1932

Abstract

The response to drug treatment in asthma is a complex trait and is markedly variable even in patients with apparently similar clinical features. Pharmaco-genomics, which is the study of variations of human genome characteristics as related to drug response, can play a role in asthma therapy. Both a traditional candidate-gene approach to conducting genetic association studies and genome-wide association studies have provided an increasing list of genes and variants associated with the three major classes of asthma medications: β-agonists, inhaled corticosteroids, and leukotriene modifiers. Moreover, a recent integrative, systems-level approach has offered a promising opportunity to identify important pharmacogenomics loci in asthma treatment. However, we are still a long way away from making this discipline directly relevant to patients. The combination of network modeling, functional validation, and integrative omics technologies will likely be needed to move asthma pharmacogenomics closer to clinical relevance.

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2015-01-06
2024-05-07
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Literature Cited

  1. Djukanović R, Roche WR, Wilson JW, Beasley CR, Twentyman OP. 1.  et al. 1990. Mucosal inflammation in asthma. Am. Rev. Respir. Dis. 142:434–57 [Google Scholar]
  2. Solway J, Fredberg JJ. 2.  1997. Perhaps airway smooth muscle dysfunction contributes to asthmatic bronchial hyperresponsiveness after all. Am. J. Respir. Cell Mol. Biol. 17:144–46 [Google Scholar]
  3. Masoli M, Fabian D, Holt S, Beasley R. 3.  2004. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy 59:469–78 [Google Scholar]
  4. Bousquet J, Bousquet PJ, Godard P, Daures JP. 4.  2005. The public health implications of asthma. Bull. World Health Organ. 83:548–54 [Google Scholar]
  5. 5. World Health Organ. (WHO) Bronchial asthma WHO Fact Sheet 206, World Health Organ., Geneva. http://www.who.int/mediacentre/factsheets/fs206/en
  6. Barnes PJ. 6.  2011. Biochemical basis of asthma therapy. J. Biol. Chem. 286:32899–905 [Google Scholar]
  7. Kerstjens HA, Engel M, Dahl R, Paggiaro P, Beck E. 7.  et al. 2012. Tiotropium in asthma poorly controlled with standard combination therapy. N. Engl. J. Med. 367:1198–207 [Google Scholar]
  8. Ogawa Y, Calhoun WJ. 8.  2006. The role of leukotrienes in airway inflammation. J. Allergy Clin. Immunol. 118:789–98 [Google Scholar]
  9. Szefler SJ, Martin RJ, King TS, Boushey HA, Cherniack RM. 9.  et al. 2002. Significant variability in response to inhaled corticosteroids for persistent asthma. J. Allergy Clin. Immunol. 109:410–18 [Google Scholar]
  10. Malmstrom K, Rodriguez-Gomez G, Guerra J, Villaran C, Piñeiro A. 10.  et al. 1999. Oral montelukast, inhaled beclomethasone, and placebo for chronic asthma: a randomized, controlled trial. Ann. Intern. Med. 130:487–95 [Google Scholar]
  11. Drazen JM, Silverman EK, Lee TH. 11.  2000. Heterogeneity of therapeutic responses in asthma. Br. Med. Bull. 56:1054–70 [Google Scholar]
  12. Szefler SJ, Phillips BR, Martinez FD, Chinchilli VM, Lemanske RF. 12.  et al. 2005. Characterization of within-subject responses to fluticasone and montelukast in childhood asthma. J. Allergy Clin. Immunol. 115:233–42 [Google Scholar]
  13. Wu AC, Tantisira K, Li L, Schuemann B, Weiss S. 13.  Childhood Asthma Management Program Research Group 2009. Repeatability of response to asthma medications. J. Allergy Clin. Immunol. 123:385–90 [Google Scholar]
  14. Niu T, Rogus JJ, Chen C, Wang B, Yang J. 14.  et al. 2000. Familial aggregation of bronchodilator response: a community-based study. Am. J. Respir. Crit. Care Med. 162:1833–37 [Google Scholar]
  15. 15. US Dep. Health Hum. Serv., Food Drug Admin 2008. Guidance for Industry: E15 Definitions for Genomic Biomarkers, Pharmacogenomics, Pharmacogenetics, Genomic Data and Sample Coding Categories Washington, DC: Food Drug Admin http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm073162.pdf
  16. Flaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA. 16.  et al. 2010. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 363:809–19 [Google Scholar]
  17. Chen P, Lin JJ, Lu CS, Ong CT, Hsieh PF. 17.  et al. 2011. Carbamazepine-induced toxic effects and HLA-B*1502 screening in Taiwan. N. Engl. J. Med. 364:1126–33 [Google Scholar]
  18. Yuyama N, Davies DE, Akaiwa M, Matsui K, Hamasaki Y. 18.  et al. 2002. Analysis of novel disease-related genes in bronchial asthma. Cytokine 19:287–96 [Google Scholar]
  19. Corren J, Lemanske RF, Hanania NA, Korenblat PE, Parsey MV. 19.  et al. 2011. Lebrikizumab treatment in adults with asthma. N. Engl. J. Med. 365:1088–98 [Google Scholar]
  20. Flockhart DA, O'Kane D, Williams MS, Watson MS, Gage B. 20.  et al. 2008. Pharmacogenetic testing of CYP2C9 and VKORC1 alleles for warfarin. Genet. Med. 10:139–50 [Google Scholar]
  21. Klein TE, Altman RB, Eriksson N, Gage BF, Kimmel SE. 21.  et al. 2009. Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360:753–64 [Google Scholar]
  22. 22. US Dep. Health Hum. Serv., Food Drug Admin 2007. FDA approves updated warfarin (coumadin) prescribing information News Release, Aug. 16. http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm108967.htm
  23. Hawkins GA, Tantisira K, Meyers DA, Ampleford EJ, Moore WC. 23.  et al. 2006. Sequence, haplotype, and association analysis of ADRβ2 in a multiethnic asthma case control study. Am. J. Respir. Crit. Care Med. 174:1101–9 [Google Scholar]
  24. Green SA, Turki J, Bejarano P, Hall IP, Liggett SB. 24.  1995. Influence of β2-adrenergic receptor genotypes on signal transduction in human airway smooth muscle cells. Am. J. Respir. Cell Mol. Biol. 13:25–33 [Google Scholar]
  25. Israel E, Chinchilli VM, Ford JG, Boushey HA, Cherniack R. 25.  et al. 2004. Use of regularly scheduled albuterol treatment in asthma: genotype stratified, randomised, placebo-controlled cross-over trial. Lancet 364:1505–12 [Google Scholar]
  26. 26. National Asthma Education and Prevention Program (NAEPP) 2007. Guidelines for the diagnosis and management of asthma Expert Panel Rep. 3, Natl. Heart Lung Blood Inst., Bethesda, MD. http://www.nhlbi.nih.gov/guidelines/asthma/asthgdln.pdf
  27. Green SA, Cole G, Jacinto M, Innis M, Liggett SB. 27.  1993. A polymorphism of the human β2-adrenergic receptor within the fourth transmembrane domain alters ligand binding and functional properties of the receptor. J. Biol. Chem. 268:23116–21 [Google Scholar]
  28. Drysdale CM, McGraw DW, Stack CB, Stephens JC, Judson RS. 28.  et al. 2000. Complex promoter and coding region β2-adrenergic receptor haplotypes alter receptor expression and predict in vivo responsiveness. Proc. Natl. Acad. Sci. USA 97:10483–88 [Google Scholar]
  29. Israel E, Drazen JM, Liggett SB, Boushey HA, Cherniack RM. 29.  et al. 2000. The effect of polymorphisms of the β2-adrenergic receptor on the response to regular use of albuterol in asthma. Am. J. Respir. Crit. Care Med. 162:75–80 [Google Scholar]
  30. Wechsler ME, Kunselman SJ, Chinchilli VM, Bleecker E, Boushey HA. 30.  et al. 2009. Effect of β2-adrenergic receptor polymorphism on response to long-acting β2 agonist in asthma (LARGE trial): a genotype-stratified, randomised, placebo-controlled, crossover trial. Lancet 374:1754–64 [Google Scholar]
  31. Bleecker ER, Nelson HS, Kraft M, Corren J, Meyers DA. 31.  et al. 2010. β2-Receptor polymorphisms in patients receiving salmeterol with or without fluticasone propionate. Am. J. Respir. Crit. Care Med. 181:676–87 [Google Scholar]
  32. Litonjua AA, Lasky-Su J, Schneiter K, Tantisira KG, Lazarus R. 32.  et al. 2008. ARG1 is a novel bronchodilator response gene: screening and replication in four asthma cohorts. Am. J. Respir. Crit. Care Med. 178:688–94 [Google Scholar]
  33. Panebra A, Schwarb MR, Glinka CB, Liggett SB. 33.  2007. Heterogeneity of transcription factor expression and regulation in human airway epithelial and smooth muscle cells. Am. J. Physiol. Lung Cell. Mol. Physiol. 293:L453–62 [Google Scholar]
  34. Duan QL, Du R, Lasky-Su J, Klanderman BJ, Partch AB. 34.  et al. 2013. A polymorphism in the thyroid hormone receptor gene is associated with bronchodilator response in asthmatics. Pharmacogenomics J. 13:130–36 [Google Scholar]
  35. Tantisira KG, Lake S, Silverman ES, Palmer LJ, Lazarus R. 35.  et al. 2004. Corticosteroid pharmacogenetics: association of sequence variants in CRHR1 with improved lung function in asthmatics treated with inhaled corticosteroids. Hum. Mol. Genet. 13:1353–59 [Google Scholar]
  36. Dijkstra A, Koppelman GH, Vonk JM, Bruinenberg M, Schouten JP. 36.  et al. 2008. Pharmacogenomics and outcome of asthma: no clinical application for long-term steroid effects by CRHR1 polymorphisms. J. Allergy Clin. Immunol. 121:1510–13 [Google Scholar]
  37. Rogers AJ, Tantisira KG, Fuhlbrigge AL, Litonjua AA, Lasky-Su JA. 37.  et al. 2009. Predictors of poor response during asthma therapy differ with definition of outcome. Pharmacogenomics 10:1231–42 [Google Scholar]
  38. Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D. 38.  et al. 2007. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448:470–73 [Google Scholar]
  39. Bouzigon E, Corda E, Aschard H, Dizier MH, Boland A. 39.  et al. 2008. Effect of 17q21 variants and smoking exposure in early-onset asthma. N. Engl. J. Med. 359:1985–94 [Google Scholar]
  40. Berce V, Kozmus CE, Potočnik U. 40.  2013. Association among ORMDL3 gene expression, 17q21 polymorphism and response to treatment with inhaled corticosteroids in children with asthma. Pharmacogenomics J. 13:523–29 [Google Scholar]
  41. Tantisira KG, Lazarus R, Litonjua AA, Klanderman B, Weiss ST. 41.  2008. Chromosome 17: association of a large inversion polymorphism with corticosteroid response in asthma. Pharmacogenet. Genomics 18:733–37 [Google Scholar]
  42. Hawkins GA, Lazarus R, Smith RS, Tantisira KG, Meyers DA. 42.  et al. 2009. The glucocorticoid receptor heterocomplex gene STIP1 is associated with improved lung function in asthmatic subjects treated with inhaled corticosteroids. J. Allergy Clin. Immunol. 123:1376–83 [Google Scholar]
  43. Tantisira KG, Hwang ES, Raby BA, Silverman ES, Lake SL. 43.  et al. 2004. TBX21: a functional variant predicts improvement in asthma with the use of inhaled corticosteroids. Proc. Natl. Acad. Sci. USA 101:18099–104 [Google Scholar]
  44. Szabo SJ, Kim ST, Costa GL, Zhang X, Fathman CG. 44.  et al. 2000. A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100:655–69 [Google Scholar]
  45. Drazen JM, Yandava CN, Dubé L, Szczerback N, Hippensteel R. 45.  et al. 1999. Pharmacogenetic association between ALOX5 promoter genotype and the response to anti-asthma treatment. Nat. Genet. 22:168–70 [Google Scholar]
  46. Klotsman M, York TP, Pillai SG, Vargas-Irwin C, Sharma SS. 46.  et al. 2007. Pharmacogenetics of the 5-lipoxygenase biosynthetic pathway and variable clinical response to montelukast. Pharmacogenet. Genomics 17:189–96 [Google Scholar]
  47. Tantisira KG, Lima J, Sylvia J, Klanderman B, Weiss ST. 47.  2009. 5-lipoxygenase pharmacogenetics in asthma: overlap with Cys-leukotriene receptor antagonist loci. Pharmacogenet. Genomics 19:244–47 [Google Scholar]
  48. Himes BE, Jiang X, Hu R, Wu AC, Lasky-Su JA. 48.  et al. 2012. Genome-wide association analysis in asthma subjects identifies SPATS2L as a novel bronchodilator response gene. PLoS Genet. 8:e1002824 [Google Scholar]
  49. Duan QL, Lasky-Su J, Himes BE, Qiu W, Litonjua AA. 49.  et al. 2014. A genome-wide association study of bronchodilator response in asthmatics. Pharmacogenomics J. 14:41–47 [Google Scholar]
  50. Drake KA, Torgerson DG, Gignoux CR, Galanter JM, Roth LA. 50.  et al. 2014. A genome-wide association study of bronchodilator response in Latinos implicates rare variants. J. Allergy Clin. Immunol. 133:2370–78.e15 [Google Scholar]
  51. Tantisira KG, Lasky-Su J, Harada M, Murphy A, Litonjua AA. 51.  et al. 2011. Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma. N. Engl. J. Med. 365:1173–83 [Google Scholar]
  52. Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C. 52.  et al. 2007. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315:848–53 [Google Scholar]
  53. Kwan T, Benovoy D, Dias C, Gurd S, Provencher C. 53.  et al. 2008. Genome-wide analysis of transcript isoform variation in humans. Nat. Genet. 40:225–31 [Google Scholar]
  54. Degner JF, Pai AA, Pique-Regi R, Veyrieras JB, Gaffney DJ. 54.  et al. 2012. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482:390–94 [Google Scholar]
  55. Tantisira KG, Damask A, Szefler SJ, Schuemann B, Markezich A. 55.  et al. 2012. Genome-wide association identifies the T gene as a novel asthma pharmacogenetic locus. Am. J. Respir. Crit. Care Med. 185:1286–91 [Google Scholar]
  56. Park HW, Dahlin A, Tse S, Duan QL, Schuemann B. 56.  et al. 2014. Genetic predictors associated with improvement of asthma symptoms in response to inhaled corticosteroids. J. Allergy Clin. Immunol. 133:664–69 [Google Scholar]
  57. Papin JA, Reed JL, Palsson BO. 57.  2004. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem. Sci. 29:641–47 [Google Scholar]
  58. Auffray C, Imbeaud S, Roux-Rouquié M, Hood L. 58.  2003. From functional genomics to systems biology: concepts and practices. C. R. Biol. 326:879–92 [Google Scholar]
  59. Rodin AS, Gogoshin G, Boerwinkle E. 59.  2011. Systems biology data analysis methodology in pharmaco-genomics. Pharmacogenomics 12:1349–60 [Google Scholar]
  60. Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR. 60.  2007. A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 3:e129 [Google Scholar]
  61. Friedman N, Linial M, Nachman I, Pe'er D. 61.  2000. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7:601–20 [Google Scholar]
  62. Sebastiani P, Ramoni M. 62.  2001. On the use of Bayesian networks to analyze survey data. Res. Off. Stat. 41:53–64 [Google Scholar]
  63. Himes BE, Wu AC, Duan QL, Klanderman B, Litonjua AA. 63.  et al. 2009. Predicting response to short-acting bronchodilator medication using Bayesian networks. Pharmacogenomics 10:1393–412 [Google Scholar]
  64. Franke L, Jansen RC. 64.  2009. eQTL analysis in humans. Methods Mol. Biol. 573:311–28 [Google Scholar]
  65. Grundberg E, Adoue V, Kwan T, Ge B, Duan QL. 65.  et al. 2011. Global analysis of the impact of environmental perturbation on cis-regulation of gene expression. PLoS Genet. 7:e1001279 [Google Scholar]
  66. Raby BA. 66.  2009. Genetic mapping of pharmacogenetic regulatory variation. Curr. Pharm. Des. 15:3773–81 [Google Scholar]
  67. Qiu W, Rogers AJ, Damask A, Raby BA, Klanderman BJ. 66a.  et al. 2014. Pharmacogenomics: novel loci identification via integrating gene differential analysis and eQTL analysis. Hum. Mol. Genet. 23:5017–24 [Google Scholar]
  68. Little JW, Shepley DP, Wert DW. 67.  1999. Robustness of a gene regulatory circuit. EMBO J. 18:4299–307 [Google Scholar]
  69. Ingolia NT. 68.  2004. Topology and robustness in the Drosophila segment polarity network. PLoS Biol. 2:e123 [Google Scholar]
  70. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ. 69.  et al. 2012. Wisdom of crowds for robust gene network inference. Nat. Methods 9:796–804 [Google Scholar]
  71. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY. 70.  et al. 2012. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148:1293–307 [Google Scholar]
  72. Miller RL, Ho SM. 71.  2008. Environmental epigenetics and asthma: current concepts and call for studies. Am. J. Respir. Crit. Care Med. 177:567–73 [Google Scholar]
  73. Holliday R, Pugh JE. 72.  1975. DNA modification mechanisms and gene activity during development. Science 187:226–32 [Google Scholar]
  74. Holliday R. 73.  1989. DNA methylation and epigenetic mechanisms. Cell Biophys. 15:15–20 [Google Scholar]
  75. Mishra PJ, Humeniuk R, Longo-Sorbello GS, Banerjee D, Bertino JR. 74.  2007. A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc. Natl. Acad. Sci. USA 104:13513–18 [Google Scholar]
  76. Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R. 75.  et al. 2008. Widespread changes in protein synthesis induced by microRNAs. Nature 455:58–63 [Google Scholar]
  77. Baek D, Villen J, Shin C, Camargo FD, Gygi SP. 76.  et al. 2008. The impact of microRNAs on protein output. Nature 455:64–71 [Google Scholar]
  78. Shen J, Ambrosone CB, DiCioccio RA, Odunsi K, Lele SB. 77.  et al. 2008. A functional polymorphism in the miR-146a gene and age of familial breast/ovarian cancer diagnosis. Carcinogenesis 29:1963–66 [Google Scholar]
  79. Lu Y, Zhang Y, Shan H, Pan Z, Li X. 78.  et al. 2009. MicroRNA-1 downregulation by propranolol in a rat model of myocardial infarction: a new mechanism for ischaemic cardioprotection. Cardiovasc. Res. 84:434–41 [Google Scholar]
  80. Granjon A, Gustin MP, Rieusset J, Lefai E, Meugnier E. 79.  et al. 2009. The microRNA signature in response to insulin reveals its implication in the transcriptional action of insulin in human skeletal muscle and the role of a sterol regulatory element–binding protein-1c/myocyte enhancer factor 2C pathway. Diabetes 58:2555–64 [Google Scholar]
  81. Nojima M, Maruyama R, Yasui H, Suzuki H, Maruyama Y. 80.  et al. 2009. Genomic screening for genes silenced by DNA methylation revealed an association between RASD1 inactivation and dexamethasone resistance in multiple myeloma. Clin. Cancer Res. 15:4356–64 [Google Scholar]
  82. Kotani A, Ha D, Hsieh J, Rao PK, Schotte D. 81.  et al. 2009. miR-128b is a potent glucocorticoid sensitizer in MLL-AF4 acute lymphocytic leukemia cells and exerts cooperative effects with miR-221. Blood 114:4169–78 [Google Scholar]
  83. Civelek M, Lusis AJ. 82.  2014. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15:34–48 [Google Scholar]
  84. Wu AC, Tantisira K, Li L, Fuhlbrigge AL, Weiss ST. 83.  et al. 2012. Effect of vitamin D and inhaled corticosteroid treatment on lung function in children. Am. J. Respir. Crit. Care Med. 186:508–13 [Google Scholar]
  85. Zhang Y, Leung DY, Goleva E. 84.  2013. Vitamin D enhances glucocorticoid action in human monocytes: involvement of granulocyte-macrophage colony-stimulating factor and mediator complex subunit 14. J. Biol. Chem. 288:14544–53 [Google Scholar]
  86. Edwards SL, Beesley J, French JD, Dunning AM. 85.  2013. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93:779–97 [Google Scholar]
  87. Zhou X, Qiu W, Sathirapongsasuti JF, Cho MH, Mancini JD. 86.  et al. 2013. Gene expression analysis uncovers novel hedgehog interacting protein (HHIP) effects in human bronchial epithelial cells. Genomics 101:263–72 [Google Scholar]
  88. Zhou X, Baron RM, Hardin M, Cho MH, Zielinski J. 87.  et al. 2012. Identification of a chronic obstructive pulmonary disease genetic determinant that regulates HHIP. Hum. Mol. Genet. 21:1325–35 [Google Scholar]
  89. Dougherty ER. 88.  2011. Validation of gene regulatory networks: scientific and inferential. Brief Bioinform. 12:245–52 [Google Scholar]
  90. Wang SJ, O'Neill RT, Hung HJ. 89.  2010. Statistical considerations in evaluating pharmacogenomics-based clinical effect for confirmatory trials. Clin. Trials 7:525–36 [Google Scholar]
  91. 90. Childhood Asthma Management Program Research Group 2000. Long-term effects of budesonide or nedocromil in children with asthma. N. Engl. J. Med. 343:1054–63 [Google Scholar]
  92. Burns DK, Hughes AR, Power A, Wang SJ, Patterson SD. 91.  2010. Designing pharmacogenomic studies to be fit for purpose. Pharmacogenomics 11:1657–67 [Google Scholar]
  93. Antman E, Weiss S, Loscalzo J. 92.  2012. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 4:367–83 [Google Scholar]
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