Breast cancer on the world wide web: cross sectional survey of quality of information and popularity of websites.

OBJECTIVES: To determine the characteristics of popular breast cancer related websites and whether more popular sites are of higher quality. DESIGN: The search engine Google was used to generate a list of websites about breast cancer. Google ranks search results by measures of link popularity---the number of links to a site from other sites. The top 200 sites returned in response to the query "breast cancer" were divided into "more popular" and "less popular" subgroups by three different measures of link popularity: Google rank and number of links reported independently by Google and by AltaVista (another search engine). MAIN OUTCOME MEASURES: Type and quality of content. RESULTS: More popular sites according to Google rank were more likely than less popular ones to contain information on ongoing clinical trials (27% v 12%, P=0.01 ), results of trials (12% v 3%, P=0.02), and opportunities for psychosocial adjustment (48% v 23%, P<0.01). These characteristics were also associated with higher number of links as reported by Google and AltaVista. More popular sites by number of linking sites were also more likely to provide updates on other breast cancer research, information on legislation and advocacy, and a message board service. Measures of quality such as display of authorship, attribution or references, currency of information, and disclosure did not differ between groups. CONCLUSIONS: Popularity of websites is associated with type rather than quality of content. Sites that include content correlated with popularity may best meet the public's desire for information about breast cancer.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

Estradiol and insulin cross -talk in breast cancer

Approximately 33% of clinical breast carcinomas require estrogens to proliferate. Epidemiological data show that insulin resistance and diabetes mellitus is 2–3 times more prevalent in women with breast cancer than those with benign breast lesions, suggesting a clinical link between insulin and estradiol. Insulin and estradiol have a synergistic effect on the growth of MCF7 breast cancer cells, and long-term estradiol treatment upregulates the expression of the key insulin signaling protein IRS-1. The goal of this study was to further define the mechanism(s) of cross-talk between insulin and estradiol in regulating the growth of breast cancer. Using MCF7 cells, acute treatment with insulin or estradiol alone was found to stimulate two activities associated with growth: Erk MAP kinase and PI 3-kinase. However, combined acute treatment had an antagonistic effect on both activities. Acute estradiol treatment inhibited the insulin-stimulated tyrosine phosphorylation of IRS-1 while increasing its serine phosphorylation; the serine phosphorylation was attenuated by the PI 3-kinase inhibitor wortmannin. The acute antagonism observed with combined estradiol and insulin are not consistent with the long-term synergistic effect on growth. In contrast, chronic estradiol treatment enhanced the insulin-sensitivity of breast cancer cells as measured by increases in total cellular insulin-stimulated tyrosine phosphorylation of IRS-1 and activation of PI 3-kinase. Estradiol stimulation of gene transcription was found to require PI 3-kinase activity but not MAP kinase activity. Insulin alone had no effect on ER transcriptional activity, but chronic treatment in combination with estradiol resulted in synergism of ER transcription. The synergistic effect of insulin and estradiol on MCF7 cell growth was also found to require PI 3-kinase but not MAP kinase activity. Therefore, chronic estradiol treatment increases insulin stimulation of PI 3-kinase, and PI 3-kinase is required for estradiol stimulation of gene transcription alone and in combined synergy with insulin. These data demonstrate that PI 3-kinase is the locus for the cross-talk between insulin and estradiol which results in enhanced breast cancer growth with long-term exposure to both hormones. This may have important clinical implications for women with high risk for breast cancer and/or diabetes mellitus. ^