Advertisement

Pharmacogenetics—Statistical Considerations

  • Aiden FlynnEmail author
  • Craig Ledgerwood
  • Caroline O’Hare
Chapter
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 9)

Abstract

The growth of Pharmacogenetics (PGx), using biomarkers to diagnose, prognose and identify patient subgroups most responsive to clinical intervention, heralds the possibility of more effectively targeted therapies and personalised medicine. Whilst demonstrating clinical significance in a number of studies, greater use of PGx has been limited by the need for further technological/methodological advancement together with a more integrated approach in study design and data analysis at the outset of clinical studies. Consideration of the statistical factors to be examined over the course of biomarker studies at the planning stage, instead of the current trend for retrospective analysis, will ensure that studies will be suitably powered to address specific questions and that subsequent data analysis will account appropriately for sources of variability. This will improve confidence levels in the conclusions drawn and the overall utility of PGx research. Greater use of PGx in the development of personalised medicine will require more guidance by statisticians and quantitative biologists in the handling and extraction of information derived from the data produced from large studies within the multidisciplinary network of researchers involved. This chapter highlights the key limiting statistical factors to be considered when embarking upon investigations using PGx, affecting the quality of information obtained from clinical data generated in personalised medicine research.

Keywords

Pharmacogenetics (PGx) Biomarkers Data analysis Statistics Study design optimisation Simulation Modelling Personalised medicine Bioinformatics 

References

  1. 1.
    Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas BD, Ge X, Megherbi DB, Symmans WF, Wang MD, Zhang J, Bitter H, Brors B, Bushel PR, Bylesjo M, Chen M, Cheng J, Chou J, Davison TS, Delorenzi M, Deng Y, Devanarayan V, Dix DJ, Dopazo J, Dorff KC, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess KR, Hong H, Huan J, Irizarry RA, Judson R, Juraeva D, Lababidi S, Lambert CG, Li L, Li Y, Li Z, Lin SM, Liu G, Lobenhofer EK, Luo J, Luo W, McCall MN, Nikolsky Y, Pennello GA, Perkins RG, Philip R, Popovici V, Price ND, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang SJ, Wu J, Wu Y, Xie Q, Yousef WA, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S, Arasappan D, Bao W, Lucas AB, Berthold F, Brennan RJ, Buness A, Catalano JG, Chang C, Chen R, Cheng Y, Cui J, Czika W, Demichelis F, Deng X, Dosymbekov D, Eils R, Feng Y, Fostel J, Fulmer-Smentek S, Fuscoe JC, Gatto L, Ge W, Goldstein DR, Guo L, Halbert DN, Han J, Harris SC, Hatzis C, Herman D, Huang J, Jensen RV, Jiang R, Johnson CD, Jurman G, Kahlert Y, Khuder SA, Kohl M, Li J, Li M, Li QZ, Li S, Liu J, Liu Y, Liu Z, Meng L, Madera M, Martinez-Murillo F, Medina I, Meehan J, Miclaus K, Moffitt RA, Montaner D, Mukherjee P, Mulligan GJ, Neville P, Nikolskaya T, Ning B, Page GP, Parker J, Parry RM, Peng X, Peterson RL, Phan JH, Quanz B, Ren Y, Riccadonna S, Roter AH, Samuelson FW, Schumacher MM, Shambaugh JD, Shi Q, Shippy R, Si S, Smalter A, Sotiriou C, Soukup M, Staedtler F, Steiner G, Stokes TH, Sun Q, Tan PY, Tang R, Tezak Z, Thorn B, Tsyganova M, Turpaz Y, Vega SC, Visintainer R, von Frese J, Wang C, Wang E, Wang J, Wang W, Westermann F, Willey JC, Woods M, Wu S, Xiao N, Xu J, Xu L, Yang L, Zeng X, Zhang M, Zhao C, Puri RK, Scherf U, Tong W, Wolfinger RD, Consortium M (2010) The MicroArray quality control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 28(8):827–838. doi:10.1038/nbt.1665PubMedCrossRefGoogle Scholar
  2. 2.
    Mattsson N, Blennow K, Zetterberg H (2010) Inter-laboratory variation in cerebrospinal fluid biomarkers for Alzheimer’s disease: united we stand, divided we fall. Clin Chem Lab Med 48(5):603–607. doi:10.1515/CCLM.2010.131PubMedCrossRefGoogle Scholar
  3. 3.
    Fenech M, Bonassi S, Turner J, Lando C, Ceppi M, Chang WP, Holland N, Kirsch-Volders M, Zeiger E, Bigatti MP, Bolognesi C, Cao J, De Luca G, Di Giorgio M, Ferguson LR, Fucic A, Lima OG, Hadjidekova VV, Hrelia P, Jaworska A, Joksic G, Krishnaja AP, Lee TK, Martelli A, McKay MJ, Migliore L, Mirkova E, Muller WU, Odagiri Y, Orsiere T, Scarfi MR, Silva MJ, Sofuni T, Surralles J, Trenta G, Vorobtsova I, Vral A, Zijno A, project HUM (2003) Intra- and inter-laboratory variation in the scoring of micronuclei and nucleoplasmic bridges in binucleated human lymphocytes. Results of an international slide-scoring exercise by the HUMN project. Mutat Res 534(1–2):45–64. doi:10.1016/S1383-5718(02)00248-6PubMedCrossRefGoogle Scholar
  4. 4.
    Hsu JC (2010) Multiplicity adjustment big and small in clinical studies. Clin Pharmacol Ther 88(2):251–254. doi:10.1038/clpt.2010.122PubMedCrossRefGoogle Scholar
  5. 5.
    Bromley CM, Close S, Cohen N, Favis R, Fijal B, Gheyas F, Liu W, Lopez-Correa C, Prokop A, Singer JB, Snapir A, Tchelet A, Wang D, Goldstaub D, Industry Pharmacogenomics Working G (2009) Designing pharmacogenetic projects in industry: practical design perspectives from the Industry Pharmacogenomics Working Group. Pharmacogenomics J 9(1):14–22. doi:10.1038/tpj.2008.11PubMedCrossRefGoogle Scholar
  6. 6.
    Mandrekar SJ, Sargent DJ (2009) Clinical trial designs for predictive biomarker validation: one size does not fit all. J Biopharm Stat 19(3):530–542. doi:10.1080/10543400902802458PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Simon R (2010) Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per Med 7(1):33–47. doi:10.2217/pme.09.49PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Freidlin B, McShane LM, Korn EL (2010) Randomized clinical trials with biomarkers: design issues. J Natl Cancer Inst 102(3):152–160. doi:10.1093/jnci/djp477PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Jiang W, Freidlin B, Simon R (2007) Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect. J Natl Cancer Inst 99(13):1036–1043. doi: 10.1093/jnci/djm022PubMedCrossRefGoogle Scholar
  10. 10.
    Goodsaid FM, Amur S, Aubrecht J, Burczynski ME, Carl K, Catalano J, Charlab R, Close S, Cornu-Artis C, Essioux L, Fornace AJ Jr, Hinman L, Hong H, Hunt I, Jacobson-Kram D, Jawaid A, Laurie D, Lesko L, Li HH, Lindpaintner K, Mayne J, Morrow P, Papaluca-Amati M, Robison TW, Roth J, Schuppe-Koistinen I, Shi L, Spleiss O, Tong W, Truter SL, Vonderscher J, Westelinck A, Zhang L, Zineh I (2010) Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact. Nat Rev Drug Discov 9(6):435–445. doi:10.1038/nrd3116PubMedCrossRefGoogle Scholar
  11. 11.
    Wang SJ, O’Neill RT, Hung HJ (2010) Statistical considerations in evaluating pharmacogenomics-based clinical effect for confirmatory trials. Clin Trials 7(5):525–536. doi:10.1177/1740774510375455PubMedCrossRefGoogle Scholar
  12. 12.
    Burns DK, Hughes AR, Power A, Wang SJ, Patterson SD (2010) Designing pharmacogenomic studies to be fit for purpose. Pharmacogenomics 11(12):1657–1667. doi:10.2217/pgs.10.140PubMedCrossRefGoogle Scholar
  13. 13.
    Hughes AR, Brothers CH, Mosteller M, Spreen WR, Burns DK (2009) Genetic association studies to detect adverse drug reactions: abacavir hypersensitivity as an example. Pharmacogenomics 10(2):225–233. doi:10.2217/14622416.10.2.225PubMedCrossRefGoogle Scholar
  14. 14.
    Weber J, McCormack PL (2008) Panitumumab: in metastatic colorectal cancer with wild-type KRAS. BioDrugs 22(6):403–411. doi:10.2165/0063030-200822060-00006PubMedCrossRefGoogle Scholar
  15. 15.
    Flynn AA (2011) Pharmacogenetics: practices and opportunities for study design and data analysis. Drug Discov Today 16(19–20):862–866. doi:10.1016/j.drudis.2011.08.008PubMedCrossRefGoogle Scholar
  16. 16.
    Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Whiteford CC, Bilke S, Krasnoselsky AL, Cenacchi N, Catchpoole D, Berthold F, Schwab M, Khan J (2004) Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Cancer Res 64(19):6883–6891PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Lee HS, Cho SB, Lee HE, Kim MA, Kim JH, Park do J, Yang HK, Lee BL, Kim WH (2007) Protein expression profiling and molecular classification of gastric cancer by the tissue array method. Clin Cancer Res 13(14):4154–4163PubMedCrossRefGoogle Scholar
  18. 18.
    Patterson SD, Cohen N, Karnoub M, Truter SL, Emison E, Khambata-Ford S, Spear B, Ibia E, Sproule R, Barnes D, Bhathena A, Bristow MR, Russell C, Wang D, Warner A, Westelinck A, Brian W, Snapir A, Franc MA, Wong P, Shaw PM (2011) Prospective-retrospective biomarker analysis for regulatory consideration: white paper from the industry pharmacogenomics working group. Pharmacogenomics 12(7):939–951. doi:10.2217/pgs.11.52PubMedCrossRefGoogle Scholar
  19. 19.
    Sanz-Pamplona R, Berenguer A, Cordero D, Riccadonna S, Sole X, Crous-Bou M, Guino E, Sanjuan X, Biondo S, Soriano A, Jurman G, Capella G, Furlanello C, Moreno V (2012) Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review. PLoS One 7(11):e48877. doi:10.1371/journal.pone.0048877PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Taylor JM, Ankerst DP, Andridge RR (2008) Validation of biomarker-based risk prediction models. Clin Cancer Res 14(19):5977–5983. doi:10.1158/1078-0432.CCR-07-4534PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Freidlin B, Jiang W, Simon R (2010) The cross-validated adaptive signature design. Clin Cancer Res 16(2):691–698. doi:10.1158/1078-0432.CCR-09-1357PubMedCrossRefGoogle Scholar
  22. 22.
    FDA (2005) Drug diagnostics co-development concept paper. http://www.fda.gov/downloads/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/UCM116689.pdf2013. Accessed 29 May 2014
  23. 23.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B 57(1):289–300Google Scholar
  24. 24.
    Chen YP, Chen F (2008) Identifying targets for drug discovery using bioinformatics. Expert Opin Ther Targets 12(4):383–389. doi:10.1517/14728222.12.4.383PubMedCrossRefGoogle Scholar
  25. 25.
    Ginsburg GS, Willard HF (2009) Genomic and personalized medicine: foundations and applications. Transl Res 154(6):277–287. doi:10.1016/j.trsl.2009.09.005PubMedCrossRefGoogle Scholar
  26. 26.
    Roos DS (2001) Computational biology. Bioinformatics–trying to swim in a sea of data. Science 291(5507):1260–1261PubMedCrossRefGoogle Scholar
  27. 27.
    Sim SC, Altman RB, Ingelman-Sundberg M (2011) Databases in the area of pharmacogenetics. Hum Mutat 32(5):526–531. doi:10.1002/humu.21454PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    ORNL The Human Genome Management Information System (HGMIS) (2014) www.ornl.gov/sci/techresources/Human_Genome/project/about.shtml. Accessed 29 May 2014
  29. 29.
    Ensembl Ensembl Genome Browser (2014) http://hapmap.ncbi.nlm.nih.gov/. Accessed 29 May 2014
  30. 30.
    NCBI SNP—Short Genetic Variations (2014) http://www.ncbi.nlm.nih.gov/SNP. Accessed 29 May 2014
  31. 31.
    NCBI International HapMap Project (2014) http://hapmap.ncbi.nlm.nih.gov/. Accessed 29 May 2014
  32. 32.
    NCBI Human Genome Resources (2015) http://www.ncbi.nlm.nih.gov/projects/genome/guide/human/index.shtml. Accessed 23 Mar 2015
  33. 33.
    Roses AD (2000) Pharmacogenetics and the practice of medicine. Nature 405(6788):857–865. doi:10.1038/35015728PubMedCrossRefGoogle Scholar
  34. 34.
    Johnson AD (2009) Single-nucleotide polymorphism bioinformatics: a comprehensive review of resources. Circ Cardiovasc Genet 2(5):530–536. doi:10.1161/CIRCGENETICS.109.872010PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    KEGG KEGG: Kyoto Encyclopedia of Genes and Genomes (2014) www.genome.jp/kegg/. Accessed 29 May 2014
  36. 36.
    PharmGKB PharmaGKB (2014) http://www.pharmagkb.org. Accessed 29 May 2014
  37. 37.
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550. doi: 10.1073/pnas.0506580102PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Leong HS, Kipling D (2009) Text-based over-representation analysis of microarray gene lists with annotation bias. Nucleic Acids Res 37(11):e79. doi:10.1093/nar/gkp310PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Heron EA, O’Dushlaine C, Segurado R, Gallagher L, Gill M (2011) Exploration of empirical Bayes hierarchical modeling for the analysis of genome-wide association study data. Biostatistics 12(3):445–461. doi:10.1093/biostatistics/kxq072PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aiden Flynn
    • 1
    Email author
  • Craig Ledgerwood
    • 1
  • Caroline O’Hare
    • 1
  1. 1.Exploristics LtdBelfastUK

Personalised recommendations