Mathematical Sciences, Chalmers University of Technology, Sweden.
Mathematical Sciences, University of Gothenburg, Sweden.
PLoS Comput Biol. 2019 Apr 1;15(4):e1006868. doi: 10.1371/journal.pcbi.1006868. eCollection 2019 Apr.
The formation of metastases is driven by the ability of cancer cells to disseminate from the site of the primary tumour to target organs. The process of dissemination is constrained by anatomical features such as the flow of blood and lymph in the circulatory system. We exploit this fact in a stochastic network model of metastasis formation, in which only anatomically feasible routes of dissemination are considered. By fitting this model to two different clinical datasets (tongue & ovarian cancer) we show that incidence data can be modelled using a small number of biologically meaningful parameters. The fitted models reveal site specific relative rates of dissemination and also allow for patient-specific predictions of metastatic involvement based on primary tumour location and stage. Applied to other data sets this type of model could yield insight about seed-soil effects, and could also be used in a clinical setting to provide personalised predictions about the extent of metastatic spread.
转移的形成是由癌细胞从原发肿瘤部位扩散到靶器官的能力驱动的。扩散的过程受到解剖特征的限制，如血液循环系统中血液和淋巴的流动。我们在转移形成的随机网络模型中利用了这一事实，在该模型中只考虑了解剖上可行的传播途径。通过将该模型拟合到两个不同的临床数据集（舌癌和卵巢癌），我们表明可以使用少数具有生物学意义的参数来对发病率数据进行建模。拟合模型揭示了特定部位的传播相对速率，并且还允许根据原发肿瘤的位置和分期对患者进行特定的转移参与预测。将这种类型的模型应用于其他数据集可以深入了解种子-土壤效应，也可以在临床环境中用于提供关于转移扩散程度的个性化预测。
