Modeling cancer-immune responses to therapy

Abstract

Cancer therapies that harness the actions of the immune response, such as targeted monoclonal antibody treatments and therapeutic vaccines, are relatively new and promising in the landscape of cancer treatment options. Mathematical modeling and simulation of immune-modifying therapies can help to offset the costs of drug discovery and development, and encourage progress toward new immunotherapies. Despite advances in cancer immunology research, questions such as how the immune system interacts with a growing tumor, and which components of the immune system play significant roles in responding to immunotherapy are still not well understood. Mathematical modeling and simulation are powerful tools that provide an analytical framework in which to address such questions. A quantitative understanding of the kinetics of the immune response to treatment is crucial in designing treatment strategies, such as dosing, timing, and predicting the response to a specific treatment. These models can be used both descriptively and predictively. In this chapter, various mathematical models that address different cancer treatments, including cytotoxic chemotherapy, immunotherapy, and combinations of both treatments, are presented. The aim of this chapter is to highlight the importance of mathematical modeling and simulation in the design of immunotherapy protocols for cancer treatment. The results demonstrate the power of these approaches in explaining determinants that are fundamental to cancer-immune dynamics, therapeutic success, and the development of efficient therapies.

An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer

“…In exploring immunotherapy in combination with other treatment modalities, de Pillis et. al developed an ODE model governing cancer growth on a cell population level with a combination of immuno-chemotherapy treatments ( [26], [27], [28], [29]). In addition, Kim and colleagues formulated a mathematical model of therapy with oncolytic viruses that simultaneously express immunostimulatory cytokines and costimulatory molecules ( [12]).…”

A primer on recent developments in cancer immunotherapy, with a focus on neoantigen vaccines

“…Mathematical modeling has begun to impact the clinic through efforts to optimize dosage and timing ("schedule optimization"), which have gained a foothold in chemotherapy [232,233] and radiotherapy [234,235] . There is now a rich literature on the modeling of immunotherapy [236,237] . As an example, modeling the kinetics of the immune response [238] reveals the possibility that a proper choice of schedule can summon a robust T cell…”

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

“…Mathematical and computational models have been developed to simulate cancer and its interactions with the immune system (e.g., [28][29][30]). Some have even been designed for single simulations of 10 9 cells or more [31][32][33], but to date they have lacked one or more of the critical elements to systematically investigate cancer-immune dynamics across high-dimensional parameter spaces (or hypothesis spaces) to identify the factors driving immunotherapy failure or success.…”