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Related Experiment Video

Updated: Jun 8, 2026

High-throughput Quantitative Real-time RT-PCR Assay for Determining Expression Profiles of Types I and III Interferon Subtypes

High-throughput Quantitative Real-time RT-PCR Assay for Determining Expression Profiles of Types I and III Interferon Subtypes

Published on: March 24, 2015

Single-nucleotide polymorphisms in response to interferon-beta therapy in multiple sclerosis.

Koen Vandenbroeck¹, Manuel Comabella

¹Neurogenomiks Group, Universidad del País Vasco (UPV/EHU), Leioa, Spain. k.vandenbroeck@ikerbasque.org

Journal of Interferon & Cytokine Research : the Official Journal of the International Society for Interferon and Cytokine Research

|September 15, 2010

Summary

Identifying biomarkers for interferon-beta (IFN-β) treatment in multiple sclerosis (MS) is crucial. Current research explores genetic variations to predict patient response, but clinical application remains distant.

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Last Updated: Jun 8, 2026

High-throughput Quantitative Real-time RT-PCR Assay for Determining Expression Profiles of Types I and III Interferon Subtypes

High-throughput Quantitative Real-time RT-PCR Assay for Determining Expression Profiles of Types I and III Interferon Subtypes

Published on: March 24, 2015

Development and Validation of an Ultrasensitive Single Molecule Array Digital Enzyme-linked Immunosorbent Assay for Human Interferon-α

Development and Validation of an Ultrasensitive Single Molecule Array Digital Enzyme-linked Immunosorbent Assay for Human Interferon-α

Published on: June 14, 2018

Area of Science:

Neuroimmunology
Pharmacogenomics
Multiple Sclerosis Therapeutics

Background:

Interferon-beta (IFN-β) is a primary treatment for multiple sclerosis (MS).
However, 20%-50% of patients do not achieve clinical benefits from IFN-β therapy.
Predictive biomarkers are needed to identify non-responders before treatment initiation.

Purpose of the Study:

To review recent advances in identifying genetic biomarkers for IFN-β response in MS.
To assess the progress and challenges in translating biomarker research into clinical practice.

Main Methods:

Review of candidate gene approaches.
Analysis of whole-genome association scans (WGAS) for MS IFN-β response biomarkers.

Main Results:

Various promising genetic variants associated with IFN-β response have been identified.
Significant progress has been made in understanding the genetic underpinnings of treatment response.
No definitive biomarkers are yet available for routine clinical use.

Conclusions:

Identifying genetic markers for IFN-β response in MS is an active research area.
While progress is substantial, further research is required for clinical implementation.
Biomarker-guided therapy could optimize MS treatment outcomes.