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RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq]

ABSTRACT: We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels (i.e., gene, transcript, and exon junction levels), RNA-Seq models based on the AceView database performed best on most endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. Sample clinical characteristics definitions: dataset: Expression data set used for classification training (1) or validation (2) Sex: M = male; F= female age at diagnosis: the age in days at diagnosis mycn status: Amplification status of the MYCN proto-oncogene (amplified = 1, no amplification = 0; no information = N/A) high risk: Clinically considered as high-risk neuroblastoma (yes=1, no= 0) INSS stage: disease stage according to International Neuroblastoma Staging System (INSS) (1, 2, 3, 4 and 4S) class label: Maximally divergent disease courses - unfavorable (= 1): patient died despite intensive chemotherapy, favorable (=0): patient survived without chemotharapy for at least 1000 days post diagnosis; not applicable (N/A) progression: Occurrence of a tumor progression event (yes=1; no=0) death from disease: Occurrence of death from the disease (yes=1; no=0)

ORGANISM(S): Homo sapiens

PROVIDER: GSE49711 | GEO | 2015/05/22

SECONDARY ACCESSION(S): PRJNA214797

REPOSITORIES: GEO

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Project description:We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels (i.e., gene, transcript, and exon junction levels), RNA-Seq models based on the AceView database performed best on most endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. Sample clinical characteristics definitions: dataset: Expression data set used for classification training (1) or validation (2) Sex: M = male; F= female age at diagnosis: the age in days at diagnosis mycn status: Amplification status of the MYCN proto-oncogene (amplified = 1, no amplification = 0; no information = N/A) high risk: Clinically considered as high-risk neuroblastoma (yes=1, no= 0) INSS stage: disease stage according to International Neuroblastoma Staging System (INSS) (1, 2, 3, 4 and 4S) class label: Maximally divergent disease courses - unfavorable (= 1): patient died despite intensive chemotherapy, favorable (=0): patient survived without chemotharapy for at least 1000 days post diagnosis; not applicable (N/A) progression: Occurrence of a tumor progression event (yes=1; no=0) death from disease: Occurrence of death from the disease (yes=1; no=0) Gene expression of 498 neuroblastoma samples was quantified by RNA sequencing as well as by microarray analyses in order to understand the neuroblastoma transcriptome and predict clinical endpoints.

Project description:In neuroblastoma, amplification of the oncogenic basic helix-loop-helix (bHLH) transcription factor (TF) MYCN is the defining prognosticator of high-risk disease, occurs in one-third of neuroblastoma, and drastically reduces overall survival rates. As a proto-oncogene, targeted MYCN overexpression in peripheral neural crest is sufficient to initiate disease in mouse models. In MYCN amplified neuroblastoma, elevated expression of the factor is crucial to maintain tumor stemness and is associated with increased proliferation and aberrant cell cycle progression, as these tumors lack the ability to arrest in G1 in response to irradiation. MYCN down-regulation broadly reverses these oncogenic phenotypes in a variety of neuroblastoma models and recent thereapeutic strategies to indirectly target MYCN production or protein stability have reduced tumor growth in vivo. These observations motivate an investigation of MYCN binding in MYCN amplified tumors as it remains fundamentally unclear how elevated levels of the factor occupy the genome and alter transcriptional programs in neuroblastoma. Here we present the first dynamic chromatin and transcriptional landscape of direct MYCN perturbation in neuroblastoma. We find that at oncogenic levels, MYCN associates with E-box (CANNTG) binding motifs in an affinity dependent manner across most active cis-regulatory promoters and enhancers. MYCN shutdown globally reduces histone acetylation and transcription, consistent with prior descriptions of MYC proteins as non-linear amplifiers of gene expression. We establish that MYCN load at the promoter and proximal enhancers predicts transcriptional responsiveness to MYCN shutdown and that MYCN enhancer binding occurs prominently at the most strongly occupied and down-regulated genes, suggesting a role for these tissue specific elements in predicating MYCN responsive âtargetâ genes. At these invaded enhancers, we identify the lineage specific bHLH TWIST1 as a key collaborator and dependency of oncogenic MYCN. These data suggest that MYCN enhancer invasion helps shape transcriptional amplification of the neuroblastoma gene expression program to promote tumorigenesis. ChIP-Seq in SHEP21, BE2C, KELLY, and NGP neuroblastoma cell lines for H3K27ac, H3K4me3, RNA PolII, MYCN, BRD4, or TWIST1

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RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq]

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