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Clinical Research in the Postgenomic Era

  • Stephane M. Meystre
  • Scott P. Narus
  • Joyce A. Mitchell
Chapter
Part of the Health Informatics book series (HI)

Abstract

Clinical research, being patient-oriented, is based predominantly on ­clinical data – symptoms reported by patients, observations of patients made by health-care providers, radiological images, and various metrics, including laboratory measurements that reflect physiological functions. Recently, however, a new type of data – genes and their products – has entered the picture, and the expectation is that given clinical conditions can ultimately be linked to the function of specific genes. The postgenomic era is characterized by the availability of the human genome as well as the complete genomes of numerous reference organisms. How genomic information feeds into clinical research is the topic of this chapter. We first review the molecules that form the “blueprint of life” and discuss the surrounding research methodologies. Then we discuss how genetic data are clinically integrated. Finally, we relate how this new type of data is used in different clinical research domains.

Keywords

Postgenomic era Genetic data Molecular biology genomic data Bioinformatics Sequence ontology Bioinformatics Sequence Markup Language Sequence analysis data Structure analysis data Functional analysis data 

References

  1. 1.
    Collins FS, Morgan M, Patrinos A. The human genome project: lessons from large-scale biology. Science. 2003;300:286–90.PubMedCrossRefGoogle Scholar
  2. 2.
    Crick FH. On protein synthesis. Symp Soc Exp Biol. 1958;12:138–63.PubMedGoogle Scholar
  3. 3.
    Mitchell JA, Fomous C, Fun J. Challenges and strategies of the genetics home reference. J Med Libr Assoc. 2006;94:336–42.PubMedGoogle Scholar
  4. 4.
    NCBI. Just the facts: a basic introduction to the science underlying NCBI resources – bioinformatics. Available from: http://www.ncbi.nlm.nih.gov/About/primer/bioinformatics.html. Accessed Aug 2011.
  5. 5.
    Luscombe NM, Greenbaum D, Gerstein M. What is bioinformatics? A proposed definition and overview of the field. Methods Inf Med. 2001;40:346–58.PubMedGoogle Scholar
  6. 6.
    Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res. 2008;36:D25–30.PubMedCrossRefGoogle Scholar
  7. 7.
    Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, et al. The sequence ontology: a tool for the unification of genome annotations. Genome Biol. 2005;6:R44.PubMedCrossRefGoogle Scholar
  8. 8.
    Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, et al. The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007;25:1251–5.PubMedCrossRefGoogle Scholar
  9. 9.
    NCBI. GenBank. 2009. Accessed Aug 2011.Google Scholar
  10. 10.
    Cuff AL, Sillitoe I, Lewis T, Redfern OC, Garratt R, Thornton J, et al. The CATH classification revisited – architectures reviewed and new ways to characterize structural divergence in superfamilies. Nucleic Acids Res. 2009;37:D310–4.PubMedCrossRefGoogle Scholar
  11. 11.
    Westbrook J, Ito N, Nakamura H, Henrick K, Berman HM. PDBML: the representation of archival macromolecular structure data in XML. Bioinformatics. 2005;21:988–92.PubMedCrossRefGoogle Scholar
  12. 12.
    RasMol. 2009. Accessed Aug 2011.Google Scholar
  13. 13.
    PyMOL. 2009. Accessed Aug 2011.Google Scholar
  14. 14.
    Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez gene: gene-centered information at NCBI. Nucleic Acids Res. 2005;33:D54–8.PubMedCrossRefGoogle Scholar
  15. 15.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.PubMedCrossRefGoogle Scholar
  16. 16.
    White JA, McAlpine PJ, Antonarakis S, Cann H, Eppig JT, Frazer K, et al. Guidelines for human gene nomenclature. HUGO Nomenclature Committee. Genomics. 1997;45:468–71.PubMedCrossRefGoogle Scholar
  17. 17.
    Yoou MH. Case study of a patient with Parkinson’s disease. Taehan Kanho. 1991;30:56–60.PubMedGoogle Scholar
  18. 18.
    Frezal J. Genatlas database, genes and development defects. C R Acad Sci III. 1998;321:805–17.PubMedGoogle Scholar
  19. 19.
    Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics. 1998;14:656–64.PubMedCrossRefGoogle Scholar
  20. 20.
    Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001;29:365–71.PubMedCrossRefGoogle Scholar
  21. 21.
    Courtot M, Bug W, Gibson F, Lister AL, Malone J, Schober D et al. The OWL of biomedical investigations. 2008. Available from: http://webont.com/owled/2008/papers/owled2008eu_submission_38.pdf. Accessed Aug 2011.
  22. 22.
    Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10.PubMedCrossRefGoogle Scholar
  23. 23.
    Oh JE, Krapfenbauer K, Fountoulakis M, Frischer T, Lubec G. Evidence for the existence of hypothetical proteins in human bronchial epithelial, fibroblast, amnion, lymphocyte, mesothelial and kidney cell lines. Amino Acids. 2004;26:9–18.PubMedCrossRefGoogle Scholar
  24. 24.
    Stoevesandt O, Taussig MJ, He M. Protein microarrays: high-throughput tools for proteomics. Expert Rev Proteomics. 2009;6:145–57.PubMedCrossRefGoogle Scholar
  25. 25.
    Natale DA, Arighi CN, Barker WC, Blake J, Chang TC, Hu Z, et al. Framework for a protein ontology. BMC Bioinformatics. 2007;8:S1.PubMedCrossRefGoogle Scholar
  26. 26.
    Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the human metabolome database. Nucleic Acids Res. 2007;35:D521–6.PubMedCrossRefGoogle Scholar
  27. 27.
    Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, et al. Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol. 2008;26:162–4.PubMedCrossRefGoogle Scholar
  28. 28.
    International HapMap Consortium. The international HapMap project. Nature. 2003;426:789–96.CrossRefGoogle Scholar
  29. 29.
    NCBI. dbSNP. Available from: www.ncbi.nlm.nih.gov/projects/SNP/. Accessed Aug 2011.
  30. 30.
    Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the genetics of common disease. J Clin Invest. 2008;118(5):1590–605.PubMedCrossRefGoogle Scholar
  31. 31.
    Kaiser J. DNA sequencing. A plan to capture human diversity in 1000 genomes. Science. 2008;319:395.PubMedCrossRefGoogle Scholar
  32. 32.
    Personal genome project. 2009. Accessed Aug 2011.Google Scholar
  33. 33.
    Drmanac R, Sparks AB, Callow MJ, Halpern AL, Burns NL, Kermani BG, et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science. 2009;327:78–81.PubMedCrossRefGoogle Scholar
  34. 34.
    NHGRI. A catalog of published genome-wide association studies. 2009. Accessed Aug 2011.Google Scholar
  35. 35.
    Manolio TA, Collins FS. The HapMap and genome-wide association studies in diagnosis and therapy. Annu Rev Med. 2009;60:443–56.PubMedCrossRefGoogle Scholar
  36. 36.
    Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, et al. The Human Gene Mutation Database: 2008 update. Genome Med. 2009;1:13.PubMedCrossRefGoogle Scholar
  37. 37.
    Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A, et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer. 2004;91:355–8.PubMedGoogle Scholar
  38. 38.
    MITOMAP: a human mitochondrial genome database. 2009 [cited 2009]. Accessed Aug 2011.Google Scholar
  39. 39.
    The human variome project. 2009. Accessed Aug 2011.Google Scholar
  40. 40.
    Cotton RG, Auerbach AD, Axton M, Barash CI, Berkovic SF, Brookes AJ, et al. Genetics. The human variome project. Science. 2008;322:861–2.PubMedCrossRefGoogle Scholar
  41. 41.
    Institute WTS. Cancer genome project. Available from: http://www.sanger.ac.uk/genetics/CGP. Accessed Aug 2011.
  42. 42.
    NCI. Cancer genome anatomy project. 2009. Accessed Aug 2011.Google Scholar
  43. 43.
    NCI. FDA-NCI clinical proteomics program. Available from: http://home.ccr.cancer.gov/ncifdaproteomics/default.asp. Accessed Aug 2011.
  44. 44.
    Martin-Sanchez F, Iakovidis I, Norager S, Maojo V, de Groen P, Van der Lei J, et al. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J Biomed Inform. 2004;37:30–42.PubMedCrossRefGoogle Scholar
  45. 45.
    Butte AJ, Kohane IS. Creation and implications of a phenome-genome network. Nat Biotechnol. 2006;24:55–62.PubMedCrossRefGoogle Scholar
  46. 46.
    Chen DP, Weber SC, Constantinou PS, Ferris TA, Lowe HJ, Butte AJ. Clinical arrays of laboratory measures, or “clinarrays”, built from an electronic health record enable disease subtyping by severity. AMIA Annu Symp Proc. 2007;2007:115–9.Google Scholar
  47. 47.
    Butte AJ, Chen R. Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics. AMIA Annu Symp Proc. 2006;2006:106–10.Google Scholar
  48. 48.
    Shah NH, Jonquet C, Chiang AP, Butte AJ, Chen R, Musen MA. Ontology-driven indexing of public datasets for translational bioinformatics. BMC Bioinformatics. 2009;10:S1.PubMedCrossRefGoogle Scholar
  49. 49.
    Murphy SN, Mendis ME, Berkowitz DA, Kohane I, Chueh HC. Integration of clinical and genetic data in the i2b2 architecture. AMIA Annu Symp Proc. 2006;2006:1040.Google Scholar
  50. 50.
    Deshmukh VG, Meystre SM, Mitchell JA. Evaluating the informatics for integrating biology and the bedside system for clinical research. BMC Med Res Methodol. 2009;9:70.PubMedCrossRefGoogle Scholar
  51. 51.
    Lee JM, Ivanova EV, Seong IS, Cashorali T, Kohane I, Gusella JF, et al. Unbiased gene expression analysis implicates the huntingtin polyglutamine tract in extra-mitochondrial energy metabolism. PLoS Genet. 2007;3:e135.PubMedCrossRefGoogle Scholar
  52. 52.
    Himes BE, Wu AC, Duan QL, Klanderman B, Litonjua AA, Tantisira K, et al. Predicting response to short-acting bronchodilator medication using Bayesian networks. Pharmaco­genomics. 2009;10:1393–412.PubMedCrossRefGoogle Scholar
  53. 53.
    NCI. caBIG: cancer biomedical informatics grid. 2009. Accessed Aug 2011.Google Scholar
  54. 54.
    Saltz J, Oster S, Hastings S, Langella S, Kurc T, Sanchez W, et al. CaGrid: design and implementation of the core architecture of the cancer biomedical informatics grid. Bioinformatics. 2006;22:1910–6.PubMedCrossRefGoogle Scholar
  55. 55.
    Amin W, Parwani AV, Schmandt L, Mohanty SK, Farhat G, Pople AK, et al. National Mesothelioma Virtual Bank: a standard based biospecimen and clinical data resource to enhance translational research. BMC Cancer. 2008;8:236.PubMedCrossRefGoogle Scholar
  56. 56.
    Sam LT, Mendonca EA, Li J, Blake J, Friedman C, Lussier YA. PhenoGO: an integrated resource for the multiscale mining of clinical and biological data. BMC Bioinformatics. 2009;10:S8.PubMedCrossRefGoogle Scholar
  57. 57.
    Liu CC, Hu J, Kalakrishnan M, Huang H, Zhou XJ. Integrative disease classification based on cross-platform microarray data. BMC Bioinformatics. 2009;10:S25.PubMedCrossRefGoogle Scholar
  58. 58.
    Pathak J, Solbrig HR, Buntrock JD, Johnson TM, Chute CG. LexGrid: a framework for representing, storing, and querying biomedical terminologies from simple to sublime. J Am Med Inform Assoc. 2009;16:305–15.PubMedCrossRefGoogle Scholar
  59. 59.
    Hewett M, Oliver DE, Rubin DL, Easton KL, Stuart JM, Altman RB, et al. PharmGKB: the Pharmacogenetics Knowledge Base. Nucleic Acids Res. 2002;30:163–5.PubMedCrossRefGoogle Scholar
  60. 60.
    Shabo A. The implications of electronic health record for personalized medicine. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2005;149(2):251–8.PubMedGoogle Scholar
  61. 61.
    CDISC. Clinical data interchange standards consortium (CDISC). 2009. Available from: http://www.cdisc.org/. Accessed Aug 2011.
  62. 62.
    BRIDG. Biomedical Research Integrated Domain Group (BRIDG). 2009. Accessed Aug 2011.Google Scholar
  63. 63.
    Schenk PW, van Fessem MA, Verploegh-Van Rij S, Mathot RA, van Gelder T, Vulto AG, et al. Association of graded allele-specific changes in CYP2D6 function with imipramine dose requirement in a large group of depressed patients. Mol Psychiatry. 2008;13:597–605.PubMedCrossRefGoogle Scholar
  64. 64.
    Loi S, Buyse M, Sotiriou C, Cardoso F. Challenges in breast cancer clinical trial design in the postgenomic era. Curr Opin Oncol. 2004;16:536–41.PubMedCrossRefGoogle Scholar
  65. 65.
    Vogel CL, Cobleigh MA, Tripathy D, Gutheil JC, Harris LN, Fehrenbacher L, et al. Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol. 2002;20:719–26.PubMedCrossRefGoogle Scholar
  66. 66.
    Jahromi MM, Eisenbarth GS. Cellular and molecular pathogenesis of type 1A diabetes. Cell Mol Life Sci. 2007;64:865–72.PubMedCrossRefGoogle Scholar
  67. 67.
    Waggoner DJ, Pagon RA. Internet resources in Medical Genetics. Curr Protoc Hum Genet. 2009;7: Chapter 9:Unit 9.12.Google Scholar
  68. 68.
    Leich E, Hartmann EM, Burek C, Ott G, Rosenwald A. Diagnostic and prognostic significance of gene expression profiling in lymphomas. APMIS. 2007;115:1135–46.PubMedCrossRefGoogle Scholar
  69. 69.
    Codony C, Crespo M, Abrisqueta P, Montserrat E, Bosch F. Gene expression profiling in chronic lymphocytic leukaemia. Best Pract Res Clin Haematol. 2009;22:211–22.PubMedCrossRefGoogle Scholar
  70. 70.
    Chan KS, Espinosa I, Chao M, Wong D, Ailles L, Diehn M, et al. Identification, molecular characterization, clinical prognosis, and therapeutic targeting of human bladder tumor-initiating cells. Proc Natl Acad Sci USA. 2009;106:14016–21.PubMedCrossRefGoogle Scholar
  71. 71.
    Hoffman AC, Danenberg KD, Taubert H, Danenberg PV, Wuerl P. A three-gene signature for outcome in soft tissue sarcoma. Clin Cancer Res. 2009;15:5191–8.PubMedCrossRefGoogle Scholar
  72. 72.
    Gold KA, Kim ES. Role of molecular markers and gene profiling in head and neck cancers. Curr Opin Oncol. 2009;21:206–11.PubMedCrossRefGoogle Scholar
  73. 73.
    Petillo D, Kort EJ, Anema J, Furge KA, Yang XJ, Teh BT. MicroRNA profiling of human kidney cancer subtypes. Int J Oncol. 2009;35:109–14.PubMedCrossRefGoogle Scholar
  74. 74.
    Yoshihara K, Tajima A, Komata D, Yamamoto T, Kodama S, Fujiwara H, et al. Gene expression profiling of advanced-stage serous ovarian cancers distinguishes novel subclasses and implicates ZEB2 in tumor progression and prognosis. Cancer Sci. 2009;100:1421–8.PubMedCrossRefGoogle Scholar
  75. 75.
    Volchenboum SL, Cohn SL. Are molecular neuroblastoma classifiers ready for prime time? Lancet Oncol. 2009;10:641–2.PubMedCrossRefGoogle Scholar
  76. 76.
    Vermeulen J, De Preter K, Naranjo A, Vercruysse L, Van Roy N, Hellemans J, et al. Predicting outcomes for children with neuroblastoma using a multigene-expression signature: a retrospective SIOPEN/COG/GPOH study. Lancet Oncol. 2009;10(7):663–71.PubMedCrossRefGoogle Scholar
  77. 77.
    Ugurel S, Utikal J, Becker JC. Tumor biomarkers in melanoma. Cancer Control. 2009;16(3):219–24.PubMedGoogle Scholar
  78. 78.
    Kim C, Taniyama Y, Paik S. Gene expression-based prognostic and predictive markers for breast cancer: a primer for practicing pathologists. Arch Pathol Lab Med. 2009;133:855–9.PubMedGoogle Scholar
  79. 79.
    Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med. 2009;360:790–800.PubMedCrossRefGoogle Scholar
  80. 80.
    Rabson AB, Weissmann D. From microarray to bedside: targeting NF-kappaB for therapy of lymphomas. Clin Cancer Res. 2005;11:2–6.PubMedGoogle Scholar
  81. 81.
    Fang KC. Clinical utilities of peripheral blood gene expression profiling in the management of cardiac transplant patients. J Immunotoxicol. 2007;4:209–17.PubMedCrossRefGoogle Scholar
  82. 82.
    XDx. XDx’s AlloMap(R) gene expression test cleared by U.S. FDA for heart transplant recipients. 2008 [cited 2009]. Accessed Aug 2011.Google Scholar
  83. 83.
    Khatri P, Sarwal MM. Using gene arrays in diagnosis of rejection. Curr Opin Organ Transplant. 2009;14:34–9.PubMedCrossRefGoogle Scholar
  84. 84.
    van Baarsen LG, Bos CL, van der Pouw Kraan TC, Verweij CL. Transcription profiling of rheumatic diseases. Arthritis Res Ther. 2009;11:207.PubMedCrossRefGoogle Scholar
  85. 85.
    Bauer JW, Bilgic H, Baechler EC. Gene-expression profiling in rheumatic disease: tools and therapeutic potential. Nat Rev Rheumatol. 2009;5:257–65.PubMedCrossRefGoogle Scholar
  86. 86.
    Lin B, Malanoski AP. Resequencing arrays for diagnostics of respiratory pathogens. Methods Mol Biol. 2009;529:231–57.PubMedCrossRefGoogle Scholar
  87. 87.
    Roche. Individualize drug dosing based on metabolic profiling with the AmpliChip CYP450 test. 2009 [cited 2009]. Accessed Aug 2011.Google Scholar
  88. 88.
    23andMe. 23andMe: Genetics just got personal. 2009. Accessed Aug 2011.Google Scholar
  89. 89.
    Navigenics. There’s DNA. And then there’s what you do with it. 2009. Accessed Aug 2011.Google Scholar
  90. 90.
    deCODEme. deCODE your health. 2009. Accessed Aug 2011.Google Scholar
  91. 91.
    PatientsLikeMe. PatientsLikeMe: patients helping patients live better every day. Available from: http://www.patientslikeme.com/. Accessed Aug 2011.
  92. 92.
    Kaput J, Rodriguez RL. Nutritional genomics: the next frontier in the postgenomic era. Physiol Genomics. 2004;16:166–77.PubMedGoogle Scholar
  93. 93.
    Cannon-Albright LA, Thomas A, Goldgar DE, Gholami K, Rowe K, Jacobsen M, et al. Familiality of cancer in Utah. Cancer Res. 1994;54:2378–85.PubMedGoogle Scholar
  94. 94.
    Hamshere ML, Schulze TG, Schumacher J, Corvin A, Owen MJ, Jamra RA, et al. Mood-incongruent psychosis in bipolar disorder: conditional linkage analysis shows genome-wide suggestive linkage at 1q32.3, 7p13 and 20q13.31. Bipolar Disord. 2009;11:610–20.PubMedCrossRefGoogle Scholar
  95. 95.
    Hamshere ML, Segurado R, Moskvina V, Nikolov I, Glaser B, Holmans PA. Large-scale linkage analysis of 1302 affected relative pairs with rheumatoid arthritis. BMC Proc. 2007;1:S100.PubMedCrossRefGoogle Scholar
  96. 96.
    Bos JM, Towbin JA, Ackerman MJ. Diagnostic, prognostic, and therapeutic implications of genetic testing for hypertrophic cardiomyopathy. J Am Coll Cardiol. 2009;54:201–11.PubMedCrossRefGoogle Scholar
  97. 97.
    de la Fuente M, Csaba N, Garcia-Fuentes M, Alonso MJ. Nanoparticles as protein and gene carriers to mucosal surfaces. Nanomedicine (Lond). 2008;3:845–57.CrossRefGoogle Scholar
  98. 98.
    Emerich DF, Thanos CG. Targeted nanoparticle-based drug delivery and diagnosis. J Drug Target. 2007;15:163–83.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Stephane M. Meystre
    • 1
  • Scott P. Narus
    • 1
  • Joyce A. Mitchell
    • 1
  1. 1.Department of Biomedical InformaticsUniversity of UtahSalt Lake CityUSA

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