Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells

Abstract

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion paper, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or non-genotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and non-genotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4h and collected 0h, 4h and 20h post-exposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24h. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid- and high concentrations at all three time points, whereas DEX was correctly classified as non-genotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24h, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells.

Using a gene expression biomarker to identify DNA damage‐inducing agents in microarray profiles

“…There were no false positives and one false negative, 2‐nitrofluorene, a weak DDI agent. These results were very similar to those from Buick et al () using the biomarker and the nearest shrunken centroid method, which resulted in predictive accuracies in HepaRG cells of 100%. While the biomarker was developed and characterized using Agilent microarrays, we could readily classify DDI using Affymetrix arrays from the studies of Kuehner et al () and Doktorova et al ().…”

“…To determine if our microarray analysis approach could be used as an alternative Tier 0 screening model to identify DDI compounds, a classification analysis was performed on 59 biosets from TK6 cells that were treated with 18 chemicals known to induce DNA damage. The biosets came from four studies (Kuehner et al, ; Buick et al, ; Yauk et al, ; Buick et al, ). In the Buick et al and Yauk et al studies, the cells were treated at 2 or 3 concentrations for 4, 7, or 8 hr and all of the chemicals except cisplatin were used to treat cells in the presence of S9 extract.…”

“…Predictive accuracy in TK6 cells was carried out using four datasets. Biosets were derived from treatments consisting of 15 chemicals carried out as described (Buick et al, ; Yauk et al, ; Buick et al, ). Additional biosets came from Kuehner et al () (Gene Expression Omnibus Accession number, GSE41296) in which TK6 cells were treated with methyl methanesulfonate, ethyl methanesulfonate, or formaldehyde for 24 hr.…”

“…Initial studies indicated that the TGx‐DDI biomarker differentiates DDI and non‐DDI compounds with 100% accuracy (Li et al, ). In follow up studies, the efficiency of the biomarker for accurately classifying compounds that require metabolic activation was demonstrated using different metabolic activating systems and TK6 cells (Buick et al, ; Yauk et al, ). In addition, using data from a microarray platform not used in the original studies (Affymetrix) that measured gene expression in the metabolically competent human liver cell line HepaRG, the biomarker correctly classified 15 out of 15 chemicals (Buick et al, ; Li et al, ; Yauk et al, ).…”

“…In follow up studies, the efficiency of the biomarker for accurately classifying compounds that require metabolic activation was demonstrated using different metabolic activating systems and TK6 cells (Buick et al, ; Yauk et al, ). In addition, using data from a microarray platform not used in the original studies (Affymetrix) that measured gene expression in the metabolically competent human liver cell line HepaRG, the biomarker correctly classified 15 out of 15 chemicals (Buick et al, ; Li et al, ; Yauk et al, ). More recently, it was demonstrated that the biomarker can accurately classify 45 test agents across a broad set of chemical classes using the nCounter high‐throughput cell‐based testing platform and probability analysis, principal components analysis, and two‐dimensional clustering (Li et al, ).…”