Neuroblastoma, like many childhood cancers, exhibits a relative paucity of somatic single nucleotide variants (SNVs). Here, we assess the contribution of structural variation (SV) in neuroblastoma using a combination of whole genome sequencing (WGS; n=135) and single nucleotide polymorphism (SNP) genotyping (n=914) of matched tumor-normal pairs. Our study design provided means for orthogonal validation of SVs as well as validation across genomic platforms. SV frequency, type, and localization varied significantly among high-risk tumors, with MYCN non-amplified tumors harboring an increased SV burden overall (P=1.12x10 -5 ). Genes disrupted by SV breakpoints were enriched in neuronal lineages and autism spectrum disorder. The postsynaptic adapter protein-coding gene SHANK2, located on chromosome 11q13, was disrupted by SVs in 14% and 10% of MYCN non-amplified highrisk tumors based on WGS and SNP array cohorts, respectively. Forced expression of SHANK2 in neuroblastoma cell models resulted in significant growth inhibition (P=2.62x10 -2 to 3.4x10 -5 ) and accelerated neuronal differentiation following treatment with all-trans retinoic acid (P=3.08x10 -13 to 2.38x10 -30 ). These data further define the complex landscape of structural variation in neuroblastoma and suggest that events leading to deregulation of neurodevelopmental processes, such as inactivation of SHANK2, are key mediators of tumorigenesis.Neuroblastoma is a cancer of the developing sympathetic nervous system that most commonly affects children under 5 years of age, with a median age at diagnosis of 17 months 1 . Approximately 50% of cases present with disseminated disease at the time of diagnosis, and despite intense multi-modal therapy, the survival rate for this high-risk subset remains less than 50% 1 . Recent whole genome and exome sequencing studies of neuroblastoma have revealed relatively few recurrent protein-coding somatic mutations including single nucleotide variations (SNVs) and small (<50b) insertion/deletions (indels) 2-5 .Large-scale structural variations (SVs) such as deletions, insertions, inversions, tandem duplications and translocations can arise from mutational processes that alter chromosome structure and evade innate mechanisms of maintaining genomic stability. These diverse SVs are commonly acquired somatically and act as driver mutations 6 .A plethora of approaches have been applied to detect SVs across large cancer datasets 6-9 .First, methods that identify copy number variations (CNVs) can be applied to intensity log R ratios from genotyping and comparative genomic hybridization (CGH) arrays as well as read-depth measures from next generation sequencing. Different segmentation algorithms have been applied to either platform in order to obtain copy number gain and loss calls, which range from a few hundred base-pair size to whole chromosomal alterations. These methods are dosage sensitive, allowing numerical quantification of amplifications and homozygous deletions. Analysis of CNVs in neuroblastoma primary tumor and matched...