
Ovarian carcinomas are a heterogeneous group of neoplasms and are traditionally subclassified based on type and degree of differentiation. Although current clinical management of ovarian carcinoma largely fails to take this heterogeneity into account, it is becoming evident that each major histological type has characteristic genetic defects that deregulate specific signaling pathways in the tumor cells. Moreover, within the most common histological types, the molecular pathogenesis of low-grade versus high-grade tumors appears to be largely distinct. Mouse models of ovarian carcinoma have been developed that recapitulate many of the morphological features, biological behavior, and gene-expression patterns of selected subtypes of ovarian cancer. Such models will likely prove useful for studying ovarian cancer biology and for preclinical testing of molecularly targeted therapeutics, which may ultimately lead to better clinical outcomes for women with ovarian cancer.
Ovarian Neoplasms, Genotype, Patient Selection, Cell Differentiation, Gene Expression Regulation, Neoplastic, Disease Models, Animal, Mice, Phenotype, Treatment Outcome, Terminology as Topic, Animals, Humans, Female, Early Detection of Cancer
Ovarian Neoplasms, Genotype, Patient Selection, Cell Differentiation, Gene Expression Regulation, Neoplastic, Disease Models, Animal, Mice, Phenotype, Treatment Outcome, Terminology as Topic, Animals, Humans, Female, Early Detection of Cancer
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 687 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 0.1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 0.1% |
