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

Abstract

High‐throughput transcriptomic technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The TGx‐DDI biomarker is a 64‐gene expression profile generated from testing 28 model chemicals or treatments (13 that cause DNA damage and 15 that do not) in human TK6 cells. While the biomarker is very accurate at predicting DNA damage inducing (DDI) potential using the nearest shrunken centroid method, the broad utility of the biomarker using other computational methods is not fully known. Here, we determined the accuracy of the biomarker used with the Running Fisher test, a nonparametric correlation test. In TK6 cells, the methods could readily differentiate DDI and non‐DDI compounds with balanced accuracies of 87–97%, depending on the threshold for determining DDI positives. The methods identified DDI agents in the metabolically competent hepatocyte cell line HepaRG (accuracy = 90%) but not in HepG2 cells or hepatocytes derived from embryonic stem cells (60 and 80%, respectively). DDI was also accurately classified when the gene expression changes were derived using the nCounter technology (accuracy = 89%). In addition, we found: (1) not all genes contributed equally to the correlations; (2) the minimal overlap in genes between the biomarker and the individual comparisons required for significant positive correlation was 10 genes, but usually was much higher; and (3) different sets of genes in the biomarker can by themselves contribute to the significant correlations. Overall, these results demonstrate the utility of the biomarker to accurately classify DDI agents. Environ. Mol. Mutagen. 59:772–784, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Assessment of the performance of the TGx‐DDI biomarker to detect DNA damage‐inducing agents using quantitative RT‐PCR in TK6 cells

“…Overall, the addition of Running Fisher test improved the sensitivity of TGx-DDI using qPCR. The results indicate that using the TGx-DDI biomarker in the context of the Running Fisher test might be advantageous for high throughput screening to identify DDI agents through assessment of gene expression by qPCR, similar to our previous studies using microarray and nCounter data (Corton et al, 2018).…”

“…The use of the Running Fisher test to determine the predictive accuracy of the original TGx-DDI biomarker has been described (Corton et al, 2018). Briefly, the TGx-DDI transcriptomic biomarker was uploaded to the BaseSpace Correlation Engine database (URL: https://www.illumina.com/products/bytype/informatics-products/basespace-correlation-engine.html; formally Next-Bio) (Kupershmidt et al, 2010) and compared to each list of fold changes The 24 chemicals and the concentrations shown above were previously described by Li et al (2017).…”

TGx-DDI, a Transcriptomic Biomarker for Genotoxicity Hazard Assessment of Pharmaceuticals and Environmental Chemicals

“…TK6, a lymphoblastoid cell line, is not metabolically competent. Using TK6 in the presence of rat liver S9 microsomal fraction enabled robust DDI classification for those chemicals requiring metabolic activation by TGx-DDI (Buick et al, 2015;Corton et al, 2018). In addition, studies showed that TGx-DDI successfully classified DDI and non-DDI compounds in a gene expression data set from cultured HepaRG cells treated with a variety of compounds (Buick et al, 2015).…”

“…Corton et al demonstrated how the TGx-DDI biomarker could be used to rapidly screen large compendia of transcriptional profiles to identify toxicants that are potentially DDI (Corton et al, 2018). In this example, expression profiles from a commercially available gene expression database called BaseSpace Correlation Engine (BSCE) (https://www.illumina.…”

Transcriptomic effect marker patterns of genotoxins – a comparative study with literature data

“…Using the search strategy as outlined in the Methods section, 9 different studies published between 2010 and 2018 were identified. Of these, 7 studies provided raw data or DEG lists and thus appeared suited for further analysis (Ates et al, 2018;Corton, Williams, & Yauk, 2018;Doktorova et al, 2013;Doktorova et al, 2014;Dumont et al, 2010;Jennen et al, 2010;Jetten, Kleinjans, Claessen, Chesné, & van Delft, 2013;Josse et al, 2012;Tryndyak et al, 2018). A list of these publications and important basic information about the studies and approaches is presented in Table 1.…”