Dec. 13, 2010
Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc.
Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed.
We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc1638N+/-mouse to test the network-based hypotheses.
The predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.
Figure We begin with a cancer driver gene of interest (e.g. Apc) and a set of putative signaling partners. The pathways between these two sets are predicted based on protein-protein interactions, coupled with mRNA coexpression and GO annotations. The prediction of pathways to the various signaling partners allows individual subnetworks to blossom into a flower with many petals. The biological relevance of each petal is assessed against proteomic evidence (i.e. 2D-DIGE), using the bimodality of mRNA coexpression to quantify this association. This results in a ranking of petals, which can then be plucked for further experimental evaluation.
Results from: Gurkan Bebek, Vishal Patel, and Mark R Chance BMC Bioinformatics 2010, 11:596 doi:10.1186/1471-2105-11-596