Jia, D.; Jolly, M.K.; Kulkarni, P.; Levine, H. Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory. Cancers2017, 9, 70.
Jia, D.; Jolly, M.K.; Kulkarni, P.; Levine, H. Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory. Cancers 2017, 9, 70.
Jia, D.; Jolly, M.K.; Kulkarni, P.; Levine, H. Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory. Cancers2017, 9, 70.
Jia, D.; Jolly, M.K.; Kulkarni, P.; Levine, H. Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory. Cancers 2017, 9, 70.
Abstract
Waddington’s epigenetic landscape, a famous metaphor in developmental biology, depicts how a stem cell progresses from an undifferentiated phenotype to a differentiated one. The concept of “landscape” in the context of dynamical system theory represents a high-dimensional cell state space, in which each cell phenotype is considered as an “attractor” that is determined by interactions among multiple variables (molecular players), and is buffered against environmental fluctuations. Further, biological noise is thought to play an important role during these cell-fate decisions and in fact controls transitions between different phenotypes. Here, we discuss these phenotypic transitions in cancer from a dynamical systems perspective and invoke the concept of “cancer attractors” – hidden stable states of the underlying regulatory network that are not occupied by normal cells. Using epithelial-to-mesenchymal transition (EMT), cancer stem-like properties, metabolic reprogramming and the emergence of drug/hormone resistance as examples, we illustrate how phenotypic plasticity in cancer cells enables them to acquire hybrid phenotypes (such as hybrid epithelial/mesenchymal and hybrid metabolic phenotypes) that tend to be more aggressive and notoriously resilient to drug/hormone treatment. Furthermore, we highlight multiple factors that may give rise to phenotypic plasticity in cancer cells, such as (a) multi-stability or oscillatory behaviors governed by underlying regulatory networks involved in cell-fate decisions in cancer cells, and (b) network rewiring due to conformational dynamics of intrinsically disordered proteins (IDPs) that are highly enriched in cancer cells. We conclude by discussing why a therapeutic approach that promotes ‘recanalization’, i.e. the exit from “cancer attractors” and re-entry into “normal attractors”, is more likely to succeed rather than a conventional approach that targets individual molecules/pathways.
Keywords
cell fate decision; cancer attractors; gene network dynamics; EMT; drug resistance; intrinsically disordered proteins
Subject
Biology and Life Sciences, Biophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
