UNASSIGNED: 175 Background: The efficient delivery of adjuvant and salvage therapy after radical prostatectomy in @entity1 with @entity263 is hampered by a lack of biomarkers to assess the risk of clinically significant recurrence and progression. METHODS: Mayo Clinic Radical Prostatectomy Registry (RP) @entity1 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 @entity922 ( @entity922 ) 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 @entity922 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 @entity922 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 @entity922 had a c-index of 0.68. In subset analysis of Gleason score 7 @entity1 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 @entity922 . 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 @entity922 in the prediction of clinical recurrence in a high-risk cohort and in subset analysis of Gleason score 7 @entity1 . The addition of clinical variables to the GC model did not significantly contribute to classifier performance in @entity1 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 @entity263 clinical risk groups.

Validation of a genomic-clinical classifier model for predicting clinical recurrence of @entity1 with XXXX in a high-risk population.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity263 :: ('MESH:D011471', 'Disease') :: ['prostate cancer'] @entity922 :: ('MESH:D013568', 'Disease') :: ['CM', 'clinical model']
@entity263 :: (MESH:D011471,Disease) :: ['localized prostate cancer']
