Despite decades of effort, most disseminated cancers remain incurable, and progression from localized to metastatic disease is largely responsible for cancer-related mortality. In metastatic disease, an ongoing battle between tumor and host occurs at each site; tumor-associated antigens, stress proteins, and danger-associated molecular patterns can both initiate and continually stimulate an immune response against a tumor. Adding an additional layer of complexity, locally activated cytotoxic T cells traffic through the host circulatory system to also surveil metastasis elsewhere in the body. Thus, metastatic tumors are highly interdependent; changes in the nonlinear tumor-immune interactions in one tumor can perturb the systemic antitumor immune response, potentially facilitating spontaneous regression or aggressive outgrowth in distant sites. This can additionally influence the clinical outcome of therapeutic intervention, which depends on therapy-induced changes in tumor-immune dynamics both locally and systemically, as well as patient-specific initial conditions of the global disease.

Using the tools of mathematical oncology we gain insights into this complex interconnectivity between metastatic sites. ODE models have been developed which incorporate both local tumor-immune interactions in each tumor site and the trafficking of activated T cells systemically, and can be parameterized by experimental and clinical investigations. Using four primary tumor sites (lung, liver, breast, kidney) we simulate the growth behavior of a primary site upon both seeding, growth, and treatment of a secondary (or several) metastatic site(s).

In the presence of several metastatic sites, the dissemination of activated T cells is not necessarily intuitive and depends on many factors including the blood flow fraction to each organ and the tumor volume to organ size ratio. Certain sites may experience inhibition of growth, and other sites may be promoted upon seeding of an additional site. We computationally analyze all combinations of metastatic sites, and predict which sites are likely to benefit (tumor growth) and which to suffer (tumor shrinkage) in each combination based upon known properties of physiological blood flow and tumor volume at initial presentation. This allows the identification of which sites in each combination would be the optimal target for surgical therapy in order to induce the maximum reduction in overall tumor burden. Furthermore, we can simulate local radiation at each respective site, quantifying the difference in model-predicted decrease in overall tumor burden between individual targets.

The results facilitate an improved understanding of general disease kinetics in the metastatic setting, emphasize that "local" therapy is highly likely to have systemic effects, and support the case for a paradigm shift in treatment target selection for metastatic disease.

Citation Format: Rachel Walker, Jan Poleszczuk, Heiko Enderling. Local and systemic tumor-immune dynamics in metastatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4543. doi:10.1158/1538-7445.AM2017-4543