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Published on: April 6, 2016
System-scale network modeling of cancer using EPoC
Tobias Abenius1, Rebecka Jörnsten, Teresia Kling
1Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, 412 96 Gothenburg, Sweden. Tobias.Abenius@chalmers.se
Abstract:
One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.
Insights
Endogenous Perturbation analysis of Cancer (EPoC) models tumor molecular profiles using copy number and mRNA data. This network modeling technique identifies genes affecting mRNA expression and predicts patient survival, aiding targeted cancer therapy development.
Area of Science:
- Cancer Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Understanding complex molecular changes in cancer is crucial for developing targeted therapies.
- Tumor molecular profiles, including copy number aberrations (CNA) and mRNA expression, offer insights into cancer biology.
- Existing methods may not fully integrate CNA and mRNA data for comprehensive network modeling.
Purpose of the Study:
- To introduce and provide a practical guide for Endogenous Perturbation analysis of Cancer (EPoC) modeling.
- To identify genes whose CNA significantly impact mRNA expression within tumor networks.
- To generate robust markers for predicting both long- and short-term patient survival.
Main Methods:
- EPoC models are constructed using a combination of regression and bootstrapping methods.
- Integration of copy number aberration (CNA) and mRNA expression data for network construction.
- Prognostic scores are derived from singular value decomposition of the constructed networks.
Main Results:
- EPoC network models successfully identify known and candidate disease-relevant genes as network hubs.
- The technique uncovers significant predictors of patient survival, validated on glioblastoma data.
- Demonstrated the utility of EPoC in analyzing The Cancer Genome Atlas (TCGA) glioblastoma dataset.
Conclusions:
- EPoC provides a powerful network modeling approach for analyzing tumor molecular profiles.
- The method effectively links genetic alterations (CNA) to gene expression (mRNA) and patient outcomes.
- This work offers a practical framework and alternative modeling strategies for cancer systems biology research.
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