Validation of a genomic-clinical classifier model for predicting clinical recurrence of patients with localized prostate cancer in a high-risk population.

Media type: E-Article

Title: Validation of a genomic-clinical classifier model for predicting clinical recurrence of patients with localized prostate cancer in a high-risk population

Contributor: Jenkins, Robert B.; Bergstralh, Eric J; Davicioni, Elai; Karnes, R. Jeffrey; Ballman, Karla V.; Fink, Stephanie; Black, Peter C.; Ghadessi, Mercedeh; Triche, Timothy J.; Klee, George G; Kollmeyer, Thomas M.; Vergara, Ismael A; Crisan, Anamaria; Erho, Nicholas; Sierocinski, Thomas; Buerki, Christine; Carlson, Rachel; Grill, Diane E; Zimmermann, Benedikt; Haddad, Zaid

imprint: American Society of Clinical Oncology (ASCO), 2012

Language: English

DOI: 10.1200/jco.2012.30.5_suppl.175

ISSN: 0732-183X; 1527-7755

Keywords: Cancer Research ; Oncology

Origination:

Footnote:

Description: 175 Background: The efficient delivery of adjuvant and salvage therapy after radical prostatectomy in patients with prostate cancer is hampered by a lack of biomarkers to assess the risk of clinically significant recurrence and progression. Methods: Mayo Clinic Radical Prostatectomy Registry (RP) patient specimens were selected from a case-control cohort with 14 years median follow-up for training and initial validation of an expression biomarker genomic classifier (GC). An independent, blinded case-cohort study of high-risk RP subjects was used to validate GC, comparing the performance of GC to a multivariate logistic regression clinical model (CM) and GC combined with clinical variables (genomic-clinical classifier, GCC) for predicting clinical recurrence (defined as positive bone or CT scan within 5 years after biochemical recurrence). The concordance index (c-index) and Cox model were used to evaluate discrimination and estimate the risk of clinical recurrence. Results: In the training subset (n=359), both GC and GCC had a c-index of 0.90 whereas CM had a c-index of 0.76. In the internal validation set (n=186), GC and GCC had a c-index of 0.76 and 0.75, while CM had a c-index of 0.69. In an independent high-risk study (n=219), GC and GCC had a c-index of 0.77 and 0.76, while CM had a c-index of 0.68. In subset analysis of Gleason score 7 patients within the high-risk group, GC and GCC showed improved discrimination with c-index of 0.78 and 0.76, respectively compared to 0.70 for CM. In the high-risk group, the risk of recurrence by GC model score quartiles at 5 years after RP was estimated at 1%, 5%, 5% and 18%. Conclusions: The GC model shows improved performance over CM in the prediction of clinical recurrence in a high-risk cohort and in subset analysis of Gleason score 7 patients. The addition of clinical variables to the GC model did not significantly contribute to classifier performance in patients with high-risk features. We are further testing the performance of the GC and GCC models and their usefulness in guiding decision-making (e.g., for the adjuvant therapy setting) in additional studies of prostate cancer clinical risk groups.

Access State: Open Access

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