Welcome to the website of the LIONS project, a ''Large-scale Integrative approach to unravel the complex relationships between differentiatiON and tumorigenesiS''. This 36-month project is being funded by INSERM and the French National Cancer Institute (INCa) through the "Cancer Plan 2014-2019" framework and brings together five research teams from France and the UK.

Scientific background

Tumor cells make use of many of the regulatory networks of normal cells for their migration, proliferation, the attraction of new vessels, and the maintenance of various differentiation states. We propose a multi-scale systems biology approach to identify and compare the networks found in normal and tumor states, and thereby assess pathways that are conserved or altered in the tumor state. We will apply this strategy to bladder cancer, a cancer derived from the bladder urothelium, because normal urothelium can be obtained and cultured at different stages of proliferation and differentiation.

Project description

We will use high-throughput heterogeneous data (miRNA and mRNA isoform expression) and protein data of bladder tumors and normal urothelial cells at different stages of proliferation/differentiation to infer, analyze and compare the regulatory networks — transcription factors, miRNA and target genes — found in the normal and pathological states. This work will build on the previous studies carried out by the partners to model gene regulation in both discrete and a continuous frameworks. It will also involve extensive cis-regulatory element analysis and tools for mathematical studies of a perturbation model combining inferred normal and tumoral regulatory networks with heterogeneous tumor data (DNA methylation, mutations and genomic alterations). Key candidate genes from deregulated pathways encoding regulators or therapeutic targets will be validated functionally.


The delineation of conserved and deregulated regulatory networks will lead to a more comprehensive understanding of the underlying biology of bladder cancers, including valuable potential therapeutic targets. This interdisciplinary systems biology project will also lead to developments in algorithmic, statistical machine learning, and computational biology for basic and applied cancer research.