Exploiting ecological principles to better understand cancer progression and treatment

Abstract

A small but growing number of people are finding interesting parallels between ecosystems as studied by ecologists (think of a savannah or the Amazon rainforest or a coral reef) and tumours. The idea of viewing cancer from an ecological perspective has many implications but, basically, it means that we should not see cancer just as a group of mutated cells. A more useful definition of cancer is to consider it a disruption in the complex balance of many interacting cellular and microenvironmental elements in a specific organ. This perspective means that organs undergoing carcinogenesis should be seen as sophisticated ecosystems in homoeostasis that cancer cells can disrupt. It also makes cancer seem even more complex but may ultimately provide insights that make it more treatable. Here, we discuss how ecological principles can be used to better understand cancer progression and treatment, using several mathematical and computational models to illustrate our argument.

Keywords: cancer; ecology; heterogeneity; homoeostasis; interactions; mathematical model.

Figure 1.

Result from a replicator equation resulting from a game where certain stromal cells (S) and certain tumour cells (D) cooperate. Treatment (sky blue) kills stromal cells effectively selecting for the I tumour population.

Figure 2.

Mutualism. Both the bee and the flower derive a benefit from their interaction (Wellcome Library, London).

Figure 3.

Parasitism: parasitic wasp cocoons attached to a caterpillar. By Jacob Scott M.D.

Figure 4.

Example of simulation in which an IBM is used to explore how the interactions between tumour cells and their environment affect progression. The screen on the top left shows tumour (red) and stromal (black and brown) interacting. Other screens show concentrations of elements of the physical microenvironment (TGFβ, matrix degrading enzymes and extracellular matrix). An IBM model can shed light on the spatial distribution of relevant cellular species.

Figure 5.

Simulation results from the HDC model under three different microenvironments: (a) uniform ECM, (b) grainy ECM and (c) low nutrient. The upper row shows the resulting tumour cell distributions obtained after three months of simulated growth; we can see that the three different microenvironments have produced distinct tumour morphologies. The lower row shows the relative abundance of a possible 100 tumour phenotypes over time as the tumour invaded each of the different microenvironments. We note that there are approximately six dominant phenotypes in the uniform tumour, two in the grainy and three in the low nutrient tumour. These phenotypes have several traits in common: low cell–cell adhesion, short proliferation age and high migration coefficients. In each tumour, one of the phenotypes is the most aggressive and also the most abundant, particularly in (b) and (c). All parameters used in the simulations are identical with the exception of the different microenvironments.

Figure 6.

Outline of the ecosystem in a prostate to bone metastasis with several types of cancer cells interacting with other cellular populations such as osteoblasts, osteoclasts, osteocytes and stem cells. Tumour cells interact to compete and cooperate for resources such as nutrients, space and growth factors.