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Mathematical model predicts tumour evolution

Researchers from Barts Cancer Institute and The Institute of Cancer Research (ICR) London have developed a computational model that maps the arms race between cancer cells and immune cells that occurs during tumour evolution.

Tumour evolution

Throughout development, tumours accumulate mutations that drive cancer evolution. However, some mutations can hinder this process and lead to an antitumour immune response due to the generation of neoantigens. Consequently, the host’s adaptive immune cells recognise these peptides as non-self and initiate a response. Therefore, the immune system is a major determinant of tumour evolution. This can be demonstrated by the prognostic value of immune infiltrating cells and the widespread success of immunotherapy.

Cancer evolution in response to the immune system is one of Hanahan and Weinberg’s Hallmarks of Cancer. Evidence has shown that the tumour-specific microenvironment shapes the repertoire of neoantigens found in tumours. Therapies that activate the immune response show particular success in cancers with high mutational burden. In fact, neoantigen profiling is a predictor of treatment response and long-term survival. However, some patients do not respond to immunotherapy despite high mutational burden. Therefore, better predictors of treatment response are needed.

A race

In a study, published in Nature Genetics, researchers used stochastic modelling to investigate how the clonal structure and immunological phenotype of growing tumours is shaped by negative selection in response to neoantigenic mutations. The team utilised genomic data from the Cancer Genome Atlas, specifically, from bowel, stomach and endometrial cancer. They calculated the allele frequencies of neoantigens present in tumours and looked at how fast they accumulated. Consequently, the model was able to predict when a cancer was likely to activate mechanisms to escape from the host’s immune response.

The team found that the model predicted that without immune escape, tumour neoantigens are either clonal or at low frequency. On the other hand, hypermutated tumours could only establish themselves after the evolution of immune escape. In other words, the hallmark of negative selection is the lack of neoantigens at intermediate subclonal frequency. Whereas the presence of numerous neoantigens is a hallmark of immune escape. Strong negative selection for neoantigens ultimately results in a strong selective pressure for the evolution of immune escape.


These results have important implications for cancer therapy. As the model is able to make predictions about people’s cancer, it could help inform whether immunotherapy is likely to be an effective treatment option. The model predicts that immunotherapy is likely to be most effective after immune escape. Therefore, oncologists could use immunotherapy at this time point to reactivate the immune system to recognise and fight the cancer.

Professor Andrea Sottoriva, team leader in evolutionary genomics and modelling at the ICR, stated:

“Our model helps us understand, through mathematics, the arms race that takes place within the body between a tumour and the patient’s immune system. Cancers are constantly adapting and evolving – and can often dodge the effects of treatment or hide themselves from the immune system. Our study gives us a valuable tool for understanding and predicting how cancers will evolve and interact with the immune system, so we can anticipate cancer’s next move, and devise new treatment strategies for patients.”

The team hope to apply their model to cancers that have been treated with immunotherapy to determine whether their predictions are accurate.

Image credit: By Meletios Verras –

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Cancer / Cancer Research / Model / Tumour