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.