Genetic subtypes predict multiple sclerosis severity and response to treatment
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Abstract
Background Predicting response to treatment and risk of long-term disability in multiple sclerosis (MS) is challenging. In other disease areas, combining genetic risk variants enabled detection of relevant clinical endophenotypes associated with important outcomes, but this has never been applied to MS. Methods We applied unsupervised hierarchical clustering to genomic risk scores of two cohorts (the prospective cohort study of 1455 Welsh MS patients was used as the discovery cohort and replication was performed in a multi-centre post-mortem Netherlands Brain Bank cohort of 272 MS patients) to predict relevant disease outcomes using survival analysis for time to disability milestones (expanded disability status scale, EDSS), and ANOVA to compare linear clinical outcomes. Results Three genomic clusters were identified, in each cluster patients had similar genetic profiles. Baseline demographic characteristics were similar between clusters. Welsh patients in cluster 1 attained key disability milestones later, reaching EDSS6, 6 years later (p=0.003) and EDSS8, 13 years later (p=0.02) than those in clusters 2 and 3. Time to EDSS6 was also significantly longer for patients in cluster 1 versus cluster 2 in the NBB-MS cohort (6 years, p=0.04). Genomic clustering is an independent predictor for disease progression compared with well-validated risk factors (Hazard ratio for time to EDSS6 1.3-2.0, all p<0.05). Welsh patients in cluster 2 and 3 also had a significantly greater annual increase in T2 lesion load on serial MR imaging (p=0.04). In cluster 2, patients who had received MS disease modifying treatments (DMT) had a longer time to EDSS6 (p=0.003) compared to those that had received no DMTs, whereas no differences were observed in either cluster 1 or cluster 3. In the NBB-MS cohort, we also observed differences in symptomatology, including earlier development of swallowing problems (p=0.02) or muscle spasticity (p= 0.0008) in cluster 2 patients. Conclusion This study demonstrates that unsupervised genetic clustering has utility to detect clinically relevant endophenotypes of MS, with genetic cluster 2 patients having a more severe phenotype and higher risk of disability. Moreover, genetic stratification is able to predict response to DMTs and could potentially be used for precision medicine in MS management.
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Abstract
Background Predicting response to treatment and risk of long-term disability in multiple sclerosis (MS) is challenging. In other disease areas, combining genetic risk variants enabled detection of relevant clinical endophenotypes associated with important outcomes, but this has never been applied to MS.
Methods
We applied unsupervised hierarchical clustering to genomic risk scores of two cohorts (the prospective cohort study of 1455 Welsh MS patients was used as the discovery cohort and replication was performed in a multi-centre post-mortem Netherlands Brain Bank cohort of 272 MS patients) to predict relevant disease outcomes using survival analysis for time to disability milestones (expanded disability status scale, EDSS), and ANOVA to compare linear clinical outcomes.
Results
Three genomic clusters were identified, in each cluster patients had similar genetic profiles. Baseline demographic characteristics were similar between clusters. Welsh patients in cluster 1 attained key disability milestones later, reaching EDSS6, 6 years later (p=0.003) and EDSS8, 13 years later (p=0.02) than those in clusters 2 and 3. Time to EDSS6 was also significantly longer for patients in cluster 1 versus cluster 2 in the NBB-MS cohort (6 years, p=0.04). Genomic clustering is an independent predictor for disease progression compared with well-validated risk factors (Hazard ratio for time to EDSS6 1.3-2.0, all p<0.05). Welsh patients in cluster 2 and 3 also had a significantly greater annual increase in T2 lesion load on serial MR imaging (p=0.04). In cluster 2, patients who had received MS disease modifying treatments (DMT) had a longer time to EDSS6 (p=0.003) compared to those that had received no DMTs, whereas no differences were observed in either cluster 1 or cluster 3. In the NBB-MS cohort, we also observed differences in symptomatology, including earlier development of swallowing problems (p=0.02) or muscle spasticity (p= 0.0008) in cluster 2 patients.
Conclusion
This study demonstrates that unsupervised genetic clustering has utility to detect clinically relevant endophenotypes of MS, with genetic cluster 2 patients having a more severe phenotype and higher risk of disability. Moreover, genetic stratification is able to predict response to DMTs and could potentially be used for precision medicine in MS management.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study did not receive any funding
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
1455 patients were recruited from the South Wales MS Registry established in 1985. This study was approved by the Wales Research Ethics Committee. The replication cohort consited of Caucasian donors with post-mortem confi rmed MS (n=272) from the Netherlands Brain Bank (NBB). The study was approved by the Free University Medical Center Medical Ethics Committee.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data Availability
All data produced in the present study are available upon reasonable request to the authors and upon completion of a signed data transfer agreement.
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