A Systems medicine approach to chronic inflammatory disease

Informacje na temat projektu

SYSCID

Identyfikator umowy o grant: 733100

DOI

10.3030/733100

Projekt został zamknięty

Data podpisania przez KE 1 Grudnia 2016

Data rozpoczęcia 1 Stycznia 2017

Data zakończenia 30 Września 2022

Finansowanie w ramach

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Koszt całkowity

€ 16 018 111,25

Wkład UE

€ 14 456 236,25

Koordynowany przez

CHRISTIAN-ALBRECHTS-UNIVERSITAET ZU KIEL
Germany

Periodic Reporting for period 4 - SYSCID (A Systems medicine approach to chronic inflammatory disease)

Okres sprawozdawczy: 2021-07-01 do 2022-09-30

Podsumowanie kontekstu i ogólnych celów projektu

In SYSCID, an interdisciplinary consortium from 9 European countries with expertise ranging from medical sciences, immunology, genomics, molecular biology, bioinformatics to computer sciences has worked on generating a personalized molecular understanding of chronic inflammatory diseases (CIDs) as an important unmet medical need. Over a period of 5.5 years it has delivered first systems-immunology based insights into new diagnostic and therapeutic tools for this group of debilitating diseases. CIDs comprise a group of incurable disorders of the immune system with a lifetime prevalence of over 10% in the EU and a globally rising incidence in countries adopting the Western industrialized lifestyle. SYSCID has focused on inflammatory bowel diseases, rheumatoid arthritis and systemic lupus erythematodes as archetypes of the CID spectrum. CIDs share large parts of their genetic risk maps and environmental risk factors. Overlap between diseases and a high degree of clinical heterogeneity within each disease suggests a complex taxonomy of CIDs, which may lead to a molecularly stratified medicine approach, which could guide expensive targeted therapies.
SYSCID has focused on several unmet clinical needs in CIDs:
• A clear diagnosis of disease: Current diagnostic methods are not sufficient to accurately predict early disease manifestation. Time from first symptoms to diagnosis of CID even in Western Europe still suffers from a diagnostic delay of 12-24 months. Diagnostic algorithms employing multimodal marker sets for early and unequivocal detection of disease are thus a major unmet need in CID.
• Disease progression and comorbidities: Early identification of patients with aggressive disease behavior or prediction of complications are decisive elements for a future individualized clinical treatment. First results have demonstrated that an exhaustion signature of specific immune cells may predict complicated disease behavior across several CID.
• Therapy Response: Several different targeted therapies neutralizing specific factors of the immune system have been approved. Such therapies are cost-intensive and suffer from significant primary and secondary non-response rates. So far on the individual patient level, a molecular rationale (biomarker) for employing a specific compound in an indication situation is missing. SYSCID followed different layers of molecular information in individual patients over time in order to develop systems-based decision support to select the right therapy at the right time.
• Clinical trajectories with high heterogeneity and activation of signal transduction pathways of CIDs have been observed in COVID-19. Immunosuppresive therapies put CID patients at risk for a more severe disease course. In response to the pandemic situation researchers in SYSCID have contributed to molecular knowledge generation around COVID-19 as a potentially devastating disease.
The consortium has employed a variety of patient sample types derived from peripheral blood: whole blood and sorted blood immune cell types to single cell analysis. The project also comprised experimental strategies to develop epigenome editing therapies to reprogram specific cell types e.g. macrophage polarization defects.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

Work in SYSCID has employed several large patient cohorts reflecting cross-sectional or longitudinal recruitment efforts of the clinical partners. Main centre point was the analysis of blood-based markers. Another focus was on fecal microbiome profiling. Apart from detailed molecular analysis, detailed clinical data was available which allowed development of prediction frameworks for clinically actionable outcome parameters, e.g. prediction of therapy escalation or non-response to a given therapy.
Main cohorts comprised (1) a cohort for assessing markers of disease severity and outcome across CID (CAU: 1022 patients and 303 controls) and (2) response to targeted therapies (anti-TNF, anti-integrin, Anti-Blys) with >220 newly treated patients (CAU) with up to 8 timepoints for longitudinal multi-Omics analysis. (3) An important reference data set was generated for RNA-analysis from whole blood, which employed longitudinally sampled healthy controls and followed them over the course of a year with several sampling time points ( >1000 samples analyzed). (4) A smaller cohort (n=23) in IBD focused on longitudinal dynamics of immune cells in the peripheral blood and used high-resolution single cell RNA sequencing. (5) Finally, we used the tools and algorithms developed in SYSCID to analyze larger cohorts of COVID-19 patients. Here, we were among the first consortia world-wide to deliver scRNAseq-based markers for a severe disease course.
Using the harmonized production pipeline, molecular analysis on most cohorts described above (cross-sectional and longitudinal therapy response cohorts, validation cohorts from Greece, Switzerland, UK and Belgium) has been finalized and data has been made available to the community using appropriate data use and access regulations. While some of these analyses still await publication, SYSCID in numerous visible and highly cited scientific publications which can be found here: https://syscid.eu/publications/
Apart from scientific publications, the consortium has actively presented its aims and results on international conferences, organized a final symposium and summer school on system medicine in CID and was active in discussing results and implications with stakeholders. SYSCID partners serve as a use case in the EU systems medicine initiative “STANDS4PM” for harmonizing data sets and predictive pharmacodynamic models and were actively participating in the European LIFETIME initiative for propagating cell-based personalized medicine (Rajewsky and Lifetime, Nature 2021).

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

Results from SYSCID have actively contributed to shaping the current understanding of the molecular underpinnings of CIDs. A particular focus has been on the development of insights into actionable prediction markers for directing precise and personalized therapeutic approaches in CID. It can be assumed that predictive markers and mechanistic insights will be instrumental for guiding further developments around clinical decision support tools. The work clearly thrived from the well-characterized patient cohorts with longitudinal molecular and clinical data, most of which were directly exported from electronic health records. The results go well beyond the current state-of-the- art, as it 1) combined molecular feature layers in large patient groups across diseases, 2) used innovative longitudinal models of molecular markers to extract early markers of therapy success and 3) extracted single-cell resolved longitudinal markers to understand disease severity and therapy response patterns. Importantly, the developed tools and algorithms demonstrated their usefulness and clinical applicability in the pandemic situation, when innovative longitudinal cell-based analyses and block-chain based data sharing algorithms (swarm learning) could be employed to contribute to a quick knowledge generation around COVID-19 as an unknown inflammatory disorder. Ultimately, SYSCID has developed first steps towards molecularly defined disease endotypes and dynamic models of disease progression and therapy response. Data sets and analyses from SYSCID will thus sustainably help to mature efficient patient stratification for future care of CID patients well beyond its funding period.

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