Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Vishal N Patel,John M Mariadason,Gurkan Bebek,Mark R Chance,Leonard H Augenlicht,Donghai Wang

doi:10.1371/journal.pone.0012497

Abstract

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 (Apc1638N+/−) 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.

Highlights

The majority of nonhereditary colorectal tumors arise via the sequential accumulation of mutations in key driver genes, where a mutation in a tumor suppressor (e.g. Apc) or oncogene (e.g. Kras) initiates the process, and a cascade of somatic mutations ensues [1]
In support of the hypothesis that these key genes function cooperatively in driving tumorigenesis, mouse models mutated at two driver genes simultaneously have shown a synergistic increase in tumor burden, including: Pten-Apc [3], KrasTgfb [4], and Apc-Trp53 [5]
To trace the connections between genes, a variety of high-throughput datasets – e.g. protein-protein interactions (PPIs), gene coexpression, and transcription factor relationships – have been employed to infer functional associations that lend themselves to analysis as networks, in which each gene or protein is represented as a node and an interaction as an edge

Summary

Introduction

The majority of nonhereditary colorectal tumors arise via the sequential accumulation of mutations in key driver genes, where a mutation in a tumor suppressor (e.g. Apc) or oncogene (e.g. Kras) initiates the process, and a cascade of somatic mutations ensues [1]. These mutations were classically thought to be comprised of a few genes (e.g. Apc, Kras, Trp53), recent large-scale sequencing efforts revealed that any given tumor includes (on average) 80 mutations, with as many as 15 lying in frequently mutated ‘‘driver’’ genes [2]. Network-based analyses can be used to identify biomarkers [6], to predict tumor progression [7], or to reveal the molecular alterations underlying disease [8]

Methods

Results

Conclusion