Main Menu
15
May
2020

Mapping how evolutionary forces affect cancer growth could help doctors choose biopsies

Sottoriva tumour growth model

Image: A spatial tumour growth model. Source: Chkhaidze et al., Figure 1D

A computer model that uses the principles of evolution to map the growth of populations of cancer cells in tumours could help doctors choose where to take biopsies from tumours, improving decisions about treatment.

The model, which shows how cancer cells with particular mutations can spread through tumours, could more accurately reflect the genetic picture across whole tumours. 

Doctors use tumour biopsies – samples taken from cancer – to learn about what is happening inside cancers and decide which treatments are likely to be effective.

We are building a new state-of-the-art drug discovery centre to develop a new generation of drugs that will make the difference to the lives of millions of people with cancer. Find out more about the challenge of cancer drug resistance and help fund research to help finish cancer.

Find out more

Mapping the patterns of cell spread

Doctors will often take several biopsy samples from tumours and use DNA sequencing to identify mutations that can be targeted for treatment.

However, biopsies do not always reflect the full diversity of mutations that make up a tumour.

Imagine trying to build up a picture of a dartboard, just from where your limited number of darts land. You could get a very different picture depending on where the darts land, which doesn’t represent the true structure of the board.

In the same way, relying on a small number of biopsies may miss details and not reflect the full diversity of mutations that make up a tumour.

If researchers could better map the patterns of cell spread through tumours, doctors could take biopsies from tumour locations that best reflect the disease’s biology.

The model, developed by scientists at The Institute of Cancer Research, London in collaboration with the Barts Cancer Institute and published in PLoS Computational Biology, shows how populations of cells with distinct sets of mutations spread through tumours.

The model accounts for the presence or absence of selective evolutionary pressure – in which conditions favour the survival of cells with certain genetic traits – and the presence or absence of physical, spatial constraints in tumours.

The ICR scientists who co-developed the model are based in our Centre for Evolution and Cancer – and are due to move into our new Centre for Cancer Drug Discovery, where they will be part of the world’s first ‘Darwinian’ drug discovery programme.

Our Centre for Evolution and Cancer, led by Professor Andrea Sottoriva aims to help us understand how cancer evolves and adapts, and to find more effective ways to treat it. 

Find out more

Modelling tumour evolution

The researchers’ model predicted that populations of cells with shared genetic characteristics grow in clumps or spreading out in strands, depending on the conditions.

These patterns look chaotic but they follows the rules of evolution, and importantly the models mimicked patterns of growth seen in real tumours.

The research was largely funded by the Medical Research Council and the Wellcome Trust, with further support from organisations including Cancer Research UK and the US National Institute of Health

Study leader Professor Andrea Sottoriva, Professor of Cancer Genomics & Evolution and Director of the ICR's Centre for Evolution and Cancer at the ICR, said:

“Tumour biopsies tell us a great deal about the biology of a patient’s cancer, but evolutionary forces lead to differences in cancer cells across space and time, and biopsies only give an incomplete picture.

“Our study helps to clarify the effect of evolutionary forces on the spread of genetic mutations across tumours. We hope, in the future, our modelling will help doctors to select the treatments most likely to counteract individual tumours, more effectively than they can with current methods to collect and analyse biopsies.”

Tags

evolution Andrea Sottoriva Centre for Evolution and Cancer modelling
comments powered by Disqus