Dicker, Adam P.

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.

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.

12 Background: In 3 published randomized trials, adjuvant radiation therapy (ART) for prostate cancer (PCa) resulted in improved progression free survival. However, the impact on metastases and overall survival is unclear. To date, there have been no published prospective trials examining the impact of salvage radiation therapy (SRT) in this disease state. Hence, we conducted a retrospective, nonrandomized comparative study of ART, SRT, or no radiation following radical prostatectomy (RP) for men with pT3 disease or positive margins (adverse pathologic features, APF). Methods: 422 PCa men treated at 4 institutions with RP and having APF were analyzed with a primary end point of clinical metastasis. ART (n = 111), early SRT (n = 70) and delayed SRT (n = 83) were defined by PSA levels of < 0.2, 0.2 to 0.5, and ≥ 0.5 ng/mL, respectively, prior to RT initiation. Remaining 157 men who did not receive additional therapy prior to metastatic onset formed the no RT arm. Clinical-genomic risk was assessed by CAPRA-S and Decipher. Cox multivariable (MVA) model was used to evaluate the impact of treatment on outcome. Results: During study follow-up, 37 men developed metastasis with a median follow-up of 8 years. Both CAPRA-S and Decipher had independent predictive value on MVA for metastatic outcome (both p < 0.05). On MVA adjusting for clinical-genomic risk, delayed SRT and no RT had an HR of 4.31 (95%CI, 1.20-15.47) and 5.42 (95% CI, 1.59-18.44) for metastasis compared to ART. No significance difference was observed between early SRT and ART (p = 0.28). Men with low to intermediate CAPRA-S and low Decipher have a low rate of metastatic events regardless of treatment selection. In contrast, men with high CAPRA-S and Decipher benefit from ART, however the cumulative incidence of metastasis remains high. Conclusions: The decision as to the timing and need for additional local therapy following RP is nuanced and requires providers and men to balance risks of morbidity with improved oncologic outcomes. This analysis provides the most robust and accurate quantification of risk for these men. Post-RP treatment can be safely avoided for men who are low risk by clinical-genomic risk, whereas those at high risk should favor enrollment in clinical trials.

106 Background: Androgen deprivation therapy (ADT) is one of the main treatment options for locally advanced and metastatic prostate cancer. Neuroendocrine prostate cancer (NEPC) is inherently less sensitive or even resistant to ADT. NEPC can be observed de novo (e.g., small cell prostate cancer) but more commonly arises after exposure to ADT. We hypothesized that a gene expression signature of NEPC when measured in primary tumor specimens (RP) of prostatic adenocarcinoma may be useful for predicting patients with innate resistance to ADT. Methods: Expression profiles of 1023 PCa patients treated with RP were obtained from the Decipher GRID database. These were split into training (n=529) and validation (n=494) sets and stratified by the receipt of adjuvant ADT (n=243) or no adjuvant ADT (n=780). A literature review of ADT resistance and neuroendocrine genes identified 1,557 genes as candidates. This set was further filtered, using logistic regression to select a 52-gene ADT resistance signature (ARS). ARS was trained using a generalized linear model with lasso regularization. Survival c-index and Kaplan Meier 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 0.69 (95% CI 0.59-0.78) as compared to 0.45 (95% CI 0.29-0.61) in patients not treated with ADT). Similarly in a separate cohort of untreated patients that received no ADT until after metastatic onset, ARS was not prognostic (c-index 0.53). Among ADT treated patients, those with low ARS scores had a 10 year MFS of 87%, versus 70% in those with high ARS scores (p<0.001). In the subset of men who received ADT after metastatic onset and who developed castrate-resistant prostate cancer (CRPC, n = 41), median time to treatment failure was 1 year in patients with high ARS compared to 2 years for those with low ARS scores (p=0.07). Conclusions: A 52-gene ADT resistance signature was developed which showed significant differences in metastasis-free survival among adjuvant hormone treated but not untreated patients.

Abstract Purpose: There is a clear need to improve risk stratification and to identify novel therapeutic targets in aggressive prostate cancer. The goal of this study was to investigate genes with outlier expression with prognostic association in high-risk prostate cancer patients as potential biomarkers and drug targets. Experimental Design: We interrogated microarray gene expression data from prostatectomy samples from 545 high-risk prostate cancer patients with long-term follow-up (mean 13.4 years). Three independent clinical datasets totaling an additional 545 patients were used for validation. Novel prognostic outlier genes were interrogated for impact on oncogenic phenotypes in vitro using siRNA-based knockdown. Association with clinical outcomes and comparison with existing prognostic instruments was assessed with multivariable models using a prognostic outlier score. Results: Analysis of the discovery cohort identified 20 prognostic outlier genes. Three top prognostic outlier genes were novel prostate cancer genes; NVL, SMC4, or SQLE knockdown reduced migration and/or invasion and outlier expression was significantly associated with poor prognosis. Increased prognostic outlier score was significantly associated with poor prognosis independent of standard clinicopathologic variables. Finally, the prognostic outlier score prognostic association is independent of, and adds to existing genomic and clinical tools for prognostication in prostate cancer (Decipher, the cell-cycle progression signature, and CAPRA-S). Conclusions: To our knowledge, this study represents the first unbiased high-throughput investigation of prognostic outlier genes in prostate cancer and demonstrates the potential biomarker and therapeutic importance of this previously unstudied class of cancer genes. Clin Cancer Res; 22(7); 1777–86. ©2015 AACR.

Abstract Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through distinct gene expression profiles. 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 and explored clinical associations using global gene expression profiles in a large cohort of PCa. Methods: We analyzed 1,577 patient Affymetrix Human Exon 1.0ST GeneChip expression profiles from 8 radical prostatectomy (RP) cohorts; 5 of them generated as part of the Decipher® platform for the Decipher® discovery or validation. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes and characterize clinical associations. Results: A random forest (RF) classifier (m-ERG) was trained and validated to predict ERG fusion status using separate subsets of a single-institution RP cohort (total n=407) with known ERG rearrangement status defined by FISH, achieving &gt;95% sensitivity and specificity in the validation subset. The model was then applied to 7 independent RP cohorts to predict ERG rearrangement status. Less frequent rearrangements involving other ETS genes (ETV1, ETV4, ETV5, FLI1) 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+, ERG—ETS+, ERG—SPINK+, and Triple Negative (ERG—/ETS—/SPINK1—), respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ERG—ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. Based on multivariable analysis, ERG+ is significantly associated with lower pre-PSA (p&lt;0.001), lower Gleason score (p&lt;0.001), the presence of extraprostatic extension (p&lt;0.001) and European Americans (p&lt;0.001), but not associated with outcome. ERG—ETS+ is significantly associated with SVI (p=0.01) and ERG—SPINK+ is enriched in African Americans (p&lt;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 and provides insights into the development of novel therapeutic approaches. Citation Format: Mohammed Alshalalfa, Scott A. Tomlins, Nicholas Erho, Kasra Yousefi, Shuang Zhao, Robert B. Den, Adam P. Dicker, Bruce Trock, Angelo DeMarzo, Edward M. Schaeffer, Ashley Ross, Eric A. Klein, Cristina Magi-Galluzzi, Jeffery R. Karnes, Robert B Jenkins, Elai Davicioni, Felix Feng. Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-64.