Abstract
Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.
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Abbreviations
- GTX:
-
Genotoxic
- NGTX:
-
Non-genotoxic
- NTP:
-
National Toxicology Program
- TG-GATE:
-
The Toxicogenomics Project Genomics Assisted Toxicity Evaluation system
References
McGovern, T. and Jacobson-Kram, D. (2006) Regulation of genotoxic and carcinogenic impurities in drug substances and products. TrAC, Trends Anal. Chem., 25, 790–795.
Hayashi, Y. (1992) Overview of genotoxic carcinogens and non-genotoxic carcinogens. Exp. Toxicol. Pathol., 44, 465–471.
Hernández, L.G., van Steeg, H., Luijten, M. and van Benthem, J. (2009) Mechanisms of non-genotoxic carcinogens and importance of a weight of evidence approach. Mutat. Res., 682, 94–109.
Robinson, J.F., Pennings, J.L. and Piersma, A.H. (2012) A review of toxicogenomic approaches in developmental toxicology. Methods Mol. Biol., 889, 347–371.
National Research Council (US) Committee on Applications of Toxicogenomic Technologies to Predictive Toxicology (2007) Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment, National Academies Press (US), Washington.
Igarashi, Y. , Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T. and Yamada, H. (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res., 43, D921–D927.
Fitzpatrick, R.B. (2008) CPDB: Carcinogenic Potency Database. Med. Ref. Serv. Q., 27, 303–311.
Ganter, B., Tugendreich, S., Pearson, C.I., Ayanoglu, E., Baumhueter, S., Bostian, K.A., Brady, L., Browne, L.J., Calvin, J.T., Day, G.J., Breckenridge, N., Dunlea, S., Eynon, B.P., Furness, L.M., Ferng, J., Fielden, M.R., Fujimoto, S.Y., Gong, L., Hu, C., Idury, R., Judo, M.S., Kolaja, K.L., Lee, M.D., McSorley, C., Minor, J.M., Nair, R.V., Natsoulis, G. , Nguyen, P., Nicholson, S.M., Pham, H., Roter, A.H., Sun, D., Tan, S., Thode, S., Tolley, A.M., Vladimirova, A., Yang, J., Zhou, Z. and Jarnagin, K. (2005) Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol., 119, 219–244.
R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
Benjamini, Y. and Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29, 1165–1188.
Díaz-Uriarte, R. and Alvarez de Andrés, S. (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 3.
Breiman, L. (2001) Random forests. Mach. Learn., 45, 5–32.
Efron, B. and Tibshirani, R. (1997) Improvements on cross-validation: the 632+ bootstrap method. J. Am. Stat. Assoc., 92, 548–560.
Alexa, A., Rahnenfuhrer, J. and Lengauer, T. (2006) Improved scoring of functional groups from gene expression data by decorrelating go graph structure. Bioinformatics, 22, 1600–1607.
Engelberg, A. (2004) Iconix Pharmaceuticals, Inc.-removing barriers to efficient drug discovery through chemogenomics. Pharmacogenomics, 5, 741–744.
Melnick, R.L., Kohn, M.C. and Portier, C.J. (1996) Implications for risk assessment of suggested non-genotoxic mechanisms of chemical carcinogenesis. Environ. Health Perspect., 104 Suppl 1, 123–134.
Wu, S.N., Li, H.F., Jan, C.R. and Shen, A.Y. (1999) Inhibition of Ca2+-activated K+ current by clotrimazole in rat anterior pituitary GH3 cells. Neuropharmacology, 38, 979–989.
Zhang, W., Ramamoorthy, Y., Kilicarslan, T., Nolte, H., Tyndale, R.F. and Sellers, E.M. (2002) Inhibition of cytochromes P450 by antifungal imidazole derivatives. Drug Metab. Dispos., 30, 314–318.
Gurer-Orhan, H., Orhan, H., Vermeulen, N.P. and Meerman, J.H. (2006) Screening the oxidative potential of several monoand di-halogenated biphenyls and biphenyl ethers in rat hepatocytes. Comb. Chem. High Throughput Screen., 9, 449–454.
El Etreby, M.F., Graf, K.J., Giinzel, P. and Neumann, F. (1979) Evaluation of effects of sexual steroids on the hypothalamic-pituitary system of animals and man in Mechanism of toxic action on some target organs drugs and other substances. Proceedings of the european society of toxicology (Chambers, P.L. and Giinzel, P. Ed.). Springer-Verlag, Berlin Heidelberg, pp. 11–40.
Singer, C.F., Kronsteiner, N., Hudelist, G. , Marton, E., Walter, I., Kubista, M., Czerwenka, K., Schreiber, M., Seifert, M. and Kubista, E. (2003) Interleukin 1 system and sex steroid receptor expression in human breast cancer: interleukin 1alpha protein secretion is correlated with malignant phenotype. Clin. Cancer Res., 9, 4877–4883.
van Delft, J.H., van Agen, E., van Breda, S.G., Herwijnen, M.H., Staal, Y.C. and Kleinjans, J.C. (2004) Discrimination of genotoxic from non-genotoxic carcinogens by gene expression profiling. Carcinogenesis, 25, 1265–1276. [Erratum in: 2004, 25, 2525; 2005, 26, 511].
Nakayama, K., Kawano, Y. , Kawakami, Y. , Moriwaki, N., Sekijima, M., Otsuka, M., Yakabe, Y., Miyaura, H., Saito, K., Sumida, K. and Shirai, T. (2006) Differences in gene expression profiles in the liver between carcinogenic and non-carcinogenic isomers of compounds given to rats. Toxicol. Appl. Pharmacol., 217, 299–307.
Fielden, M.R., Brennan, R. and Gollub, J. (2007) A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by non-genotoxic chemicals. Toxicol. Sci., 99, 90–100.
Ellinger-Ziegelbauer, H., Gmuender, H., Bandenburg, A. and Ahr, H.J. (2008) Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of shortterm in vivo studies. Mutat. Res., 637, 23–39.
Auerbach, S.S., Shah, R.R., Mav, D., Smit, C.S., Walker, N.J., Vallant, M.K., Boorman, G.A. and Irwin, R.D. (2010) Predicting the hepatocarcinogenic potential of alkenylbenzene flavoring agents using toxicogenomics and machine learning. Toxicol. Appl. Pharmacol., 243, 300–314.
Uehara, T., Minowa, Y. , Morikawa, Y. , Kondo, C., Maruyama, T., Kato, I., Nakatsu, N., Igarashi, Y. , Ono, A., Hayashi, H., Mitsumori, K., Yamada, H., Ohno, Y. and Urushidani, T. (2011) Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database. Toxicol. Appl. Pharmacol., 255, 297–306.
Lee, S.J., Yum, Y.N., Kim, S.C., Kim, Y. , Lim, J., Lee, W.J., Koo, K.H., Kim, J.H., Kim, J.E., Lee, W.S., Sohn, S., Park, S.N., Park, J.H., Lee, J. and Kwon, S.W. (2013) Distinguishing between genotoxic and non-genotoxic hepatocarcinogens by gene expression profiling and bioinformatics pathway analysis. Sci. Rep., 3, 2783.
Gusenleitner, D., Auerbach, S.S., Melia, T., Gómez, H.F., Sherr, D.H. and Monti, S. (2014) Genomic models of shortterm exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action. PLoS ONE, 9, e102579.
He, L., Vasiliou, K. and Nebert, D.W. (2009) Analysis and update of the human solute carrier (SLC) gene superfamily. Hum. Genomics, 3, 195–206.
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Pérez, L.O., González-José, R. & García, P.P. Prediction of Non-Genotoxic Carcinogenicity Based on Genetic Profiles of Short Term Exposure Assays. Toxicol Res. 32, 289–300 (2016). https://doi.org/10.5487/TR.2016.32.4.289
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DOI: https://doi.org/10.5487/TR.2016.32.4.289