Targets for the Treatment of Breast Cancer

The completion of the human genome sequence provides unique opportunities to identify new molecular targets for a variety of diseased conditions, especially for neoplastic diseases. Breast cancer is an ideal disease for the implementation of the recently developed, sophisticated genomic technologies, which permit the study of expression of many genes or proteins simultaneously, an approach known as molecular profiling. This approach is considered a major step forward in the development of new drugs that are more effective and less toxic than the current generation of antitumor agents. In this paper, we briefly review the current and future genomics technologies, such as DNA microarrays and proteomics techniques, and their use in the identification of new molecular targets for the treatment of breast cancer. We also discuss the challenge associated with the development of bioinformatics tools to analyze the massive number of data points generated by these technologies. Proof of principle is now emerging, demonstrating that selective agents against abnormal or mutated gene products can indeed be useful in the treatment of cancer. However, despite heavy investment in genomics research by the pharmaceutical industry, the full impact of genomics on drug discovery has yet to be fully demonstrated

The role of oxidative stress in chemical carcinogenesis.

Oxidative stress results when the balance between the production of reactive oxygen species (ROS) overrides the antioxidant capability of the target cell; oxidative damage from the interaction of reactive oxygen with critical cellular macromolecules may occur. ROS may interact with and modify cellular protein, lipid, and DNA, which results in altered target cell function. The accumulation of oxidative damage has been implicated in both acute and chronic cell injury including possible participation in the formation of cancer. Acute oxidative injury may produce selective cell death and a compensatory increase in cell proliferation. This stimulus may result in the formation of newly initiated preneoplastic cells and/or enhance the selective clonal expansion of latent initiated preneoplastic cells. Similarly, sublethal acute oxidative injury may produce unrepaired DNA damage and result in the formation of new mutations and, potentially, new initiated cells. In contrast, sustained chronic oxidative injury may lead to a nonlethal modification of normal cellular growth control mechanisms. Cellular oxidative stress can modify intercellular communication, protein kinase activity, membrane structure and function, and gene expression, and result in modulation of cell growth. We examined the role of oxidative stress as a possible mechanism by which nongenotoxic carcinogens may function. In studies with the selective mouse liver carcinogen dieldrin, a species-specific and dose-dependent decrease in liver antioxidant concentrations with a concomitant increase in ROS formation and oxidative damage was seen. This increase in oxidative stress correlated with an increase in hepatocyte DNA synthesis. Antioxidant supplementation prevented the dieldrin-induced cellular changes. Our findings suggest that the effect of nongenotoxic carcinogens (if they function through oxidative mechanisms) may be amplified in rodents but not in primates because of rodents' greater sensitivity to ROS. These results and findings reported by others support a potential role for oxidative-induced injury in the cancer process specifically during the promotion stage

Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity