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Clinical Chemistry 48: 1835-1843, 2002;
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(Clinical Chemistry. 2002;48:1835-1843.)
© 2002 American Association for Clinical Chemistry, Inc.

Oak Ridge Conference

Boosted Decision Tree Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer from Noncancer Patients

Yinsheng Qu1, Bao-Ling Adam2,4, Yutaka Yasui1, Michael D. Ward2,4, Lisa H. Cazares2,4, Paul F. Schellhammer2,3,4, Ziding Feng1, O. John Semmes2,4 and George L. Wright, Jr.2,3,4a

1 Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109.
Departments of
2 Microbiology and Molecular Cell Biology and
3 Urology, Eastern Virginia Medical School, Norfolk, VA 23501.

4 Virginia Prostate Center, Eastern Virginia Medical School and Sentara Cancer Institute, Norfolk, VA 23501.

aAddress correspondence to this author at: Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, 700 West Olney Rd., Norfolk, VA 23507. Fax 757-624-2255; e-mail wrightgl{at}evms.edu.


Abstract

Background: The low specificity of the prostate-specific antigen (PSA) test makes it a poor biomarker for early detection of prostate cancer (PCA). Because single biomarkers most likely will not be found that are expressed by all genetic forms of PCA, we evaluated and developed a proteomic approach for the simultaneous detection and analysis of multiple proteins for the differentiation of PCA from noncancer patients.

Methods: Serum samples from 386 men [197 with PCA, 92 with benign prostatic hyperplasia (BPH), and 96 healthy individuals], randomly divided into training (n = 326) and test (n = 60) sets, were analyzed by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. The 124 peaks detected by computer analyses were analyzed in the training set by a boosting tree algorithm to develop a classifier for separating PCA from the noncancer groups. The classifier was then challenged with the test set (30 PCA samples, 15 BPH samples, 15 samples from healthy men) to determine the validity and accuracy of the classification system.

Results: Two classifiers were developed. The AdaBoost classifier completely separated the PCA from the noncancer samples, achieving 100% sensitivity and specificity. The second classifier, the Boosted Decision Stump Feature Selection classifier, was easier to interpret and used only 21 (compared with 74) peaks and a combination of 21 (vs 500) base classifiers to achieve a sensitivity and specificity of 97% for the test set.

Conclusions: The high sensitivity and specificity achieved in this study provides support of the potential for SELDI, coupled with a bioinformatics learning algorithm, to improve the early detection/diagnosis of PCA.




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