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  • Title: Pharmacological targeting of mammalian target of rapamycin inhibits ovarian granulosa cell tumor growth.
    Author: Rico C, Laguë MN, Lefèvre P, Tsoi M, Dodelet-Devillers A, Kumar V, Lapointe E, Paquet M, Nadeau MÈ, Boerboom D.
    Journal: Carcinogenesis; 2012 Nov; 33(11):2283-92. PubMed ID: 22871496.
    Abstract:
    Few targeted therapies have been developed for ovarian granulosa cell tumor (GCT), even though it represents 5% of all malignant ovarian tumors in women. As misregulation of PI3K/AKT signaling has been implicated in GCT development, we hypothesized that the AKT signaling effector mammalian target of rapamycin (mTOR) may play a role in the pathogenesis of GCT and could represent a therapeutic target. Analyses of human GCT samples showed an increase in protein levels of mTOR and its downstream effectors RPS6KB1, RPS6, eIF4B and PPARG relative to normal granulosa cells, suggestive of an increase in mTOR pathway activity and increased translational activity and/or protein stability. We next sought to evaluate mTOR as a GCT therapeutic target using the Pten (tm1Hwu/tmiHwu);Ctnnb1 (tm1Mmt/+);Amhr2 (tm3(cre)Bhr/+) (PCA) mouse model, in which mTOR, RPS6KB1, eIF4B and PPARG are upregulated in tumor cells in a manner similar to human GCT. Treatment of PCA mice with the mTOR-specific inhibitor everolimus reduced tumor growth rate (1.5-fold; P < 0.05) and also reduced total tumor burden (4.7-fold; P < 0.05) and increased survival rate (78 versus 44% in the vehicle group) in a PCA surgical model of GCT peritoneal carcinomatosis. Everolimus decreased tumor cell proliferation and tumor cell volume relative to controls (P < 0.05), whereas apoptosis was unaffected. Phosphorylation of RPS6KB1 and RPS6 were decreased (P < 0.05) by everolimus, but RPS6KB1, RPS6, eIF4B and PPARG expressions were not affected. These results suggest that mTOR is a valid and clinically useful pharmacological target for the treatment of GCT, although its inhibition does not reverse all consequences of aberrant PI3K/AKT signaling in the PCA model.
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