A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity
A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity
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DOI：
10.1098/rsif.2015.0276
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发表时间：
2015-07-06
影响因子：
3.9
通讯作者：
Mantalaris, Athanasios
中科院分区：
文献类型：
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作者：
Fuentes-Gari, Maria;Misener, Ruth;Mantalaris, Athanasios
急性髓系白血病的特点是显著的患者间和患者内异质性，识别这种异质性对于个性化治疗的设计至关重要。白血病细胞的异质性是由最终影响细胞周期的突变决定的。我们已经开发并验证了一个生物学相关的细胞周期数学模型，该模型基于独特的细胞周期特征，由细胞周期阶段的持续时间和由流式细胞术确定的细胞周期蛋白谱定义，用于三种白血病细胞系。该模型在各自的进程变量（周期蛋白和DNA）中离散化不同阶段，得到一组随时间变化的常微分方程。细胞周期期分布和细胞周期蛋白浓度分布在种群追踪实验中得到验证。在盲法实验设置中，通过组合三种细胞系来模拟培养中的异质性。基于个体动力学，该模型能够识别和量化细胞异质性。当仅提供初始条件时，该模型预测了未来的细胞群体动态并估计了以前细胞的异质性组成。使用这样的数学平台在诊断和治疗后识别异质性白血病克隆，有可能预测诱导和巩固化疗的多种未来结果以及复发动力学。
Acute myeloid leukaemia is characterized by marked inter- and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.