Epigenetics as a mediator of plasticity in cancer

Abstract

The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.

Figure 1.. Gene expression and epigenetic landscapes control normal and cancer cell functions.

(A) Gene regulatory networks and availability of genes for expression can define the probabilistic distributions of proteins expressed within a cell population. In the example here, the network of interacting proteins that includes the molecules A and B (top) and the underlying epigenetic control determining the availability of the corresponding genes for expression define the distribution of expression of A and B (middle). This probability distribution can be experimentally measured and converted into a gene expression landscape by calculating the corresponding quasi-potential distribution (bottom, see Box). The epigenetic landscape can be similarly determined by experimentally measuring the probabilistic distributions of epi-alleles, DNA methylation marks at specific loci or by performing other measurements of epigenetic regulation across populations of cells and tissues, and then also converting these probability distributions into corresponding underlying quasi-potential landscapes. The landscape analysis allows conceptual accounting for abundance and dynamics of molecular species, shown here as a trajectory of particle inside a quasi-potential well, with the particle position defined by the current concentrations of A and B that can change probabilistically in time, with the quasi-potential wells interpreted as the landscape attractors. (B) Various scenarios of landscape alterations and the corresponding changes in the molecular distributions, shown as joint distributions of the molecules A and B and the corresponding entropies H1–5. Implementation of these scenarios in the context of carcinogens is extensively illustrated and discussed in the text. Oncogenic mutations of epigenetic modifiers and modulators or environmental inputs can lead to formation of new stable attractors with the overall entropy H2 greater than the original entropy H1 (H2>H1) generating phenotypic heterogeneity (Input 1’) or, alternatively, enlarge the existing attractor with the new entropy H3>H1, generating a more plastic state (phenotypic plasticity), with cells capable of stochastically and dynamically ‘exploring’ this attractor and thus transiently adopting different phenotypes. Note that in both cases entropy increases vs H1, and it is possible that H2=H3, thus making entropy less discriminating than the full landscape picture in the analysis of cell states. These new landscapes can be further altered by oncogenic and environmental inputs, so that one of the attractors becomes dominant (Inout 2’), associated with a lower entropy value (H4<H2), or, alternatively, with the narrowing of the wider (and more plastic) attractor (Inout 2, H5<H3). Again, it is possible that H4=H5, requiring the landscape analysis rather than entropy analysis alone for full characterization. The narrowing of the wide attractor due to either environmental or intrinsic inputs (Input 2) is frequently reversible and context dependent, further elaborating the more plastic overall state (transient nature of Input 2 described by a bidirectional arrow). The transiently occupied attractors can be simultaneously occupied by discerns cells in the population. Small arrows correspond to stochastic fluctuations of molecular concentrations within individual attractors. (C) Gene regulation and epigenetic landscapes of cancer cells can be complex and have multiple attractors, corresponding to distinct and stable cell states and phenotypes, which may be reshaped by oncogenic mutations, cell aging, environmental inputs and other perturbations, leading to mutual accessibility of the attractors, more plastic cell states and an increase in the phenotypic plasticity.

Figure 2.. Interplay between epigenetic and gene expression landscapes.

Developmental and environmental factors and genetic mutations can impact diverse modulators of epigenetic control and gene expression, such as signaling and cell communication networks, frequently leading to diversification of cell states. These modulators may directly impact modulators of epigenetic states, such as DNA demethylases, and gene expression, such as transcription factors, which also can directly interact with each other. Examples of these molecular regulators discussed in the text are shown here. The result is alterations of the epigenetic and gene regulation landscapes that are tightly coupled, e.g., through the action of mediators of epigenetic control, influencing accessibility of genes for regulation, and the magnitude and variability of gene expression. Certain additional inputs may be more specific to each of the landscapes, such as the epigenetic drift with cell aging primarily leading to a widening of the landscape attractors, higher plasticity and higher entropy of the state, and protein-protein interaction and gene regulatory networks, stabilizing various attractors and serving to decrease the plasticity and entropy.

Figure 3.. Connection between an epigenetic landscape and variable phenotypic outcomes.

(A) An epigenetic alteration – loss of imprinting (LOI) of the insulin-like growth factor 2 gene (IGF2), implicated in Wilms tumor, doubling of the signaling input, can lead to rewiring of the signaling network activated by the IGF2 receptor IGF1R (depicted as IGF1Rp) through altered receptor trafficking (IGF1Rint), degradation (ϕ) and altered balance of activation of the downstream signaling pathways activating Erk (Erkpp) and Akt (Aktpp) kinases. Rebalancing of Erk and Akt activities translates into transcriptional upregulation of IGF1R, a higher proliferation rate but also rebalancing of pro- and anti-apoptotic protein abundances (BAX versus Bcl-2, respectively) leading to an increased propensity for cell death (113). The integral signs represent integration over time of signaling activities. (B) The landscape alterations that correspond to a change in phenotype (upper panel) is the altered expression and activity of signaling pathway molecules (and thus gene regulatory landscapes in the lower panel) in response to alteration of epigenetic landscapes (IGF2 loss of imprinting, LOI). This leads to emergence of a new attractor in addition to the wild type attractor, resulting in a mosaic wild type/LOI cell distribution in the tissue. This landscape alteration can be ‘mapped’ onto, for example, the apoptosis phenotype-defining network by a quantitative analysis of the dependence of the BCL family protein distributions on the signaling inputs, thus enabling a direct translation of the landscape alterations into phenotype distributions. In this example, the mapping can be visualized as wild type and LOI cell distributions mapped with respect to the areas of cell survival and death on the (BAX, Bcl-2) phenotypic plane, suggesting how treatments targeting LOI cells may be developed to spare the wild type cells. Arrows in the lower panel represent the effect of drugs, such as IGF1R inhibitors shifting the landscape and phenotypic distribution towards the boundary separating survival and death, with the red areas depicting the effect on the wild type and LOI cell populations. (C) A more general view of landscape mapping onto the apoptosis phenotypic plane. By analogy with Fig. 1B, one can contrast mapping of a large attractor versus two more limited attractors, representing the difference between a plastic and stochastic state (phenotypic plasticity) versus a state with two alternative stable attractors (phenotypic diversity). The more plastic state can allow cells to escape from the death area to the survival area even in the presence of a treatment (such as in (B)), by stochastically ‘exploring’ the available attractor, whereas a combination of more stable attractors (with the same overall entropy as the more plastic state) can allow for selective targeting by one but not the other attractor. Therefore, the treatment strategy suggested in (B) may benefit from the initial intervention stabilizing smaller attractors within a larger one and thus decreasing the plasticity of the state, particularly through epigenetic perturbations.