Dicker, Adam P

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.

Abstract Purpose: Currently, no genomic signature exists to distinguish men most likely to progress on adjuvant androgen deprivation therapy (ADT) after radical prostatectomy for high-risk prostate cancer. Here we develop and validate a gene expression signature to predict response to postoperative ADT. Experimental Design: A training set consisting of 284 radical prostatectomy patients was established after 1:1 propensity score matching metastasis between adjuvant-ADT (a-ADT)-treated and no ADT–treated groups. An ADT Response Signature (ADT-RS) was identified from neuroendocrine and AR signaling–related genes. Two independent cohorts were used to form three separate data sets for validation (set I, n = 232; set II, n = 435; set III, n = 612). The primary endpoint of the analysis was postoperative metastasis. Results: Increases in ADT-RS score were associated with a reduction in risk of metastasis only in a-ADT patients. On multivariable analysis, ADT-RS by ADT treatment interaction term remained associated with metastasis in both validation sets (set I: HR = 0.18, Pinteraction = 0.009; set II: HR = 0.25, Pinteraction = 0.019). In a matched validation set III, patients with Low ADT-RS scores had similar 10-year metastasis rates in the a-ADT and no-ADT groups (30.1% vs. 31.0%, P = 0.989). Among High ADT-RS patients, 10-year metastasis rates were significantly lower for a-ADT versus no-ADT patients (9.4% vs. 29.2%, P = 0.021). The marginal ADT-RS by ADT interaction remained significant in the matched dataset (Pinteraction = 0.035). Conclusions: Patients with High ADT-RS benefited from a-ADT. In combination with prognostic risk factors, use of ADT-RS may thus allow for identification of ADT-responsive tumors that may benefit most from early androgen blockade after radical prostatectomy. We discovered a gene signature that when present in primary prostate tumors may be useful to predict patients who may respond to early ADT after surgery. Clin Cancer Res; 24(16); 3908–16. ©2018 AACR.

2 Background: Significant genomic diversity exists in the androgen receptor ( AR) and its activity (AR-A) in mCRPC. In localized prostate cancer, the biologic, prognostic, and therapeutic clinical implications of AR-A heterogeneity have yet to be interrogated Methods: Genome-wide expression profiles of FFPE RP or biopsy tumor samples were evaluated from a prospective registry cohort (n = 5,239, NCT02609269) and six retrospective institutional cohorts (n = 1,170). AR-A was calculated based on expression of 9 targets of AR.. Results: Utilizing 6,409 localized prostate adenocarcinomas with full transcriptomic data, we found there was marked inter-individual transcriptomic diversity in AR and AR-A expression, and weak correlation between them (r = 0.08 to 0.36 based on cohort), in contrast to mCRPC that has a strong correlation between AR and AR-A expression (r = 0.76). Additionally, serum PSA had no correlation to intratumoral AR-A (r = 0.06). Unsupervised hierarchical clustering identified a distinct subclass of low AR-A prostate tumors, which had increased markers of immunogenicity (decreased T-regs and MDSCs, and increased CD3 effector T-cells), increased neuroendocrine marker expression ( NCAM1, ENO2, and SCG2), and decreased DNA repair pathway expression (all p < 0.001). Clinically, low AR-A tumors had more rapid development of metastatic disease in three independent cohorts, were more prone to develop resistance to hormonal therapy and develop CRPC, and were found at an increased frequency in African-American men. Interrogating in vitro drug sensitivity analyses utilizing the NCI-60 panel, low AR-A tumors appear more sensitive to platinum chemotherapy and PARP inhibition, and less sensitive to hormone therapy and taxanes. Conclusions: The diversity in AR-signaling in localized prostate cancer represents important biological heterogeneity that is both prognostic and predictive of treatment response. These findings are provocative in that low AR-A tumors may be more susceptible to immunotherapy, PARP inhibition, platinum chemotherapy, and/or radiotherapy. Patients with low AR-A tumors warrant dedicated biomarker enhanced clinical trials.

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.

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

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.