Antineoplastic Cytotoxicity and Immune Adjuvancy of a Recombinant Oncolytic Poliovirus

Our group has pioneered the development of a live-attenuated poliovirus, called PVSRIPO, for the purpose of targeting cancer. Despite clinical progress, the cancer selective cytotoxicity and immunotherapeutic potential of PVSRIPO has not yet been mechanistically dissected. Defining such mechanisms may inform its clinical application.

Herein I describe the discovery of a mechanism by which the MAP-Kinase Interacting Kinases (MNKs) regulate PVSRIPO cytotoxicity in cancer. In doing so, I delineate a novel, intricate network connecting the MNK and mTOR signaling pathway that regulates activity of a splicing kinase called the Ser-Arg Rich Protein Kinase (SRPK), and define SRPK as an impediment to IRES mediated translation. Moreover, I demonstrate that MNK regulates mTORC1 associations that determine its substrate proximity and thus, activity. In a collaborative effort, we found that PVSRIPO oncolysis produces antigen specific, cytolytic anti-tumor immunity in an in vitro human system and that much of the observed adjuvancy is due to the direct infection of dendritic cells (DCs) by the virus itself; implicating PVSRIPO as a potent adjuvant. In summary, oncogenic signaling in part through MNK leads to cancer specific cytotoxicity by PVSRIPO that engages an inflammatory environment conducive to DC activation and antigen specific T cell antigen immunity.

Game-Theoretically Allocating Resources to Catch Evaders and Payments to Stabilize Teams

Allocating resources optimally is a nontrivial task, especially when multiple

self-interested agents with conflicting goals are involved. This dissertation

uses techniques from game theory to study two classes of such problems:

allocating resources to catch agents that attempt to evade them, and allocating

payments to agents in a team in order to stabilize it. Besides discussing what

allocations are optimal from various game-theoretic perspectives, we also study

how to efficiently compute them, and if no such algorithms are found, what

computational hardness results can be proved.

The first class of problems is inspired by real-world applications such as the

TOEFL iBT test, course final exams, driver's license tests, and airport security

patrols. We call them test games and security games. This dissertation first

studies test games separately, and then proposes a framework of Catcher-Evader

games (CE games) that generalizes both test games and security games. We show

that the optimal test strategy can be efficiently computed for scored test

games, but it is hard to compute for many binary test games. Optimal Stackelberg

strategies are hard to compute for CE games, but we give an empirically

efficient algorithm for computing their Nash equilibria. We also prove that the

Nash equilibria of a CE game are interchangeable.

The second class of problems involves how to split a reward that is collectively

obtained by a team. For example, how should a startup distribute its shares, and

what salary should an enterprise pay to its employees. Several stability-based

solution concepts in cooperative game theory, such as the core, the least core,

and the nucleolus, are well suited to this purpose when the goal is to avoid

coalitions of agents breaking off. We show that some of these solution concepts

can be justified as the most stable payments under noise. Moreover, by adjusting

the noise models (to be arguably more realistic), we obtain new solution

concepts including the partial nucleolus, the multiplicative least core, and the

multiplicative nucleolus. We then study the computational complexity of those

solution concepts under the constraint of superadditivity. Our result is based

on what we call Small-Issues-Large-Team games and it applies to popular

representation schemes such as MC-nets.