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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

Schematic representation of Model (3), describing the interplay between DNA copy number aberrations and gene expression within a regulatory network. The solid arrows correspond to the cis-effect () of the gene dosage, whereas the elements of  are displayed as dashed arrows (Color figure online)
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Fig3: Schematic representation of Model (3), describing the interplay between DNA copy number aberrations and gene expression within a regulatory network. The solid arrows correspond to the cis-effect () of the gene dosage, whereas the elements of are displayed as dashed arrows (Color figure online)

Mentions: Let and be p-dimensional vectors of DNA copy number and gene expression information, respectively. The relation between the two may be described by the rate equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \nabla _t \mathbf{Y}= & {} \mathbf{f}(\mathbf{Y}) - \varvec{\gamma }\circ \mathbf{Y} + \varvec{\beta }\circ \mathbf{X}, \end{aligned}$$\end{document}∇tY=f(Y)-γ∘Y+β∘X,where the -operator denotes the Hadamard product, the decay rate of the mRNAs, and the effect of DNA copy number changes on the expression levels. This equation links the change in gene expression with time to the p-dimensional vector-valued transcription function , the decay rate (the second summand on the right-hand side), and the cis-effect of the genomic aberration (third summand). In order for the rate equation to be applicable to data from integrative genomic studies, where the two molecular levels of a random sample are measured in an observational experimental setup, two simplifying assumptions are made: (1) a steady state and (2) a linear form of (although not strictly necessary). After regrouping of terms and the introduction of an error term , with , we arrive at:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {\varvec{\Theta }} \mathbf{Y}= & {} \varvec{\beta }\circ \mathbf{X} + \varvec{\varepsilon }, \end{aligned}$$\end{document}ΘY=β∘X+ε,where contains the edges between the genes in the regulatory network. For example, an element represents the effect of gene on gene . Model (3) is visually portrayed in Fig. 3. For the identifiability and estimation of Model (3), refer to Van Wieringen and Van de Wiel (2014).Fig. 3


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)

Schematic representation of Model (3), describing the interplay between DNA copy number aberrations and gene expression within a regulatory network. The solid arrows correspond to the cis-effect () of the gene dosage, whereas the elements of  are displayed as dashed arrows (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Schematic representation of Model (3), describing the interplay between DNA copy number aberrations and gene expression within a regulatory network. The solid arrows correspond to the cis-effect () of the gene dosage, whereas the elements of are displayed as dashed arrows (Color figure online)
Mentions: Let and be p-dimensional vectors of DNA copy number and gene expression information, respectively. The relation between the two may be described by the rate equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \nabla _t \mathbf{Y}= & {} \mathbf{f}(\mathbf{Y}) - \varvec{\gamma }\circ \mathbf{Y} + \varvec{\beta }\circ \mathbf{X}, \end{aligned}$$\end{document}∇tY=f(Y)-γ∘Y+β∘X,where the -operator denotes the Hadamard product, the decay rate of the mRNAs, and the effect of DNA copy number changes on the expression levels. This equation links the change in gene expression with time to the p-dimensional vector-valued transcription function , the decay rate (the second summand on the right-hand side), and the cis-effect of the genomic aberration (third summand). In order for the rate equation to be applicable to data from integrative genomic studies, where the two molecular levels of a random sample are measured in an observational experimental setup, two simplifying assumptions are made: (1) a steady state and (2) a linear form of (although not strictly necessary). After regrouping of terms and the introduction of an error term , with , we arrive at:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {\varvec{\Theta }} \mathbf{Y}= & {} \varvec{\beta }\circ \mathbf{X} + \varvec{\varepsilon }, \end{aligned}$$\end{document}ΘY=β∘X+ε,where contains the edges between the genes in the regulatory network. For example, an element represents the effect of gene on gene . Model (3) is visually portrayed in Fig. 3. For the identifiability and estimation of Model (3), refer to Van Wieringen and Van de Wiel (2014).Fig. 3

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