Buerki, Christine

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.

133 Background: The genomic classifier, Decipher, has been validated to predict risk of metastasis after radical prostatectomy (RP). However, the cohort size and event rate in the previous studies did not allow for a thorough investigation into performance within individual clinicopathologic or treatment subgroups. In this study, we present the first meta-analysis of the performance of the 22-marker genomic classifier in men with prostate cancer (PCa) post-RP. Methods: MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports of men with PCa treated by RP between 2010 and 2016 where the benefit of the Decipher genomic classifier test was assessed. The primary end point was the ability of Decipher to independently improve prognostication of regional or distant metastasis over routine clinicopathologic factors. Meta-analysis was performed with random-effects modeling, and extent of heterogeneity between studies was determined with the I2 test. Results: Five studies (975 total patients, and 855 with individual patient genomic and clinicopathologic data) were eligible for analysis. The median follow-up was 8 years. All patients had clinical high-risk disease, yet 60.9%, 22.6%, and 16.5% of patients were classified as low, intermediate, and high-risk, respectively by Decipher and had 10-year cumulative incidence rates of metastases of 5.5%, 15.0% and 26.7% (p < 0.001), respectively. Adjusting for standard clinicopathologic variables, on multivariable analysis Decipher remained a statistically significant predictor of metastasis (hazard ratio [HR] 1.30 per 0.1 unit, 95% confidence interval [CI] 1.14-1.47, p < 0.001), and the summary HR for metastasis of Decipher across the 5 studies was 1.52 (95% CI 1.39-1.67) per 0.1 unit. Conclusions: The genomic classifier test, Decipher, has the ability to independently improve prognostication of men post-RP, as well as within nearly all clinicopathologic and treatment subgroups. Strong consideration should be given to incorporating the use of genomic testing in clinical decision making and clinical trials to better individualize treatment.

90 Background: Biochemical recurrence (BCR) may be an indicator of metastatic prostate cancer; however, it isn't specific enough to differentiate patients who will experience rapid onset of metastases from those who do not. Such lack of specificity leads to overtreatment of men who will likely never experience metastatic disease in their lifetime. We hypothesize that genomic biomarkers, detectable in primary tumor tissue, may better identify rapidly progressing metastatic prostate cancer. Methods: FFPE prostatectomy specimens (n = 309) from the Mayo Clinic tumor registry were profiled using a high-density expression array. Patients were retrospectively classified into three outcome groups: NED (no evidence of disease recurrence); BCR-only (patients that experienced BCR but no metastatic progression); and MET (metastatic disease confirmed by positive CT or bone scans) and were matched on several criteria for analysis . Uni- and multi-variable analyses between outcome groups were used to analyze clinical and genomic variables (adjusting for adjuvant hormone therapy as a confounding factor). Results: Median follow-up was 16.8 years and MET patients were diagnosed a mean of 3.1 years following BCR. Univariable and multivariable analysis of clinical variables failed to show statistically significant differences between NED and BCR-only outcome groups. Differential expression analysis between NED and BCR-only groups showed no significant differences after false discovery correction. Conversely, 6,277 and 52,020 biomarkers were differentially expressed between MET and the BCR-only and NED groups, respectively. Conclusions: Considering the clinical and genomic results together, patients who experienced rapid metastatic disease were significantly different from both the NED and BCR-only groups. However, no significant differences were identified between the BCR-only and NED outcome groups. These results imply that BCR is not a good surrogate for men who are likely to experience rapid onset of metastatic disease and lethal prostate cancer.

40 Background: Gleason score (GS) is the most widely used grading system of cell differentiation in prostate cancer and one of the best pathological predictors of disease progression. Patients with high GS (> 7) are the most likely to experience metastasis, whereas patients with low GS (< 7) are expected to have favorable long-term outcomes. In addition to the subjective nature of GS assessment, patients with GS 7 represent a heterogeneous group in terms of patient outcomes. In this study a biomarker signature is developed and validated which could improve the prediction of high risk disease among GS 7 radical prostatectomy (RP) patients. Methods: Patient specimens from the Mayo Clinic RP Registry (n = 764) were used for feature selection and training of a 392-feature K-nearest neighbor (KNN, k = 11) classifier. These 392 genomic features were identified by assessing differential expression between patients with GS < 7 and those with GS > 7, using a Bonferroni adjusted T-Test with a p-value threshold of 0.05. The classifier was subsequently validated in two independent patient cohorts (Memorial Sloan Kettering [MSKCC] and German Cancer Research Center [DKFZ]) and compared to a state of the art biomarker signature (Penney et al. 2011). Results: In the MSKCC dataset (GSE21034) our model segregated GS < 7 from GS > 7 patients (n = 56) with an area under the receiver operating characteristic curve (AUC) of 0.97, comparable to the AUC of 0.94 obtained by Penney et al. in their independent validation set (n = 45). Strong performance was observed by our model when discriminating between primary Gleason grade (pGG) 3 and pGG 4 & 5 patients in the MSKCC (n = 130) and DKFZ prostate cancer (GSE29079, n = 47) datasets, achieving AUCs of 0.75 and 0.87, respectively. In the challenging GS 7 subset, our model segregated GS 3 + 4 and 4 + 3 patients in the DKFZ dataset (n = 64) with an AUC of 0.81 outperforming the Penney et al. signature (AUC = 0.60). Conclusions: A biomarker signature was developed which discriminates between low and high GS patients, outperforming a previously reported signature. Further validation of this biomarker signature in additional post RP patients, as well as, pretreatment biopsy specimens is warranted.

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.

8 Background: A number of biomarkers related to cell cycle, angiogenesis or apoptosis have been found to be associated with patient outcome in tissue samples from men treated with first line radiation therapy in RTOG clinical trials using immunohistochemical staining and analysis. In a prior study, four biomarkers (Ki-67, MDM2, p16 and Cox-2) and clinical covariates were included in a model of distant metastasis (DM; Pollack et al, Clin Cancer Res, 2014 [epub ahead of print]) risk. The current study tested the hypothesis that these genes are prognostic for DM in men treated primarily with total prostatectomy using RNA expression profiling. Methods: RNA fromprostatectomy samples from Cleveland Clinic (CC, n=182); Mayo Clinic (MC)-I (n = 545) and II (n=235); Memorial Sloan Kettering Cancer Center (MSKCC, n=131); Erasmus Medical Center (EMC, n=48) and Thomas Jefferson University (TJU, n=130) were profiled using 1.4 million RNA features. A Cox proportional hazards model was built on the MC-I training set to combine the 4 biomarkers into a prognostic risk score (4BMSig). 4BMSig was subsequently evaluated for its prognostic significance separately and in combination with clinical risk factors (biopsy Gleason Score, cT-category and Preop-PSA) for DM. Results: 4BMSig was found to discriminate DM patients significantly for the MC-II (AUC = 0.66, p < 0.001), CCF (AUC = 0.68, p < 0.001), and MSKCC (AUC = 0.71, p = 0.04) datasets, and achieved borderline significance for EMC (AUC = 0.70, p = 0.06). 4BMSig did not discriminate DM in the TJU dataset (only 10 DM events). Pooled multivariable analysis (n = 726) with clinical covariates revealed that 4BMSig is a strong independent prognostic covariate for DM (p < 0.001) and prostate cancer specific mortality (p = 0.005). Conclusions: The four genes identified previously as being associated with DM in radiotherapy patients were incorporated herein into 4BMSig, which was found to have potential as a pretreatment prognostic DM risk assessment tool for men treated with prostatectomy. Further validation would consist of testing 4BMSig from RNA in diagnostic tissue from prostate cancer patients prior to prostatectomy.

4 Background: Management of patients with a postoperative rising prostate-specific antigen (PSA) level is complex. Additional local treatment such as salvage radiation therapy (SRT) may be sufficient for many patients but some may require concurrent systemic therapy in order to delay or prevent metastatic disease. As PSA recurrence on its own is a poor surrogate for metastatic disease we hypothesized that the Decipher genomic classifier (GC), a validated predictor of metastasis may be able to better distinguish those patients where additional therapy is beneficial from those where SRT on its own is likely sufficient. Methods: Genomic classifier (GC) scores were calculated from 170 prostate cancer patients, who received SRT at the Veteran Affairs Medical Center Durham, Thomas Jefferson University and Mayo Clinic, between 1990 and 2010. SRT was defined as administration of RT with Pre-RT PSA levels > 0.2 ng/ml. GC and CAPRA-S scores were compared using survival c-index, competing-risks and Cox regression analysis for the prediction of metastasis. Results: Survival c-index for predicting metastasis 5 years post SRT was 0.85 (95% CI: 0.73-0.88) for GC and 0.63 (95% CI: 0.49-0.77) for CAPRA-S. The cumulative incidence of metastasis at 5 years post-SRT was 2.7%, 8.4%, and 33.1% for low, average, and high GC scores (p < 0.001) and 16.9%, 2.3% and 17.2% for low, average and high CAPRA-S scores (p = 0.113). In univariable analysis only GC, extraprostatic extension, path GS and Pre-RT PSA were significant predictors of metastasis. In multivariable analyses with clinical risk factors or the CAPRA-S risk model, GC was the only independent predictor of metastasis with a HR of 1.63 (1.22-2.18, p < 0.001) for a 10% unit increase in risk score. Conclusions: In patients treated with postoperative SRT for PSA recurrence, GC is a powerful predictor of metastasis. Patients with low Decipher have excellent prognosis with SRT and may avoid concurrent hormonal therapy. Patients with high Decipher risk are at highest risk for metastatic disease and SRT failure and may benefit from intensified systemic therapy.

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.

Objective To better characterize the genomics of patients with biochemical recurrence ( BCR ) who have metastatic disease progression in order to improve treatment decisions for prostate cancer. Methods The expression profiles of three clinical outcome groups after radical prostatectomy ( RP ) were compared: those with no evidence of disease ( NED ; n = 108); those with BCR (rise in prostate‐specific antigen [ PSA ] level without metastasis; n = 163); and those with metastasis ( n = 192). The patients were profiled using Human Exon 1.0 ST microarrays, and outcomes were supported by a median 18 years of follow‐up. A metastasis signature was defined and verified in an independent RP cohort to ensure the robustness of the signature. Furthermore, bioinformatics characterization of the signature was conducted to decipher its biology. Results Minimal gene expression differences were observed between adjuvant treatment‐naïve patients in the NED group and patients without metastasis in the BCR group. More than 95% of the differentially expressed genes (metastasis signature) were found in comparisons between primary tumours of metastasis patients and the two other outcome groups. The metastasis signature was validated in an independent cohort and was significantly associated with cell cycle genes, ubiquitin‐mediated proteolysis, DNA repair, androgen, G‐protein coupled and NOTCH signal transduction pathways. Conclusion This study shows that metastasis development after BCR is associated with a distinct transcriptional programme that can be detected in the primary tumour. Patients with NED and BCR have highly similar transcriptional profiles, suggesting that measurement of PSA on its own is a poor surrogate for lethal disease. Use of genomic testing in patients undergoing RP with an initial rise in PSA level may be useful to improve secondary therapy decision‐making.