Investigating the genetic influence on multiple sclerosis outcomes

Dr Vilija Jokubaitis is first author on the
  Brain paper

by Dr Loretta Piccenna

Researchers from the Department of Neuroscience at Central Clinical School have found that no common genetic variations (those found in 5% or more of the population) are strongly linked to disease severity in people with relapsing-remitting multiple sclerosis (RRMS).

Published in one of the top neuroscience journals, Brain, the multicentre study instead revealed that multiple genetic loci with small effect sizes were associated with clinical outcomes in people with RRMS. Further, a machine learning algorithm using this information, together with clinical and demographic variables available at disease onset, accurately predicted the severity of the disease.

Multiple sclerosis is an autoimmune, degenerative neurological disease interfering with the central nervous system’s ability to send messages along nerves from the brain to the body.

MS is most common in young adults with 2.8 million people diagnosed worldwide. It results in neurological disability that affects activities of daily living to differing extents for people living with the condition. Relapsing-Remitting Multiple Sclerosis (RRMS) is the most common form of the disease, that affects about 85% of those diagnosed with MS. Clinicians are able to manage the disease with disease-modifying treatments that target the immune system.

Patients with RRMS accumulate disability at different rates, some very slowly over many decades, others rapidly, within 10 years of diagnosis, or less. It is not understood why there is such a large variability in the rates of disability progression, or MS outcomes. Previous studies have shown that common genetic variants are linked to the risk of developing MS, therefore the team wanted to determine whether the same might be true for disability accumulation.

To better understand the large heterogeneity that occurs with disease outcomes from one person to another the research team used prospectively collected longitudinal data from the largest international MS clinical outcomes registry, MSBase (www.msbase.org).

Teasing out the genetics of MS severity:
Figure from the Brain paper

Clinical, demographic and genetic data were collected from 1,813 patients who met the study criteria and were further categorised by clinical and demographic characteristics including age at onset of the disease. A genome-wide association study was performed to look for novel single nucleotide variants associated with the characteristics identified in the study cohort. In addition, a machine learning algorithm was applied to the data to predict MS severity.

First author of the study, Dr Vilija Jokubaitis, Head of the Neuroimmunology Genomics and Prognostics research group, said, “We showed that the genetics underlying the severity of MS implicates the central nervous system (CNS), specifically synaptic plasticity and myelination pathways - mechanisms functionally distinct to MS risk.

“Given that no genetic variant associates strongly with disease outcomes, the findings suggest that MS disease outcomes are, to an extent, modifiable with appropriate and early treatment with disease-modifying therapies. 

“We were also able to demonstrate using machine learning the potential of common genetic variants to serve as prognostic biomarkers when combined with demographic data. Once independently validated, the machine learning algorithm could enable clinicians to provide patients with more accurate prognostic information, that would then also help to select the most appropriate disease-modifying therapy based on a person’s likely long-term MS outcomes.” 

Further, the machine learning algorithm may also help to stratify patients for participation in future randomized controlled drug trials, and other clinical studies to where genetic predisposition to severe or mild disease outcomes may be important.

The study marks an important milestone in research progress for gaining insight into the high variation of disability and long-term outcomes in people living with RRMS.

“The work was a long journey, in fact it was 10 years in the making from writing the first ethics application through to recruiting participants, and performing the analyses. It would not have been possible without our amazing team and collaborators, nurses involved in research and people with MS who generously donated their samples,” said Dr Jokubaitis.

The research team are now excited about validating the machine learning algorithm locally in the Multiple Sclerosis Neuro-Immunology clinics at The Alfred Hospital, a close healthcare partner of the Central Clinical School at Monash University, with the hope of seeing it implemented more widely in clinical practice in the future.

This study was supported by funding from MS Australia, the MSBase Foundation, the RMH Home Lottery and Charity Works for MS.

Reference

Vilija G Jokubaitis, Maria Pia Campagna, Omar Ibrahim, Jim Stankovich, Pavlina Kleinova, Fuencisla Matesanz, Daniel Hui, Sara Eichau, Mark Slee, Jeannette Lechner-Scott, Rodney Lea, Trevor J Kilpatrick, Tomas Kalincik, Philip L De Jager, Ashley Beecham, Jacob L McCauley, Bruce V Taylor, Steve Vucic, Louise Laverick, Karolina Vodehnalova, Maria-Isabel García-Sanchéz, Antonio Alcina, Anneke van der Walt, Eva Kubala Havrdova, Guillermo Izquierdo, Nikolaos Patsopoulos, Dana Horakova, Helmut Butzkueven. Not all roads lead to the immune system: the genetic basis of multiple sclerosis severity, Brain, 2022;, ac449, https://doi.org/10.1093/brain/awac44