Prediction of Non-genotoxic Hepatocarcinogenicity Using Chemical-Protein Interactions

  • Chun-Wei Tung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)

Abstract

The assessment of non-genotoxic hepatocarcinogenicity of chemicals is currently based on 2-year rodent bioassays. It is desirable to develop a fast and effective method to accelerate the identification of potential hepatocarcinogenicity of non-genotoxic chemicals. In this study, a novel method CPI is proposed to predict potential hepatocarcinogenicity of non-genotoxic chemicals. The CPI method is based on chemical-protein interactions and interpretable decision tree classifiers.The interpretable rules generated by the CPI method are analyzed to provide insights into the mechanism and biomarkers of non-genotoxic hepatocarcinogenicity. The CPI method with an independent test accuracy of 86% using only 1 protein biomarker outperforms the state-of-the-art methods of gene expression profile-based toxicogenomics using 90 gene biomarkers. A protein ABCC3 was identified as a potential protein biomarker for further exploration. This study presents the potential application of CPI method for assessing non-genotoxic hepatocarcinogenicity of chemicals.

Keywords

Non-Genotoxic Hepatocarcinogenicity Decision Tree Chemical-Protein Interaction Interpretable Rule Toxicology 

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References

  1. 1.
    Hayashi, Y.: Overview of genotoxic carcinogens and non-genotoxic carcinogens. Exp. Toxicol. Pathol. 44, 465–471 (1992)CrossRefGoogle Scholar
  2. 2.
    Melnick, R.L., Kohn, M.C., Portier, C.J.: Implications for risk assessment of suggested nongenotoxic mechanisms of chemical carcinogenesis. Environ. Health Perspect. 104 (suppl. 1), 123–134 (1996)Google Scholar
  3. 3.
    Weisburger, J.H., Williams, G.M.: The distinction between genotoxic and epigenetic carcinogens and implication for cancer risk. Toxicol. Sci. 57, 4–5 (2000)CrossRefGoogle Scholar
  4. 4.
    Gold, L.S., Manley, N.B., Slone, T.H., Rohrbach, L., Garfinkel, G.B.: Supplement to the carcinogenic potency database (cpdb): results of animal bioassays published in the general literature through 1997 and by the national toxicology program in 1997-1998. Toxicol. Sci. 85, 747–808 (2005)CrossRefGoogle Scholar
  5. 5.
    Kar, S., Deeb, O., Roy, K.: Development of classification and regression based qsar models to predict rodent carcinogenic potency using oral slope factor. Ecotoxicol. Environ. Saf. 82, 85–95 (2012)CrossRefGoogle Scholar
  6. 6.
    Kar, S., Roy, K.: First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using oecd guidelines. Chemosphere 87, 339–355 (2012)CrossRefGoogle Scholar
  7. 7.
    Tanabe, K., Kurita, T., Nishida, K., Lucic, B., Amic, D., Suzuki, T.: Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of svm models. SAR QSAR Environ. Res. (2013)Google Scholar
  8. 8.
    Yuan, J., Pu, Y., Yin, L.: Qsar study of liver specificity of carcinogenicity of n-nitroso compounds. Ecotoxicol. Environ. Saf. 84, 282–292 (2012)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Kelly, R., Fang, H., Ding, D., Tong, W.: Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships. Chem. Res. Toxicol. 24, 1062–1070 (2011)CrossRefGoogle Scholar
  10. 10.
    Yamada, F., Sumida, K., Uehara, T., Morikawa, Y., Yamada, H., Urushidani, T., Ohno, Y.: Toxicogenomics discrimination of potential hepatocarcinogenicity of non-genotoxic compounds in rat liver. J. Appl. Toxicol. (2012)Google Scholar
  11. 11.
    Uehara, T., Hirode, M., Ono, A., Kiyosawa, N., Omura, K., Shimizu, T., Mizukawa, Y., Miyagishima, T., Nagao, T., Urushidani, T.: A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats. Toxicology 250, 15–26 (2008)CrossRefGoogle Scholar
  12. 12.
    Kuhn, M., von Mering, C., Campillos, M., Jensen, L.J., Bork, P.: Stitch: interaction networks of chemicals and proteins. Nucleic Acids Res. 36, D684–D688 (2008)Google Scholar
  13. 13.
    Kuhn, M., Szklarczyk, D., Franceschini, A., Campillos, M., von Mering, C., Jensen, L.J., Beyer, A., Bork, P.: Stitch 2: an interaction network database for small molecules and proteins. Nucleic Acids Res. 38, D552–D556 (2010)Google Scholar
  14. 14.
    Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L.J., Bork, P.: Stitch 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 40, D876–D880 (2012)Google Scholar
  15. 15.
    Kim Kjaerulff, S., Wich, L., Kringelum, J., Jacobsen, U.P., Kouskoumvekaki, I., Audouze, K., Lund, O., Brunak, S., Oprea, T.I., Taboureau, O.: Chemprot-2.0: visual navigation in a disease chemical biology database. Nucleic Acids Res. 41, 464–469 (2013)Google Scholar
  16. 16.
    Taboureau, O., Nielsen, S.K., Audouze, K., Weinhold, N., Edsgard, D., Roque, F.S., Kouskoumvekaki, I., Bora, A., Curpan, R., Jensen, T.S., Brunak, S., Oprea, T.I.: Chemprot: a disease chemical biology database. Nucleic Acids Res. 39, D367–D372 (2011)Google Scholar
  17. 17.
    Mattingly, C.J., Colby, G.T., Forrest, J.N., Boyer, J.L.: The comparative toxicogenomics database (ctd). Environ. Health Perspec.t 111, 793–795 (2003)CrossRefGoogle Scholar
  18. 18.
    Young, J., Tong, W., Fang, H., Xie, Q., Pearce, B., Hashemi, R., Beger, R., Cheeseman, M., Chen, J., Chang, Y.C., Kodell, R.: Building an organ-specific carcinogenic database for sar analyses. J. Toxicol. Environ. Health A 67, 1363–1389 (2004)CrossRefGoogle Scholar
  19. 19.
    von Mering, C., Jensen, L.J., Snel, B., Hooper, S.D., Krupp, M., Foglierini, M., Jouffre, N., Huynen, M.A., Bork, P.: String: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33, D433–D437 (2005)Google Scholar
  20. 20.
    Tung, C.W., Ho, S.Y.: Popi: predicting immunogenicity of mhc class i binding peptides by mining informative physicochemical properties. Bioinformatics 23, 942–949 (2007)CrossRefGoogle Scholar
  21. 21.
    Tung, C.W., Ho, S.Y.: Computational identification of ubiquitylation sites from protein sequences. BMC Bioinformatics 9, 310 (2008)CrossRefGoogle Scholar
  22. 22.
    Liaw, C., Tung, C.W., Ho, S.J., Ho, S.Y.: Sequence-based prediction of gamma-turn types using a physicochemical property-based decision tree method. Proceeding of World Academy of Science, Engineering and Technology 41, 898–902 (2010)Google Scholar
  23. 23.
    Huang, W.L., Tung, C.W., Ho, S.W., Ho, S.Y.: Proloc-rgo: Using rule-based knowledge with gene ontology terms for prediction of protein subnuclear localization. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2008, pp. 201–206. IEEE (2008)Google Scholar
  24. 24.
    Quinlan, J.: C4. 5: programs for machine learning. Morgan kaufmann (1993)Google Scholar
  25. 25.
    Kuhn, M., Weston, S.: code for C5.0 by R. Quinlan, N.C.C.: C50: C5.0 Decision Trees and Rule-Based Models (2012); R package version 0.1.0-013Google Scholar
  26. 26.
    Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3, 185–205 (2005)CrossRefGoogle Scholar
  27. 27.
    Kryston, T.B., Georgiev, A.B., Pissis, P., Georgakilas, A.G.: Role of oxidative stress and dna damage in human carcinogenesis. Mutat. Res. 711, 193–201 (2011)CrossRefGoogle Scholar
  28. 28.
    Ziech, D., Franco, R., Georgakilas, A.G., Georgakila, S., Malamou-Mitsi, V., Schoneveld, O., Pappa, A., Panayiotidis, M.I.: The role of reactive oxygen species and oxidative stress in environmental carcinogenesis and biomarker development. Chem. Biol. Interact. 188, 334–339 (2010)CrossRefGoogle Scholar
  29. 29.
    Ponka, P., Beaumont, C., Richardson, D.R.: Function and regulation of transferrin and ferritin. Semin. Hematol. 35, 35–54 (1998)Google Scholar
  30. 30.
    McCord, J.M.: Iron, free radicals, and oxidative injury. Semin. Hematol. 35, 5–12 (1998)Google Scholar
  31. 31.
    Linn, S.: Dna damage by iron and hydrogen peroxide in vitro and in vivo. Drug Metab. Rev. 30, 313–326 (1998)CrossRefGoogle Scholar
  32. 32.
    Gill, J.H., Brickell, P., Dive, C., Roberts, R.A.: The rodent non-genotoxic hepatocarcinogen nafenopin suppresses apoptosis preferentially in non-cycling hepatocytes but also elevates cdk4, a cell cycle progression factor. Carcinogenesis 19, 1743–1747 (1998)MATHCrossRefGoogle Scholar
  33. 33.
    Weinberg, R.A.: The retinoblastoma protein and cell cycle control. Cell 81, 323–330 (1995)CrossRefGoogle Scholar
  34. 34.
    Butterworth, B.E., Bogdanffy, M.S.: A comprehensive approach for integration of toxicity and cancer risk assessments. Regul. Toxicol. Pharmacol. 29, 23–36 (1999)CrossRefGoogle Scholar
  35. 35.
    Nguyen-Ba, G., Vasseur, P.: Epigenetic events during the process of cell transformation induced by carcinogens (review). Oncol. Rep. 6, 925–932 (1999)Google Scholar
  36. 36.
    Silva Lima, B., Van der Laan, J.W.: Mechanisms of nongenotoxic carcinogenesis and assessment of the human hazard. Regul. Toxicol. Pharmacol. 32, 135–143 (2000)CrossRefGoogle Scholar
  37. 37.
    Williams, G.M., Iatropoulos, M.J., Weisburger, J.H.: Chemical carcinogen mechanisms of action and implications for testing methodology. Exp. Toxicol. Pathol. 48, 101–111 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chun-Wei Tung
    • 1
    • 2
  1. 1.School of PharmacyKaohsiung Medical UniversityTaiwan
  2. 2.ToxicologyKaohsiung Medical UniversityTaiwan

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