Pathway-Informed Machine Learning Identifies Genetic Predictors of High-Dose Methotrexate-Induced Mucositis in Pediatric Acute Lymphoblastic Leukemia.

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 signific

SUBMITTER: Zhang XYC 

PROVIDER: S-EPMC12816430 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature