Model prediction and validation of an order mechanism controlling the spatio-temporal phenotype of early hepatocellular carcinoma

The aggressiveness of a tumor may be reflected by its micro-architecture. To gain a deeper understanding of the mechanisms controlling spatial organization of tumors at early stages after tumor initiation, we used an agent-based spatio-temporal model previously established to simulate features of liver regeneration. Here, this model was further developed to simulate scenarios in early tumor development, when individual initiated hepatocytes gain increased proliferation capacity. The model simulations were performed in realistic liver microarchitectures obtained from 3D reconstruction of confocal laser scanning micrographs. Interestingly, the here established model predicted that initially initiated hepatocytes arrange in elongated patterns. Only when the tumor progresses to cell numbers of approximately 4,000, it adopts spherical structures. This model prediction was validated by the analysis of initiated cells in a rat liver tumor initiation study using single doses of 250 mg/kg of the genotoxic carcinogen Nnitrosomorpholine (NNM). Indeed, small clusters of GST-P positive cells induced by NNM were elongated, almost columnar, while larger GDT-P positive foci of approximately the size of liver lobuli, adopted spherical shapes. Simulation of numerous possible mechanisms demonstrated that only hepatocyte-sinusoidal-alignment (HSA), a previously discovered order mechanism involved in coordination of liver tissue architecture, could explain the experimentally observed initial deviation from sphericalshape. The present study demonstrates that the architecture of small hepatocellular tumor cell clusters early after initiation is still controlled by physiological control mechanisms. However, this coordinating influence is lost when the tumor grows to approximately 4,000 cells, leading to further growth in spherical shape. Our findings stress the potential importance of organ micro-architecture in understanding tumor phenotypes