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Delineating the cytogenomic and epigenomic landscapes of glioma stem cell lines.

Baronchelli S, Bentivegna A, Redaelli S, Riva G, Butta V, Paoletta L, Isimbaldi G, Miozzo M, Tabano S, Daga A, Marubbi D, Cattaneo M, Biunno I, Dalprà L - PLoS ONE (2013)

Bottom Line: We found several canonical cytogenetic alterations associated with GBM and a common minimal deleted region (MDR) at 1p36.31, including CAMTA1 gene, a putative tumor suppressor gene, specific for the GSC population.Therefore, beyond the differences that can create apparent heterogeneity of alterations among GSC lines, there's a sort of selective force acting on them in order to converge towards the impairment of cell development and differentiation processes.This new overview could have a huge importance in therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery and Translational Medicine, University of Milan-Bicocca, Monza, Italy.

ABSTRACT
Glioblastoma multiforme (GBM), the most common and malignant type of glioma, is characterized by a poor prognosis and the lack of an effective treatment, which are due to a small sub-population of cells with stem-like properties, termed glioma stem cells (GSCs). The term "multiforme" describes the histological features of this tumor, that is, the cellular and morphological heterogeneity. At the molecular level multiple layers of alterations may reflect this heterogeneity providing together the driving force for tumor initiation and development. In order to decipher the common "signature" of the ancestral GSC population, we examined six already characterized GSC lines evaluating their cytogenomic and epigenomic profiles through a multilevel approach (conventional cytogenetic, FISH, aCGH, MeDIP-Chip and functional bioinformatic analysis). We found several canonical cytogenetic alterations associated with GBM and a common minimal deleted region (MDR) at 1p36.31, including CAMTA1 gene, a putative tumor suppressor gene, specific for the GSC population. Therefore, on one hand our data confirm a role of driver mutations for copy number alterations (CNAs) included in the GBM genomic-signature (gain of chromosome 7- EGFR gene, loss of chromosome 13- RB1 gene, loss of chromosome 10-PTEN gene); on the other, it is not obvious that the new identified CNAs are passenger mutations, as they may be necessary for tumor progression specific for the individual patient. Through our approach, we were able to demonstrate that not only individual genes into a pathway can be perturbed through multiple mechanisms and at different levels, but also that different combinations of perturbed genes can incapacitate functional modules within a cellular networks. Therefore, beyond the differences that can create apparent heterogeneity of alterations among GSC lines, there's a sort of selective force acting on them in order to converge towards the impairment of cell development and differentiation processes. This new overview could have a huge importance in therapy.

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Functional characterization of cytogenomic landscapes.(A) Categories of genes determined by GO analysis and included in gain and loss regions. Each category is associated to a percentage of frequency which was calculated on the ratio between the number of genes associated to a specific category and the total number of genes associated to at least one GO term. (B) Tree topology of overlapping network established using IPA software. Genes in new “exclusive” gain and loss regions identified in GSCs profiles of aCGH were assigned to gene networks which were strictly interconnected one to each other and revealed cancer-relevant annotations. Different genes can be grouped in several networks, underlying the same mechanism (i.e. cancer or cell cycle). (C) New ‘exclusive’ CNA region-associated pathways. Each pathway is associated with a p-value (calculated by Ingenuity Pathway Analysis, IPA, software), which represents the probability that such association could have occurred by chance.
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pone-0057462-g004: Functional characterization of cytogenomic landscapes.(A) Categories of genes determined by GO analysis and included in gain and loss regions. Each category is associated to a percentage of frequency which was calculated on the ratio between the number of genes associated to a specific category and the total number of genes associated to at least one GO term. (B) Tree topology of overlapping network established using IPA software. Genes in new “exclusive” gain and loss regions identified in GSCs profiles of aCGH were assigned to gene networks which were strictly interconnected one to each other and revealed cancer-relevant annotations. Different genes can be grouped in several networks, underlying the same mechanism (i.e. cancer or cell cycle). (C) New ‘exclusive’ CNA region-associated pathways. Each pathway is associated with a p-value (calculated by Ingenuity Pathway Analysis, IPA, software), which represents the probability that such association could have occurred by chance.

Mentions: Genome-wide data were analyzed through GOstat and IPA software in order to identify biological functions and pathways related to input gene lists, respectively. Cancer-related GO terms were grouped in different functional categories, as described in the materials and methods section. Each category was scored based on its own percentage of genes belonging to that specific category [47] and normalized to the total number of genes. Cell signaling and development and morphogenesis were the most represented biological functions in gain and loss regions, which underlie a de-regulation of genes related to these categories by amplification or deletion of genomic regions (Figure 4A). Moreover, other categories resulted affected by CNAs, i.e. cell cycle, apoptosis, cell differentiation, response to stimulus and cytoskeleton organization. Furthermore, even if at lower frequencies, cell motility and immune response categories were associated with deleted regions (Figure 4A).


Delineating the cytogenomic and epigenomic landscapes of glioma stem cell lines.

Baronchelli S, Bentivegna A, Redaelli S, Riva G, Butta V, Paoletta L, Isimbaldi G, Miozzo M, Tabano S, Daga A, Marubbi D, Cattaneo M, Biunno I, Dalprà L - PLoS ONE (2013)

Functional characterization of cytogenomic landscapes.(A) Categories of genes determined by GO analysis and included in gain and loss regions. Each category is associated to a percentage of frequency which was calculated on the ratio between the number of genes associated to a specific category and the total number of genes associated to at least one GO term. (B) Tree topology of overlapping network established using IPA software. Genes in new “exclusive” gain and loss regions identified in GSCs profiles of aCGH were assigned to gene networks which were strictly interconnected one to each other and revealed cancer-relevant annotations. Different genes can be grouped in several networks, underlying the same mechanism (i.e. cancer or cell cycle). (C) New ‘exclusive’ CNA region-associated pathways. Each pathway is associated with a p-value (calculated by Ingenuity Pathway Analysis, IPA, software), which represents the probability that such association could have occurred by chance.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585345&req=5

pone-0057462-g004: Functional characterization of cytogenomic landscapes.(A) Categories of genes determined by GO analysis and included in gain and loss regions. Each category is associated to a percentage of frequency which was calculated on the ratio between the number of genes associated to a specific category and the total number of genes associated to at least one GO term. (B) Tree topology of overlapping network established using IPA software. Genes in new “exclusive” gain and loss regions identified in GSCs profiles of aCGH were assigned to gene networks which were strictly interconnected one to each other and revealed cancer-relevant annotations. Different genes can be grouped in several networks, underlying the same mechanism (i.e. cancer or cell cycle). (C) New ‘exclusive’ CNA region-associated pathways. Each pathway is associated with a p-value (calculated by Ingenuity Pathway Analysis, IPA, software), which represents the probability that such association could have occurred by chance.
Mentions: Genome-wide data were analyzed through GOstat and IPA software in order to identify biological functions and pathways related to input gene lists, respectively. Cancer-related GO terms were grouped in different functional categories, as described in the materials and methods section. Each category was scored based on its own percentage of genes belonging to that specific category [47] and normalized to the total number of genes. Cell signaling and development and morphogenesis were the most represented biological functions in gain and loss regions, which underlie a de-regulation of genes related to these categories by amplification or deletion of genomic regions (Figure 4A). Moreover, other categories resulted affected by CNAs, i.e. cell cycle, apoptosis, cell differentiation, response to stimulus and cytoskeleton organization. Furthermore, even if at lower frequencies, cell motility and immune response categories were associated with deleted regions (Figure 4A).

Bottom Line: We found several canonical cytogenetic alterations associated with GBM and a common minimal deleted region (MDR) at 1p36.31, including CAMTA1 gene, a putative tumor suppressor gene, specific for the GSC population.Therefore, beyond the differences that can create apparent heterogeneity of alterations among GSC lines, there's a sort of selective force acting on them in order to converge towards the impairment of cell development and differentiation processes.This new overview could have a huge importance in therapy.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery and Translational Medicine, University of Milan-Bicocca, Monza, Italy.

ABSTRACT
Glioblastoma multiforme (GBM), the most common and malignant type of glioma, is characterized by a poor prognosis and the lack of an effective treatment, which are due to a small sub-population of cells with stem-like properties, termed glioma stem cells (GSCs). The term "multiforme" describes the histological features of this tumor, that is, the cellular and morphological heterogeneity. At the molecular level multiple layers of alterations may reflect this heterogeneity providing together the driving force for tumor initiation and development. In order to decipher the common "signature" of the ancestral GSC population, we examined six already characterized GSC lines evaluating their cytogenomic and epigenomic profiles through a multilevel approach (conventional cytogenetic, FISH, aCGH, MeDIP-Chip and functional bioinformatic analysis). We found several canonical cytogenetic alterations associated with GBM and a common minimal deleted region (MDR) at 1p36.31, including CAMTA1 gene, a putative tumor suppressor gene, specific for the GSC population. Therefore, on one hand our data confirm a role of driver mutations for copy number alterations (CNAs) included in the GBM genomic-signature (gain of chromosome 7- EGFR gene, loss of chromosome 13- RB1 gene, loss of chromosome 10-PTEN gene); on the other, it is not obvious that the new identified CNAs are passenger mutations, as they may be necessary for tumor progression specific for the individual patient. Through our approach, we were able to demonstrate that not only individual genes into a pathway can be perturbed through multiple mechanisms and at different levels, but also that different combinations of perturbed genes can incapacitate functional modules within a cellular networks. Therefore, beyond the differences that can create apparent heterogeneity of alterations among GSC lines, there's a sort of selective force acting on them in order to converge towards the impairment of cell development and differentiation processes. This new overview could have a huge importance in therapy.

Show MeSH
Related in: MedlinePlus