Antitumor Effect of Everolimus in Preclinical Models of High Grade Gastroenteropancreatic Neuroendocrine Carcinomas.

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Bollard, Julien | Couderc, Christophe | Blanc, Martine | Poncet, Gilles | Lepinasse, Florian | Hervieu, Valérie | Gouysse, Géraldine | Ferraro-Peyret, Carole | Benslama, Noura | Walter, Thomas | Scoazec, Jean-Yves | Roche, Colette

Edité par CCSD ; Karger -

International audience. Background/Aims: While the range of therapeutic options for well-differentiated gastroenteropancreatic neuroendocrine tumors (GEP NETs) has recently increased with the emergence of targeted therapies, such as mTOR inhibitors, there is no recent progress in the treatment of poorly differentiated neuroendocrine carcinomas (PDNECs). Since PDNECs have been shown to strongly express mTOR pathway components, the aim of the present study was to assess the antitumor effect of the mTOR inhibitor everolimus in preclinical models of PDNECs. Methods: Thee expression of mTOR pathway components and their response to everolimus were assessed in two neuroendocrine cell lines: STC-1 and GluTag. A xenograft model of intra-hepatic dissemination in the nude mouse, based on the intrasplenic injection of either STC-1 and GluTag tumor cells, was used. Animals were started on everolimus treatment 3 days after injection. The effects of treatment on tumor growth, proliferative capacities, apoptosis and in situ expression of mTOR pathway components were assessed. Results: The expression of mTOR pathway components was comparable in STC-1 and GluTag cells and in human PDNECs and could be inhibited in vitro by everolimus. In vivo, the tumor volume of STC-1 and GluTag xenografts was significantly reduced in treated animals (6.05% ± 1.84 as compared to 21.76 ± 3.88% in controls). Everolimus treatment also induced a significant decrease in Ki67 index and in the phosphorylation levels of the two major effectors of mTOR, p70S6K and 4E-BP1. Conclusion: Our experimental data suggest that mTOR inhibition could be considered a therapeutic option for high grade GEP NETs.

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