Clinical Relevance of ABCB1, ABCG2, and ABCC2 Gene Polymorphisms in Chronic Myeloid Leukemia Patients Treated With Nilotinib
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKIs) have radically changed the outcome of chronic myeloid leukemia (CML) patients in the last 20 years. Moreover, the advent of second generation TKIs, namely nilotinib and dasatinib, have largely increased the number of CML patients achieving deep and sustained molecular responses. However, the possible mechanisms capable of influencing the maintenance of the long-term molecular response are not yet fully known and understood. In this light, polymorphisms in MDR-ABC transporters may influence the efficacy and safety of TKIs. In this study, we examined seven single nucleotide polymorphisms (SNPs) in four ABC transporter genes: ABCC1 rs212090 (5463T>A), ABCC2 rs3740066 (3972C>T), ABCC2 rs4148386 G>A, ABCC2 rs1885301 (1549G>A), ABCG2 rs2231137 (34G>A), ABCG2 rs2231142 G>C, ABCB1 rs1045642 (3435C>T), to determine their effect on the achievement and/or loss of molecular response in 90 CML patients treated with nilotinib. We found that ABCC2 rs3740066 CC and CT as well as the ABCB1 rs1045642 TT genotypes correlated with a higher probability to achieve MR3 in a shorter time ( p =0.02, p =0.004, and p =0.01), whereas ABCG2 rs2231137 GG was associated with lower probability of MR3 achievement ( p =0.005). Moreover, ABCC2 rs3740066 CC genotype, the ABCB1 rs1045642 CC and TT genotypes were positively correlated with MR4 achievement ( p =0.02, p =0.007, and p =0.003). We then generated a predictive model incorporating the information of four genotypes, to evaluate the combined effect of the SNPs. The combination of SNPs present in the model affected the probability and the time to molecular response. This model had a high prognostic significance for both MR3 and MR4 ( p =0.005 and p =0.008, respectively). Finally, we found ABCG2 rs2231142 GG genotype to be associated with a decrease risk of MR3 loss. In conclusion, MDR-transporters SNPs may significantly affect the achievement and loss of molecular response in CML patients treated with nilotinib.
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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) | 0.001 | 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 | 0.001 |
| 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 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".