Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets

Songjian Lu*, Kevin N. Lu, Shi Yuan Cheng, Bo Hu, Xiaojun Ma, Nicholas Nystrom, Xinghua Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways.

Original languageEnglish (US)
Article numbere1004257
JournalPLoS computational biology
Issue number8
StatePublished - Aug 1 2015

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation


Dive into the research topics of 'Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets'. Together they form a unique fingerprint.

Cite this