Format

Send to

Choose Destination
Front Immunol. 2018 Jul 25;9:1693. doi: 10.3389/fimmu.2018.01693. eCollection 2018.

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.

Author information

1
Neuroimmunology Unit, Puerta de Hierro-Segovia de Arana Health Research Institute, Madrid, Spain.
2
Autonomous University of Madrid, Madrid, Spain.
3
Centre for Plant Biotechnology and Genomics, Madrid, Spain.
4
Flow Cytometry Core Facility, Puerta de Hierro-Segovia de Arana Health Research Institute, Madrid, Spain.
5
Spanish National Cardiovascular Research Center (CNIC), Madrid, Spain.
6
IMT Lille Douai & CRIStAL, Univ. de Lille, Douai, France.
7
Sequencing Core Facility, Puerta de Hierro-Segovia de Arana Health Research Institute, Madrid, Spain.
8
Bioinformatics Unit of Spanish National Cancer Research Center (CNIO), Madrid, Spain.
9
Neurology Department, Puerta de Hierro University Hospital, Madrid, Spain.
10
Red Española de Esclerosis Múltiple (REEM), Barcelona, Spain.
11
Biobank, Puerta de Hierro University Hospital-IDIPHISA, Madrid, Spain.

Abstract

Background:

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.

Materials and methods:

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.

Results:

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.

Conclusion:

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.

KEYWORDS:

RNA-seq; biomarkers; fingolimod; lymphocyte subpopulations; multiple sclerosis; transcriptome

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center