Can Our Genes Predict How Well Multiple Sclerosis Patients Respond to Glatiramer Acetate?
Multiple sclerosis (MS) is a lifelong immune-mediated disease in which the immune system mistakenly attacks the central nervous system, leading to neurodegeneration and disability. For decades, patients have relied on disease-modifying therapies (DMTs) to slow progression and reduce relapses. Among the first-line therapies is glatiramer acetate (GA), a synthetic peptide mixture marketed as Copaxone.
GA has a solid safety record and is particularly recommended for patients with milder MS courses and even during pregnancy. However, its effectiveness varies widely: while some patients experience long-term remission, others continue to relapse. The question is—why the difference?
A team of researchers led by Olga Kulakova set out to answer this through the lens of pharmacogenomics—the study of how genetic differences influence drug responses.
The Study: Looking at MS Risk Genes in Treatment Response
We already know from genome-wide association studies (GWAS) that over 100 genetic regions are linked to MS susceptibility, many of them tied to immune system regulation. Kulakova and colleagues hypothesized that these same genetic risk factors might also determine how patients respond to GA.
They studied 296 Russian patients with relapsing–remitting MS who were treated with GA for at least two years. Patients were grouped as:
Responders (Rs): no relapses or disability progression.
Non-responders (NRs): continued relapses or worsening disability.
Intermediate responders (IRs): fell somewhere in between.
Researchers analyzed 17 GWAS-identified immune response genes, using SNP genotyping and advanced multilocus association methods.
Key Genetic Findings
Several genes stood out as potential biomarkers for GA response:
EOMES (rs2371108*T) – associated with improved GA response.
Encodes a transcription factor important in T-cell function.
CLEC16A (rs6498169*A) – strongly linked to good treatment outcomes.
Plays a role in antigen presentation, critical for immune regulation.
IL22RA2 (rs202573*G/G) – associated with event-free survival.
Encodes a soluble receptor that dampens IL-22 activity, implicated in inflammation.
PVT1 (rs2114358*A) – also linked with better outcomes.
A long noncoding RNA region, less understood but tied to immune regulation.
Interestingly, while HLA-DRB1*4 showed a weak positive association, the well-known MS susceptibility allele HLA-DRB1*15 had no effect on GA response.
Beyond Single Genes: Combinations Matter
One of the most powerful aspects of this study was looking at gene–gene interactions. The team discovered that combinations of variants had stronger predictive power than single markers. For example:
EOMEST + CLEC16AA greatly increased the chance of a positive GA response.
PVT1A + IRF8G showed a significant synergistic effect, boosting treatment success.
Conversely, EOMESG/G + TYK2C was linked to poor GA response.
This highlights the complexity of genetic influence—not just one “good” or “bad” gene, but how different variants interact in the immune system’s network.
Predicting Treatment Response: Toward Personalized Medicine
The team built predictive models using these genetic markers.
A model with just EOMES, CLEC16A, and IL22RA2 reached an AUC (area under ROC curve) of 0.657—moderately predictive.
Adding epistatic combinations (EOMES + TYK2 and PVT1 + IRF8) increased predictive power to an AUC of 0.701.
While not perfect, these results suggest that composite genetic models could help forecast whether a patient will benefit from GA.
Why This Matters
For patients, starting MS therapy can feel like trial and error. Some drugs work wonders; others don’t. If clinicians could genetically screen patients before prescribing, they could:
Identify who is most likely to respond well to GA.
Avoid wasting time on ineffective treatment.
Tailor therapy more quickly, improving long-term outcomes.
This study marks an important step toward that vision. By combining GWAS insights with pharmacogenomics, researchers are laying the groundwork for more personalized MS treatment strategies.
Takeaway
The research by Kulakova and colleagues demonstrates that MS risk genes are not just about disease susceptibility—they also influence treatment response. Variants in EOMES, CLEC16A, IL22RA2, and PVT1, along with their interactions with other immune genes, could become valuable biomarkers for predicting GA efficacy.
While replication in larger, diverse cohorts is needed, this work brings us closer to a future where an MS patient’s treatment plan could be guided by their genetic profile—turning trial-and-error medicine into precision medicine.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Kulakova, O., Bashinskaya, V., Kiselev, I., Baulina, N., Tsareva, E., Nikolaev, R., ... & Favorova, O. (2017). Pharmacogenetics of glatiramer acetate therapy for multiple sclerosis: the impact of genome-wide association studies identified disease risk loci. Pharmacogenomics, 18(17), 1563-1574.
