Evaluation of Adoptive T Cell Therapy and Oncolytic Virotherapy for Treatment of Brain Tumors

Adoptive immunotherapy and oncolytic virotherapy are two promising strategies for treating primary and metastatic malignant brain tumors. We demonstrate the ability of adoptively transferred tumor-specific T cells to rapidly mediate the clearance of established brain tumors in several mouse models. Similar to the clinical situation, tumor recurrences are frequent and result from immune editing of tumors. T cells can eliminate antigen-expressing tumor cells but are not effective against antigen loss variant (ALV) cancer cells that multiply and repopulate a tumor. We show that the level of tumor antigen present affects the success of adoptive T cell therapy. When high levels of antigen are present, tumor stromal cells such as microglia and macrophages present tumor peptide on their surface. As a result, T cells directly eliminate cancer cells and cross-presenting stromal cells and indirectly eliminate ALV cells. We were able to show the first direct evidence of tumor antigen cross-presentation by CD11b+ stromal cells in the brain using soluble, high-affinity T cell receptor monomers. Strategies that target brain tumor stroma or increase antigen shedding from tumor cells leading to increased crosspresentation by stromal cells may improve the clinical success of T cell adoptive therapies. We evaluated one potential strategy to complement adoptive T cell therapy by characterizing the oncolytic effects of myxoma virus (MYXV) in a syngeneic mouse brain tumor model of metastatic melanoma. MYXV is a rabbit poxvirus with strict species tropism for European rabbits. MYXV can also infect mouse and human cancer cell lines due to signaling defects in innate antiviral mechanisms and hyperphosphorylation of Akt. MYXV kills B16.SIY melanoma cells in vitro, and intratumoral injection of virus leads to robust, selective and transient infection of the tumor. We observed that virus treatment recruits innate immune cells iii to the tumor, induces TNFα and IFNβ production in the brain, and results in limited oncolytic effects in vivo. To overcome this, we evaluated the safety and efficacy of co-administering 2C T cells, MYXV, and neutralizing antibodies against IFNβ. Mice that received the triple combination therapy survived significantly longer with no apparent side effects, but eventually relapsed. Based on these findings, methods to enhance viral replication in the tumor and limit immune clearance of the virus will be pursued. We conclude that myxoma virus should be further explored as a vector for transient delivery of therapeutic genes to a tumor to enhance T cell responses.

A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.