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Front Genet. 2018 Jul 10;9:228. doi: 10.3389/fgene.2018.00228. eCollection 2018.

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients.

Author information

1
J. Craig Venter Institute, La Jolla, CA, United States.
2
Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.
3
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States.
4
The Translational Genomics Research Institute, Phoenix, AZ, United States.
5
Department of Medical Oncology, Yale Cancer Center, Yale University, New Haven, CT, United States.
6
Department of Psychiatry, University of California, San Diego, San Diego, CA, United States.

Abstract

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between "unsupervised" and "supervised" network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.

KEYWORDS:

bioinformatics and computational biology; data science; drug interactions; machine learning; melanoma; network rewiring; pathway analysis; simulation models

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