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The Utility of Cabozantinib in the Therapy of Endocrine Tumours
Eleni Armeni1, Tu-Vinh Luong2, Ashley Grossman3
1Department of Diabetes and Endocrinology, and Neuroendocrine Tumour Unit, ENETS Centre of Excellence, Royal Free Hospital NHS Foundation Trust, London, NW3 2QG, UK. eleni.armeni@nhs.net.
Abstract:
Endocrine and neuroendocrine malignancies, including epithelial neuroendocrine neoplasms (NENs), phaeochromocytoma/paraganglioma (PPGL), adrenocortical carcinoma (ACC) and thyroid cancers, represent a heterogeneous group of tumours often characterised by dysregulated receptor tyrosine kinase signalling and with limited systemic treatment options. Cabozantinib is a multikinase inhibitor implicated in tumour angiogenesis, growth, and therapeutic resistance, and its use has been reported in many of these tumours. We performed a narrative review assessing cabozantinib monotherapy or combination regimens in patients with progressive neuroendocrine neoplasms. In NENs, monotherapy achieved a disease control rate (DCR) of up to 83% and a progression-free survival (PFS) of 8.4 months in extra-pancreatic subtypes, and 13.8 months in pancreatic subtypes. Combination therapies yielded modest efficacy with a PFS up to 13.0 months. In metastatic PPGLs, monotherapy achieved an objective response rate (ORR) of 25%, a median PFS of 16.6 months and overall survival (OS) of 24.9 months; combination with atezolizumab showed an ORR of 15.4% and a PFS of 8.4 months. In adrenocortical cancer, the DCR reached 78%, PFS up to 7.2 months, and OS up to 23.9 months. In differentiated thyroid cancer, PFS 11.4 months and OS 26.3 months; in RET M918T-mutant medullary thyroid cancer, OS improved to 44.3 months. Cabozantinib represents a promising therapeutic option across endocrine and neuroendocrine malignancies, particularly in settings with limited treatment alternatives, although the reported rates of control have not been dramatic and adverse effects not insignificant. However, it offers the possibility of exploring more effective molecular approaches, especially with biomarker-based stratification and combinatorial approaches.
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