Cancer arises from a deregulation of both intracellular and intercellular control systems. Understanding the architecture of these control systems and how they are changed in diseases could present opportunities for therapeutic targets to restore normal control. However, since intercellular control structures only appear in intact systems, it is difficult to identify how these control structures become altered using in vitro models and it can be difficult to determine if an in vivo model system appropriately replicates what occurs in human disease. In order to overcome this, we use the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas database of human breast cancer to identify intercellular control topology in vivo. To improve the underlying biological signals from the noisy gene expression data, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly reported with different immune and cancer states. From these directional, acyclic graphs, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. Furthermore, we also found that it was possible to identify the relationship between EMT and macrophage polarization with fewer datasets when the Bayesian network was generated from malignant samples alone, while it was possible to identify the relationship between proliferation and macrophage polarization with fewer samples when the samples were taken from a combination of the normal and malignant samples. When the same technique was applied to other cancers, we found a common result that proliferation was associated with a type 1 cell-mediated anti-tumor immunity and EMT was associated with a pro-tumor anti-inflammatory response. All together, these networks give us an understanding of what relationships are occurring in human cancer progression, and this knowledge can be used to help identify model system that more closely mimic human disease progression.
|Commitee:||Barnett, John, Cuff, Chrisopher, Schafer, Rosana|
|School:||West Virginia University|
|Department:||School of Medicine|
|School Location:||United States -- West Virginia|
|Source:||MAI 53/06M(E), Masters Abstracts International|
|Keywords:||Bayesian networks, Cancer progression, Control topography, Tcga|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be