Kasra Yousefi

Background: Inflammation is linked to prostate cancer progression and is mediated by NF-κB. Tristetraprolin is a key node of NF-κB activation and we investigated its biological and prognostic role in lethal prostate cancer. Methods: In vitro assays assessed the function of tristetraprolin and the association between low mRNA tristetraprolin levels and lethal prostate cancer (metastatic disease or death) was assessed across independent prostatectomy cohorts: (i) nested case-control studies from Health Professionals Follow-up Study and Physicians’ Health Study, and (ii) prostatectomy samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and Memorial Sloan Kettering Cancer Center. Tristetraprolin expression levels in prostatectomy samples from patients with localized disease and biopsies of metastatic castration–resistant prostate cancer (mCRPC) were assessed in a Cornell University cohort. Results: In vitro tristetraprolin expression was inversely associated with NF-κB–controlled genes, proliferation, and enzalutamide sensitivity. Men with localized prostate cancer and lower quartile of tumor tristetraprolin expression had a significant, nearly two-fold higher risk of lethal prostate cancer after adjusting for known clinical and histologic prognostic features (age, RP Gleason score, T-stage). Tristetraprolin expression was also significantly lower in mCRPC compared with localized prostate cancer. Conclusions: Lower levels of tristetraprolin in human prostate cancer prostatectomy tissue are associated with more aggressive prostate cancer and may serve as an actionable prognostic and predictive biomarker. Impact: There is a clear need for improved biomarkers to identify patients with localized prostate cancer in need of treatment intensification, such as adjuvant testosterone suppression, or treatment de-intensification, such as active surveillance. Tristetraprolin levels may serve as informative biomarkers in localized prostate cancer.

5062 Background: Inflammation has been linked to prostate cancer (PCa) progression which may be mediated by the transcription factor nuclear factor kappa B (NFκB). Using a bioinformatic screen focused on NFκB pathway activation in lethal PCa, we identified the tumor suppressor ZFP36 as a key node of the NFκB network. We also showed in vitro that ZFP36 expression was inversely associated with both NFκB-controlled gene levels and proliferation and sensitivity to enzalutamide. We examined the role of ZFP36 and PCa progression in patient cohorts. Methods: The association between low mRNA expression of ZFP36 (levels in the lower quartile) and lethal PCa (defined as metastatic disease or death) was assessed with logistic regression among men with localized PCa in two independent cohorts treated with radical prostatectomy (RP). The discovery cohort was RP samples from Health Professional Follow-up Study and Physicians Health Study and the validation cohort was RP samples from Cleveland Clinic, Mayo Clinic, Johns Hopkins and MSKCC. In a third cohort from Cornell University, ZFP36 expression levels were assessed in RP samples from patients with localized PCa and biopsies of metastatic castration resistant prostate cancer (mCRPC). Results: Table 1 shows men with localized PCa and lower quartile of tumor ZFP36 expression had a nearly 2-fold higher risk of lethal PCa after adjusting for known clinical and histological prognostic features (age, RP Gleason score, T-stage); an association confirmed in the validation cohort. ZFP36 gene expression was also significantly lower in mCRPC (n=53) compared with a localized RP cohort (n=68) (p-value

5000 Background: It is clinically challenging to integrate genomic classifier results that report a continuous numerical risk of recurrence into treatment decisions for prostate cancer (PCa). We aimed to develop a novel clinical-genomic risk system that can readily be incorporated into treatment guidelines for localized PCa. Methods: Four multi-center cohorts (n = 6928 men; 5937 prospective samples and 991 retrospective samples with long-term follow-up) were utilized to identify and validate our clinical-genomic risk system in radical prostatectomy (RP) samples and subsequently in pre-treatment biopsy samples. All patients’ FFPE tissue underwent microarray analysis, and the expression values for 22 prespecified biomarkers that constitute Decipher were extracted. Cumulative incidence curves were constructed to estimate metastasis risk. C-indices were calculated to compare NCCN and CAPRA score to our clinical-genomic system. Results: With a median follow-up of 8 years for men in our RP cohort, the 10-year distant metastasis rates for NCCN low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.8%, 9.4%, 40.1%, and 41.4%, respectively. Our 3-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.7%, 30.7%, and 57.7%, for low, intermediate, and high-risk, which were validated in our pre-treatment biopsy cohort with 10-year rate of distant metastasis of 0%, 30.3%, and 63.2%, respectively. C-indices for the clinical-genomic system (0.84, 95%CI 0.62-0.92) were significantly improved over NCCN (0.71, 95%CI 0.59-0.84) and CAPRA (0.71, 95%CI 0.60-0.81) score. A total of 33.4% of men would be reclassified by the clinical-genomic system, and specifically 17.1%, 41.3%, and 19.4% of men in NCCN low, intermediate and high risk groups would be reclassified by our new system. Conclusions: The use of a readily available genomic classifier in combination with clinicopathologic variables can generate a simple to use 3-tier clinical-genomic risk system that is highly prognostic for distant metastasis, is more accurate than clinical risk, and can be easily incorporated into NCCN guidelines to inform treatment decisions.

51 Background: Currently utilized pre-treatment nomograms for prostate cancer were developed and validated using populations primarily composed of men with low and intermediate risk disease. This study aims to construct a nomogram that predicts for biochemical recurrence (BCR) and metastasis (mets) from a contemporary cohort of men with with NCCN high (HR) and very high risk (VHR) prostate cancer. Methods: From 2005 to 2015, 1,241 men with NCCN HR or VHR prostate cancer were identified from two large academic medical centers. The cohort was divided into training (n = 620) and validation (n = 621) cohorts. Primary endpoints were BCR and mets. Cox multivariable regression was performed to model characteristics and outcomes in the training cohort. Model accuracy was assessed using the time-dependent area under the receiver operator characteristic curve (AUC) in the validation cohort. Results: 494 men (245 training and 249 validation) developed BCR, and 123 men (64 training and 59 validation) developed mets, with BCR-free and mets-free probability of 49.0% and 86.5% at 5- years, respectively. Predictive nomograms including age, ethnicity, PSA, Gleason grade, clinical stage, and the number of cores with Gleason 8-10 disease were developed. Models for BCR and mets had AUCs of 0.72 and 0.75. By comparison, the MSKCC preoperative nomogram and CAPRA nomogram provided AUCs of 0.69 and 0.68 for predicting BCR and 0.66 and 0.67 for mets. Conclusions: Individualized risk assessment is imperative for optimal decision making and to design and power clinical trials. The nomograms described here, created from a population exclusively comprised of HR/VHR men, have better discrimination than those previously established on cohorts of primarily low and intermediate risk men, and may represent an ideal way by which oncologic outcomes can be predicted in men with HR or VHR disease.

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.

38 Background: The Androgen-receptor (AR) signaling plays a pivotal role in prostate cancer initiation and progression. AR amplification and overexpression have been associated with lethal PCa and CRPC; however, much of the impact of these events on cancer progression is poorly understood with an unclear prognostic significance of down-stream targets of AR. Methods: A total of 2,555 RP expression profiles were extracted from the Decipher GRID database; 262 of which were from retrospective natural history cohort with known metastasic outcomes and the remaining from anonymized prospective cases with basic demographic and pathological data available. We built a 9 gene AR-output signature (ARoS) score based on canonical androgen regulated genes to represent AR-output activity in RP tissues. The 9 genes were weighted based on their distribution skewness in the prospective data (n = 2,293) and then scores were calculating by summing the weighted expression of the 9 genes. Results: AR expression and ARoS score were not strongly correlated with approximately 50% of samples with low ARoS have relatively high AR expression. In a prospective cohort of 2,293 patients, low ARoS was associated with higher Decipher metastasis risk scores (OR: 3.1, p < 0.001), higher Gleason grade (OR: 2.4, p < 0.001), ERG-fusion negative (OR: 1.8, p < 0.001) and SVI (OR: 2.1, p < 0.001). Additionally, low ARoS was associated with higher expression of neuroendocrine biomarkers (NCAM1, ENO2). In a large retrospective cohort with long term clinical follow-up, patients with low ARoS score had a higher probability of 10-year metastasis rate (p = 0.002). In 55 patients from this cohort who received ADT post metastasis, the ARoS signature derived from primary tissue was lower in patients who developed CRPC (p < 0.001). Furthermore, ARoS was significantly lower in mCRPC samples (p < 0.001) compared to primary PCa tissues. Conclusions: ARoS derived from primary prostate cancer tissue is correlated with metastatic outcomes and progression to CRPC. With further validation, such a signature may better predict those patients which require early intensification of systemic therapy.

24 Background: The decision to provide adjuvant therapy to men with high risk pathology after radical prostatectomy (RP) is confounded by tremendous uncertainty. We prospectively evaluated the impact of the Decipher test, which predicts metastases after RP, on men and providers decision quality. Methods: 150 adjuvant pts were enrolled by 43 urologists from 19 practices. Pts with pathologic T3 stage or positive surgical margins (SM+) after RP were included. Participating physicians provided a treatment (Tx) recommendation before and after exposure to Decipher test results. Pts completed validated surveys on health-related quality of life, decisional conflict, and PCa-related anxiety. Results: Median patient age at RP was 64 years; 67% and 50% had pT3 and SM+ pathology, respectively. Decipher classified 46%, 22% and 32% of men as low-, intermediate- and high-risk, respectively. Pre-Decipher, observation was recommended for 89%. Post-Decipher, 18% (95% CI 12-25%) of Tx recommendations changed. Men’s Decisional Conflict Scale (DCS) scores decreased (indicating higher decision quality) after exposure to Decipher results (median DCS pre-Decipher 25 [IQR 8-44], median DCS post-Decipher 19 [IQR 2-30], p

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.

5018 Background: Androgen deprivation therapy (ADT) is one of the main treatment options for locally advanced, recurrent and metastatic prostate cancer. Neuroendocrine prostate cancer (NEPC) is inherently less sensitive to ADT. NEPC can be observed de novo (e.g., small cell prostate cancer) but more commonly arises after exposure to ADT. In some patients, even short exposure to ADT may ‘unmask’ a NEPC phenotype. We hypothesized that a gene expression signature of NEPC when measured in primary tumor specimens (RP) may be useful for predicting patients with innate resistance to ADT. Methods: Expression profiles of 786 PCa patients treated with RP were obtained from the Decipher GRID database. These were split into training (n=360) and validation (n=426) sets and stratified by the receipt of adjuvant ADT (n=222) or no adjuvant ADT (n=564). A literature review of ADT resistance and neuroendocrine genes identified 1,632 genes as candidates. This set was further filtered, using logistic regression and forward feature selection to select a genomic ADT resistance signature (ARS). ARS was trained using a generalized linear model with lasso regularization. Survival c-index, Kaplan Meier and Cox regression analysis was used to compare survival differences between treated and untreated patients with high and low ARS scores (defined by median split). Results: In validation cohorts, the ARS was predictive of metastasis in cohorts receiving adjuvant ADT (10-year metastasis free survival c-index of 0.64 (95% CI 0.55-0.71) as compared to 0.50 (95% CI 0.36-0.63) in patients not treated with ADT). Among ADT treated patients, those with low ARS scores had a 10 year MFS of 86%, versus 68% in those with high ARS scores (p=0.03). In multivariable analysis adjusting for confounding variableswith metastasis as an endpoint, ARS validated with a significant interaction with ADT treatment (p=0.02) which demonstrates ARS’s adjuvant ADT predictive capability. Conclusions: An ADT resistance signature validated as a predictive biomarker with a significant interaction term for predicting metastasis after hormonal treatment. ARS may allow for identification of patients that may be optimal candidates for trials of novel systemic agents