Tumor-immune ecosystem dynamics exploration via a high-resolution agent-based model

Thomas Dombrowski

Moffitt Cancer Center

"Tumor-immune ecosystem dynamics exploration via a high-resolution agent-based model"

BACKGROUND: Radiation therapy is the single most utilized therapeutic agent in oncology, yet in the biology-driven medicine era, advances in radiation oncology have primarily focused on improving physical dose properties. As a result, the field of radiation oncology currently does not individualize radiation dose prescription based on the intrinsic biology of a patient’s tumor. METHODS: We develop a high resolution, 3D multiscale agent-based model that simulates the interactions of cancer cells with antitumor immune effector T-cells and immune-inhibitory suppressor cells. The immune cells and cancer cells are treated to be on a staggered lattice, where the immune cells are located at the cell vertices and the cancer cells are located at the centroid of the 3D unit lattice. Each cell is considered as an individual agent, and their behavior at any time is determined by a stochastic decision-making process based on biological-driven mechanistic rules. The absolute numbers of effector and suppressor immune cells in conjunction with the cancer cell burden were used to define the tumor-immune ecosystem (TIES). RESULTS: Simulations of tumor growth in various TIES reveal that in our model, the tumor-immune ecosystem yields 2 functional phenotypes: where tumors evade immune predation and where tumors are eradicated by the immune system. The immune cells are seen to dynamically move via chemokinesis with components of Brownian motion (exploration) and of directed motion toward the highest gradient of dead cancer cells (exploitation). Mechanistic rules are defined at a local and individual level to impose spatial restrictions on the immune cells and prevent immediate infiltration to the center of the tumor. The resulting movement and spatial rules lead to an emergent local immune swarming and formation of tertiary lymphoid structures. CONCLUSION: This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy. This work was supported by the NIH/NCI 1U01CA244100
Additional authors: Daniel Grass (Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA); Javier Torres Roca (Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA); Shari Pilon-Thomas (Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA); Steven Eschrich (Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA); Heiko Enderling (Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA)

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