Project Interview: Personalizing health care in Multiple Sclerosis using systems medicine tools – Sys4MS

What are general and long term goals of the project?
The Sys4MS consortium aims to use systems medicine to develop new tools to improve and personalize the management of patients with a complex disease, Multiple Sclerosis (MS). The first step of our strategy is to develop mathematical models of the disease into which we can introduce the clinical information and “omics” data obtained from the patients. These models are based on our current knowledge of the disease and they allow us to generate algorithms that can predict both the disease course and future disability in specific subgroups of MS patients. These algorithms can also provide information that helps to select the best therapy for each individual. We will test these tools in small clinical studies, which will help to demonstrate their usefulness in clinical practice. In this way we will show how systems medicine can be used to produce clinically relevant tools. Moreover, these studies will provide evidence of how omics data can be used for healthcare programmes directed at both care and prevention, allowing informed decisions to be made regarding the organisation of health and care systems.

Please state a few more specific objectives of the project.
There are 3 main objectives in this project:

  1. To achieve a better stratification of MS patient by integrating clinical, “omics” and imaging information into computational models of MS, and to develop algorithms that predict disease activity, future disability and response to therapy;
  2. To validate the clinical algorithms and evaluate their benefits in short clinical studies, as well as in prospective database studies;
  3. To develop and validate the computational models to aid the design of anticipative therapeutic strategies and the identification of combination therapies for MS.

Achieving these objectives will translate the concepts of systems medicine into real applications that will help personalize the clinical care of patients with MS. Our approach will identify tools and data that are truly useful to answer specific clinical questions that are currently of significant value to patients and physicians, such as the prognosis of disease severity and the potential response to therapy. Therefore, we aim to find clear application for systems biology concepts to a specific medical field.

Describe the methodology, approach and technologies used.
Our approach will involve first upgrading the existing computational models specific to MS so that they take into consideration the novel phenotypic aspects of MS derived from selected ‘omics, clinical and imaging studies. These models will be optimized on the basis of the experimental dataset obtained, and through these updated models and the data collected, we will be able to develop algorithms that can be applied to relevant clinical questions. These clinically relevant algorithms will then be validated in short clinical studies, assessing their ethical and health economic impact, as well as their clinical usefulness. The algorithms showing good performance and clinical utility will be further developed as a CDSS (clinical decision support system), which will be implemented at the four clinical centres involved in the project. Finally, we aim to make use of the computational models developed initially to propose new therapies based on drug reprofiling and combinations with current disease modifying drugs (DMDs). We are in a position to perform ex vivo assays that will provide the necessary validation of these therapies opening the way to more rigorous clinical testing.

The overall strategy can be outlined in 5 steps:

  1. To obtain experimental “omics” data from a big cohort of MS patients by performing genomics (SNPs, HLA typing, RNA seq), phosphoproteomics and cytomics studies on blood samples from patients (PBMCs). Currently available imaging (magnetic resonance imaging –MRI- and optical coherent tomography -OCT) and clinical data will also be collected. All this information will be integrated into a harmonized database and used to stratify MS patients and optimize computational models of MS pathogenesis that we developed previously.
  2. By performing simulations and analysing the optimized models, we will define the configurations that are useful to predict specific clinical outcomes. These predictors will be translated into clinically specific algorithms that are based on the most relevant information collected from patients.
  3. Once the integrated model(s) and algorithms have been established, we will test them in clinical practice, evaluating their capacity to address a set of clinically relevant questions from both a patient’s and physician’s perspective. This will be performed in short clinical studies performed on specific patient sub-groups at the clinical centres participating in the project. These studies will test the predictions for each classifier in each patient.
  4. Based on the accuracy of the predictions and on the users (physicians and patients) perceived usefulness, we will optimize the algorithms as a CDSS. This web-based tool will then be optimized at the participating clinical centres focusing on its usability and utility, as perceived by physicians and by stratified patient sub-groups.
  5. A second set of algorithms will be defined to search for new single and combination therapies to treat MS.

The models developed will then be used to identify biomarkers of each patient subtype and to predict therapies that may revert these biomarkers to the healthy state. Multiplexed in vitro assays performed on PBMCs and phosphoproteome network analysis of therapeutic agents will be used to identify therapies, and the effects of the drugs proposed will then be tested ex vivo using PBMCs isolated from MS patients. These drugs will be evaluated in functional assays, using phosphoproteomic and cytomic profiles as a read-out of the phenotype and efficacy.

How is the project progressing, any results you wish to highlight?
The Sys4MS project has not yet started. Its planned start date is 1st March, 2016. It is worth noting that the Sys4MS approach will benefit from previous work carried out by some members of the consortium under the FP7 EU project CombiMS in terms of the application of systems medicine to MS. The CombiMS project established the network models of signalling pathways and the mathematical models of immune cell dynamics that will serve as the foundation for the work in Sys4MS. Likewise, the database used in this earlier project will serve as the basic design for the harmonized multilevel database used in SYS4MS to develop the clinical decision support systems. By testing such tools in small clinical studies, we shall improve the usefulness of systems medicine tools in clinical practice.

Funding source and funding duration:
The National Institute of Health Carlos III (Spain), The Federal Ministry of Education and Research (Germany), The Italian Ministry of Health (Italy), and The Research Council of Norway (Norway) under the frame of ERACoSysMed-1, the ERA-Net for Systems Medicine in clinical research and medical practice. From 03/2016 to 02/2019.

Pablo Villoslada