Modeling breast cancer progression to bone: how driver mutation order and metabolism matter
BMC Medical Genomics
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Ascolani, G., & Liò, P. (2019). Modeling breast cancer progression to bone: how driver mutation order and metabolism matter. BMC Medical Genomics, 12 (Suppl 6)https://doi.org/10.1186/s12920-019-0541-4
Abstract: Background: Not all the mutations are equally important for the development of metastasis. What about their order? The survival of cancer cells from the primary tumour site to the secondary seeding sites depends on the occurrence of very few driver mutations promoting oncogenic cell behaviours. Usually these driver mutations are among the most effective clinically actionable target markers. The quantitative evaluation of the effects of a mutation across primary and secondary sites is an important challenging problem that can lead to better predictability of cancer progression trajectory. Results: We introduce a quantitative model in the framework of Cellular Automata to investigate the effects of metabolic mutations and mutation order on cancer stemness and tumour cell migration from breast, blood to bone metastasised sites. Our approach models three types of mutations: driver, the order of which is relevant for the dynamics, metabolic which support cancer growth and are estimated from existing databases, and non–driver mutations. We integrate the model with bioinformatics analysis on a cancer mutation database that shows metabolism-modifying alterations constitute an important class of key cancer mutations. Conclusions: Our work provides a quantitative basis of how the order of driver mutations and the number of mutations altering metabolic processis matter for different cancer clones through their progression in breast, blood and bone compartments. This work is innovative because of multi compartment analysis and could impact proliferation of therapy-resistant clonal populations and patient survival. Mathematical modelling of the order of mutations is presented in terms of operators in an accessible way to the broad community of researchers in cancer models so to inspire further developments of this useful (and underused in biomedical models) methodology. We believe our results and the theoretical framework could also suggest experiments to measure the overall personalised cancer mutational signature.
Research, Driver mutations, Metabolic mutations, Mutation order, Breast cancer, Subordinated processes, Non-commuting operators
External DOI: https://doi.org/10.1186/s12920-019-0541-4
This record's URL: https://www.repository.cam.ac.uk/handle/1810/308362