. Epithelial cell behavior is tightly regulated by the surrounding mE. This control is mediated through the coordinated actions of cell-cell adhesion, paracrine/autocrine growth factors and through adhesion to the extracellular matrix. Together, these mechanisms ensure that cells do not proliferate inappropriately or stray from their immediate mE niche. The process of oncogenic transformation and tumor progression entails the escape from these mechanisms, and the evolution ofthe tumor cell population towards phenotypes that allow them to become independent ofthe normal tissue mE. Activation ofthe underlying stromal fibroblasts, leading to the increased production of paracrine growth factors and pro-survival ECM is one way that developing tumors can achieve mE independence. The complexity of the host-tumor interaction in the carcinogenic process lends itself well to integrated experimental/mathematical based approaches, which are designed to handle multiple variables simultaneously. The current project will initially consider the mechanisms which control normal tissue homeostasis and subsequently homeostatic escape by using three different modeling approaches that examine the roles physical constraints, cell-mE interactions and evolutionary dynamics play in carcinogenesis. In the second part we will use novel in vitro organotypic cell culture models to test whether the presence of an activated stroma can provide the second 'hit' in the transformation of epithelial cells that have been immortalized using the step-wise introduction of activating oncogenes. The final part of the study will integrate our understanding of homeostasis to develop methods for homeostatic control that may require new experimental and theoretical developments. We expect that a deeper understanding of homeostatic escape, in terms of host-tumor interactions, will have major implications for cancer prevention and novel treatment strategies. As with the other projects in the PS-OC, Project 1 is built on the research paradigm that closely integrates mathematical modeling with empirical observations. The proposed research relies heavily on imaging (primarily microscopy) as the enabling technology that bridges cancer biology with the mathematical models. As in the other projects, we will pay close attention to the accuracy of information extraction from the images and critically examine the limits of the integration of imaging in informing model parameters and comparing to system dynamics predicted by model simulations.
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