title: Efficient methods for identifying mutated driver pathways in cancer.
abstract: BACKGROUND
  The first step for clinical diagnostics, prognostics and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations, driver genes and driver pathways promoting cancer proliferation and filter out the unfunctional and passenger ones.
  
  
  RESULTS
  In this study, we propose two methods to solve the so-called maximum weight submatrix problem, which is designed to de novo identify mutated driver pathways from mutation data in cancer. The first one is an exact method that can be helpful for assessing other approximate or/and heuristic algorithms. The second one is a stochastic and flexible method that can be employed to incorporate other types of information to improve the first method. Particularly, we propose an integrative model to combine mutation and expression data. We first apply our methods onto simulated data to show their efficiency. We further apply the proposed methods onto several real biological datasets, such as the mutation profiles of 74 head and neck squamous cell carcinomas samples, 90 glioblastoma tumor samples and 313 ovarian carcinoma samples. The gene expression profiles were also considered for the later two data. The results show that our integrative model can identify more biologically relevant gene sets. We have implemented all these methods and made a package called mutated driver pathway finder, which can be easily used for other researchers.
  
  
  BACKGROUND
  A MATLAB package of MDPFinder is available at http://zhangroup.aporc.org/ShiHuaZhang.
  
  
  BACKGROUND
  zsh@amss.ac.cn.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Junfei
authors.initials: J
authors.lastname: Zhao
authors.forename: Shihua
authors.initials: S
authors.lastname: Zhang
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Wu
authors.forename: Xiang-Sun
authors.initials: XS
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D004252:DNA Mutational Analysis / Q000379:methods; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*