From a biological question to a mathematical model: an example of patterns of genetic alterations in bladder tumorigenesis
Laurence Calzone

04 December 2014, 14:30 - 04 December 2014, 15:30 Salle/Bat : 475/PCRI-N
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Activités de recherche : System Biology

Résumé :

I will start introducing our systems biology approach, from the definition of the question to the construction of a mathematical model and the formulation of predictions. I will then show how we applied our approach to bladder cancer and attempted to explain the co-occurrence and exclusivity of genetic alterations in a set of genes frequently mutated in bladder tumours.
Bladder tumours progress along two main pathways: the CIS pathway and the Ta pathway. The Ta pathway, less aggressive than the CIS pathway, is characterized by a high frequency of activating fibroblast growth factor receptor 3 (FGFR3) gene mutations, which are rare in the CIS pathway. We combined mathematical modelling and statistical analyses to better understand the diverse alterations observed in bladder tumorigenesis.
In a dataset of 178 patients including both invasive and non-invasive tumours, we performed statistical tests on a list of genes known to be frequently altered in bladder cancer in order to identify co-occurrences or exclusivities between all these alterations. We focused on genetic alterations (mutations, homozygous losses, and amplifications) of genes frequently mutated in bladder cancer. We identified 10 associations and verified them in 3 other public datasets. We then constructed a logical model of cell cycle and apoptosis entry in order to explain the context for these patterns of genetic alterations. With the model, we formulated some predictions that we verified back in each of the three datasets when possible. Finally, we explored a method linking our transcriptomics dataset of the 178 tumours to the stable states of our logical model and confirmed that the invasive tumours are associated with proliferative stable states and non-invasive tumours with apoptosis. We attempted to stratify patients based on the solutions of the mathematical model.