Erratum to "Prediction of non-genotoxic carcinogenesis in rats using changes in gene expression following acute dosing"

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer

The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification.