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

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 ident...

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Autores principales: Ferrè, Laura, Clarelli, Ferdinando, Pignolet, Beatrice, Mascia, Elisabetta, Frasca, Marco, Santoro, Silvia, Sorosina, Melissa, Bucciarelli, Florence, Moiola, Lucia, Martinelli, Vittorio, Comi, Giancarlo, Liblau, Roland, Filippi, Massimo, Valentini, Giorgio, Esposito, Federica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861774/
https://www.ncbi.nlm.nih.gov/pubmed/36675783
http://dx.doi.org/10.3390/jpm13010122
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author Ferrè, Laura
Clarelli, Ferdinando
Pignolet, Beatrice
Mascia, Elisabetta
Frasca, Marco
Santoro, Silvia
Sorosina, Melissa
Bucciarelli, Florence
Moiola, Lucia
Martinelli, Vittorio
Comi, Giancarlo
Liblau, Roland
Filippi, Massimo
Valentini, Giorgio
Esposito, Federica
author_facet Ferrè, Laura
Clarelli, Ferdinando
Pignolet, Beatrice
Mascia, Elisabetta
Frasca, Marco
Santoro, Silvia
Sorosina, Melissa
Bucciarelli, Florence
Moiola, Lucia
Martinelli, Vittorio
Comi, Giancarlo
Liblau, Roland
Filippi, Massimo
Valentini, Giorgio
Esposito, Federica
author_sort Ferrè, Laura
collection PubMed
description 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
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spelling pubmed-98617742023-01-22 Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach Ferrè, Laura Clarelli, Ferdinando Pignolet, Beatrice Mascia, Elisabetta Frasca, Marco Santoro, Silvia Sorosina, Melissa Bucciarelli, Florence Moiola, Lucia Martinelli, Vittorio Comi, Giancarlo Liblau, Roland Filippi, Massimo Valentini, Giorgio Esposito, Federica J Pers Med Article 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 MDPI 2023-01-06 /pmc/articles/PMC9861774/ /pubmed/36675783 http://dx.doi.org/10.3390/jpm13010122 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ferrè, Laura
Clarelli, Ferdinando
Pignolet, Beatrice
Mascia, Elisabetta
Frasca, Marco
Santoro, Silvia
Sorosina, Melissa
Bucciarelli, Florence
Moiola, Lucia
Martinelli, Vittorio
Comi, Giancarlo
Liblau, Roland
Filippi, Massimo
Valentini, Giorgio
Esposito, Federica
Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
title Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
title_full Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
title_fullStr Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
title_full_unstemmed Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
title_short Combining Clinical and Genetic Data to Predict Response to Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients: A Precision Medicine Approach
title_sort combining clinical and genetic data to predict response to fingolimod treatment in relapsing remitting multiple sclerosis patients: a precision medicine approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861774/
https://www.ncbi.nlm.nih.gov/pubmed/36675783
http://dx.doi.org/10.3390/jpm13010122
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