Abstract A13: Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer

Abstract

Abstract The ability to rapidly assess the efficacy of therapeutic strategies for incurable bone metastatic prostate cancer is an important and urgent need. Pre-clinical in vivo models provide insights yet are limited in their capacity to interrogate temporal multi-cellular interactions occurring in the cancer-bone microenvironment. Computational and mathematical models that integrate and, are validated experimentally, can overcome this limitation and accelerate biological research. Here, we describe how a biologically driven discrete continuum hybrid cellular automaton (HCA) model approach can dissect the pleiotropic effects of inhibiting putative therapeutic targets such as, TGFβ. Using our HCA model, we tested five different therapeutic doses delivered in two therapeutic windows. In silico results predict that TGFβ inhibition, applied prior to tumor seeding acts by directly impacting prostate cancer cell viability but also by simultaneously restricting osteoclast formation and unexpectedly, promoting osteoblast differentiation. This effect was dependent on the prostate cancer cell expression of TGFβ receptors. In silico predictions were validated with two independent in vivo models of bone metastatic prostate cancer (PAIII and C4-2B). Using immunohistochemical information from human bone metastatic prostate cancer samples, we also demonstrate how the HCA can be used to predict the evolution of heterogeneous disease in response to applied therapies. Collectively, these data underscore the power of a combined HCA/biological approach in optimizing the efficacy of applied therapies and measuring their impact on bone metastatic prostate cancer. Citation Format: Leah Cook, Arturo Araujo, Julio Pow-Sang, Mikalai Budzevich, David Basanta, Conor C. Lynch. Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A13.