Mathematical modeling of differential effects of neo-adjuvant Sunitinib on primary tumor and metastatic growth

International audienceSunitinib 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 [1, 2]. In this work, we evaluated a previously validated mechanistic mathematical model of metastasis [3] to describe primary tumor and metastatic dynamics in response to neoadjuvant anti-angiogenic treatment in clinically relevant mouse models of spontaneous metastatic breast and kidney cancers that develop after surgical removal of orthotopically implanted primary tumors. The data of more than 380 mice receiving either vehicle or sunitinib in the neoadjuvant (presurgical) setting according to different schedules was analyzed. The experimental datasets comprise measurements of primary tumor and metastatic burden kinetics as well as pre-surgical molecular and cellular biomarkers, including vascular cell Ki67 and CD31 expression, circulating tumor cells (CTCs) and myeloid derived suppressor cell counts (MDSCs). Estimation of the mathematical model's parameters was performed using a mixed-effects population approach. Population fits obtained modeling the effect of treatment only on primary tumor growth described well the experimental data of all the treated groups considered, suggesting a negligible effect of the neo-adjuvant treatment on early metastatic spread and growth. When inserting in the model the available biomarkers as covariates, measurements of Ki67+/CD31+, CTCs and granulocytic MDSCs were found significantly correlated with a specific model parameter expressing the metastatic aggressiveness of the tumor. Together, our mathematical model confirms a differential effect of sunitinib on primary (localized) tumors compared to secondary (metastatic) disease. Our results suggest that CTCs and MDSCs might help in predicting metastatic potential and provide a biologically-based computational model integrating these biomarkers into personalized predictions of metastatic benefit of pre-operative treatments. [1] Ebos, J. M. L., Lee, C. R., Cruz-Munoz, W., Bjarnason, G. A., Christensen, J. G., and Kerbel, R. S. (2009). Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell, 15(3):232-239