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PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling
Gurkan Bebek1, Vishal Patel, Mark R Chance
1Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan@case.edu
Background:
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.
Results:
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 Apc¹⁶³⁸(N+/)⁻ mouse to test the network-based hypotheses.
Conclusions:
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.
Insights
Computational methods predict colon cancer pathways. The PETALS pipeline identified a key Apc-Hapln1 pathway, validated by proteomic data, offering insights into cancer driver gene interactions.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Colon cancer is driven by mutations in genes like Apc.
- Understanding Apc signaling in tumors is complex due to other cancer driver (CAN) gene mutations.
- Predictive computational methods are needed to identify operative pathways in Apc-mutated tumors.
Purpose of the Study:
- To develop and apply a computational pipeline (PETALS) for predicting and testing signaling pathways involving Apc and other CAN genes.
- To identify specific Apc-CAN-gene subnetworks ('petals') within a larger interaction network ('blossom').
Main Methods:
- Developed the PETALS pipeline to model Apc-CAN-gene interactions.
- Constructed an Apc interaction network ('blossom') with 24 predicted 'petals'.
- Utilized a novel bimodality measure to evaluate petal coexpression against proteomic data (2D-DIGE) from Apc¹⁶³⁸(N+/)⁻ mice.
Main Results:
- The PETALS pipeline identified and prioritized potential signaling pathways.
- The Apc-Hapln1 pathway demonstrated significant bimodal coexpression with proteomic targets.
- This suggests the Apc-Hapln1 petal is a key regulator of observed proteome-level effects.
Conclusions:
- Functional 'omics data can validate in silico predictions of CAN-gene pathways.
- The study reveals an integrated approach combining genetic models with measured downstream effects.
- The Apc-Hapln1 pathway is highlighted as a potentially critical player in colon cancer progression.
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