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Association of Age at Diagnosis and Genetic Mutations in Patients with Neuroblastoma

Neuroblastoma is diagnosed over a wide age range from birth through young adulthood, and older age at diagnosis is associated with a decline in survivability. We performed whole genome sequencing of DNA from diagnostic tumors and their matched germlines from 40 patients with metastatic neuroblastoma obtained between 1987 and 2009. To identify genetic lesions that are associated with age at diagnosis in patients with metastatic neuroblastoma. Age groups at diagnosis included infants (0-18 months), children (18 months-12 years), and adolescents and young adults (>12 years). To confirm the findings from this discovery cohort, validation testing using tumors from an additional 64 patients obtained between 1985 and 2009 was also performed. Formalin-fixed paraffin-embedded tumor tissue was used for immunohistochemistry and fluorescent in situ hybridization. Telomere lengths were analyzed using the whole genome sequencing data, quantitative polymerase chain reaction and fluorescent in situ hybridization

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001000135 Illumina HiSeq 2000 80
EGAD00001001432 Illumina HiSeq 2000 1337
EGAD00001001433 Illumina HiSeq 2000 906
EGAD00001006196 Illumina HiSeq 2000 3
EGAD00001006344 30
Publications Citations
Association of age at diagnosis and genetic mutations in patients with neuroblastoma.
JAMA 307: 2012 1062-1071
251
Assessing telomeric DNA content in pediatric cancers using whole-genome sequencing data.
Genome Biol 13: 2012 R113
22
The genetics of splicing in neuroblastoma.
Cancer Discov 5: 2015 380-395
14
Computel: computation of mean telomere length from whole-genome next-generation sequencing data.
PLoS One 10: 2015 e0125201
39
The Childhood Solid Tumor Network: A new resource for the developmental biology and oncology research communities.
Dev Biol 411: 2016 287-293
45
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.
Int J Mol Sci 18: 2016 E37
23
Pan-neuroblastoma analysis reveals age- and signature-associated driver alterations.
Nat Commun 11: 2020 5183
67
The landscape of coding RNA editing events in pediatric cancer.
BMC Cancer 21: 2021 1233
5
Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers.
Nat Cancer 4: 2023 276-289
9