Chronic inflammatory diseases (CID) comprise a group of disorders of the immune system with a lifetime prevalence of over 10% in the EU and a continuously rising incidence in Western countries. Despite different clinical manifestations, CID show a vast overlap of genetic risk maps and environmental risk factors.

SYSCID will focus on three major CID (inflammatory bowel disease, systemic lupus erythematodes and rheumatoid arthritis) to identify a common set of unifying mechanisms that contribute to their pathogenesis. Identifying sub entities of CID via molecular biomarkers will lay the ground to develop a prediction framework for disease outcome and/or response to different therapies. Improved predictability will guide therapy decisions on an individual patient level enabling the choice of the right therapy at the right time. At the same time, SYSCID will investigate the development of new causative therapies by editing the epigenome and transcriptionally reprogram specific cell types. Unlike current therapeutic interventions which only alleviate the symptoms, SYSCID thus aims to develop a therapeutic strategy that will eventually offer a first causal therapy.

To this end, SYSCID will build on previous and ongoing research activities by partners and international initiatives to exploit already collected and established patient data - e.g. in IHEC, ICGC, TwinsUK and Meta-HIT - and enable their utilisation for the development of new clinical applications.

In summary, SYSCID’s fundamental aims are:

Identification of a "Core disease signature"

The hypothesis is that CID as incurable diseases have unique and shared molecular correlates which remain present and stable even under conditions of symptomatic disease control over time. Finding these correlates in large longitudinal disease data sets with clinical information and control cohorts which can be used to test stability of markers sets and pathway states will yield powerful and early multimodal biomarkers and mechanistic insights into CID for clinical applications.

Defining a "Predictive model of disease outcome"

CID may have very different disease behaviour, from nearly self-limiting to highly aggressive and life-threatening. We have shown that pathways determining disease outcome may be distinct from initial disease risk and that a simple transcriptional signature in CD8 cells is sufficient to predict CID outcome. Using longitudinal and cross-sectional data SYSCID will develop clinically validated multimodal models for disease course prediction to guide „hit hard and early“ versus „wait and see“ therapy decisions on an individual patient basis.

Reprogramming disease

SYSCID will use longitudinal therapy cohorts with dense clinical sampling to understand kinetics of immunological network changes associated with response and non-response to current treatment regimes (e.g. anti-TNF, anti-IL6) Following the hypotheses, (a) network states can be inferred which likely respond to one therapy regime, but not the other and (b) even under the most controlled conditions (full remission under biological therapy) a molecular “memory” of disease should be detectable, which will be targeted in epigenome editing experiments.