Extending pathways and processes using molecular interaction networks to analyse cancer genome data
- Equal contributors
1 School of Computer Science, Nottingham University, Jubilee Campus, NG81BB Nottingham, UK
2 Structural Biology and Biocomputing Program, Spanish National Cancer Research Centre, CNIO, E-28029 Madrid, Spain
3 Luminy Institute of Mathematics, UMR6206, Campus de Luminy, Case 907, 13288 Marseilles Cedex 9, France
BMC Bioinformatics 2010, 11:597 doi:10.1186/1471-2105-11-597Published: 13 December 2010
Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.
We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes.
The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.