Current and future use of genomics data in toxicology: Opportunities and challenges for regulatory applications

Regulatory Toxicology and Pharmacology

Volumn 61, Issue 2, 2011, Pages 141-153

  Goetz, Amber K ^a   Singh, Bhanu P ^b   Battalora, Michael ^b   Breier, Joseph M ^c   Bailey, Jason P ^d   Chukwudebe, Amechi C ^e   Janus, Erik R ^f  

^a SYNGENTA CROP PROTECTION   (United States)

^b DUPONT COMPANY   (United States)

^c BAYER CROPSCIENCE   (United States)

^d DOW AGROSCIENCES LLC   (United States)

^e BASF CORPORATION   (United States)

^f Steptoe & Johnson LLP   (United States)

Author keywords

Cancer; Gene expression; Microarray; Risk assessment; Toxicogenomics

Indexed keywords

BIOTECHNOLOGY; CANCER RISK; CARCINOGENICITY; CYTOLOGY; DATA ANALYSIS; DECISION MAKING; DOSE RESPONSE; DRUG MECHANISM; ENVIRONMENTAL HEALTH; FOOD AND DRUG ADMINISTRATION; GENE EXPRESSION; GENETIC TRANSCRIPTION; GENETICS; HUMAN; MOLECULAR BIOLOGY; MOLECULAR GENETICS; NOTE; PRIORITY JOURNAL; RISK ASSESSMENT; TOXICOGENETICS; TOXICOLOGY; VALIDATION STUDY;

ANIMALS; CELL TRANSFORMATION, NEOPLASTIC; DOSE-RESPONSE RELATIONSHIP, DRUG; GENOMICS; HUMANS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; TOXICOGENETICS; TOXICOLOGY;

EID: 84860390137     PISSN: 02732300     EISSN: 10960295     Source Type: Journal    
DOI: 10.1016/j.yrtph.2011.07.012     Document Type: Note

References (98)

1. Afshari C.A., et al. The evolution of bioinformatics in toxicology: advancing toxicogenomics. Toxicol. Sci. 2010, 10.1093/toxsci/kfq373.
2. Alexa A., et al. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 2006, 22:1600-1607.
3. Allen J.W., et al. Toxicity profiles in mice treated with hepatotumorigenic and non-hepatotumorigenic triazole conazole fungicides: propiconazole, triadimefon, and myclobutanil. Toxicol. Pathol. 2006, 34:853-862.
4. Alon U. An Introduction to Systems Biology 2007, Chapman & Hall/CRC, Taylor & Francis Group.
5. Ankley G.T., et al. Toxicogenomics in regulatory ecotoxicology. Environ. Sci. Technol. 2006, 40:4055-4065.
6. Anonymous Microarray standards at last - not a moment too soon, the microarray community has issued guidelines that will make their data much more useful and accessible. Nature and the Nature research journals will respond accordingly. Nature 2002, 419:323.
7. Ashburner M., et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat. Genet. 2000, 25:25-29.
8. Audouze K., et al. Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks. PLoS Comput. Biol. 2010, 6:e1000788.
9. Ball C.A., et al. Submission of microarray data to public repositories. PLoS Biol. 2004, 2:E317.
10. Bercu J.P., et al. Toxicogenomics and cancer risk assessment: a framework for key event analysis and dose-response assessment for nongenotoxic carcinogens. Regul. Toxicol. Pharmacol. 2010, 58:369-381.
11. Boess F., et al. Gene expression in two hepatic cell lines, cultured primary hepatocytes, and liver slices compared to the in vivo liver gene expression in rats: possible implications for toxicogenomics use of in vitro systems. Toxicol. Sci. 2003, 73:386-402.
12. Boobis A.R., et al. IPCS framework for analyzing the relevance of a cancer mode of action for humans. Crit. Rev. Toxicol. 2006, 36:781-792.
13. Boobis A.R., et al. IPCS framework for analyzing the relevance of a noncancer mode of action for humans. Crit. Rev. Toxicol. 2008, 38:87-96.
14. Boorman G.A., et al. Hepatic gene expression changes throughout the day in the Fischer rat: implications for toxicogenomic experiments. Toxicol. Sci. 2005, 86:185-193.
15. Brazma A., et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 2001, 29:365-371.
16. Breitling R. Biological microarray interpretation: the rules of engagement. Biochim. Biophys. Acta. 2006, 1759:319-327.
17. Coombes K.R., et al. Microarrays: retracing steps. Nat. Med. 2007, 13:1276-1277. [author reply 1277-8].
18. Corvi R., et al. Meeting report: validation of toxicogenomics-based test systems: ECVAM-ICCVAM/NICEATM considerations for regulatory use. Environ. Health Perspect. 2006, 114:420-429.
19. Crump K.S. A new method for determining allowable daily intakes. Fundam. Appl. Toxicol. 1984, 4:854-871.
20. de Haan J.R., et al. Interpretation of ANOVA models for microarray data using PCA. Bioinformatics 2007, 23:184-190.
21. Doak S.H., et al. Mechanistic influences for mutation induction curves after exposure to DNA-reactive carcinogens. Cancer Res. 2007, 67:3904-3911.
22. Draghici S., et al. A systems biology approach for pathway level analysis. Genome Res. 2007, 17:1537-1545.
23. ECETOC, 2007. Workshop on the Application of 'Omic Technologies in Toxicology and Ecotoxicology: Case Studies and Risk Assessment. 6-7 December 2007, Malaga. Workshop Report No. 11 European Centre for Ecotoxicology and Toxicology of Chemicals.
24. Ein-Dor L., et al. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl. Acad. Sci. USA 2006, 103:5923-5928.
25. Eisen M.B., et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 1998, 95:14863-14868.
26. Fang H., et al. Transcriptome analysis of early organogenesis in human embryos. Dev. Cell. 2010, 19:174-184.
27. Fernandes T.G., et al. High-throughput cellular microarray platforms: applications in drug discovery, toxicology and stem cell research. Trends Biotechnol. 2009, 27:342-349.
28. Fielden M.R., Zacharewski T.R. Challenges and limitations of gene expression profiling in mechanistic and predictive toxicology. Toxicol. Sci. 2001, 60:6-10.
29. Fielden M.R., et al. Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci. 2008, 103:28-34.
30. Foley J.F., et al. Optimal sampling of rat liver tissue for toxicogenomic studies. Toxicol Pathol. 2006, 34:795-801.
31. Geng X., Oliver G. Elucidating the molecular characteristics of organogenesis in human embryos. Genome Biol. 2010, 11:130.
32. Genter M.B., et al. Genomic analysis of alachlor-induced oncogenesis in rat olfactory mucosa. Physiol. Genomics 2002, 12:35-45.
33. Goodsaid F.M., et al. Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact. Nat. Rev. Drug Discov. 2010, 9:435-445.
34. Grawe J., et al. Low dose effects of chemicals as assessed by the flow cytometric in vivo micronucleus assay. Mutat. Res. 1998, 405:199-208.
35. Guo L., et al. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol. 2006, 24:1162-1169.
36. Harris A.J., et al. Comparison of basal gene expression profiles and effects of hepatocarcinogens on gene expression in cultured primary human hepatocytes and HepG2 cells. Mutat. Res. 2004, 549:79-99.
37. Hoffman E.P., et al. Expression profiling-best practices for data generation and interpretation in clinical trials. Nat. Rev. Genet. 2004, 5:229-237.
38. ICMJE, 2010. International Committee of Medical Journal Editors (ICMJE). Uniform Requirements for Manuscripts Submitted to Biomedical Journals: Writing and Editing for Biomedical Publication. International Committee of Medical Journal Editors. http://www.icmje.org/#prepare.
39. Ioannidis J.P., et al. Repeatability of published microarray gene expression analyses. Nat. Genet. 2009, 41:149-155.
40. IPCS, 2003. International Programme on Chemical Safety (IPCS) Workshop Report: Toxicogenomics and the Risk assessment of Chemicals for the Protection of Human Health. Berlin, Germany 17-19 November 2003.
41. Irizarry R.A., et al. Multiple-laboratory comparison of microarray platforms. Nat. Methods. 2005, 2:345-350.
42. Jansen J.G., et al. Marked differences in the role of O6-alkylguanine in hprt mutagenesis in T-lymphocytes of rats exposed in vivo to ethylmethanesulfonate, N-(2-hydroxyethyl)-N-nitrosourea, or N-ethyl-N-nitrosourea. Cancer Res. 1995, 55:1875-1882.
43. Kanehisa M., Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28:27-30.
44. Khatri P., Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 2005, 21:3587-3595.
45. Kier L.D., et al. Applications of microarrays with toxicologically relevant genes (tox genes) for the evaluation of chemical toxicants in Sprague Dawley rats in vivo and human hepatocytes in vitro. Mutat. Res. 2004, 549:101-113.
46. Lockhart D.J., et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 1996, 14:1675-1680.
47. Martin M.T., et al. Toxicogenomic study of triazole fungicides and perfluoroalkyl acids in rat livers predicts toxicity and categorizes chemicals based on mechanisms of toxicity. Toxicol. Sci. 2007, 97:595-613.
48. Mauthe R.J., et al. Comparison of DNA-adduct and tissue-available dose levels of MeIQx in human and rodent colon following administration of a very low dose. Int. J. Cancer. 1999, 80:539-545.
49. Mendrick, D.L., Mattes, W.B., Essential concepts in toxicogenomics. Methods Mol. Biol., Humana, 2008.
50. MGED, 2004. A proposed framework structure for reporting biological investigations. Microarray Gene Expression Data Society RSBI Tiered Checklist. http://www.mged.org/Workgroups/rsbi/index.html.
51. Moggs J.G., et al. Phenotypic anchoring of gene expression changes during estrogen-induced uterine growth. Environ. Health. Perspect. 2004, 112:1589-1606.
52. Naciff J.M., et al. Uterine temporal response to acute exposure to 17alpha-ethinyl estradiol in the immature rat. Toxicol. Sci. 2007, 97:467-490.
53. Nesnow S., et al. Discrimination of tumorigenic triazole conazoles from phenobarbital by transcriptional analyses of mouse liver gene expression. Toxicol. Sci. 2009, 110:68-83.
54. NRC Applications of toxicogenomics technologies to predictive toxicology and risk assessment 2007, National Research Council (NRC). The National Academies Press, Washington, DC.
55. NRC Validiation of toxicogenomic technologies: a workshop summary 2007, National Research Council (NRC). The National Academies Press, Washington, DC.
56. NRC Toxicogenomic Technology and Risk Assessment of Environmental Carcinogens: A Workshop Summary. National Research Council (NRC) 2005, The National Academies Press, Washington DC.
57. NRC Science and decisions: advancing risk assessment. National Research Council (NRC) 2009, The National Academies Press, Washington DC.
58. Oberemm A., et al. How can toxicogenomics inform risk assessment?. Toxicol. Appl. Pharmacol. 2005, 207:592-598.
59. OECD, 2004. Report of the OECD/IPCS Workshop on Toxicogenomics. Kyoto, 13-15 October 2004. Environmental Directorate, Joint Meeting of the Chemicals Committee and the Working Party on Chemicals, Pesticides and Biotechnology. OECD Series on Testing and Assessment, Number 50.
60. Patterson T.A., et al. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat. Biotechnol. 2006, 24:1140-1150.
61. Pennie W., et al. Toxicogenomics in risk assessment: an overview of an HESI collaborative research program. Environ. Health Perspect. 2004, 112:417-419.
62. Pettit S., et al. Current and future applications of toxicogenomics: Results summary of a survey from the HESI Genomics State of Science Subcommittee. Environ. Health Perspect. 2010, 118:992-997.
63. Phillips J.M., et al. Phenobarbital elicits unique, early changes in the expression of hepatic genes that affect critical pathways in tumor-prone B6C3F1 mice. Toxicol. Sci. 2009, 109:193-205.
64. Quackenbush J. Extracting meaning from functional genomics experiments. Toxicol. Appl. Pharmacol. 2005, 207:195-199.
65. Raghavan N., et al. Class prediction in toxicogenomics. J. Biopharm. Stat. 2005, 15:327-341.
66. Rouquie D., et al. Standard and molecular NOAELs for rat testicular toxicity induced by flutamide. Toxicol. Sci. 2009, 109:59-65.
67. Schuler G.D. Pieces of the puzzle: expressed sequence tags and the catalog of human genes. J. Mol. Med. 1997, 75:694-698.
68. Sen B., et al. Gene expression profiling of responses to dimethylarsinic acid in female F344 rat urothelium. Toxicology. 2005, 215:214-226.
69. Shi L., et al. Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinf. 2005, 6(Suppl. 2):S12.
70. Shi L., et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 2006, 24:1151-1161.
71. Shi L., et al. Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential. Curr. Opin. Biotechnol. 2008, 19:10-18.
72. Shi L.M., et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 2010, 28:827-838.
73. Thomas R.S., et al. A method to integrate benchmark dose estimates with genomic data to assess the functional effects of chemical exposure. Toxicol. Sci. 2007, 98:240-248.
74. Thomas R., et al. Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structure. Genome Biol. 2009, 10:R44.
75. Thukral S.K., et al. Prediction of nephrotoxicant action and identification of candidate toxicity-related biomarkers. Toxicol. Pathol. 2005, 33:343-355.
76. Tibshirani R., et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA 2002, 99:6567-6572.
77. Turteltaub K.W., et al. MeIQx-DNA adduct formation in rodent and human tissues at low doses. Mutat. Res. 1997, 376:243-252.
78. US EPA National Center for Environmental Assessment (NCEA): Applying Science to Improve Risk Assessment & Environmental Decision Making. EPA/600/F-00/008 2000, US EPA, Washington, DC.
79. US EPA, 2002. Interim Policy on Genomics. US EPA, Washington, DC, 2002. http://www.epa.gov/osa/spc/genomics.htm.
80. US EPA Potential Implication of Genomics for Regulatory and Risk Assessment Applications at EPA. EPA 100/B-04/002 2004, US EPA, Washington, DC.
81. US EPA, 2005. Notice of Availability; Documents Entitled Guidelines for Carcinogen Risk Assessment. Federal Register April 7, 2005.
82. US EPA Summary of the NCEA Colloquium on Current Use and Future Needs of Genomics in Ecological and Human Health Risk Assessment. EPA/600/R-04/039F 2006, US EPA, Washington, DC.
83. US EPA Alachlor: PP#8F05000 and 8F5025. FQPA Human Health Risk Assessment for Section 3 New Uses on Cotton, Sunflower, and for Inadvertent Tolerances on Various Rotational Crops (Cereal Grains and Nongrass Animal Feeds). PC Code: 090501. DP Barcode D330812, 247976. OPPTS 2007, US EPA, Washington, DC, USA.
84. US EPA, 2007b. External review draft of Interim guidance for microarray-based assays: Data submission, quality, analysis, management, and training considerations. http://www.epa.gov/spc/pdfs/epa_interim_guidance_for_microarray-based_assays-external-review_draft.pdf.
85. US EPA, 2008. White Paper on Use of Structure-Activity Relationship (SAR) Information and Quantitative SAR (QSAR) Modeling for Fulfilling Data Requirements for Antimicrobial Pesticide Chemicals and Informing EPA's Risk Management Process (Docket: 2008-0110 Document 0045).
86. US EPA An approach to using toxicogenomic data in U.S. EPA human health risk assessments a dibutyl phthalate case study.EPA/600/R-09/028F 2009, US EPA, Washington, DC.
87. US EPA The U.S. Environmental Protection Agency's Strategic Plan for Evaluating the Toxicity of Chemicals. EPA 100/K-09/001 2009, US EPA, Washington, DC.
88. US FDA, 2004. Innovation/Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. (FDA, March 2004). http://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/default.htm.
89. US FDA, 2005. Guidance for Industry. Pharmacogenomic Data Submissions. (FDA, March 2005). http://www.fda.gov/OHRMS/DOCKETS/98fr/2003d-0497-gdl0002.pdf.
90. US FDA, 2006. Innovation/Stagnation: Critical Path Opportunities List. U.S. Department of Health and Human Services. (FDA, March 2006). http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/UCM077258.pdf.
91. Villeneuve D.L., Garcia-Reyero N. Vision & strategy: predictive ecotoxicology in the 21st century. Environ. Toxicol. Chem. 2011, 30:1-8.
92. Ward W.O., et al. Transcriptional profiles in liver from mice treated with hepatotumorigenic and nonhepatotumorigenic triazole conazole fungicides: propiconazole, triadimefon, and myclobutanil. Toxicol. Pathol. 2006, 34:863-878.
93. Waring J.F., et al. Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol. Lett. 2001, 120:359-368.
94. Waring J.F., et al. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol. Appl. Pharmacol. 2001, 175:28-42.
95. Watari A., Yutsudo M. Multi-functional gene ASY/Nogo/RTN-X/RTN4: apoptosis, tumor suppression, and inhibition of neuronal regeneration. Apoptosis 2003, 8:5-9.
96. Yang C., et al. Computational toxicology approaches at the US Food and Drug Administration. Altern. Lab. Anim. 2009, 37:523-531.
97. Yuryev A. Pathway analysis for drug discovery: computational infrastructure and applications 2008, John Wiley & Sons, Hoboken, N.J.
98. Zhou T., et al. Toxicogenomics: transcription profiling for toxicology assessment. EXS 2009, 99:325-366.