10.3389/fimmu.2018.01693.s001
Irene Moreno-Torres
Irene
Moreno-Torres
Coral González-García
Coral
González-García
Marco Marconi
Marco
Marconi
Aranzazu García-Grande
Aranzazu
García-Grande
Luis Rodríguez-Esparragoza
Luis
Rodríguez-Esparragoza
Víctor Elvira
Víctor
Elvira
Elvira Ramil
Elvira
Ramil
Lucía Campos-Ruíz
Lucía
Campos-Ruíz
Ruth García-Hernández
Ruth
García-Hernández
Fátima Al-Shahrour
Fátima
Al-Shahrour
Coral Fustero-Torre
Coral
Fustero-Torre
Alicia Sánchez-Sanz
Alicia
Sánchez-Sanz
Antonio García-Merino
Antonio
García-Merino
Antonio José Sánchez López
Antonio José Sánchez
López
Data_Sheet_1_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.PDF
Frontiers
2018
multiple sclerosis
biomarkers
fingolimod
lymphocyte subpopulations
RNA-seq
transcriptome
2018-07-25 04:03:54
Dataset
https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_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_PDF/6859229
Background<p>Fingolimod is a functional sphingosine-1-phosphate 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 (NRs) to treatment.</p>Materials and methods<p>Samples were obtained from relapsing–remitting multiple sclerosis 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.</p>Results<p>Fingolimod affects practically all lymphocyte subpopulations and exerts a strong effect on genetic transcription switching toward an anti-inflammatory and antioxidant response. Fingolimod induces a differential effect in lymphocyte subpopulations after 6 months of treatment in responder and NR patients. Patients who achieved a good response to the drug compared to NR patients exhibited higher percentages of NK bright cells and plasmablasts, higher levels of FOXP3, glucose phosphate isomerase, lower levels of FCRL1, and lower Expanded Disability Status Scale at baseline. The combination of these possible markers enabled us to build a probabilistic linear model to predict the clinical response to fingolimod.</p>Conclusion<p>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.</p>