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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.

Jörnsten R, Abenius T, Kling T, Schmidt L, Johansson E, Nordling TE, Nordlander B, Sander C, Gennemark P, Funa K, Nilsson B, Lindahl L, Nelander S - Mol. Syst. Biol. (2011)

Bottom Line: Prognostic scores are obtained from a singular value decomposition of the networks.Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth.Free software in MATLAB and R is provided.

View Article: PubMed Central - PubMed

Affiliation: Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden.

ABSTRACT
DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

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Derivation of prognostic scores from the network model. (A) Kaplan–Meier curves to assess prognostic scores extracted from the CNA-driven network. Prognostic scores are computed by a sparse singular value decomposition of the CNA-driven network G (Materials and methods). Patients are divided into two groups by projecting their CNA profiles and mRNA profiles onto the main left and right axis of the singular vectors of G, respectively. This separates patients with favorable and poor prognosis (upper panels). By contrast, the corresponding analysis of the transcriptional network A (middle panels) does not produce any significant separation of patients in terms of survival, nor does a standard singular value decomposition (SVD) of the mRNA profiles or CNA profiles (lower panels). The panels show the results obtained by projection onto the first SVD components. The results obtained when projecting onto additional components are given in Table II. (B) The sparse singular value decomposition of the CNA-driven network G identifies genes with strong scores for signal amplification, i.e., genes whose perturbations are highly amplified by the network system (here illustrated as yellow nodes, e.g., PDGFRA), as well as mRNA transcripts that are most affected by these perturbations (green nodes, e.g., GRIK1; Figure 1C).
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f5: Derivation of prognostic scores from the network model. (A) Kaplan–Meier curves to assess prognostic scores extracted from the CNA-driven network. Prognostic scores are computed by a sparse singular value decomposition of the CNA-driven network G (Materials and methods). Patients are divided into two groups by projecting their CNA profiles and mRNA profiles onto the main left and right axis of the singular vectors of G, respectively. This separates patients with favorable and poor prognosis (upper panels). By contrast, the corresponding analysis of the transcriptional network A (middle panels) does not produce any significant separation of patients in terms of survival, nor does a standard singular value decomposition (SVD) of the mRNA profiles or CNA profiles (lower panels). The panels show the results obtained by projection onto the first SVD components. The results obtained when projecting onto additional components are given in Table II. (B) The sparse singular value decomposition of the CNA-driven network G identifies genes with strong scores for signal amplification, i.e., genes whose perturbations are highly amplified by the network system (here illustrated as yellow nodes, e.g., PDGFRA), as well as mRNA transcripts that are most affected by these perturbations (green nodes, e.g., GRIK1; Figure 1C).

Mentions: As predicted, we find that these scores achieve a significant degree of prognostic separation (Figure 5A). In contrast, when we examine the prognostic properties of the transcriptional network, A, we find no evident survival stratification when separating patients along the leading components of the SVD of A. We also demonstrate that a standard singular value decomposition (SVD) calculated from mRNA profiles or CNA profiles fails to detect survival differences in the data. We further calculate survival curves for the first six components of both mRNA and CNA data in the G, A and data SVD cases, revealing that survival differences are only seen in the first SVD component of G (Table II).


Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.

Jörnsten R, Abenius T, Kling T, Schmidt L, Johansson E, Nordling TE, Nordlander B, Sander C, Gennemark P, Funa K, Nilsson B, Lindahl L, Nelander S - Mol. Syst. Biol. (2011)

Derivation of prognostic scores from the network model. (A) Kaplan–Meier curves to assess prognostic scores extracted from the CNA-driven network. Prognostic scores are computed by a sparse singular value decomposition of the CNA-driven network G (Materials and methods). Patients are divided into two groups by projecting their CNA profiles and mRNA profiles onto the main left and right axis of the singular vectors of G, respectively. This separates patients with favorable and poor prognosis (upper panels). By contrast, the corresponding analysis of the transcriptional network A (middle panels) does not produce any significant separation of patients in terms of survival, nor does a standard singular value decomposition (SVD) of the mRNA profiles or CNA profiles (lower panels). The panels show the results obtained by projection onto the first SVD components. The results obtained when projecting onto additional components are given in Table II. (B) The sparse singular value decomposition of the CNA-driven network G identifies genes with strong scores for signal amplification, i.e., genes whose perturbations are highly amplified by the network system (here illustrated as yellow nodes, e.g., PDGFRA), as well as mRNA transcripts that are most affected by these perturbations (green nodes, e.g., GRIK1; Figure 1C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3101951&req=5

f5: Derivation of prognostic scores from the network model. (A) Kaplan–Meier curves to assess prognostic scores extracted from the CNA-driven network. Prognostic scores are computed by a sparse singular value decomposition of the CNA-driven network G (Materials and methods). Patients are divided into two groups by projecting their CNA profiles and mRNA profiles onto the main left and right axis of the singular vectors of G, respectively. This separates patients with favorable and poor prognosis (upper panels). By contrast, the corresponding analysis of the transcriptional network A (middle panels) does not produce any significant separation of patients in terms of survival, nor does a standard singular value decomposition (SVD) of the mRNA profiles or CNA profiles (lower panels). The panels show the results obtained by projection onto the first SVD components. The results obtained when projecting onto additional components are given in Table II. (B) The sparse singular value decomposition of the CNA-driven network G identifies genes with strong scores for signal amplification, i.e., genes whose perturbations are highly amplified by the network system (here illustrated as yellow nodes, e.g., PDGFRA), as well as mRNA transcripts that are most affected by these perturbations (green nodes, e.g., GRIK1; Figure 1C).
Mentions: As predicted, we find that these scores achieve a significant degree of prognostic separation (Figure 5A). In contrast, when we examine the prognostic properties of the transcriptional network, A, we find no evident survival stratification when separating patients along the leading components of the SVD of A. We also demonstrate that a standard singular value decomposition (SVD) calculated from mRNA profiles or CNA profiles fails to detect survival differences in the data. We further calculate survival curves for the first six components of both mRNA and CNA data in the G, A and data SVD cases, revealing that survival differences are only seen in the first SVD component of G (Table II).

Bottom Line: Prognostic scores are obtained from a singular value decomposition of the networks.Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth.Free software in MATLAB and R is provided.

View Article: PubMed Central - PubMed

Affiliation: Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden.

ABSTRACT
DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

Show MeSH
Related in: MedlinePlus