Microenvironmental Variables Must Influence Intrinsic Phenotypic Parameters of Cancer Stem Cells to Affect Tumourigenicity

<div><p>Since the discovery of tumour initiating cells (TICs) in solid tumours, studies focussing on their role in cancer initiation and progression have abounded. The biological interrogation of these cells continues to yield volumes of information on their pro-tumourigenic behaviour, but actionable generalised conclusions have been scarce. Further, new information suggesting a dependence of tumour composition and growth on the microenvironment has yet to be studied theoretically. To address this point, we created a hybrid, discrete/continuous computational cellular automaton model of a generalised stem-cell driven tissue with a simple microenvironment. Using the model we explored the phenotypic traits inherent to the tumour initiating cells and the effect of the microenvironment on tissue growth. We identify the regions in phenotype parameter space where TICs are able to cause a disruption in homeostasis, leading to tissue overgrowth and tumour maintenance. As our parameters and model are non-specific, they could apply to any tissue TIC and do not assume specific genetic mutations. Targeting these phenotypic traits could represent a generalizable therapeutic strategy across cancer types. Further, we find that the microenvironmental variable does not strongly affect the outcomes, suggesting a need for direct feedback from the microenvironment onto stem-cell behaviour in future modelling endeavours.</p></div

The three qualitatively different tissue scale phenotypes plotted as cell numbers over time for the example simulations in figure 4.

<p>The black trace, representing the unsustainable simulation, grows quickly though never expands its stem population and then outstrips the available oxygen and collapses. The blue trace, representing the homeostatic simulation, reaches a critical size and then maintains a steady birth-death balance. The red trace, representing the tumorigenic simulation, settles into an effectively linear trace on this log-log plot, suggesting power law growth.</p

Model parameters.

<p>Model parameters.</p

Cartoon representing the hierarchical model of stem-cell driven tissues.

<p>In this formulation, each stem can undergo two types of division, either symmetric (with probability ) or asymmetric (with probability ). Each subsequently generated transient amplifying cell (TAC) can then undergo a certain number () of round of amplification before differentiating into a terminally differentiated cell (TD) which will live for a certain amount of time before dying ( timesteps). It is these three parameters, which we assume are intrinsic to a given stem cell, which we explore in this paper.</p

Differential phenotypes in cultures enriched for brain tumour initiating cells.

<p>Bright field images of CD133+ patient derived glioblastoma cell lines cultured in Neurobasal supplemented with EGF, FGF and B27, exhibiting striking phenotypic variability. These differences highlight the heterogeneity present even in a highly controlled static environment between cells that are putatively the same.</p

Three different examples of simulations resulting from the computational model. Each simulation represents one of the typical outcomes.

<p>Each begins with a single TIC seeded in the middle of the computational domain. In each situation the phenotype parameters are slightly different, resulting in (A) An unsustainable tissue (parameters: , , and day), (B) A homeostatic tissue where the balance of stem cell renewal and progenitor proliferation leads to a tissue whose overall size remains relatively constant over time, possibly representing a dormant tumor (parameters: , , and day) and, (C) Neoplastic-like tissue where the tissue overgrows the computational domain (parameters: , , and day). On the right of the final time point, we have shown an example of the oxygen tension in the computational domain. n.b. these images are zoomed in to illustrate detail and do not represent the entire computational domain.</p

Computational model description.

<p>(A) The model includes three different cell types: stem, progenitor and differentiated. All cell types interact with the microenvironment in the form of oxygen tension. (B) The behaviour of each cell type is captured by a flowchart. The last segment with discontinuous arrows represents behaviour that is specific to the stem cells. (C) The cells are represented as agents inhabiting points in a grid in a 2D space with 500×500 grid points. Stem cells are represented as red points, progenitor as green and fully differentiated as blue. The vasculature is represented as oxygen source points in black.</p

Size of tissues vs. progenitor proliferative potential achieved by simulations using different levels of vascularisation and rates of symmetric vs. asymmetric divisions.

<p>Lines represent averages for each of the three realisations in each scenario. (Left). Low vascularisation density of 0.01 (Centre) Normal vascularisation density of 0.05 (Right) High vascularisation density of 0.1. In each of these cases, the maximum tissue size will depend on the right combination of and .</p

c-Myc modulates cell cycle regulators of glioma cancer stem cells.

<p>(A) Early passage (WAF1/CIP1, cyclin D₁ (cycD1) and cyclin D₂ (cycD2) were determined by quantitative real-time PCR 3 days after infection. (B) Protein levels of c-Myc, p53, cyclin D₁, cyclin D₂, cyclin E and p21^WAF1/CIP1 were determined by immunoblotting.</p

c-Myc is highly expressed in glioma cancer stem cells.

<p>(A) CD133− and CD133+ cells were isolated from glioma surgical biopsy specimens passaged short-term in immunocompromised mice and briefly cultured. Total RNA was isolated from both CD133− and CD133+ cells. cDNA was prepared by reverse transcription. Expression of c-Myc was then determined by quantitative real-time PCR and normalized to β-actin and HPRT1. Relative mRNA levels of c-Myc in CD133− cells were assigned a value of 1. Data are represented as mean±S.E.M in this and all subsequent graphs (#: p<0.001). (B) Total cellular lysates were resolved by SDS-PAGE. Protein levels of c-Myc and Olig2 were determined by immunoblotting. Actin was blotted as the loading control. (C) Glioma cells were isolated directly from human surgical biopsy specimens, fixed in 4% paraformaldehyde following dissociation, labeled with anti-CD133-APC and anti-c-Myc-FITC, and subjected to FACS analysis. (D) Percentage of cells expressing high levels of c-Myc within either the CD133− fraction or the CD133+ fraction was demonstrated (#: p<0.001). (E) Sections of freshly frozen human glioma surgical biopsy specimens were fixed and co-stained for c-Myc (green) and Nestin (red). Nuclei were counterstained with Hoechst 33342. Representative images (630×) were demonstrated.</p