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Using MEMo to discover mutual exclusivity modules in cancer

Giovanni Ciriello1, Ethan Cerami1, Bulent Arman Aksoy1

  • 1Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York.

Current Protocols in Bioinformatics
|March 19, 2013
PubMed

Insights

This study introduces Mutual Exclusivity Modules in Cancer (MEMo), a new computational method to identify cancer gene pathway modules. MEMo reveals that cancer gene alterations often occur in specific pathways and are mutually exclusive within patients.

Area of Science:

  • Computational biology
  • Cancer genomics
  • Systems biology

Background:

  • Tumors exhibit diverse genomic alterations, often confined to specific biological pathways.
  • Understanding oncogenic pathway modules is crucial but incomplete in cancer genomics.
  • Existing pathway analysis tools have limitations in identifying complex gene interactions.

Purpose of the Study:

  • To develop a novel computational method for systematically identifying oncogenic pathway modules.
  • To characterize modules based on recurrent alterations, shared biological processes, and mutual exclusivity.
  • To integrate multiple data types for mapping genomic alterations to biological pathways.

Main Methods:

  • Developed Mutual Exclusivity Modules in Cancer (MEMo) method.
  • Identified modules with recurrently altered genes across tumor samples.
  • Ensured member genes participate in the same biological process.
  • Applied a statistical model to ensure mutual exclusivity of alteration events within modules.

Main Results:

  • MEMo successfully identifies modules of genes with mutually exclusive alterations.
  • The method integrates diverse data types to map genomic alterations to pathways.
  • The statistical model preserves gene and sample alteration counts, enhancing accuracy.
  • Identified previously unknown oncogenic pathway modules.

Conclusions:

  • MEMo provides a robust framework for discovering cancer-relevant pathway modules.
  • The identified modules offer insights into cancer driver genes and pathways.
  • This approach advances the understanding of cancer genomics and potential therapeutic targets.

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