Using proteomics to predict long-term disability outcomes in MS

A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The identified proteins could be used to tailor treatments to the individual based on the expected severity of the disease. Researchers from Linköping University, the University of Skövde and the Karolinska Institute have found that measuring these proteins in cerebrospinal fluid, instead of the blood, better reflects activity in the central nervous system (CNS).   

In individuals with multiple sclerosis (MS) the immune system attacks their own body, damaging nerves in the brain and spinal cord. Myelin, a fatty compound which surrounds and insulates the nerve axons so that signals can be transmitted, is primarily attacked, meaning transmission becomes less efficient. 

Disease progression in multiple sclerosis varies considerably between individuals. It is important not to lose time at the onset of the disease and to get the correct treatment for those whom a more severe disease is predicted. Scientists in the current study aimed to discover whether it was possible to detect at an early stage of disease which patients would require a more powerful treatment.  

Dr Mika Gustafsson, professor of Bioinformatics at the Department of Physics, Chemistry and Biology at Linköping University, led the study. He said: “I think we’ve come one step closer to an analysis tool for selecting which patients would need more effective treatment in an early stage of the disease. But such a treatment may have side effects and be relatively expensive, and some patients don’t need it.”

Finding biomarkers

It is very difficult to find markers linked to disease severity many years ahead. The scientists analysed almost 1,500 proteins in samples from 92 people with suspected or recently diagnosed MS.

Data from the protein analyses was combined with a large amount of information from the patients’ journals, such as disability, results from MRI scans of the nervous system, and treatments received. Then, using machine learning, the team discovered various proteins that could predict disease progression.

Sara Hojjati, doctoral student at the Department of Biomedical and Clinical Sciences at Linköping University, explained: “Having a panel consisting of only 11 proteins makes it easy should anyone want to develop analysis for this. It won’t be as costly as measuring 1,500 proteins, so we’ve really narrowed it down to make it useful for others wanting to take this further.”  

Also, the team found that a protein named neurofilament light chain (NfL), leaking from damaged nerve axons, is a reliable biomarker for disease activity in the short term. These results confirm earlier research on the use of NfL to identify nerve damage and suggest that the protein indicates how active the disease is.

The combination of proteins found in the patient group from which samples were taken at Linköping University Hospital was later confirmed in a separate group consisting of 51 MS patients sampled at the Karolinska University Hospital.

This is the first study to measure such a large number of proteins with a highly sensitive method, proximity extension assay, combined with next-generation sequencing, PEA-NGS. Also, this technology allows for high accuracy measuring of very small amounts, which is crucial because these proteins are often present in very low levels.

The study was published in Nature Communications.