Abstract 1137: Next generation sequencing of the neuroblastoma transcriptome identifies multiple protein disrupting mutations

Introduction: Neuroblastoma (NB) is one of the small round blue cell tumors of childhood arising in the peripheral nervous system. Fifty percent of patients present with metastatic or high risk disease and despite aggressive multimodal therapy approximately 60% percent of these patients eventually succumb to NB. Currently only a handful of molecular alterations are known to influence prognosis, but no clear mechanism of pathogenesis has been demonstrated. Recently, targeted sequencing efforts identified recurrent single nucleotide variants (SNVs) in the ALK gene in 6.1-15% of NB. In an effort to better understand the biology driving this tumor and to identify new targets for therapy, we sequenced the transcriptomes of 20 stage four tumors, including ten MYCN amplified and ten MYCN non-amplified samples, using massively parallel sequencing technology. Methods: In our analysis pipeline, 50 base nucleotide reads are filtered against a database of rRNA, tRNA, repetitive regions, and adapter sequences. The remaining reads were aligned to the reference human genome (hg 18) and a database of spice junctions. Reads that align were analyzed for: 1) base coverage, 2) transcript expression levels, 3) calling SNVs and 4) determination of which of the SNVs were damaging by Sorting Intolerant From Tolerant (SIFT) analysis Results: Initial analysis of the first six samples, yielded an average of 86.6 million uniquely mapped reads per sample. On average we detected the expression of 6,000 genes to a depth of 10x. The RNA seq expression profile correlated well with expression array data from the same sample (r=0.62), while the sequencing data identified an additional 3,000 genes that were not detected by microarray. Our sequencing method maintains “strandedness” allowing the identification of multiple novel antisense non-coding transcripts. Using the SAMtool, an average of 1,255 nonsynonymous SNVs predicted per sample. Of these nonsynonymous SNVs, 69-160 per sample were predicted by the SIFT algorithm to be damaging. Interestingly, 3 different genes had damaging nonsynonymous SNVs in at least 30% of the samples. Conclusion: Next generation sequencing of transcriptome is a powerful and more sensitive method than microarrays for expression profiling and allows for the identification of novel transcripts including non-coding RNAs. Here we report the most extensive profiling of the neuroblastoma transcriptome to date using this technology. We identified several hundred protein disrupting SNVs, and of these 3 were commonly altered. Ongoing analysis is underway to validate our results. The identification of recurrent genetic alterations in NB will assist in developing a better understanding of the mechanisms of pathogenesis of this pediatric cancer and lead to new therapeutic targets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1137.