Citations

4565 Background: The efficient delivery of adjuvant therapy after radical prostatectomy (RP) in patients with prostate cancer is limited by the lack of biomarkers, beyond clinicopathologic factors, that are able to assess the risk of clinically significant disease progression. Previously, routine FFPE patient specimens from the Mayo Clinic Radical Prostatectomy Registry with long term follow-up were selected to develop a genomic classifier (GC) to predict clinical progression. Here, we present the validation of a GC in a cohort of patients at high risk of disease progression. Methods: A case-cohort study of high-risk RP patients from the Mayo Clinic (N=219) was used to validate the genomic classifier (GC) for predicting clinical progression (defined by positive bone or CT scan post-RP). Its performance was compared to a multivariable clinical classifier (CC) and a genomic-clinical classifier (GCC) which combines GC with established clinicopathologic variables. Concordance index, Cox modeling and decision curve analysis were used to compare the different models. Results: GC and GCC were predictive of clinical progression in the high-risk cohort with c-indices of 0.79 and 0.82, respectively, compared to the clinical classifier (0.70). Multivariable survival analysis showed that the majority of prognostic information of GCC came from the GC with a minor contribution from Gleason score. Decision curve analysis showed that GCC had a higher overall net benefit compared to CC over a wide range of ‘decision-to-treat’ thresholds for the risk of progression. Conclusions: In this high-risk cohort, GC and GCC classifiers showed improved performance over CC in prediction of clinical progression. GC is an independent prognostic factor in this cohort and captures the majority of prognostic information. GC and GCC’s prognostic performance and their usefulness in guiding decision-making in the adjuvant setting after RP need further testing in studies of additional prostate cancer risk groups.

337 Background: Current risk stratification schema have limited prognostic performance in predicting outcome within National Comprehensive Cancer Network (NCCN) high-risk to very high-risk prostate cancer. Methods: Two multicenter high-risk cohorts were used for training (n = 214) and validation (n = 151) of a novel RNA microarray-based integrated clinical-genomic Classifier Optimized for Outcome in High-risk Prostate cancer (COOHP) to classify patients as COOHP favorable high-risk, standard high-risk, or very high-risk. Cox analysis was used to model metastasis-free survival (MFS), prostate cancer-specific survival (PCSS), and overall survival (OS). Model performance was compared to prior sub-classification systems using time-dependent c-indices. Results: Among NCCN high/very high-risk patients in the training cohort, 11% were classified as COOHP favorable high-risk, 70% as COOHP standard high-risk, and 18% as COOHP very high-risk. Patients with COOHP favorable high-risk disease had better rates of 5-year MFS compared to those with COOHP standard high-risk disease (94% vs 76%, hazard ratio [HR] 0.10, p = 0.02), and patients with COOHP very high-risk disease had worse 5-year MFS compared to those with COOHP standard high-risk disease (34% vs 76%, HR 3.5, p < 0.0001). Similarly, patients with COOHP very high-risk disease had worse 10-year PCSS compared to those with COOHP standard high-risk disease (36% vs 82%, HR 4.4, p < 0.0001). The c-indices for 5-year MFS and 10-year PCSS in the training cohort were 0.80 and 0.74, significantly improved compared to prior clinical and clinical-genomic risk stratification systems (0.62-0.69 for 5-year MFS and 0.56-0.63 for 10-year PCSS). These results were consistent in the validation cohort, where 5-year MFS significantly varied among the three COOHP subgroups (100% vs 89% vs 79%, p = 0.020), as did 10-year OS (100% vs 71% vs 53%, p = .040). Conclusions: A clinical-genomic risk stratification system specifically designed to discriminate prognosis in high-risk prostate cancer better identified favorable high-risk and very high-risk subsets of disease compared to prior clinical and clinical-genomic stratification systems.

295 Background: While 18F-fluciclovine PET/CT is approved in the US and recommended by the NCCN, prostate-specific membrane antigen (PSMA) PET/CT is more common in Europe/Australia and recommended by the EAU. Less is known about the biology of lesions detected by either modality. 18F-fluciclovine PET relies on radiotracer uptake by amino acid transporters LAT1-4 and ASCT1-2. PSMA PET is dependent on surface expression of PSMA. We compared relative expression of PSMA and fluciclovine transporter genes in radical prostatectomy (RP) samples to determine their distribution across subtypes and correlation with outcomes. Methods: Gene expression data of 19,102 RP samples were analyzed using the Affymetrix Human Exon 1.0 ST microarray. 1,135 patients had long term follow up. Associations between expression of PSMA and fluciclovine transporter genes (LAT1-4 and ASCT1-2) and pathologic variables, molecular subtypes, and clinical outcomes were conducted. Results: All fluciclovine transporter genes (LAT 1-4, ASCT1-2) were expressed at lower levels than PSMA (p <0.0001). PSMA expression was positively correlated with genomic risk score and pathologic Gleason score (p<0.0001), but LAT2-3 and ASCT2 were inversely correlated with genomic risk in primary tumors (p<0.0001) and less expressed in GS 9-10 tumors (p<0.0001). PSMA expression was associated with worse metastasis-free survival (MFS) (HR 1.45, p=0.001) and lymph node involvement (HR 2.14, p<0.0001). Expression of LAT2, LAT3, ASCT2 expression was associated with better MFS (HR 0.85, 0.63, 0.74, p<0.0001-0.04). After multivariable adjustment, PSMA expression remained independently prognostic of poorer MFS (HR 1.3, p=0.028). Luminal B subtype was notable for PSMA overexpression; Luminal A was enriched in ASCT2 and LAT2 (p<0.0001). PSMA expression did not correlate with ERG fusion prostate cancers, but LAT2, ASCT1, and ASCT2 were overexpressed in ERG fusion negative tumors (p<0.0001). Conclusions: PSMA expression is associated with more aggressive disease and poorer clinical outcomes than fluciclovine transporter genes in localized prostate cancer. Molecular subtypes of prostate cancer vary in PSMA and fluciclovine transporter gene expression.

276 Background: Decipher is a genomic classifier (GC) that estimates risk of prostate cancer (PCa) distant metastases (DM) post-radical prostatectomy (RP). Herein, we validate the GC within the context of a randomized phase 3 trial. Methods: RP specimens from patients on the NRG/RTOG 9601 phase 3 placebo-controlled randomized trial of salvage radiotherapy (sRT) +/- 2 years of bicalutamide (NCT00002874) were centrally reviewed and the highest-grade tumor underwent RNA extraction. Samples passing quality control (QC) were run on a clinical-grade whole-transcriptome assay to assign the GC score (scale 0-1). Our NCTN-CCSC approved pre-specified statistical plan (NRG-GU-TS002 CSC0075) included the primary objective of validating the ability of the GC to independently prognosticate the cumulative incidence of DM, with secondary endpoints of prostate cancer-specific mortality (PCSM) and overall survival (OS). Results: Of patients with tissue available, 352 passed QC and were included for analysis. The final GC cohort was a representative sample of the overall cohort, with a median follow-up of 13 years. On multivariable analysis, the GC (continuous variable) was independently associated with DM (HR 1.19 [95%CI 1.06-1.35], p=0.003), PCSM (HR 1.37 [95%CI 1.18-1.61], p<0.001), and OS (HR 1.16 [95%CI 1.06-1.28], p=0.002) after adjusting for age, race, Gleason score, T-stage, margin status, entry PSA, and treatment arm. The estimated absolute impact of bicalutamide on 12-year OS was less in patients with lower vs higher GC scores (2.4% vs 8.9%), further demonstrated in men receiving early sRT at PSA <0.7 ng/mL (-2.0% vs 5.0%). Conclusions: This is the first validation of this GC in a prospective randomized trial cohort and demonstrates association of the GC with DM and PCSM independent of standard clinicopathologic variables. The GC may help personalize shared decision-making to weigh the absolute benefit from the addition of bicalutamide to sRT.

149 Background: Patients with metastatic hormone-sensitive prostate cancer (mHSPC) have a variable response to ADT and some benefit from the addition of docetaxel or an androgen signaling pathway inhibitor. We found that loss of the epigenetic regulator KDM5D is associated with more aggressive prostate cancer (PC). We sought to determine whether MYBL2 which is regulated by KDM5D mediates this effect. MYBL2 is a transcription factor which controls key genes (e.g. FOXM1) and increases cell cycle progression and survival. Methods: AR expressing hormone-sensitive cell lines, LNCaP and LAPC4 were used. Motif analysis and CHiPseq of LNCaP with and without siKDM5D was performed and impact of modulation of KDM5D and MYBL2 in both cells on cell survival was assessed. Gene expression profiling (GEP) data assessed MYBL2’s association with KDM5D levels in localized disease (publicly available data) and mHSPC (Decipher whole Affymetrix platform on CHAARTED samples). Results: Silencing KDM5D increased H3K4me3 and increased MYBL2 expression. GEP showed a strong negative correlation between KDM5D and MYBL2 in patients with localized PC (-0.66; TCGA) but not from primary prostate cancer tissue with mHSPC (-0.03; CHAARTED). Cells with low KDM5D and high MYBL2 were androgen independent and more resistant to docetaxel. In TCGA, patients with high MYBL2 had a higher rate of relapse from localized disease. In patients with metastatic disease (CHAARTED) low MYBL2 was associated with a better overall survival (OS) on multivariable analysis when treated with ADT or ADT + docetaxel. Conclusions: Low MYBL2 is associated with a longer OS with ADT alone and ADT and docetaxel independent of clinical variables. Patients with high MYBL2 expression had better OS with ADT plus docetaxel compared with patients with high MYBL2 treated with ADT alone.[Table: see text]

BackgroundThe progression of prostate cancer is a complex, multistep process that involves molecular alterations in cells of the tumor and the microenvironment, with associated interactions between the stroma and epithelium. Genomic expression analyses of stromal infiltration markers were performed to determine the significance thereof in prostate cancer.MethodsGenome‐wide expression profiles of formalin‐fixed, paraffin‐embedded radical prostatectomy samples were evaluated from a prospective registry cohort (n = 5239) and 3 retrospective institutional cohorts (n = 1135). Two independent stromal gene expression signatures implied stromal infiltration. Cox proportional hazards regression defined the association between stromal infiltration expression and metastasis‐free survival (MFS).ResultsStromal expression scores were correlated with stromal signature genes and with other key stromal markers (CAV1, VIM, and TAGLN), basal activity, and CD3 and CD4 immune biomarkers (r > 0.5 for all). The top decile of stromal expression was associated with high genomic risk scores (Decipher ≥ 0.6) , high Cancer of the Prostate Risk Assessment–Postsurgical scores, Gleason 9 to 10 disease, and a higher risk for metastasis (hazard ratio, 2.35; 95% CI, 1.37‐4.02; P = .001). A higher stromal infiltration score was also associated with decreased expression of DNA repair genes and higher radiation sensitivity genomic scores. Postoperative radiation therapy (RT) was associated with an MFS benefit for patients with high stromal scores, but not for patients with low stromal scores (Pinteraction = .02).ConclusionsExpression of stromal infiltration markers is correlated with prostate cancer aggressiveness/progression and may be predictive of a response to RT. Stromal infiltration markers should be studied and considered for incorporation into clinical prognostication and decision making.

Abstract Purpose: The heterogeneity of androgen receptor (AR)-activity (AR-A) is well-characterized in heavily treated metastatic castration-resistant prostate cancer (mCRPC). However, the diversity and clinical implications of AR-A in treatment-naïve primary prostate cancer is largely unknown. We sought to characterize AR-A in localized prostate cancer and understand its molecular and clinical implications. Experimental Design: Genome-wide expression profiles from prostatectomy or biopsy samples from 19,470 patients were used, all with independent pathology review. This was comprised of prospective discovery (n = 5,239) and validation (n = 12,728) cohorts, six retrospective institutional cohorts with long-term clinical outcomes data (n = 1,170), and The Cancer Genome Atlas (n = 333). Results: A low AR-active subclass was identified, which comprised 9%–11% of each cohort, and was characterized by increased immune signaling, neuroendocrine expression, and decreased DNA repair. These tumors were predominantly ERG and basal subtype. Low AR-active tumors had significantly more rapid development of recurrence or metastatic disease across cohorts, which was maintained on multivariable analysis [HR, 2.61; 95% confidence interval (CI), 1.22–5.60; P = 0.014]. Low AR-active tumors were predicted to be more sensitive to PARP inhibition, platinum chemotherapy, and radiotherapy, and less sensitive to docetaxel and androgen-deprivation therapy. This was validated clinically, in that low AR-active tumors were less sensitive to androgen-deprivation therapy (OR, 0.41; 95% CI, 0.21–0.80; P = 0.008). Conclusions: Leveraging large-scale transcriptomic data allowed the identification of an aggressive subtype of treatment-naïve primary prostate cancer that harbors molecular features more analogous to mCRPC. This suggests that a preexisting subgroup of patients may have tumors that are predisposed to fail multiple current standard-of-care therapies and warrant dedicated therapeutic investigation.

AbstractBackgroundMolecular studies have tried to address the unmet need for prognostic biomarkers in prostate cancer (PCa). Some gene expression tests improve upon clinical factors for prediction of outcomes, but additional tools for accurate prediction of tumor aggressiveness are needed.MethodsBased on a previously published panel of 23 gene transcripts that distinguished patients with metastatic progression, we constructed a prediction model using independent training and testing datasets. Using the validated messenger RNAs and Gleason score (GS), we performed model selection in the training set to define a final locked model to classify patients who developed metastatic‐lethal events from those who remained recurrence‐free. In an independent testing dataset, we compared our locked model to established clinical prognostic factors and utilized Kaplan‐Meier curves and receiver operating characteristic analyses to evaluate the model's performance.ResultsThirteen of 23 previously identified gene transcripts that stratified patients with aggressive PCa were validated in the training dataset. These biomarkers plus GS were used to develop a four‐gene (CST2, FBLN1, TNFRSF19, and ZNF704) transcript (4GT) score that was significantly higher in patients who progressed to metastatic‐lethal events compared to those without recurrence in the testing dataset (P = 5.7 × 10−11). The 4GT score provided higher prediction accuracy (area under the ROC curve [AUC] = 0.76; 95% confidence interval [CI] = 0.69‐0.83; partial area under the ROC curve [pAUC] = 0.008) than GS alone (AUC = 0.63; 95% CI = 0.56‐0.70; pAUC = 0.002), and it improved risk stratification in subgroups defined by a combination of clinicopathological features (ie, Cancer of the Prostate Risk Assessment‐Surgery).ConclusionOur validated 4GT score has prognostic value for metastatic‐lethal progression in men treated for localized PCa and warrants further evaluation for its clinical utility.