Genetic subtypes predict multiple sclerosis severity and response to treatment
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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.
