Clinical Relevance of a Pharmacogenetic Approach Using Multiple Candidate Genes to Predict Response and Resistance to Imatinib Therapy in Chronic Myeloid Leukemia
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".