Bergstralh, Eric J.

4565 Background: The efficient delivery of adjuvant therapy after radical prostatectomy (RP) in patients with prostate cancer is limited by the lack of biomarkers, beyond clinicopathologic factors, that are able to assess the risk of clinically significant disease progression. Previously, routine FFPE patient specimens from the Mayo Clinic Radical Prostatectomy Registry with long term follow-up were selected to develop a genomic classifier (GC) to predict clinical progression. Here, we present the validation of a GC in a cohort of patients at high risk of disease progression. Methods: A case-cohort study of high-risk RP patients from the Mayo Clinic (N=219) was used to validate the genomic classifier (GC) for predicting clinical progression (defined by positive bone or CT scan post-RP). Its performance was compared to a multivariable clinical classifier (CC) and a genomic-clinical classifier (GCC) which combines GC with established clinicopathologic variables. Concordance index, Cox modeling and decision curve analysis were used to compare the different models. Results: GC and GCC were predictive of clinical progression in the high-risk cohort with c-indices of 0.79 and 0.82, respectively, compared to the clinical classifier (0.70). Multivariable survival analysis showed that the majority of prognostic information of GCC came from the GC with a minor contribution from Gleason score. Decision curve analysis showed that GCC had a higher overall net benefit compared to CC over a wide range of ‘decision-to-treat’ thresholds for the risk of progression. Conclusions: In this high-risk cohort, GC and GCC classifiers showed improved performance over CC in prediction of clinical progression. GC is an independent prognostic factor in this cohort and captures the majority of prognostic information. GC and GCC’s prognostic performance and their usefulness in guiding decision-making in the adjuvant setting after RP need further testing in studies of additional prostate cancer risk groups.

36 Background: Following radical prostatectomy (RP), 30-40% of patients have adverse pathology and are deemed at high risk for metastatic progression. The objective of this study was to validate the ability of Decipher, a genomic classifier (GC), to improve prediction of metastatic disease progression compared with clinical variables in order to better identify candidates for therapy intensification. Methods: A previously developed 22-feature GC model was validated in a prospectively designed case-cohort study of a clinically high-risk population (i.e., with one or more adverse pathological features) of 1,010 RP patients treated at Mayo Clinic between 2000-2006. A random sample of 20% of the cohort was subjected to microarray analysis and GC scores were generated for 219 patients. The primary endpoint, the c-index for predicting metastatic disease progression (i.e., positive bone or CT scans) was evaluated in a blinded analysis. Cox modeling and decision curve analyses were used to compare the performance of GC to individual clinical variables and prediction models. Results: GC had a c-index 0.79 (95% CI 0.71-0.86) that was significantly better than any single clinical variable. Cumulative incidence curves in the cohort showed that 72% of patients had low GC scores with only 3% and 6% incidence of metastatic disease at 5 and 10 years post RP. In contrast, for the 28% of patients with high GC scores, the cumulative incidence was 17% and 25% at 5 and 10 years post RP. Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ‘decision-to-treat’ thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR=1.51, for each 0.1 unit increment, p<0.001). Conclusions: GC can better predict metastatic disease progression compared with clinical variables and may select among patients with adverse pathology a majority that is in fact at low risk for metastasis.

106 Background: Some patients with lymph node metastases experience prolonged survival following radical prostatectomy. The purpose of this study was to determine the outcome of patients with clinically suspicious lymph nodes on preoperative imaging who underwent radical prostatectomy and lymphadenectomy. Methods: Patients with lymph node metastases diagnosed during radical prostatectomy from 1988-2003 were reviewed. Patients with preoperative CT or MRI images were included in the study. Radiology reports were reviewed to determine if patients had clinically suspicious lymphadenopathy (cN+). For all analyses, patients with cN+ were compared to those with clinically negative nodes (cN−). Results: Preoperative imaging was available in 202 men with lymph node metastasis at the time of prostatectomy. Of these 17% (34/202) were cN+. None had pre-operative lymph node biopsy and none had abandoned prostatectomy. At a median follow-up of 11.1 years, PSA recurrence occurred in 50% (17/34) and 49% (82/186), local recurrence in 18% (6/34) and 13% (22/186) and systemic progression in 32% (11/34) and 24% (40/186) of patients with cN+ and cN-, respectively. On multivariate analysis, cN+ was not associated with increased risk of death (HR 1.66, p=0.1). Conclusions: cN+ patients undergoing surgical therapy for prostate cancer may experience similar outcomes to cN− patients. The presence of clinically suspicious lymph nodes on preoperative imaging should not be an absolute contraindication for surgical therapy. No significant financial relationships to disclose.

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.

Context.— Prostate-specific antigen (PSA) is a 34-kDa glycoprotein with chymotrypsin-like enzyme activity that circulates both in free forms and complexed to various enzyme inhibitors including antichymotrypsin and α 2 -macroglobulin. Prostate-specific antigen bound to α 2 -macroglobulin is not detected by commercial PSA immunoassays. Objective.— To develop a mass spectrometry assay that detects the same forms of PSA as the immunoassays, which could serve as a reference for harmonizing PSA immunoassays. Design.— Prostate-specific antigen was immune extracted from serum, trypsin was digested, and the LSEPAELTDAVK peptide was quantitated on an API 5000 spectrometer. Calibrators were made by adding 10% free and 90% antichymotrypsin-bound PSA to female sera. The assay was standardized to the World Health Organization 96/670 reference standard. Validation of clinical utility and comparisons with 2 immunoassays (Roche cobas and Beckman Access) were performed using frozen sera aliquots from 100 men undergoing prostate biopsy (50 negative, 50 with cancer) and 5 serial samples collected over time from 5 men with advanced prostate cancer. Results.— The antibody extraction efficiency was greater than 99%. The assay has an analytic range from 1.2 to 76 ng/mL, with precision ranging from 8.6% at 1.5 ng/mL to 5.4% at 27 ng/mL. The mass spectrometry assay correlated well with 2 immunoassays. All 3 assays showed statistically equivalent separation of prostate cancer from benign disease using receiver operating characteristic curve analysis. Conclusions.— This mass spectrometry assay can reliably measure PSA concentrations in human serum and could serve as a reference standard for harmonizing PSA immunoassays.