Animal models of spontaneous pancreatic neuroendocrine tumors. (5th February 2016)

• Record Type: Journal Article
• Authors: Yu, Run
• Is Part Of: Molecular and cellular endocrinology. Volume 421(2016)
• Page Start: 60
• Page End: 67
• Publication Date: 2016-02-05
• Subjects: Pancreatic neuroendocrine tumors -- Animal models -- Murine models; Endocrinology -- Periodicals; Molecular biology -- Periodicals; Cytology -- Periodicals; Endocrinology -- Periodicals; Hormones -- Periodicals; Endocrinologie -- Périodiques; Cytology; Endocrinology; Molecular biology; Periodicals; 573.4
• DOI: 10.1016/j.mce.2015.08.004
• Languages: English
• ISSNs: 0303-7207

Abstract

Abstract: Pancreatic neuroendocrine tumors (PNETs) are usually low-grade neoplasms derived from the endocrine pancreas. PNETs can be functioning and cause well-described hormonal hypersecretion syndromes or non-functioning and cause only tumor mass effect. PNETs appear to be more common recently likely due to incidental detection by imaging. Although the diagnosis and management of PNETs have been evolving rapidly, much remains to be studied in the areas of molecular pathogenesis, molecular markers of tumor behavior, early detection, and targeted drug therapy. Unique challenges facing PNETs studies are long disease course, the deep location of pancreas and difficult access to pancreatic tissue, and the variety of tumors, which make animal models valuable tools for PNETs studies. Existing animal models of PNETs have provided insights into the pathogenesis and natural history of human PNETs. Future studies on animal models of PNETs should address early tumor detection, molecular markers of tumor behavior, and novel targeted therapies. Graphical abstract: Highlights: More than 20 murine and rat models of pancreatic neuroendocrine tumors (PNETs) are available. Some animal models recapitulate PNET pathogenesis in human PNET syndromes. The animal models universally demonstrate a multi-stage PNET pathogenesis from hyperplasia, dysplasia, to frank PNETs. The animal models of PNETs have already contributed much to the understanding of human PNETs.