Characterization and interlaboratory comparison of a gene expression signature for differentiating genotoxic mechanisms.

The genotoxicity testing battery is highly sensitive for detection of chemical carcinogens. However, it features a low specificity and provides only limited mechanistic information required for risk assessment of positive findings. This is especially important in case of positive findings in the in vitro chromosome damage assays, because chromosome damage may be also induced secondarily to cell death. An increasing body of evidence indicates that toxicogenomic analysis of cellular stress responses provides an insight into mechanisms of action of genotoxicants. To evaluate the utility of such a toxicogenomic analysis we evaluated gene expression profiles of TK6 cells treated with four model genotoxic agents using a targeted high density real-time PCR approach in a multilaboratory project coordinated by the Health and Environmental Sciences Institute Committee on the Application of Genomics in Mechanism-based Risk Assessment. We show that this gene profiling technology produced reproducible data across laboratories allowing us to conclude that expression analysis of a relevant gene set is capable of distinguishing compounds that cause DNA adducts or double strand breaks from those that interfere with mitotic spindle function or that cause chromosome damage as a consequence of cytotoxicity. Furthermore, our data suggest that the gene expression profiles at early time points are most likely to provide information relevant to mechanisms of genotoxic damage and that larger gene expression arrays will likely provide richer information for differentiating molecular mechanisms of action of genotoxicants. Although more compounds need to be tested to identify a robust molecular signature, this study confirms the potential of toxicogenomic analysis for investigation of genotoxic mechanisms.

Application of toxicogenomics to genetic toxicology risk assessment.

Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.

Characterization of DNA reactive and non-DNA reactive anticancer drugs by gene expression profiling.

Gene expression profiling technology is expected to advance our understanding of genotoxic mechanisms involving direct or indirect interaction with DNA. We exposed human lymphoblastoid TK6 cells to 14 anticancer drugs (vincristine, paclitaxel, etoposide, daunorubicin, camptothecin, amsacrine, cytosine arabinoside, hydroxyurea, methotrexate, 5-fluorouracil, cisplatin, 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea (CCNU), 1,3-bis (2-chloroethyl)-1-nitrosourea (BCNU), and bleomycin) for 4-h and examined them immediately or after a 20-h recovery period. Cytotoxicity and genotoxicity, respectively, were evaluated by cell counting and by in vitro micronucleus assay at 24h. Effects on the cell cycle were determined by flow cytometry at 4 and 24h. Gene expression was profiled at both sampling times by using human Affymetrix U133A GeneChips (22K). Bioanalysis was done with Resolver/Rosetta software and an in-house annotation program. Cell cycle analysis and gene expression profiling allowed us to classify the drugs according to their mechanisms of action. The molecular signature is composed of 28 marker genes mainly involved in signal transduction and cell cycle pathways. Our results suggest that these marker genes could be used as a predictive model to classify genotoxins according to their direct or indirect interaction with DNA.

Laboratory variability does not preclude identification of biological functions impacted by hydroxyurea.

The multi-lab International Life Sciences Institute (ILSI) project on the application of genomics to risk assessment offered the unique opportunity to investigate the influence of variability within and between laboratories on identifying biologically relevant gene expression changes. We assessed the gene expression profiles of mouse lymphoma L5178Y cells treated with hydroxyurea (HU) in three independent studies from two different laboratories, Sanofi Aventis and Procter and Gamble. Cells were dosed for 4 hr and harvested immediately at the end of the treatment or after a 20-hr recovery period. Cytotoxicity and genotoxicity were evaluated by standard assays. Statistical analysis of these data revealed that, while gene expression responses to HU treatment were markedly different at 4 hr vs. 24 hr, there was otherwise a consistent pattern of dose-response across the three studies. Therefore, the studies were merged and each time point was evaluated separately. At 4 hr, we identified 173 (P < 0.0001) dose-responsive genes with a common trend in all three studies. These were mainly associated with the cell cycle, DNA repair and DNA metabolism, and in particular, the intra-S and G2/M phase checkpoints. At 24 hr, we identified 434 dose-responsive genes common across studies. These genes were involved in lymphocyte-specific activities and the activation of apoptosis via the caspase cascade. Our results show that despite inter-laboratory variability, combining the three studies in a single statistical analysis identifies more significantly-modulated genes than in any of the individual studies, due to improved statistical sensitivity. The genes identified in our study provide information that is relevant to HU biology.