Karnes, R. Jeffrey

Purpose Despite documented oncologic benefit, use of postoperative adjuvant radiotherapy (aRT) in patients with prostate cancer is still limited in the United States. We aimed to develop and internally validate a risk-stratification tool incorporating the Decipher score, along with routinely available clinicopathologic features, to identify patients who would benefit the most from aRT. Patient and Methods Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of four US academic centers between 1990 and 2010. All patients had ≥ pT3a disease, positive surgical margins, and/or pathologic lymph node invasion. Multivariable Cox regression analysis tested the relationship between available predictors (including Decipher score) and clinical recurrence (CR), which were then used to develop a novel risk-stratification tool. Our study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for development of prognostic models. Results Overall, 21.9% of patients received aRT. Median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with aRT versus initial observation ( P < .001). Pathologic T3b/T4 stage, Gleason score 8-10, lymph node invasion, and Decipher score > 0.6 were independent predictors of CR (all P < .01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. aRT was associated with decreased CR rate in patients with two or more risk factors (10-year CR rate 10.1% in aRT v 42.1% in initial observation; P = .012), but not in those with fewer than two risk factors ( P = .18). Conclusion Using the new model to indicate aRT might reduce overtreatment, decrease unnecessary adverse effects, and reduce risk of CR in the subset of patients (approximately 25% of all patients with aggressive pathologic disease in our cohort) who benefit from this therapy.

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