A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours
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Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies.
Macfarlane , F R , Chaplain , M A J & Lorenzi , T 2019 , ' A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours ' , Journal of Theoretical Biology , vol. 480 , pp. 43-55 . https://doi.org/10.1016/j.jtbi.2019.07.019
Journal of Theoretical Biology
© 2019, Elsevier Ltd. All rights reserved. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.jtbi.2019.07.019
DescriptionFRM is funded by the Engineering and Physical Sciences Research Council (EPSRC).
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