TP53 mutations in early-stage ovarian carcinoma, relation to long-term survival

Wang, Y; Helland, Åslaug; Holm, Ruth; Skomedal, Hanne; Vm, Abeler; He, Danielsen; Cg, Tropé; Børresen-Dale, A-L; Gb, Kristensen

doi:10.1038/sj.bjc.6601537

Cited by 54 publications

(32 citation statements)

References 31 publications

(38 reference statements)

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“…TP53 mutations are found in 35 to 40% of human ovarian tumors (32)(33)(34). The identified rat Tp53 mutations of codons 173 and 218 correspond to human codons 175 and 220, respectively, which are among the most frequent in human ovarian cancer (6.8% and 2.4, respectively).…”

Section: Discussionmentioning

confidence: 99%

Characterization of a Carcinogenesis Rat Model of Ovarian Preneoplasia and Neoplasia

Stewart¹,

Querec²,

Ochman³

et al. 2004

Cancer Research

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Animal models of ovarian cancer are crucial for understanding the pathogenesis of the disease and for testing new treatment strategies. A model of ovarian carcinogenesis in the rat was modified and improved to yield ovarian preneoplastic and neoplastic lesions that pathogenetically resemble human ovarian cancer. A significantly lower dose (2 to 5 g per ovary) of 7,12-dimethylbenz(a)anthracene (DMBA) was applied to the one ovary to maximally preserve its structural integrity. DMBA-induced mutagenesis was additionally combined with repetitive gonadotropin hormone stimulation to induce multiple cycles of active proliferation of the ovarian surface epithelium. Animals were treated in three arms of different doses of DMBA alone or followed by hormone administration. Comparison of the DMBA-treated ovaries with the contralateral control organs revealed the presence of epithelial cell origin lesions at morphologically distinct stages of preneoplasia and neoplasia. Their histopathology and path of dissemination to other organs are very similar to human ovarian cancer. Hormone cotreatment led to an increased lesion severity, indicating that gonadotropins may promote ovarian cancer progression. Point mutations in the Tp53 and Ki-Ras genes were detected that are also characteristic of human ovarian carcinomas. Additionally, an overexpression of estrogen and progesterone receptors was observed in preneoplastic and early neoplastic lesions, suggesting a role of these receptors in ovarian cancer development. These data indicate that this DMBA animal model gives rise to ovarian lesions that closely resemble human ovarian cancer and it is adequate for additional studies on the mechanisms of the disease and its clinical management.

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Section: Discussionmentioning

confidence: 99%

Characterization of a Carcinogenesis Rat Model of Ovarian Preneoplasia and Neoplasia

Stewart¹,

Querec²,

Ochman³

et al. 2004

Cancer Research

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“…1). Mutations are more frequent in advanced stage or in cancer subtypes with aggressive behavior (such as triple negative or HER2-amplified breast cancers) (Wang et al 2004a;Wang et al 2004b;Langerod et al 2007). In cancers with low mutation rates, p53 is often inactivated by alternative mechanisms.…”

Section: Somatic Mutationsmentioning

confidence: 99%

TP53 Mutations in Human Cancers: Origins, Consequences, and Clinical Use

Olivier¹,

Hollstein²,

Hainaut³

2009

Cold Spring Harbor Perspectives in Biology

1,700

1,321

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Somatic mutations in the TP53 gene are one of the most frequent alterations in human cancers, and germline mutations are the underlying cause of Li-Fraumeni syndrome, which predisposes to a wide spectrum of early-onset cancers. Most mutations are single-base substitutions distributed throughout the coding sequence. Their diverse types and positions may inform on the nature of mutagenic mechanisms involved in cancer etiology. TP53 mutations are also potential prognostic and predictive markers, as well as targets for pharmacological intervention. All mutations found in human cancers are compiled in the IARC TP53 Database (http://www-p53.iarc.fr/). A human TP53 knockin mouse model (Hupki mouse) provides an experimental model to study mutagenesis in the context of a human TP53 sequence. Here, we summarize current knowledge on TP53 gene variations observed in human cancers and populations, and current clinical applications derived from this knowledge.

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“…Even more alarmingly, many reports have questioned or rejected a correlation between a proposed biomarker and clinical outcome. Doubts were raised regarding the markers CA125 (Cruickshank et al 1987, van der Burg et al 1988, Rustin et al 1989, Sevelda et al 1989, cyclin D1 (Masciullo et al 1997, Dhar et al 1999, p16 (Milde-Langosch et al 2003, Khouja et al 2007), p21 (Baekelandt et al 1999, Levesque et al 2000, Schuyer et al 2001, p27 (Schmider et al 2000), p53 (Smith-Sorensen et al 1998, Wang et al 2004, Green et al 2006, Bcl-xl (Baekelandt et al 2000), cIAP (Kleinberg et al 2007), survivin (Cohen et al 2003, Ferrandina et al 2005, hTERT (Wisman et al 2003, Widschwendter et al 2004), ERBB1 (Berchuck et al 1991, Meden et al 1995, Nielsen et al 2004, and ERBB2 (Rubin et al 1993, Meden et al 1995, Ross et al 1999, Nielsen et al 2004, Riener et al 2004.…”

Section: Introductionmentioning

confidence: 99%

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

Győrffy¹,

Lánczky²,

Szállási³

2012

Endocrine-Related Cancer

786

824

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The validation of prognostic biomarkers in large independent patient cohorts is a major bottleneck in ovarian cancer research. We implemented an online tool to assess the prognostic value of the expression levels of all microarray-quantified genes in ovarian cancer patients. First, a database was set up using gene expression data and survival information of 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 microarrays). After quality control and normalization, only probes present on all three Affymetrix platforms were retained (nZ22 277). To analyze the prognostic value of the selected gene, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (nZ1090) or overall survival (nZ1287). A Kaplan-Meier survival plot was generated and significance was computed. The tool can be accessed online at www.kmplot. com/ovar. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature. Of these, CA125 (MUC16; PZ3.7!10 K5

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TP53 mutations in early-stage ovarian carcinoma, relation to long-term survival

Cited by 54 publications

References 31 publications

Characterization of a Carcinogenesis Rat Model of Ovarian Preneoplasia and Neoplasia

Characterization of a Carcinogenesis Rat Model of Ovarian Preneoplasia and Neoplasia

TP53 Mutations in Human Cancers: Origins, Consequences, and Clinical Use

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

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