Discrimination of genotoxic from non-genotoxic carcinogens by gene expression profiling

• Published in: Carcinogenesis (New York) Vol. 25; no. 7; pp. 1265 - 1276
• Main Authors: van Delft, J. H. M., van Agen, E., van Breda, S. G. J., Herwijnen, M. H., Staal, Y. C. M., Kleinjans, J. C. S.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.07.2004; Oxford Publishing Limited (England)
• Subjects: Biological and medical sciences; Carcinogenesis, carcinogens and anticarcinogens; Carcinogens - classification; Carcinogens - pharmacology; Chemical agents; Data Interpretation, Statistical; Gene Expression - drug effects; Gene Expression Profiling; genotoxic; GTX; Humans; Medical sciences; Mutagens - pharmacology; N-GTX; N-NGTX; NGTX; non-genotoxic; not similar to GTX; not similar to NGTX; Oligonucleotide Array Sequence Analysis; Tumors; Carcinogen; Discrimination; Toxicity; Genotoxicity; Gene expression profile
• ISSN: 0143-3334; 1460-2180; 1460-2180
• DOI: 10.1093/carcin/bgh108

Two general mechanisms are implicated in chemical carcinogenesis. The first involves direct damage to DNA, referred to as genotoxic (GTX), to which the cell responds by repair of the damages, arrest of the cell cycle or induction of apoptosis. The second is non-DNA damaging, non-genotoxic (NGTX), in which a wide variety of cellular processes may be involved. Therefore, it can be hypothesized that modulation of the underlying gene expression patterns is profoundly distinct between GTX and NGTX carcinogens, and thus that expression profiling is applicable for classification of chemical carcinogens as GTX or NGTX. We investigated this hypothesis by analysing modulation of gene expression profiles induced by 20 chemical carcinogens in HepG2 cells with application of cDNA microarrays that contain 597 toxicologically relevant genes. In total, 22 treatments were included, divided in two sets. The training set consisted of 16 treatments (nine genotoxins and seven non-genotoxins) and the validation set of six treatments (three and three). Class discrimination models based on Pearson correlation analyses for the 20 most discriminating genes were developed with data from the training set, where after the models were tested with all data. Using all data, the correctness for classification of the carcinogens from the training set was clearly better than that for the validation set, namely 81 and 33%, respectively. Exclusion of the treatments that had only marginal effects on the expression profiles, improved the discrimination for the training and validation sets to 92 and 100% correctness, respectively. Exclusion of the gene expression signals that were hardly altered also improved classification, namely to 94 and 80%. Therefore, our study proves the principle that gene expression profiling can discriminate carcinogens with major differences in their mode of actions, namely genotoxins versus non-genotoxins.