Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system of autoimmune etiopathogenesis, and is characterized by various neurological symptoms. Glatiramer acetate and interferon-β are administered as first-line treatments for this disease. In non-responsive patients, several second-line therapies are available, including natalizumab; however, a percentage of MS patients does not respond, or respond partially. Therefore, it is of the utmost importance to develop a diagnostic test for the prediction of drug response in patients suffering from complex diseases, such as MS, where several therapeutic options are already available. By a machine learning approach, the UnCorrelated Shrunken Centroid algorithm was applied to identify a subset of genes of CD 4+ T cells that may predict the pharmacological response of relapsing-remitting MS patients to natalizumab, before the initiation of therapy. The results from the present study may provide a basis for the design of personalized therapeutic strategies for patients with MS.

Identification of CD4+ T cell biomarkers for predicting the response of patients with relapsing-remitting multiple sclerosis to natalizumab treatment

Fagone P.;Mazzon E.;Mammana S.;Basile M. S.;Petralia M. C.;Nicoletti F.;Mangano K.
2019

Abstract

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system of autoimmune etiopathogenesis, and is characterized by various neurological symptoms. Glatiramer acetate and interferon-β are administered as first-line treatments for this disease. In non-responsive patients, several second-line therapies are available, including natalizumab; however, a percentage of MS patients does not respond, or respond partially. Therefore, it is of the utmost importance to develop a diagnostic test for the prediction of drug response in patients suffering from complex diseases, such as MS, where several therapeutic options are already available. By a machine learning approach, the UnCorrelated Shrunken Centroid algorithm was applied to identify a subset of genes of CD 4+ T cells that may predict the pharmacological response of relapsing-remitting MS patients to natalizumab, before the initiation of therapy. The results from the present study may provide a basis for the design of personalized therapeutic strategies for patients with MS.
Biomarkers; Machine learning; Multiple sclerosis; Natalizumab; Personalized medicine; UnCorrelated Shrunken Centroid
File in questo prodotto:
File Dimensione Formato  
Identification of CD4+ T cell biomarkers.pdf

accesso aperto

780.67 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/368862
Citazioni
  • ???jsp.display-item.citation.pmc??? 19
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 24
social impact