1. Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia
- Author
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Olga Krali, Yanara Marincevic-Zuniga, Gustav Arvidsson, Anna Pia Enblad, Anders Lundmark, Shumaila Sayyab, Vasilios Zachariadis, Merja Heinäniemi, Janne Suhonen, Laura Oksa, Kaisa Vepsäläinen, Ingegerd Öfverholm, Gisela Barbany, Ann Nordgren, Henrik Lilljebjörn, Thoas Fioretos, Hans O. Madsen, Hanne Vibeke Marquart, Trond Flaegstad, Erik Forestier, Ólafur G. Jónsson, Jukka Kanerva, Olli Lohi, Ulrika Norén-Nyström, Kjeld Schmiegelow, Arja Harila, Mats Heyman, Gudmar Lönnerholm, Ann-Christine Syvänen, and Jessica Nordlund
- Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Genomic 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 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.
- Published
- 2023
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