OnKO3T-TC Leaders : Monique Dontenwill et Damien Reita
OnKO•3T-TC is developing translational research aimed at characterising the heterogeneity and plasticity of glioblastomas with the aim of identifying innovative therapeutic targets for the treatment of glioblastomas. Our work, through the study of integrin α5β1, has highlighted the very high heterogeneity and plasticity of glioblastomas for which therapeutic targets are modulated locoregionally and longitudinally during tumour progression (Sani et al., 2022; Messe et al., in revision, 2024). They also emphasise the fundamental role of the microenvironment in tumour progression and recurrence mechanisms. Thanks to a collaboration with bioinformaticians, we have identified a GBM-specific regulatory network whose originality lies in the fact that it takes into account the activity of regulatory genes, rather than just gene expression levels (Bernhard et al., in revision, 2024). The use of this network is an innovative and relevant tool for understanding the heterogeneity of glioblastomas. It enables a finer classification of glioblastomas and makes it possible to re-analyse transcriptome data by integrating notions of activity and interaction, highlighting new genes of interest.
Following on from this work, a new research theme aims to determine the role of different tumour niches (tumour, perinecrotic, invasive and peritumoral) in the acquisition of molecular phenotypes at the origin of glioblastoma recurrence. This project is based on an innovative integrative analysis of intra-tumour heterogeneity, taking into account the spatial organisation and temporal evolution (before and after treatment) of clonal and sub-clonal populations. Our aim is to characterise the molecular phenotype (transcriptome, methylome, proteome) of each niche and its evolution over time (before and after treatment). The aim is to identify 1) potential markers predictive of recurrence at diagnosis, and 2) new therapeutic strategies. These strategies will then be validated on different models mimicking tumour heterogeneity. Our work is based on transdisciplinary collaborations with clinicians, chemists, bioinformatics experts and biologists, as well as scientific networks (GOPA-PETRA network).
