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随机竞争释放与适应性化疗。

Stochastic competitive release and adaptive chemotherapy.

机构信息

Department of Mathematics, University of Southern California, Los Angeles, California 90089-1191, USA.

Department of Aerospace & Mechanical Engineering, Department of Mathematics, and Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089-1191, USA.

出版信息

Phys Rev E. 2023 Sep;108(3-1):034407. doi: 10.1103/PhysRevE.108.034407.

Abstract

We develop a finite-cell model of tumor natural selection dynamics to investigate the stochastic fluctuations associated with multiple rounds of adaptive chemotherapy. The adaptive cycles are designed to avoid chemoresistance in the tumor by managing the ecological mechanism of competitive release of a resistant subpopulation. Our model is based on a three-component evolutionary game played among healthy (H), sensitive (S), and resistant (R) populations of N cells, with a chemotherapy control parameter, C(t), which we use to dynamically impose selection pressure on the sensitive subpopulation to slow tumor growth and manage competitive release of the resistant population. The adaptive chemoschedule is designed based on the deterministic (N→∞) adjusted replicator dynamical system, then implemented using the finite-cell stochastic frequency dependent Moran process model (N=10K-50K) to ascertain the cumulative effect of the stochastic fluctuations on the efficacy of the adaptive schedules over multiple rounds. We quantify the stochastic fixation probability regions of the R and S populations in the HSR trilinear phase plane as a function of the control parameter C∈[0,1], showing that the size of the R region increases with increasing C. We then implement an adaptive time-dependent schedule C(t) for the stochastic model and quantify the variances (using principal component coordinates) associated with the evolutionary cycles over multiple rounds of adaptive therapy. The variances increase subquadratically through several rounds before the evolutionary cycle begins to break down. Despite this, we show the stochastic adaptive schedules are more effective at delaying resistance than standard maximum tolerated dose and low-dose metronomic schedules. The simplified low-dimensional model provides some insights on how well multiple rounds of adaptive therapies are likely to perform over a range of tumor sizes (i.e., different values of N) if the goal is to maintain a sustained balance among competing subpopulations of cells to avoid chemoresistance via competitive release in a stochastic environment.

摘要

我们开发了一个肿瘤自然选择动力学的有限单元模型,以研究与多轮适应性化疗相关的随机波动。适应性循环旨在通过管理抵抗亚群竞争释放的生态机制来避免肿瘤的耐药性。我们的模型基于一个由 N 个细胞组成的健康(H)、敏感(S)和抵抗(R)三种成分的进化博弈,其中化疗控制参数 C(t),我们用它来对敏感亚群施加选择压力,以减缓肿瘤生长并管理抵抗种群的竞争释放。适应性化疗方案是基于确定性(N→∞)调整复制者动力系统设计的,然后使用有限单元随机频率相关 Moran 过程模型(N=10K-50K)来确定随机波动对多个回合适应性方案效果的累积影响。我们将 R 和 S 种群在 HSR 三线相平面中的随机固定概率区域量化为控制参数 C∈[0,1]的函数,表明随着 C 的增加,R 区域的大小增加。然后,我们为随机模型实施了一个自适应时变的控制参数 C(t),并量化了多个回合自适应治疗中与进化循环相关的方差(使用主成分坐标)。方差在几个回合内呈次二次增加,然后进化循环开始崩溃。尽管如此,我们还是表明,与标准最大耐受剂量和低剂量节拍化疗方案相比,随机自适应方案在延迟耐药方面更有效。简化的低维模型提供了一些见解,如果目标是通过竞争释放在随机环境中避免耐药性,那么在不同的肿瘤大小(即,不同的 N 值)范围内,多轮适应性治疗的表现如何。

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