Modeling evolutionary dynamics of epigenetic mutations in hierarchically organized tumors - PubMed (original) (raw)

Modeling evolutionary dynamics of epigenetic mutations in hierarchically organized tumors

Andrea Sottoriva et al. PLoS Comput Biol. 2011 May.

Abstract

The cancer stem cell (CSC) concept is a highly debated topic in cancer research. While experimental evidence in favor of the cancer stem cell theory is apparently abundant, the results are often criticized as being difficult to interpret. An important reason for this is that most experimental data that support this model rely on transplantation studies. In this study we use a novel cellular Potts model to elucidate the dynamics of established malignancies that are driven by a small subset of CSCs. Our results demonstrate that epigenetic mutations that occur during mitosis display highly altered dynamics in CSC-driven malignancies compared to a classical, non-hierarchical model of growth. In particular, the heterogeneity observed in CSC-driven tumors is considerably higher. We speculate that this feature could be used in combination with epigenetic (methylation) sequencing studies of human malignancies to prove or refute the CSC hypothesis in established tumors without the need for transplantation. Moreover our tumor growth simulations indicate that CSC-driven tumors display evolutionary features that can be considered beneficial during tumor progression. Besides an increased heterogeneity they also exhibit properties that allow the escape of clones from local fitness peaks. This leads to more aggressive phenotypes in the long run and makes the neoplasm more adaptable to stringent selective forces such as cancer treatment. Indeed when therapy is applied the clone landscape of the regrown tumor is more aggressive with respect to the primary tumor, whereas the classical model demonstrated similar patterns before and after therapy. Understanding these often counter-intuitive fundamental properties of (non-)hierarchically organized malignancies is a crucial step in validating the CSC concept as well as providing insight into the therapeutical consequences of this model.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. Morphology of the classical model and the CSC model.

Tumor morphology appears spherical in the classical model (A) whereas tumor borders in the CSC model are irregular (B). Red: CSCs, yellow: TACs, blue: DCCs (zoom box, black: cell borders). The distribution of the neutral methylation patterns is radial in the classical model (C) versus patch-like in the CSC one (D).

Figure 2

Figure 2. The CSC model enhances methylation pattern heterogeneity.

Despite its much smaller effective population size, the CSC model (red) shows consistently higher heterogeneity (A) with respect to the classical (blue) model of malignancies (p = 10−7 at 100,000 cells, a = 0.01). Importantly this measure is even enhanced when considering the CSC compartment only (B) (p = 10−7 at 100,000 cells). Error bars represent SD with_n = 16_.

Figure 3

Figure 3. The CSC model escapes local fitness peaks and achieves better fitness in the long run.

Within a linear fitness function fL(x) = x+8 (A) the CSC model tends to spread towards low fitness regions too, rather than just selecting for the fastest replicating clone. In the case of a symmetrical fitness function with peaks and valleys_fS(x) = 3−x sin(x/2)_ and_f'S(x) = x sin(x)+2_ (B,C) the CSC model shows evolutionary superiority and the ability to escape local peaks and reach higher fitness in the long run. Even more clearly, the same evolutionary differences are present under an asymmetrical fitness function_fC(x) = x cos(x/2)+2_ (D). Error bars represent SD with n = 8.

Figure 4

Figure 4. The CSC model stimulates malignant features in relapsing tumors after therapy.

Whereas the relapsing tumors in a classical model are highly similar to the primary ones, displaying an unaltered average fitness (A, p = 0.04), the CSC model not only shows a different clonal distribution, but also the average fitness is considerably increased (B, p = 0.0023). Error bars represent SD with_n = 12_.

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