Header

UZH-Logo

Maintenance Infos

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer


Petralia, Francesca; Tignor, Nicole; Reva, Boris; et al; Nazarian, Javad (2020). Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer. Cell, 183(7):1962-1985.e31.

Abstract

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Abstract

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

23 downloads since deposited on 27 Jan 2021
23 downloads since 12 months
Detailed statistics

Additional indexing

Contributors:Children’s Brain Tumor Network, Clinical Proteomic Tumor Analysis Consortium
Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Language:English
Date:23 December 2020
Deposited On:27 Jan 2021 06:40
Last Modified:28 Jan 2021 21:01
Publisher:Cell Press (Elsevier)
ISSN:0092-8674
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.cell.2020.10.044
PubMed ID:33242424

Download

Hybrid Open Access

Download PDF  'Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer'.
Preview
Content: Published Version
Filetype: PDF
Size: 15MB
View at publisher
Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)