Pharmacogenetics of Childhood Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Patients vary widely in their responses to drug therapy, often manifested as adverse drug reactions, drug resistance or drug-associated toxicity. Functional polymorphisms in genes encoding enzymes, which are involved in drug action, may underlie these differences thus offering a powerful tool in predicting a treatment outcome. Several studies have addressed the potential for tailoring individual acute lymphoblastic leukemia (ALL) therapy based on patients genetics. Several candidate genes have been shown to have a predictive role, among which the best examples are the well-characterized polymorphisms in the thiopurine methyltransferase (TPMT) gene. The impact of TPMT genotypes on 6-MP tolerance, affecting the duration of treatment and the appearance of severe hematotoxicity or secondary malignancies has been well documented. Recent studies suggest that polymorphisms in enzymes of the folate cycle may modify the therapeutic effectiveness and toxicity of antifolates. Polymorphism in the enhancer element located in 5-UTR of thymidylate synthase gene influenced the outcome of ALL, whereas variants of methylene tetrahydrofolate reductase gene correlated with methotrexate sensitivity. Efforts have been also made to gain a closer insight into the role of polymorphisms in genes that might affect both disease susceptibility and drug metabolism, and some of them (e.g. glutathione S-transferase or quinone oxidoreductase) seem to affect the risk of recurrent disease in children with ALL. Extending the pharmacogenetics concept to other candidate genes / enzymes and to other drugs might, through comprehensive genetic evaluation, help clinical management of ALL patients. Keywords: pharmacogenetics, childhood acute lymphoblastic leukemia, acute lymphoblastic, thiopurine methyltransferase, thymidylate synthase gene, quinone oxidoreductase
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".