Frontiers in Pharmacology (Nov 2021)
Preliminary Pharmacogenomic-Based Predictive Models of Tamoxifen Response in Hormone-dependent Chilean Breast Cancer Patients
Abstract
Tamoxifen (TAM), a selective oestrogen receptor modulator, is one of the most used treatments in oestrogen receptor-positive (ER+) early and metastatic breast cancer (BC) patients. The response to TAM has a high degree of inter-individual variability. This is mainly due to genetic variants in CYP2D6 gene, as well as other genes encoding proteins involved in the TAM pharmacokinetic and/or pharmacodynamic. Therefore, prediction of the TAM response using these genetic factors together with other non-genetic variables may be relevant to improve breast cancer treatment. Thus, in this work, we used genetic polymorphisms and clinical variables for TAM response modelling. One hundred sixty-two ER + BC patients with 2 years of TAM treatment were retrospectively recruited, and the genetic polymorphisms CYP2D6*4, CYP3A4*1B (CYP3A4*1.001), CYP3A5*3, UGT2B7*2, UGT2B15*2, SULT1A1*2, and ESRA V364E were analyzed by PCR-RFLP. Concomitantly, the therapeutic response was obtained from clinical records for association with genotypes using univariate and multivariate biostatistical models. Our results show that UGT2B15*1/*2 genotype protects against relapse (OR = 0.09; p = 0.02), CYP3A5*3/*3 genotype avoids endometrial hyperplasia (OR = 0.07; p = 0.01), SULT1A1*1/*2 genotype avoids vaginal bleeding (OR = 0.09; p = 0.03) and ESRA 364E/364E genotype increases the probability of vaginal bleeding (OR = 5.68; p = 0.02). Logistic regression models, including genomic and non-genomic variables, allowed us to obtain preliminary predictive models to explain relapse (p = 0.010), endometrial hyperplasia (p = 0.002) and vaginal bleeding (p = 0.014). Our results suggest that the response to TAM treatment in ER + BC patients might be associated with the presence of the studied genetic variants in UGT2B15, CYP3A5, SULT1A1 and ESRA genes. After clinical validation protocols, these models might be used to help to predict a percentage of BC relapse and adverse reactions, improving the individual response to TAM-based treatment.
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