Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

In vitro evaluation of the whitening effect of mouth rinses containing hydrogen peroxide

The aim of this study was to evaluate the bleaching effect of two mouth rinses containing hydrogen peroxide. Thirty premolars were randomly divided into two groups (n = 15): Listerine Whitening (LW) and Colgate Plax Whitening (PW). The teeth were fixed on a wax plate and with acrylic resin, at a distance of 5 mm between each other, exposing the buccal surfaces. All teeth were stored in artificial saliva for 45 days, being removed twice a day to be immersed for 1 min in each mouthwash, followed by 10-second washing in tap water. The pH of each product was measured. Digital images of each tooth were captured under standardized conditions. These images were cut in areas previously demarcated and analyzed in Adobe Photoshop 7.0 using the CIEL*a*b* color space system. Data were statistically analyzed by a paired t test and an independent samples t test (p < 0.05). The pH values were 5.6 and 3.4 for LW and PW, respectively. Both treatment groups showed a decrease in the b* parameter (p < 0.01), but a decrease of a* was observed only for PW (p < 0.01). While the LW group showed an improvement in lightness (L*) (p = 0.03), the PW group had a decrease in the L* parameter (p = 0.02). Within the limitations of this study, it is possible to conclude that both products caused some degree of whitening; however, extreme care should be taken when using Colgate Plax Whitening, since its decline in luminosity might be due to its lower pH