BCSBC, Institut Curie, Paris
Date(s) : 20/06/2016 iCal
14 h 00 min - 16 h 00 min
Cancer is driven by mutations leading to dysfunctions of the complex network of molecular interactions regulating signalling pathways and thus, affecting multiple cellular functions. Successful applications of systems biology methods for analysis of high-throughput data require detailed reconstructions of signalling networks amenable for computational analyses. For that purpose, we have developed a comprehensive map of molecular mechanisms implicated in cancer, the “Atlas of Cancer Signalling Networks” (ACSN). The resource is combined with tools for map navigation and data visualization in the biological network context, which constitutes a good starting point when building a mathematical model to explain particular datasets.
Using the information gathered in ACSN, we have constructed a regulatory network with the purpose of elucidating the role of individual mutations or their combinations affecting the metastatic development. The network was then translated into a logical model 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.
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. The genetic interaction profile for this model reveals putative candidates for targeted therapy able to diminish the probability of metastasis. In particular, we have shown that the double mutation Notch gain-of-function and p53 loss-of-function has the highest probability to acquire metastasis, which is in agreement with a recent published experiment in a mouse model of gut cancer.
In summary, 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.