Pharmacogenetic prediction of individual variability in drug response based on CYP2D6, CYP2C9 and CYP2C19 genetic polymorphisms.

Interindividual variability in drug response depends on a number of genetic and environmental factors. The metabolic enzymes are well known for their contribution to this variability due to drug-drug interactions and genetic polymorphisms. The phase I drug metabolism is highly dependent upon the cytochrome P450 mono-oxygenases (CYP) and their genetic polymorphism leads to the variable internal drug exposures. The highly polymorphic CYP2C9, CYP2C19 and CYP2D6 isozymes are responsible for metabolizing a large portion of routinely prescribed drugs and contribute significantly to adverse drug reactions and therapeutic failures. In this review, two attractive and easily implementable approaches are highlighted to recommend drug doses ensuring similar internal exposures in the face of these polymorphisms. The first approach relies on subpopulation-based dose recommendations that consider the original population dose as an average of the doses recommended in genetically polymorphic subpopulations. By using bioequivalence principles and assuming linear gene-dose effect, dose recommendations can be made for different metabolic phenotypes. The second approach relates area under the curve to two characteristic parameters; the contribution ratio (CR), computes for the contribution of the metabolic enzyme and the fractional activity (FA), considers the impact of the genetic polymorphism. This approach provides valid and error free internal drug exposure predictions and can take into consideration genetic polymorphisms and drug interactions and/ or both simultaneously. Despite certain advantages and limitations, both approaches provide a good initial frame-work for devising models to predict internal exposure and individualize drug therapy, one of the promises from human genome project.

Hypermethylation of the PTEN gene in ovarian cancer cell lines.

This study was performed to investigate the hypermethylation status of the PTEN gene in ovarian cancer. To this end, we incubated eight ovarian cancer cell lines with the demethylating agent 5-aza-2' deoxycytidine in three different concentrations for 5 days. Subsequently, the PTEN expression was quantified by both real time RT-PCR and quantitative western analyses. PTEN mRNA varied considerably in response to demethylation whereas PTEN protein concentrations remained constant in all cell lines except OAW42 cells (12.5%). The data suggest that PTEN is highly regulated at translational level. However, methylation of the PTEN gene plays a subordinate role in ovarian cancer.

The V109G polymorphism of the p27 gene CDKN1B indicates a worse outcome in node-negative breast cancer patients.

Although p27 plays a central role in cell cycle regulation, its role in breast cancer prognosis is controversial. Furthermore, the p27 gene CDKN1B carries a polymorphism with unknown functional relevance. This study was designed to evaluate p27 expression and p27 genotyping with respect to early breast cancer prognosis. 279 patients with infiltrating metastasis-free breast cancer were included in this study. p27 expression was determined in tumor tissue specimens from 261 patients by immunohistochemistry. From 108 patients, the CDKN1B genotype was examined by PCR and subsequent direct sequencing. 55.2% of the tumors were considered p27 positive. p27 expression did not correlate with any of the established parameters except for nodal involvement but significantly correlated to prolonged disease-free survival. In 35% of the tumors analyzed, the CDKN1B gene showed a polymorphism at codon 109 (V109G). The V109G polymorphism correlated with greater nodal involvement. In the node-negative subgroup, V109G correlated significantly with a shortened disease-free survival. In conclusion, the determination of the CDKN1B genotype might be a powerful tool for the prognosis of patients with early breast cancer.