Pathway‐Informed Machine Learning Identifies Genetic Predictors of High‐Dose Methotrexate‐Induced Mucositis in Pediatric Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
High-dose methotrexate for pediatric cancer treatment is frequently associated with mucositis, which can lead to delayed or discontinued treatment and impact survival. While individual genetic variants have been implicated, the cumulative impact of genetic variation within relevant biological pathways remains unexplored. We evaluated single nucleotide polymorphisms across 18 pathways previously identified as relevant to mucositis in 278 pediatric patients with acute lymphoblastic leukemia from six academic health centers across Canada. Pathway enrichment was assessed using the Joint Association of Genetic variants tool, and a predictive model was developed using XGBoost, a supervised machine learning algorithm based on gradient-boosted decision trees. Pathway enrichment identified significant associations in IL6 (P = 0.04) and WNT/β-catenin (P = 0.048) signaling pathways. The predictive model (area under the curve [AUC] = 0.76) highlighted single nucleotide polymorphisms associated with inflammation- and mucosa-related genes, including PRKCD, IL17B, MAST3, and CAPN9, with both risk and protective effects. Model performance dropped by 0.15 in AUC (from 0.76 to 0.61) after removing single nucleotide polymorphism features, underscoring their predictive value. This pathway-informed approach identifies genetic contributors to methotrexate-induced mucositis and supports polygenic risk prediction. Our findings provide a foundation for individualized toxicity risk profiling and suggest potential therapeutic targets to mitigate treatment-limiting mucositis in pediatric oncology.
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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 | 0.001 | 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".