Proceedings of the National Academy of Sciences· 2013Q1
Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics
- 1,765citations
- Q1SCImago
- 2013year
Short summary
Glioblastoma tumors harbor extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor, revealed by genomic analysis of spatially distinct fragments.
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Key points
- Most glioblastoma patients exhibit multiple GB subtypes within a single tumor.
- Genomic analysis reconstructed tumor evolution, identifying early (EGFR, CDKN2A/B/p14ARF) and late (PDGFRA, PTEN) copy number alterations.
- Multiple coexisting cell lineages were detected at the single-molecule level.
- Intratumor heterogeneity follows patient-specific evolutionary patterns across multiple spatial scales.
AI-generated from the title and abstract; the full text is not read.
Abstract
Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.
The authors' abstract, as published at the source. Proceedings of the National Academy of Sciences, 2013 · DOI ↗
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Field: Genetics (Medicine)
GeneticsMedicine