Automated network analysis identifies core pathways in glioblastoma

Area of Science:

• Genomics
• Computational Biology
• Oncology

Background:

• Glioblastoma multiforme (GBM) is an aggressive human brain tumor.
• The Cancer Genome Atlas (TCGA) project mapped GBM genomic profiles.
• Distinguishing driver mutations from passenger mutations is a key challenge in cancer genomics.

Purpose of the Study:

• To develop an automated network-based approach for identifying driver genes and oncogenic processes in GBM.
• To analyze genomic alterations within the context of molecular interaction networks.
• To differentiate between cancer-causing and incidental mutations.

Main Methods:

• Combined analysis of DNA sequence mutations and copy number alterations.
• Utilized a unified molecular interaction network including protein-protein interactions and signaling pathways.
• Identified and statistically assessed network modules (cohesive gene groups).

Main Results:

• GBM alterations are concentrated within specific functional modules, despite patient variability.
• Key modules identified involve signaling pathways such as p53, Rb, PI3K, and receptor protein kinases.
• New candidate GBM drivers, including AGAP2/CENTG1, and modules related to microtubule organization were discovered.

Conclusions:

• GBM alterations exhibit modularity, targeting critical cellular network components.
• The network-based approach successfully identifies known and novel GBM driver genes and pathways.
• The developed method, NetBox, is available for application to other cancer types.