Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals.
ABSTRACT The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of 3 years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest fivefold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of current data set was sufficient to provide a robust classifier. The classification results showed that a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than with efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models overpredicted the tumor response, but the variability in predictions was significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically induced mouse lung tumors and a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related end points.
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ABSTRACT: Translation elongation factor-1d (TEF-1δ) has been identified as a novel cadmium-responsive proto-oncogene. However, it is still unclear whether TEF-1δ could be a potential biomarker of cadmium exposure. Rats were treated with CdCl at different concentrations (high dose 1.225, mid-dose 0.612 and low dose 0.306 mg/kg body weight, respectively) for 14 weeks, and the cadmium levels, weight coefficients, serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (SCR), 24-h urine protein (24hPro), urinary creatinine (Cr) and pathological features were determined. The TEF-1δ expression in white blood cells and multiple organs were examined by reverse transcription polymerase chain reaction (PCR) and were also confirmed with fluorescence quantitative PCR. A cadmium dose-dependent increase (p < 0.05) of cadmium levels in blood, urine, liver, kidney, heart and lung, and the weight coefficients was observed. The liver and renal function indictors including AST, ALT, SCR, BUN and 24hPro, were elevated in a cadmium dose-dependent manner (p < 0.05). Significant pathological changes in liver, kidney, heart and lung were indicated. The TEF-1δ expression was up-regulated in both blood and organs (p < 0.05). Moreover, the expression level of blood TEF-1δ was positively correlated to TEF-1δ expression level, cadmium level and toxicity in the organs (p < 0.01). This study indicates that blood TEF-1δ is a novel valuable biomarker for cadmium exposure and its organ toxicity.International Journal of Molecular Sciences 01/2013; 14(3):5182-97. · 2.46 Impact Factor
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ABSTRACT: Under REACH, the European Community Regulation on chemicals, the testing strategy for carcinogenicity is based on in vitro and in vivo genotoxicity assays. Given that non-genotoxic carcinogens are negative for genotoxicity and chronic bioassays are no longer regularly performed, this class of carcinogens will go undetected. Therefore, test systems detecting non-genotoxic carcinogens, or even better their modes of action, are required. Here, we investigated whether gene expression profiling in primary hepatocytes can be used to distinguish different modes of action of non-genotoxic carcinogens. For this, primary mouse hepatocytes were exposed to 16 non-genotoxic carcinogens with diverse modes of action. Upon profiling, pathway analysis was performed to obtain insight into the biological relevance of the observed changes in gene expression. Subsequently, both a supervised and an unsupervised comparison approach were applied to recognize the modes of action at the transcriptomic level. These analyses resulted in the detection of three of eight compound classes, that is, peroxisome proliferators, metalloids and skin tumor promotors. In conclusion, gene expression profiles in primary hepatocytes, at least in rodent hepatocytes, appear to be useful to detect some, certainly not all, modes of action of non-genotoxic carcinogens.Archives of Toxicology 06/2012; 86(11):1717-27. · 5.22 Impact Factor
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ABSTRACT: Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.PLoS ONE 01/2013; 8(5):e63308. · 3.73 Impact Factor