Pharmacogenomics: Success and Challenges

  • Mohammad Omar Hussaini
  • Howard L. McLeodEmail author


Pharmacogenomics is a field of study that explores the impact of genetic variation on pharmacokinetics and pharmacodynamics, with the goal of rational therapeutic selection. The past decade has brought together substantial advances in human genomic analysis and a maturation of our understanding of tumor biology. While there is much progress still to be had, there are now several prominent examples in which tumor-associated somatic mutations have been used to identify cellular signaling pathways in tumors. This in turn has led to the development of targeted therapies, with somatic mutations serving as genomic predictors of tumor response and providing new leads for drug development. There is also a realization that germline DNA variants can help optimize drug dosing and predict the susceptibility of patients to the adverse side effects of these drugs, knowledge that ultimately can be used to improve the benefit: risk ratio of therapeutics for individual patients.


Pharmacogenomics Tumor profiling Pharmacodynamics Pharmacokinetics Side effects Targeted therapy Personalized medicine Biomarkers Cellular pathways Next-generation sequencing Drugs Efficacy Chemotherapy 


  1. 1.
    Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature. 2015;526(7573):343–50.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Zhang G, Nebert DW. Personalized medicine: genetic risk prediction of drug response. Pharmacol Ther. 2017;175:75–90.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Weinshilboum R, Wang L. Pharmacogenomics: bench to bedside. Discov Med. 2005;5(25):30–6.PubMedGoogle Scholar
  4. 4.
    Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011;364(12):1144–53.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lehmann H, Ryan E. The familial incidence of low pseudocholinesterase level. Lancet. 1956;271(6934):124.CrossRefGoogle Scholar
  6. 6.
    Meyer UA. Pharmacogenetics and adverse drug reactions. Lancet. 2000;356(9242):1667–71.CrossRefGoogle Scholar
  7. 7.
    Stearns V, et al. Active tamoxifen metabolite plasma concentrations after coadministration of tamoxifen and the selective serotonin reuptake inhibitor paroxetine. J Natl Cancer Inst. 2003;95(23):1758–64.CrossRefGoogle Scholar
  8. 8.
    Kelly CM, et al. Selective serotonin reuptake inhibitors and breast cancer mortality in women receiving tamoxifen: a population based cohort study. BMJ. 2010;340:c693.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Cancer Genome Atlas Research Network. Electronic address, w.b.e. and N. Cancer Genome Atlas Research. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell. 2017;169(7):1327–1341 e23.CrossRefGoogle Scholar
  10. 10.
    Cancer Genome Atlas Research N, et al. Integrated genomic and molecular characterization of cervical cancer. Nature. 2017;543(7645):378–84.Google Scholar
  11. 11.
    Cancer Genome Atlas N. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576–82.CrossRefGoogle Scholar
  12. 12.
    Cancer Genome Atlas Research N. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–8.CrossRefGoogle Scholar
  13. 13.
    Weng L, et al. Pharmacogenetics and pharmacogenomics: a bridge to individualized cancer therapy. Pharmacogenomics. 2013;14(3):315–24.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    United States Food and Drug Administration. Table of Pharmacogenomic Biomarkers in Drug Labeling. updated 8/3/2018.Google Scholar
  15. 15.
    M. Whirl-Carrillo, et al. “Pharmacogenomics Knowledge for Personalized Medicine” Clinical Pharmacology & Therapeutics (2012) 92(4):414–17.Google Scholar
  16. 16.
    Lee JW, et al. The emerging era of pharmacogenomics: current successes, future potential, and challenges. Clin Genet. 2014;86(1):21–8.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lee SY, McLeod HL. Pharmacogenetic tests in cancer chemotherapy: what physicians should know for clinical application. J Pathol. 2011;223(1):15–27.CrossRefGoogle Scholar
  18. 18.
    Pratz KW, Levis M. How I treat FLT3-mutated AML. Blood. 2017;129(5):565–71.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Chapman PB, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364(26):2507–16.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Soverini S, et al. BCR-ABL kinase domain mutation analysis in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors: recommendations from an expert panel on behalf of European LeukemiaNet. Blood. 2011;118(5):1208–15.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Fujii T, et al. Targeting isocitrate dehydrogenase (IDH) in cancer. Discov Med. 2016;21(117):373–80.PubMedPubMedCentralGoogle Scholar
  22. 22.
    Pui CH, Evans WE. A 50-year journey to cure childhood acute lymphoblastic leukemia. Semin Hematol. 2013;50(3):185–96.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Vici P, et al. Outcomes of HER2-positive early breast cancer patients in the pre-trastuzumab and trastuzumab eras: a real-world multicenter observational analysis. The RETROHER study. Breast Cancer Res Treat. 2014;147(3):599–607.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Xu ZQ, et al. Efficacy and safety of lapatinib and trastuzumab for HER2-positive breast cancer: a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2017;7(3):e013053.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Denduluri N, et al. Selection of optimal adjuvant chemotherapy regimens for human epidermal growth factor receptor 2 (HER2) -negative and adjuvant targeted therapy for HER2-positive breast cancers: an American Society of Clinical Oncology Guideline Adaptation of the Cancer Care Ontario Clinical Practice Guideline. J Clin Oncol. 2016;34(20):2416–27.CrossRefGoogle Scholar
  26. 26.
    Braggio E, et al. Lessons from next-generation sequencing analysis in hematological malignancies. Blood Cancer J. 2013;3:e127.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Dewitt ND, Yaffe MP, Trounson A. Building stem-cell genomics in California and beyond. Nat Biotechnol. 2012;30(1):20–5.CrossRefGoogle Scholar
  28. 28.
    Arranz EE, et al. Gene signatures in breast cancer: current and future uses. Transl Oncol. 2012;5(6):398–403.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Cancer Genome Atlas Research N. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489(7417):519–25.CrossRefGoogle Scholar
  30. 30.
    Cancer Genome Atlas N. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330–7.CrossRefGoogle Scholar
  31. 31.
    Consortium EP. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.CrossRefGoogle Scholar
  32. 32.
    Lipson D, et al. Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nat Med. 2012;18(3):382–4.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Goodman AM, Choi M, Wieduwilt M, Mulroney C, Costello C, Frampton G, Miller V, Kurzrock R. Next-generation sequencing reveals potentially actionable alterations in the majority of patients with lymphoid malignancies. JCO Precis Oncol. 2017;1:1–13.PubMedCentralPubMedGoogle Scholar
  34. 34.
    Muller KE, et al. Targeted next-generation sequencing detects a high frequency of potentially actionable mutations in metastatic breast cancers. Exp Mol Pathol. 2016;100(3):421–5.CrossRefGoogle Scholar
  35. 35.
    Vasan N, et al. A targeted next-generation sequencing assay detects a high frequency of therapeutically targetable alterations in primary and metastatic breast cancers: implications for clinical practice. Oncologist. 2014;19(5):453–8.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Blumenthal DT, et al. Clinical utility and treatment outcome of comprehensive genomic profiling in high grade glioma patients. J Neuro-Oncol. 2016;130(1):211–9.CrossRefGoogle Scholar
  37. 37.
    Rankin A, et al. Broad detection of alterations predicted to confer lack of benefit from EGFR antibodies or sensitivity to targeted therapy in advanced colorectal cancer. Oncologist. 2016;21:1306.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Hagemann IS, et al. Diagnostic yield of targeted next generation sequencing in various cancer types: an information-theoretic approach. Cancer Genet. 2015;208(9):441–7.CrossRefGoogle Scholar
  39. 39.
    da Cunha Santos G, Shepherd FA, Tsao MS. EGFR mutations and lung cancer. Annu Rev Pathol. 2011;6:49–69.CrossRefGoogle Scholar
  40. 40.
    Paez JG, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304(5676):1497–500.CrossRefGoogle Scholar
  41. 41.
    Kreso A, et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science. 2013;339(6119):543–8.CrossRefGoogle Scholar
  42. 42.
    Amado RG, et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol. 2008;26(10):1626–34.CrossRefGoogle Scholar
  43. 43.
    Abramson, R. 2018. Overview of Targeted Therapies for Cancer. My Cancer Genome (Updated May 25).
  44. 44.
    Engstrom PF, et al. NCCN molecular testing white paper: effectiveness, efficiency, and reimbursement. J Natl Compr Cancer Netw. 2011;9(Suppl 6):S1–16.Google Scholar
  45. 45.
    Walter MJ, et al. Clonal diversity of recurrently mutated genes in myelodysplastic syndromes. Leukemia. 2013;27(6):1275–82.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Walter MJ, et al. Clonal architecture of secondary acute myeloid leukemia. N Engl J Med. 2012;366(12):1090–8.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Jacoby MA, Duncavage EJ, Walter MJ. Implications of tumor clonal heterogeneity in the era of next-generation sequencing. Trends Cancer. 2015;1(4):231–41.CrossRefGoogle Scholar
  48. 48.
    Hussaini M. Biomarkers in hematological malignancies: a review of molecular testing in hematopathology. Cancer Control. 2015;22(2):158–66.CrossRefGoogle Scholar
  49. 49.
    Cancer Genome Atlas Research N, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059–74.Google Scholar
  50. 50.
    Velizheva NP, et al. Cytology smears as excellent starting material for next-generation sequencing-based molecular testing of patients with adenocarcinoma of the lung. Cancer Cytopathol. 2017;125(1):30–40.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Berg JS, et al. An informatics approach to analyzing the incidentalome. Genet Med. 2013;15(1):36–44.CrossRefGoogle Scholar
  52. 52.
    Zhou SF, et al. Clinical pharmacogenetics and potential application in personalized medicine. Curr Drug Metab. 2008;9(8):738–84.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Investigators G, Investigators M, Investigators SD. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry. 2013;170(2):207–17.CrossRefGoogle Scholar
  54. 54.
    Tansey KE, et al. Contribution of common genetic variants to antidepressant response. Biol Psychiatry. 2013;73(7):679–82.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Reynolds GP. The pharmacogenetics of symptom response to antipsychotic drugs. Psychiatry Investig. 2012;9(1):1–7.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Drozda K, Muller DJ, Bishop JR. Pharmacogenomic testing for neuropsychiatric drugs: current status of drug labeling, guidelines for using genetic information, and test options. Pharmacotherapy. 2014;34(2):166–84.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Hicks JK, et al. Clinical Pharmacogenetics Implementation Consortium guideline for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants. Clin Pharmacol Ther. 2013;93(5):402–8.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Leckband SG, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for HLA-B genotype and carbamazepine dosing. Clin Pharmacol Ther. 2013;94(3):324–8.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Berinstein E, Levy A. Recent developments and future directions for the use of pharmacogenomics in cardiovascular disease treatments. Expert Opin Drug Metab Toxicol. 2017;13(9):973–83.CrossRefGoogle Scholar
  60. 60.
    Giudicessi JR, Kullo IJ, Ackerman MJ. Precision cardiovascular medicine: state of genetic testing. Mayo Clin Proc. 2017;92(4):642–62.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Aceti A. Pharmacogenomics for infectious diseases. J MedMicrob Diagn. 2016;5:e223.Google Scholar
  62. 62.
    Young B, et al. First large, multicenter, open-label study utilizing HLA-B*5701 screening for abacavir hypersensitivity in North America. AIDS. 2008;22(13):1673–5.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Soon-U LL, Amur S. Chapter 12: “Pharmacogenomics and pharmacogenetics for infectious diseases.” in Pharmacogenomics: an introduction and clinical perspective. McGraw Hill, New York, NY, USA. 2013.Google Scholar
  64. 64.
    Adams JU. Pharmacogenomics and personalized medicine. Nat Educ. 2008;1:194.Google Scholar
  65. 65.
    Daly AK. Pharmacogenomics of adverse drug reactions. Genome Med. 2013;5(1):5.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Dong Y, et al. Analysis of genetic variations in CYP2C9, CYP2C19, CYP2D6 and CYP3A5 genes using oligonucleotide microarray. Int J Clin Exp Med. 2015;8(10):18917–26.PubMedPubMedCentralGoogle Scholar
  67. 67.
    Eechoute K, et al. A long-term prospective population pharmacokinetic study on imatinib plasma concentrations in GIST patients. Clin Cancer Res. 2012;18(20):5780–7.CrossRefGoogle Scholar
  68. 68.
    Ma Q, Lu AY. Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol Rev. 2011;63(2):437–59.CrossRefGoogle Scholar
  69. 69.
    Ahmad A, et al. Endoxifen, a new cornerstone of breast cancer therapy: demonstration of safety, tolerability, and systemic bioavailability in healthy human subjects. Clin Pharmacol Ther. 2010;88(6):814–7.CrossRefGoogle Scholar
  70. 70.
    Hertz DL, McLeod HL, Irvin WJ Jr. Tamoxifen and CYP2D6: a contradiction of data. Oncologist. 2012;17(5):620–30.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Irvin WJ Jr, et al. Genotype-guided tamoxifen dosing increases active metabolite exposure in women with reduced CYP2D6 metabolism: a multicenter study. J Clin Oncol. 2011;29(24):3232–9.CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Walko CM, McLeod H. Use of CYP2D6 genotyping in practice: tamoxifen dose adjustment. Pharmacogenomics. 2012;13(6):691–7.CrossRefGoogle Scholar
  73. 73.
    Baldwin RM, et al. A genome-wide association study identifies novel loci for paclitaxel-induced sensory peripheral neuropathy in CALGB 40101. Clin Cancer Res. 2012;18(18):5099–109.CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Hertz DL, et al. CYP2C8*3 predicts benefit/risk profile in breast cancer patients receiving neoadjuvant paclitaxel. Breast Cancer Res Treat. 2012;134(1):401–10.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Ross CJ, et al. Genetic variants in TPMT and COMT are associated with hearing loss in children receiving cisplatin chemotherapy. Nat Genet. 2009;41(12):1345–9.CrossRefGoogle Scholar
  76. 76.
    Visscher H, et al. Pharmacogenomic prediction of anthracycline-induced cardiotoxicity in children. J Clin Oncol. 2012;30(13):1422–8.CrossRefGoogle Scholar
  77. 77.
    McWhinney SR, Goldberg RM, McLeod HL. Platinum neurotoxicity pharmacogenetics. Mol Cancer Ther. 2009;8(1):10–6.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Peters EJ, et al. Pharmacogenomic characterization of US FDA-approved cytotoxic drugs. Pharmacogenomics. 2011;12(10):1407–15.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    McLeod HL. Cancer pharmacogenomics: early promise, but concerted effort needed. Science. 2013;339(6127):1563–6.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Cox NJ, et al. Clinical translation of cell-based pharmacogenomic discovery. Clin Pharmacol Ther. 2012;92(4):425–7.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Weinshilboum RM, Wang L. Pharmacogenomics: precision medicine and drug response. Mayo Clin Proc. 2017;92(11):1711–22.CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Ratain MJ, et al. The cancer and leukemia group B pharmacology and experimental therapeutics committee: a historical perspective. Clin Cancer Res. 2006;12(11 Pt 2):3612s–6s.CrossRefGoogle Scholar
  83. 83.
    Innocenti F, et al. A genome-wide association study of overall survival in pancreatic cancer patients treated with gemcitabine in CALGB 80303. Clin Cancer Res. 2012;18(2):577–84.CrossRefGoogle Scholar
  84. 84.
    Relling MV, Klein TE. CPIC: clinical pharmacogenetics implementation consortium of the pharmacogenomics research network. Clin Pharmacol Ther. 2011;89(3):464–7.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Hicks JK, et al. Patient decisions to receive secondary pharmacogenomic findings and development of a multidisciplinary practice model to integrate results into patient care. Clin Transl Sci. 2018;11(1):71–6.CrossRefGoogle Scholar
  86. 86.
    Hicks JK, et al. Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clin Pharmacol Ther. 2016;102:37.CrossRefGoogle Scholar
  87. 87.
    Saldivar JS, et al. Initial assessment of the benefits of implementing pharmacogenetics into the medical management of patients in a long-term care facility. Pharmgenomics Pers Med. 2016;9:1–6.PubMedPubMedCentralGoogle Scholar
  88. 88.
    Lopez-Lopez E, et al. Polymorphisms of the SLCO1B1 gene predict methotrexate-related toxicity in childhood acute lymphoblastic leukemia. Pediatr Blood Cancer. 2011;57(4):612–9.CrossRefGoogle Scholar
  89. 89.
    Wheeler HE, et al. Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet. 2013;14(1):23–34.CrossRefGoogle Scholar
  90. 90.
    van Staveren MC, et al. Evaluation of predictive tests for screening for dihydropyrimidine dehydrogenase deficiency. Pharmacogenomics J. 2013;13(5):389–95.CrossRefGoogle Scholar
  91. 91.
    Kalia M. Biomarkers for personalized oncology: recent advances and future challenges. Metabolism. 2015;64(3 Suppl 1):S16–21.CrossRefGoogle Scholar
  92. 92.
    Patil SA, et al. Novel approaches to glioma drug design and drug screening. Expert Opin Drug Discovery. 2013;8(9):1135–51.CrossRefGoogle Scholar
  93. 93.
    Ranieri G, et al. Vascular endothelial growth factor (VEGF) as a target of bevacizumab in cancer: from the biology to the clinic. Curr Med Chem. 2006;13(16):1845–57.CrossRefGoogle Scholar
  94. 94.
    Konstantinopoulos PA, et al. Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer. J Clin Oncol. 2010;28(22):3555–61.CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Hematopathology and Laboratory MedicineMoffitt Cancer CenterTampaUSA
  2. 2.Department of Cancer Epidemiology, Individualized Cancer MedicineMoffitt Cancer CenterTampaUSA
  3. 3.Department of Individualized Cancer MedicineMoffitt Cancer CenterTampaUSA

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