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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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Experimental perturbations of a network region controlled by NDN and PDGFRA. (A–D) NDN overexpression slows the growth of glioblastoma cell lines. (A) Interactions in the network around EGFR, NDN and PDGFRA. (B) Perturbation of NDN by stable overexpression in two separate U343-derived cell lines, denoted as NDN+ (moderate overexpression) and NDN++ (high overexpression). (C) Growth curves collected during 6 days showed that NDN overexpression inhibits growth of U343 cells. Error bars indicate 95% confidence intervals. (D) Single-time point (7 days) measurement of cell number in NDN-overexpressing cells. Error bars indicate s.e.m. (E) Perturbation of PDGFRA by PDGF-AA protein (ligand) and imatinib (STI-571; Gleevec™; inhibits PDGFRA and certain other tyrosine kinases), respectively, produces opposite responses in target genes KCNH8 and FGF9, which were identified as downstream targets of PDGFRA in the model. NDN overexpression induces CPNE8 target genes and modulates FGF9 response to PDGFRA. Error bars indicate 95% confidence intervals of mRNA expression log2-relative to untreated controls. (F) Perturbation of EGFR by its ligand EGF and gefitinib (ZD-1839 Iressa™; inhibits EGFR) produces opposite responses in the predicted EGFR target genes SOCS2 and NR2E1.
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f4: Experimental perturbations of a network region controlled by NDN and PDGFRA. (A–D) NDN overexpression slows the growth of glioblastoma cell lines. (A) Interactions in the network around EGFR, NDN and PDGFRA. (B) Perturbation of NDN by stable overexpression in two separate U343-derived cell lines, denoted as NDN+ (moderate overexpression) and NDN++ (high overexpression). (C) Growth curves collected during 6 days showed that NDN overexpression inhibits growth of U343 cells. Error bars indicate 95% confidence intervals. (D) Single-time point (7 days) measurement of cell number in NDN-overexpressing cells. Error bars indicate s.e.m. (E) Perturbation of PDGFRA by PDGF-AA protein (ligand) and imatinib (STI-571; Gleevec™; inhibits PDGFRA and certain other tyrosine kinases), respectively, produces opposite responses in target genes KCNH8 and FGF9, which were identified as downstream targets of PDGFRA in the model. NDN overexpression induces CPNE8 target genes and modulates FGF9 response to PDGFRA. Error bars indicate 95% confidence intervals of mRNA expression log2-relative to untreated controls. (F) Perturbation of EGFR by its ligand EGF and gefitinib (ZD-1839 Iressa™; inhibits EGFR) produces opposite responses in the predicted EGFR target genes SOCS2 and NR2E1.

Mentions: To assess the biological relevance of a hub gene in the G network that has not been previously associated with glioblastoma, we have chosen to perform directed validation experiments on NDN. This gene has five downstream targets in the G network and shares a common target, fibroblast growth factor 9 (FGF9; also known as glia-activating factor), with PDGFRA which is frequently amplified in glioblastoma (Figure 3B). NDN is maternally imprinted, located on chromosome 15, and encodes a p53-interacting protein that belongs to the melanoma antigen family (Taniura et al, 1998). In the TCGA data, NDN is deleted in 29/186 patients. We introduce perturbations of NDN by overexpressing the gene in four glioblastoma cell lines (T98G, U-87MG, U-343MG and U-373MG), leading to decreased cell cycling time in all cell lines, except T98G (Figure 4A–C). Using the U-343MG cell line, we measure the expression of a set of downstream targets of NDN and PDGFRA by qPCR to assess the transcriptional response of NDN overexpression and inhibition/stimulation of PDGFRA, respectively. The results confirm a set of EPoC predictions, including induction of CPNE8 by NDN, induction of KCNH8 by PDGF-AA protein dimers (i.e., a PDGFRA agonist) and suppression of KCNH8 by Imatinib (a selective inhibitor of PDGFRA and other tyrosine kinases). Further, when NDN is overexpressed, FGF9 does not respond to PDGFRA perturbation. This is not only consistent with the prediction that NDN and PDGFRA regulate the transcription of FGF9 in opposite directions, but may also suggest a more complicated mechanism that is not captured by our model because NDN perturbation by itself did not suppress FGF9 levels (Figure 4E). For the other two of the tested transcripts, GALNT13 and ALK, which are both expressed at very low levels in U-343MG cells, we did not detect any significant changes. Further, we perturbed the activity of the EGFR by activating it using one of its ligands (EGF) and inhibiting it with a selective EGFR inhibitor (Gefitinib). As readout, we measured the transcriptional effect on SOCS2 (a modulator of STAT signaling), NR2E1 (also known as TLX, a transcription factor believed to be important for neural stem cell renewal), yielding results compatible with a coupling between hub perturbation and transcriptional response (Figure 4F).


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)

Experimental perturbations of a network region controlled by NDN and PDGFRA. (A–D) NDN overexpression slows the growth of glioblastoma cell lines. (A) Interactions in the network around EGFR, NDN and PDGFRA. (B) Perturbation of NDN by stable overexpression in two separate U343-derived cell lines, denoted as NDN+ (moderate overexpression) and NDN++ (high overexpression). (C) Growth curves collected during 6 days showed that NDN overexpression inhibits growth of U343 cells. Error bars indicate 95% confidence intervals. (D) Single-time point (7 days) measurement of cell number in NDN-overexpressing cells. Error bars indicate s.e.m. (E) Perturbation of PDGFRA by PDGF-AA protein (ligand) and imatinib (STI-571; Gleevec™; inhibits PDGFRA and certain other tyrosine kinases), respectively, produces opposite responses in target genes KCNH8 and FGF9, which were identified as downstream targets of PDGFRA in the model. NDN overexpression induces CPNE8 target genes and modulates FGF9 response to PDGFRA. Error bars indicate 95% confidence intervals of mRNA expression log2-relative to untreated controls. (F) Perturbation of EGFR by its ligand EGF and gefitinib (ZD-1839 Iressa™; inhibits EGFR) produces opposite responses in the predicted EGFR target genes SOCS2 and NR2E1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Experimental perturbations of a network region controlled by NDN and PDGFRA. (A–D) NDN overexpression slows the growth of glioblastoma cell lines. (A) Interactions in the network around EGFR, NDN and PDGFRA. (B) Perturbation of NDN by stable overexpression in two separate U343-derived cell lines, denoted as NDN+ (moderate overexpression) and NDN++ (high overexpression). (C) Growth curves collected during 6 days showed that NDN overexpression inhibits growth of U343 cells. Error bars indicate 95% confidence intervals. (D) Single-time point (7 days) measurement of cell number in NDN-overexpressing cells. Error bars indicate s.e.m. (E) Perturbation of PDGFRA by PDGF-AA protein (ligand) and imatinib (STI-571; Gleevec™; inhibits PDGFRA and certain other tyrosine kinases), respectively, produces opposite responses in target genes KCNH8 and FGF9, which were identified as downstream targets of PDGFRA in the model. NDN overexpression induces CPNE8 target genes and modulates FGF9 response to PDGFRA. Error bars indicate 95% confidence intervals of mRNA expression log2-relative to untreated controls. (F) Perturbation of EGFR by its ligand EGF and gefitinib (ZD-1839 Iressa™; inhibits EGFR) produces opposite responses in the predicted EGFR target genes SOCS2 and NR2E1.
Mentions: To assess the biological relevance of a hub gene in the G network that has not been previously associated with glioblastoma, we have chosen to perform directed validation experiments on NDN. This gene has five downstream targets in the G network and shares a common target, fibroblast growth factor 9 (FGF9; also known as glia-activating factor), with PDGFRA which is frequently amplified in glioblastoma (Figure 3B). NDN is maternally imprinted, located on chromosome 15, and encodes a p53-interacting protein that belongs to the melanoma antigen family (Taniura et al, 1998). In the TCGA data, NDN is deleted in 29/186 patients. We introduce perturbations of NDN by overexpressing the gene in four glioblastoma cell lines (T98G, U-87MG, U-343MG and U-373MG), leading to decreased cell cycling time in all cell lines, except T98G (Figure 4A–C). Using the U-343MG cell line, we measure the expression of a set of downstream targets of NDN and PDGFRA by qPCR to assess the transcriptional response of NDN overexpression and inhibition/stimulation of PDGFRA, respectively. The results confirm a set of EPoC predictions, including induction of CPNE8 by NDN, induction of KCNH8 by PDGF-AA protein dimers (i.e., a PDGFRA agonist) and suppression of KCNH8 by Imatinib (a selective inhibitor of PDGFRA and other tyrosine kinases). Further, when NDN is overexpressed, FGF9 does not respond to PDGFRA perturbation. This is not only consistent with the prediction that NDN and PDGFRA regulate the transcription of FGF9 in opposite directions, but may also suggest a more complicated mechanism that is not captured by our model because NDN perturbation by itself did not suppress FGF9 levels (Figure 4E). For the other two of the tested transcripts, GALNT13 and ALK, which are both expressed at very low levels in U-343MG cells, we did not detect any significant changes. Further, we perturbed the activity of the EGFR by activating it using one of its ligands (EGF) and inhibiting it with a selective EGFR inhibitor (Gefitinib). As readout, we measured the transcriptional effect on SOCS2 (a modulator of STAT signaling), NR2E1 (also known as TLX, a transcription factor believed to be important for neural stem cell renewal), yielding results compatible with a coupling between hub perturbation and transcriptional response (Figure 4F).

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