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. 2023 Jan 6;13(1):122.
doi: 10.3390/jpm13010122.

Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach

Laura Ferrè  1   2   3 , Ferdinando Clarelli  2 , Beatrice Pignolet  4   5 , Elisabetta Mascia  2 , Marco Frasca  6   7   8 , Silvia Santoro  2 , Melissa Sorosina  2 , Florence Bucciarelli  4   5 , Lucia Moiola  1 , Vittorio Martinelli  1 , Giancarlo Comi  3 , Roland Liblau  5   9 , Massimo Filippi  1   3   10   11 , Giorgio Valentini  6   7   8 , Federica Esposito  1   2
Affiliations

Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach

Laura Ferrè et al. J Pers Med. .

Abstract

A personalized approach is strongly advocated for treatment selection in Multiple Sclerosis patients due to the high number of available drugs. Machine learning methods proved to be valuable tools in the context of precision medicine. In the present work, we applied machine learning methods to identify a combined clinical and genetic signature of response to fingolimod that could support the prediction of drug response. Two cohorts of fingolimod-treated patients from Italy and France were enrolled and divided into training, validation, and test set. Random forest training and robust feature selection were performed in the first two sets respectively, and the independent test set was used to evaluate model performance. A genetic-only model and a combined clinical-genetic model were obtained. Overall, 381 patients were classified according to the NEDA-3 criterion at 2 years; we identified a genetic model, including 123 SNPs, that was able to predict fingolimod response with an AUROC= 0.65 in the independent test set. When combining clinical data, the model accuracy increased to an AUROC= 0.71. Integrating clinical and genetic data by means of machine learning methods can help in the prediction of response to fingolimod, even though further studies are required to definitely extend this approach to clinical applications.

Keywords: fingolimod; genetic markers; machine learning; multiple sclerosis; precision medicine; predictive model.

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Conflict of interest statement

Ferdinando Clarelli, Beatrice Pignolet, Elisabetta Mascia, Marco Frasca, Santoro Silvia, Sorosina Melissa, Florence Bucciarelli and Giorgio Valentini have nothing to disclose. Laura Ferrè received compensation for speaking activities from Novartis. Lucia Moiola received compensation for consulting services, travel grants, and/or speaking activities from Biogen, Serono, Sanofi, Teva, Roche, and Novartis. Vittorio Martinelli has received compensations for consulting services and/or speaking activities from Novartis, Genzyme, Almirall, TEVA, Biogen, and Merck-Serono. Giancarlo Comi has received personal compensation for consulting and speaking activities from F. Hoffmann-La Roche Ltd., Roche SpA, Novartis, Teva Pharmaceutical Industries Ltd, Teva Italia Srl, Sanofi Genzyme, Genzyme Corporation, Genzyme Europe, Merck KGgA, Merck Serono SpA, Celgene Group, Biogen Idec, Biogen Italia Srl, Almirall SpA, Forward Pharma, Medday, and Excemed. Roland Liblau received grant support from Pierre Fabre, GlaxoSmithKline, and BMS. He received speaker or scientific board honoraria from Biogen, Servier, Novartis, and Sanofi-Genzyme. Massimo Filippi is Editor-in-Chief of the Journal of Neurology; received compensation for consulting services and/or speaking activities from Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). Federica Esposito has received compensation for consulting services and/or speaking activities from Novartis, Sanofi Genzyme, Almirall, TEVA, and Merck-Serono.

Figures

Figure 1
Figure 1
Comparison of disease activity levels between patients predicted to be non-responders to FTY (PrNR) and patients likely to respond to treatment (PrR). (A) Proportion of patients with No Evidence of Disease Activity (NEDA) and Evidence of Disease Activity (EDA) in the PrR and PrNR groups. (B) Number of new and/or active lesions in the PrR and PrNR groups. (C) Proportion of patients with or without MRI activity in the PrR and PrNR groups. (D) Number of clinical relapses in the PrR and PrNR groups. (E) Proportion of patients with or without clinical activity in the PrR and PrNR groups.
See this image and copyright information in PMC

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