title: Quantifying cancer progression with conjunctive Bayesian networks.
abstract: BACKGROUND
  Cancer is an evolutionary process characterized by accumulating mutations. However, the precise timing and the order of genetic alterations that drive tumor progression remain enigmatic.
  
  
  RESULTS
  We present a specific probabilistic graphical model for the accumulation of mutations and their interdependencies. The Bayesian network models cancer progression by an explicit unobservable accumulation process in time that is separated from the observable but error-prone detection of mutations. Model parameters are estimated by an Expectation-Maximization algorithm and the underlying interaction graph is obtained by a simulated annealing procedure. Applying this method to cytogenetic data for different cancer types, we find multiple complex oncogenetic pathways deviating substantially from simplified models, such as linear pathways or trees. We further demonstrate how the inferred progression dynamics can be used to improve genetics-based survival predictions which could support diagnostics and prognosis.
  
  
  BACKGROUND
  The software package ct-cbn is available under a GPL license on the web site cbg.ethz.ch/software/ct-cbn
  
  
  BACKGROUND
  moritz.gerstung@bsse.ethz.ch.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. moritz.gerstung@bsse.ethz.ch
authors.forename: Moritz
authors.initials: M
authors.lastname: Gerstung
authors.forename: Michael
authors.initials: M
authors.lastname: Baudis
authors.forename: Holger
authors.initials: H
authors.lastname: Moch
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*
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