Single-Nucleotide Polymorphisms in Reductase Genes Are not Associated with Response to Daunorubicin-Based Remission Induction
Bibliographic record
Abstract
BACKGROUND: To improve the quality of care for patients with acute myeloid leukemia (AML), biomarkers predictive of response to the standard daunorubicin-based induction therapy are needed. Genetic variants affecting daunorubicin metabolism are attractive candidates for such biomarkers. METHODS: We have previously shown that 13 of the naturally occurring nonsynonymous single-nucleotide polymorphisms (SNP) in the reductase genes affect daunorubicin metabolism in vitro. Here, we test these SNPs individually and jointly for association with response to one cycle of daunorubicin-based chemotherapy in a sample of 189 patients with acute myelogenous leukemia. RESULTS: Of the 13 SNPs included in this study, only 5 passed quality control filters. No association was found between these 5 SNPs and response to one cycle of daunorubicin-based induction therapy in either individual or joint effect tests. CONCLUSIONS: Despite their showing in vitro effect on metabolic rate of daunorubicin, the nonsynonymous SNPs in the reductase genes on their own are not significant contributors to the observed variability in response to daunorubicin therapy and thus, as singularities, are not useful biomarkers of this outcome. IMPACT: The results of this investigation provide important information for studies on personalization of anthracycline-based therapies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".