Culture dimensionality influences the resistance of glioblastoma stem-like cells to multikinase inhibitors
Menée in vitro à l'aide de lignées cellulaires de glioblastome, cette étude met en évidence des mécanismes différents de réponse au sunitinib selon que les cellules sont cultivées dans un environnement bi-dimensionnel ou tri-dimensionnel
Résumé en anglais
Sunitinib, an inhibitor of kinases, including VEGFR and PDGFR, efficiently induces apoptosis in vitro in glioblastoma (GBM) cells, but does not show any survival benefit in vivo. One detrimental aspect of current in vitro models is that they do not take into account the contribution of extrinsic factors to the cellular response to drug treatment. Here, we studied the effects of substrate properties including elasticity, dimensionality and matrix composition on the response of glioblastoma (GBM) stem-like cells (GSCs) to chemotherapeutic agents. Thirty seven cell cultures, including GSCs, parenchymal GBM cells and GBM cell lines, were treated with nine antitumor compounds. Contrary to the expected chemoresistance of GSCs, these cells were more sensitive to most agents than GBM parenchymal cells or GBM cell lines cultured on flat (2D) plastic or collagen-coated surfaces. However, GSCs cultured in collagen-based 3D environments increased their resistance, particularly to receptor tyrosine kinase inhibitors, such as Sunitinib, BIBF1120 and Imatinib. Differences in substrate rigidity or matrix components did not modify the response of GSCs to the inhibitors. Moreover, MEK-ERK and PI3K-Akt pathways but not PDGFR mediate, at least in part, this dimensionality-dependent chemoresistance. These findings suggest that survival of GSCs on 2D substrates, but not in a 3D environment, relies on kinases that can be efficiently targeted by sunitinib-like inhibitors. Overall, our data may help explain the lack of correlation between in vitro and in vivo models used to study the therapeutic potential of kinase inhibitors, and provide a rationale for developing more robust drug screening models.
