Cancer–immune coevolution dictated by antigenic mutation accumulation

The immune system is one of the first lines of defence against cancer. When effector cells attempt to suppress tumour, cancer cells can evolve methods of escape or inhibition. Knowledge of this coevolutionary system can help to understand tumour-immune dynamics both during tumourigenesis and during immunotherapy treatments. Here, we present an individual-based model of mutation accumulation, where random mutations in cancer cells trigger specialised immune responses. Unlike previous research, we explicitly model interactions between cancer and effector cells and incorporate stochastic effects, which are important for the expansion and extinction of small populations. We find that the parameters governing interactions between the cancer and effector cells induce different outcomes of tumour progress, such as suppression and evasion. While it is hard to measure the cancer-immune dynamics directly, genetic information of the cancer may indicate the presence of such interactions. Our model demonstrates signatures of selection in sequencing-derived summary statistics, such as the single-cell mutational burden distribution. Thus, bulk and single-cell sequencing may provide information about the coevolutionary dynamics.

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Publication Details

Journal
eLife
Published
2025-04-16
DOI
https://doi.org/10.7554/elife.103970
Citations
1
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
1.37
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article

Cancer–immune coevolution dictated by antigenic mutation accumulation

Long Wang, Weini Huang, Christo Morison
1 citations
eLife
Mathematical Biology Tumor Growth
1.37
article

Cancer–immune coevolution dictated by antigenic mutation accumulation

Long Wang, Weini Huang, Christo Morison
article en
1 citations

Abstract

The immune system is one of the first lines of defence against cancer. When effector cells attempt to suppress tumour, cancer cells can evolve methods of escape or inhibition. Knowledge of this coevolutionary system can help to understand tumour-immune dynamics both during tumourigenesis and during immunotherapy treatments. Here, we present an individual-based model of mutation accumulation, where random mutations in cancer cells trigger specialised immune responses. Unlike previous research, we explicitly model interactions between cancer and effector cells and incorporate stochastic effects, which are important for the expansion and extinction of small populations. We find that the parameters governing interactions between the cancer and effector cells induce different outcomes of tumour progress, such as suppression and evasion. While it is hard to measure the cancer-immune dynamics directly, genetic information of the cancer may indicate the presence of such interactions. Our model demonstrates signatures of selection in sequencing-derived summary statistics, such as the single-cell mutational burden distribution. Thus, bulk and single-cell sequencing may provide information about the coevolutionary dynamics.

eLifeVol. 14
Sun Yat-sen University (CN), Queen Mary University of London (GB), Evolutionary Genomics (United States) (US)
Openalex Percentile: Top 23%
Mathematical Biology Tumor Growth
1.37
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