Stochastic modeling of tumor progression and immune evasion

Abstract

It is now well-established that the host's adaptive immune system plays an important role in identifying and eliminating cancer cells in much the same way that intracellular pathogens are cleared during an adaptive immune response to infection. From a therapeutic standpoint, the adaptive immune system is unique in that it can co-evolve alongside a developing tumor. Tumor acquisition of immune evasive phenotypes, such as class-I MHC down-regulation, remains a major limitation of successful T-cell immunotherapy. Here, we consider a population dynamical model coupling tumor and adaptive immune compartments in order to study the dynamics and survival of an evolving threat when faced with adaptive immune pressure. We demonstrate that predicted optimal growth strategies depend on whether or not the threat may acquire an immune-evasive phenotype as well as the mode of immune detection. We parameterize adaptive immune functioning by T-cell turnover and repertoire diversity and predict that decreases in the latter quantity which occur in advanced age may substantially affect the ability to recognize, and therefore control, an immune evasive threat like cancer. This framework recapitulates general features of age-dependent AML incidence, thereby providing a probable association between cancer frequency and adaptive immune functioning. Lastly, we quantify therapeutic efficacy of adjuvant immunotherapeutic strategies, and predict their benefits and limitations with regard to handling immune evasion. Our model generates survival behavior consistent with known growth-dependent characteristics, and serves as a first attempt at modeling stochastic cancer evolution alongside an adaptive immune compartment.

Keywords: Applied probability; Cancer immunotherapy; Immune evasion; Stochastic processes.