Integrative discovery of treatments for high-risk neuroblastoma

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• High-risk neuroblastoma remains a significant challenge in paediatric oncology, with limited effective therapeutic options for approximately 50% of patients.
• Advances in molecular profiling have identified risk signatures but translating this knowledge into targeted therapies is difficult.

Purpose of the Study:

• To identify and validate novel therapeutic targets for high-risk neuroblastoma by integrating diverse biological data.
• To develop a predictive algorithm, TargetTranslator, to accelerate the discovery of treatments for paediatric cancers.

Main Methods:

• Development of the TargetTranslator algorithm, which integrates tumour biobank data, pharmacological databases, and cellular networks.
• Prediction of targeted intervention effects on mRNA signatures associated with neuroblastoma risk and differentiation.
• Experimental validation of predicted targets in patient-derived xenograft cell lines and neuroblastoma xenograft models.

Main Results:

• Identification of over 80 targets associated with neuroblastoma risk and differentiation signatures.
• Demonstration of the reversal of risk signatures and malignant phenotypes in cell lines upon targeting selected candidates.
• Establishment of CNR2 and MAPK8 as promising therapeutic targets for high-risk neuroblastoma.

Conclusions:

• The TargetTranslator algorithm effectively predicts therapeutic targets for high-risk neuroblastoma.
• CNR2 and MAPK8 represent promising candidates for novel neuroblastoma treatments.
• The TargetTranslator tool has the potential to expedite target discovery for both paediatric and adult cancers.