A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Abstract excerpt

<h4>ABSTRACT</h4> Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency ( < 2%) mutations 1 , and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived fr...

Topics

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• Algorithms
• Computational Biology
• Computer Simulation
• Disease Progression
• Gene Regulatory Networks
• Mutation Rate
• Oncogenes
• Protein Interaction Maps

Identifiers and source

Literature Corpus work

cf3fb80e-01c6-5612-8fd9-ba731b1da1ca

DOI

10.1101/025445