To achieve our goals, the LiSyM-Cancer network requires the integration of activities in a matrix structure which, in addition to the coordination of the three consortia and data management, includes a combination of clinical, technological and modeling activities across the individual projects as overarching topics.
A Systems Medicine Approach to Early Detection and Prevention of Hepatocellular Carcinoma in Non-Alcoholic Fatty Liver Disease – is coordinated by Prof. Dr. Ursula Klingmüller, Head of the Department of Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ) Heidelberg, Prof. Dr. Jens Timmer, Head of the Department of Data Analysis and Modeling of Dynamic Processes in the Life Sciences, Freiburg Institute for Advanced Studies FRIAS), University of Freiburg and Prof. Dr. Thomas Berg, Head of Hepatology, Clinic and Polyclinic for Oncology, Gastroenterology, Hepatology, Pneumology and Infectiology, University Hospital Leipzig.
This network brings together 12 groups that are researching on urgently needed innovative strategies for the early detection of patients with non-alcoholic fatty liver disease (NAFLD)
SMART-NAFLD: Project concept - Multi-stage approach to the integration of cell and tissue level models, based on quantitative metabolic and proteomic data and the analysis of tissue architecture using imaging techniques at different stages of progression from NAFLD to HCC.
A central hypothesis in SMART-NAFLD is that altered glucose and lipid metabolism in hepatocytes of NAFLD patients promotes increased cell proliferation. Together with elevated levels of proinflammatory cytokines, this metabolic state drives passing through the tipping point 1 and thus the direct transition from NAFLD to HCC formation in the non-cirrhotic liver. For the early detection of HCC, it is therefore essential to characterise tumor-promoting changes in the interaction of metabolism and signal transduction, which is induced by growth factors and / or proinflammatory factors. To understand these complex relationships, an integrative mathematical model of metabolism and signal transduction will be developed that quantitatively describes the metabolic state and the extent of cellular reactions in hepatocytes of different stages of the disease. The integration of the cellular model into a tissue model that describes the structural changes during the course of the disease allows quantitative predictions regarding the temporal course of the disease. Global studies of the plasma proteome and metabolome are used to identify characteristic changes in longitudinal blood samples and link them to mechanistic changes in tissue. Based on these findings a classification tool will be developed that allows early detection of NAFLD patients at high risk of progression to HCC. The model is intended to support individualized therapeutic decisions for curative chirurgic therapy of early lesions.
The main objectives in SMART-NAFLD are:
Mechanism-based Multi-scale Model for the Dissection of the Tipping-Point from Liver Cirrhosis to Hepatocellular Carcinoma – is coordinated by Prof. Dr. Steven Dooley, Head of Department of Molecular Hepatology, Medical Clinic II, University Medical Center Mannheim, University of Heidelberg, Prof. Dr. Heike Bantel, Head of Translational Hepatology and Senior Physician, Department of Gastroenenterology, Hepatology, Endocrinology, Hannover Medical School and PD Dr. Dirk Drasdo, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund/Director of Research, National Institute for Research in Computer Science and Control (INRIA, France).
A total of 12 groups are working together to identify signatures from tissue and cell parameters in patients with non-alcoholic fatty liver disease and liver cirrhosis that are crucial for exceeding the tipping point (TIP) from liver cirrhosis to HCC via a non-invasive systems medicine approach. The TIP is defined as the stage of cirrhosis of the liver, in which minor changes in cellular and tissue factors produce a micro-environment that promotes malignant transformation and cancer cell development. Critical tissues and cell parameters include structural changes in cirrhotic liver architecture, including changes in cellular phenotypes, especially hepatic stellate cells (HSC), macrophages, and hepatocytes, qualitative and quantitative changes in the matrisome, and changes in the TGFβ signaling pathway. In addition, longitudinal clinical data from very large national and European patient cohorts are used, some of which have already been collected in LiSyM (previous project). Imaging, -omics, and dynamic signaling pathway data are used to further develop the already developed mathematical models on the tissue, cell and molecular scale, thus defining the patient-specific TIP in the cirrhotic regeneration node. Technically, a mechanistic spatially and temporally resolved multiscale model is developed, which is based on the tissue and cell parameters and can determine the specific risk of a single patient with cirrhosis passing the TIP for tumor development by model simulation. The model should be able to identify at-risk patients much earlier than is the case with current clinical used approaches that focus mainly on the early detection of already established HCC.
C-TIP-HCC: scientific concept: Cirrhosis of the liver is the strongest risk factor for the development of HCC. However, not all patients with cirrhosis of the liver develop towards HCC. A model in which a tipping point (TIP) in a cirrhotic regeneration node must be overcome for a malignant transformation is proposed.
In-Depth Spatial Organisation of Hepatocellular Carcinoma Initiation – is coordinated by Prof. Dr. Jochen Hampe, Director of the Medical Clinic I and Head of the Department of Gastroenterology and Hepatology, University Hospital Dresden, Prof. Dr. Marino Zerial, Director of the Max-Planck-Institute of Molecular Cell Biology and Genetics, Head of the Working Group Principles of cell and tissue organization, Dresden and Dr. Lutz Brusch, Deputy Head of the Department Innovative Methods of Computing, Center for Information Services and High Performance Computing (ZIH), Technical University Dresden.
In the 17 groups, the medical need to improve the chances of survival and cure of patients with liver cancer through a system-controlled discovery of novel blood-based and imaging biomarkers for early detection or potential target molecules for the prevention of HCC is addressed. DEEP-HCC focuses on early-stage HCC in humans and provides deep multidimensional functional and spatial characterization from tissue to individual cells. A comprehensive data set with digital 3D tissue reconstruction is created based on the analysis of human liver tissue in LiSyM, spatial transcriptomics, epigenetics, lipidomics, pseudo-temporally ordered somatic mutations and personalized complex (multicompartiment) HCC organoids. This unique convergence of data sets is integrated into a comprehensive metabolism/signaling, spatio-temporal and stochastic modeling workflow, revealing multimodal HCC signatures in DEEP-HCC that are beyond the resolution of purely OMIC-based approaches. By reconstructing the developmental stages of the tumor along the course of the disease, DEEP-HCC will provide a mechanistic understanding of early HCC progression, system-level associations between emerging HCC signatures and accessible biomarkers, potentially new PET/CT contrast agents, and blood-based markers for early detection and prevention of HCC. The resulting predictions and discoveries will be tested and validated by model-based experiments in complex human organoids, by selected experiments in mice, and by validation in large prospective human cohorts together with industry partners.
The Data Management is coordinated by PD Dr. Wolfgang Müller, head of the Scientific Databases and Visualisation (SDBV) working group at the Heidelberg Institute for Theoretical Studies, HITS gGmbH. The team ensures the flow of data, data exchange and data backup within the consortia and across the network according to the FAIR criteria: Findability – Accessibility –Interoperability – Reusability. The Data Management team supports the researchers with training, software, the provision and maintenance of various, demand-oriented data exchange platforms as well as ethical & legal advice. The Project and Communications Management Team works closely with the Program Director, SAB, and Network Coordinators to facilitate the successful implementation of the program and the publication and usability of the results.