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Gene Panel Model Predictive of Outcome in Men at High-Risk of Systemic Progression and Death From Prostate Cancer After Radical Retropubic Prostatectomy

2008; Lippincott Williams & Wilkins; Volume: 26; Issue: 24 Linguagem: Inglês

10.1200/jco.2007.15.6752

1527-7755

John C. Cheville, R. Jeffrey Karnes, Terry M. Therneau, Farhad Kosari, Jan-Marie Munz, Lori S. Tillmans, Eati Basal, Laureano J. Rangel, Eric J. Bergstralh, Irina V. Kovtun, C. Dilara Savci-Heijink, Eric W. Klee, George Vasmatzis,

Cancer, Lipids, and Metabolism

Purpose In men who are at high-risk of prostate cancer, progression and death from cancer after radical retropubic prostatectomy (RRP), limited prognostic information is provided by established prognostic features. The objective of this study was to develop a model predictive of outcome in this group of patients. Methods Candidate genes were identified from microarray expression data from 102 laser capture microdissected prostate tissue samples. Candidates were overexpressed in tumor compared with normal prostate and more frequently in Gleason patterns 4 and 5 than in 3. A case control study of 157 high-risk patients, matched on Gleason score and stage with systemic progression or death of prostate cancer as the end point, was used to evaluate the expression of candidate genes and build a multivariate model. Tumor was collected from the highest Gleason score in paraffin-embedded blocks and the gene expression was quantified by real-time reverse transcription polymerase chain reaction. Validation of the final model was performed on a separate case-control study of 57 high-risk patients who underwent RRP. Results A model incorporating gene expression of topoisomerase-2a, cadherin-10, the fusion status based on ERG, ETV1, and ETV4 expression, and the aneuploidy status resulted in a 0.81 area under the curve (AUC) in receiver operating characteristic statistical analysis for the identification of men with systemic progression and death from high grade prostate cancer. The AUC was 0.79 in the independent validation study. Conclusion The model can identify men with high-risk prostate cancer who may benefit from more intensive postoperative follow-up and adjuvant therapies.