Skip Navigation


Bioinformatics Advance Access originally published online on August 19, 2009
Bioinformatics 2009 25(21):2809-2815; doi:10.1093/bioinformatics/btp505
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
25/21/2809    most recent
btp505v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Gerstung, M.
Right arrow Articles by Beerenwinkel, N.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Gerstung, M.
Right arrow Articles by Beerenwinkel, N.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Quantifying cancer progression with conjunctive Bayesian networks

Moritz Gerstung 1,*, Michael Baudis 2, Holger Moch 3 and Niko Beerenwinkel 1

1 Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, 2 Institute of Molecular Biology, University of Zurich, Winterthurerstrasse 190, 8057 Zurich and 3 Institute of Surgical Pathology, Department of Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091 Zurich, Switzerland

* To whom correspondence should be addressed.


  Abstract

Motivation: 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.

Availability: The software package ct-cbn is available under a GPL license on the web site cbg.ethz.ch/software/ct-cbn

Contact: moritz.gerstung{at}bsse.ethz.ch

Associate Editor: Jeffrey Barrett

Received on April 29, 2009; revised on July 17, 2009; accepted on August 13, 2009

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.