Gene regulatory networks: A new conceptual framework to analyse breast cancer behaviour

Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale Tumori, Milano, Italy.
Annals of Oncology (Impact Factor: 7.04). 11/2010; 22(6):1259-65. DOI: 10.1093/annonc/mdq546
Source: PubMed


The study of complex systems has clearly evidenced that a few overall behavioural properties cannot be inferred from the properties of their single parts and are rather determined by their architecture. Such an approach has been recently proposed in biology to understand genome functioning and in oncology to endeavour a more consistent explanation of the variegated cancer behaviours. In the present perspective, we summarise the basic concepts of the proposed global approach and then we reconsider, in this new context, tumour dormancy and primary tumour removal effects, which recently emerged as critical points for breast cancer understanding.

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    • "The inference of GRNs has already been successfully applied to other malignances such as leukemia [14], breast cancer [48, 49] or ovarian tumors [50], with relevant findings regarding breast cancer metastasis prognostic markers or prioritization of druggable gene targets for ovarian cancer. In colorectal cancer some researchers have also explored the reconstruction of GRNs, but with limited approaches to one transcription factor [23] or only tumor tissue [21, 22]. "
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    ABSTRACT: Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Our study aims to characterize global differences between transcriptional regulatory programs of normal and tumor cells of the colon. Affymetrix Human Genome U219 expression arrays were used to assess gene expression in 100 samples of colon tumor and their paired adjacent normal mucosa. Transcriptional networks were reconstructed using ARACNe algorithm using 1,000 bootstrap replicates consolidated into a consensus network. Networks were compared regarding topology parameters and identified well-connected clusters. Functional enrichment was performed with SIGORA method. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors. The normal network contained 1,177 transcription factors, 5,466 target genes and 61,226 transcriptional interactions. A large loss of transcriptional interactions in the tumor network was observed (11,585; 81% reduction), which also contained fewer transcription factors (621; 47% reduction) and target genes (2,190; 60% reduction) than the normal network. Gene silencing was not a main determinant of this loss of regulatory activity, since the average gene expression was essentially conserved. Also, 91 transcription factors increased their connectivity in the tumor network. These genes revealed a tumor-specific emergent transcriptional regulatory program with significant functional enrichment related to colorectal cancer pathway. In addition, the analysis of clusters again identified subnetworks in the tumors enriched for cancer related pathways (immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others). Also multiple metabolism pathways show differential clustering between the tumor and normal network. These findings will allow a better understanding of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy.
    BMC Cancer 09/2014; 14(1):708. DOI:10.1186/1471-2407-14-708 · 3.36 Impact Factor
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    • "LUCIDATION of the interactions between molecular structures in biological organisms can provide valuable insights into human diseases, including cancer [1]. Spurred by advances in molecular profiling technology, computational models of cellular networks have been sought, and a variety of algorithms to infer the structure of molecular networks have been proposed and evaluated [2]–[7]. "
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    ABSTRACT: In this paper, we develop a comprehensive framework for optimal perturbation control of dynamic networks. The aim of the perturbation is to drive the network away from an undesirable steady-state distribution and to force it to converge towards a desired steady-state distribution. The proposed framework does not make any assumptions about the topology of the initial network, and is thus applicable to general-topology networks. We define the optimal perturbation control as the minimum-energy perturbation measured in terms of the Frobenius-norm between the initial and perturbed probability transition matrices of the dynamic network. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state distribution. In the event where the optimal perturbation does not exist, we construct a family of suboptimal perturbations, and show that the suboptimal perturbation can be used to approximate the optimal limiting distribution arbitrarily closely. Moreover, we investigate the robustness of the optimal perturbation control to errors in the probability transition matrix, and demonstrate that the proposed optimal perturbation control is robust to data and inference errors in the probability transition matrix of the initial network. Finally, we apply the proposed optimal perturbation control method to the Human melanoma gene regulatory network in order to force the network from an initial steady-state distribution associated with melanoma and into a desirable steady-state distribution corresponding to a benign cell.
    IEEE Transactions on Signal Processing 04/2013; 61(7):1733-1742. DOI:10.1109/TSP.2013.2241054 · 2.79 Impact Factor
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    • "Under the adaptive landscape theory, transformation of a normal cell to a cancer cell is an evolutionary , dynamic process driven by some driver-mutations under various environmental selection forces. The cells may transit from one state of gene expression to another on the adaptive landscape by some gene mutation or by responding to changes in its microenvironment (Demicheli and Coradini 2010). The mutations and the changes in microenvironment may only destabilize the present state of gene expression, and natural selection will drive the cells to another adaptive state. "
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    ABSTRACT: Tandem 3' UTRs produced by alternative polyadenylation (APA) play an important role in gene expression by impacting mRNA stability, translation, and translocation in cells. Several studies have investigated APA site switching in various physiological states; nevertheless, they only focused on either the genes with two known APA sites or several candidate genes. Here, we developed a strategy to study APA sites in a genome-wide fashion with second-generation sequencing technology which could not only identify new polyadenylation sites but also analyze the APA site switching of all genes, especially those with more than two APA sites. We used this strategy to explore the profiling of APA sites in two human breast cancer cell lines, MCF7 and MB231, and one cultured mammary epithelial cell line, MCF10A. More than half of the identified polyadenylation sites are not included in human poly(A) databases. While MCF7 showed shortening 3' UTRs, more genes in MB231 switched to distal poly(A) sites. Several gene ontology (GO) terms and pathways were enriched in the list of genes with switched APA sites, including cell cycle, apoptosis, and metabolism. These results suggest a more complex regulation of APA sites in cancer cells than previously thought. In short, our novel unbiased method can be a powerful approach to cost-effectively investigate the complex mechanism of 3' UTR switching in a genome-wide fashion among various physiological processes and diseases.
    Genome Research 05/2011; 21(5):741-7. DOI:10.1101/gr.115295.110 · 14.63 Impact Factor
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