A Bayesian-Based Integrative Bioinformatics Analysis Nominates Oncogenic Drivers in Neuroblastoma

Identifying targetable oncogenic drivers remains a challenge in neuroblastoma, the most common extracranial solid malignancy in children. We applied a Bayesian algorithm for integrative analysis of expression and copy-number, iExCN, to nominate oncogenic drivers in neuroblastoma. The iExCN algorithm found 47 oncogenes from a dataset of 195 neuroblastoma biopsy specimens. The nominated oncogenes included some previously implicated in neuroblastoma, including ALK, MDM2, MYCN, and XPO1, but most have not been linked to this disease. A CRISPR/Cas9-based screen testing those candidates revealed neuroblastoma cell line-specific and shared vulnerabilities in three well-characterized models, and dependencies were confirmed by complementary RNA knockdown. Analysis of more than 30 additional neuroblastoma cell lines using DepMap confirmed statistically significant dependency scores in the iExCN genes. Finally, analysis of two cohorts of children with neuroblastoma revealed a trend toward worse survival in cases in which more iExCN genes showed copy number gains. Our findings uncover multiple, potentially targetable oncogenic drivers that appear to influence neuroblastoma biology, demonstrating a generalizable capacity for iExCN to unmask disease genes and therapeutic targets in cancer.