Personalizing health care in Multiple Sclerosis using systems medicine tools

Tailoring or personalizing healthcare for people diagnosed with Multiple Sclerosis (MS) is critical to control their illness and maximize their quality of life. In the Sys4MS research project, we aim to take advantage of the power of systems medicine to integrate different types of clinical data, allowing us to define how the disease will progress and thus, to make better informed decisions in order to care for each MS patient. To achieve this we have recruited a prospective cohort of MS patients followed at the MS centers in Barcelona, Genova, Berlin and Oslo. Throughout the project period, we have collected clinical, imaging and genetic data and obtained samples for proteomics assays based on blood-derived immunological cells from 424 MS patients and healthy controls, of which 95 of the MS patients have been collected in Oslo. We are in the process of finalizing the follow-up examinations two years after the baseline investigation. Using computational tools, we will now integrate these diverse and complex information, and we will develop new algorithms that will match a given patient to a specific disease subgroup. This will allow clinicians to better predict the course of each patient´s disease and thereby to adopt the best therapeutic approach.

Dette prosjektet har medført at fem Europeiske forskningssentre har samarbeidet tett over tre år for å innhente omfattende forskningsdata fra 328 personer med multippel sklerose og 90 friske kontroller. Det er også gjort oppfølgingsundersøkelser og innhenting av ytterligere forskningsdata etter to år som sluttføres rett over sommeren 2019. Det er etablert avanserte statistiske modeller for å nyttegjøre seg av en system-medisin tilnærming til det innsamlede materialet. Videre arbeid med det innsamlede materialet vil pågå i flere år fremover og lede til flere internasjonale publikasjoner i etablerte medisinske tidsskrifter. Vi vil arbeide videre for at resultatene oppnådd i dette prosjekte også skal inngå i andre, store internasjonale samarbeidsprosjekter for å kunne bekrefte våre funn og studere videre sykdomsmekanismene ved multippel sklerose og legge grunnlag for en mer persontilpasset behandling.

Development of personalized health care for complex diseases like Multiple Sclerosis (MS) is hindered by a poor understanding of the biological processes underlying the disease and their interactions, as well as by the heterogeneity between patients. These shortcomings also represent a significant limitation in terms of monitoring or predicting the disease course, as well as in the prescription of the most efficacious or safer therapies. By integrating clinical information with omics data and mathematical models of MS, we aim to develop algorithms that can be used in clinical practice to define the prognosis of the disease and that will help in selecting the best therapeutic approach based on the patients phenotype. We shall focus on 5 different levels of biological complexity to capture and integrate the most relevant information: 1) genomics to evaluate the individuals genetic predisposition; 2) phosphoproteomics to capture the activity ofsignalling pathways involved in the immune response; 3) cytomics to capture the dynamics of the autoimmune response; 4) Imaging to quantify the damage of the central nervous system; 5) clinical phenotype (clinical scales, comorbidities, drug usage, quality of life, health economics) to define the clinical outcomes to be reached. Such an approach will benefit from previous work carried out by members of the consortium in the application of systems medicine to MS, including the development of network models of signalling pathways, mathematical models of the dynamics of immune cells, and the harmonization of multilevel and complex databases to develop clinical decision support systems. By testing such tools in small clinical studies, we shall improve the usefulness of systems medicine tools in clinical practice.