Prediction and Testing of Biological Networks Underlying Intestinal Cancer
AuthorPatel, VN; Bebek, G; Mariadason, JM; Wang, D; Augenlicht, LH; Chance, MR
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
University of Melbourne Author/sMariadason, John
AffiliationMedicine and Radiology
Document TypeJournal Article
CitationsPatel, V. N., Bebek, G., Mariadason, J. M., Wang, D., Augenlicht, L. H. & Chance, M. R. (2010). Prediction and Testing of Biological Networks Underlying Intestinal Cancer. PLOS ONE, 5 (9), https://doi.org/10.1371/journal.pone.0012497.
Access StatusOpen Access
Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called "driver" genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections--both precedented and novel--between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc(1638N+/-)) or Cdkn1a (Cdkn1a(-/-)), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.
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