Den, Robert B

PURPOSE Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. PATIENTS AND METHODS A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269 ) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). RESULTS In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). CONCLUSION The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

Abstract Purpose: The role of immune-oncologic mechanisms of racial disparities in prostate cancer remains understudied. Limited research exists to evaluate the molecular underpinnings of immune differences in African American men (AAM) and European American men (EAM) prostate tumor microenvironment (TME). Experimental Design: A total of 1,173 radiation-naïve radical prostatectomy samples with whole transcriptome data from the Decipher GRID registry were used. Transcriptomic expressions of 1,260 immune-specific genes were selected to assess immune-oncologic differences between AAM and EAM prostate tumors. Race-specific differential expression of genes was assessed using a rank test, and intergene correlational matrix and gene set enrichment was used for pathway analysis. Results: AAM prostate tumors have significant enrichment of major immune-oncologic pathways, including proinflammatory cytokines, IFNα, IFNγ, TNFα signaling, ILs, and epithelial–mesenchymal transition. AAM TME has higher total immune content score (ICSHIGH) compared with 0 (37.8% vs. 21.9%, P = 0.003). AAM tumors also have lower DNA damage repair and are genomically radiosensitive as compared with EAM. IFITM3 (IFN-inducible transmembrane protein 3) was one of the major proinflammatory genes overexpressed in AAM that predicted increased risk of biochemical recurrence selectively for AAM in both discovery [HRAAM = 2.30; 95% confidence interval (CI), 1.21–4.34; P = 0.01] and validation (HRAAM = 2.42; 95% CI, 1.52–3.86; P = 0.0001) but not in EAM. Conclusions: Prostate tumors of AAM manifest a unique immune repertoire and have significant enrichment of proinflammatory immune pathways that are associated with poorer outcomes. Observed immune-oncologic differences can aid in a genomically adaptive approach to treating prostate cancer in AAM.

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.

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.

Abstract Serum levels of miR-194 have been reported to predict prostate cancer recurrence after surgery, but its functional contributions to this disease have not been studied. Herein, it is demonstrated that miR-194 is a driver of prostate cancer metastasis. Prostate tissue levels of miR-194 were associated with disease aggressiveness and poor outcome. Ectopic delivery of miR-194 stimulated migration, invasion, and epithelial–mesenchymal transition in human prostate cancer cell lines, and stable overexpression of miR-194 enhanced metastasis of intravenous and intraprostatic tumor xenografts. Conversely, inhibition of miR-194 activity suppressed the invasive capacity of prostate cancer cell lines in vitro and in vivo. Mechanistic investigations identified the ubiquitin ligase suppressor of cytokine signaling 2 (SOCS2) as a direct, biologically relevant target of miR-194 in prostate cancer. Low levels of SOCS2 correlated strongly with disease recurrence and metastasis in clinical specimens. SOCS2 downregulation recapitulated miR-194–driven metastatic phenotypes, whereas overexpression of a nontargetable SOCS2 reduced miR-194–stimulated invasion. Targeting of SOCS2 by miR-194 resulted in derepression of the oncogenic kinases FLT3 and JAK2, leading to enhanced ERK and STAT3 signaling. Pharmacologic inhibition of ERK and JAK/STAT pathways reversed miR-194–driven phenotypes. The GATA2 transcription factor was identified as an upstream regulator of miR-194, consistent with a strong concordance between GATA2 and miR-194 levels in clinical specimens. Overall, these results offer new insights into the molecular mechanisms of metastatic progression in prostate cancer. Cancer Res; 77(4); 1021–34. ©2016 AACR.

Purpose We aimed to update a previously published, multi-institutional nomogram of outcomes for salvage radiotherapy (SRT) following radical prostatectomy (RP) for prostate cancer, including patients treated in the contemporary era. Methods Individual data from node-negative patients with a detectable post-RP prostate-specific antigen (PSA) treated with SRT with or without concurrent androgen-deprivation therapy (ADT) were obtained from 10 academic institutions. Freedom from biochemical failure (FFBF) and distant metastases (DM) rates were estimated, and predictive nomograms were generated. Results Overall, 2,460 patients with a median follow-up of 5 years were included; 599 patients (24%) had a Gleason score (GS) ≤ 6, 1,387 (56%) had a GS of 7, 244 (10%) had a GS of 8, and 230 (9%) had a GS of 9 to 10. There were 1,370 patients (56%) with extraprostatic extension (EPE), 452 (18%) with seminal vesicle invasion (SVI), 1,434 (58%) with positive surgical margins, and 390 (16%) who received ADT (median, 6 months). The median pre-SRT PSA was 0.5 ng/mL (interquartile range, 0.3 to 1.1). The 5-yr FFBF rate was 56% overall, 71% for those with a pre-SRT PSA level of 0.01 to 0.2 ng/mL (n = 441), 63% for those with a PSA of 0.21 to 0.50 ng/mL (n = 822), 54% for those with a PSA of 0.51 to 1.0 ng/mL (n = 533), 43% for those with a PSA of 1.01 to 2.0 ng/mL (n = 341), and 37% for those with a PSA > 2.0 ng/mL (n = 323); P < .001. On multivariable analysis, pre-SRT PSA, GS, EPE, SVI, surgical margins, ADT use, and SRT dose were associated with FFBF. Pre-SRT PSA, GS, SVI, surgical margins, and ADT use were associated with DM, whereas EPE and SRT dose were not. The nomogram concordance indices were 0.68 (FFBF) and 0.74 (DM). Conclusion Early SRT at low PSA levels after RP is associated with improved FFBF and DM rates. Contemporary nomograms can estimate individual patient outcomes after SRT in the modern era.

Abstract Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1–3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948–58. ©2016 AACR.