Predicting tumor dynamics in treated patients from patient-derived-xenograft mouse models: a translational model-based approach.

Abstract

This study presents a translational modeling framework designed to predict tumor size dynamics in cancer patients undergoing anticancer treatment, using data from patient-derived xenograft (PDX) mice. In the first step, a population tumor growth inhibition (TGI) model to estimate the distribution of exponential tumor growth rates and anticancer drug potency in PDX mice was built. Then, model parameters were allometrically scaled from mice to humans to inform a TGI model predicting tumor size dynamics in cancer patients. Longitudinal tumor dynamics predicted by the PDX-informed TGI model were expressed in terms of tumor progression events to allow validation against literature time-to-progression (TTP) data. The proposed approach was tested on two case studies: gemcitabine treatment for pancreatic cancer and sorafenib treatment for hepatocellular cancer. The framework successfully predicted median tumor size dynamics, closely aligned with clinical TTP curves for gemcitabine-pancreatic cancer case study. While predictions for extreme tumor size percentiles highlighted potential avenues for refinement, such as incorporating resistance mechanisms, the overall accuracy underscored the goodness of the approach. For the sorafenib-hepatocellular cancer case study, the framework provided plausible tumor size predictions, with TTP curves closely aligned with clinical observations, despite the limited availability of clinical data prevented a full validation. Overall, the translational modeling framework showed potential for predicting tumor dynamics in cancer patients, with results suggesting its applicability as a valid tool to support early decision-making in oncology.