Dissecting Tumor Response to Radiation Therapy Using Genetically Engineered Mouse Models

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2015

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Approximately 50% of all patients with cancer receive radiation therapy at some point during the course of their illness. Despite advances in radiation delivery and treatment planning, normal tissue toxicity often limits the ability of radiation to eradicate tumors. The tumor microenvironment consists of tumor cells and stromal cells such as endothelial cells that contribute to tumor initiation, progression and response to therapy. Although endothelial cells can contribute to normal tissue injury following radiation, the contribution of stromal cells to tumor response to radiation therapy remains controversial. To investigate the contribution of endothelial cells to the radiation response of primary tumors, we have developed the technology to contemporaneously mutate different genes in the tumor cells and stromal cells of a genetically engineered mouse model of soft tissue sarcoma. Using this dual recombinase technology, we deleted the DNA damage response gene Atm in sarcoma and heart endothelial cells. Although deletion of Atm increased cell death of proliferating tumor endothelial cells, Atm deletion in quiescent endothelial cells of the heart did not sensitize mice to radiation-induced myocardial necrosis. In addition, the ATM inhibitor NVP-BEZ235 selectively radiosensitized primary sarcomas, demonstrating a therapeutic window for inhibiting ATM during radiation therapy. Sensitizing tumor endothelial cells to radiation by deleting Atm prolonged tumor growth delay following a non-curative dose of radiation, but failed to increase local control. In contrast, deletion of Atm in tumor parenchymal cells increased the probability of tumor eradication. These results demonstrate that tumor parenchymal cells rather than endothelial cells are the critical targets that regulate tumor eradicaiton by radiation therapy.

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Moding, Everett James (2015). Dissecting Tumor Response to Radiation Therapy Using Genetically Engineered Mouse Models. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/9790.

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