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http://dx.doi.org/10.5487/TR.2016.32.4.289

Prediction of Non-Genotoxic Carcinogenicity Based on Genetic Profiles of Short Term Exposure Assays  

Perez, Luis Orlando (Instituto Patagonico de Ciencias Sociales y Humanas (IPCSH), Centro Nacional Patagonico (CENPAT))
Gonzalez-Jose, Rolando (Instituto Patagonico de Ciencias Sociales y Humanas (IPCSH), Centro Nacional Patagonico (CENPAT))
Garcia, Pilar Peral (Instituto de Genetica Veterinaria "Fernando Noel Dulout"-CONICET, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata)
Publication Information
Toxicological Research / v.32, no.4, 2016 , pp. 289-300 More about this Journal
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.
Keywords
Toxicogenomics; Non-genotoxic carcinogen; Random forest;
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1 Engelberg, A. (2004) Iconix Pharmaceuticals, Inc.-removing barriers to efficient drug discovery through chemogenomics. Pharmacogenomics, 5, 741-744.   DOI
2 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.   DOI
3 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.   DOI
4 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.   DOI
5 Gurer-Orhan, H., Orhan, H., Vermeulen, N.P. and Meerman, J.H. (2006) Screening the oxidative potential of several mono- and di-halogenated biphenyls and biphenyl ethers in rat hepatocytes. Comb. Chem. High Throughput Screen., 9, 449-454.   DOI
6 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.
7 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.
8 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].
9 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.   DOI
10 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.   DOI
11 Ellinger-Ziegelbauer, H., Gmuender, H., Bandenburg, A. and Ahr, H.J. (2008) Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. Mutat. Res., 637, 23-39.   DOI
12 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.   DOI
13 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.   DOI
14 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.   DOI
15 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.   DOI
16 Gusenleitner, D., Auerbach, S.S., Melia, T., Gomez, H.F., Sherr, D.H. and Monti, S. (2014) Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action. PLoS ONE, 9, e102579.   DOI
17 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.   DOI
18 Hayashi, Y. (1992) Overview of genotoxic carcinogens and non-genotoxic carcinogens. Exp. Toxicol. Pathol., 44, 465-471.   DOI
19 Hernandez, 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.   DOI
20 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.
21 Breiman, L. (2001) Random forests. Mach. Learn., 45, 5-32.   DOI
22 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.   DOI
23 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.   DOI
24 Fitzpatrick, R.B. (2008) CPDB: Carcinogenic Potency Database. Med. Ref. Serv. Q., 27, 303-311.   DOI
25 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.   DOI
26 R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
27 Benjamini, Y. and Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29, 1165-1188.   DOI
28 Diaz-Uriarte, R. and Alvarez de Andres, S. (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 3.   DOI
29 Efron, B. and Tibshirani, R. (1997) Improvements on cross-validation: the 632+ bootstrap method. J. Am. Stat. Assoc., 92, 548-560.
30 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.   DOI