A Bayesian statistical analysis of stochastic phenotypic plasticity model of cancer cells

The phenotypic plasticity of cancer cells has received special attention in recent years. Even though related models have been widely studied in terms of mathematical properties, a thorough statistical analysis on parameter estimation and model selection is still very lacking. In this study, we present a Bayesian approach which is devised to deal with the data sets containing both mean and variance information of relative frequencies of cancer stem cells (CSCs). Both Gibbs sampling and Metropolis-Hastings (MH) algorithm are used to perform point and interval estimations of cell-state transition rates between CSCs and non-CSCs. Extensive simulations demonstrate the validity of our model and algorithm. By applying this method to a published data on SW620 colon cancer cell line, the model selection favors the phenotypic plasticity model, relative to conventional hierarchical model of cancer cells. Further quantitative analysis shows that, in the presence of phenotypic equilibrium, the variance data greatly influences the time-variant pattern of the parameters. Moreover, it is found that the occurrence of self-renewal of CSCs shows a strong negative correlation with de-differentiation rate from non-CSCs to CSCs, suggesting a balancing mechanism in the heterogenous population of cancer cells.