CYP2D6 polymorphisms as predictors of outcome in breast cancer patients treated with tamoxifen: Expanded polymorphism coverage improves risk stratification

Abstract

<h4>Purpose</h4>This study aimed to validate matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS)/Taqman copy number assay (CNA) CYP2D6 genotyping by AmpliChip CYP450 Test for the prediction of tamoxifen metabolizer phenotypes in breast cancer, and to investigate the influence of CYP2D6 variant coverage on genotype-phenotype relationships and tamoxifen outcome.<h4>Experimental design</h4>Hormone receptor-positive postmenopausal breast cancer patients (n = 492) treated with adjuvant tamoxifen, previously analyzed by MALDI-TOF MS/CNA, were reanalyzed by AmpliChip CYP450 Test and validated by independent methods. Cox proportional hazard ratios (HR) were calculated for recurrence of poor (PM) relative to extensive metabolizer (EM) phenotypes with increasing numbers of CYP2D6 variants. Kaplan-Meier distributions were calculated for different phenotype classifications.<h4>Results</h4>Concordance was 99.2% to 99.5% for CNA and 99.8% to 100% per CYP2D6 allele (*3, *4, *5, *9, *10, and *41). The prevalence of predicted phenotypes was 1.2% for ultrarapid metabolizer (UM), 37.2% for EM without variant, 43.5% for heterozygous EM, 9.7% for intermediate metabolizer (IM), and 8.3% for PM. Approximately, one third of patients were misclassified based on a *4 analysis only, but inclusion of all reduced-function alleles increased the PM-associated HR from 1.33 (P = 0.58) to 2.87 (P = 0.006). Kaplan-Meier analyses showed highest and lowest clinical benefit for UM and PM with respect to both the AmpliChip-based and a redefined phenotype assignment. The latter revealed significant allele-dose-dependent associations (P = 0.011) and largest effect size (HR(PM_EM) = 2.77; 95% confidence interval, 1.31-5.89).<h4>Conclusions</h4>MALDI-TOF MS/CNA is suitable for accurate CYP2D6 genotyping. For tamoxifen pharmacogenetics, broad CYP2D6 allele coverage is recommended to reduce phenotype misclassification. Classification based on refined EM and reduced-function metabolizers is advisable. AACR.