Scientists find that the way tumours grow impacts their genetics

Researchers at the Francis Crick Institute, UCL, The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London have developed a computer model to analyse how the way in which tumours grow affects their genetic makeup. Using this new model, they have identified links between tumour growth and shape, and how quickly a patient’s cancer might progress.  

Background 

As cancer cells mutate, some gain an advantage through mutations which make them more likely to survive, divide and create a group of ‘fitter’ cells. This group may outcompete others to become dominant, for example, if they have evolved to survive in conditions where there is a low supply of nutrients or oxygen. 

This process of tumour evolution is highly complex and is impacted by many factors, including how the tumour is growing. But it is not fully understood.  

The study 

In the study, published in Nature Ecology and Evolution, the scientists used their computer model to study two types of tumour growth in kidney cancers: one where growth is consistent throughout the tumour, the ‘volume growth model’ and one where growth is restricted to the surface, the ‘surface growth model’.  

Two scenarios occurred in the volume growth model. In some cases, a single ‘fit’ group of genetically related cancer cells arose in the tumour at an early stage. In others, the tumours did not develop a new ‘fit’ group but rather the original group of parental cancer cells remained dominant. 

In the surface growth model, there was extensive genetic diversity with different groups of ‘fit’ cells forming on the surface. The team suggest that this creates a competitive environment where different groups of cells are pushed to evolve more rapidly. 

The results 

The researchers validated their model using data from 66 tumours analysed through the TRACERx Renal study. By cross-referencing the model and this tumour data, they found that different rates of real-world tumour progression corresponded with different growth models. For example, tumours which rapidly progressed fitted with the volume growth model where one ‘fit’ group of cells was present from early on. While cases which did not progress fitted with the volume growth model where the parental group of cells remained dominant.  

The model also provided insights into how different types of growth impact the shape of tumours. Volume growth tumours grew outwards in a more consistent shape, while surface growth tumours showed bulges on the surface, where the ‘fitter’ groups were growing.   

The researchers also used their model to analyse the impact of necrosis, the death of tissue within the tumour, on its evolution. When necrosis was present under the surface growth model, the tumours quickly developed more ‘fit’ groups of genetically distinct cells.  

Official comments 

Xiao Fu, first author and postdoctoral training fellow in the Biomolecular Modelling lab at the Crick said: “These findings are just the start of what we hope to uncover with this model. What’s exciting is how this structural information could be used as a window into the evolution of a tumour. More research is needed but it could be used to help determine what sort of growth a tumour is undergoing, for example, if radiological imaging of an early tumour shows bulges this means it’s more likely to be undergoing surface growth. This information could help inform medical teams and treatment decisions.” 

Paul Bates, paper author and group leader of the Biomolecular Modelling lab at the Crick, said: “Computer simulations are extremely valuable in furthering our understanding of how tumours evolve over time. By developing these models and using them to analyse how cancers change, we hope to find periods in their evolution and growth where the cancer may be most vulnerable to treatments.”  

Samra Turajlic, author and group leader of the Crick’s Cancer Dynamics Laboratory and consultant oncologist at The Royal Marsden NHS Foundation Trust, said: “The most important observations regarding cancer behaviour are gleaned through analyses of patients’ tumours because they reflect the time-scales and complexities of actual cancer evolution. However, every instance of cancer evolution in a patient is unique, cannot be rewound, and repeated, making it hard to predict how likely tumours are to go down certain paths. This is where mathematical modelling can be a powerful tool to help us understand how the patterns we observe in real tumours come about. Informed mathematical models combined with detailed clinical, molecular, histological and radiological data from real-life tumours can bring about critical insights that will translate into patient benefit.” 

Funding 

This work was funded by the Wellcome Trust, the Francis Crick Institute, Cancer Research UK, the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden Hospital and The Institute of Cancer Research, The Royal Marsden Cancer Charity, The Rosetrees Trust, Ventana Medical Systems Inc, the National Institute of Health and Melanoma Research Alliance, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant. 

Image credit: Trust Katsande

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