Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment

References

• Aardema MJ, MacGregor JT. 2002. Toxicology and genetic toxicology in the new era of “toxicogenomics”: impact of “-omics” technologies. Mutat Res. 499(1):13–25.
   PubMed Web of Science ®Google Scholar
• Borgert CJ. 2007. Predicting interactions from mechanistic information: can omic data validate theories? Toxicol Appl Pharmacol. 223(2):114–120.
   PubMed Web of Science ®Google Scholar
• Boss MK, Bristow R, Dewhirst MW. 2014. Linking the history of radiation biology to the hallmarks of cancer. Radiat Res. 181(6):561–577.
   PubMed Web of Science ®Google Scholar
• Bourdon JA, Williams A, Kuo B, Moffat I, White PA, Halappanavar S, Vogel U, Wallin H, Yauk CL. 2013. Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure. Toxicology. 303:83–93.
   PubMed Web of Science ®Google Scholar
• Buesen R, Chorley BN, da Silva Lima B, Daston G, Deferme L, Ebbels T, Gant TW, Goetz A, Greally J, Gribaldo L, et al. 2017. Applying ‘omics’ technologies in chemicals risk assessment: report of an ECETOC workshop. Regul Toxicol Pharmacol. 91(Suppl 1):S3–S13.
   PubMed Web of Science ®Google Scholar
• Buick JK, Williams A, Kuo B, Wills JW, Swartz CD, Recio L, Li HH, Fornace AJ, Jr, Aubrecht J, Yauk CL. 2017. Integration of the TGx-28.65 genomic biomarker with the flow cytometry micronucleus test to assess the genotoxicity of disperse orange and 1,2,4-benzenetriol in human TK6 cells. Mutat Res. 806:51–62. Epub 2017 Sep 13.
   PubMed Web of Science ®Google Scholar
• Chauhan V, Kuo B, McNamee JP, Wilkins RC, Yauk CL. 2016. Transcriptional benchmark dose modeling: exploring how advances in chemical risk assessment may be applied to the radiation field. Environ Mol Mutagen. 57(8):589–604.
   PubMed Web of Science ®Google Scholar
• Chauhan V, Rowan-Carroll A, Gagné R, Kuo B, Williams A, Yauk CL. 2018. The use of in vitro Transcriptional data to Identify Thresholds of Effects in a Human Lens Epithelial Cell-line Exposed to Ionizing Radiation. Int J Radiat Biol. 5:1–40.
   Google Scholar
• Chepelev NL, Moffat ID, Labib S, Bourdon-Lacombe J, Kuo B, Buick JK, Lemieux F, Malik AI, Halappanavar S, Williams A, Yauk CL. 2015. Integrating toxicogenomics into human health risk assessment: lessons learned from the benzo[a]pyrene case study. Crit Rev Toxicol. 45(1):44–52.
   PubMed Web of Science ®Google Scholar
• Clewell HJ, Efremenko A, Campbell JL, Dodd DE, Thomas RS. 2014. Transcriptional responses in the rat nasal epithelium following subchronic inhalation of naphthalene vapor. Toxicol Appl Pharmacol. 280(1):78–85.
   PubMed Web of Science ®Google Scholar
• Collins FS, Green ED, Guttmacher A, Guyer MS. 2003. A vision for the future of genomics research. Nature. 422(6934):835–847.
   PubMed Web of Science ®Google Scholar
• Crump K. 1984. A new method for determining allowable daily intakes. Fundam Appl Toxicol. 4(5):854–871.
   PubMedGoogle Scholar
• Davis JA, Gift JS, Zhao QJ. 2011. Introduction to benchmark dose methods and U.S. EPA’s benchmark dose software (BMDS) version 2.1.1. Toxicol Appl Pharmacol. 254(2):181–191.
   PubMed Web of Science ®Google Scholar
• Farmahin R, Williams A, Kuo B, Chepelev NL, Thomas RS, Barton-Maclaren TS, Curran IH, Nong A, Wade MG, Yauk CL, et al. 2017. Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Arch Toxicol. 91(5):2045–2065.
   PubMed Web of Science ®Google Scholar
• Filipsson AF, Sand S, Nilsson J, Victorin K. 2003. The benchmark dose method—review of available models, and recommendations for application in health risk assessment. Crit Rev Toxicol. 33(5):505–542.
   PubMed Web of Science ®Google Scholar
• Flegal FN, Devantier Y, Marro L, Wilkins RC. 2012. Validation of QuickScan dicentric chromosome analysis for high throughput radiation biological dosimetry. Health Phys. 102(2):143–153.
   PubMed Web of Science ®Google Scholar
• Haber LT, Dourson ML, Allen BC, Hertzberg RC, Parker A, Vincent MJ, Maier A, Boobis AR. 2018. Benchmark dose (BMD) modeling: current practice, issues, and challenges. Crit Rev Toxicol. 48(5):387–415.
   PubMed Web of Science ®Google Scholar
• Jackson AF, Williams A, Recio L, Waters MD, Lambert IB, Yauk CL. 2014. Case study on the utility of hepatic global gene expression profiling in the risk assessment of the carcinogen furan. Toxicol Appl Pharmacol. 274(1):63–77.
   PubMed Web of Science ®Google Scholar
• Johnson KJ, Auerbach SS, Costa E. 2020. A rat liver transcriptomic point of departure predicts a prospective liver or non-liver apical point of departure. Toxicol Sci. 2020;176(1):86-102.
   Google Scholar
• Kyrtopoulos SA. 2013. Making sense of OMICS data in population-based environmental health studies. Environ Mol Mutagen. 54(7):468–479.
   PubMed Web of Science ®Google Scholar
• Labib S, Williams A, Yauk CL, Nikota JK, Wallin H, Vogel U, Halappanavar S. 2016. Nano-risk Science: application of toxicogenomics in an adverse outcome pathway framework for risk assessment of multi-walled carbon nanotubes. Part Fibre Toxicol. 13:15–15.
   PubMed Web of Science ®Google Scholar
• McNamee JP, Flegal FN, Greene HB, Marro L, Wilkins RC. 2009. Validation of the cytokinesis-block micronucleus (CBMN) assay for use as a triage biological dosimetry tool. Radiat Prot Dosimetry. 135(4):232–242.
   PubMed Web of Science ®Google Scholar
• National Toxicology Program. 2018. NTP Research Report on National Toxicology Program Approach to Genomic Dose-Response Modeling: Research Report 5 [Internet]. Durham (NC): National Toxicology Program; [accessed 2020 July 22]. http://www.ncbi.nlm.nih.gov/books/NBK531562/.
   Google Scholar
• Phillips JR, Svoboda DL, Tandon A, Patel S, Sedykh A, Mav D, Kuo B, Yauk CL, Yang L, Thomas RS, et al. BMDExpress 2: enhanced transcriptomic dose-response analysis workflow. Bioinformatics. 2019;35(10):1780–1782.
   Google Scholar
• Qutob SS, Chauhan V, Kuo B, Williams A, Yauk CL, McNamee JP, Gollapudi B. 2018. The application of transcriptional benchmark dose modeling for deriving thresholds of effects associated with solar-simulated ultraviolet radiation exposure. Environ Mol Mutagen. 59(6):502–515.
   PubMed Web of Science ®Google Scholar
• Sand S, Victorin K, Filipsson AF. 2008. The current state of knowledge on the use of the benchmark dose concept in risk assessment. J Appl Toxicol. 28(4):405–421.
   PubMed Web of Science ®Google Scholar
• Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, Lomsadze A, Pruitt KD, Borodovsky M, Ostell J, et al. 2016. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 44(14):6614–6624.
   PubMed Web of Science ®Google Scholar
• Thomas RS, Philbert MA, Auerbach SS, Wetmore BA, Devito MJ, Cote I, Rowlands JC, Whelan MP, Hays SM, Andersen ME, Meek ME, Reiter LW, et al. 2013. Incorporating new technologies into toxicity testing and risk assessment: moving from 21st century vision to a data-driven framework. Toxicol Sci. 136(1):4–18.
   PubMed Web of Science ®Google Scholar
• Thomas RS, Wesselkamper SC, Wang NC, Zhao QJ, Petersen DD, Lambert JC, Cote I, Yang L, Healy E, Black MB, Clewell HJ, 3rd, et al. 2013. Temporal concordance between apical and transcriptional points of departure for chemical risk assessment. Toxicol Sci. 134(1):180–194.
   PubMed Web of Science ®Google Scholar
• United Nations Scientific Committee on Atomic Radiation (UNSCEAR). 2012. The 2012 white paper to guide the scientific committee’s white paper future programme or work. Biological mechanisms of radiation actions at low doses. New York: United Nations Publications.
   Google Scholar
• US EPA. 2012. Benchmark Dose Technical Guidance (EPA/100/R-12/001); [accessed 2015 Oct 7]. http://www2.epa.gov/sites/production/files/2015-01/documents/benchmark_dose_guidance.pdf.
   Google Scholar
• Webster AF, Chepelev N, Gagne R, Kuo B, Recio L, Williams A, Yauk CL. 2015. Impact of genomics platform and statistical filtering on transcriptional Benchmark Doses (BMD) and multiple approaches for selection of chemical Point of Departure (PoD). PLoS One. 10(8):e0136764.
   PubMed Web of Science ®Google Scholar
• Webster AF, Zumbo P, Fostel J, Gandara J, Hester SD, Recio L, Williams A, Wood CE, Yauk CL, Mason CE. 2015. Mining the archives: a cross-platform analysis of gene expression profiles in archival formalin-fixed paraffin-embedded tissues. Toxicol Sci. 148(2):460–472.
   PubMed Web of Science ®Google Scholar
• William MG, Auerbach SS, Parham F, Stout MD, Waidyanatha S, Mutlu E, Collins B, Paules RS, Merrick BA, Ferguson S, et al. Evaluation of 5-day in vivo rat liver and kidney with high-throughput transcriptomics for estimating benchmark doses of apical outcomes. Toxicol Sci. 2020:kfaa081. DOI:https://doi.org/10.1093/toxsci/kfaa081.
   Google Scholar
• Yang L, Allen BC, Thomas RS. 2007. BMDExpress: a software tool for the benchmark dose analyses of genomic data. BMC Genomics. 8:350–387.
   PubMed Web of Science ®Google Scholar