A mechanistic classification of clinical phenotypes in neuroblastoma
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ABSTRACT:
PROVIDER: PRJNA493612 | ENA |
REPOSITORIES: ENA
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Project description:A mechanistic classification of clinical phenotypes in neuroblastoma (telomere maintenance cohort)
| PRJNA493570 | ENA
Project description:Our study proposes a precise mechanistic classification of clinical neuroblastoma phenotypes that is based on telomere maintenance mechanisms and RAS or p53 pathway mutations. A crucial factor in telomere maintenance is overexpression of TERT. We therefore determined a TERT expression threshold to identify MYCN-WT TERT-WT tumors whose TERT mRNA levels are comparable to those of tumors bearing MYCN or TERT alterations.
2018-10-25 | GSE120559 | GEO
Project description:Our study proposes a precise mechanistic classification of clinical neuroblastoma phenotypes that is based on telomere maintenance mechanisms and RAS or p53 pathway mutations. A crucial factor in telomere maintenance is overexpression of TERT. We therefore determined a TERT expression threshold to identify MYCNWT TERTWT tumors whose TERT mRNA levels are comparable to those of tumors bearing MYCN or TERT alterations.
2018-10-25 | GSE120568 | GEO
Project description:A mechanistic classification of clinical phenotypes in neuroblastoma (TERT expression threshold cohort)
| PRJNA493609 | ENA
Project description:Clinical courses of the pediatric tumor neuroblastoma range from fatal progression to spontaneous regression. To gain insights into the pathogenesis of these divergent subtypes, we performed massively parallel sequencing of 416 pretreatment neuroblastomas and assessed telomere maintenance mechanisms in 208/416 cases. We observed that telomere maintenance and mutations in RAS or p53 pathway genes strongly affected disease courses. Fatal outcome occurred only in the presence of telomere maintenance, whereas patients whose tumors lacked such mechanisms had excellent outcome. In patients with telomere maintenance-positive tumors, survival was dramatically inferior when additional RAS/p53 pathway mutations were present. By contrast, spontaneous regression occurred both in the presence and absence of RAS/p53 pathway mutations in patients with telomere maintenance-negative tumors. Together, our data provide a precise mechanistic classification of clinical neuroblastoma phenotypes based on telomere maintenance mechanisms and genetic alterations of the tumor.
| EGAS00001003244 | EGA
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-10-25 | GSE120572 | GEO
Project description:Neuroblastoma is a pediatric tumor of the sympathetic nervous system. Its clinical course ranges from spontaneous tumor regression to fatal progression. To investigate the molecular features of the divergent tumor subtypes, we performed genome sequencing on 416 pretreatment neuroblastomas and assessed telomere maintenance mechanisms in 208 of these tumors. We found that patients whose tumors lacked telomere maintenance mechanisms had an excellent prognosis, whereas the prognosis of patients whose tumors harbored telomere maintenance mechanisms was substantially worse. Survival rates were lowest for neuroblastoma patients whose tumors harbored telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Spontaneous tumor regression occurred both in the presence and absence of these mutations in patients with telomere maintenance-negative tumors. On the basis of these data, we propose a mechanistic classification of neuroblastoma that may benefit the clinical management of patients.
| S-EPMC7875194 | biostudies-literature
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.
2014-10-21 | E-GEOD-49710 | biostudies-arrayexpress
Project description:Ependymoma subpopulation lineages underlie clinical classification and outcome
| PRJNA518917 | ENA
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)
2014-10-21 | GSE49710 | GEO