World Journal of Surgery

, Volume 35, Issue 8, pp 1693–1699 | Cite as

Overview of the Development of Personalized Genomic Medicine and Surgery

  • F. Charles Brunicardi
  • Richard A. Gibbs
  • David A. Wheeler
  • John Nemunaitis
  • William Fisher
  • John Goss
  • Changyi Chen
Article

Abstract

Personalized genomic medicine and surgery (PGMS) represents a new approach to health care that customizes patients’ medical treatment according to their own genetic information. This new approach is the result of increased knowledge of the human genome and ways this information can be applied by physicians in the medical and surgical management of their patients. A patient’s genotype can yield important information concerning disease susceptibility and the effectiveness of medications, therefore guiding specific, targeted imaging and treatment therapies. This review summarizes major achievements of human genomic studies and applications of genomics in health care. Five years ago we developed a model for the development of PGMS in which genomic profile guides choice of therapy. In this article we discussed our progress, including an updating of the model, and a future vision of PGMS.

Notes

Acknowledgments

The authors are grateful to Katie Elsbury for editorial assistance. This study was partially supported by grants from the following sources: National Institutes of Health (NIH) grants NIDDK R01-DK46441 and NCI R01-CA095731 (to F.C. Brunicardi); U54-HG003273; U54-HG004973 (to R. A. Gibbs); Cancer Prevention & Research Institution of Texas (CPRIT) grant RP101353-P01/P07 (to R. A. Gibbs); the Vivian L. Smith Foundation, the MD Anderson Foundation, the Elkins Pancreas Center at the Baylor College of Medicine, the MacDonald General Research Fund Award, St. Luke’s Episcopal Hospital (09RDM006, to C. Chen), and the Dan L. Duncan Cancer Center at the Baylor College of Medicine (DLDCC PILOT PROJECT 09-10 to C. Chen).

References

  1. 1.
    Obama B (2007) Genomics and personalized medicine act of 2007. U.S. Congress, Washington, DCGoogle Scholar
  2. 2.
    President’s Council of Advisors on Science Technology. Priorities for Personalised Medicine (2008) http://www.ostp.gov/galleries/PCAST/pcast_report_v2.pdf. Accessed Feb 12, 2011
  3. 3.
    U.S. Department of health and Human Services (HHS). Personalized health care: opportunities, pathways, resources. http://www.hhs.gov/myhealthcare/
  4. 4.
    Ginsburg GS, Willard HF (2009) Genomic and personalized medicine: foundations and applications. Transl Res 154:277–287PubMedCrossRefGoogle Scholar
  5. 5.
    Spear BB, Heath-Chiozzi M, Huff J (2001) Clinical application of pharmacogenetics. Trends Mol Med 7:201–204PubMedCrossRefGoogle Scholar
  6. 6.
    International Human Genome Sequencing Consortium (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921CrossRefGoogle Scholar
  7. 7.
    International Human Genome Sequencing Consortium (2004) Finishing the euchromatic sequence of the human genome. Nature 431:931–945CrossRefGoogle Scholar
  8. 8.
    Collins FS (2010) Has the revolution arrived? Nature 464:674–675PubMedCrossRefGoogle Scholar
  9. 9.
    International HapMap Consortium (2003) The international HapMap project. Nature 426:789–796CrossRefGoogle Scholar
  10. 10.
    International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861CrossRefGoogle Scholar
  11. 11.
    The International HapMap 3 Consortium (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58CrossRefGoogle Scholar
  12. 12.
    Artiga MJ, Bullido MJ, Frank A (1998) Risk for Alzheimer’s disease correlates with transcriptional activity of the APOE gene. Hum Mol Genet 7:1887–1892PubMedCrossRefGoogle Scholar
  13. 13.
    Holmes C (2002) Genotype and phenotype in Alzheimer’s disease. Br J Psychiatry 180:131–134PubMedCrossRefGoogle Scholar
  14. 14.
    Belbin O, Dunn JL, Ling Y et al (2007) Regulatory region single nucleotide polymorphisms of the apolipoprotein E gene and the rate of cognitive decline in Alzheimer’s disease. Hum Mol Genet 16:2199–2208PubMedCrossRefGoogle Scholar
  15. 15.
    van Vliet P, Westendorp RG, Eikelenboom P et al (2009) Parental history of Alzheimer disease associated with lower plasma apolipoprotein E levels. Neurology 73:681–687PubMedCrossRefGoogle Scholar
  16. 16.
    International Consortium Announces the 1000 Genomes Project. National Human Genome Research Institute. http://www.genome.gov/26524516. Accessed Feb 12, 2011
  17. 17.
    Kidd JM, Cooper GM, Donahue WF et al (2008) Mapping and sequencing of structural variation from eight human genomes. Nature 453:56–64PubMedCrossRefGoogle Scholar
  18. 18.
    Sebat J, Lakshmi B, Troge J et al (2004) Large-scale copy number polymorphism in the human genome. Science 305:525–528PubMedCrossRefGoogle Scholar
  19. 19.
    Iafratel AJ, Feuk L, Rivera MN et al (2004) Detection of large-scale variation in the human genome. Nat Genet 36:949–951CrossRefGoogle Scholar
  20. 20.
    Lee JA, Carvalho CM, Lupski JR (2007) A DNA replication mechanism for generating nonrecurrent rearrangements associated with genomic disorders. Cell 131:1235–1247PubMedCrossRefGoogle Scholar
  21. 21.
    Redon R, Ishikawa S, Fitch KR et al (2006) Global variation in copy number in the human genome. Nature 444:444–454PubMedCrossRefGoogle Scholar
  22. 22.
    Freeman JL, Perry GH, Feuk L et al (2006) Copy number variation: new insights into genome diversity. Genome Res 16:949–961PubMedCrossRefGoogle Scholar
  23. 23.
    Zhang F, Gu W, Hurles ME et al (2009) Copy number variation in human health, disease, and evolution. Annu Rev Genomics Hum Genet 10:451–481PubMedCrossRefGoogle Scholar
  24. 24.
    Gonzalez E, Kulkarni H, Bolivar H (2005) The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science 307:1434–1440PubMedCrossRefGoogle Scholar
  25. 25.
    Aitman TJ, Dong R, Vyse TJ et al (2006) Copy number polymorphism in Fcgr3 predisposes to glomerulonephritis in rats and humans. Nature 439:851–855PubMedCrossRefGoogle Scholar
  26. 26.
    Wheeler DA, Srinivasan M, Egholm M et al (2008) The complete genome of an individual by massively parallel DNA sequencing. Nature 452:872–876PubMedCrossRefGoogle Scholar
  27. 27.
    Levy S, Sutton G, Ng PC et al (2007) The diploid genome sequence of an individual human. PLoS Biol 5:e254PubMedCrossRefGoogle Scholar
  28. 28.
    Fujimoto A, Nakagawa H, Hosono N et al (2010) Whole-genome sequencing and comprehensive variant analysis of a Japanese individual using massively parallel sequencing. Nat Genet 42:931–936PubMedCrossRefGoogle Scholar
  29. 29.
    Sobreira NLM, Cirulli ET, Avramopoulos D et al (2010) Whole-genome sequencing of a single proband together with linkage analysis identifies a mendelian disease gene. PLoS Genet 6:e1000991PubMedCrossRefGoogle Scholar
  30. 30.
    Lupski JR, Reid JG, Gonzaga-Jauregui C et al (2010) Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N Engl J Med 362:1181–1191PubMedCrossRefGoogle Scholar
  31. 31.
    Mardis ER (2006) Anticipating the $1, 000 genome. Genome Biol 7:112PubMedCrossRefGoogle Scholar
  32. 32.
    Farmer H, McCabe N, Lord CJ et al (2005) Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434:917–921PubMedCrossRefGoogle Scholar
  33. 33.
    Venkitaraman AR (2002) Cancer susceptibility and the functions of BRCA1 and BRCA2. Cell 108:171–182PubMedCrossRefGoogle Scholar
  34. 34.
    Roukos DH, Briasoulis E (2007) Individualized preventive and therapeutic management of hereditary breast ovarian cancer. Nat Clin Pract Oncol 4:578–590PubMedCrossRefGoogle Scholar
  35. 35.
    National Cancer Institute. BRCA1 and BRCA2: cancer risk and genetic testing. http://www.cancer.gov/cancertopics/factsheet/risk/brca. Accessed Feb 12, 2011
  36. 36.
    Struewing JP, Hartge P, Wacholder S et al (1997) The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. N Engl J Med 336:1401–1408PubMedCrossRefGoogle Scholar
  37. 37.
    Ries LAG, Harkins D, Krapcho M et al (2006) SEER cancer statistics review, 1975–2003. National Cancer Institute, BethesdaGoogle Scholar
  38. 38.
    Narod SA, Offit K (2005) Prevention and management of hereditary breast cancer. J Clin Oncol 23:1656–1663PubMedCrossRefGoogle Scholar
  39. 39.
    Wiesner G, Slavin T, Barnholtz-Sloan J (2009) Colorectal cancer. In: Willard H, Ginsburg GS (eds) Genomic it personalized medicine. Elsevier, Durham, pp 879–897CrossRefGoogle Scholar
  40. 40.
    Manolio T, Brooks L, Collins FS (2008) A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118:1590–1605PubMedCrossRefGoogle Scholar
  41. 41.
    National Center for Biotechnology Information (NCBI) (2011) One size does not fit all: the promise of pharacogenomics. http://www.ncbi.nlm.nih.gov/About/primer/pharm.html. Accessed Feb. 12, 2011
  42. 42.
    Schwarz UI (2003) Clinical relevance of genetic polymorphisms in the human CYP2C9 gene. Eur J Clin Invest 33:23–30PubMedCrossRefGoogle Scholar
  43. 43.
    Rost S, Fregin A, Ivaskevicius V et al (2004) Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2. Nature 427:537–541PubMedCrossRefGoogle Scholar
  44. 44.
    Oldenburg J, Watzka M, Rost S et al (2007) VKORC1: molecular target of coumarins. J Thromb Haemost 5(1):1–6PubMedCrossRefGoogle Scholar
  45. 45.
    FDA Approves Updated Warfarin (Coumadin) Prescribing Information. http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm108967.htm. Accessed Apr. 8, 2009
  46. 46.
    Higgins MJ, Stearns V (2011) Pharmacogenetics of endocrine therapy for breast cancer. Annu Rev Med 62:281–293PubMedCrossRefGoogle Scholar
  47. 47.
    Dezentjé VO, Guchelaar HJ, Nortier JW et al (2009) Clinical implications of CYP2D6 genotyping in tamoxifen treatment for breast cancer. Clin Cancer Res 15:15–21PubMedCrossRefGoogle Scholar
  48. 48.
    Jain KK (2005) Applications of AmpliChip CYP450. Mol Diagn 9:119–127PubMedCrossRefGoogle Scholar
  49. 49.
    FDA (2009) Table of valid genomic biomarkers in the context of approved drug labels, 2009. http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm. Accessed Feb 12, 2011
  50. 50.
    Dowsett M, Dunbier AK (2008) Emerging biomarkers and new understanding of traditional markers in personalized therapy for breast cancer. Clin Cancer Res 14:8019–8026PubMedCrossRefGoogle Scholar
  51. 51.
    Romond EH, Perez EA, Bryant J et al (2005) Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 353:1673–1684PubMedCrossRefGoogle Scholar
  52. 52.
    Piccart-Gebhart MJ, Procter M, Leyland-Jones B et al (2005) Herceptin adjuvant (HERA) trial study team. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Eng J Med 353:1659–1672CrossRefGoogle Scholar
  53. 53.
    Ross JS, Slodkowska EA, Symmans WF et al (2009) The HER-2 receptor and breast cancer: ten years of targeted anti-HER-2 therapy and personalized medicine. Oncologist 14:320–368PubMedCrossRefGoogle Scholar
  54. 54.
    Bang Y, Chung H, Xu J et al (2009) Pathological features of advanced gastric cancer (GC): relationship to human epidermal growth factor receptor 2 (HER2) positivity in the global screening programme of the ToGA trial. J Clin Oncol 27 abstract 4556Google Scholar
  55. 55.
    Van Cutsem E, Kang Y, Chung H et al (2009) Efficacy results from the ToGA trial: a phase III study of trastuzumab added to standard chemotherapy (CT) in first-line human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer (GC). J Clin Oncol 27 abstract 2409Google Scholar
  56. 56.
    Joske DJ (2008) Chronic myeloid leukaemia: the evolution of gene-targeted therapy. Med J Aust 189:277–282PubMedGoogle Scholar
  57. 57.
    Druker BJ (2008) Translation of the Philadelphia chromosome into therapy for CML. Blood 112:4808–4817PubMedCrossRefGoogle Scholar
  58. 58.
    Schindler T, Bornmann W, Pellicena P (2000) A structural mechanism for STI-571 inhibition of abelson tyrosine kinase. Science 289:1857–1859CrossRefGoogle Scholar
  59. 59.
    National Comprehensive Cancer Network (2009) clinical practice guidelines in oncology chronic myelogenous leukemia. http://www.nccn.org/professionals/physician_gls/PDF/cml.pdf
  60. 60.
    Pao W, Miller V, Zakowski M et al (2004) EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 101:13306–13311PubMedCrossRefGoogle Scholar
  61. 61.
    Sordella R, Bell DW, Haber DA et al (2004) Gefitinib-sensitizing EGFR mutations in lung cancer activate anti-apoptotic pathways. Science 305:1163–1167PubMedCrossRefGoogle Scholar
  62. 62.
    Lynch TJ, Bell DW, Sordella R et al (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350:2129–2139PubMedCrossRefGoogle Scholar
  63. 63.
    Paez JG, Jänne PA, Lee JC et al (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304:1497–1500PubMedCrossRefGoogle Scholar
  64. 64.
    Mack GS (2009) FDA holds court on post hoc data linking KRAS status to drug response. Nat Biotechnol 27:110–112PubMedCrossRefGoogle Scholar
  65. 65.
    Amado RG, Wolf M, Peeters M et al (2008) Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol 26:1626–1634PubMedCrossRefGoogle Scholar
  66. 66.
    Lièvre A, Bachet JB, Le Corre D et al (2006) KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Cancer Res 66:3992–3995PubMedCrossRefGoogle Scholar
  67. 67.
    Nemunaitis J, Clayman G, Agarwala SS et al (2009) Biomarkers predict p53 gene therapy efficacy in recurrent squamous cell carcinoma of the head and neck. Clin Cancer Res 15:7719–7725PubMedCrossRefGoogle Scholar
  68. 68.
    U.S. Food and Drug Administration (2009) Table of valid biomarkers in the context of approved drug labels. http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm
  69. 69.
    Levy JA (2009) HIV pathogenesis: 25 years of progress and persistent challenges. AIDS 23:147–160PubMedCrossRefGoogle Scholar
  70. 70.
    Biswas P, Tambussi G, Lazzarin A (2007) Access denied? The status of co-receptor inhibition to counter HIV entry. Expert Opin Pharmacother 8:923–933PubMedCrossRefGoogle Scholar
  71. 71.
    Brunicardi FC, Gibbs RA, Fisher W et al (2009) Overview of the molecular surgeon symposium on personalized genomic medicine and surgery. World J Surg 33:612–614PubMedCrossRefGoogle Scholar
  72. 72.
    Voidonikolas G, Kreml SS, Chen C et al (2009) Basic principles and technologies for deciphering the genetic map of cancer. World J Surg 33:615–629PubMedCrossRefGoogle Scholar
  73. 73.
    Voidonikolas G, Gingras MC, Hodges S et al (2009) Developing a tissue resource to characterize the genome of pancreatic cancer. World J Surg 33:723–731PubMedCrossRefGoogle Scholar

Copyright information

© Société Internationale de Chirurgie 2011

Authors and Affiliations

  • F. Charles Brunicardi
    • 1
  • Richard A. Gibbs
    • 2
  • David A. Wheeler
    • 2
  • John Nemunaitis
    • 3
  • William Fisher
    • 1
  • John Goss
    • 1
  • Changyi Chen
    • 1
  1. 1.Michael E. DeBakey Department of SurgeryBaylor College of MedicineHoustonUSA
  2. 2.Human Genome Sequencing Center, Department of Molecular and Human GeneticsBaylor College of MedicineHoustonUSA
  3. 3.Mary Crowley Cancer Research CenterDallasUSA

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