Immunophenotype and Transcriptome Profile of Patients With Multiple Sclerosis Treated With Fingolimod: Setting Up a Model for Prediction of Response in a 2-Year Translational Study - Moreno-Torres, Irene; González-García, Coral; Marconi, Marco; García-Grande, Aranzazu; Rodríguez-Esparragoza, Luis; Elvira, Víctor; Ramil, Elvira; Campos-Ruíz, Lucía; García-Hernández, Ruth; Al-Shahrour, Fátima; Fustero-Torre, Coral; Sánchez-Sanz, Alicia; et al. (Frontiers in Immunology, 2018)
Background Fingolimod is a functional sphingosine-1-phosphate (S1P) antagonist approved for the treatment of multiple sclerosis (MS). Fingolimod affects lymphocyte subpopulations and regulates gene expression in the lymphocyte transcriptome. Translational studies are necessary to identify cellular and molecular biomarkers that might be used to predict the clinical response to the drug. In MS patients, we aimed to clarify the differential effects of fingolimod on T, B and natural killer (NK) cell subsets and to identify differentially expressed genes in responders and non-responders to treatment. Materials and Methods Samples were obtained from relapsing-remitting multiple sclerosis (RRMS) patients before and 6 months after starting fingolimod. Forty-eight lymphocyte subpopulations were measured by flow cytometry based on surface and intracellular marker analysis. Transcriptome sequencing by next-generation technologies was used to define the gene expression profiling in lymphocytes at the same time points. NEDA-3 (no evidence of disease activity) and NEDA-4 scores were measured for all patients at 1 and 2 years after beginning fingolimod treatment to investigate an association with cellular and molecular characteristics. Results Fingolimod affects practically all lymphocyte subpopulations and exerts a strong effect on genetic transcription switching towards an anti-inflammatory and antioxidant response. Fingolimod induces a differential effect in lymphocyte subpopulations after 6 months of treatment in responder and non-responder patients. Patients who achieved a good response to the drug compared to non-responder patients exhibited higher percentages of NK bright cells and plasmablasts, higher levels of FOXP3, GPI, lower levels of FCRL1 and lower EDSS at baseline. The combination of these possible markers enabled us to build a probabilistic linear model to predict the clinical response to fingolimod. Conclusions MS patients responsive to fingolimod exhibit a recognizable distribution of lymphocyte subpopulations and a different pretreatment gene expression signature that might be useful as a biomarker.