Biomarkers of aHSCT

Overview

The underlying disease mechanisms which occur in patients with immune mediation neurological diseases, such as Multiple Sclerosis (MS), are incompletely understood. For such patients, autologous haematopoietic stem cell transplantation (aHSCT) has been increasingly used as a highly successful one-off treatment for some patients. This treatment aims to delete the faulty immune system with a course of chemotherapy and then 'reboot' the immune system using a patients' own stem cells (a cell with the unique ability of being a building block to create many different cells in the body) to stop further damage. Over the last 20 years more than 1800 patients with MS have been treated in Europe with high levels of success. It may be more successful than disease modifying treatment but unfortunately, a small portion of people do not respond to this treatment optimally and continue to accumulate disability. There is a risk of side effects, restricted largely to the time of treatment, which necessitates the need to ensure appropriate patients are treated. Whilst aHSCT is a very effective therapy, it is still in its early phase of development, is not in widespread use, and there is incomplete knowledge regarding how it works and importantly, why it does not work in some patients, and how to monitor response to treatment. Unfortunately, there is no way of detecting which patients will, and will not, benefit from the different treatments available or a way of monitoring the immune system to ensure further treatment is provided before irreversible damage occurs. This study will investigate the immune system which is found in the fluid surrounding the brain and spinal cord, blood and stool of patients undergoing aHSCT and compare it to those receiving disease modifying treatment. This study will therefore further the understanding of biomarkers of aHSCT to develop an awareness of how it can be refined, may improve monitoring of patients following treatment and permit the development of markers which can predict potential treatment success or failure before patients are exposed to the risks.

Conditions