Citations

94 Background: Technetium (Tc99m) trofolastat (USANC) is a novel urea-based small molecule SPECT radiotracer with utility in imaging overexpression of PSMA in prostate cancer (PCa). Unlike earlier agents targeting PSMA, this ligand is rapidly internalized and retained in tumors. We conducted an open-label, multicenter phase 2 study in the US and Europe (NCT01667536). An interim analysis was performed at approximately 50% accrual. Methods: Patients (pts) with confirmed adenocarcinoma of the prostate scheduled for RP with EPLND at high risk for disease outside of the prostate gland were eligible. High risk pts were ≥cT3 or Kattan nomogram score ≥130. Within 30 days of screening, pts required a bone scan and pelvic MRI. After enrollment, pts received trofolastat followed by whole-body planar and SPECT/CT imaging 3 to 6 hrs later. Pts then underwent RP with EPLND within 21 days. SPECT/CT images were evaluated centrally by 3 readers blinded to clinical information and compared to on-site pathology assessments using a common scoring template. The primary endpoint was the ability of trofolastat to detect PCa within the gland. Secondary endpoints included detection of extent and location within the gland, pelvic lymph nodes and comparative performance against MRI. Results: 84 pts were enrolled to date from 16 centers. Interim data is available for 54 pts. A majority (≥2/3) of SPECT/CT readers correctly identified the presence or absence of primary PCa in 51/54 (94%, 85-98 95% CI) patients including 2 true-negative cases treated with neoadjuvant enzalutamide. Sensitivity and specificity were 94% (84-98 95% CI) and 100% (34-100 95% CI) respectively. Conclusions: Based on the interim data available, trofolastat has accurately detected primary prostate carcinoma within the gland with high sensitivity and specificity in high-risk pts prior to surgery. Updated results, analyses of secondary endpoints, pelvic lymph nodes, and comparative performance vs. MRI from this ongoing study will be presented. Clinical trial information: NCT01667536. [Table: see text]

167 Background: Physicians have a variety of resources available about the treatment of localized prostate cancer. Yet, it is largely unknown what resources and evidence radiation oncologists and urologists commonly use to inform patients and possibly alter clinical practice. In this national survey study, we assessed the types of resources prostate cancer specialists’ use and the level of evidence needed to change treatment recommendations in localized prostate cancer. Methods: From a random sample, 1,422 physicians were sent a mail survey assessing what type of information was used to guide prostate cancer treatment. The survey also queried what level of evidence could alter recommendations. Multivariable logistic regression models were used to identify for differences in physician characteristics for each outcome. Results: Survey response rates were similar for radiation oncologists and urologists (44% vs. 46%; p=0.46). Specialty-specific journals represented the most commonly used resource to inform the clinical practice for radiation oncologists (65%) and urologists (70%). Relative to radiation oncologists, urologists were less likely to utilize medical journals (25% vs. 38.7%, OR: 0.50; p=0.01), cancer journals (21.7% vs. 50.8%, OR: 0.50; p<0.001), but more likely to rely on clinical guidelines (46.3% vs. 37.7%,OR: 1.6; p=0.006). Radiation oncologists and urologists both viewed RCT’s as the most likely source of evidence needed to change treatment recommendations (85% vs. 77%). Conclusions: Both specialties most commonly rely on their specialty-specific journals to inform their clinical practice and view RCT’s as the level of evidence needed to change treatment recommendations for localized prostate cancer. Radiation oncologists embraced greater diversity in literature, while urologists were more likely to use clinical guidelines. Utilizing this information may overcome barriers to the dissemination and adoption of new treatment paradigms for localized prostate cancer.

140 Background: High-risk prostate cancer (HRPC) is a challenging disease and the role of surgery is often considered in the context of a multimodal approach but patients with positive section margins (R1) disease have not always the same cancer-specific survival (CSS). The current study aims to analyze current postoperative pathological features in order to predict CSS of HRPC patients with R1, but with negative lymph nodes (pN0), treated with surgery. Methods: From a multi-institutional retrospective cohort of 5,876 HRPC patients treated by radical prostatectomy and pelvic lymph node dissection, 1541 patients with pN0 and R1 were selected. Following surgery, adjuvant EBRT and/or ADT were delivered according to institutional protocols. Patients were subdivided into four groups according to pT stage (pT≥3 and pT<3) and p-Gleason score (pGS≥8 and pGS<8). Kaplan-Meier plots with log-rank tests and a Cox proportional hazards model were applied to study CSS. All significance levels were set at 0.05. MedCalc was used for all statistical analyses. Results: Median age at surgery was 66 years (42-89). Of all patients, 399 (25.9%) had GS≥8 and 999 (64.8%) had pT≥3 at definitive histopathology. Patients were classified as COMBO groups: C1 (423; 27.4%; GS<8,pT<3), C2 (674; 43.7%; GS<8, pT≥3), C3 (83; 5.4%; GS≥8, pT<3), C4 (362; 23.5%; GS≥8, pT≥3). Adjuvant EBRT and ADT, respectively, were delivered in C1 3%/5%, C2 15%/21%, C3 21%/20%, C4 28%/40%. Kaplan-Meier plots demonstrated statistically different 10-yr CSS between groups: C1 97%, C2 93.8%, C3 85.1% and C4 77.3% (p<0.0001). COMBO groups were also compared using a Cox model and results are shown in the Table. Conclusions: COMBO groups demonstrated to be able to subdivide margin-positive, pN0 HRPC into 4 demarcated groups with significantly different CSS. This subdivision could be considered an easy-to-use tool which can help for counseling patients for adjuvant treatment strategies. [Table: see text]

114 Background: High-risk prostate cancer (HRPC) is a challenging disease and the role of surgery is often considered in the context of a multimodal approach. The indication for adjuvant therapy after surgery for HRPC patients who have specimen-confined disease (R0, pN0, <pT3b) is still difficult. The current study aims to analyze postoperative pathological features which help to predict CSS in specimen-confined HRPC and thus may aid in the decision to administer adjuvant EBRT or ADT. Methods: From a multi-institutional retrospective cohort of 5876 HRPC patients treated by radical prostatectomy and pelvic lymph node dissection, 1391 patients with specimen-confined disease were selected. Following surgery, adjuvant EBRT and/or ADT were delivered according to institutional protocols. Patients were subdivided into four groups according to pT stage (pT≥3 and pT<3) and final Gleason score (GS≥8 and GS<8). Kaplan-Meier plots with log-rank tests and a Cox proportional hazards model were applied to study CSS. All significance levels were set at 0.05. MedCalc was used for all statistical analyses. Results: Median age was 65 years (43-84). Of all patients, 346 (24.9%) had GS≥8 and 794 (57.1%) had pT≥3 at definitive histopathology. Patients were classified into COMBO groups: C1 (478; 34.4%; GS<8,pT<3), C2 (567; 40.8%; GS<8, pT≥3), C3 (119; 8.6%; GS≥8, pT<3), C4 (227; 16.3%; GS≥8, pT≥3). Adjuvant EBRT and ADT, respectively, were delivered in C1 2%/2%, C2 15%/22%, C3 3%/10%, C4 18%/25%. Kaplan Meier plots demonstrated statistically different 10-yr CSS between groups: C1 97.4%, C2 95.2%, C3 89.9% and C4 84.4% (p<0.0001). COMBO groups were also compared using a Cox model and results are shown in the Table. Conclusions: COMBO groups demonstrated to be able to subdivide specimen-confined HRPC into 4 demarcated groups with significantly different CSS. This subdivision could be considered an easy-to-use tool which can help for counseling patients for adjuvant treatment strategies. [Table: see text]

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.

9 Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through outlier gene expression profiling analysis. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping in a large sample of PCa treated with radical prostatectomy. Methods: We analyzed Affymetrix Human Exon 1.0ST GeneChip expression profiles for 1,577 patients from 8 radical prostatectomy (RP) cohorts. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes. Results: A random forest (RF) classifier was trained and validated to predict ERG fusion status using a subset with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. Less frequent rearrangements involving other ETS genes or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ETS+, SPINK+, and Triple Negative, respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. A series of multivariable analyses further revealed, ERG+ to be associated with lower pre PSA and Gleason scores but more likely to have EPE and occur in patients with European American ancestry compared to the ETS+, SPINK+/Triple Negative tumors (p<0.001). In contrast, patients with ETS+ were more likely to have SVI compared to both ERG+ and SPINK/Triple Negative (p=0.01), while SPINK+/Triple Negative had higher Gleason scores and were more likely to occur in African Americans (p<0.001). Conclusions: The Decipher platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests

84 Background: In elderly patients, Charlson score among other features, might allow clinicians to limit the use of aggressive adjuvant treatment strategies or even primary surgical treatment to those who might not achieve benefit during their lifetime. Methods: Retrospective analysis, 7,650 case multicenter high-risk prostate cancer (Pca) radical prostatectomy database selecting >= 70 years old cases. We predicted death from all causes (DAC) and cancer related death (CRD) including all clinical and pathological data. Multivariable analysis were performed to identify independent predictors of DAC and CRD with binary logistic regression, using STATA® software, version 13.1. Results: 2,106 patients from 14 high-volume centers were included. Mean age was 72.8 years (SD 2.46). 206 (9.78%) patients were classified as ASA 3-4 and 497 (23.6%) as CS >=1. Mean PSA was 21.7 ng/ml (SD 50.5). At final histopathology, 800 (38%) had pT3b-T4 disease, GS was 8-10 in 589 (28%), LNI was found in 518 (24.6%) and 822 (39%) PSM. Adjuvant RT, ADT and RT+ADT were administered in 359 (17%), 391 (18.6%) and 437 (20.7%), respectively. Mean follow up was 5.18 years (DS 4.47). BCR occur in 649 (30.8%) and CF in 150 (7.1%) of which distant in 59 (2.8%). Total deaths accounted 341 (16.2%) and CRD for 100 (4.7%) cases. Conclusions: Multicenter data confirms that elderly patients survival harboring high risk prostate cancer will benefit from radical treatment if they are Charlson score 1 or less. [Table: see text] [Table: see text]

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.

7 Background: Improved prognostic biomarkers are needed for localized prostate cancer. We undertook an unbiased large-scale analysis of genes associated with metastatic progression and validated the prognostic ability of the top candidate gene. Methods: Prostate cancer samples from prostatectomy patients were analyzed for gene expression using a clinical-grade, high-density Affymetrix GeneChip platform, encompassing >1 million genomic loci and assessed in a CLIA-certified laboratory. Nomination of prognostic candidate genes was performed on a discovery cohort (n=545) and validated on 3 independent cohorts (n=463). Multivariate analyses were performed for the primary endpoint of metastasis. The top prostate-specific gene was further evaluated in 208 additional tumor samples with a novel RNA in-situ hybridization (ISH) assay and in urine samples from 230 patients using PCR. Results: Of all known genes, the long noncoding RNA SChLAP1 ranked first for elevated expression in patients with metastatic progression by receiver-operator-curve analyses. Validation in three independent cohorts confirmed the prognostic value of SChLAP1. On multivariate modeling, SChLAP1 expression independently predicted metastasis within 10 years (odds ratio (OR) = 2.45, 95% confidence interval (CI) 1.70 – 3.53), death within 10 years (OR = 1.93, 95% CI 1.31 – 2.85), and biochemical recurrence within 5 years (OR = 1.76, 95% CI 1.28 – 2.41) with ORs comparable to Gleason score. Evaluation of SChLAP1 expression by RNA ISH confirmed a significant association with disease recurrence (OR = 1.99, 95% CI 1.06 – 3.73). Evaluation of urine SChLAP1 levels demonstrated increased expression in patients at higher risk for disease progression. Conclusions: We perform the largest high-throughput, unbiased study of prostate cancer prognostic biomarkers to date and discover SChLAP1 as a top gene predictive of metastatic progression. We validate SChLAP1 extensively with a clinical-grade assay. We show feasibility of a RNA ISH assay and a non-invasive urine test for SChLAP1. Our results, spanning 1,446 patients from 6 independent patient cohorts, suggest that SChLAP1 represents a very promising biomarker for aggressive clinical course.

168 Background: The optimal timing of postoperative radiotherapy following radical prostatectomy (post-RP RT) is unclear. We hypothesized that a genomic classifier (GC) would provide prognostic and predictive insight into the development of clinical metastases in men receiving post-RP RT and inform decision-making. Methods: GC scores were calculated from 188 patients with pT3 or margin positive PCa, who received post-RP RT at Thomas Jefferson University and Mayo Clinic, between 1990 and 2009. The primary endpoint was clinical metastasis. Prognostic accuracy of the models were tested using c-index and decision curve analysis. Cox regression tested the relationship between GC and metastasis. Results: The cumulative incidence of metastasis at 5 years post-RT was 0%, 9%, and 29% for low, average, and high GC scores, respectively (p=0.002). In multivariable analysis, GC and pre-RP PSA were independent predictors of metastasis (both p<0.01). Within the low GC score (<0.4), there were no differences in the cumulative incidence of metastasis comparing those who received adjuvant or salvage RT (p=0.79). However, for patients with higher GC scores (≥0.4) cumulative incidence of metastasis at 5-year was 6% vs. 23% for patients treated with adjuvant vs. salvage RT (p<0.01). Conclusions: In patients treated with post-RP RT, GC is prognostic for the development of clinical metastasis beyond routine clinical/pathologic features. Though preliminary, patients with low GC are best treated with salvage radiation, while those with high GC benefit from adjuvant therapy. These findings provide the first rationale selection of timing of post-RP RT.