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J Theor Biol. 2018 Dec 14;459:67-78. doi: 10.1016/j.jtbi.2018.09.022. Epub 2018 Sep 20.

Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer.

Author information

1
Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA; Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands. Electronic address: jessica.cunningham@maastrichtuniversity.nl.
2
Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA; Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.
3
Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA; Department of Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center & Research Institute, Tampa, Florida, USA.
4
Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands; Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.

Abstract

In metastatic castrate resistant prostate cancer (mCRPC), abiraterone is conventionally administered continuously at maximal tolerated dose until treatment failure. The majority of patients initially respond well to abiraterone but the cancer cells evolve resistance and the cancer progresses within a median time of 16 months. Incorporating techniques that attempt to delay or prevent the growth of the resistant cancer cell phenotype responsible for disease progression have only recently entered the clinical setting. Here we use evolutionary game theory to model the evolutionary dynamics of patients with mCRPC subject to abiraterone therapy. In evaluating therapy options, we adopt an optimal control theory approach and seek the best treatment schedule using nonlinear constrained optimization. We compare patient outcomes from standard clinical treatments to those with other treatment objectives, such as maintaining a constant total tumor volume or minimizing the fraction of resistant cancer cells within the tumor. Our model predicts that continuous high doses of abiraterone as well as other therapies aimed at curing the patient result in accelerated competitive release of the resistant phenotype and rapid subsequent tumor progression. We find that long term control is achievable using optimized therapy through the restrained and judicious application of abiraterone, maintaining its effectiveness while providing acceptable patient quality of life. Implementing this strategy will require overcoming psychological and emotional barriers in patients and physicians as well as acquisition of a new class of clinical data designed to accurately estimate intratumoral eco-evolutionary dynamics during therapy.

KEYWORDS:

Adaptive therapy; Competitive release; Eco-evolutionary dynamics; Evolutionary game theory; Metastatic castrate-resistant prostate cancer; Optimal control

PMID:
30243754
DOI:
10.1016/j.jtbi.2018.09.022

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