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Strategic grant

Developing an integrated "in vitro carcinogenicity predictive tool" utilising in vitro cell signalling and cell behaviour assessment coupled with in vitro genotoxicity data

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At a glance

Completed
Award date
April 2012 - September 2015
Grant amount
£384,143
Principal investigator
Professor Gareth Jenkins

Co-investigator(s)

Institute
Swansea University

R

  • Replacement

Contents

Application abstract

Cancer arises due to the accumulation of multiple disparate abnormalities governing how cells control their growth. Some of these abnormalities involve the accumulation of DNA mutations, hence DNA mutagens (agents that alter DNA) are nearly always capable of inducing cancer. Therefore a traditional way to assess cancer risk of new chemical agents is merely to focus on DNA mutability. Unfortunately, this approach does not detect chemicals that cause cancer in other (non-DNA) ways. It is also not 100% accurate in detecting chemicals that do mutate DNA, and even throws up numerous false positives (wrongly classifies safe chemicals as mutagens), which require animal experiments for clarification. In this proposal we aim to develop a better approach to detecting carcinogens, by focussing on induced abnormalities in cell signals controlling cellular growth and on how cells behave by monitoring multiple cellular parameters after exposure to the test chemicals. We hypothesise that this more holistic approach will yield a better prediction of which chemicals are potential carcinogens. As this apporach is entirely in vitro (cultured cells), this could have a major impact on the use of animals in safety testing and reduce the numbers of animals needed by tens of thousands per year.

Impacts

Publications

  1. Chapman KE et al. (2020). Multiple-endpoint in vitro carcinogenicity test in human cell line TK6 distinguishes carcinogens from non-carcinogens and highlights mechanisms of action. Genotoxicity and Carcinogenicity 95: 321-336. doi: 10.1007/s00204-020-02902-3
  2. Wilde EC et al. (2018). A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells. Archives of Toxicology 92(2):935-51. doi: 10.1007/s00204-017-2102-y
  3. Chapman KE et al. (2017). Investigation of J-shaped dose-responses induced by exposure to the alkylating agent N-methyl-N-nitrosourea. Mutation Research 819:38-46. doi: 10.1016/j.mrgentox.2017.05.002
  4. Stannard L et al. (2017). Is Nickel Chloride really a Non-Genotoxic Carcinogen? Basic Clin Pharmacol Toxicol 121:10-15. doi: 10.1111/bcpt.12689
  5. Brüsehafer K et al. (2016). The clastogenicity of 4NQO is cell-type dependent and linked to cytotoxicity, length of exposure and p53 proficiency. Mutagenesis 31(2):171-80. doi: 10.1093/mutage/gev069
  6. Shah UK et al. (2016). A comparison of the genotoxicity of benzo[a]pyrene in four cell lines with differing metabolic capacity. Mutat Res Genet Toxicol Environ Mutagen 808:8-19. doi: 10.1016/j.mrgentox.2016.06.009
  7. Verma J et al. (2016). Evaluation of the automated MicroFlow® and Metafer™ platforms for high-throughput micronucleus scoring and dose response analysis in human lymphoblastoid TK6 cells. Archives of Toxicology 91:2689–2698. doi: 10.1007/s00204-016-1903-8
  8. Wills JW et al. (2016). Empirical analysis of BMD metrics in genetic toxicology part II: in vivo potency comparisons to promote reductions in the use of experimental animals for genetic toxicity assessment. Mutagenesis 31(3):265-75. doi: 10.1093/mutage/gew009 
  9. Chapman KE et al. (2015). Acute dosing and p53-deficiency promote cellular sensitivity to DNA methylating agents. Toxicological Sciences 144:2(357-65). doi: 10.1093/toxsci/kfv004
  10. MacGregor JT et al. (2015). IWGT report on quantitative approaches to genotoxicity risk assessment I. Methods and metrics for defining exposure-response relationships and points of departure (PoDs). Mutation Research 783:55-65. doi: 10.1016/j.mrgentox.2014.09.011
  11. Chapman KE et al. (2014). Automation and validation of micronucleus detection in the 3D EpiDerm™ human reconstructed skin assay and correlation with 2D dose responses. Mutagenesis 29(3):165-75. doi: 10.1093/mutage/geu011
  12. Johnson GE et al. (2014) Derivation of point of departure (PoD) estimates in genetic toxicology studies and their potential applications in risk assessment. Environ. Mol. Mutagen. doi: 10.1002/em.21870
  13. Gollapudi BB et al. (2013) Quantitative approaches for assessing dose-response relationships in genetic toxicology studies. Environ. Mol. Mutagen. 54(1):8-18. doi: 10.1002/em.21727