Impact of combined UGT2B17 and GSTA1 genotypes on exemestane pharmacogenetics
Bibliographic record
Abstract
Exemestane (EXE) is an aromatase inhibitor used for the treatment of estrogen receptor-positive breast cancer. The metabolism of EXE includes reduction to form 17-Beta-hydroxy-EXE (17β-DHE) and subsequent UGT2B17-mediated glucuronidation to form 17-Beta-hydroxy-EXE-17-O-Beta-D-glucuronide (17β-DHE-Gluc), and GSTA1-mediated glutathione conjugation of EXE and 17β-DHE and subsequent sequential metabolism by γ-glutamyl transferases and dipeptidases to form 6-methylcysteinylandrosta-1,4-diene-3,17-dione (EXE-Cys) and 6-methylcysteinylandrosta-1,4-diene-17-Beta-hydroxy-3-one (17β-DHE-Cys). The aim of the present study was to determine the effects of UGT2B17 and GSTA1 genotype on the serum levels of EXE and its metabolites among subjects taking EXE. Genotypes of UGT2B17 and GSTA1 were determined by real-time PCR and serum EXE,17β-DHE, 17β-DHE-Gluc, EXE-Cys and 17β-DHE-Cys were quantified by UPLC-MS. Shunting was observed between the two metabolic pathways of EXE, with serum EXE levels increased with increasing numbers of either the UGT2B17*2 or GSTA1*B alleles ( P trend <0.0001). 17β-DHE-Gluc levels decreased ( P trend <0.0001) and EXE-Cys levels increased ( P trend <0.0001) with combined increasing numbers of the UGT2B17*2 allele and decreasing numbers of the GSTA1*B allele. While GSTA1 genotype alone showed no effect on serum 17β-DHE-Gluc levels, the UGT2B17 (*2/*2) genotype was associated with a 10.4-fold decrease ( P <0.0001) in serum 17β-DHE-Gluc levels as compared to wild-type UGT2B17 . The GSTA1 (*B/*B) genotype was associated with 1.4- ( P <0.0001) and 1.3-fold ( P =0.0005) decreases while UGT2B17 (*2/*2) genotype was associated with 2.1- ( P <0.0001) and 2.3-fold ( P <0.0001) increases in EXE-Cys and 17β-DHE-Cys formation, respectively, as compared to their respective wild-type genotypes. These results suggest that GSTA1 and UGT2B17 genotypes play an important role in EXE metabolism variability and potentially in patient response to EXE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".