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Identifying cellular cancer mechanisms through pathway-driven data integration.

Sam F L Windels1,2, Noël Malod-Dognin1,2, Nataša Pržulj1,2,3

  • 1Department of Computer Science, University College London, London WC1E 6BT, UK.

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Summary
This summary is machine-generated.

We identified cancer pathways by analyzing changes in pathway relationships, not just gene expression. This approach predicts novel cancer-associated genes and potential drug targets.

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Cancer is a genetic disease driven by mutations affecting cellular pathways.
  • Current methods focus on internal pathway perturbations, overlooking the role of driver genes as hubs between pathways.

Purpose of the Study:

  • To develop a novel method for identifying cancer pathways by analyzing changes in pathway-pathway relationships.
  • To predict cancer-associated genes and potential therapeutic targets.

Main Methods:

  • Pathway-driven non-negative matrix tri-factorization (NMTF) to learn pathway and gene embeddings.
  • Defining 'NMTF centrality' and 'moving distance' to assess functional importance and relationship changes.
  • Utilizing graphlet adjacency to model network data and identify hub driver genes.

Main Results:

  • Predicted 15 genes and pathways involved in four major cancers, yielding 60 gene-cancer associations.
  • Identified genes that rewire immune system pathway interactions.
  • Found 15 out of 28 predicted genes are druggable and 47 out of 60 associations are cancer-implicated.
  • Predicted six druggable, cancer-specific drug targets.

Conclusions:

  • Changes in pathway-pathway relationships are crucial for identifying cancer pathways.
  • The proposed method effectively predicts cancer-associated genes, their functional roles, and potential therapeutic targets.
  • This approach offers a new perspective for cancer pathway identification and drug discovery.