Paper Info
Title
Mathematical Modelling Of Molecular Pathways Enabling Tumour Cell Invasion And Migration
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-beta-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.
Year
DOI
Venue
2015
10.1371/journal.pcbi.1004571
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Metastasis,Gene,Biology,Phenotype,Transcriptome,Cell,Bioinformatics,Genetics,Cancer,Epithelial–mesenchymal transition,Mutation
Journal
11
Issue
ISSN
Citations 
11
1553-734X
7
PageRank 
References 
Authors
0.61
7
6
Name
Order
Citations
PageRank
David P A Cohen1141.06
Loredana Martignetti271.29
Sylvie Robine370.61
Emmanuel Barillot4950165.00
Andrei Yu. Zinovyev59311.87
Laurence Calzone627617.90