Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory

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.

Group phenotypic composition in cancer

“…More globally, insights can be gained on the role of phenotypic plasticity in affecting tumor GPC using dynamical systems theory ( Huang et al, 2009 ; Jia et al, 2017 ) where reshaping of the epigenetic landscape in cancer cells allows acquisition of novel abnormal cell states corresponding to ‘cancer attractors’ in the gene regulatory network ( Huang et al, 2009 ). The enhanced cellular stochasticity in cancer cells would facilitate these phenotypic conversions toward novel states ( Huang et al, 2009 ; Li et al, 2016 ).…”

Cancer Hallmarks Define a Continuum of Plastic Cell States between Small Cell Lung Cancer Archetypes

“…While plasticity has been discussed in the context of differentiation 89,[119][120][121][122][123][124][125][126][127] and cancer 17,[21][22][23]25,47,71,120,123,[128][129][130][131][132][133][134] many times, it has not been rigorously quantified for individual cells. In quantifying plasticity, we wanted to capture the local likelihood of phenotypic transition (that is, change in gene expression profile) for each transcriptional state sampled.…”

“…5,[9][10][11][12][13] Phenotypic heterogeneity, both genetic and non-genetic, is intensively studied across cancer types because of its perceived impact on progression, acquired resistance, and relapse. [14][15][16][17][18][19][20][21][22][23][24][25] Studies of intratumoral heterogeneity are especially relevant for SCLC, since cooperativity and transitions among SCLC subtypes have been reported but remain poorly understood. 11,12,26 The phenotypic heterogeneity of SCLC cells has been provisionally classified into four consensus subtypes defined by enrichment for the transcription factors (TFs) ASCL1 (A), NEUROD1 (N), YAP1 (Y), or POU2F3 (P).…”

Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics

“…The cancer process often exhibits switch-like behavior between two steady cell states, in accordance with the landscape-switching model. As a dysregulated cell developmental process [48], cancer progression is often determined by the regulation of the bistable gene expression states [49,50], which correspond to the normal and cancer cell. Previous theoretical studies using a simple circuitry of the bistable switch between distinct gene expression states to study the cancer development well captured many characteristics of the cancer process [51,30,31].…”