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首页> 外文期刊>BMC Medical Genomics >Modeling breast cancer progression to bone: how driver mutation order and metabolism matter
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Modeling breast cancer progression to bone: how driver mutation order and metabolism matter

机译：建立乳腺癌向骨骼发展的模型：驱动程序突变顺序和新陈代谢如何起作用

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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. 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. 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.
机译：并非所有的突变对于转移的发展都同样重要。他们的订单呢？癌细胞从原发肿瘤部位到继发接种部位的存活取决于促进致癌细胞行为的极少数驱动突变。通常，这些驱动程序突变是最有效的临床可操作靶标标记之一。对主要和次要位点突变的影响进行定量评估是一个重要的挑战性问题，可导致癌症进展轨迹的更好可预测性。我们在细胞自动机的框架中引入一个定量模型，以研究代谢突变和突变顺序对癌症干性和肿瘤细胞从乳腺，血液向骨转移部位迁移的影响。我们的方法对三种类型的突变进行建模：驱动程序，其顺序与动力学相关，代谢支持癌症的生长并根据现有数据库进行估算，以及非驱动程序突变。我们在癌症突变数据库上将该模型与生物信息学分析集成在一起，该数据库显示代谢修饰改变构成关键癌症突变的重要类别。我们的工作提供了定量的依据，即不同的癌症克隆通过其在乳房，血液和骨腔中的进展，对驱动程序突变的顺序和改变代谢过程的突变数量有何影响。由于多区室分析，这项工作是创新的，并且可能影响对治疗有抵抗力的克隆种群的扩散和患者的生存。突变顺序的数学模型以操作员的方式呈现给癌症模型研究者广泛的群体，以激发这种有用的方法的发展（并在生物医学模型中未得到充分利用）。我们相信我们的结果和理论框架还可以建议进行实验，以测量总体个性化癌症突变特征。

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