Analyses of p53 expression pattern and BRCA mutations in patients with double primary breast and ovarian cancer

BRCA1: An Endocrine and Metabolic Regulator

The breast and ovarian cancer susceptibility gene (BRCA1) is a tumor suppressor whose mutation has been associated with the development of breast, ovarian and, probably, other malignancies at young ages. The BRCA1 gene product participates in multiple biological pathways including the DNA damage response, transcriptional control, cell growth and apoptosis. Inactivating germline mutations of the BRCA1 gene can be detected in a substantial portion of families with inherited breast and/or ovarian cancer. While the genomic and cancer-related actions of BRCA1 have been extensively investigated, not much information exists regarding the cellular and circulating factors involved in regulation of BRCA1 expression and action. The present review article dissects the emerging role of BRCA1 as an important regulator of various endocrine and metabolic axes. Experimental and clinical evidence links BRCA1 with a number of peptide and steroid hormones. Furthermore, comprehensive analyses identified complex interactions between the insulin/insulin-like growth factor-1 (IGF1) signaling axis and BRCA1. The correlation between metabolic disorders, including diabetes and the metabolic syndrome, and BRCA1 mutations, are discussed in this article.

The potential of biologic network models in understanding the etiopathogenesis of ovarian cancer

Ovarian cancer is one of the most common gynecologic malignancies and is the 5th leading cause of cancer mortality in women in the United States. Understanding the biology and molecular pathogenesis of ovarian epithelial tumors is key to developing improved prognostic indicators and effective therapies. The selection of ovarian serous carcinomas as one of the three cancer types for extensive genomic and proteomic characterization of The Cancer Genome Atlas (TCGA) project offers an important opportunity to extend our knowledge of ovarian cancer. The data portal includes molecular characterization, high throughput sequencing, and clinical data. Models to determine which of these genes act as "key drivers" of ovarian carcinogenesis and which are innocent "passengers" are needed. Standard statistical approaches often fail to differentiate between these driver and passenger genes, given that the correlation between sets of genes or genes and endpoints alone does not establish causality. As contrasted to basic correlations analyses, biological network models offer the ability to resolve causality by elucidating the directional linkages between genetics, molecular characterizations of the system, and clinical measures. This article describes the use of a novel, supercomputer-driven approach named REFS to learn network models directly from the TGCA ovarian cancer data set and simulate these models to learn the "key drivers" of ovarian carcinogenesis. The model can be validated by out-of-sample testing, and may provide a powerful new tool for ovarian cancer research.

[Overexpression and prognostic value of p53 and HER2/neu proteins in benign ovarian tissue and in ovarian cancer]

BACKGROUND AND OBJECTIVE

To investigate the prognostic value of p53 and HER2/neu overexpression in epithelial ovarian cancer (EOC).

PATIENTS AND METHOD

p53 and HER2/neu immunostaining were performed in 198 tissue samples, 124 EOC, 44 benign ovarian tumors and 30 normal ovaries. Nuclear p53 and membranous HER2/neu immunostaining were evaluated.

RESULTS

Neither p53 nor HER2/neu overexpression was seen in the benign ovarian tumors. HER2/neu immunostaining was observed in one normal ovary. P53 overexpression was found in 25% EOC and was related with advanced stage, endometrioid, clear cell and undifferentiated types, grade G3, and sub-optimal surgery. HER2/neu immunostaining was observed in 24.2% and it was associated with advanced stage, clear cell and undifferentiated types, and suboptimal surgery. Both, p53 and HER2/neu overexpression decreased overall and progression-free survival, but in the multivariant analysis, only HER2/neu overexpression was an independent prognostic factor of overall survival (RR = 2.8; 95% confidence interval [CI], 1.2-5.6) and recurrence (RR = 2.8; 95% CI, 1.1-7.1). Simultaneous p53 and HER2/neu overexpression made the prognosis worse (p < 0.01).

CONCLUSIONS

HER2/neu overexpression (but not p53 overexpression) is a major prognostic factor in EOC.