Cancer Ecosystems and how to collapse them

by Phillip Swanepoel

The Cancer Ecosystem

When thinking of ecosystems, cancer isn’t the first thing that comes to mind. However, tumours aren’t just a simple mass of identical cells. They can consist of a wide range of cell types, each with different set of properties. This is called “Tumour Heterogeneity”. Furthermore, tumours don’t exist in isolation. The tumour cells are constantly interacting with their surroundings, they influence it physically and chemically, and the environment responds in kind. This collection of blood vessels, signalling molecules, immune cells and more interact with the tumour to form the “Tumour microenvironment”.

Evolution, competition and cooperation all take place within and around the tumour, forming a complex web of biological interactions – an ecosystem.

One recent example comes from Glioblastomas, a type of brain cancer. Cells in Glioblastomas have been found to organise into four well-defined subtypes. Rather disturbingly, experiments have shown that tumours grown from any one of these cell types develop, once again, into this formation of four different types. This shows these cell types are not static, and similar dynamics have been shown in other types of cancer.

Understanding the Ecosystem Dynamics

The authors of a recently released paper, Transition therapy: tackling the ecology of tumor phenotypic plasticity, set out to better understand how these distinct tumour cell populations change over time, and how this is influenced by tumour treatments.

With this goal in mind they developed a mathematical model to provide some insight into the cell type switching dynamics, and how these cell types respond to targeted treatments. I won’t go into any detail about the formulas used, but the basic idea was to capture the population growth of each cell type with four terms. Each cell type has its own equation.

  1. A term that represents the base replication rate, increasing the population.
  2. A term for the switch rate of cells to a different type, reducing the population.
  3. A term for the switch rate of other cells into this type, increasing the population.
  4. A term which captures the impact of treatment, reducing the population.

Implications for cancer treatment strategies

One of the interesting results they found is that tumour heterogeneity can spontaneously develop in this model mirroring real life experiments. They also found that cell populations where heavily dependent on the “switch rate” or transition rate of the cell types between each other. Cancer treatments which target one phenotype are often not effective on some cells, which survive, and subsequently regrow the tumour. This also steers the evolution of the cancer cells, potentially causing more harm than good.

This suggested a potential treatment: transition therapy. By manipulating the rate at which these cell types transition, you can increase the effectiveness of targeted treatments. Therapeutic strategies that target cell differentiation are already being developed, and knowledge about cell differentiation and re-programming is constantly growing.

A simple example of a transition treatment strategy could be: increasing the rate at which the resistant cell type transitions into vulnerable types. This increases the number of cells targeted by the drugs, as well as reducing the population of cells available for tumour regrowth.

This example illustrates the strategy needed to collapse the cancer ecosystem – you attack its diversity.

Reference:

Aguade, G.; Kauffman, S.; Sole, R. Transition Therapy: Tackling the Ecology of Tumour Phenotypic Plasticity. Preprints 2020, 2020070547. doi: 10.20944/preprints202007.0547.v1

 

 

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s