Effect of combined treatment with progesterone and tamoxifen on the growth and apoptosis of human ovarian cancer cells.
ABSTRACT Progesterone has a potential protective effect against ovarian carcinoma induced by estrogen. Progesterone is also known to cause apoptosis while tamoxifen induces growth arrest. Therefore, we attempted to determine whether combined treatment with progesterone and tamoxifen has a synergistic effect on anti-cancer activity. Although progesterone is known to cause apoptosis while tamoxifen induces growth arrest in many cancer cells, the detailed action of progesterone and tamoxifen and the anticancer effect of combined treatment have not been tested in ovarian cancer cells. Therefore, we tested the growth and apoptosis activity of progesterone and tamoxifen and the anticancer effect of combined treatment of progesterone and tamoxifen in ovarian cancer cells. Ovarian cancer cells, PA-1, were treated with progesterone, tamoxifen, or a combination of progesterone and tamoxifen. The anti-cancer effects were investigated by use of flow cytometry, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, DNA fragmentation analysis, and Western blot analysis. We found that 100 µM progesterone induced typical apoptosis in PA-1 cells. Treatment of PA-1 cells with 10 µM tamoxifen resulted in an increase in the levels of p21, p27, p16 and phospho-pRb, indicating typical G1 arrest. Co-treatment of PA-1 cells with 100 µM progesterone and 10 µM tamoxifen resulted in typical apoptosis, similar to that induced by treatment with 100 µM progesterone alone. These results indicate that progesterone caused apoptosis and tamoxifen induced G1 arrest. Combined treatment with tamoxifen and progesterone caused apoptosis similar to that induced by treatment with progesterone alone and had no additional anti-cancer effect in ovarian cancer cells.
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ABSTRACT: Serous ovarian cancer (SOC) is a significant cause of morbidity and mortality in females with poor prognosis because of advanced stage at presentation. Recently, neoadjuvant chemotherapy (NACT) is being used for management of advanced SOC, but role of tissue biomarkers in prognostication following NACT is not well established. The study was conducted on advanced stage SOC patients (n = 100) that were treated either conventionally (n = 50) or with NACT (n = 50), followed by surgery. In order to evaluate the expression of tissue biomarkers (p53, MIB1, estrogen and progesterone receptors, Her-2/neu, E-cadherin, and Bcl2), immunohistochemistry and semiquantitative scoring were done following morphological examination. Following NACT, significant differences in tumor histomorphology were observed as compared to the native neoplasms. MIB 1 was significantly lower in cases treated with NACT and survival outcome was significantly better in cases with low MIB 1. ER expression was associated with poor overall survival. No other marker displayed any significant difference in expression or correlation with survival between the two groups. Immunophenotype of SOC does not differ significantly in samples from cases treated with NACT, compared to upfront surgically treated cases. The proliferating capacity of the residual tumor cells is less, depicted by low mean MIB1 LI. MIB 1 and ER inversely correlate with survival.BioMed Research International 01/2014; 2014:401245. · 2.88 Impact Factor
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ABSTRACT: Computational drug repositioning leverages computational technology and high volume of biomedical data to identify new indications for existing drugs. Since it does not require costly experiments that have a high risk of failure, it has attracted increasing interest from diverse fields such as biomedical, pharmaceutical, and informatics areas. In this study, we used pharmacogenomics data generated from pharmacogenomics studies, applied informatics and Semantic Web technologies to address the drug repositioning problem. Specifically, we explored PharmGKB to identify pharmacogenomics related associations as pharmacogenomics profiles for US Food and Drug Administration (FDA) approved breast cancer drugs. We then converted and represented these profiles in Semantic Web notations, which support automated semantic inference. We successfully evaluated the performance and efficacy of the breast cancer drug pharmacogenomics profiles by case studies. Our results demonstrate that combination of pharmacogenomics data and Semantic Web technology/Cheminformatics approaches yields better performance of new indication and possible adverse effects prediction for breast cancer drugs.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 01/2014; 19:172-82.