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Modeling breast cancer progression to bone: how driver mutation order and metabolism matter
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. Usua...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657267/ https://www.ncbi.nlm.nih.gov/pubmed/31345216 http://dx.doi.org/10.1186/s12920-019-0541-4 |
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author | Ascolani, Gianluca Liò, Pietro |
author_facet | Ascolani, Gianluca Liò, Pietro |
author_sort | Ascolani, Gianluca |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0541-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6657267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66572672019-07-31 Modeling breast cancer progression to bone: how driver mutation order and metabolism matter Ascolani, Gianluca Liò, Pietro BMC Med Genomics Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0541-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-25 /pmc/articles/PMC6657267/ /pubmed/31345216 http://dx.doi.org/10.1186/s12920-019-0541-4 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Ascolani, Gianluca Liò, Pietro Modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
title | Modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
title_full | Modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
title_fullStr | Modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
title_full_unstemmed | Modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
title_short | Modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
title_sort | modeling breast cancer progression to bone: how driver mutation order and metabolism matter |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657267/ https://www.ncbi.nlm.nih.gov/pubmed/31345216 http://dx.doi.org/10.1186/s12920-019-0541-4 |
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