Polymorphisms in genes encoding drugs and xenobiotic metabolizing enzymes, DNA repair enzymes, and response to treatment of childhood acute lymphoblastic leukemia.
Bibliographic record
Abstract
PURPOSE: The most common childhood malignancy, acute lymphoblastic leukemia (ALL), remains the leading cause of cancer-related death in children because of resistant cases in which underlying predisposing factors are poorly understood. The interindividual variation in the activity of xenobiotic metabolizing enzymes that modify individual somatic mutation burden in the context of environmental exposure was shown to modify susceptibility to childhood ALL. Variable DNA repair capacity may further modulate induced DNA lesions. Similarly, differential capacity of ALL patients to process carcinogens and chemotherapeutic drugs could both modify an individual's risk of recurrent malignancy and response to therapy. EXPERIMENTAL DESIGN: We investigated the relationship between the risk of relapse in ALL patients and functional polymorphisms in genes encoding carcinogen-metabolizing enzymes, including CYP1A1, CYP2D6, CYP2E1, MPO, GSTM1, GSTT1, GSTP1, NAT1, NAT2, NQO1, as well as DNA-repair enzymes hMLH1, hMSH3, XRCC1, XPF, and APE. Our study included 320 children with ALL, of which 68 relapsed or died because of this disease within 5 years of follow-up. RESULTS: Among children of the latter group, we found that carriers of CYP1A1*2A and NQO1*2 variants had worse disease prognosis according to Kaplan-Meier (P = 0.003) and Cox regression (P <or= 0.03) analyses. hMLH1 Ile219 contributed to the increased risk of relapse when combined with the CYP1A1*2A variant. CONCLUSIONS: Our findings suggest that determining individual genotypes can become important in predicting disease outcome. Genotyping could also guide the therapeutic protocol.
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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 | 0.000 | 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".