Journal
BREAST CANCER RESEARCH AND TREATMENT
Volume 139, Issue 1, Pages 95-105Publisher
SPRINGER
DOI: 10.1007/s10549-013-2511-4
Keywords
Endoxifen; CYP3A4; Vitamin D; CYP2D6; Tamoxifen efficacy; Therapeutic threshold
Categories
Funding
- Cancer Care Ontario (CCO) Research Chair Award (Tier-1) in Experimental Therapeutics
- Ontario Institute for Cancer Research (OICR)
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Tamoxifen is a widely utilized adjuvant anti-estrogen agent for hormone receptor-positive breast cancer, known to undergo CYP2D6-mediated bioactivation to endoxifen. However, little is known regarding additional genetic and non-genetic determinants of optimal endoxifen plasma concentration. Therefore, 196 breast cancer patients on tamoxifen were enrolled in this prospective study over a 24-month period. Blood samples were collected for pharmacogenetic and drug-level analysis of tamoxifen and metabolites. Regression analysis indicated that besides CYP2D6, the recently described CYP3A4*22 genotype, seasonal variation, and concomitant use of CYP2D6-inhibiting antidepressants were significant predictors of endoxifen concentration. Of note, genetic variation explained 33 % of the variability while non-genetic variables accounted for 13 %. Given the proposed notion of a sub-therapeutic endoxifen concentration for predicting breast cancer recurrence, we set the therapeutic threshold at 18 nM, the 20th percentile for endoxifen level among enrolled patients in this cohort. Nearly 70 % of CYP2D6 poor metabolizers as well as extensive metabolizers on potent CYP2D6-inhibiting antidepressants exhibited endoxifen levels below 18 nM, while carriers of CYP3A4*22 were twofold less likely to be in sub-therapeutic range. Unexpectedly, endoxifen levels were 20 % lower during winter months than mean levels across seasons, which was also associated with lower vitamin D levels. CYP3A4*22 genotype along with sunshine exposure and vitamin D status may be unappreciated contributors of tamoxifen efficacy. The identified covariates along with demographic variables were integrated to create an endoxifen concentration prediction algorithm to pre-emptively evaluate the likelihood of individual patients falling below the optimal endoxifen concentration.
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