Recent clinical studies suggest that the efficacy of hormone therapy for prostate cancer depends on the characteristics of individual patients. In this paper, we develop a computational framework for identifying patient-specific androgen ablation therapy schedules for postponing the potential cancer relapse. We model the population dynamics of heterogeneous prostate cancer cells in response to androgen suppression as a nonlinear hybrid automaton. We estimate personalized kinetic parameters to characterize patients and employ δ-reachability analysis to predict patient-specific therapeutic strategies. The results show that our methods are promising and may lead to a prognostic tool for personalized cancer therapy.
@article{arxiv.1410.7346,
title = {Towards Personalized Prostate Cancer Therapy Using Delta-Reachability Analysis},
author = {Bing Liu and Soonho Kong and Sicun Gao and Paolo Zuliani and Edmund M. Clarke},
journal= {arXiv preprint arXiv:1410.7346},
year = {2015}
}