFNβ treatment. Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions.Results: We were able to predict future immunogenicity from baseline metabolomics data. Lasso logistic regression with/without interactions and support vector machines were the most successful at identifying ADA+ or ADA– cases, respectively. Furthermore, patients who become ADA+ had a distinct metabolic response to IFNβ in the first 3 months, with 29 differentially regulated metabolites. Machine learning algorithms could also predict ADA status based on metabolite concentrations at 3 months. Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. Finally, we hypothesized that serum lipids could contribute to ADA development by altering immune-cell lipid rafts. This was supported by experimental evidence demonstrating that, prior to IFNβ exposure, lipid raft-associated lipids were differentially expressed between MS patients who became ADA+ or remained ADA–.Conclusion: Serum metabolites are a promising biomarker for prediction of ADA development in MS patients treated with IFNβ, and could provide novel insight into mechanisms of immunogenicity.https://www.frontiersin.org/article/10.3389/fimmu.2020.01527/fullimmunogenicityanti-drug antibodiesmultiple sclerosismetabolomicscholesterolmachine learning |
| spellingShingle | Kirsty E. Waddington Kirsty E. Waddington Artemis Papadaki Leda Coelewij Leda Coelewij Marsilio Adriani Petra Nytrova Eva Kubala Havrdova Anna Fogdell-Hahn Rachel Farrell Pierre Dönnes Pierre Dönnes Inés Pineda-Torra Elizabeth C. Jury Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ Frontiers in Immunology immunogenicity anti-drug antibodies multiple sclerosis metabolomics cholesterol machine learning |
| title | Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ |
| title_full | Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ |
| title_fullStr | Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ |
| title_full_unstemmed | Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ |
| title_short | Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ |
| title_sort | using serum metabolomics to predict development of anti drug antibodies in multiple sclerosis patients treated with ifnβ |
| topic | immunogenicity anti-drug antibodies multiple sclerosis metabolomics cholesterol machine learning |
| url | https://www.frontiersin.org/article/10.3389/fimmu.2020.01527/full |
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