Markovian modelling of phenotypic state transitions in glioblastoma cancer cells.
Résumé
Introduction. Cancer stem cell (CSC) hypothesis suggests that tumor progression and recurrence rely on a small subpopulation of cancer cells with stem-like properties. The unresolved question is whether cancer stem cells lead to organisation of intratumoral phenotypic heterogeneity by hierarchical differentiation events or whether they represent one of the transitory phenotypic states. This is crucial not only for our understanding of tumor progression, but also for the successful design of novel therapeutic strategies targeting CSCs. Purpose. Cancer cell populations exist in distinct phenotypic states but the mechanisms by which state transitions occur are poorly understood. Gupta et al. (2011) proposed a Markov model to study the phenotypic proportions observed in populations of cancer cells. They found that over time the phenotypic proportions move toward equilibrium. In the present study we addressed the question whether this model is relevant to explain the state transitions between four phenotypic populations of glioblastoma stem-like cells. Methods and Materials. In vitro data were provided by fluorescence-activated cell sorting and a discrete-time Markov chain was used to describe the phenotypic transitions. Model implementation and parameter estimation were performed in Matlab. Results. We have compared our in vitro data from glioblastoma cells with the predictions provided by the previously estimated Markov model. Our initial results confirm the presence of phenotypic transitions, but may not accurately follow the Gupta's cancer stem cell Markov chain model to explain the dynamic state transitions between the four phenotypic subpopulations. Conclusions. Our study exemplifies a tight integration between hypothesis- and data-driven modelling approaches to biological knowledge discovery. Ongoing experiments will show whether the phenotypic equilibrium observed in breast cancer cells is confirmed in glioblastoma cancer cells.