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.
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.
J Theor Biol. 2018 Dec 14;459:67-78. doi: 10.1016/j.jtbi.2018.09.022. Epub 2018 Sep 20.
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.
在转移性去势抵抗性前列腺癌（mCRPC）中，阿比特龙通常以最大耐受剂量连续给药，直到治疗失败。大多数患者最初对阿比特龙反应良好，但癌细胞会产生耐药性，癌症在中位数为 16 个月的时间内进展。最近，一些旨在延迟或预防导致疾病进展的耐药癌细胞表型生长的技术才刚刚进入临床实践。在这里，我们使用进化博弈论来模拟接受阿比特龙治疗的 mCRPC 患者的进化动态。在评估治疗方案时，我们采用最优控制理论方法，并通过非线性约束优化寻求最佳治疗方案。我们将标准临床治疗的患者结果与其他治疗目标（如保持肿瘤总体积不变或最小化肿瘤内耐药癌细胞的比例）进行比较。我们的模型预测，连续高剂量的阿比特龙以及其他旨在治愈患者的治疗方法会加速耐药表型的竞争释放，并随后迅速导致肿瘤进展。我们发现，通过谨慎和明智地应用阿比特龙进行优化治疗，可以实现长期控制，在保持其有效性的同时，为患者提供可接受的生活质量。实施这一策略需要克服患者和医生的心理和情感障碍，以及获取一类新的临床数据，旨在在治疗过程中准确估计肿瘤内生态进化动态。
