Dataset Information

Spatial and temporal genomic evolution in glioblastoma

ABSTRACT: Tumor heterogeneity is a challenging barrier to successful targeted therapy in glioblastoma. Identifying the mechanisms that drive cancer cells as they genetically evolve and adapt in response to treatment is a vital goal of modern cancer research. Analysis of sequencing data provides a method for uncovering the mechanisms sustaining tumor heterogeneity and driving evolution. Here, we analyzed genomic profiles of GBM biopsies taken from pre- and post-treatment GBMs (with matched germline controls), to understand 1) the intratumoral clonal compositions of primary GBM and 2) how GBM responds to therapeutic intervention. Our results provide a molecular portrait of GBM recurrence.

PROVIDER: EGAS00001001033 | EGA |

REPOSITORIES: EGA

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Publications

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution.

Kim Hoon H Zheng Siyuan S Amini Seyed S SS Virk Selene M SM Mikkelsen Tom T Brat Daniel J DJ Grimsby Jonna J Sougnez Carrie C Muller Florian F Hu Jian J Sloan Andrew E AE Cohen Mark L ML Van Meir Erwin G EG Scarpace Lisa L Laird Peter W PW Weinstein John N JN Lander Eric S ES Gabriel Stacey S Getz Gad G Meyerson Matthew M Chin Lynda L Barnholtz-Sloan Jill S JS Verhaak Roel G W RG

Genome research 20150203 3

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ∼ 7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to rec ...[more]

PMID: 25650244

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Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated. Gene expression profiling experiments were conducted for 25 patient-derived xenograft glioblastoma samples using Affymetrix Human Gene 1.0 ST arrays according to manufacturer's protocol.

Dataset Information

Spatial and temporal genomic evolution in glioblastoma

Publications

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution.

Similar Datasets

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