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Bioinformatics Advance Access originally published online on July 15, 2008
Bioinformatics 2009 25(1):54-60; doi:10.1093/bioinformatics/btn354
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis

James A. Koziol 1, Anne C. Feng 1, Zhenyu Jia 2, Yipeng Wang 2,3, Seven Goodison 4, Michael McClelland 3 and Dan Mercola 2,*

1The Scripps Research Institute, La Jolla, 2Translational Cancer Biology, Department of Pathology and Laboratory Medicine, University of California, Irvine, 3The Sidney Kimmel Cancer Center, San Diego, CA and 4Department of Surgery, University of Florida, Shands Health Science Center, Jacksonville, FL, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Classification and regression trees have long been used for cancer diagnosis and prognosis. Nevertheless, instability and variable selection bias, as well as overfitting, are well-known problems of tree-based methods. In this article, we investigate whether ensemble tree classifiers can ameliorate these difficulties, using data from two recent studies of radical prostatectomy in prostate cancer.

Results: Using time to progression following prostatectomy as the relevant clinical endpoint, we found that ensemble tree classifiers robustly and reproducibly identified three subgroups of patients in the two clinical datasets: non-progressors, early progressors and late progressors. Moreover, the consensus classifications were independent predictors of time to progression compared to known clinical prognostic factors.

Contact: dmercola{at}uci.edu

Associate Editor: Martin Bishop

Received on April 3, 2008; revised on July 9, 2008; accepted on July 10, 2008

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