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Systems Pathology Approach for the Prediction of Prostate Cancer Progression After Radical Prostatectomy

Michael J. Donovan, Stefan Hamann, Mark Clayton, Faisal M. Khan, Marina Sapir, Valentina Bayer-Zubek, Gerardo Fernandez, Ricardo Mesa-Tejada, Mikhail Teverovskiy, Victor E. Reuter, Peter T. Scardino, Carlos Cordon-Cardo

From the Memorial Sloan-Kettering Cancer Center; Herbert Irving Comprehensive Cancer Center and Department of Pathology, Columbia University, New York; and Aureon Laboratories Inc, Yonkers, NY

Corresponding author: Michael J. Donovan, MD, PhD, Aureon Laboratories Inc, 28 Wells Ave, Yonkers, NY 10701; e-mail: mdonovan{at}pathocon.com

Purpose For patients with prostate cancer treated by radical prostatectomy, no current personalized tools predict clinical failure (CF; metastasis and/or androgen-independent disease). We developed such a tool through integration of clinicopathologic data with image analysis and quantitative immunofluorescence of prostate cancer tissue.

Patients and Methods A prospectively designed algorithm was applied retrospectively to a cohort of 758 patients with clinically localized or locally advanced prostate cancer. A model predicting distant metastasis and/or androgen-independent recurrence was derived from features selected through supervised multivariate learning. Performance of the model was estimated using the concordance index (CI).

Results We developed a predictive model using a training set of 373 patients with 33 CF events. The model includes androgen receptor (AR) levels, dominant prostatectomy Gleason grade, lymph node involvement, and three quantitative characteristics from hematoxylin and eosin staining of prostate tissue. The model had a CI of 0.92, sensitivity of 90%, and specificity of 91% for predicting CF within 5 years after prostatectomy. Model validation on an independent cohort of 385 patients with 29 CF events yielded a CI of 0.84, sensitivity of 84%, and specificity of 85%. High levels of AR predicted shorter time to castrate prostate-specific antigen increase after androgen deprivation therapy (ADT).

Conclusion The integration of clinicopathologic variables with imaging and biomarker data (systems pathology) resulted in a highly accurate tool for predicting CF within 5 years after prostatectomy. The data support a role for AR signaling in clinical progression and duration of response to ADT.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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				E. A. Klein, A. J. Stephenson, D. Raghavan, and R. Dreicer Systems Pathology and Predicting Outcome After Radical Prostatectomy J. Clin. Oncol., August 20, 2008; 26(24): 3916 - 3917. [Full Text] [PDF]