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Inferring rates of metastatic dissemination using stochastic network models

Journal article
Authors Philip Gerlee
Mia Johansson
Published in Plos Computational Biology
Volume 15
Issue 4
ISSN 1553-7358
Publication year 2019
Published at Department of Mathematical Sciences
Institute of Clinical Sciences, Department of Oncology
Language en
Links dx.doi.org/10.1371/journal.pcbi.100...
Keywords circulating tumor-cells, ovarian-cancer, hematogenous metastasis, lung-cancer, neck, blood, head, identification, carcinoma, patterns, Biochemistry & Molecular Biology, Mathematical & Computational Biology
Subject categories Cancer and Oncology

Abstract

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. Author summary For most cancer patients the occurrence of metastases equals incurable disease. Despite this fact our quantitative knowledge about the process of metastatic dissemination is limited. In this manuscript we improve on a previously published mathematical model by incorporating known biological facts about metastatic spread and also consider the temporal dimension of dissemination. The model is fit to two different cancer types with very different patterns of spread, which highlights the versatility of our framework. Properly parametrised this type of model can be used for making personalised predictions about metastatic burden.

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