Endometrial Carcinoma—Molecular Genetic Aspects

Abstract

The molecular genetic characterization of endometrial carcinoma has clearly contributed to the understanding of this common disease. However, the interpretation of such studies has relied on careful clinicopathological classification of the tumors. Many of the early molecular genetic studies of endometrial carcinoma were hampered by the lack of careful tumor classification. Much of the problem was related to the fact that the classification scheme, described in the previous chapter, initially described in 1983 has only recently gained widespread acceptance. Consequently, and understandably, many of the early studies did not clearly state the type (or types) of endometrial carcinomas that were included. Additionally, many studies that classified tumors lacked significant numbers to allow the results from the different tumor types to be assessed independently, partly because of the fact that type II tumors are relatively uncommon. However, recent molecular genetic studies have supported the dualistic categorization of endometrial carcinoma and have resulted in important insights into the pathogenesis of the two major types of carcinoma (Fig. 1). In addition, such studies are beginning to elucidate the molecular underpinnings of some of the more uncommon types of endometrial carcinoma that are not so easily classified in the dualistic model of endometrial carcinoma. This chapter will focus primarily on the molecular studies that have supported the dualistic model and a short discussion on one of the more uncommon forms of endometrial carcinoma. In addition, the relationship between genetics and hormones in the context of endometrial carcinoma will be briefly discussed followed by a short introduction of a genetic mouse model of endometrial carcinoma.KeywordsEndometrial CancerEndometrial CarcinomaClear Cell CarcinomaSerous CarcinomaEndometrioid CarcinomaThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.