Integrative discovery of treatments for high-risk neuroblastoma

Research output: Contribution to journalArticle


Despite advances in the molecular exploration of paediatric cancers, approximately 50% of children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group of high-risk patients, we combine predictive data mining with experimental evaluation in patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates data from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures associated with high patient risk or disease processes. We find more than 80 targets to be associated with neuroblastoma risk and differentiation signatures. Selected targets are evaluated in cell lines derived from high-risk patients to demonstrate reversal of risk signatures and malignant phenotypes. Using neuroblastoma xenograft models, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method, available as a public tool (, will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.


  • Elin Almstedt
  • Ramy Elgendy
  • Neda Hekmati
  • Emil Rosén
  • Caroline Wärn
  • Thale Kristin Olsen
  • Cecilia Dyberg
  • Milena Doroszko
  • Ida Larsson
  • Anders Sundström
  • Marie Arsenian Henriksson
  • Sven Påhlman
  • Daniel Bexell
  • Michael Vanlandewijck
  • Per Kogner
  • Rebecka Jörnsten
  • Cecilia Krona
  • Sven Nelander
External organisations
  • Uppsala University
  • Karolinska Institutet
  • Chalmers University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cancer and Oncology
Original languageEnglish
Article number71
JournalNature Communications
Publication statusPublished - 2020 Jan 3
Publication categoryResearch