最近的证据表明，在癌症演变的所有阶段，非遗传（表观遗传）机制发挥着重要的作用。在许多癌症中，这些机制被观察到在两个或多个细胞状态之间诱导动态转换，这些状态通常对药物治疗表现出不同的反应。为了理解这些癌症随着时间的推移如何演变以及它们如何对治疗作出反应，我们需要了解细胞增殖和表型转换的状态依赖速率。在这项工作中，我们提出了一个严格的统计框架，利用常见的细胞系实验数据估计这些参数，其中表型在培养中被分选和扩展。该框架明确模拟了细胞分裂，细胞死亡和表型转换的随机动力学，并为模型参数提供基于似然的置信区间。输入数据可以是一个或多个时间点中每种状态的细胞的分数或数量。通过理论分析和数值模拟的组合，我们展示了当使用细胞分数数据时，转换速率可能是唯一可以准确估计的参数。另一方面，使用细胞数量数据可以准确估计每种表型的净分裂速率，甚至可以估计状态依赖性的细胞分裂和细胞死亡速率。最后，我们将我们的框架应用于一个公开可用的数据集。版权所有© 2023 Elsevier Ltd.。

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.Copyright © 2023 Elsevier Ltd. All rights reserved.