Dataset Information

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

ABSTRACT: The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances.

SUBMITTER: Doktorova TY

PROVIDER: S-EPMC4464292 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

Doktorova T Y TY Yildirimman Reha R Ceelen Liesbeth L Vilardell Mireia M Vanhaecke Tamara T Vinken Mathieu M Ates Gamze G Heymans Anja A Gmuender Hans H Bort Roque R Corvi Raffaella R Phrakonkham Pascal P Li Ruoya R Mouchet Nicolas N Chesne Christophe C van Delft Joost J Kleinjans Jos J Castell Jose J Herwig Ralf R Rogiers Vera V

EXCLI journal 20140528

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biosta ...[more]

PMID: 26417288

Similar Datasets

Project description:Background: Information on the carcinogenic potential of chemicals is only availably for High Production Volume products. There is however, a pressing need for alternative methods allowing for the chronic toxicity of substances, including carcinogenicity, to be detected earlier and more reliably. Here we applied advanced genomics to a cellular transformation assay to identify gene signatures useful for the prediction of risk for carcinogenicity. Methods: Genome wide gene expression analysis and qRT-PCR were applied to untransformed and transformed Balb/c 3T3 cells that exposed to 2, 4-diaminotoluene (DAT), benzo(a)pyrene (BaP), 2-Acetylaminoflourene (AAF) and 3-methycholanthrene (MCA) for 24h and 120h, at different concentrations, respectively. Furthermore, various bioinformatics tools were used to identify gene signatures predicting for the carcinogenic risk. Results: Bioinformatics analysis revealed distinct datasets for the individual chemicals tested while the number of significantly regulated genes increased with ascending treatment concentration of the cell cultures. Filtering of the data revealed a common gene signature that comprised of 13 genes whose regulation in cancer tissue has already been established. Strikingly, this gene signature was already identified prior to cell transformation therefore confirming the predictive power of this gene signature in identifying carcinogenic risks of chemicals. Comparison of fold changes determined by microarray analysis and qRT-PCR were in good agreement. Conclusion: Our data describes selective and commonly regulated carcinogenic pathways observed in an easy to use in vitro carcinogenicity assay. Here we defined a set of genes which can serve as a simply assay to predict the risk for carcinogenicity by use of an alternative in vitro testing strategy. Balb/c 3T3 cells were seeded at 200 cells in each 60 x 15 mm culture dish with 4 ml M10F, using six culture dishes for every treatment. When cells reached a confluence of 60-65%, the culture medium was removed and replaced with fresh medium containing all tested chemicals at a specific concentration and two time points (24 and 120h). First we treated the cells both 24h and 120h with concentrations reported in the literature (0.5ÂµM BaP, 50ÂµM DAT, 25ÂµM AAF and 2ÂµM MCA). In a second approach IC20 concentrations were investigated for each chemical at both time points. The concentrations determined for IC20 ranged from 1.5 ÂµM BaP to 700 ÂµM DAT for 24h of treatment, and from 0.1 ÂµM BaP or MCA to 10ÂµM AAF for 120h of treatment. Balb/c 3T3 cells treated with 0.75% DMSO alone were kept as controls. Each experiment was run in triplicate.

Project description:BackgroundOne of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.ResultsModels for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.ConclusionCarcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Dataset Information

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

Publications

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets