HIT’nDRIVE: Multi-driver Gene Prioritization Based on Hitting Time

  • Raunak Shrestha
  • Ermin Hodzic
  • Jake Yeung
  • Kendric Wang
  • Thomas Sauerwald
  • Phuong Dao
  • Shawn Anderson
  • Himisha Beltran
  • Mark A. Rubin
  • Colin C. Collins
  • Gholamreza Haffari
  • S. Cenk Sahinalp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8394)

Abstract

A key challenge in cancer genomics is the identification and prioritization of genomic aberrations that potentially act as drivers of cancer. In this paper we introduce HIT’nDRIVE, a combinatorial method to identify aberrant genes that can collectively influence possibly distant “outlier” genes based on what we call the “random-walk facility location” (RWFL) problem on an interaction network. RWFL differs from the standard facility location problem by its use of “multi-hitting time”, the expected minimum number of hops in a random walk originating from any aberrant gene to reach an outlier. HIT’nDRIVE thus aims to find the smallest set of aberrant genes from which one can reach outliers within a desired multi-hitting time. For that it estimates multi-hitting time based on the independent hitting times from the drivers to any given outlier and reduces the RWFL to a weighted multi-set cover problem, which it solves as an integer linear program (ILP). We apply HIT’nDRIVE to identify aberrant genes that potentially act as drivers in a cancer data set and make phenotype predictions using only the potential drivers - more accurately than alternative approaches.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raunak Shrestha
    • 1
    • 2
  • Ermin Hodzic
    • 3
  • Jake Yeung
    • 2
    • 4
  • Kendric Wang
    • 2
  • Thomas Sauerwald
    • 5
  • Phuong Dao
    • 6
  • Shawn Anderson
    • 2
  • Himisha Beltran
    • 7
  • Mark A. Rubin
    • 7
  • Colin C. Collins
    • 2
    • 8
  • Gholamreza Haffari
    • 9
  • S. Cenk Sahinalp
    • 3
    • 10
  1. 1.CIHR Bioinformatics Training ProgramUniversity of British ColumbiaVancouverCanada
  2. 2.Laboratory for Advanced Genome AnalysisVancouver Prostate CentreVancouverCanada
  3. 3.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  4. 4.Genome Science and Technology ProgramUniversity of British ColumbiaVancouverCanada
  5. 5.Computer LaboratoryUniversity of CambridgeCambridgeUnited Kingdom
  6. 6.NLM, NIHNational Center for Biotechnology InformationBethesdaUSA
  7. 7.Weill Cornell Cancer CenterNew YorkUSA
  8. 8.Department of Urologic SciencesUniversity of British ColumbiaVancouverCanada
  9. 9.Faculty of Information TechnologyMonash UniversityMelbourneAustralia
  10. 10.School of Informatics and ComputingIndiana UniversityBloomingtonUSA

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