Integrative discovery of treatments for high-risk neuroblastoma

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.

We lack effective treatment for half of children with high-risk neuroblastoma. Here, the authors introduce an algorithm that can predict the effect of interventions on gene expression signatures associated with high disease processes and risk, and identify and validate promising drug targets.

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

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.

Abstract 3395: TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma

Despite the many advances in the molecular characterization of neuroblastoma, effective treatments for high-risk patients are currently lacking. Using publically available data, integrated computational analysis offers new opportunities to uncover druggable subgroups. In the TargetTranslator project, we have combined state-of-the-art Big Data techniques to identify new targets in high-risk subgroups of neuroblastoma. Starting with clinical or genomic risk factors, TargetTranslator builds a consensus molecular signature across cohorts. This signature is scored against a massive amount of data from (i) childhood tumor biobanks (TARGET, R2), (ii) drug profiling data from cancer cell models (NIH-LINCS), and (iii) drug targets and pathways (STITCH, STRING, MSIGDB). As output, the system provides ranked lists of compounds, molecular targets and pathways for each risk factor, as well as an aggregated probability score. In a proof-of-concept study, we used TargetTranslator to recommend treatments for high-risk neuroblastoma subgroups, including COG high-risk, MYCN amplification, 11q deletion, and signatures of differentiation and ALK activation. Depending on risk group stratification, we detected between 21 and 680 substances (p<0.001), most of which were strongly associated to approximately 10 key targets. In addition to confirming a key role for the PI3K/mTOR and MAPK pathways, we detected high scoring compounds in non-canonical pathways, including neurotransmission, GLI/SMO, ROCK and PKA. Experimental validation of 11 predicted drugs in patient-derived neuroblastoma lines confirmed our signatures and reduced viability. We also identified four new compounds that significantly (p<0.05) suppressed MYCN protein levels (ROCK inhibitor fasudil, CNR2 agonist GW405833, CDK inhibitor AZD5438, lovastatin) at concentrations non-toxic in zebrafish embryos. By integrating data from patients, drugs, and drug-protein networks, we establish a new computational pipeline that predicts protein targets in specific patient subgroups. The TargetTranslator introduces a way to bridge drug effects with patient data and will be available as a user-friendly web tool on www.targettranslator.org in 2018.

Citation Format: Elin Almstedt, Caroline Wärn, Ramy Elgendy, Neda Hekmati, Emil Rosén, Ida Larsson, Rebecka Jörnsten, Cecilia Crona, Sven Nelander. TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3395.