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Gene Expression-Based Biomarkers of Drug Safety

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Biomarker Methods in Drug Discovery and Development

Part of the book series: Methods in Pharmacology and Toxicology™ ((MIPT))

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Summary

Large-scale gene expression profiling with microarray platforms represents a new approach to identify much needed, novel mechanism-based biomarkers of toxicity for use in preclinical and clinical studies. These biomarkers may have diagnostic and/or predictive values and may consist of single gene products or of gene sets or gene expression signatures. Derivation and validation of these molecular markers involves supervised classification methods of reference data with sophisticated statistical methodologies. In this chapter, we review the methods for the identification and development of toxicity biomarkers with gene expression profiling. This chapter also describes how these novel multigene molecular markers can be integrated in a discovery pipeline, using the examples of hepatotoxicity, nephrotoxicity, and blood-based markers to illustrate successful or promising applications in toxicology.

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References

  1. Rawlings MD. Cutting the cost of drug development? Nat Rev Drug Discov 2004;3:360–364.

    Article  Google Scholar 

  2. Service RF. Surviving the blockbuster syndrome. Science 2004;303:1796–1799.

    Article  PubMed  Google Scholar 

  3. Prentis RA, Lis Y, Walker SR. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964–1985). Br J Clin Pharmacol 1988;25:387–396.

    CAS  PubMed  Google Scholar 

  4. Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004;3:711–715.

    Article  CAS  PubMed  Google Scholar 

  5. Yang Y, Blomme EA, Waring JF. Toxicogenomics in drug discovery: from preclinical studies to clinical trials. Chem Biol Interact 2004;150:71–85.

    Article  CAS  PubMed  Google Scholar 

  6. Searfoss GH, Ryan TP, Jolly RA. The role of transcriptome analysis in pre-clinical toxicology. Curr Mol Med 2005;5:53–64.

    Article  CAS  PubMed  Google Scholar 

  7. Luhe A, Suter L, Ruepp S, Singer T, Weiser T, Albertini S. Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit? Mutat Res 2005; 575:102–115.

    PubMed  Google Scholar 

  8. Segal E, Friedman N, Kaminski N, Regev A, Koller D. From signatures to models: understanding cancer using microarrays. Nat Genet 2005;37(Suppl): S38–S45.

    Article  CAS  PubMed  Google Scholar 

  9. Hamadeh HK, Bushel PR, Jayadev S, Martin K, DiSorbo O, Sieber S, et al. Gene expression analysis reveals chemical-specific profiles. Toxicol Sci 2002; 67:219–231.

    Article  CAS  PubMed  Google Scholar 

  10. Fielden MR, Eynon BP, Natsoulis G, Jarnagin K, Banas D, Kolaja KL. A gene expression signature that predicts the future onset of drug-induced renal tubular toxicity. Toxicol Pathol 2005;33:675–683.

    Article  CAS  PubMed  Google Scholar 

  11. Yang Y, Abel SJ, Ciurlionis R, Waring JF. Development of a toxicogenomics in vitro assay for the efficient characterization of compounds. Pharmacogenomics 2006;7:177–186.

    Article  CAS  PubMed  Google Scholar 

  12. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001;7:673–679.

    Article  CAS  PubMed  Google Scholar 

  13. Ringner M, Peterson C. Microarray-based cancer diagnosis with artificial neural networks. Biotechniques 2003;Suppl:30–35.

    PubMed  Google Scholar 

  14. Xu Y, Selaru FM, Yin J, Zou TT, Shustova V, Mori Y, et al. Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett’s esophagus and esophageal cancer. Cancer Res 2002;62:3493–3497.

    CAS  PubMed  Google Scholar 

  15. Gant TW. Classifying toxicity and pathology by gene-expression profile—taking a lead from studies in neoplasia. Trends Pharmacol Sci 2002;23:388–393.

    Article  CAS  PubMed  Google Scholar 

  16. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69: 89–95.

    Article  Google Scholar 

  17. Searfoss GH, Jordan WH, Calligaro DO, Galbreath EJ, Schirtzinger LM, Berridge BR, et al. Adipsin, a biomarker of gastrointestinal toxicity mediated by a functional gamma-secretase inhibitor. J Biol Chem 2003;278:46107–46116.

    Article  CAS  PubMed  Google Scholar 

  18. Milano J, McKay J, Dagenais C, Foster-Brown L, Pognan F, Gadient R, et al. Modulation of notch processing by gamma-secretase inhibitors causes intestinal goblet cell metaplasia and induction of genes known to specify gut secretory lineage differentiation. Toxicol Sci 2004;82:341–358.

    Article  CAS  PubMed  Google Scholar 

  19. Guerreiro N, Staedtler F, Grenet O, Kehren J, Chibout SD. Toxicogenomics in drug development. Toxicol Pathol 2003;31:471–479.

    CAS  PubMed  Google Scholar 

  20. Whitlock JP Jr. Induction of cytochrome P4501A1. Annu Rev Pharmacol Toxicol 1999;39:103–125.

    Article  CAS  PubMed  Google Scholar 

  21. Mimura J, Fujii-Kuriyama Y. Functional role of AhR in the expression of toxic effects by TCDD. Biochim Biophys Acta 2003;1619:263–268.

    CAS  PubMed  Google Scholar 

  22. Nebert DW, Dalton TP, Okey AB, Gonzalez FJ. Role of aryl hydrocarbon receptor-mediated induction of the CYP1 enzymes in environmental toxicity and cancer. J Biol Chem 2004;279:23847–23850.

    Article  CAS  PubMed  Google Scholar 

  23. Hu W, Sorrentino C, Denison MS, Kolaja K, Fielden MR. Induction of cyp1a1 is a nonspecific biomarker of aryl hydrocarbon receptor activation: results of large scale screening of pharmaceuticals and toxicants in vivo and in vitro. Mol Pharmacol 2007;71:1475–1486.

    Article  CAS  PubMed  Google Scholar 

  24. Ganter B, Tugendreich S, Pearson CI, Ayanoglu E, Baumhueter S, Bostian KA, et al. Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol 2005;119:219–244.

    Article  CAS  PubMed  Google Scholar 

  25. Kolaja K, Fielden M. The impact of toxicogenomics on preclinical development: from promises to realized value to regulatory implications. Preclinica 2004;2:122–129.

    Google Scholar 

  26. Fostel JM. Future of toxicogenomics and safety signatures: balancing public access to data with proprietary drug discovery. Pharmacogenomics 2007;8:425–430.

    Article  PubMed  Google Scholar 

  27. Fielden MR, Halbert DN. Iconix Biosciences, Inc. Pharmacogenomics 2007;8: 401–405.

    Article  PubMed  Google Scholar 

  28. Castle AL, Carver MP, Mendrick DL. Toxicogenomics: a new revolution in drug safety. Drug Discov Today 2002;7:728–736.

    Article  CAS  PubMed  Google Scholar 

  29. Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics 2006;7:1025–1044.

    Article  CAS  PubMed  Google Scholar 

  30. Schulte-Hermann R. Induction of liver growth by xenobiotic compounds and other stimuli. CRC Crit Rev Toxicol 1974;3:97–158.

    Article  CAS  PubMed  Google Scholar 

  31. Amacher DE, Schomaker SJ, Burkhardt JE. The relationship among microsomal enzyme induction, liver weight and histological change in rat toxicology studies. Food Chem Toxicol 1998;36:831–839.

    Article  CAS  PubMed  Google Scholar 

  32. Suter L, Babiss LE, Wheeldon EB. Toxicogenomics in predictive toxicology in drug development. Chem Biol 2004;11:161–171.

    CAS  PubMed  Google Scholar 

  33. Nikolsky Y, Ekins S, Nikolskaya T, Bugrim A. A novel method for generation of signature networks as biomarkers from complex high throughput data. Toxicol Lett 2005;158:20–29.

    Article  CAS  PubMed  Google Scholar 

  34. Natsoulis G, El Ghaoui L, Lanckriet GR, Tolley AM, Leroy F, Dunlea S, et al. Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. Genome Res 2005;15:724–736.

    Article  CAS  PubMed  Google Scholar 

  35. Maggioli J, Hoover A, Weng L. Toxicogenomic analysis methods for predictive toxicology. J Pharmacol Toxicol Methods 2006;53:31–37.

    Article  CAS  PubMed  Google Scholar 

  36. Bushel PR, Hamadeh HK, Bennett L, Green J, Ableson A, Misener S, et al. Computational selection of distinct class- and subclass-specific gene expression signatures. J Biomed Inform 2002;35:160–170.

    Article  PubMed  Google Scholar 

  37. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 2000;97:262–267.

    Article  CAS  PubMed  Google Scholar 

  38. Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon; 1995.

    Google Scholar 

  39. Thomas RS, Rank DR, Penn SG, Zastrow GM, Hayes KR, Pande K, et al. Identification of toxicologically predictive gene sets using cDNA microarrays. Mol Pharmacol 2001;60:1189–1194.

    CAS  PubMed  Google Scholar 

  40. Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge, UK: Cambridge University Press; 2000.

    Google Scholar 

  41. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000;16:906–914.

    Article  CAS  PubMed  Google Scholar 

  42. Martin R, Yu K. Assessing performance of prediction rules in machine learning. Pharmacogenomics 2006;7:543–550.

    Article  PubMed  Google Scholar 

  43. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, et al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 2000;32:56–67.

    Article  CAS  PubMed  Google Scholar 

  44. Waring JF, Ciurlionis R, Jolly RA, Heindel M, Ulrich RG. Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol Lett 2001;120:359–368.

    Article  CAS  PubMed  Google Scholar 

  45. Hamadeh HK, Bushel PR, Jayadev S, DiSorbo O, Bennett L, Li L, et al. Prediction of compound signature using high density gene expression profiling. Toxicol Sci 2002;67:232–240.

    Article  CAS  PubMed  Google Scholar 

  46. Sawada H, Takami K, Asahi S. A toxicogenomic approach to drug-induced phospholipidosis: analysis of its induction mechanism and establishment of a novel in vitro screening system. Toxicol Sci 2005;83:282–292.

    Article  CAS  PubMed  Google Scholar 

  47. Hong Y, Muller UR, Lai F. Discriminating two classes of toxicants through expression analysis of HepG2 cells with DNA arrays. Toxicol in vitro 2003;17: 85–92.

    Article  CAS  PubMed  Google Scholar 

  48. Martin R, Rose D, Yu K, Barros S. Toxicogenomics strategies for predicting drug toxicity. Pharmacogenomics 2006;7:1003–1016.

    Article  CAS  PubMed  Google Scholar 

  49. Burczynski ME, McMillian M, Ciervo J, Li L, Parker JB, Dunn RT, et al. Toxicogenomics-based discrimination of toxic mechanism in HepG2 human hepatoma cells. Toxicol Sci 2000;58:399–415.

    Article  CAS  PubMed  Google Scholar 

  50. Atienzar F, Gerets H, Dufrane S, Tilmant K, Cornet M, Dhalluin S, et al. Determination of phospholipidosis potential based on gene expression analysis in HepG2 cells. Toxicol Sci 2007;96:101–114.

    Article  CAS  PubMed  Google Scholar 

  51. Snyder RD, Green JW. A review of the genotoxicity of marketed pharmaceuticals. Mutat Res 2001;488:151–169.

    Article  CAS  PubMed  Google Scholar 

  52. Newton RK, Aardema M, Aubrecht J. The utility of DNA microarrays for characterizing genotoxicity. Environ Health Perspect 2004;112:420–422.

    Article  CAS  PubMed  Google Scholar 

  53. Aubrecht J, Caba E. Gene expression profile analysis: An emerging approach to investigate mechanisms of genotoxicity. Pharmacogenomics 2005;6:419–428.

    Article  CAS  PubMed  Google Scholar 

  54. Dickinson DA, Warnes GR, Quievryn G, Messer J, Zhitkovich A, Rubitski E, et al. Differentiation of DNA reactive and non-reactive genotoxic mechanisms using gene expression profile analysis. Mutat Res 2004;549:29–41.

    CAS  PubMed  Google Scholar 

  55. Sawada H, Taniguchi K, Takami K. Improved toxicogenomic screening for drug-induced phospholipidosis using a multiplexed quantitative gene expression ArrayPlate assay. Toxicol in vitro 2006;20:1506–1513.

    Article  CAS  PubMed  Google Scholar 

  56. Thukral SK, Nordone PJ, Hu R, Sullivan L, Galambos E, Fitzpatrick VD, et al. Prediction of nephrotoxicant action and identification of candidate toxicity-related biomarkers. Toxicol Pathol 2005;33:343–355.

    Article  CAS  PubMed  Google Scholar 

  57. Peters EH, Rojas-Caro S, Brigell MG, Zahorchak RJ, des Etages SA, Ruppel PL, et al. Quality-controlled measurement methods for quantification of variations in transcript abundance in whole blood samples from healthy volunteers. Clin Chem 2007;53:1030–1037.

    Article  CAS  PubMed  Google Scholar 

  58. Willey JC, Crawford EL, Knight CR, Warner KA, Motten CA, Herness EA, et al. Standardized RT-PCR and the standardized expression measurement center. Methods Mol Biol 2004;258:13–41.

    CAS  PubMed  Google Scholar 

  59. Willey JC, Knight CR, Crawford EL, Olson DE, Hammersly J, Yoon Y, et al. Use of standardized reverse transcription-polymerase chain reaction and the standardized expression measurement center in multi-institutional trials to develop meaningful lung cancer classification based on molecular genetic criteria. Chest 2004;125:155S–156S.

    Article  PubMed  Google Scholar 

  60. Rockett JC, Burczynski ME, Fornace AJ, Herrmann PC, Krawetz SA, Dix DJ. Surrogate tissue analysis: monitoring toxicant exposure and health status of inaccessible tissues through the analysis of accessible tissues and cells. Toxicol Appl Pharmacol 2004;194:189–199.

    Article  CAS  PubMed  Google Scholar 

  61. Burczynski ME, Dorner AJ. Transcriptional profiling of peripheral blood cells in clinical pharmacogenomic studies. Pharmacogenomics 2006;7:187–202.

    Article  CAS  PubMed  Google Scholar 

  62. Burczynski ME, Peterson RL, Twine NC, Zuberek KA, Brodeur BJ, Casciotti L, et al. Molecular classification of Crohn’s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J Mol Diagn 2006;8:51–61.

    Article  CAS  PubMed  Google Scholar 

  63. Rockett JC, Kavlock RJ, Lambright CR, Parks LG, Schmid JE, Wilson VS, et al. DNA arrays to monitor gene expression in rat blood and uterus following 17beta-estradiol exposure: biomonitoring environmental effects using surrogate tissues. Toxicol Sci 2002;69:49–59.

    Article  CAS  PubMed  Google Scholar 

  64. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003;100:2610–2615.

    Article  CAS  PubMed  Google Scholar 

  65. Aune TM, Maas K, Moore JH, Olsen NJ. Gene expression profiles in human autoimmune disease. Curr Pharm Des 2003;9:1905–1917.

    Article  CAS  PubMed  Google Scholar 

  66. Olsen NJ, Moore JH, Aune TM. Gene expression signatures for autoimmune disease in peripheral blood mononuclear cells. Arthritis Res Ther 2004;6:120–128.

    Article  CAS  PubMed  Google Scholar 

  67. Tang Y, Gilbert DL, Glauser TA, Hershey AD, Sharp FR. Blood gene expression profiling of neurologic diseases: a pilot microarray study. Arch Neurol 2005;62:210–215.

    Article  PubMed  Google Scholar 

  68. Scherzer CR, Eklund AC, Morse LJ, Liao Z, Locascio JJ, Fefer D, et al. Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc Natl Acad Sci U S A 2007;104:955–960.

    Article  CAS  PubMed  Google Scholar 

  69. Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, et al. Genome-wide expression profiling of human blood reveals biomarkers for Huntington’s disease. Proc Natl Acad Sci U S A 2005;102:11023–11028.

    Article  CAS  PubMed  Google Scholar 

  70. Dressman HK, Muramoto GG, Chao NJ, Meadows S, Marshall D, Ginsburg GS, et al. Gene expression signatures that predict radiation exposure in mice and humans. PLoS Med 2007;4:e106.

    Article  PubMed  Google Scholar 

  71. Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al. Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U S A 2003;100:1896–1901.

    Article  CAS  PubMed  Google Scholar 

  72. Kim SJ, Dix DJ, Thompson KE, Murrell RN, Schmid JE, Gallagher JE, et al. Effects of storage, RNA extraction, genechip type, and donor sex on gene expression profiling of human whole blood. Clin Chem 2007;53:1038–1045.

    Article  CAS  PubMed  Google Scholar 

  73. Steppan S, Kupfer K, Mayer A, Evans M, Yamasaki G, Greve JM, et al. Genome wide expression profiling of human peripheral blood mononuclear cells stimulated with BAY 50–4798, a novel T cell selective interleukin-2 analog. J Immunother 2007;30:150–168.

    Article  CAS  PubMed  Google Scholar 

  74. van Leeuwen DM, Gottschalk RW, van Herwijnen MH, Moonen EJ, Kleinjans JC, van Delft JH. Differential gene expression in human peripheral blood mononuclear cells induced by cigarette smoke and its constituents. Toxicol Sci 2005;86:200–210.

    Article  PubMed  Google Scholar 

  75. Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006;24:1151–1161.

    Article  CAS  PubMed  Google Scholar 

  76. Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005;5:845–856.

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Global Pharmaceutical Research and Development, Department of Cellular, Molecular and Exploratory Toxicology, Abbott Laboratories, Abbott Park, Illinois

    Eric A.G. Blomme DVM, Phd

  2. Department of Molecular Sciences, Amgen Inc., Thousand Oaks, Illinois

    Scott E. Warder Phd

Authors
  1. Eric A.G. Blomme DVM, Phd
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  2. Scott E. Warder Phd
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Editors and Affiliations

  1. Procter & Gamble Pharmaceutical Inc, Mason, OH, USA, 8700 Mason-Montgomery Road

    Feng Wang PhD

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© 2008 Humana Press, a part of Springer Science+Business Media, LLC

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Blomme, E.A., Warder, S.E. (2008). Gene Expression-Based Biomarkers of Drug Safety. In: Wang, F. (eds) Biomarker Methods in Drug Discovery and Development. Methods in Pharmacology and Toxicology™. Humana Press. https://doi.org/10.1007/978-1-59745-463-6_2

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  • DOI: https://doi.org/10.1007/978-1-59745-463-6_2

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-934115-23-7

  • Online ISBN: 978-1-59745-463-6

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