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Despite major advances in the molecular exploration of pediatric cancers, approximately 50 % of children with high-risk neuroblastoma lack effective treatment. To identify new therapeutic options for this group of high-risk patients, we have combined integrative data analysis with experimental evaluation in patient-derived xenograft cells. We propose a new algorithm, TargetTranslator, which combines data from tumor biobanks, pharmacological databases, and cellular networks, to predict how particular targeted interventions will affect mRNA signatures associated with high patient risk. We find more than 80 known and novel targets to be associated with neuroblastoma risk and differentiation signatures. To evaluate these predictions, we performed RNA sequencing of drug-treated cell lines derived from high-risk patients and show that predicted compounds suppress risk signatures and malignant phenotypes. Using a xenograft model, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method (made available as a public tool, targettranslator.org) will enhance and expedite the discovery of risk-associated targets for pediatric and adult cancers.

PDTM-43. THERAPEUTIC TARGETS FROM BIG DATA: INTEGRATIVE DISCOVERY OF TREATMENTS FOR HIGH-RISK NEUROBLASTOMA