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) 20411723 (eISSN)
Vol. 11 1 71Subject Categories
Hematology
Medical Genetics
Cancer and Oncology
DOI
10.1038/s41467-019-13817-8
PubMed
31900415