Abstract 4613: Mathematical modeling of tumor growth in mouse models

Abstract

Abstract A variety of tumor models are used in preclinical oncology drug discovery, including patient-derived xenografts (PDXs), cell line-derived xenografts (CDXs), and syngeneic homografts. Tumors in mouse models exhibit heterogeneous growth patterns with or without drug treatment, which is further complicated by measurement errors. Mathematical modeling of tumor growth helps us better understand such patterns and analyze drug efficacy in transplanted tumor models. Many mathematical models have been proposed to describe tumor growth curves, each with certain assumptions and equations, suitable for specific situations or data sets1-5. It is yet unknown which models are best in fitting the growth curves for a large collection of mouse models. In this study, we systematically evaluated a set of mathematical models including exponential models, logistic and Gompertz model, biphasic models, dynamic carrying capacity model, and power law model on more than 50,000 tumor growth datasets collected from in vivo efficacy studies in PDXs, CDXs and syngeneic models. A set of metrics were used to evaluate the goodness of fit, including Akaike Information Criterion (AIC), root mean square error (RMSE), coefficient of determination, mean absolute relative error, etc. We also proposed a new composite metric to combine many criteria for easy selection of growth models. Our results show that tumor growth patterns are less complex under vehicle treatment, and different mathematical models have relatively small difference in modeling the growth curves. Tumor growth patterns are more diverse under drug treatment. Tumors may shrink or completely remit, though some may grow in the first days after inoculation before drug effect takes place. Still some tumors stop growing or even shrink before drug resistance develops and resume fast growing. Therefore, different mathematical models are needed to describe the different patterns. Finally, we present summary statistics to give an overall picture on the applicability of the mathematical models, and suggest statistical models for subsequent analysis for drug efficacy evaluation and biomarker discovery. Citation Format: Chao Zhang, Xiaoqian Jiang, Sheng Guo, Qixiang Li. Mathematical modeling of tumor growth in mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4613.