MEMo

MEMo identifies mutually exclusive driver networks in cancer by integrating somatic mutation and copy number alteration data with biological pathway and network knowledge to reveal oncogenic pathway modules.

Key Features:

• Recurrent alterations: Identifies genes recurrently altered across tumor samples using somatic mutations and copy number changes.
• Pathway and network integration: Leverages prior knowledge of biological pathways and networks to determine whether member genes participate in the same biological process.
• Mutual exclusivity detection: Detects statistically significant levels of mutual exclusivity among genomic events within identified networks.
• Multi-data mapping: Maps multiple types of genomic alterations directly onto biological pathways.
• Preserved alteration counts: Employs a statistical model that preserves the number of alterations per gene and per sample to ensure robustness in significance testing.
• Oncogenic module recovery: Identifies oncogenic pathway modules even when pathway membership or alteration patterns are incomplete.

Scientific Applications:

• Driver network identification: Systematic discovery of mutually exclusive driver networks in cancer genomes.
• Pathway-centric interpretation: Interpretation of how disparate genomic events converge on shared biological pathways.
• Candidate driver and target prioritization: Prioritization of critical driver mutations and pathway components as potential therapeutic targets.

Methodology:

Maps somatic mutations and copy number alterations onto pathway networks, identifies recurrently altered genes across samples, tests for statistically significant mutual exclusivity, and uses a statistical model that preserves per-gene and per-sample alteration counts.