Journal Article

Inferring progression models for CGH data

Jun Liu, Nirmalya Bandyopadhyay, Sanjay Ranka, M. Baudis and Tamer Kahveci

in Bioinformatics

Volume 25, issue 17, pages 2208-2215
Published in print September 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp365
Inferring progression models for CGH data

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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 article 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 article are available at http://bioinformatics.cise.ufl.edu/phylogeny.html.

Contact: nirmalya@cise.ufl.edu; tamer@cise.ufl.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  7221 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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