Header

UZH-Logo

Maintenance Infos

Quantifying cancer progression with conjunctive Bayesian networks


Gerstung, M; Baudis, M; Moch, H; Beerenwinkel, N (2009). Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics, 25(21):2809-2815.

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@bsse.ethz.ch.

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@bsse.ethz.ch.

Statistics

Citations

Dimensions.ai Metrics
57 citations in Web of Science®
63 citations in Scopus®
86 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

135 downloads since deposited on 16 Nov 2009
33 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:08 Research Priority Programs > Systems Biology / Functional Genomics
07 Faculty of Science > Institute of Molecular Life Sciences
04 Faculty of Medicine > University Hospital Zurich > Institute of Pathology and Molecular Pathology
04 Faculty of Medicine > Institute of Molecular Cancer Research
07 Faculty of Science > Institute of Molecular Cancer Research
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2009
Deposited On:16 Nov 2009 10:16
Last Modified:24 Sep 2019 16:22
Publisher:Oxford University Press
ISSN:1367-4803
OA Status:Green
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btp505
PubMed ID:19692554

Download

Green Open Access

Download PDF  'Quantifying cancer progression with conjunctive Bayesian networks'.
Preview
Content: Published Version
Language: English
Filetype: PDF (Nationallizenz 142-005)
Size: 365kB
View at publisher