Integrative discovery of treatments for high-risk neuroblastoma
Artikel i vetenskaplig tidskrift, 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.
Författare
Elin Almstedt
Uppsala universitet
Ramy Elgendy
Uppsala universitet
Neda Hekmati
Uppsala universitet
Emil Rosén
Uppsala universitet
Caroline Wärn
Uppsala universitet
Thale Kristin Olsen
Karolinska Institutet
Cecilia Dyberg
Karolinska Institutet
Milena Doroszko
Uppsala universitet
Ida Larsson
Uppsala universitet
Anders Sundström
Uppsala universitet
Marie Arsenian Henriksson
Karolinska Institutet
Sven Påhlman
Lunds universitet
Daniel Bexell
Lunds universitet
Michael Vanlandewijck
Uppsala universitet
Karolinska Institutet
Per Kogner
Karolinska Institutet
Rebecka Jörnsten
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Cecilia Krona
Uppsala universitet
S. Nelander
Uppsala universitet
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 11 1 71Ämneskategorier
Hematologi
Medicinsk genetik
Cancer och onkologi
DOI
10.1038/s41467-019-13817-8
PubMed
31900415