Effects of Trough Concentration and Solute Carrier Polymorphisms on Imatinib Efficacy in Chinese Patients with Chronic Myeloid Leukemia
Bibliographic record
Abstract
PURPOSE: We investigated the relationship between imatinib trough concentrations and genetic polymorphisms with efficacy of imatinib in Chinese patients with chronic myeloid leukemia (CML). METHODS: There were 171 eligible patients. Peripheral blood samples were collected from 171 eligible patients between 21 and 27 hours after the last imatinib administration. Complete cytogenetic response (CCyR), major molecular response (MMR) and complete molecular response (CMR) were used as metrics for efficacy. Nine single nucleotide polymorphisms in 5 genes, SLC22A4 (917 T>C, -248 C>G and -538 C>G), SLC22A5 (-945 T>G and -1889 T>C), SLCO1A2 (-361 G>A), SLCO1B3 (334 T>G and 699 G>A) and ABCG2 (421C>A) were selected for genotyping. RESULTS: Patients with CCyR achieve higher trough concentrations than those without CCyR (1478.18±659.83 vs 984.89±454.06 ng mL-1, p<0.001). Patients with MMR and CMR achieve higher trough concentrations than those without MMR and CMR, respectively (1486.40±703.38 vs 1121.17±527.14 ng mL-1, p=0.007; 1528.00±709.98 vs 1112.67±518.35 ng mL-1, p=0.003, respectively). Carriers of A allele in SLCO1A2 -361G>A achieve higher CCyR and MMR rates (p=0.047, OR=4.320, 95% CI: 0.924-20.206; p=0.042, OR=2.825, 95% CI: 1.016-7.853, respectively). Both trough concentrations and SLCO1A2 -361G>A genotypes are independent factors affecting imatinib efficacy. The positive and negative predictive values for CCyR are 71.01% and 68.75%, respectively. The positive and negative predictive values for MMR are 62.86% and 69.70%, respectively. CONCLUSION: Imatinib trough concentrations and SLCO1A2 -361G>A genotypes are associated with imatinib efficacy in Chinese patients with CML.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".