Single Nucleotide Polymorphisms as Genomic Markers for High-Throughput Pharmacogenomic Studies

  • Annalisa Lonetti
  • Maria Chiara Fontana
  • Giovanni Martinelli
  • Ilaria IacobucciEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1368)


Genetic variations in patients have strong impact on their drug therapies and responses because the variations may contribute to the efficacy and/or produce undesirable side effects for any given drug. The Drug Metabolizing Enzymes and Transporters (DMET) assay is a high-throughput technology by Affymetrix that is able to simultaneously genotype variants in multiple genes involved in absorption, distribution, metabolism, and excretion of drugs for subsequent clinical applications, i.e., the assay allows for a precise genetic map that can guide therapeutic interventions and avoid side effects.

Key words

Single nucleotide polymorphisms Drug metabolizing enzymes and transporters 



We are grateful to the financial support by European LeukemiaNet, Associazione Italiana control le Leucemie, AIRC, progetto Regione-Università 2010–2012 (L. Bolondi), and FP7 NGS-PTL project.


  1. 1.
    Brookes AJ (1999) The essence of SNPs. Gene 234(2):177–186CrossRefPubMedGoogle Scholar
  2. 2.
    Collins FS, Guyer MS, Charkravarti A (1997) Variations on a theme: cataloging human DNA sequence variation. Science 278(5343):1580–1581CrossRefPubMedGoogle Scholar
  3. 3.
    Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE et al (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422):56–65CrossRefPubMedGoogle Scholar
  4. 4.
    Sachidanandam R, Weissman D, Schmidt SC, Kakol JM, Stein LD, Marth G et al (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409(6822):928–933CrossRefPubMedGoogle Scholar
  5. 5.
    Scott SA (2011) Personalizing medicine with clinical pharmacogenetics. Genet Med 13(12):987–995PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Iacobucci I, Lonetti A, Candoni A, Sazzini M, Papayannidis C, Formica S et al (2013) Profiling of drug-metabolizing enzymes/transporters in CD33+ acute myeloid leukemia patients treated with Gemtuzumab-Ozogamicin and Fludarabine, Cytarabine and Idarubicin. Pharmacogenomics J 13(4):335–341CrossRefPubMedGoogle Scholar
  7. 7.
    Affymetrix (2012) DMET plus allele translation reports: summary of comprehensive drug disposition genotyping into commonly recognized allele names. Affymetrix White Paper 1–19Google Scholar
  8. 8.
    Deeken J (2009) The Affymetrix DMET platform and pharmacogenetics in drug development. Curr Opin Mol Ther 11(3):260–268PubMedGoogle Scholar
  9. 9.
    Karlin-Neumann G et al (2007) Molecular inversion probes and universal tag arrays: application to highplex targeted SNP genotyping. In: Weiner MP, Gabriel SB, Stephens JC (eds) Genetic variation: a laboratory manual. Cold Spring Harbor Lab, Cold Spring Harbor, NY, pp 199–211Google Scholar
  10. 10.
    Robarge JD, Li L, Desta Z, Nguyen A, Flockhart DA (2007) The star-allele nomenclature: retooling for translational genomics. Clin Pharmacol Ther 82(3):244–248CrossRefPubMedGoogle Scholar
  11. 11.
    Harris M, Bhuvaneshwar K, Natarajan T, Sheahan L, Wang D, Tadesse MG et al (2014) Pharmacogenomic characterization of gemcitabine response – a framework for data integration to enable personalized medicine. Pharmacogenet Genomics 24(2):81–93PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Hertz DL, Roy S, Jack J, Motsinger-Reif AA, Drobish A, Clark LS et al (2014) Genetic heterogeneity beyond CYP2C8*3 does not explain differential sensitivity to paclitaxel-induced neuropathy. Breast Cancer Res Treat 145(1):245–254PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Shiotani A, Murao T, Fujita Y, Fujimura Y, Sakakibara T, Nishio K et al (2014) Single nucleotide polymorphism markers for low-dose aspirin-associated peptic ulcer and ulcer bleeding. J Gastroenterol Hepatol 29(Suppl 4):47–52CrossRefPubMedGoogle Scholar
  14. 14.
    Bonifaz-Pena V, Contreras AV, Struchiner CJ, Roela RA, Furuya-Mazzotti TK, Chammas R et al (2014) Exploring the distribution of genetic markers of pharmacogenomics relevance in Brazilian and Mexican populations. PLoS One 9(11):e112640PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Deeken JF, Cormier T, Price DK, Sissung TM, Steinberg SM, Tran K et al (2010) A pharmacogenetic study of docetaxel and thalidomide in patients with castration-resistant prostate cancer using the DMET genotyping platform. Pharmacogenomics J 10(3):191–199CrossRefPubMedGoogle Scholar
  16. 16.
    Hu Y, Ehli EA, Nelson K, Bohlen K, Lynch C, Huizenga P et al (2012) Genotyping performance between saliva and blood-derived genomic DNAs on the DMET array: a comparison. PloS One 7(3):e33968PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Affymetrix (2012) DMET™ console 1.3 user manual. DMET™ console 13 user manualGoogle Scholar
  18. 18.
    Guzzi PH, Agapito G, Di Martino MT, Arbitrio M, Tassone P, Tagliaferri P et al (2012) DMET-analyzer: automatic analysis of Affymetrix DMET data. BMC Bioinformatics 13:258PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Day IN (2010) dbSNP in the detail and copy number complexities. Hum Mutat 31(1):2–4CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Annalisa Lonetti
    • 1
  • Maria Chiara Fontana
    • 2
  • Giovanni Martinelli
    • 2
  • Ilaria Iacobucci
    • 2
    Email author
  1. 1.Department of Biomedical and Neuromotor SciencesUniversity of BolognaBolognaItaly
  2. 2.Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. and A. Seràgnoli”University of BolognaBolognaItaly

Personalised recommendations