Better treatments for multiple sclerosis through biomarkers
More than 2.3 million people worldwide suffer from multiple sclerosis (MS). A hallmark of MS is its unpredictable progression and extensive phenotypic variability. This makes therapeutic decisions and “life planning” difficult and, in most cases, unsuccessful.
MS diagnosis and monitoring mostly depends on analyzing cerebrospinal fluid (CSF), which requires an invasive procedure that carries risks that range from headache to nerve damage and paraplegia. Consequently, regular CSF sampling is unacceptable to most patients and their physicians. The identification of biomarkers (indicators of a particular biological condition) can lead to the development of less invasive and more time-effective diagnostic blood tests to improve and personalize the care of MS, as well as other diseases, such as cancer and heart failure.
New technologies exist that can measure genes and proteins in the blood and enable minimally invasive clinical tests for diagnosing and monitoring various diseases. However, new statistical methods are needed in order to distill the information generated by these technologies.
Gabriela Cohen Freue, Canada Research Chair in Statistical Proteomics, is conducting computational approaches to find protein biomarkers that may not be detected by traditional statistical methods. The statistical framework she is using will allow for proper analysis of the rich information contained in proteomics datasets.
Cohen Freue’s research will lead to new methodologies that will completely change the way biomarkers are identified and searched for. It could also make it easier to develop personalized care and new drugs for treating diseases such as MS.