Multiscale HCC

Systems Biology Supports Multiscale Analysis of Imaging, Omics and Clinical Data to Improve Diagnosis and Therapy of HCCs

We will develop models which will predict and mirror tumor development of advanced hepatocellular carcinoma (HCC) under antiangiogenic therapies, specifically Sorafenib and after transarterial chemoembolisation (TACE). Using our existing base of multifunctional imaging data and pre-existing simulation framework we will reveal functional factors of anti-angiogenic drugs and TACE treatments on the vascular system and tumor cells.

Model-based predictions will be correlated with clinical data from the same patient which subsequently validates and refines our models. The validated and improved mathematical model will then be applied  to analyse and optimize different administration schedules for combination therapies. Existing data will be supplemented by multiparametric imaging, metabolomics, transcriptomics, genomics, pathology and clinical data of genetically engineered mouse models as well as by an observational clinical study in HCC patients. Volume perfusion CT and combined PET/MR will reveal complementary functional-molecular parameters of the disease and treatment effects, leading to an image fingerprint of investigated tumors. Network models on the molecular scale will be coupled to spatially resolved tissue models, resulting in a multi-scale description of HCC.

These models will be developed, calibrated, and validated against imaging data and omics data, expanding the usual scope of systems biology. Predictions made by the model will be validated in mouse models. Non-invasive clinical imaging will thus be directly coupled to molecular mechanistic data and answer the question, which type of data will be most effective for selecting optimal therapies. The consortium is an interdisciplinary group of the Universities Tübingen and Stuttgart and our SME Chimaera, covering all required areas of clinical disciplines, advanced mouse models, multimodal imaging, image analysis, data mining, omics data analysis, modeling and bioinformatics.

Subprojects in Multiscale HCC:

SP 1   Image-guided multiscale modeling of vascular tumor growth as a systems biology-based tool to predict therapeutic outcome in HCC

SP 2   In vivo imaging and molecular profiling of treatment responses to anti-angiogenic therapies in murine liver carcinomas of defined genetic origin

SP 3   Data management and Multiscale Computational Modeling

SP 4   Image analysis and mining for the development of predictive and prognostic models for HCC diagnosis and therapy response

SP 5   Interventional clinical trial to test 3D-multiscale HCC model derived decisions in human patients with HCC

SP 6   Management of the Consortium