Clinical Relevance of a Pharmacogenetic Approach Using Multiple Candidate Genes to Predict Response and Resistance to Imatinib Therapy in Chronic Myeloid Leukemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Imatinib resistance is major cause of imatinib mesylate (IM) treatment failure in chronic myeloid leukemia (CML) patients. Several cellular and genetic mechanisms of imatinib resistance have been proposed, including amplification and overexpression of the BCR/ABL gene, the tyrosine kinase domain point mutations, and MDR1 gene overexpression. EXPERIMENTAL DESIGN: We investigated the impact of 16 single nucleotide polymorphisms (SNP) in five genes potentially associated with pharmacogenetics of IM, namely ABCB1, multidrug resistance 1; ABCG2, breast-cancer resistance protein; CYP3A5, cytochrome P450-3A5; SLC22A1, human organic cation transporter 1; and AGP, alpha1-acid glycoprotein. The DNAs from peripheral blood samples in 229 patients were genotyped. RESULTS: The GG genotype in ABCG2 (rs2231137), AA genotype in CYP3A5 (rs776746), and advanced stage were significantly associated with poor response to IM especially for major or complete cytogenetic response, whereas the GG genotype at SLC22A1 (rs683369) and advanced stage correlated with high rate of loss of response or treatment failure to IM therapy. CONCLUSIONS: We showed that the treatment outcomes of imatinib therapy could be predicted using a novel, multiple candidate gene approach based on the pharmacogenetics of IM.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it