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Background: A limited number of approved therapeutic options are available to metastatic medullary thyroid cancer (MTC) patients, and the response to conventional chemotherapy and/or radiotherapy strategies is inadequate. Sporadic and inherited mutations in the tyrosine kinase RET result in oncogenic activation that is associated with the pathogenesis of MTC. Cabozantinib is a potent inhibitor of MET, RET, and vascular endothelial factor receptor 2 (VEGFR2), as well as other tyrosine kinases that have been implicated in tumor development and progression. The object of this study was to determine the in vitro biochemical and cellular inhibitory profile of cabozantinib against RET, and in vivo antitumor efficacy using a xenograft model of MTC. Methods: Cabozantinib was evaluated in biochemical and cell-based assays that determined the potency of the compound against wild type and activating mutant forms of RET. Additionally, the pharmacodynamic modulation of RET and MET and in vivo antitumor activity of cabozantinib was examined in a MTC tumor model following subchronic oral administration. Results: In biochemical assays, cabozantinib inhibited multiple forms of oncogenic RET kinase activity, including M918T and Y791F mutants. Additionally, it inhibited proliferation of TT tumor cells that harbor a C634W activating mutation of RET that is most often associated with MEN2A and familial MTC. In these same cells grown as xenograft tumors in nude mice, oral administration of cabozantinib resulted in dose-dependent tumor growth inhibition that correlated with a reduction in circulating plasma calcitonin levels. Moreover, immunohistochemical analyses of tumors revealed that cabozantinib reduced levels of phosphorylated MET and RET, and decreased tumor cellularity, proliferation, and vascularization. Conclusions: Cabozantinib is a potent inhibitor of RET and prevalent mutationally activated forms of RET known to be associated with MTC, and effectively inhibits the growth of a MTC tumor cell model in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Background: Many proteins that are dysregulated or mutated in cancer cells rely on the molecular chaperone HSP90 for their proper folding and activity, which has led to considerable interest in HSP90 as a cancer drug target. The diverse array of HSP90 client proteins encompasses oncogenic drivers, cell cycle components, and a variety of regulatory factors, so inhibition of HSP90 perturbs multiple cellular processes, including mitogenic signaling and cell cycle control. Although many reports have investigated HSP90 inhibition in the context of the cell cycle, no large-scale studies have examined potential correlations between cell genotype and the cell cycle phenotypes of HSP90 inhibition. Methodology/Principal Findings: To address this question, we developed a novel high-content, high-throughput cell cycle assay and profiled the effects of two distinct small molecule HSP90 inhibitors (XL888 and 17-AAG [17-allylamino-17- demethoxygeldanamycin]) in a large, genetically diverse panel of cancer cell lines. The cell cycle phenotypes of both inhibitors were strikingly similar and fell into three classes: accumulation in M-phase, G2-phase, or G1-phase. Accumulation in M-phase was the most prominent phenotype and notably, was also correlated with TP53 mutant status. We additionally observed unexpected complexity in the response of the cell cycle-associated client PLK1 to HSP90 inhibition, and we suggest that inhibitor-induced PLK1 depletion may contribute to the striking metaphase arrest phenotype seen in many of the M-arrested cell lines. Conclusions/Significance: Our analysis of the cell cycle phenotypes induced by HSP90 inhibition in 25 cancer cell lines revealed that the phenotypic response was highly dependent on cellular genotype as well as on the concentration of HSP90 inhibitor and the time of treatment. M-phase arrest correlated with the presence of TP53 mutations, while G2 or G1 arrest was more commonly seen in cells bearing wt TP53. We draw upon previous literature to suggest an integrated model that accounts for these varying observations. [ABSTRACT FROM AUTHOR]