Comprehensive genomic profiling of glioblastoma tumors, BTICs, and xenografts reveals stability and adaptation to growth environments.
Adult
Aged
Aged, 80 and over
Animals
Apoptosis
Biomarkers, Tumor
/ genetics
Brain Neoplasms
/ genetics
Case-Control Studies
Cell Proliferation
DNA Methylation
Drug Resistance, Neoplasm
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Genomics
/ methods
Glioblastoma
/ genetics
Humans
Male
Mice
Mice, SCID
Middle Aged
Neoplastic Stem Cells
/ metabolism
Transcriptome
Tumor Cells, Cultured
Tumor Microenvironment
/ genetics
Whole Genome Sequencing
Xenograft Model Antitumor Assays
BTICs
genome
glioblastoma
therapy
transcriptome
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
17 09 2019
17 09 2019
Historique:
pubmed:
1
9
2019
medline:
11
4
2020
entrez:
1
9
2019
Statut:
ppublish
Résumé
Glioblastoma multiforme (GBM) is the most deadly brain tumor, and currently lacks effective treatment options. Brain tumor-initiating cells (BTICs) and orthotopic xenografts are widely used in investigating GBM biology and new therapies for this aggressive disease. However, the genomic characteristics and molecular resemblance of these models to GBM tumors remain undetermined. We used massively parallel sequencing technology to decode the genomes and transcriptomes of BTICs and xenografts and their matched tumors in order to delineate the potential impacts of the distinct growth environments. Using data generated from whole-genome sequencing of 201 samples and RNA sequencing of 118 samples, we show that BTICs and xenografts resemble their parental tumor at the genomic level but differ at the mRNA expression and epigenomic levels, likely due to the different growth environment for each sample type. These findings suggest that a comprehensive genomic understanding of in vitro and in vivo GBM model systems is crucial for interpreting data from drug screens, and can help control for biases introduced by cell-culture conditions and the microenvironment in mouse models. We also found that lack of
Identifiants
pubmed: 31471491
pii: 1813495116
doi: 10.1073/pnas.1813495116
pmc: PMC6754609
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
19098-19108Subventions
Organisme : CIHR
ID : FDN-143288
Pays : Canada
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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