The AAPS Journal

, Volume 15, Issue 2, pp 427–437 | Cite as

Strategic Applications of Gene Expression: From Drug Discovery/Development to Bedside

  • Jane P. F. Bai
  • Alexander V. Alekseyenko
  • Alexander Statnikov
  • I-Ming Wang
  • Peggy H. Wong
Review Article

ABSTRACT

Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.

KEY WORDS

clinical molecular signatures molecular signatures of disease signature genes target engagement toxicological pathways 

Notes

Acknowledgment

Alexander Statnikov was supported in part by NIH/NLM grant 1 R01 LM011179-01.

REFERENCES

  1. 1.
    Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35. doi:10.126/science.1132939.PubMedCrossRefGoogle Scholar
  2. 2.
    Mahadevan B, Snyder RD, Waters MD, Benz RD, Kemper RA, Tice RR, et al. Genetic toxicology in the 21st century: reflections and future directions. Environ Mol Mutagen. 2011;52(5):339–54. doi:10.1002/em.20653.PubMedCrossRefGoogle Scholar
  3. 3.
    Liu H, D’Andrade P, Fulmer-Smentek S, Lorenzi P, Kohn KW, Weinstein JN, et al. mRNA and microRNA expression profiles of the NCI-60 integrated with drug activities. Mol Cancer Ther. 2010;9(5):1080–91. doi:10.1158/535-7163.MCT-09-0965.PubMedCrossRefGoogle Scholar
  4. 4.
    Wagner BK, Kitami T, Gilbert TJ, Peck D, Ramanathan A, Schreiber SL, et al. Large-scale chemical dissection of mitochondrial function. Nat Biotechnol. 2008;26(3):343–51. doi:10.038/nbt.PubMedCrossRefGoogle Scholar
  5. 5.
    Connectivity Map. http://wwwbroadinstituteorg/cmap/. Accessed July 2012.
  6. 6.
    Developing Orphan Products: FDA and Rare Disease Day. http://wwwfdagov/ForIndustry/DevelopingProductsforRareDiseasesConditions/ucm239698htm. Accessed July 2012.
  7. 7.
    Kao KJ, Chang KM, Hsu HC, Huang AT. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization. BMC Cancer. 2011;11:143. doi:10.1186/471-2407-11-143.PubMedCrossRefGoogle Scholar
  8. 8.
    Mehta R, Jain RK, Badve S. Personalized medicine: the road ahead. Clin Breast Cancer. 2011;11(1):20–6. doi:10.3816/CBC.2011.n.004. Review.PubMedCrossRefGoogle Scholar
  9. 9.
    Mizuarai S, Yamanaka K, Itadani H, Arai T, Nishibata T, Hirai H, et al. Discovery of gene expression-based pharmacodynamic biomarker for a p53 context-specific anti-tumor drug Wee1 inhibitor. Mol Cancer. 2009;8:34. doi:10.1186/476-4598-8-34.PubMedCrossRefGoogle Scholar
  10. 10.
    Liebler DC, Guengerich FP. Elucidating mechanisms of drug-induced toxicity. Nat Rev Drug Discov. 2005;4(5):410–20. doi:10.1038/nrd720.PubMedCrossRefGoogle Scholar
  11. 11.
    D’Haeseleer P. How does gene expression clustering work? Nat Biotechnol. 2005;23(12):1499–501. doi:10.038/nbt205.PubMedCrossRefGoogle Scholar
  12. 12.
    Yang X, Regan K, Huang Y, Zhang Q, Li J, Seiwert TY, et al. Single sample expression-anchored mechanisms predict survival in head and neck cancer. PLoS Comput Biol. 2012;8(1):e1002350. doi:10.1371/journal.pcbi.PubMedCrossRefGoogle Scholar
  13. 13.
    KEGG (Kyoto Encyclopedia of Genes and Genomes). http://wwwgenomejp/kegg. Accessed July 2011.
  14. 14.
    Reactome. http://wwwreactomeorg/ReactomeGWT/entrypointhtml. Accessed January 2012.
  15. 15.
    Kirouac DC, Saez-Rodriguez J, Swantek J, Burke JM, Lauffenburger DA, Sorger PK. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Syst Biol. 2012;6(1):29. doi:10.1186/752-0509-6-29.PubMedCrossRefGoogle Scholar
  16. 16.
    Alexopoulos LG, Saez-Rodriguez J, Cosgrove BD, Lauffenburger DA, Sorger PK. Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. Mol Cell Proteomics. 2010;9(9):1849–65. doi:10.074/mcp.M110.000406.PubMedCrossRefGoogle Scholar
  17. 17.
    Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Med. 2012;4(5):41. doi:10.1186/gm340.PubMedCrossRefGoogle Scholar
  18. 18.
    Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med. 2011;3(96):96ra77. doi:10.1126/scitranslmed.3001318.PubMedCrossRefGoogle Scholar
  19. 19.
    Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med. 2011;3(96):96ra76. doi:10.1126/scitranslmed.3002648.PubMedCrossRefGoogle Scholar
  20. 20.
    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–7. doi:10.1038/nature11003.PubMedCrossRefGoogle Scholar
  21. 21.
    Novershtern N, Subramanian A, Lawton LN, Mak RH, Haining WN, McConkey ME, et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell. 2011;144(2):296–309. doi:10.1016/j.cell.2011.01.004.PubMedCrossRefGoogle Scholar
  22. 22.
    Campbell JD, Spira A, Lenburg ME. Applying gene expression microarrays to pulmonary disease. Respirology. 2011;16(3):407–18. doi:10.1111/j.440-843.2011.01942.x. Review.PubMedCrossRefGoogle Scholar
  23. 23.
    Zeskind JE, Lenburg ME, Spira A. Translating the COPD transcriptome: insights into pathogenesis and tools for clinical management. Proc Am Thorac Soc. 2008;5(8):834–41. doi:10.1513/pats.200807-074TH.PubMedCrossRefGoogle Scholar
  24. 24.
    Jenner RG, Young RA. Insights into host responses against pathogens from transcriptional profiling. Nat Rev Microbiol. 2005;3(4):281–94. doi:10.1038/nrmicro126.PubMedCrossRefGoogle Scholar
  25. 25.
    van’t Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature. 2008;452(7187):564–70. doi:10.1038/nature06915.PubMedCrossRefGoogle Scholar
  26. 26.
    Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity. 2008;29(1):150–64. doi:10.1016/j.immuni.2008.05.012.PubMedCrossRefGoogle Scholar
  27. 27.
    Banchereau R, Jordan-Villegas A, Ardura M, Mejias A, Baldwin N, Xu H, et al. Host immune transcriptional profiles reflect the variability in clinical disease manifestations in patients with Staphylococcus aureus infections. PLoS One. 2012;7(4):e34390. doi:10.1371/journal.pone.0034390.PubMedCrossRefGoogle Scholar
  28. 28.
    Puig O, Wang IM, Cheng P, Zhou P, Roy S, Cully D, et al. Transcriptome profiling and network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension. Physiol Genomics. 2010;42A(1):24–32. doi:10.1152/physiolgenomics.00010.2010.PubMedCrossRefGoogle Scholar
  29. 29.
    Yang Y, Adelstein SJ, Kassis AI. Integrated bioinformatics analysis for cancer target identification. Methods Mol Biol. 2011;719:527–45. doi:10.1007/978-1-61779-027-0_25.PubMedCrossRefGoogle Scholar
  30. 30.
    Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, et al. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008;452(7186):429–35. doi:10.1038/nature06757.PubMedCrossRefGoogle Scholar
  31. 31.
    Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, et al. Genetics of gene expression and its effect on disease. Nature. 2008;452(7186):423–8. doi:10.1038/nature06758.PubMedCrossRefGoogle Scholar
  32. 32.
    Wang IM, Zhang B, Yang X, Zhu J, Stepaniants S, Zhang C, et al. Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers. Mol Syst Biol. 2012;8:594.PubMedCrossRefGoogle Scholar
  33. 33.
    Yanagisawa S, Sugiura H, Yokoyama T, Yamagata T, Ichikawa T, Akamatsu K, et al. The possible role of hematopoietic cell kinase in the pathophysiology of COPD. Chest. 2009;135(1):94–101. doi:10.1378/chest.07-3020.PubMedCrossRefGoogle Scholar
  34. 34.
    Lee F, Fandi A, Voi M. Overcoming kinase resistance in chronic myeloid leukemia. Int J Biochem Cell Biol. 2008;40(3):334–43.PubMedCrossRefGoogle Scholar
  35. 35.
    Paloneva J, Kestila M, Wu J, Salminen A, Bohling T, Ruotsalainen V, et al. Loss-of-function mutations in TYROBP (DAP12) result in a presenile dementia with bone cysts. Nat Genet. 2000;25(3):357–61. doi:10.1038/77153.PubMedCrossRefGoogle Scholar
  36. 36.
    Thrash JC, Torbett BE, Carson MJ. Developmental regulation of TREM2 and DAP12 expression in the murine CNS: implications for Nasu–Hakola disease. Neurochem Res. 2009;34(1):38–45. doi:10.1007/s11064-008-9657-1.PubMedCrossRefGoogle Scholar
  37. 37.
    O’Neill LA. Targeting signal transduction as a strategy to treat inflammatory diseases. Nat Rev Drug Discov. 2006;5(7):549–63. doi:10.1038/nrd2070.PubMedCrossRefGoogle Scholar
  38. 38.
    Lum PY, He YD, Slatter JG, Waring JF, Zelinsky N, Cavet G, et al. Gene expression profiling of rat liver reveals a mechanistic basis for ritonavir-induced hyperlipidemia. Genomics. 2007;90(4):464–73. doi:10.1016/j.ygeno.2007.06.004.PubMedCrossRefGoogle Scholar
  39. 39.
    Bhat KP, Greer SF. Proteolytic and non-proteolytic roles of ubiquitin and the ubiquitin proteasome system in transcriptional regulation. Biochim Biophys Acta. 2011;1809(2):150–5. doi:10.1016/j.bbagrm.2010.11.006.PubMedCrossRefGoogle Scholar
  40. 40.
    Waring JF, Ciurlionis R, Marsh K, Klein LL, Degoey DA, Randolph JT, et al. Identification of proteasome gene regulation in a rat model for HIV protease inhibitor-induced hyperlipidemia. Arch Toxicol. 2010;84(4):263–70. doi:10.1007/s00204-010-0527-7.PubMedCrossRefGoogle Scholar
  41. 41.
    Kuperman DA, Huang X, Koth LL, Chang GH, Dolganov GM, Zhu Z, et al. Direct effects of interleukin-13 on epithelial cells cause airway hyperreactivity and mucus overproduction in asthma. Nat Med. 2002;8(8):885–9. doi:10.1038/nm734.PubMedGoogle Scholar
  42. 42.
    Hershey GK. IL-13 receptors and signaling pathways: an evolving web. J Allergy Clin Immunol. 2003;111(4):677–90. Quiz 91.PubMedCrossRefGoogle Scholar
  43. 43.
    Kole R, Krainer AR, Altman S. RNA therapeutics: beyond RNA interference and antisense oligonucleotides. Nat Rev Drug Discov. 2012;11(2):125–40. doi:10.1038/nrd3625. Review.PubMedGoogle Scholar
  44. 44.
    Fedorov Y, Anderson EM, Birmingham A, Reynolds A, Karpilow J, Robinson K, et al. Off-target effects by siRNA can induce toxic phenotype. RNA. 2006;12(7):1188–96. doi:10.261/rna.28106.PubMedCrossRefGoogle Scholar
  45. 45.
    Fucini RV, Haringsma HJ, Deng P, Flanagan WM, Willingham AT. Adenosine modification may be preferred for reducing siRNA immune stimulation. Nucleic Acid Ther. 2012;22(3):205–10. doi:10.1089/nat.2011.0334.PubMedGoogle Scholar
  46. 46.
    Yuan Z, Wu X, Liu C, Xu G, Wu Z. Asymmetric siRNA: new strategy to improve specificity and reduce off-target gene expression. Hum Gene Ther. 2012;23(5):521–32. doi:10.1089/hum.2011.145.PubMedCrossRefGoogle Scholar
  47. 47.
    Jackson AL, Burchard J, Leake D, Reynolds A, Schelter J, Guo J, et al. Position-specific chemical modification of siRNAs reduces “off-target” transcript silencing. RNA. 2006;12(7):1197–205. doi:10.261/rna.30706.PubMedCrossRefGoogle Scholar
  48. 48.
    Jackson AL, Bartz SR, Schelter J, Kobayashi SV, Burchard J, Mao M, et al. Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol. 2003;21(6):635–7. doi:10.1038/nbt831.PubMedCrossRefGoogle Scholar
  49. 49.
    Anderson E, Boese Q, Khvorova A, Karpilow J. Identifying siRNA-induced off-targets by microarray analysis. Methods Mol Biol. 2008;442:45–63. doi:10.1007/978-1-59745-191-8_4.PubMedCrossRefGoogle Scholar
  50. 50.
    Sommerer C, Hartschuh W, Enk A, Meuer S, Zeier M, Giese T. Pharmacodynamic immune monitoring of NFAT-regulated genes predicts skin cancer in elderly long-term renal transplant recipients. Clin Transplant. 2008;22(5):549–54. doi:10.1111/j.399-0012.2008.00819.x.PubMedCrossRefGoogle Scholar
  51. 51.
    Billing H, Breil T, Schmidt J, Tonshoff B, Schmitt C, Giese T, et al. Pharmacodynamic monitoring by residual NFAT-regulated gene expression in stable pediatric liver transplant recipients. Pediatr Transplant. 2012;16(2):187–94. doi:10.1111/j.399-3046.2012.01660.x.PubMedCrossRefGoogle Scholar
  52. 52.
    Locatelli G, Bosotti R, Ciomei M, Brasca MG, Calogero R, Mercurio C, et al. Transcriptional analysis of an E2F gene signature as a biomarker of activity of the cyclin-dependent kinase inhibitor PHA-793887 in tumor and skin biopsies from a phase I clinical study. Mol Cancer Ther. 2010;9(5):1265–73. doi:10.58/535-7163.MCT-09-1163.PubMedCrossRefGoogle Scholar
  53. 53.
    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(2):189–99.PubMedCrossRefGoogle Scholar
  54. 54.
    Berkofsky-Fessler W, Nguyen TQ, Delmar P, Molnos J, Kanwal C, DePinto W, et al. Preclinical biomarkers for a cyclin-dependent kinase inhibitor translate to candidate pharmacodynamic biomarkers in phase I patients. Mol Cancer Ther. 2009;8(9):2517–25. doi:10.1158/535-7163.MCT-09-0083.PubMedCrossRefGoogle Scholar
  55. 55.
    Boni JP, Leister C, Bender G, Fitzpatrick V, Twine N, Stover J, et al. Population pharmacokinetics of CCI-779: correlations to safety and pharmacogenomic responses in patients with advanced renal cancer. Clin Pharmacol Ther. 2005;77(1):76–89. doi:10.1016/j.clpt.2004.08.025.PubMedCrossRefGoogle Scholar
  56. 56.
    Baselga J, Semiglazov V, van Dam P, Manikhas A, Bellet M, Mayordomo J, et al. Phase II randomized study of neoadjuvant everolimus plus letrozole compared with placebo plus letrozole in patients with estrogen receptor-positive breast cancer. J Clin Oncol. 2009;27(16):2630–7. doi:10.1200/JCO.2008.18.8391.PubMedCrossRefGoogle Scholar
  57. 57.
    deGraffenried LA, Friedrichs WE, Russell DH, Donzis EJ, Middleton AK, Silva JM, et al. Inhibition of mTOR activity restores tamoxifen response in breast cancer cells with aberrant Akt activity. Clin Cancer Res. 2004;10(23):8059–67. doi:10.1158/078-0432.CCR-04-035.PubMedCrossRefGoogle Scholar
  58. 58.
    Beeram M, Tan QT, Tekmal RR, Russell D, Middleton A, DeGraffenried LA. Akt-induced endocrine therapy resistance is reversed by inhibition of mTOR signaling. Ann Oncol. 2007;18(8):1323–8.PubMedCrossRefGoogle Scholar
  59. 59.
    Zheng M, Lv LL, Cao YH, Liu H, Ni J, Dai HY, et al. A pilot trial assessing urinary gene expression profiling with an mRNA array for diabetic nephropathy. PLoS One. 2012;7(5):e34824. doi:10.1371/journal.pone.0034824.PubMedCrossRefGoogle Scholar
  60. 60.
    Affo S, Dominguez M, Lozano JJ, Sancho-Bru P, Rodrigo-Torres D, Morales-Ibanez O, et al. Transcriptome analysis identifies TNF superfamily receptors as potential therapeutic targets in alcoholic hepatitis. Gut. 2012. doi:10.1136/gutjnl-2011-301146.
  61. 61.
    Bijlsma JW, Berenbaum F, Lafeber FP. Osteoarthritis: an update with relevance for clinical practice. Lancet. 2011;377(9783):2115–26. doi:10.1016/S0140-6736(11)60243-2. Review.PubMedCrossRefGoogle Scholar
  62. 62.
    Kraus VB. Osteoarthritis year 2010 in review: biochemical markers. Osteoarthr Cartil. 2011;19(4):346–53. doi:10.1016/j.joca.2011.02.002.PubMedCrossRefGoogle Scholar
  63. 63.
    Bobinac D, Spanjol J, Zoricic S, Maric I. Changes in articular cartilage and subchondral bone histomorphometry in osteoarthritic knee joints in humans. Bone. 2003;32(3):284–90.PubMedCrossRefGoogle Scholar
  64. 64.
    Watters JW, Cheng C, Pickarski M, Wesolowski GA, Zhuo Y, Hayami T, et al. Inverse relationship between matrix remodeling and lipid metabolism during osteoarthritis progression in the STR/Ort mouse. Arthritis Rheum. 2007;56(9):2999–3009. doi:10.1002/art.22836.PubMedCrossRefGoogle Scholar
  65. 65.
    Kizawa H, Kou I, Iida A, Sudo A, Miyamoto Y, Fukuda A, et al. An aspartic acid repeat polymorphism in asporin inhibits chondrogenesis and increases susceptibility to osteoarthritis. Nat Genet. 2005;37(2):138–44. doi:10.1038/ng496.PubMedCrossRefGoogle Scholar
  66. 66.
    Gazzerro E, Pereira RC, Jorgetti V, Olson S, Economides AN, Canalis E. Skeletal overexpression of gremlin impairs bone formation and causes osteopenia. Endocrinology. 2005;146(2):655–65. doi:10.1210/en.2004-0766.PubMedCrossRefGoogle Scholar
  67. 67.
    Pullig O, Tagariello A, Schweizer A, Swoboda B, Schaller P, Winterpacht A. MATN3 (matrilin-3) sequence variation (pT303M) is a risk factor for osteoarthritis of the CMC1 joint of the hand, but not for knee osteoarthritis. Ann Rheum Dis. 2007;66(2):279–80. doi:10.1136/ard.2006.058263.PubMedCrossRefGoogle Scholar
  68. 68.
    van der Weyden L, Wei L, Luo J, Yang X, Birk DE, Adams DJ, et al. Functional knockout of the matrilin-3 gene causes premature chondrocyte maturation to hypertrophy and increases bone mineral density and osteoarthritis. Am J Pathol. 2006;169(2):515–27. doi:10.2353/ajpath.006.050981.PubMedCrossRefGoogle Scholar
  69. 69.
    Broyl A, Corthals SL, Jongen JL, van der Holt B, Kuiper R, de Knegt Y, et al. Mechanisms of peripheral neuropathy associated with bortezomib and vincristine in patients with newly diagnosed multiple myeloma: a prospective analysis of data from the HOVON-65/GMMG-HD4 trial. Lancet Oncol. 2010;11(11):1057–65. doi:10.1016/S1470-2045(10)-0.PubMedCrossRefGoogle Scholar
  70. 70.
    Arastu-Kapur S, Anderl JL, Kraus M, Parlati F, Shenk KD, Lee SJ, et al. Nonproteasomal targets of the proteasome inhibitors bortezomib and carfilzomib: a link to clinical adverse events. Clin Cancer Res. 2011;17(9):2734–43. doi:10.1158/078-0432.CCR-10-1950.PubMedCrossRefGoogle Scholar
  71. 71.
    Vande Walle L, Lamkanfi M, Vandenabeele P. The mitochondrial serine protease HtrA2/Omi: an overview. Cell Death Differ. 2008;15(3):453–60. doi:10.1038/sj.cdd.4402291.PubMedCrossRefGoogle Scholar
  72. 72.
    Momose H, Mizukami T, Ochiai M, Hamaguchi I, Yamaguchi K. A new method for the evaluation of vaccine safety based on comprehensive gene expression analysis. J Biomed Biotechnol. 2010;2010:361841. doi:10.1155/2010/.PubMedCrossRefGoogle Scholar
  73. 73.
    Hamaguchi I, Imai J, Momose H, Kawamura M, Mizukami T, Naito S, et al. Application of quantitative gene expression analysis for pertussis vaccine safety control. Vaccine. 2008;26(36):4686–96. doi:10.1016/j.vaccine.2008.06.086.PubMedCrossRefGoogle Scholar
  74. 74.
    Mizukami T, Imai J, Hamaguchi I, Kawamura M, Momose H, Naito S, et al. Application of DNA microarray technology to influenza A/Vietnam/1194/2004 (H5N1) vaccine safety evaluation. Vaccine. 2008;26(18):2270–83. doi:10.1016/j.vaccine.2008.02.031.PubMedCrossRefGoogle Scholar
  75. 75.
    Gaucher D, Therrien R, Kettaf N, Angermann BR, Boucher G, Filali-Mouhim A, et al. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J Exp Med. 2008;205(13):3119–31. doi:10.1084/jem.20082292.PubMedCrossRefGoogle Scholar
  76. 76.
    Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10(1):116–25. doi:10.1038/ni.688.PubMedCrossRefGoogle Scholar
  77. 77.
    Palermo RE, Patterson LJ, Aicher LD, Korth MJ, Robert-Guroff M, Katze MG. Genomic analysis reveals pre- and postchallenge differences in a rhesus macaque AIDS vaccine trial: insights into mechanisms of vaccine efficacy. J Virol. 2011;85(2):1099–116. doi:10.128/JVI.01522-10.PubMedCrossRefGoogle Scholar
  78. 78.
    Balas C, Kennel A, Deauvieau F, Sodoyer R, Arnaud-Barbe N, Lang J, et al. Different innate signatures induced in human monocyte-derived dendritic cells by wild-type dengue 3 virus, attenuated but reactogenic dengue 3 vaccine virus, or attenuated nonreactogenic dengue 1–4 vaccine virus strains. J Infect Dis. 2011;203(1):103–8. doi:10.1093/infdis/jiq022.PubMedCrossRefGoogle Scholar
  79. 79.
    Huang E, Ishida S, Pittman J, Dressman H, Bild A, Kloos M, et al. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet. 2003;34(2):226–30. doi:10.1038/ng167.PubMedCrossRefGoogle Scholar
  80. 80.
    Ganter B, Giroux CN. Emerging applications of network and pathway analysis in drug discovery and development. Curr Opin Drug Discov Devel. 2008;11(1):86–94.PubMedGoogle Scholar
  81. 81.
    Reif DM, McKinney BA, Motsinger AA, Chanock SJ, Edwards KM, Rock MT, et al. Genetic basis for adverse events after smallpox vaccination. J Infect Dis. 2008;198(1):16–22. doi:10.1086/588670.PubMedCrossRefGoogle Scholar
  82. 82.
    Wei G, Margolin AA, Haery L, Brown E, Cucolo L, Julian B, et al. Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency. Cancer Cell. 2012;21(4):547–62. doi:10.1016/j.ccr.2012.02.028.PubMedCrossRefGoogle Scholar
  83. 83.
    Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Miriami E, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–307. doi:10.016/j.cell.2012.02.009.PubMedCrossRefGoogle Scholar
  84. 84.
    Field LA, Love B, Deyarmin B, Hooke JA, Shriver CD, Ellsworth RE. Identification of differentially expressed genes in breast tumors from African American compared with Caucasian women. Cancer. 2012;118(5):1334–44. doi:10.002/cncr.26405.PubMedCrossRefGoogle Scholar
  85. 85.
    Becker H, Marcucci G, Maharry K, Radmacher MD, Mrozek K, Margeson D, et al. Favorable prognostic impact of NPM1 mutations in older patients with cytogenetically normal de novo acute myeloid leukemia and associated gene- and microRNA-expression signatures: a Cancer and Leukemia Group B study. J Clin Oncol. 2010;28(4):596–604. doi:10.200/JCO.2009.25.1496.PubMedCrossRefGoogle Scholar
  86. 86.
    Iwamoto T, Bianchini G, Booser D, Qi Y, Coutant C, Shiang CY, et al. Gene pathways associated with prognosis and chemotherapy sensitivity in molecular subtypes of breast cancer. J Natl Cancer Inst. 2011;103(3):264–72. doi:10.1093/jnci/djq524.PubMedCrossRefGoogle Scholar
  87. 87.
    Gatza ML, Lucas JE, Barry WT, Kim JW, Wang Q, Crawford MD, et al. A pathway-based classification of human breast cancer. Proc Natl Acad Sci U S A. 2010;107(15):6994–9. doi:10.1073/pnas.PubMedCrossRefGoogle Scholar
  88. 88.
    Jonsson G, Staaf J, Vallon-Christersson J, Ringner M, Holm K, Hegardt C, et al. Genomic subtypes of breast cancer identified by array-comparative genomic hybridization display distinct molecular and clinical characteristics. Breast Cancer Res. 2010;12(3):R42. doi:10.1186/bcr2596.PubMedCrossRefGoogle Scholar
  89. 89.
    Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A. 2005;102(38):13550–5. doi:10.1073/pnas.PubMedCrossRefGoogle Scholar
  90. 90.
    Bai JP, Bell R, Buckman S, Burckart GJ, Eichler HG, Fang KC, et al. Translational biomarkers: from preclinical to clinical a report of 2009 AAPS/ACCP Biomarker Workshop. AAPS J. 2011;13(2):274–83. doi:10.1208/s12248-011-9265-x.PubMedCrossRefGoogle Scholar
  91. 91.
    Wagner JA, Williams SA, Webster CJ. Biomarkers and surrogate end points for fit-for-purpose development and regulatory evaluation of new drugs. Clin Pharmacol Ther. 2007;81(1):104–7. doi:10.1038/sj.clpt.6100017.PubMedCrossRefGoogle Scholar
  92. 92.
    Zhou HH, Chin CN, Wu M, Ni W, Quan S, Liu F, et al. Suppression of PC-1/ENPP-1 expression improves insulin sensitivity in vitro and in vivo. Eur J Pharmacol. 2009;616(1–3):346–52. doi:10.1016/j.ejphar.2009.06.057.PubMedCrossRefGoogle Scholar
  93. 93.
    Uehara T, Ono A, Maruyama T, Kato I, Yamada H, Ohno Y, et al. The Japanese toxicogenomics project: application of toxicogenomics. Mol Nutr Food Res. 2010;54(2):218–27. doi:10.1002/mnfr.200900169.PubMedCrossRefGoogle Scholar
  94. 94.
    PharmGKB. The Pharmacogenomics Knowledgebase. http://www.pharmgkb.org. Accessed July 2011.
  95. 95.
    Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008;321(5886):263–6. doi:10.1126/science.1158140.PubMedCrossRefGoogle Scholar
  96. 96.
    Toyoshiba H, Sawada H, Naeshiro I, Horinouchi A. Similar compounds searching system by using the gene expression microarray database. Toxicol Lett. 2009;186(1):52–7. doi:10.1016/j.toxlet.2008.08.009.PubMedCrossRefGoogle Scholar
  97. 97.
    Lytkin NI, McVoy L, Weitkamp JH, Aliferis CF, Statnikov A. Expanding the understanding of biases in development of clinical-grade molecular signatures: a case study in acute respiratory viral infections. PLoS One. 2011;6(6):e20662. doi:10.1371/journal.pone.0020662.PubMedCrossRefGoogle Scholar
  98. 98.
    Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003;95(1):14–8.PubMedCrossRefGoogle Scholar
  99. 99.
    Statnikov A, Aliferis CF. Analysis and computational dissection of molecular signature multiplicity. PLoS Comput Biol. 2010;6(5):e1000790. doi:10.1371/journal.pcbi.PubMedCrossRefGoogle Scholar
  100. 100.
    Qiu X, Brooks AI, Klebanov L, Yakovlev N. The effects of normalization on the correlation structure of microarray data. BMC Bioinforma. 2005;6:120. doi:10.1186/471-2105-6-120.CrossRefGoogle Scholar
  101. 101.
    Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99(2):147–57. doi:10.1093/jnci/djk018.PubMedCrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2013

Authors and Affiliations

  • Jane P. F. Bai
    • 1
  • Alexander V. Alekseyenko
    • 2
  • Alexander Statnikov
    • 2
  • I-Ming Wang
    • 3
  • Peggy H. Wong
    • 4
  1. 1.Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringUSA
  2. 2.Center for Health Informatics and Bioinformatics, Division of Translational Medicine, Department of MedicineNew York University Langone Medical CenterNew YorkUSA
  3. 3.Informatics and Analysis DepartmentMerck Research LaboratoryWest PointUSA
  4. 4.Merck Research LaboratoriesRahwayUSA

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