Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy

Authors

  • Andrew J. Stephenson M.D.,

    1. Department of Urology, Sidney Kimmel Center for Prostate and Urologic Cancers, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Alex Smith M.S.,

    1. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Michael W. Kattan Ph.D.,

    1. Department of Urology, Sidney Kimmel Center for Prostate and Urologic Cancers, Memorial Sloan-Kettering Cancer Center, New York, New York
    2. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Jaya Satagopan Ph.D.,

    1. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Victor E. Reuter M.D.,

    1. Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Peter T. Scardino M.D.,

    1. Department of Urology, Sidney Kimmel Center for Prostate and Urologic Cancers, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • William L. Gerald M.D., Ph.D.

    Corresponding author
    1. Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York
    • Department of Pathology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021
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    • Fax: (212) 639-4559


Abstract

BACKGROUND

Gene expression profiling of prostate carcinoma offers an alternative means to distinguish aggressive tumor biology and may improve the accuracy of outcome prediction for patients with prostate carcinoma treated by radical prostatectomy.

METHODS

Gene expression differences between 37 recurrent and 42 nonrecurrent primary prostate tumor specimens were analyzed by oligonucleotide microarrays. Two logistic regression modeling approaches were used to predict prostate carcinoma recurrence after radical prostatectomy. One approach was based exclusively on gene expression differences between the two classes. The second approach integrated prognostic gene variables with a validated postoperative predictive model based on standard variables (nomogram). The predictive accuracy of these modeling approaches was evaluated by leave-one-out cross-validation (LOOCV) and compared with the nomogram.

RESULTS

The modeling approach using gene variables alone accurately classified 59 (75%) tissue samples in LOOCV, a classification rate substantially higher than expected by chance. However, this predictive accuracy was inferior to the nomogram (concordance index, 0.75 vs. 0.84, P = 0.01). Models combining clinical and gene variables accurately classified 70 (89%) tissue samples and the predictive accuracy using this approach (concordance index, 0.89) was superior to the nomogram (P = 0.009) and models based on gene variables alone (P < 0.001). Importantly, the combined approach provided a marked improvement for patients whose nomogram-predicted likelihood of disease recurrence was in the indeterminate range (7-year disease progression-free probability, 30–70%; concordance index, 0.83 vs. 0.59, P = 0.01).

CONCLUSIONS

Integration of gene expression signatures and clinical variables produced predictive models for prostate carcinoma recurrence that perform significantly better than those based on either clinical variables or gene expression information alone. Cancer 2005. © 2005 American Cancer Society.

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