Abstract
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.
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Brookes AJ (1999) The essence of SNPs. Gene 234(2):177–186
Collins FS, Guyer MS, Charkravarti A (1997) Variations on a theme: cataloging human DNA sequence variation. Science 278(5343):1580–1581
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):56–65
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):928–933
Scott SA (2011) Personalizing medicine with clinical pharmacogenetics. Genet Med 13(12):987–995
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):335–341
Affymetrix (2012) DMET plus allele translation reports: summary of comprehensive drug disposition genotyping into commonly recognized allele names. Affymetrix White Paper 1–19
Deeken J (2009) The Affymetrix DMET platform and pharmacogenetics in drug development. Curr Opin Mol Ther 11(3):260–268
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 199–211
Robarge JD, Li L, Desta Z, Nguyen A, Flockhart DA (2007) The star-allele nomenclature: retooling for translational genomics. Clin Pharmacol Ther 82(3):244–248
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):81–93
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):245–254
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):47–52
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):e112640
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):191–199
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):e33968
Affymetrix (2012) DMET™ console 1.3 user manual. DMET™ console 13 user manual
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 13:258
Day IN (2010) dbSNP in the detail and copy number complexities. Hum Mutat 31(1):2–4
Acknowledgements
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.
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Lonetti, A., Fontana, M.C., Martinelli, G., Iacobucci, I. (2016). Single Nucleotide Polymorphisms as Genomic Markers for High-Throughput Pharmacogenomic Studies. In: Li, P., Sedighi, A., Wang, L. (eds) Microarray Technology. Methods in Molecular Biology, vol 1368. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3136-1_11
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DOI: https://doi.org/10.1007/978-1-4939-3136-1_11
Publisher Name: Humana Press, New York, NY
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