Title: Statistical inference of the rates of cell proliferation and phenotypic switching in cancer Show Chinese title

Title: 癌细胞增殖和表型转换率的统计推断

Abstract: 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. Show Chinese abstract

Abstract: 最近的证据表明，非遗传（表观遗传）机制在癌症演化的所有阶段都起着重要作用。 在许多癌症中，这些机制已被观察到在两种或多种细胞状态之间引起动态转换，这些状态通常对药物治疗有不同的反应。 为了理解这些癌症随时间的演化方式以及它们对治疗的反应，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，用于估计这些参数，使用的是常见的细胞系实验数据，其中表型在培养中被分选和扩增。 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，并为模型参数提供了基于似然的置信区间。 输入数据可以是每个时间点的一个或多个状态下细胞的比例或数量。 通过理论分析和数值模拟的结合，我们表明当使用细胞比例数据时，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率，并且甚至可以估计状态依赖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的数据集。