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Development of clinically relevant models for tumor growth
Recent advances in molecular biology and biotechnology have helped identify novel therapeutic targets that have led to the development of many new anticancer agents. However, only a handful of them show efficacy on human tumors due to a wide range genetic and epigenetic factors. As tumors grow in size, tumor cells and micro-environments in different regions become heterogeneous. Tumor tissue is heterogeneous with respect to metabolic milieu, micro-vascular density and permeability, drug susceptibility of local cell populations, all of which directly affects chemotherapeutic efficacy. Cancer cells are known to have altered metabolism and tumor metabolic microenvironment is characterized by hypoxia, high levels of lactate and lower pH. We incorporated a model of primary energy metabolism with glucose, oxygen, and lactate as substrates within a reaction-convection model of tumor spheroid growth to better understand and explain observed nutrient concentration profiles and tumor physiology. The spheroid model was extended further to include cell-cycle progression, cellular drug effects, and drug pharmacokinetics; thereby quantifying the interaction between drug and tumor micro-environment. We found that oxygen transport has a greater effect than glucose transport on the distribution of drug-resistant quiescent cells. Model simulations showed the existence of an optimum drug diffusion coefficient: a low diffusivity prevents effective penetration before the drug is cleared from the blood and a high diffusivity limits drug retention. The simulations also showed that fast growing tumors are less responsive to therapy than are slower tumors with more quiescent cells, demonstrating the competing effects of regrowth and cytotoxicity. Dynamic contrast enhanced Magnetic Resonance Images of breast tumors are routinely used in clinics to size tumors. These contain dynamic information that reveal the spatial distribution of vasculature and vascular permeability, and can be used to characterize the accessibility of different regions in tumors. We have developed a predictive framework integrating functional patient specific information about tumor micro-environment from DCE-MRI’s into a dynamic mathematical model of tumor growth that predicts the outcome of a treatment modality. Our model predictions for the tumor growth and tumor drug response for patient tumors were correlated to the average tumor trans-vascular transport rate. Drug response predictions for tumors with heterogeneity incorporated were found to differ significantly those for homogeneous tumors suggesting that the transport heterogeneity present within a tumor must be taken into account for generating drug response predictions. The interaction of drug/nutrient transport dynamics and cell growth/death dynamics is central to the efficacy of chemotherapy and these models can be developed further and used as a tool to predict therapeutic outcome.
Venkatasubramanian, Raja, "Development of clinically relevant models for tumor growth" (2010). Doctoral Dissertations Available from Proquest. AAI3397750.