Abstract 3751: Defining the temporal effects of TGFβ inhibition on the cellular heterogeneity of the bone metastatic prostate cancer microenvironment

Abstract

Abstract Introduction. Bone metastatic prostate cancer is currently incurable. Traditional in vivo experimentation has provided insight into the circuitry driving the osteolytic and osteogenic nature of the disease but is limited in its ability to interrogate multiple simultaneous interactions occurring in the tumor-bone microenvironment. We hypothesize that this limitation can be overcome by combining the power of predictive computational modeling with biological approaches. To this end, we developed a clinically relevant hybrid cellular automaton model of bone metastatic prostate cancer to test the impact of putative targeted therapies. Herein, we examined the impact of inhibiting transforming growth factor beta (TGFβ) because of its well-described pleiotropic effects on the tumor-bone microenvironment. Results. In silico, simulations of 250 days (n = 27) were performed for five different levels of TGFβ inhibition (0-100%), applied either pre- or post- metastatic seeding. The computational outputs predicted that TGFβ inhibition will only work if administered as a pre-treatment. In this scenario, TGFβ inhibition (at 80% inhibition); 1) reduced tumor cell number by ∼25%, 2) prevented osteoclast precursor infiltration and maturation by 40% and surprisingly, 3) reduced tumor-induced osteogenesis by ∼14%; all data were significant with p<0.0001. Next we tested these predictions in vivo with an osteogenic and TGFβ responsive model of bone metastatic prostate cancer (PAIII). Pre-treatment of mice (n = 9/group) with a TGFβ inhibitor (1D11) prior to intratibial inoculation of luciferase expressing PAIII cells remarkably validated computational model predictions while post-treatment (n = 7/group) with 1D11 had little or no effect. Unlike our homogeneous in vivo model, analysis of human clinical specimens (n = 20) of bone metastatic prostate cancer revealed heterogeneous expression of TGFβ ligand and receptor in prostate cancer cells. Again, computational modeling is a powerful way to address the issue of heterogeneity and using TGFβ as an example, the computational model was seeded with equal ratios of TRP (ligand and receptor-producing), TR (receptor-expressing), and TN (neutral) cells. In silico control simulations (n = 30) reveal that TR cancer cells are the dominant population. However, application of a TGFβ inhibitor in silico results in the eventual emergence (∼100 days later) of the TN population. These data suggest that adaptive application of an inhibitor such as TGFβ could prevent the emergence of resistant populations over time. Conclusions. Integration of computational and biological approaches can be a powerful tactic in determining the temporal impact of putative therapies on heterogeneous tumor microenvironments. Further, the computational model can be of major benefit in optimizing treatments for the eradication of bone metastatic prostate cancer. Citation Format: Leah M. Cook, Arturo Araujo, David Basanta, Conor C. Lynch. Defining the temporal effects of TGFβ inhibition on the cellular heterogeneity of the bone metastatic prostate cancer microenvironment. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3751. doi:10.1158/1538-7445.AM2015-3751