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

TL;DR

This paper develops a Bayesian statistical framework to analyze phenotypic plasticity in cancer cells, focusing on estimating transition rates between cell states and validating the model with real data.

Contribution

It introduces a Bayesian approach with Gibbs sampling and MH algorithms for parameter estimation in cancer cell plasticity models, filling a gap in statistical analysis.

Findings

Model selection favors phenotypic plasticity over hierarchical models.

Initial CSC state significantly affects plasticity occurrence.

Simulation results validate the model and algorithms.

Abstract

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 on the 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. Moreover, it is found that the initial state of CSCs after cell sorting significantly influences the occurrence of phenotypic plasticity.