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Full-text held externally
- PMID: 22418701
- UKPMCID: 22418701
- DOI: 10.1007/s10549-012-2010-z
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Polymorphisms of CYP19A1 and response to aromatase inhibitors in metastatic breast cancer patients.
Ferraldeschi, Roberta; Arnedos, Monica; Hadfield, Kristen D; A'Hern, Roger; Drury, Suzie; Wardley, Andrew; Howell, Anthony; Evans, D Gareth; Roberts, Stephen A; Smith, Ian; Newman, William G; Dowsett, Mitch
Breast cancer research and treatment. 2012;133(3):1191-8.
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Full-text held externally
- PMID: 22418701
- UKPMCID: 22418701
- DOI: 10.1007/s10549-012-2010-z
Abstract
Single nucleotide polymorphisms (SNPs) in the gene encoding aromatase (CYP19A1) have been associated with differential benefit from letrozole treatment in metastatic breast cancer (mBC) patients, but validation is lacking. The aim was to investigate whether polymorphic variation of CYP19A1 and enzymes involved in estrogen and aromatase inhibitors (AIs) metabolism are associated with efficacy of AIs. 308 Women with estrogen-receptor-positive metastatic mBC treated with a third-generation AI were identified retrospectively. DNA was extracted from archival formalin-fixed paraffin embedded tumors and genotyped for 71 variants in 16 candidate genes, including CYP19A1. Time to treatment failure (TTF) was significantly improved in patients carrying the minor (T) allele of rs4775936 when compared to patients with the reference allele [HR = 0.79 per T allele (0.66-0.95); P = 0.012]. Patients with >7 TTTA repeats on either allele of CYP19A1 intron 4 had a lower risk of failure than those with a smaller repeat size [HR = 0.84 per >7 TTTA repeats (0.7-0.99); P = 0.04]. However, importantly in multivariate analysis, adjusting for the number of disease sites; disease-free interval from diagnosis to first recurrence, grade at diagnosis and first recurrence type neither variant maintained independent predictive significance. None of the 56 SNPs analyzed as an exploratory set showed significant association with TTF. Variants in CYP19A1 or other selected genes associated with AI metabolism were not independently associated with improved AI efficacy and emphasize the importance in pharmacogenetic studies of considering genetic biomarkers in the context of relevant prognostic factors.