Abstract 4264: Mathematical modeling of differential effects of sunitinib on primary tumor and metastatic growth

Abstract

Abstract Sunitinib is a drug with anti-angiogenic activity used in the treatment of patients with metastases from renal cell carcinoma or gastrointestinal tumors. However, despite clear efficacy in reducing established tumor growth, recent preclinical studies have shown limited, or even opposing, efficacies in preventing metastatic spread. In this work, we evaluated a mathematical metastatic model to describe primary tumor and metastatic dynamics in response to sunitinib in a clinically relevant mouse model of spontaneous metastatic breast cancer that develops after surgical removal of an orthotopically implanted primary tumor. Mice received either vehicle or sunitinib in the neoadjuvant (presurgical) setting according to different schedules. The experimental dataset comprises measurements of primary tumor kinetics, metastatic burden and pre-surgical molecular and cellular biomarkers, including vascular cell Ki67 and CD31 expression, circulating tumor cells (CTCs) and myeloid derived suppressor cells (MDSCs). Estimation of the model's parameters was performed using a mixed-effects population approach. To describe tumor growth under sunitinib treatment, a simple ordinary differential equation model of tumor growth inhibition was used. Population fits obtained modeling the effect of treatment only on primary tumor growth described well the experimental data of all the treated groups considered. On the contrary, simulations of treatment also on metastasis could not reproduce the behavior of the data. By inserting in the model the available biomarkers as covariates, through a Wald-test, measurements of Ki67+/CD31+, CTCs and granulocytic MDSCs were found significantly correlated with the model parameter expressing the metastatic aggressiveness of the tumor. Together, these mathematical models confirm a differential effect of sunitinib on primary (localized) tumors compared to secondary (metastatic) disease. Our results suggest that Ki67+/CD31+, CTCs and MDSCs measurements might help in predicting metastatic potential and thus aid in predicting benefit in overall survival for preoperative antiangiogenic treatments. Citation Format: Chiara Nicolò, Michalis Mastri, Amanda Tracz, John M. Ebos, Sébastien Benzekry. Mathematical modeling of differential effects of sunitinib on primary tumor and metastatic growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4264.