Understanding tumor invasion and metastasis is of crucial importance for bothfundamental cancer research and clinical practice. In vitro experiments haveestablished that the invasive growth of malignant tumors is characterized bythe dendritic invasive branches composed of chains of tumor cells emanatingfrom the primary tumor mass. The preponderance of previous tumor simulationsfocused on non-invasive (or proliferative) growth. The formation of theinvasive cell chains and their interactions with the primary tumor mass andhost microenvironment are not well understood. Here, we present a novelcellular automaton (CA) model that enables one to efficiently simulate invasivetumor growth in a heterogeneous host microenvironment. By taking into account avariety of microscopic-scale tumor-host interactions, including the short-rangemechanical interactions between tumor cells and tumor stroma, degradation ofextracellular matrix by the invasive cells and oxygen/nutrient gradient drivencell motions, our CA model predicts a rich spectrum of growth dynamics andemergent behaviors of invasive tumors. Besides robustly reproducing the salientfeatures of dendritic invasive growth, such as least resistance and intrabranchhomotype attraction, we also predict nontrivial coupling of the growth dynamicsof the primary tumor mass and the invasive cells. In addition, we show that theproperties of the host microenvironment can significantly affect tumormorphology and growth dynamics, emphasizing the importance of understanding thetumor-host interaction. The capability of our CA model suggests thatwell-developed in silico tools could eventually be utilized in clinicalsituations to predict neoplastic progression and propose individualized optimaltreatment strategies.
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