p53 and Chemosensitivity in a Mouse Lymphoma Model

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p53 acts in a complex tumor suppressor network that mediates cellular responses to stress. Thus, p53 is activated in response to diverse cellular insults, including mitogenic oncogenes, hypoxia, oxidative stress, and DNA damage. Once activated, p53 can trigger a variety of anti-proliferative programs, including apoptosis and cellular senescence, by targeting multiple components of each program's effector machineries. Since many of the chemotherapeutic agents currently used to treat cancer directly or indirectly damage DNA, they often rely on the integrity of the p53 pathway to elicit their anti-tumor effects. As a consequence, drug resistance can arise as a byproduct of tumor evolution. This project is interested in how p53 modulates the action of conventional and targeted drugs, and the implications of disrupting the p53 network at different points from tumor evolution to drug resistance. To study these processes, we use the E-mu-myc transgenic model of B-cell lymphoma as a tractable yet physiological test system. Over the last funding period, we developed methods to rapidly produce lymphomas with complex genotypes, and then study the impact of these lesions on tumor cell responses to therapy using approaches that parallel clinical trials. Using this system, we identified a number of biological (apoptosis, senescence, translational control) and genetic (p53, ARF, INK4a/ARF, Bcl-2, Akt, elF4E) determinants of drug action. In the current proposal, we will continue to study components of the p53 network that influence apoptosis and senescence for their impact on chemosensitivity, and will examine how survival signaling through the PI3kinase/Akt network affects p53 action to promote chemoresistance. However, we also will initiate efforts to explore the utility of the E-mu-myc system to study new drugs, in particular, by testing strategies to reverse chemoresistance in vivo. Furthermore, we will exploit the unique features of the E-mu-myc model to conduct high throughput screens to identify new genes that influence treatment responses in vivo. We expect that our results will produce a better understanding of the p53 and Akt networks and how they influence drug sensitivity and resistance in vivo. By understanding mechanisms of drug resistance and testing strategies to circumvent it, we may identify new therapeutic targets or treatment strategies that can be extended to clinical trials.