Abstract
The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24 h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ashby J (1996) Alternatives to the 2-species bioassay for the identification of potential human carcinogens. Hum Exp Toxicol 15(3):183–202. https://doi.org/10.1177/096032719601500301
Ates G, Mertens B, Heymans A et al (2018) A novel genotoxin-specific qPCR array based on the metabolically competent human HepaRG(™) cell line as a rapid and reliable tool for improved in vitro hazard assessment. Arch Toxicol 92(4):1593–1608. https://doi.org/10.1007/s00204-018-2172-5
Beal MA, Chen G, Dearfield KL et al (2023) Interpretation of In vitro concentration-response data for risk assessment and regulatory decision-making: report from the 2022 IWGT Quantitative Analysis Expert Working Group Meeting. Environ Mol Mutagen. https://doi.org/10.1002/em.22582
Benya TJ, Busey WM, Dorato MA, Berteau PE (1982) Inhalation carcinogenicity bioassay of vinyl bromide in rats. Toxicol Appl Pharmacol 64(3):367–379. https://doi.org/10.1016/0041-008x(82)90233-2
Bolt HM (2008) The concept of “Practical Thresholds” in the Derivation of Occupational Exposure Limits for Carcinogens by the Scientific Committee on Occupational Exposure Limits (SCOEL) of the European Union. Genes Environ 30(4):114–119. https://doi.org/10.3123/jemsge.30.114
Buick JK, Moffat I, Williams A et al (2015) Integration of metabolic activation with a predictive toxicogenomics signature to classify genotoxic versus nongenotoxic chemicals in human TK6 cells. Environ Mol Mutagen 56(6):520–534. https://doi.org/10.1002/em.21940
Burnum KE, Hirota Y, Baker ES et al (2012) Uterine deletion of Trp53 compromises antioxidant responses in the mouse decidua. Endocrinology 153(9):4568–4579. https://doi.org/10.1210/en.2012-1335
Chen Y, Ohki R (2020) p53-PHLDA3-akt network: the key regulators of neuroendocrine tumorigenesis. Int J Mol Sci. https://doi.org/10.3390/ijms21114098
Chen T, Wu D, Moskaluk CA, Fu Z (2013) Distinct expression patterns of ICK/MAK/MOK protein kinases in the intestine implicate functional diversity. PLoS ONE 8(11):e79359. https://doi.org/10.1371/journal.pone.0079359
Cohen SM (2010a) An enhanced 13-week bioassay: an alternative to the 2-year bioassay to screen for human carcinogenesis. Exp Toxicol Pathol 62(5):497–502. https://doi.org/10.1016/j.etp.2009.06.011
Cohen SM (2010b) Evaluation of possible carcinogenic risk to humans based on liver tumors in rodent assays: the two-year bioassay is no longer necessary. Toxicol Pathol 38(3):487–501. https://doi.org/10.1177/0192623310363813
Cohen SM, Robinson D, MacDonald J (2001) Alternative models for carcinogenicity testing. Toxicol Sci 64(1):14–19. https://doi.org/10.1093/toxsci/64.1.14
Cohen SM, Boobis AR, Dellarco VL et al (2019) Chemical carcinogenicity revisited 3: Risk assessment of carcinogenic potential based on the current state of knowledge of carcinogenesis in humans. Regul Toxicol Pharmacol 103:100–105. https://doi.org/10.1016/j.yrtph.2019.01.017
Corton JC, Hill T, Sutherland JJ, Stevens JL, Rooney J (2020) A set of six gene expression biomarkers identify rat liver tumorigens in short-term assays. Toxicol Sci 177(1):11–26. https://doi.org/10.1093/toxsci/kfaa101
Corton JC, Mitchell CA, Auerbach S et al (2022) A collaborative initiative to establish genomic biomarkers for assessing tumorigenic potential to reduce reliance on conventional rodent carcinogenicity studies. Toxicol Sci 188(1):4–16. https://doi.org/10.1093/toxsci/kfac041
Eastmond DA, Vulimiri SV, French JE, Sonawane B (2013) The use of genetically modified mice in cancer risk assessment: challenges and limitations. Crit Rev Toxicol 43(8):611–631. https://doi.org/10.3109/10408444.2013.822844
Ellinger-Ziegelbauer H, Gmuender H, Bandenburg A, Ahr HJ (2008) Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. Mutat Res 637(1–2):23–39. https://doi.org/10.1016/j.mrfmmm.2007.06.010
Ellinger-Ziegelbauer H, Aubrecht J, Kleinjans JC, Ahr HJ (2009) Application of toxicogenomics to study mechanisms of genotoxicity and carcinogenicity. Toxicol Lett 186(1):36–44. https://doi.org/10.1016/j.toxlet.2008.08.017
Engeland K (2022) Cell cycle regulation: p53–p21-RB signaling. Cell Death Differ 29(5):946–960. https://doi.org/10.1038/s41418-022-00988-z
Ennever FK, Lave LB (2003) Implications of the lack of accuracy of the lifetime rodent bioassay for predicting human carcinogenicity. Regul Toxicol Pharmacol 38(1):52–57. https://doi.org/10.1016/s0273-2300(03)00068-0
EPA (2005) United States Environmental Protection Agency's guidelines for carcinogen risk assessment. https://www.epa.gov/risk/guidelines-carcinogen-risk-assessment. Accessed 22 Mar 2024
Fielden MR, Adai A, Dunn RT 2nd et al (2011) Development and evaluation of a genomic signature for the prediction and mechanistic assessment of nongenotoxic hepatocarcinogens in the rat. Toxicol Sci 124(1):54–74. https://doi.org/10.1093/toxsci/kfr202
Furihata C, Watanabe T, Suzuki T, Hamada S, Nakajima M (2016) Collaborative studies in toxicogenomics in rodent liver in JEMS.MMS; a useful application of principal component analysis on toxicogenomics. Genes Environ 38:15. https://doi.org/10.1186/s41021-016-0041-0
Gant TW, Auerbach SS, Von Bergen M et al (2023) Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity. Arch Toxicol 97(8):2291–2302. https://doi.org/10.1007/s00204-023-03522-3
Gold LS, Slone TH, Manley NB, Bernstein L (1991) Target organs in chronic bioassays of 533 chemical carcinogens. Environ Health Perspect 93:233–246. https://doi.org/10.1289/ehp.9193233
Gold LS, Manley NB, Slone TH, Ward JM (2001) Compendium of chemical carcinogens by target organ: results of chronic bioassays in rats, mice, hamsters, dogs, and monkeys. Toxicol Pathol 29(6):639–652. https://doi.org/10.1080/019262301753385979
Hamza AH, Abdulfattah HM, Mahmoud RH, Khalil WK, Ahmed HH (2015) Current concepts in pathophysiology and management of hepatocellular carcinoma. Acta Biochim Pol 62(3):573–580. https://doi.org/10.18388/abp.2015_1030
Hartwig A, Arand M, Epe B et al (2020) Mode of action-based risk assessment of genotoxic carcinogens. Arch Toxicol 94(6):1787–1877. https://doi.org/10.1007/s00204-020-02733-2
Heflich RH, Johnson GE, Zeller A et al (2020) Mutation as a toxicological endpoint for regulatory decision-making. Environ Mol Mutagen 61(1):34–41. https://doi.org/10.1002/em.22338
Hill T, Rooney J, Abedini J, El-Masri H, Wood CE, Corton JC (2020) Gene expression thresholds derived from short-term exposures identify rat liver tumorigens. Toxicol Sci 177(1):41–59. https://doi.org/10.1093/toxsci/kfaa102
Hori M, Satou K, Harashima H, Kamiya H (2010) Suppression of mutagenesis by 8-hydroxy-2’-deoxyguanosine 5’-triphosphate (7,8-dihydro-8-oxo-2’-deoxyguanosine 5’-triphosphate) by human MTH1, MTH2, and NUDT5. Free Radic Biol Med 48(9):1197–1201. https://doi.org/10.1016/j.freeradbiomed.2010.02.002
Hu X, Li D, Zhu H, Yu T, Xiong X, Xu X (2023) ATP6V1F is a novel prognostic biomarker and potential immunotherapy target for hepatocellular carcinoma. BMC Med Genomics 16(1):188. https://doi.org/10.1186/s12920-023-01624-6
Hui AM, Makuuchi M, Li X (1998) Cell cycle regulators and human hepatocarcinogenesis. Hepatogastroenterology 45(23):1635–1642
ICH (2008) International Conference on Harmonisation. Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use (ICH Topic S2 (R1).
Igarashi Y, Nakatsu N, Yamashita T et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res 43(Database issue):D921–D927. https://doi.org/10.1093/nar/gku955
Ito N, Tamano S, Shirai T (2003) A medium-term rat liver bioassay for rapid in vivo detection of carcinogenic potential of chemicals. Cancer Sci 94(1):3–8. https://doi.org/10.1111/j.1349-7006.2003.tb01343.x
Ito R, Hayakawa H, Sekiguchi M, Ishibashi T (2005) Multiple enzyme activities of Escherichia coli MutT protein for sanitization of DNA and RNA precursor pools. Biochemistry 44(17):6670–6674. https://doi.org/10.1021/bi047550k
Kanki M, Gi M, Fujioka M, Wanibuchi H (2016) Detection of non-genotoxic hepatocarcinogens and prediction of their mechanism of action in rats using gene marker sets. J Toxicol Sci 41(2):281–292. https://doi.org/10.2131/jts.41.281
Kawase T, Ichikawa H, Ohta T et al (2008) p53 target gene AEN is a nuclear exonuclease required for p53-dependent apoptosis. Oncogene 27(27):3797–3810. https://doi.org/10.1038/onc.2008.32
Kirkland D, Aardema M, Henderson L, Müller L (2005) Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens I. Sensitivity, specificity and relative predictivity. Mutat Res 584(1–2):1–256. https://doi.org/10.1016/j.mrgentox.2005.02.004
Kirkland DJ, Aardema M, Banduhn N et al (2007) In vitro approaches to develop weight of evidence (WoE) and mode of action (MoA) discussions with positive in vitro genotoxicity results. Mutagenesis 22(3):161–175. https://doi.org/10.1093/mutage/gem006
Kreis NN, Louwen F, Yuan J (2019) the Multifaceted p21 (Cip1/Waf1/CDKN1A) in cell differentiation migration and cancer therapy. Cancers 11(9):10. https://doi.org/10.3390/cancers11091220
Le AH, Yelland T, Paul NR et al (2021) CYRI-A limits invasive migration through macropinosome formation and integrin uptake regulation. J Cell Biol. https://doi.org/10.1083/jcb.202012114
Li B, Chen M, Lu M, Xin-Xiang J, Meng-Xiong P, Jun-Wu M (2018) Glutaredoxin 3 promotes migration and invasion via the Notch signalling pathway in oral squamous cell carcinoma. Free Radic Res 52(4):390–401. https://doi.org/10.1080/10715762.2018.1435871
Lu Y, Zhao X, Li K et al (2013) Thioredoxin-like protein 2 is overexpressed in colon cancer and promotes cancer cell metastasis by interaction with ran. Antioxid Redox Signal 19(9):899–911. https://doi.org/10.1089/ars.2012.4736
Machesky LM (2023) CYRI proteins: controllers of actin dynamics in the cellular “eat vs walk” decision. Biochem Soc Trans 51(2):579–585. https://doi.org/10.1042/BST20221354
Mateo EM, Tarazona A, Jimenez M, Mateo F (2022) Lactic acid bacteria as potential agents for biocontrol of aflatoxigenic and ochratoxigenic fungi. Toxins (basel). https://doi.org/10.3390/toxins14110807
Merkulova TI, Kropachev KY, Timofeeva OA et al (2005) Species-specific effects of the hepatocarcinogens 3’-methyl-4-dimethyl-aminoazobenzene and ortho-aminoazotoluene in mouse and rat liver. Mol Carcinog 44(4):223–232. https://doi.org/10.1002/mc.20090
Mollbrink A, Jawad R, Vlamis-Gardikas A et al (2014) Expression of thioredoxins and glutaredoxins in human hepatocellular carcinoma: correlation to cell proliferation, tumor size and metabolic syndrome. Int J Immunopathol Pharmacol 27(2):169–183. https://doi.org/10.1177/039463201402700204
Muller-Tegethoff K, Kasper P, Muller L (1995) Evaluation studies on the in vitro rat hepatocyte micronucleus assay. Mutat Res 335(3):293–307. https://doi.org/10.1016/0165-1161(95)00033-x
Niska-Blakie J, Gopinathan L, Low KN et al (2020) Knockout of the non-essential gene SUGCT creates diet-linked, age-related microbiome disbalance with a diabetes-like metabolic syndrome phenotype. Cell Mol Life Sci 77(17):3423–3439. https://doi.org/10.1007/s00018-019-03359-z
Nohmi T (2018) Thresholds of genotoxic and non-genotoxic carcinogens. Toxicol Res 34(4):281–290. https://doi.org/10.5487/tr.2018.34.4.281
Nohmi T, Suzuki T, Masumura K (2000) Recent advances in the protocols of transgenic mouse mutation assays. Mutat Res 455(1–2):191–215
NTP (2002) Final report on Carcinogens (RcC), Background Ducument for Selected Heterocyclic Amines: PhIP, MeIQ, and MeIQx. National Toxicology Program
O’Brien J, Renwick AG, Constable A et al (2006) Approaches to the risk assessment of genotoxic carcinogens in food: a critical appraisal. Food Chem Toxicol 44(10):1613–1635. https://doi.org/10.1016/j.fct.2006.07.004
Ozturk M, Arslan-Ergul A, Bagislar S, Senturk S, Yuzugullu H (2009) Senescence and immortality in hepatocellular carcinoma. Cancer Lett 286(1):103–113. https://doi.org/10.1016/j.canlet.2008.10.048
Pinato DJ, Pai M, Reccia I et al (2018) Preliminary qualification of a novel, hypoxic-based radiologic signature for trans-arterial chemoembolization in hepatocellular carcinoma. BMC Cancer 18(1):211. https://doi.org/10.1186/s12885-018-4120-4
Reznik-Schüller HM, Gregg M (1981) Pathogenesis of lung tumors induced by N-nitrosoheptamethyleneimine in F344 rats. Virchows Archiv A 393:333–343
Rivlin N, Katz S, Doody M et al (2014) Rescue of embryonic stem cells from cellular transformation by proteomic stabilization of mutant p53 and conversion into WT conformation. Proc Natl Acad Sci U S A 111(19):7006–7011. https://doi.org/10.1073/pnas.1320428111
Saggese P, Martinez CA, Tran LM et al (2021) Genotoxic treatment enhances immune response in a genetic model of lung cancer. Cancers. https://doi.org/10.3390/cancers13143595
Saito M, Sada A, Fukuyo M et al (2022) PHLDA3 is an important downstream mediator of p53 in squamous cell carcinogenesis. J Invest Dermatol 142(4):1040–10498. https://doi.org/10.1016/j.jid.2021.09.007
SCHER (2009) Risk assessment methodologies and approaches for genotoxic and carcinogenic substances (European Commission, Scientific Committee on Health and Environmental Risks) http://ec.europa.eu/health/ph_risk/committees/04_scher/docs/scher_o_113.pdf. Accessed 22 Mar 2024
Smith BP, Auvil LS, Welge M et al (2020) Identification of early liver toxicity gene biomarkers using comparative supervised machine learning. Sci Rep 10(1):19128. https://doi.org/10.1038/s41598-020-76129-8
Takayama S, Nakatsuru Y, Masuda M, Ohgaki H, Sato S, Sugimura T (1984) Demonstration of carcinogenicity in F344 rats of 2-amino-3-methyl-imidazo[4,5-f]quinoline from broiled sardine, fried beef and beef extract. Gan 75(6):467–470
Uehara T, Minowa Y, Morikawa Y et al (2011) Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database. Toxicol Appl Pharmacol 255(3):297–306. https://doi.org/10.1016/j.taap.2011.07.001
Uhlen M, Zhang C, Lee S, et al (2017) A pathology atlas of the human cancer transcriptome Science 357(6352) doi:https://doi.org/10.1126/science.aan2507. Data available from v23.0.proteinatlas.org. (https://www.proteinatlas.org/ENSG00000108010-GLRX3/pathology/liver+cancer)
Valovicova Z, Marvanova S, Meszarosova M et al (2009) Differences in DNA damage and repair produced by systemic, hepatocarcinogenic and sarcomagenic dibenzocarbazole derivatives in a model of rat liver progenitor cells. Mutat Res 665(1–2):51–60. https://doi.org/10.1016/j.mrfmmm.2009.02.014
Vogel EW, Nivard MJ, Ballering LA et al (1996) DNA damage and repair in mutagenesis and carcinogenesis: implications of structure-activity relationships for cross-species extrapolation. Mutat Res 353(1–2):177–218. https://doi.org/10.1016/0027-5107(96)00032-2
Waters MD, Jackson M, Lea I (2010) Characterizing and predicting carcinogenicity and mode of action using conventional and toxicogenomics methods. Mutat Res 705(3):184–200. https://doi.org/10.1016/j.mrrev.2010.04.005
WHO (2021) Evaluation of certain food additives: eighty-ninth report of the Joint FAO/WHO Expert Committee on Food Additives. World Health Organization and Food and Agriculture Organization of the United Nations. WHO; FAO, Geneva
Xie B, Liu Y, Zhao Z et al (2020) MYB Proto-oncogene-like 1-TWIST1 axis promotes growth and metastasis of hepatocellular carcinoma cells. Mol Ther Oncolytics 18:58–69. https://doi.org/10.1016/j.omto.2020.05.016
Yamada F, Sumida K, Saito K (2016) An improved model of predicting hepatocarcinogenic potential in rats by using gene expression data. J Appl Toxicol 36(2):296–308. https://doi.org/10.1002/jat.3184
Yoon H, Liyanarachchi S, Wright FA et al (2002) Gene expression profiling of isogenic cells with different TP53 gene dosage reveals numerous genes that are affected by TP53 dosage and identifies CSPG2 as a direct target of p53. Proc Natl Acad Sci U S A 99(24):15632–15637. https://doi.org/10.1073/pnas.242597299
Zhu J, Wu Y, Yu Y, Li Y, Shen J, Zhang R (2022) MYBL1 induces transcriptional activation of ANGPT2 to promote tumor angiogenesis and confer sorafenib resistance in human hepatocellular carcinoma. Cell Death Dis 13(8):727. https://doi.org/10.1038/s41419-022-05180-2
Acknowledgements
This work was supported by Health and Labour Sciences Research Grants from the Ministry of Health, Labour and Welfare of Japan (20KD0101 and 22KD1003), and a grant from Japan Society for the Promotion of Science (23K09652). Guiyu Qiu is supported by a scholarship from Ichikawa International Scholarship Foundation. Runjie Guo is supported by scholarships from Nishimura International Scholarship Foundation, Japan and Association for Promotion of Research on Risk Assessment, Japan. The authors gratefully acknowledge the technical assistance of Rie Onodera, Keiko Sakata, Yuko Hisabayashi, and Yukiko Iura (Department of Molecular Pathology, Graduate School of Medicine School, Osaka Metropolitan University Osaka, Japan).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gi, M., Suzuki, S., Kanki, M. et al. A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats. Arch Toxicol (2024). https://doi.org/10.1007/s00204-024-03755-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00204-024-03755-w