Limits...
Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network.

van Wieringen WN, van der Vaart AW - Bull. Math. Biol. (2015)

Bottom Line: Dysregulation of the regulatory network results in less control of transcript levels in the cell.Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity.These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P. O. Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.

ABSTRACT
Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene-gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

No MeSH data available.


Related in: MedlinePlus

The cubic spline smoothed relationship between the node degree of the node with eliminated conditional dependencies versus the entropy of the resulting  dimensional covariance matrix with an underlying scale-free topology. Each spline represents the results for an independently drawn covariance matrix. In total, a hundred splines are displayed (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4644214&req=5

Fig7: The cubic spline smoothed relationship between the node degree of the node with eliminated conditional dependencies versus the entropy of the resulting dimensional covariance matrix with an underlying scale-free topology. Each spline represents the results for an independently drawn covariance matrix. In total, a hundred splines are displayed (Color figure online)

Mentions: We now ask ourselves, from an entropy perspective, whether it matters which gene is silenced. In particular, we investigate—by simulation—the relation between entropy and a gene’s connectivity. This is motivated by the observation of Jonsson and Bates (2006) that cancer genes tend to be more highly connected in the regulatory network. TP53, a well-known tumor-suppressor gene and lost (i.e., silenced) in many cancers, is indeed highly connected (Vogelstein et al. 2000). In the simulation study, the effect of connectivity on the entropy is assessed by eliminating dependencies. Starting point of the simulation is a graph (either small world or scale free) and an associated covariance matrix . For node j, we calculate its degree , eliminate its edges (conditional dependencies) with the other nodes, and calculate (the entropy), where is obtained from by setting all conditional dependencies of node j to zero. This is done for each node. Finally, is plotted against , where the range of is restricted to the degrees present in the network. Figure 7 shows the results of conditional dependency removal for pathways of genes with a scale-free regulatory network. It reveals a clear monotonously increasing trend: the higher the edge degree of a node, the larger the entropy increase as its conditional dependencies are removed. This holds also for pathways with a small-world topology (refer to the SM J). The plots even suggest a dependence of this relation on the size of the network, but this needs further exploration. In all, the simulation suggests a cancer cell gains most by silencing a highly connected gene, as a it explores different paths of random variation in its evolution and naturally selects the path that leads to a faster entropy increase (Kaila and Annila 2008).Fig. 8


Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network.

van Wieringen WN, van der Vaart AW - Bull. Math. Biol. (2015)

The cubic spline smoothed relationship between the node degree of the node with eliminated conditional dependencies versus the entropy of the resulting  dimensional covariance matrix with an underlying scale-free topology. Each spline represents the results for an independently drawn covariance matrix. In total, a hundred splines are displayed (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644214&req=5

Fig7: The cubic spline smoothed relationship between the node degree of the node with eliminated conditional dependencies versus the entropy of the resulting dimensional covariance matrix with an underlying scale-free topology. Each spline represents the results for an independently drawn covariance matrix. In total, a hundred splines are displayed (Color figure online)
Mentions: We now ask ourselves, from an entropy perspective, whether it matters which gene is silenced. In particular, we investigate—by simulation—the relation between entropy and a gene’s connectivity. This is motivated by the observation of Jonsson and Bates (2006) that cancer genes tend to be more highly connected in the regulatory network. TP53, a well-known tumor-suppressor gene and lost (i.e., silenced) in many cancers, is indeed highly connected (Vogelstein et al. 2000). In the simulation study, the effect of connectivity on the entropy is assessed by eliminating dependencies. Starting point of the simulation is a graph (either small world or scale free) and an associated covariance matrix . For node j, we calculate its degree , eliminate its edges (conditional dependencies) with the other nodes, and calculate (the entropy), where is obtained from by setting all conditional dependencies of node j to zero. This is done for each node. Finally, is plotted against , where the range of is restricted to the degrees present in the network. Figure 7 shows the results of conditional dependency removal for pathways of genes with a scale-free regulatory network. It reveals a clear monotonously increasing trend: the higher the edge degree of a node, the larger the entropy increase as its conditional dependencies are removed. This holds also for pathways with a small-world topology (refer to the SM J). The plots even suggest a dependence of this relation on the size of the network, but this needs further exploration. In all, the simulation suggests a cancer cell gains most by silencing a highly connected gene, as a it explores different paths of random variation in its evolution and naturally selects the path that leads to a faster entropy increase (Kaila and Annila 2008).Fig. 8

Bottom Line: Dysregulation of the regulatory network results in less control of transcript levels in the cell.Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity.These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P. O. Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.

ABSTRACT
Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene-gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

No MeSH data available.


Related in: MedlinePlus