Extraction of tamoxifen and its metabolites from formalin-fixed, paraffin-embedded tissues: an innovative quantitation method using liquid chromatography and tandem mass spectrometry
Bibliographic record
Abstract
PURPOSE: Tamoxifen is a key therapeutic option for breast cancer treatment. Understanding its complex metabolism and pharmacokinetics is important for dose optimization. We examined the possibility of utilizing archival formalin-fixed paraffin-embedded (FFPE) tissue as an alternative sample source for quantification since well-annotated retrospective samples were always limited. METHODS: Six 15 μm sections of FFPE tissues were deparaffinized with xylene and purified using solid-phase extraction. Tamoxifen and its metabolites were separated and detected by liquid chromatography-tandem mass spectrometry using multiple-reaction monitoring. RESULTS: This method was linear between 0.4 and 200 ng/g for 4-hydroxy-tamoxifen and endoxifen, and 4-2,000 ng/g for tamoxifen and N-desmethyl-tamoxifen. Inter- and intra-assay precisions were <9 %, and mean accuracies ranged from 81 to 106 %. Extraction recoveries were between 83 and 88 %. The validated method was applied to FFPE tissues from two groups of patients, who received 20 mg/day of tamoxifen for >6 months, and were classified into breast tumor recurrence and non-recurrence. Our preliminary data show that levels of tamoxifen metabolites were significantly lower in patients with recurrent cancer, suggesting that inter-individual variability in tamoxifen metabolism might partly account for the development of cancer recurrence. Nevertheless, other causes such as non-compliance or stopping therapy of tamoxifen could possibly lead to the concentration differences. CONCLUSIONS: The ability to successfully study tamoxifen metabolism in such tissue samples will rapidly increase our knowledge of how tamoxifen's action, metabolism and tissue distribution contribute to breast cancer control. However, larger population studies are required to understand the underlying mechanism of tamoxifen metabolism for optimization of its treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".