TBR-760 (formerly BIM-23A760) is a chimeric dopamine (DA)-somatostatin (SST) compound with potent agonist activity at both DA type 2 (D2R) and SST type 2 (SSTR2) receptors. Studies have shown that chimeric DA-SST compounds are more efficacious than individual DA and/or SST analogues, either alone or combined, in inhibiting secretion from primary cultures of human somatotroph and lactotroph tumor cells. Nonfunctioning pituitary adenomas (NFPAs) express both D2R and SSTR2 and, consequently, may respond to TBR-760. We used a mouse model with the pro-opiomelanocortin (POMC) gene knocked out that spontaneously develops aggressive NFPAs. Genomic microarray and DA and SST receptor messenger RNA expression analysis indicate that POMC KO mouse tumors and human NFPAs have similar expression profiles, despite arising from different cell lineages, establishing POMC KO mice as a model for study of NFPAs. Treatment with TBR-760 for 8 weeks resulted in nearly complete inhibition of established tumor growth, whereas tumors from vehicle-treated mice increased in size by 890 ± 0.7%. Comparing TBR-760 with its individual DA and SST components, TBR-760 arrested tumor growth. Treatment with equimolar or 10×-higher doses of the individual SST or DA agonists, either alone or in combination, had no significant effect. One exception was the lower dose of DA agonist that induced modest suppression of tumor growth. Only the chimeric compound TBR-760 arrested tumor growth in this mouse model of NFPA. Further, significant tumor shrinkage was observed in 20% of the mice treated with TBR-760. These results support the development of TBR-760 as a therapy for patients with NFPA.
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http://dx.doi.org/10.1210/endocr/bqaa101 | DOI Listing |
Sci Rep
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Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Patients with recurrent high-grade glioma (rHGG) have a poor prognosis with median progression-free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow-up imaging studies to adaptively predict the risk of progression.
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