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Bioinformatics Advance Access published online on February 10, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti312
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received November 28, 2004
Revised January 21, 2005
Accepted February 7, 2005

Article

Estimating cancer survival and clinical outcome based on genetic tumor progression scores

Jörg Rahnenführer 1*, Niko Beerenwinkel 1, Wolfgang A. Schulz 2, Christian Hartmann 3, Andreas von Deimling 3, Bernd Wullich 4, and Thomas Lengauer 1

1 Department of Computational Biology and Applied Algorithmics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany
2 Department of Urology, Heinrich-Heine University, D-40225 Düsseldorf, Germany
3 Department of Neuropathology, Charité, Humboldt University, D-13353 Berlin, Germany
4 Department of Urology and Pediatric Urology, University of the Saarland, D-66421 Homburg/Saar, Germany

* To whom correspondence should be addressed.
Jörg Rahnenführer, E-mail: rahnenfj{at}mpi-sb.mpg.de


  Abstract

Motivation: In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. Survival prognosis is typically based on clinical and histological parameters. There is increasing interest in identifying genetic markers that better capture the status of a tumor in order to improve on existing predictions. The accumulation of genetic alterations during tumor progression can be used for the assessment of the genetic status of the tumor. For modelling dependencies between the genetic events, evolutionary tree models have been applied.

Results: Mixture models of oncogenetic trees provide a probabilistic framework for the estimation of typical pathogenetic routes. From these models we derive a genetic progression score (GPS) that estimates the genetic status of a tumor. GPS is calculated for glioblastoma patients from LOH (loss of heterozygosity) and for prostate cancer patients from CGH (comparative genomic hybridization) measurements. Cox proportional hazard models are then fitted to observed survival times of glioblastoma patients and to times until PSA relapse following radical prostatectomy of prostate cancer patients. It turns out that the genetically defined GPS is predictive even after adjustment for classical clinical markers and thus can be considered a medically relevant prognostic factor.

Availability: Mtreemix, a software package for estimating tree mixture models, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de. The raw cancer data sets and R code for the analysis with Cox models are available upon request from the corresponding author.


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