Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Dokumenter

  • Fulltext

    Forlagets udgivne version, 2,63 MB, PDF-dokument

  • 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
  • Trond Flaegstad
  • Erik Forestier
  • Ólafur G. Jónsson
  • Jukka Kanerva
  • Olli Lohi
  • Ulrika Norén-Nyström
  • Arja Harila
  • Mats Heyman
  • Gudmar Lönnerholm
  • Ann Christine Syvänen
  • Jessica Nordlund
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.
OriginalsprogEngelsk
Artikelnummer131
Tidsskriftnpj Precision Oncology
Vol/bind7
Udgave nummer1
Antal sider13
ISSN2397-768X
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was supported by grants from the Swedish Research Council (2019-01976 to JN), the Swedish Cancer Society (CAN2018-623 to ACS and CAN2022-2395 to JN), the Swedish Childhood Cancer Foundation (PR2017-0023 to ACS and PR2019-0046 to JN), the Göran Gustafsons Foundation (to JN), the Jane and Aatos Erkko Foundation and the Academy of Finland #321550 (to OL and MH). DNA methylation array analysis and RNA-sequencing was performed with assistance from the SciLifeLab National Genomics Infrastructure, SNP&SEQ Technology Platform, which is funded by the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Computational resources were provided by the Swedish National Infrastructure for Computing (SNIC), National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Finnish IT Centre of Science (CSC) and University of Eastern Finland Bioinformatics Center. SNIC and NAISS are partially funded by the Swedish Research Council. We thank Sara Nystedt and Sara Nilsson for technical assistance, Jonas Carlsson Almlöf and Christofer Bäcklin for input on DNA methylation classification, and our colleagues from NOPHO LL Biology Group, Lucia Cavelier, Anna Bremer and Tatjana Pandzic for valuable input on the study design. We especially thank the ALL patients who contributed samples to this study. Figures 1 , 5f and Supplementary Figs. 2 –3 were made with Biorender.com.

Funding Information:
This work was supported by grants from the Swedish Research Council (2019-01976 to JN), the Swedish Cancer Society (CAN2018-623 to ACS and CAN2022-2395 to JN), the Swedish Childhood Cancer Foundation (PR2017-0023 to ACS and PR2019-0046 to JN), the Göran Gustafsons Foundation (to JN), the Jane and Aatos Erkko Foundation and the Academy of Finland #321550 (to OL and MH). DNA methylation array analysis and RNA-sequencing was performed with assistance from the SciLifeLab National Genomics Infrastructure, SNP&SEQ Technology Platform, which is funded by the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Computational resources were provided by the Swedish National Infrastructure for Computing (SNIC), National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Finnish IT Centre of Science (CSC) and University of Eastern Finland Bioinformatics Center. SNIC and NAISS are partially funded by the Swedish Research Council. We thank Sara Nystedt and Sara Nilsson for technical assistance, Jonas Carlsson Almlöf and Christofer Bäcklin for input on DNA methylation classification, and our colleagues from NOPHO LL Biology Group, Lucia Cavelier, Anna Bremer and Tatjana Pandzic for valuable input on the study design. We especially thank the ALL patients who contributed samples to this study. Figures , and Supplementary Figs. – were made with Biorender.com.

Publisher Copyright:
© 2023, The Author(s).

ID: 386373032