| Summary: | <p>Abstract</p> <p>Background</p> <p>Colon cancer is driven by mutations in a number of genes, the most notorious of which is <it>Apc</it>. Though much of <it>Apc</it>'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 <it>Apc</it>.</p> <p>Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in <it>Apc </it>is observed.</p> <p>Results</p> <p>We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting <it>Apc </it>to other CAN-genes, where the interaction network originating at <it>Apc </it>is defined as a "blossom," with each <it>Apc</it>-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 <it>Apc</it><sup><it>1638N</it>+/-</sup>mouse to test the network-based hypotheses.</p> <p>Conclusions</p> <p>The predicted pathways linking <it>Apc </it>and <it>Hapln1 </it>exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the <it>Apc-Hapln1 </it>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 <it>silico </it>predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.</p>
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