Christine Buerki

72 Background: Active surveillance (AS) is becoming standard of care for men with low-risk prostate cancer; however a need exists for better tools to assess which men are optimal candidates for AS. In this study we compare genomic expression profiles of AS candidates against higher-risk radical prostatectomy (RP) patients to characterize the genomics of clinically low-risk prostate cancer. Methods: Biopsies from 473 UCSF patients potentially suitable for AS (stage ≤ cT2N0M0, PSA ≤ 10 ng/ml, Gleason 3+3 or low-volume 3+4 ) were profiled using the Affymetrix HuEx microarray to generate RNA expression data. These cases were compared to 2043 RP cases previously profiled on the same microarray platform. Scores for 21 published prognostic signatures were calculated and pathway associated genes were summarized to provide levels of patient risk and pathway activity. Results: Of the 473 AS biopsies profiled, 408 (86%) passed quality control and were used for analysis. Based on the quartiles of average scores for 21 prognostic signature risk models, 49%, 36%, 11%, and 4%, respectively, were classified into the 1st, 2nd, 3rd, or 4th score quartiles. Considering only the clinically low-risk patients at diagnosis, 356 (87%) were low, 45 (11%) were intermediate and 7 (2%) were high risk. Genomic risk was positively associated with cell cycle related pathways (p < 0.001) and negatively associated with apical junction (p < 0.001), epithelial−mesenchymal transition (p < 0.001), and androgen receptor (p < 0.05) pathways. Clustering of patients based on the expression of 36 pathways revealed two biologic groups corresponding to putative basal and luminal subtypes. Compared to higher risk RP patients, the low risk prostate cancer tumors at diagnosis were enriched for basal-like tumors (20% vs 33%, p < 0.001). Conclusions: Although only 2% of low risk AS candidates have high risk genomic characteristics, very substantial genomic heterogeneity exists in this population, and pathway activation overlaps significantly with higher-risk RP patients. These results suggest that even in potential AS candidates, genomic profiling could eventually be used to better guide management.

4 Background: Accurate risk stratification after diagnosis of prostate cancer (PCa) is key to optimal treatment decision-making. Decipher RP is an extensively validated genomic classifier used to determine biological potential for metastasis. Here, in a multi-institutional cohort, we aimed to evaluate its ability to predict metastasis and prostate cancer-specific mortality from analysis of PCa needle biopsy tumor tissue specimens. Methods: We identified 175 patients treated with either first-line RP or first-line radiation therapy (RT) + androgen deprivation therapy (ADT) with available genomic expression profiles generated from diagnostic biopsy specimens obtained from three tertiary referral centers: Cleveland Clinic, Brigham and Women’s Hospital and Johns Hopkins. The core with the highest grade was sampled and Decipher was calculated based on a locked random forest model. Cox univariable and multivariable (MVA) proportional hazards model and survival c-index were used to evaluate the performance of Decipher. Results: Overall, 85% of patients had NCCN intermediate and high-risk disease. Of the 175 patients, 43% and 57% were treated with first-line RP and RT+ADT, respectively. With a median follow-up of 6 years, 32 patients developed metastases and 11 of these patients died of PCa. For prediction of metastasis 5 years post biopsy, Decipher had a c-index of 0.74 (95% confidence interval [CI] 0.63-0.84) compared to 0.66 (95% CI 0.53-0.77) for CAPRA and 0.66 (95% CI 0.55-0.77) for NCCN risk group. On MVA, when modeled with CAPRA, Bx Decipher remained a significant predictor of metastasis (Decipher Bx hazard ratio [HR] 1.33 per 10% increase in score, 95% CI 1.06–1.69, P = 0.01). Decipher Bx was also a significant predictor of PCSM (Decipher Bx HR 1.57 per 10%, 95% CI 1.07–2.40, P = 0.02) Conclusions: Decipher Bx was able to predict metastasis and PCSM from diagnostic biopsy specimens in a cohort of primarily intermediate and high-risk men regardless of first line treatment. This additional genomic information provides important risk stratification to help guide therapy for men with intermediate- and high-risk disease.

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.

Clinical features of the primary prostate tumor remain insufficient for clinicians to accurately define patients at highest risk of developing metastases. However, the primary tumor is the source of these metastases, and thus should contain information on its metastatic potential. We hypothesized that the combination of a primary tumor’s copy number alterations (CNAs) and genome-wide transcriptome information would give us more insight in the biology of metastases-prone localized disease. We designed a retrospective clinically matched cohort study of patients with high-risk localized prostate cancer who have been treated with radical prostatectomy. A cohort of patients who have developed metastases during long-term follow-up (M+ cohort) was matched with patients who did not develop clinical recurrence (M− cohort). Paraffin embedded tumor blocks were retrieved for tissue collection and used for DNA and RNA extraction. CNAs were analyzed using GISTIC and transcriptome analysis was performed using the Decipher GRID platform. Forty-four patients were withheld with both high quality CNA and gene expression data of which 19 are part of the M+ cohort and 25 of the M− cohort. GISTIC analysis showed distinct CNA profiles, with significantly more focal amplifications in the M+ cohort, whilst the M− cohort harbors a 6q15 deletion. Transcriptome-wide gene expression analysis allowed us to identify a selection of genes in the amplified or deleted regions that were up- or downregulated, respectively. Ongoing investigations are focused on identifying which of these genes are driving the primary tumors’ metastatic potential. Funding: T Van den Broeck is holder of a doctoral fellowship of the Fund for Scientific Research Flanders (FWO). This work supported by research grants from the KU Leuven (GOA/15/017). Claessens F. is the principal investigator for all the grants previously mentioned. Presenting author: Thomas Van den Broeck, Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. Email: thomas.vandenbroeck@med.kuleuven.be.

59 Background: Accurate riskstratification after diagnosis of prostate cancer (PCa) is key to optimal treatment decision-making. Decipher is an extensively validated genomic classifier of metastasis after radical prostatectomy (RP). Here, we evaluate its ability to predict metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens in a cohort of intermediate risk PCa patients treated with RP. Methods: Fifty-seven patients with available diagnostic biopsy specimens were identified from a previously reported post-RP validation study of Decipher in a cohort of 169 patients treated at Cleveland Clinic. The core with at least 1mm tumor of the highest Gleason grade was sampled and subjected to whole transcriptome analysis. Decipher was calculated based on a locked random forest model. Cox multivariable (MVA) proportional hazards model and survival c-index were used to evaluate the performance of Decipher. Results: 61% of patients had biopsy Gleason score 6 and 67% of patients had NCCN intermediate risk disease. With a median 8 years follow up, 8 patients metastasized and 3 of these patients died of PCa. Decipher had a c-index of 0.80 (95% confidence interval [CI], 0.58-0.95) compared to 0.58 (95% CI, 0.18-0.91) for biopsy Gleason score and 0.57 (0.57; 95% CI, 0.23-0.89) for preoperative PSA at 10 years post-RP for prediction of metastasis. A combined model consisting of Decipher, preoperative PSA, and Gleason score had a c-index of 0.84 (95% CI, 0.68-0.96). On MVA, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative PSA and biopsy Gleason score (Decipher hazard ratio per 10% increase: 1.72; 95% CI, 1.04–2.83; P = 0.02). Conclusions: Decipher was able to predict metastatic outcome from diagnostic biopsy specimens in a cohort of primarily intermediate risk men treated with RP. This additional genomic information may help identify patients who may not be optimal candidates for active surveillance and better identify appropriate first line therapy for men with intermediate risk disease.

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.

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.

25 Background: Current methods for prostate cancer risk stratification are often insufficient to accurately predict outcome after definitive therapy. As tumor multi-focality and genetic heterogeneity can lead to diagnostic prostate biopsy sampling bias, we hypothesize that quantitative imaging with multiparametric (MP)-MRI will more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features, and improve accuracy of current risk classification systems. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2w, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 51 radiographic features (including intensity, volume, perfusion, and diffusion paramters). Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values ( 0.7). Furthermore, genomic features were found to be significantly enriched for prostate cancer related pathways (p < 0.05), representing a potential biologically meaningful link between imaging and genomic data. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk classification by directing pathological and genomic analysis to highest risk index lesions. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies.

60 Background: Biomarkers may improve ascertainment of progression risk after radical prostatectomy (RP). We compared two validated post-RP classifiers: the Decipher genomic classifier (GC) and CAPRA-S (based on standard clinicopathologic parameters), to predict prostate cancer-specific mortality (CSM) in a contemporary cohort of RP patients. Methods: From a cohort of 1,010 RP patients treated from 2000-06, a case-cohort design was used to analyze a subset of 219 men with one or more high risk features and available paraffin-embedded tissue. Median follow-up was 6 years. The GC, derived from expression levels of 22 biomarkers and dichotomized to denote low- and high-risk, and CAPRA-S, calculated from preoperative PSA and pathologic grade and staging variables, scores were determined. The scores were evaluated individually and in combination using concordance index, decision curve, (DC), re-classification, and Cox analyses for prediction of CSM. Results: 212 men had full data available to calculate the CAPRA-S; 27 experienced CSM. The c-index for GC (0.78) and CAPRA-S (0.76) were similar, although GC showed improved calibration and higher net-benefit on DC analysis. In 103 patients with high-risk CAPRA-S scores (≥6), GC scores were likewise high-risk for 49, among whom 19 had CSM events. The other 54 men were reclassified as low-risk by GC; among these only 1 CSM event was observed. In multivariable Cox analysis both GC and CAPRA-S were independently prognostic of CSM, with hazard ratios of 1.62 (p50% of men stratified to high risk based on CAPRA-S alone. Thus the GC provides independent prognostic information, and a model integrating GC and CAPRA-S may further improve the prediction of lethal prostate cancer.