Mechanisms of Adenovirus-Mediated Autophagy

A patient diagnosed with a glioma, generally, has an average of 14 months year to live after implementation of conventional therapies such as surgery, chemotherapy, and radiation. Glioblastomas are highly lethal because of their aggressive nature and resistance to conventional therapies and apoptosis. Thus other avenues of cell death urgently need to be explored. Autophagy, which is also known as programmed cell death type II, has recently been identified as an alternative mechanism to kill apoptosis- resistant cancer cells. Traditionally, researchers have studied how cells undergo autophagy during viral infection as an immune response mechanism, but recently researchers have discovered how viruses have evolved to manipulate autophagy for their benefit. Extensive studies of viral-induced autophagy provide a rationale to investigate other viruses, such as the adenovirus, which may be developed as part of a therapy against cancers resistant to apoptosis. Despite the present and relatively poor understanding of the mechanisms behind adenoviral-induced autophagy, adenovirus is a promising candidate, because of its ability to efficiently eradicate tumors. A better understanding of how the adenovirus induces autophagy will allow for the development of viruses with increased oncolytic potency. We hypothesized that adenovirus induces autophagy in order to aid in lysis. We found that replication, not infection, was required for adenovirus-mediated autophagy. Loss of function analysis of early genes revealed that, of the early genes tested, no single gene was sufficient to induce autophagy alone. Examination of cellular pathways for their role in autophagy during adenovirus infection revealed a function for the eIF2α pathway and more specifically the GCN2 kinase. Cells lacking GCN2 are more resistant to adenovirus-mediated autophagy in vitro; in vivo we also found these cells fail to undergo autophagy, but display more cell death. We believe that autophagy is a protective mechanism the cell employs during adenoviral infection, and in the in vivo environment, cells cannot recover from virus infection and are more susceptible to death. Congruently, infected cells deficient for autophagy through deletion of ATG5 are not able undergo productive cell lysis, providing evidence that the destruction of the cytoplasm and cell membrane through autophagy is crucial to the viral life cycle. This project is the first to describe a gene, other than a named autophagy gene, to be required for adenovirus- mediated autophagy. It is also the first to examine autophagic cell death as a means to aid in viral-induced cell lysis.

Improving the accuracy of radiation pneumonitis dose response models

The prognosis for lung cancer patients remains poor. Five year survival rates have been reported to be 15%. Studies have shown that dose escalation to the tumor can lead to better local control and subsequently better overall survival. However, dose to lung tumor is limited by normal tissue toxicity. The most prevalent thoracic toxicity is radiation pneumonitis. In order to determine a safe dose that can be delivered to the healthy lung, researchers have turned to mathematical models predicting the rate of radiation pneumonitis. However, these models rely on simple metrics based on the dose-volume histogram and are not yet accurate enough to be used for dose escalation trials. The purpose of this work was to improve the fit of predictive risk models for radiation pneumonitis and to show the dosimetric benefit of using the models to guide patient treatment planning. The study was divided into 3 specific aims. The first two specifics aims were focused on improving the fit of the predictive model. In Specific Aim 1 we incorporated information about the spatial location of the lung dose distribution into a predictive model. In Specific Aim 2 we incorporated ventilation-based functional information into a predictive pneumonitis model. In the third specific aim a proof of principle virtual simulation was performed where a model-determined limit was used to scale the prescription dose. The data showed that for our patient cohort, the fit of the model to the data was not improved by incorporating spatial information. Although we were not able to achieve a significant improvement in model fit using pre-treatment ventilation, we show some promising results indicating that ventilation imaging can provide useful information about lung function in lung cancer patients. The virtual simulation trial demonstrated that using a personalized lung dose limit derived from a predictive model will result in a different prescription than what was achieved with the clinically used plan; thus demonstrating the utility of a normal tissue toxicity model in personalizing the prescription dose.