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 systems theory represents a high-dimensional space, in which each cell phenotype is considered as an “attractor” that is determined by interactions between multiple molecular players, and is buffered against environmental fluctuations. In addition, 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 the 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. Phenotypic transitions in cancer occur at varying levels depending on the context. Using epithelial-to-mesenchymal transition (EMT), cancer stem-like properties, metabolic reprogramming and the emergence of therapy 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 therapies such as chemotherapy and androgen-deprivation therapy. 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.
Highlights
gene regulatory networks (GRNs) are dynamical systems that start from context-dependent conditions, develop temporally due to the mutual interactions between molecular regulators and later settle down into “attractors”, each of which is characterized by a unique gene expression pattern (Figure 1C)
The regulatory network underlying the landscape can give rise to various “attractors”, i.e., “stable states” corresponding to different cell phenotypes, each of which is characterized by a unique gene expression pattern
Emerging insights demonstrate that cancer cells are often behaving as “moving targets” and often find new adaptive ways to resist therapeutic attacks
Summary
“The woods are lovely, dark and deep, but I have promises to keep, and miles to go before I sleep, and miles to go before I sleep.”. Y are usually the master regulators of the two sister cell-fates Such a “self-activating toggle switch” usually generates three stable “attractors’ that are characterized by Xhigh /Ylow , Xlow /Yhigh and Xmedium /Ymedium corresponding to two differentiated cell fates and an undifferentiated progenitor state respectively [2,6,7] (Figure 1A). This asymmetry a cascade of events where the levels a gene regulatory network (GRN) governing the differentiation of “1” to two lineages “1_1” and of X (Y) continually increase and those of Y (X) continually decrease, because X (Y) can progressively “1_2”. Since the perspective is intended to encourage cross pollination of ideas between biologists, especially cancer biologists, and physicists interested in exploring the physics of biology, technical jargon is limited to its minimum and equations are omitted
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