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Role of p53 and Rb in Ovarian Cancer

  • David C. Corney
  • Andrea Flesken-Nikitin
  • Jinhyang Choi
  • Alexander Yu. Nikitin
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 622)

Ovarian cancer is the second most common gynecological neoplasm with over 20,000 new cases and 15,000 deaths predicted in 2006 (1). Although significant decreases in mortality have been observed in cancers of the breast and cervix, mortality rates for cancer of the ovary has remained essentially constant over the past 30 years. The majority of cases present at advanced stages, at which point the disease is rarely curable using the existing treatment schemes. Accordingly, the 5-year survival rate for advanced ovarian cancer is 29%. In addition to asymptomatic development, a scarcity of accurate animal models has resulted in a marked lack of knowledge of how the disease progresses, which in turn has precluded the development of desperately needed treatment regimens and screening programs.

Ovarian cancer is a wide-ranging term that groups together a diverse set of neoplasms originating from the ovary, with carcinomas comprising 90% of ovarian cancers. On the basis of the morphological criteria, epithelial ovarian cancers (EOCs) are classified as serous, mucinous, endometrioid, clear cell, transitional cell, squamous cell, and mixed epithelial neoplasms (2). The ovarian surface epithelium (OSE) is a single layer of flat-to-cuboidal cells covering the ovary and is the presumed cell of origin for EOCs (3–6). Recent studies indicate that this layer may possess stem cell properties, and both tumors and cell lines of transformed mouse OSE cells contain a side population (7), which is considered by many investigators as an indicator of cancer stem cells in other tissues (8–11).

Keywords

Ovarian Cancer Ovarian Carcinoma Epithelial Ovarian Cancer Side Population Serous Carcinoma 
These 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.

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Copyright information

© Springer 2008

Authors and Affiliations

  • David C. Corney
    • 1
  • Andrea Flesken-Nikitin
    • 1
  • Jinhyang Choi
    • 1
  • Alexander Yu. Nikitin
    • 1
  1. 1.Department of Biomedical SciencesCornell UniversityIthaca

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