Reassessment of the Risk-stratified GD-2008 ALL Protocol

PML mutation and familial pediatric acute lymphoblastic leukemia: A case report

Hereditary factors contribute to the pathogenesis of pediatric leukemia. However, few studies have reported gene mutation pathopoeias. This paper reports genetic mutations associated with hereditary acute lymphoblastic leukemia. We reported a case of siblings diagnosed with acute lymphoblastic leukemia when aged 3 and 7 years, both siblings are alive after chemotherapy, and whole exome sequencing analysis was performed on the siblings and their parents. It was observed that both siblings had diheterozygous mutations in PML gene (PML, NM_033250, exon7, c.2170A>G, p.S724G; PML, NM_033250, exon7, c.2195G>T, p.G732V), and their parents had heterozygous mutations in one mutation site of PML gene, respectively, suggesting that the diheterozygous mutations of PML gene might be causal genetic genes for the occurrence of acute lymphoblastic leukemia.

Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning

This study aimed to establish a predictive model to identify children with hematologic malignancy at high risk for delayed clearance of high-dose methotrexate (HD-MTX) based on machine learning. A total of 205 patients were recruited. Five variables (hematocrit, risk classification, dose, SLC19A1 rs2838958, sex) and three variables (SLC19A1 rs2838958, sex, dose) were statistically significant in univariable analysis and, separately, multivariate logistic regression. The data was randomly split into a "training cohort" and a "validation cohort". A nomogram for prediction of delayed HD-MTX clearance was constructed using the three variables in the training dataset and validated in the validation dataset. Five machine learning algorithms (cart classification and regression trees, naïve Bayes, support vector machine, random forest, C5.0 decision tree) combined with different resampling methods were used for model building with five or three variables. When developed machine learning models were evaluated in the validation dataset, the C5.0 decision tree combined with the synthetic minority oversampling technique (SMOTE) using five variables had the highest area under the receiver operating characteristic curve (AUC 0.807 [95% CI 0.724-0.889]), a better performance than the nomogram (AUC 0.69 [95% CI 0.594-0.787]). The results support potential clinical application of machine learning for patient risk classification.