Ovarian carcinoma is one of the most aggressive gynecological diseases and generally diagnosed at advanced stages. Osteopontin (OPN) is one of the proteins overexpressed in ovarian cancer and is involved in tumorigenesis and metastasis. Alternative splicing of OPN leads to 3 isoforms, OPNa, OPNb, and OPNc. However, the expression pattern and the roles of each of these isoforms have not been previously characterized in ovarian cancer. Herein, we have evaluated the expression profiling of OPN isoforms in ovarian tumor and nontumor samples and their putative roles in ovarian cancer biology using in vitro and in vivo functional assays. OPNa and OPNb were expressed both in tumor and nontumor ovarian samples, whereas OPNc was specifically expressed in ovarian tumor samples. The isoform OPNc significantly activated OvCar-3 cell proliferation, migration, invasion, anchorage-independent growth and tumor formation in vivo. Additionally, we have also shown that some of the OPNc-dependent protumorigenic roles are mediated by PI3K/Akt signaling pathway. OPNc stimulated immortalized ovarian epithelial IOSE cell proliferation, indicating a role for this isoform in ovarian cancer tumorigenesis. Functional assays using OPNc conditioned medium and an anti-OPNc antibody have shown that most cellular effects observed herein were promoted by the secreted OPNc. According to our data, OPNc-specific expression in ovarian tumor samples and its role on favoring different aspects of ovarian cancer progression suggest that secreted OPNc contributes to the physiopathology of ovarian cancer progression and tumorigenesis. Altogether, the data open possibilities of new therapeutic approaches for ovarian cancer that selectively down regulate OPNc, altering its properties favoring ovarian tumor progression. Mol Cancer Res; 9(3); 280–93. ©2011 AACR.

The selection of suitable internal control genes is crucial for proper interpretation of real-time PCR data. Here we outline a strategy to identify housekeeping genes that could serve as suitable internal control for comparative analyses of gene expression data in breast cancer cell lines and tissues obtained by high throughput sequencing and quantitative real-time PCR (qRT-PCR). The strategy proposed includes the large-scale screening of potential candidate reference genes from RNA-seq data as well as their validation by qRT-PCR, and careful examination of reference data from the International Cancer Genome Consortium, The Cancer Genome Atlas and Gene Expression Omnibus repositories. The identified set of reference genes, also called novel housekeeping genes that includes CCSER2, SYMPK, ANKRD17 and PUM1, proved to be less variable and thus potentially more accurate for research and clinical analyses of breast cell lines and tissue samples compared to the traditional housekeeping genes used to this end. These results highlight the importance of a massive evaluation of housekeeping genes for their relevance as internal control for optimized intra- and inter-assay comparison of gene expression. We developed a strategy to identify and evaluate the significance of housekeeping genes as internal control for the intra- and inter-assay comparison of gene expression in breast cancer that could be applied to other tumor types and diseases.

In this review, we report on breast cancer’s molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development