Stochastic cancer-immune coevolution: Implications for cancer incidence and immunotherapeutic efficacy.
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e14023 Background: Despite recent progress, robust treatment strategies that lead to durable remission are still lacking for many cancer types. This disease is difficult to treat owing in part to the complexity introduced by a heterogeneous population of cancer cells capable of evolving mechanisms of resistance to traditional therapy. Nonetheless, the discovery and continued optimization of T-cell immunotherapy has revolutionized the treatment of many cancers. This treatment strategy stands out from other approaches in its unique ability to co-evolve alongside an evading tumor. While promising, such therapies are also complex. For example, allogeneic stem cell transplantation leverages a donor-derived T-cell repertoire to treat patients with refractory hematologic malignancies and relies upon a delicate balance between desirable anti-tumor effects and potentially life-threatening graft-versus-host-disease. Currently, the decision to utilize this therapy and others like it is largely influenced by prior empirical evidence. Thus, there is great need for quantitative models of the cancer-immune interaction to generate testable predictions of treatment outcome, which could then be validated prior to T-cell immunotherapy administration. Methods: We develop a foundational mathematical model to investigate the properties of stochastic tumor-immune co-evolution using applied stochastic process theory and probabilistic analysis. We use this model to predict the effects of reduced immunity, T-cell diversity, and thymic turnover rates on cancer incidence, and compare model simulations to cancer evolutionary data. Results: We predict that changes in T-cell diversity, and to a lesser degree thymic turnover, increase the chance of tumor progression. When applied to experimental data, we demonstrate that the observations are consistent with co-evolution between an indolent cancer population and the adaptive immune system prior to clinical disease. Conclusions: Our results provide a fundamental framework for analyzing the interaction dynamics of an evolving threat like cancer and the adaptive immune system in order to better understand and predict immunotherapeutic efficacy.
