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Gastroenteropancreatic neuroendocrine neoplasms: genes, therapies and models
Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) refer to a group of heterogeneous cancers of neuroendocrine cell phenotype that mainly fall into one of two subtypes: gastroenteropancreatic neuroendocrine tumors (GEP-NETs; well differentiated) or gastroenteropancreatic neuroendocrine carci...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Company of Biologists Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5894937/ https://www.ncbi.nlm.nih.gov/pubmed/29590641 http://dx.doi.org/10.1242/dmm.029595 |
Sumario: | Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) refer to a group of heterogeneous cancers of neuroendocrine cell phenotype that mainly fall into one of two subtypes: gastroenteropancreatic neuroendocrine tumors (GEP-NETs; well differentiated) or gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs; poorly differentiated). Although originally defined as orphan cancers, their steadily increasing incidence highlights the need to better understand their etiology. Accumulating epidemiological and clinical data have shed light on the pathological characteristics of these diseases. However, the relatively low number of patients has hampered conducting large-scale clinical trials and hence the development of novel treatment strategies. To overcome this limitation, tractable disease models that faithfully reflect clinical features of these diseases are needed. In this Review, we summarize the current understanding of the genetics and biology of these diseases based on conventional disease models, such as genetically engineered mouse models (GEMMs) and cell lines, and discuss the phenotypic differences between the models and affected humans. We also highlight the emerging disease models derived from human clinical samples, including patient-derived xenograft models and organoids, which may provide biological and therapeutic insights into GEP-NENs. |
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