PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling
- Equal contributors
1 Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH, 44106, USA
2 Case Comprehensive Cancer Center, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH, 44106, USA
3 Genomic Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland OH, 44195, USA
4 Department of Genetics, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH, 44106, USA
5 Department of Physiology and Biophysics, Case Western Reserve University, 10900 Euclid Ave, Cleveland OH, 44106, USA
BMC Bioinformatics 2010, 11:596 doi:10.1186/1471-2105-11-596Published: 13 December 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.