Integrative discovery of treatments for high-risk neuroblastoma
Journal article, 2020

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 (targettranslator.org), will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.

Author

Elin Almstedt

Uppsala University

Ramy Elgendy

Uppsala University

Neda Hekmati

Uppsala University

Emil Rosén

Uppsala University

Caroline Wärn

Uppsala University

Thale Kristin Olsen

Karolinska Institutet

Cecilia Dyberg

Karolinska Institutet

Milena Doroszko

Uppsala University

Ida Larsson

Uppsala University

Anders Sundström

Uppsala University

Marie Arsenian Henriksson

Karolinska Institutet

Sven Påhlman

Lund University

Daniel Bexell

Lund University

Michael Vanlandewijck

Uppsala University

Karolinska Institutet

Per Kogner

Karolinska Institutet

Rebecka Jörnsten

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Cecilia Krona

Uppsala University

S. Nelander

Uppsala University

Nature Communications

2041-1723 (ISSN)

Vol. 11 1 71

Subject Categories

Hematology

Medical Genetics

Cancer and Oncology

DOI

10.1038/s41467-019-13817-8

PubMed

31900415

More information

Latest update

2/12/2020