Mechanisms controlling the compensation of liver injury and towards model-based biomarkers for early detection of liver cancer

The liver has a central role in metabolism and detoxification. As the organ is constantly exposed to damaging insults, it has a remarkable capacity to regenerate and ensure survival. To maintain physiological function, these responses require tight regulatory mechanisms. In contrast to machine learning approaches that require large data sets, mechanistic models can be calibrated based on small data sets and employed as predictive tools to establish a causal relationship between inputs and outputs (D'Alessandro et al, Biochem J 2022). TGFβ is a key controlling factor coordinating the termination of liver regeneration. By model-based selection we identified the three most relevant SMAD-complexes in liver cells. This mathematical model suggests that elevated levels of SMAD proteins and SMAD2 phosphorylation are hallmarks of liver cancer (Lucarelli et al., Cell Syst 2018). Another factor implicated in the regulation of liver regeneration is tumor necrosis factor alpha (TNFα). We showed by dynamic pathway modelling that multiple parameters in the TNFα network are affected by the non-steroidal anti-inflammatory compound Diclofenac leading to amplified toxicity and defining the toxicity threshold. (Oppelt et al. NPJ Syst Biol Appl 2018). Besides drugs, hepatotropic viruses such as hepatitis B virus (HBV) or hepatitis C virus (HCV) can cause liver damage and trigger interferon alpha (IFNα) antiviral responses. Dynamic pathway modeling enabled us to resolve the molecular mechanisms mediating hypersensitization or desensitization of the pathway. Adaptation of the mathematical model for individual patients in a virtual patient cohort revealed patient-specific predictors for pathway desensitization (Kok et al, Mol Syst Biol 2020). Due to the key role of the liver in metabolism a major interest is to elucidate interfaces between signal transduction involved in liver regeneration and metabolism. In a first step we contributed to a genome scale model that identified a high amino acid consumption rate in hepatoma cells and pointed to the importance of glutamate excretion, representing a potential cancer-specific intervention (Nilsson et al., Proc Natl Acad Sci U S A 2020). Currently, a major emphasis is on addressing the importance of remodelling of the ECM and the metabolic state on cancer progression. The detection of indicators of these mechanisms in the blood plasma of patients is utilized to facilitate early detection of liver cancer when surgical resection with curative intend is still possible.