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From the core to beyond the margin: a genomic picture of glioblastoma intratumor heterogeneity.

Aubry M, de Tayrac M, Etcheverry A, Clavreul A, Saikali S, Menei P, Mosser J - Oncotarget (2015)

Bottom Line: At the genome level, we identified common GB copy number alterations and but a strong interindividual molecular heterogeneity.We provide a signature of key cancer-heterogeneity genes highly associated with the intratumor spatial gradient and show that it is enriched in genes with correlation between methylation and expression levels.Our study confirms that GBs are molecularly highly diverse and that a single tumor can harbor different transcriptional GB subtypes depending on its spatial architecture.

View Article: PubMed Central - PubMed

Affiliation: Université Rennes1, UEB, UMS 3480 Biosit, Faculté de Médecine, Rennes F-35043, France.

ABSTRACT
Glioblastoma (GB) is a highly invasive primary brain tumor that almost systematically recurs despite aggressive therapies. One of the most challenging problems in therapy of GB is its extremely complex and heterogeneous molecular biology. To explore this heterogeneity, we performed a genome-wide integrative screening of three molecular levels: genome, transcriptome, and methylome. We analyzed tumor biopsies obtained by neuro-navigation in four distinct areas for 10 GB patients (necrotic zone, tumor zone, interface, and peripheral brain zone). We classified samples and deciphered a key genes signature of intratumor heterogeneity by Principal Component Analysis and Weighted Gene Co-expression Network Analysis. At the genome level, we identified common GB copy number alterations and but a strong interindividual molecular heterogeneity. Transcriptome analysis highlighted a pronounced intratumor architecture reflecting the surgical sampling plan of the study and identified gene modules associated with hallmarks of cancer. We provide a signature of key cancer-heterogeneity genes highly associated with the intratumor spatial gradient and show that it is enriched in genes with correlation between methylation and expression levels. Our study confirms that GBs are molecularly highly diverse and that a single tumor can harbor different transcriptional GB subtypes depending on its spatial architecture.

No MeSH data available.


Related in: MedlinePlus

GB subtypesA. Samples GB subtypes according to the Verhaak signature. Gene Set Enrichment Analysis enrichment scores of each samples are reported as a gray-based color gradient. B. Samples GB subtypes and zone-specific profiles determined by PCA. Samples are colored according to their GB subtype. Squares: barycenter of GB subtypes.
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Figure 3: GB subtypesA. Samples GB subtypes according to the Verhaak signature. Gene Set Enrichment Analysis enrichment scores of each samples are reported as a gray-based color gradient. B. Samples GB subtypes and zone-specific profiles determined by PCA. Samples are colored according to their GB subtype. Squares: barycenter of GB subtypes.

Mentions: We assigned each biopsy to one of four subtypes: Proneural, Neural, Classical, and Mesenchymal using the Verhaak classifier [10], which is based on an 840-gene signature. Samples from peripheral brain zone (PBZ) and interface (I) biopsies showed the highest correlations with the Neural or Proneural subtypes. In contrast, tumor (TZ) and necrotic (NZ) biopsies showed the highest correlations with the Mesenchymal and Classical subtypes (Figure 3A). All samples in HCPC #4 were classified as Neural. In the other HCPC clusters, we found that in 9 of 10 cases, biopsies from the same patient were classified into at least two different subtypes. Only FT07 was classified as Mesenchymal on both PBZ and TZ biopsies. FT02 was classified as Neural and Proneural indicating a strong Neural component in this tumor. In the other cases, we observed mainly the combination [(Neural or Proneural) and Mesenchymal] (6 cases), but also the [(Neural or Proneural) and Classical] combination (1 case). FT08 presented strong tumor heterogeneity with two Mesenchymal and two Classical biopsies (Supplementary File 2). Taken as a whole, the Verhaak GB classes were highly associated with the zone-specific profiles, as determined by the PCA performed on the whole transcriptome dataset (ANOVA, p = 9.10-9) (Figure 3B). This highlighted that the definition of GB subtype based on gene expression was related to the biopsy zone.


From the core to beyond the margin: a genomic picture of glioblastoma intratumor heterogeneity.

Aubry M, de Tayrac M, Etcheverry A, Clavreul A, Saikali S, Menei P, Mosser J - Oncotarget (2015)

GB subtypesA. Samples GB subtypes according to the Verhaak signature. Gene Set Enrichment Analysis enrichment scores of each samples are reported as a gray-based color gradient. B. Samples GB subtypes and zone-specific profiles determined by PCA. Samples are colored according to their GB subtype. Squares: barycenter of GB subtypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4494925&req=5

Figure 3: GB subtypesA. Samples GB subtypes according to the Verhaak signature. Gene Set Enrichment Analysis enrichment scores of each samples are reported as a gray-based color gradient. B. Samples GB subtypes and zone-specific profiles determined by PCA. Samples are colored according to their GB subtype. Squares: barycenter of GB subtypes.
Mentions: We assigned each biopsy to one of four subtypes: Proneural, Neural, Classical, and Mesenchymal using the Verhaak classifier [10], which is based on an 840-gene signature. Samples from peripheral brain zone (PBZ) and interface (I) biopsies showed the highest correlations with the Neural or Proneural subtypes. In contrast, tumor (TZ) and necrotic (NZ) biopsies showed the highest correlations with the Mesenchymal and Classical subtypes (Figure 3A). All samples in HCPC #4 were classified as Neural. In the other HCPC clusters, we found that in 9 of 10 cases, biopsies from the same patient were classified into at least two different subtypes. Only FT07 was classified as Mesenchymal on both PBZ and TZ biopsies. FT02 was classified as Neural and Proneural indicating a strong Neural component in this tumor. In the other cases, we observed mainly the combination [(Neural or Proneural) and Mesenchymal] (6 cases), but also the [(Neural or Proneural) and Classical] combination (1 case). FT08 presented strong tumor heterogeneity with two Mesenchymal and two Classical biopsies (Supplementary File 2). Taken as a whole, the Verhaak GB classes were highly associated with the zone-specific profiles, as determined by the PCA performed on the whole transcriptome dataset (ANOVA, p = 9.10-9) (Figure 3B). This highlighted that the definition of GB subtype based on gene expression was related to the biopsy zone.

Bottom Line: At the genome level, we identified common GB copy number alterations and but a strong interindividual molecular heterogeneity.We provide a signature of key cancer-heterogeneity genes highly associated with the intratumor spatial gradient and show that it is enriched in genes with correlation between methylation and expression levels.Our study confirms that GBs are molecularly highly diverse and that a single tumor can harbor different transcriptional GB subtypes depending on its spatial architecture.

View Article: PubMed Central - PubMed

Affiliation: Université Rennes1, UEB, UMS 3480 Biosit, Faculté de Médecine, Rennes F-35043, France.

ABSTRACT
Glioblastoma (GB) is a highly invasive primary brain tumor that almost systematically recurs despite aggressive therapies. One of the most challenging problems in therapy of GB is its extremely complex and heterogeneous molecular biology. To explore this heterogeneity, we performed a genome-wide integrative screening of three molecular levels: genome, transcriptome, and methylome. We analyzed tumor biopsies obtained by neuro-navigation in four distinct areas for 10 GB patients (necrotic zone, tumor zone, interface, and peripheral brain zone). We classified samples and deciphered a key genes signature of intratumor heterogeneity by Principal Component Analysis and Weighted Gene Co-expression Network Analysis. At the genome level, we identified common GB copy number alterations and but a strong interindividual molecular heterogeneity. Transcriptome analysis highlighted a pronounced intratumor architecture reflecting the surgical sampling plan of the study and identified gene modules associated with hallmarks of cancer. We provide a signature of key cancer-heterogeneity genes highly associated with the intratumor spatial gradient and show that it is enriched in genes with correlation between methylation and expression levels. Our study confirms that GBs are molecularly highly diverse and that a single tumor can harbor different transcriptional GB subtypes depending on its spatial architecture.

No MeSH data available.


Related in: MedlinePlus