Limits...
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

Weighted gene co-expression network analysisA. Cluster dendrogram and co-expression modules. B. Clustering of module eigengenes. C. Module eigengenes expression across the sampling plan. Boxplots are colored according to HCPC classification. The gray boxplot represents reference control brain samples (n = 4). The green boxplot includes HCPC1 samples minus reference control brain samples. D. Gene significance (mean and standard error) for each module. Above each bar is a scatter plot of gene significance (GS) versus module membership (MM). GS is based on p-values from the ANOVA performed between HCPC clusters (BH corrected p-values). Number of probes in each module is also reported.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4494925&req=5

Figure 4: Weighted gene co-expression network analysisA. Cluster dendrogram and co-expression modules. B. Clustering of module eigengenes. C. Module eigengenes expression across the sampling plan. Boxplots are colored according to HCPC classification. The gray boxplot represents reference control brain samples (n = 4). The green boxplot includes HCPC1 samples minus reference control brain samples. D. Gene significance (mean and standard error) for each module. Above each bar is a scatter plot of gene significance (GS) versus module membership (MM). GS is based on p-values from the ANOVA performed between HCPC clusters (BH corrected p-values). Number of probes in each module is also reported.

Mentions: We performed weighted gene co-expression network analysis to map genes and pathways onto the intra-tumor heterogeneity highlighted by PCA. We restricted this analysis to the 8000 most informative probes (see methods). It identified seven co-expression modules hierarchically organized into two distinct groups (Figure 4A and Figure 4B). The fist group gathered 60% of the genes and was composed of only two modules (turquoise and yellow) whereas the second group was composed of five modules. The turquoise and yellow modules gathered the downregulated genes from the periphery to the center of the tumor (PBZ to TZ/NZ biopsies). The five other modules (black, blue, brown, green and red) gathered the probes upregulated from the periphery to the center of the tumor (Figure 4C). We identified modules related to the intratumor heterogeneity by computing a gene significance measure for each probe. This measure identified four significant modules: black, blue, brown and turquoise (Figure 4D). These modules are very coherent as they showed strong correlations between gene significance and module membership. Functional enrichments were performed to assess the biological significance of each module (Table 2 and Supplementary File 3). This analysis pointed out a clear distinction between the turquoise/yellow modules and the other modules. The turquoise and yellow modules were characterized by a strong composition of genes involved in the nervous system development and function. The five other modules were mainly characterized by genes involved in cellular growth and proliferation (3 modules), cellular development (3 modules), cellular movement (3 modules), and cell cycle (2 modules). Modules were highly coherent as core modules displayed functional enrichments similar to those of entire modules (Supplementary File 4). Based on the Verhaak signature, we found that the brown module was enriched with Mesenchymal genes (chi-squared test, p < 1e-16) and that the turquoise module was enriched with Proneural (p = 0.002) and Neural (p = 0.02) genes.


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)

Weighted gene co-expression network analysisA. Cluster dendrogram and co-expression modules. B. Clustering of module eigengenes. C. Module eigengenes expression across the sampling plan. Boxplots are colored according to HCPC classification. The gray boxplot represents reference control brain samples (n = 4). The green boxplot includes HCPC1 samples minus reference control brain samples. D. Gene significance (mean and standard error) for each module. Above each bar is a scatter plot of gene significance (GS) versus module membership (MM). GS is based on p-values from the ANOVA performed between HCPC clusters (BH corrected p-values). Number of probes in each module is also reported.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Weighted gene co-expression network analysisA. Cluster dendrogram and co-expression modules. B. Clustering of module eigengenes. C. Module eigengenes expression across the sampling plan. Boxplots are colored according to HCPC classification. The gray boxplot represents reference control brain samples (n = 4). The green boxplot includes HCPC1 samples minus reference control brain samples. D. Gene significance (mean and standard error) for each module. Above each bar is a scatter plot of gene significance (GS) versus module membership (MM). GS is based on p-values from the ANOVA performed between HCPC clusters (BH corrected p-values). Number of probes in each module is also reported.
Mentions: We performed weighted gene co-expression network analysis to map genes and pathways onto the intra-tumor heterogeneity highlighted by PCA. We restricted this analysis to the 8000 most informative probes (see methods). It identified seven co-expression modules hierarchically organized into two distinct groups (Figure 4A and Figure 4B). The fist group gathered 60% of the genes and was composed of only two modules (turquoise and yellow) whereas the second group was composed of five modules. The turquoise and yellow modules gathered the downregulated genes from the periphery to the center of the tumor (PBZ to TZ/NZ biopsies). The five other modules (black, blue, brown, green and red) gathered the probes upregulated from the periphery to the center of the tumor (Figure 4C). We identified modules related to the intratumor heterogeneity by computing a gene significance measure for each probe. This measure identified four significant modules: black, blue, brown and turquoise (Figure 4D). These modules are very coherent as they showed strong correlations between gene significance and module membership. Functional enrichments were performed to assess the biological significance of each module (Table 2 and Supplementary File 3). This analysis pointed out a clear distinction between the turquoise/yellow modules and the other modules. The turquoise and yellow modules were characterized by a strong composition of genes involved in the nervous system development and function. The five other modules were mainly characterized by genes involved in cellular growth and proliferation (3 modules), cellular development (3 modules), cellular movement (3 modules), and cell cycle (2 modules). Modules were highly coherent as core modules displayed functional enrichments similar to those of entire modules (Supplementary File 4). Based on the Verhaak signature, we found that the brown module was enriched with Mesenchymal genes (chi-squared test, p < 1e-16) and that the turquoise module was enriched with Proneural (p = 0.002) and Neural (p = 0.02) genes.

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