Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia

Abstract

AbstractGenomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 280 cases with undefined/B-other molecular phenotype, resulting in a single revised subtype for 85.4% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides the first comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries, identifying subgroups with differential survival outcomes.