Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration

David P A Cohen, Loredana Martignetti, Sylvie Robine, Emmanuel Barillot, Andrei Zinovyev and Laurence Calzone

PLOS Computational Biology, 2015, vol. 11, issue 11, 1-29

Abstract: Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.Author Summary: We provide here a logical model that proposes gene/pathway candidates that could abrogate metastasis. The model explores the mechanisms and interplays between pathways that are involved in the process, identifies the main players in these mechanisms and gives some insight on how the pathways could be altered. The model reproduces phenotypes of published experimental results such as the double mutant Notch+/+/p53-/-. We have also developed two methods: (1) to predict genetic interactions and (2) to match transcriptomics data to the attractors of a logical model and validate the model on cell line experiments.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004571

DOI: 10.1371/journal.pcbi.1004571

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