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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-19325

Liu, J; Bandyopadhyay, N; Ranka, S; Baudis, M; Kahveci, T (2009). Inferring Progression Models for CGH data. Bioinformatics, 25(17):2208-2215.

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Abstract

MOTIVATION: One of the mutational processes that has been monitored genome-wide is the occurrence of regional DNA Copy Number Alterations (CNAs), which may lead to deletion or over-expression of tumor suppressors or oncogenes, respectively. Understanding the relationship between CNAs and different cancer types is a fundamental problem in cancer studies. RESULTS: This paper develops an efficient method that can accurately model the progression of the cancer markers and reconstruct evolutionary relationship between multiple types of cancers using Comparative Genomic Hybridization (CGH) data. Such modeling can lead to better understanding of the commonalities and differences between multiple cancer types and potential therapies. We have developed an automatic method to infer a graph model for the markers of multiple cancers from a large population of CGH data. Our method identifies highly related markers across different cancer types. It then builds a directed acyclic graph that shows the evolutionary history of these markers based on how common each marker is in different cancer types. We demonstrated the use of this model in determining the importance of markers in cancer evolution. We have also developed a new method to measure the evolutionary distance between different cancers based on their markers. This method employs the graph model we developed for the individual markers to measure the distance between pairs of cancers. We used this measure to create an evolutionary tree for multiple cancers. Our experiments on Progenetix database show that our markers are largely consistent to the reported hot-spot imbalances and most frequent imbalances. The results show that our distance measure can accurately reconstruct the evolutionary relationship between multiple cancer types. AVAILABILITY: All the code developed in this paper are available at http: //bioinformatics.cise.ufl.edu/phylogeny.html. subtypes of the same cancer. CONTACT: nirmalya@cise.ufl.edu.

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
07 Faculty of Science > Institute of Molecular Life Sciences
04 Faculty of Medicine > Institute of Molecular Cancer Research
07 Faculty of Science > Institute of Molecular Cancer Research

08 University Research Priority Programs > Systems Biology / Functional Genomics
08 University Research Priority Programs > Systems Biology / Functional Genomics
DDC:570 Life sciences; biology
610 Medicine & health
Language:English
Date:1 September 2009
Deposited On:22 Jun 2009 10:43
Last Modified:27 Nov 2013 20:15
Publisher:Oxford University Press
ISSN:1367-4803
Additional Information:This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics. The definitive publisher-authenticated version Inferring Progression Models for CGH data is available online at: http://bioinformatics.oxfordjournals.org/cgi/reprint/btp365v1
Publisher DOI:10.1093/bioinformatics/btp365
PubMed ID:19528087
Citations:Web of Science®. Times Cited: 6
Google Scholar™
Scopus®. Citation Count: 5

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