Stratification Of Prostate Cancer Research Articles

ADC Metrics From Multiparametric MRI: Histologic Downgrading of Gleason Score 9 or 10 Prostate Cancers Diagnosed at Nontargeted Transrectal Ultrasound-Guided Biopsy.

The purpose of this study was to evaluate quantitative apparent diffusion coefficient (ADC) metrics for the downgrading of Gleason score (GS) 9 or 10 prostate cancer (PCa) diagnosed by means of nontargeted transrectal ultrasound-guided biopsy. Between 2012 and 2015, 30 men with a diagnosis of GS 9 or 10 PCa at nontargeted transrectal ultrasound-guided biopsy underwent 3-T multiparametric MRI before radical prostatectomy (RP). Two radiologists blinded to the histopathologic results independently assessed multiparametric MR images using Prostate Imaging Reporting and Data System (PI-RADS) version 2. Whole-lesion ADC mean, centile, and texture features were extracted from coregistered ADC and RP maps by a third blinded radiologist. Comparisons were performed by chi-square, multivariable logistic regression, and ROC analysis. Tumors were downgraded to intermediate risk (GS 4 + 3 [n = 7] and GS 3 + 4 [n = 2]) PCa in 30.0% (9/30) of men after RP. There were no statistically significant differences between groups with respect to age (p = 0.028), prostate-specific antigen level (p = 0.018), or clinical stage (p = 0.021). PI-RADS version 2 scores did not differ between groups (p = 0.035, p = 0.091) with moderate agreement (κ = 0.48). There were no differences in mean or centile ADC (p = 0.269-0.634) between the two groups. ADC entropy was significantly lower in downgraded tumors (5.542 ± 0.721 [SD] vs 8.089 ± 1.237, p < 0.001) with no difference in kurtosis or skewness (p = 0.133, p = 0.296). The ROC AUC for the diagnosis of downgrading was 0.93 (95% CI, 0.84-1.00) with sensitivity of 85.7% and specificity of 88.9% when entropy was less than 6.31. ADC entropy was significantly lower in GS 9 and 10 tumors diagnosed by means of nontargeted transrectal ultrasound-guided biopsy that were eventually downgraded to intermediate risk (GS 7) after RP. ADC texture analysis may be useful for further risk stratification of PCa diagnosed at biopsy.

Abstract LB-219: Clinical utility of a serum protein biomarker panel (FLNA, KRT19) in stratification of prostate cancer from benign prostate hyperplasia patients

Abstract Background: Prostate-specific antigen (PSA) based screening tests have been considered as a benchmark for PCa diagnosis (PSA &amp;gt;4 ng/mL), however, in recent years their use has been widely debated due to various limitations. In particular, increased PSA levels are also observed in patients with benign prostatic hyperplasia (BPH) thus generating a false positive. These patients are often subjected to unnecessary prostate biopsies and have to endure both physical and mental anguish. Thus, there is a clear unmet need to develop molecular diagnostics that stratifies men with BPH from those that have PCa and prevent unwarranted prostate biopsies in symptomatic, DRE negative men. Here in, we demonstrate clinical utility of a validated biomarker panel (FLNA, KRT 19) in combination with age and prostate volume in stratifying BPH from PCa patients. Methods: This study was conducted using retrospectively collected and clinically annotated serum samples from 203 BPH patients and 333 PCa patients with PSA values ranging from 4-10ng/mL. These samples included BPH patients with negative Digital Rectum Exam, single biopsy and repeat biopsies, and prostate cancer patients undergoing radical prostatectomy. Validated bioanalytical assays (FLNA ELISA, FLNA IPMRM and KRT19 ELISA) were used to quantitate the serum biomarkers. Regression models were built and compared for their ability to distinguish patients with BPH from those with PCa. Results: Table 1: Summary of predictive power analysis of PCa panel (49 characters) BERG TestPSAAUC0.80.54Sensitivity0.8n/aSpecificity0.62n/aPPV0.78n/aNPV0.65n/aOR (CI)6.67 (4.49,9.90)n/a Conclusion: The data demonstrates that these PCa biomarkers in combination with age, and prostatic volume were significantly better than (0.8) PSA (0.54 AUC in this population) in identifying men with BPH rather than PCa thus preventing unnecessary biopsies for this population. Moreover, this panel may be used in clinical decision support to physicians to fill a clinical unmet need. Citation Format: Michael A. Kiebish, Poornima Tekmulla, Shobha Ravipaty, Wenfang Wu, Tracey Friss, Chenchen Liao, Allison Klotz, Joe Andreazi, Elisabeth Hutchins, Albert Dobi, Shiv Srivastava, Jennifer Cullen, Amina Ali, Stephen Freedland, Kagan Griffin, Sandra Laszlo, Michele Petrovic, Neil Fleshner, Jeonifer Garren, Leonardo Rodrigues, Mark D. Kellog, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan, Niven R. Narain. Clinical utility of a serum protein biomarker panel (FLNA, KRT19) in stratification of prostate cancer from benign prostate hyperplasia patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-219.

Abstract 371: Utilizing metabolic pathways to improve diagnosis and risk stratification of prostate cancer

Abstract Prostate cancer has well-characterized changes that take place metabolically in the citric acid cycle (CAC) such as increased lactate formation, reversal of physiologic buildup of citrate and breakdown into downstream CAC products. Metabolic MR spectroscopy utilizing 13C-labeled pyruvate now allows for clinical characterization of metabolic pathways in tissue. However, the traditionally utilized substrate of [1-13C]pyruvate has limitations because as the pyruvate is broken down, the labeled carbon does not enter the CAC and instead is lost as CO2. We propose that [2-13C]pyruvate, is better to characterize changes in the CAC. Specifically, [2-13]C-pyruvate can be converted to 13C-labeled acetyl CoA and subsequently metabolized in the CAC as 13C-labeled intermediate compounds (such as glutamate which rapidly pools alpha-ketogluterate). Alternatively in altered bioenergetic states, [2-13C]pyruvate is reduced to 13C-labeled lactate (Warburg effect) or transaminated to 13C-labeled alanine. By exposing prostate cancer cells to [2-13C]pyruvate and determining the relative labeling of lactate, glutamate, and alanine, we can elucidate which metabolic pathways (CAC, Warburg, transamination) are utilized in prostate cancer metabolism. The aim of this study was to determine how the metabolism of [2-13C]pyruvate via the CAC, reduction to lactate, and transamination differed in high and low malignancy prostate cancer cells.Methods: Tumorigenic prostate cells LNCaP and PC3 were exposed a medium containing [2-13C]pyruvate for 4 hours. PCA extraction was then performed to obtain the metabolites. 13C labeled compounds formed intracellularly and those released into the medium were analyzed using NMR chemical shifts with focus on peaks associated with pyruvate, lactate, alanine, glutamate, and other compounds related to the CAC. Dioxane was used as an internal standard for NMR characterization. Results: The labeled carbon from the [2-13C]pyruvate can be identified in the downstream metabolites of the citric acid cycle, after reduction to lactate, and/or transamination by determining labeled metabolites by 13C-NMR analysis. This allows for the ability to calculate the relative metabolism of pyruvate via the three pathways; transamination (74%), lactate formation (16%), and CAC (10%). Conclusions: [2-13C]pyruvate can be successfully utilized as a tool to explore the alterations in pathways of metabolism, including the citric acid cycle, in prostate cancer cells. Citation Format: Aymen Alqazzaz, Dexue Fu, Arman Karimi, Gustavo Ferreira, Mary McKenna, Mohummad M. Siddiqui. Utilizing metabolic pathways to improve diagnosis and risk stratification of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 371.

MP14-10 PROSTATE CANCER HETEROGENEITY: TEXTURE ANALYSIS OF MULTIPLE MRI SEQUENCES FOR DETECTION AND SELECTION OF BIOPSY TARGETS

You have accessJournal of UrologyImaging/Radiology: Uroradiology I1 Apr 2018MP14-10 PROSTATE CANCER HETEROGENEITY: TEXTURE ANALYSIS OF MULTIPLE MRI SEQUENCES FOR DETECTION AND SELECTION OF BIOPSY TARGETS Clement Orczyk, Arnauld Villers, Henry Rusinek, Vincent Le Pennec, Celine Bazille, Artem Mikheev, Myriam Bernaudin, Audrey Fohlen, and Samuel Valable Clement OrczykClement Orczyk More articles by this author , Arnauld VillersArnauld Villers More articles by this author , Henry RusinekHenry Rusinek More articles by this author , Vincent Le PennecVincent Le Pennec More articles by this author , Celine BazilleCeline Bazille More articles by this author , Artem MikheevArtem Mikheev More articles by this author , Myriam BernaudinMyriam Bernaudin More articles by this author , Audrey FohlenAudrey Fohlen More articles by this author , and Samuel ValableSamuel Valable More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.503AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES In oncology, heterogeneity of cancer is a driver to progression. This feature can be captured quantitatively by image analysis, especially MRI. Prostate mpMRI already showed capabilities in prostate cancer diagnosis pathway. To develop and test in a biopsy population a texture analysis score, entropy score, capturing heterogeneity, based on analysis of multiple MRI sequences for detection and stratification of prostate cancer in view of selection of targets for biopsy. METHODS Under ethical approval, 134 Volume of Interest (VOIs) were generated from 20 consecutive patients with a clinical 1.5T mpMRI (T2WI, ADC map and DCE WI) at time of biopsy. VOIs comprised zonal anatomy segmentation, biopsy targets and cancer. Reference test was targeted and systematic saturation biopsy. Calibrated Entropy (E) was computed and plotted as a multiparametric score defined as Entropy Score (ES)= E ADC+ E Ktrans + E Ve+ E T2WI. Performances were assessed for detection of significant prostate cancer (SPCa). RESULTS Cancer (GS 6- 8) was found in 12 of the 20 patients with a median PSA of 8.22ng/ml. ES performed respectively an Area Under the Curve of 0.89 and 0.88 for detection of SPCa among the targets and cancer and all VOIs. Best ES estimated threshold of 16.61 NAT led to a sensitivity of 100% and negative predictive value of 100%. SPca (ES=17.96 ±0.72 NAT; CI 95%) showed a significant higher ES than non-SPCa (ES=15.33 ±0.76 NAT). ES correlated with Gleason Score (rs =0.5683, p=0.033) and maximum cancer core length (? = 0.781; p=0.0009). CONCLUSIONS Capturing heterogeneity of PCa across multiple MRI sequences with ES performed high performances for detection and stratification of SPca in this biopsy population with potential to select accurately targets for biopsy. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e183 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Clement Orczyk More articles by this author Arnauld Villers More articles by this author Henry Rusinek More articles by this author Vincent Le Pennec More articles by this author Celine Bazille More articles by this author Artem Mikheev More articles by this author Myriam Bernaudin More articles by this author Audrey Fohlen More articles by this author Samuel Valable More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

PD42-10 PROGNOSTIC VALUE OF CELL CYCLE PROGRESSION SCORE IN PROSTATE CANCER PATIENTS WITH LOCALLY ADVERSE PATHOLOGY AFTER RADICAL PROSTATECTOMY: IMPLICATIONS FOR TREATMENT GUIDANCE

You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy VI1 Apr 2018PD42-10 PROGNOSTIC VALUE OF CELL CYCLE PROGRESSION SCORE IN PROSTATE CANCER PATIENTS WITH LOCALLY ADVERSE PATHOLOGY AFTER RADICAL PROSTATECTOMY: IMPLICATIONS FOR TREATMENT GUIDANCE Xun Shangguan, Wei Xue, Baijun Dong, Jiahua Pan, Hongyang Qian, and Fan Xu Xun ShangguanXun Shangguan More articles by this author , Wei XueWei Xue More articles by this author , Baijun DongBaijun Dong More articles by this author , Jiahua PanJiahua Pan More articles by this author , Hongyang QianHongyang Qian More articles by this author , and Fan XuFan Xu More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1963AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate cancer (PCa) patients with locally adverse pathologic features (including extracapsular extension (ECE), seminal vesicle invasion (SVI), or positive surgical margins (PSM)) are considered as poor prognostic factors. Evidences form randomized trials (SWOG-8794, EORTC 22911, and ARO 96-02) suggests that adjuvant radiotherapy is appropriate for patients with high-risk features because it can significantly improve disease-free survival. However, the outcomes of surgically treated patients with locally adverse pathologic features are not invariably poor, with not every patient will suffer eventual cancer recurrence and the policy of adjuvant radiotherapy could lead to the potential for overtreatment. Therefore, correct identification of patients who most likely to benefit from adjuvant therapy is of paramount importance. Tissue-based genomic tests, such as the cell cycle progression (CCP) score, can help decrease prognostic uncertainty and improve risk stratification of PCa. The aim of this study was to determine the prognostic utility of the CCP score in men with locally adverse pathology undergoing radical prostatectomy. METHODS Data on 100patients with locally adverse pathologic features (including ECE, SVI, or PSM) who had been treated with wait and see policy after RP were retrospectively analyzed for biochemical recurrence (BCR)-free survival. CCP score were retrospectively derived from prostatectomy specimens. Kaplan–Meier survival analysis and Cox proportional hazard regression models were used to test the association between the CCP score and BCR. RESULTS Among the 100 men identified, 47(46.5%) had BCR. Five-year BCR-free survival for the vary low (<-1), low (<0), intermediate (0–1), high (>1) and vary high (>2) CCP score groups was 100%, 82.9%, 38.8%, 28.7%, 0% respectively (p<0.001). In multivariable models adjusting for clinical and pathological variables with the Cancer of the Prostate Risk Assessment (CAPRA) score, the CCP score was an independent predictor of BCR (HR:1.67; P = 0.001). In addition, CCP score had higher predictive accuracy for BPFS (increment in Harrell concordance index by 0.026–0.036), compared with the CAPRA score. CONCLUSIONS For the appropriate patient, depending on age, physical condition, patient desire, etc., could be a candidate for wait and see policy with low specimen CCP score, so to distinguish this from high CCP score may facilitate discussions at the point of treatment decision making. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e818 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Xun Shangguan More articles by this author Wei Xue More articles by this author Baijun Dong More articles by this author Jiahua Pan More articles by this author Hongyang Qian More articles by this author Fan Xu More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

The prostate cancer focal therapy.

Despite prostate cancer (PCa) is the leading form of non-cutaneous cancer in men, most patients with PCa die with disease rather than of the disease. Therefore, the risk of overtreatment should be considered by clinicians who have to distinguish between patients with high risk PCa (who would benefit from radical treatment) and patients who may be managed more conservatively, such as through active surveillance or emerging focal therapy (FT). The aim of FT is to eradicate clinically significant disease while protecting key genito-urinary structures and function from injury. While effectiveness studies comparing FT with conventional care options are still lacking, the rationale supporting FT relies on evidence-based advances such as the understanding of the index lesion's central role in the natural history of the PCa and the improvement of multiparametric magnetic resonance imaging (mpMRI) in the detection and risk stratification of PCa. In this literature review, we want to highlight the rationale for FT in PCa management and the current evidence on patient eligibility. Furthermore, we summarize the best imaging modalities to localize the target lesion, describe the current FT techniques in PCa, provide an update on their oncological outcomes and highlight trends for future research.

Comparison of a low-cost immunohistochemistry marker panel with a cell-cycle progression assay for the prediction of outcome after radical prostatectomy.

118 Background: Gene expression assays appear to stratify prostate cancer risk, however tests involving amplification techniques are expensive and may strain payment systems. We aimed to compare the prognostic utility of a low-cost immunohistochemistry (IHC) panel consisting of three validated markers with an RNA expression based cell-cycle progression (CCP) score. Methods: Among 5,748 patients with PCa within an institutional database, we identified 424 with localized PCa treated with radical prostatectomy (RP). IHC analysis was performed using tissue microarrays to examine the expression status of PTEN, Ki67, and ERG compared with previously calculated CCP scores derived from 31 genes normalized to 15 housekeeper genes. Associations of IHC status, CCP scores, adjusted for clinical and pathologic characteristics (Cancer of the Prostate Risk Assessment Score, CAPRA-S) were performed using Cox proportional hazards regression to examine risk of biochemical recurrence (BCR), and metastasis or PCa- specific mortality (PCSM). We compared models using concordance index (c-index) testing. Results: Median age at treatment was 59 years and patients were followed for a median of 114 months post RP. By 10 years after RP, 27% experienced BCR and 4% developed metastasis or PCSM. Adjusted for CAPRA-S, the triad of PTEN loss, high Ki67, and positive ERG expression was independently associated with risk of BCR (HR 2.3, 95% CI 1.1-4.7) and metastasis/PCSM (HR 8.3, 95% CI 3.3-21.2). In a model including CAPRA-S, IHC panel, and CCP, the CCP score was independently associated with both recurrence and metastasis/PCSM (HR 1.9, 95% CI 1.5-2.5 and HR 2.5, 95% CI 1.5-4.1, respectively), but IHC expression status of PTEN/Ki67/ERG remained an independent predictor of both endpoints. The addition of the CCP assay marginally improved the c-index of a combined IHC and CAPRA-S model from 0.78 to 0.81 for the prediction of metastasis/PCSM. Conclusions: A low-cost three-marker IHC panel examining PTEN, Ki67, and ERG expression was independently associated with downstream PCa endpoints following RP. Efforts are warranted to reduce the cost of PCa prognostic tools in order to expand access.

68Ga-PSMA Positron Emission Tomography/Computerized Tomography for Primary Diagnosis of Prostate Cancer in Men with Contraindications to or Negative Multiparametric Magnetic Resonance Imaging: A Prospective Observational Study

68Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography may represent the most promising imaging modality to identify and risk stratify prostate cancer in patients with contraindications to or negative multiparametric magnetic resonance imaging. In this prospective observational study we analyzed 68Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography in a select group of patients with persistently elevated prostate specific antigen and/or Prostate Health Index suspicious for prostate cancer, negative digital rectal examination and at least 1 negative biopsy. The cohort comprised men with equivocal multiparametric magnetic resonance imaging (Prostate Imaging-Reporting and Data System, version 2 score of 2 or less), or an absolute or relative contraindication to multiparametric magnetic resonance imaging. Sensitivity, specificity and CIs were calculated compared to histopathology findings. ROC analysis was applied to determine the optimal cutoff values of 68Ga labeled prostate specific membrane antigen uptake to identify clinically significant prostate cancer (Gleason score 7 or greater). A total of 45 patients with a median age of 64 years were referred for 68Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography between January and August 2017. The 25 patients (55.5%) considered to have positive positron emission tomography results underwent software assisted fusion biopsy. We determined the uptake values of regions of interest, including a median maximum standardized uptake value of 5.34 (range 2.25 to 30.41) and a maximum-to-background standardized uptake value ratio of 1.99 (range 1.06 to 14.42). Mean and median uptake values on 68Galabeled prostate specific membrane antigen positron emission tomography/computerized tomography (ie the maximum standardized uptake value or the maximum-to-background standardized uptake value ratio) were significantly higher for Gleason score 7 lesions than for Gleason score 6 or benign lesions (p <0.001). On ROC analysis a maximum standardized uptake value of 5.4 and a maximum-to-background standardized uptake value ratio of 2 discriminated clinically relevant prostate cancer with 100% overall sensitivity in each case, and 76% and 88% specificity, respectively. Our findings support the use of 68Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography for primary detection of prostate cancer in a specific subset of men.

Optimising preoperative risk stratification tools for prostate cancer using mpMRI

PurposeTo improve preoperative risk stratification for prostate cancer (PCa) by incorporating multiparametric MRI (mpMRI) features into risk stratification tools for PCa, CAPRA and D’Amico.Methods807 consecutive patients operated on by robot-assisted radical prostatectomy at our institution during the period 2010–2015 were followed to identify biochemical recurrence (BCR). 591 patients were eligible for final analysis. We employed stepwise backward likelihood methodology and penalised Cox cross-validation to identify the most significant predictors of BCR including mpMRI features. mpMRI features were then integrated into image-adjusted (IA) risk prediction models and the two risk prediction tools were then evaluated both with and without image adjustment using receiver operating characteristics, survival and decision curve analyses.Results37 patients suffered BCR. Apparent diffusion coefficient (ADC) and radiological extraprostatic extension (rEPE) from mpMRI were both significant predictors of BCR. Both IA prediction models reallocated more than 20% of intermediate-risk patients to the low-risk group, reducing their estimated cumulative BCR risk from approximately 5% to 1.1%. Both IA models showed improved prognostic performance with a better separation of the survival curves.ConclusionIntegrating ADC and rEPE from mpMRI of the prostate into risk stratification tools improves preoperative risk estimation for BCR.Key points• MRI-derived features, ADC and EPE, improve risk stratification of biochemical recurrence.• Using mpMRI to stratify prostate cancer patients improves the differentiation between risk groups.• Using preoperative mpMRI will help urologists in selecting the most appropriate treatment.

A circulating miRNA signature to better stratify prostate cancer patients at diagnosis

A circulating miRNA signature to better stratify prostate cancer patients at diagnosis

Plasma microRNA signature is associated with risk stratification in prostate cancer patients.

The aim of this study was to establish a unique expression profile of circulating cell-free microRNAs (miRNAs) capable of differentiating between prostate cancer (PCa) patients with high-risk and intermediate-risk Gleason scores. MiRNA expression profiles were determined in plasma samples from 79 treatment-naïve PCa patients, 1-2 follow-up samples after radical prostatectomy (RP) from 51 out of the 79 PCa patients, and 33 healthy men, using a quantitative real-time PCR-based array containing 48 selected miRNAs. We identified 27 up- and 2 downregulated plasma miRNAs in PCa patients compared with healthy men. Most of the upregulated miRNA levels were also associated with increasing PSA levels and Gleason scores. Particularly, the levels of miR-16 (p = 0.002), miR-148a (p = 0.006) and miR-195 (p = 0.006) significantly correlated with high-risk Gleason scores, whereby miR-148a (p = 0.003) was also significantly associated with increasing PSA values. The high miRNA levels before RP remained increased in the postsurgical plasma samples. Our findings show a network of deregulated plasma miRNAs. In particular, miR-16, miR-148a and miR-195 are involved in the regulation of the PI3K/Akt signaling pathway. These miRNAs may be promising therapeutic targets for high-risk PCa stratification.

Predicting outcome: role of gene signatures

Pathological assessments, such as Gleason grading, which is a strong clinical predictor of prostate cancer progression 1, have a role to play in predicting the outcome of a patient's response to therapy. The addition of PSA and TNM staging are further used to inform appropriate treatment strategies in patients who are at low, intermediate and high risk of disease progression. Unfortunately, approximately 30% of men with intermediate-risk prostate cancer will fail to be cured by surgery or radiation therapy approaches. This is not surprising as primary prostate cancer represents a complex heterogeneous disease that is clearly not fully explained by the current clinical prognostic factors, and further molecular characterization is required. There is now significant emerging evidence that the molecular characterization of tumours is important to enable us to stratify prostate cancer patients by their response to primary therapy and to identify the next appropriate steps in their treatment pathway. Long et al. 2 have identified gene signatures that can define different genomic subtypes of prostate cancer and are predictive of biochemical recurrence. Erho et al. 3 discovered and validated a 22-gene signature, which they termed a genomic classifier for the prediction of early metastasis after radical prostatectomy, while Penny et al. 4 have identified an mRNA expression signature of Gleason grade which is predictive of lethal prostate cancer. Long et al. 2 identified a 24-gene signature, which they discovered through RNA sequencing analysis of 100 formalin-fixed paraffin-embedded prostatectomy samples. They went on to validate their findings in a publically available independent gene expression microarray dataset of 140 patients. This 24-gene signature forms the basis of the present study by Pellegrini et al. 5, who refer to this gene signature as Sig24. In their study, they firstly undertook to determine if Sig24 was associated with pathological Gleason score as a marker of tumour aggressiveness, and then if it had prognostic value for the identification of patients at risk of metastasis or prostate cancer-specific mortality after radical prostatectomy. The Gleason score association study was carried out using the data from three independent case–control sets, including 182 patients from the Cleveland Clinic and two cohorts (cohort I, n = 545; cohort II, n = 235) from the Mayo Clinic, for which gene expression analysis had previously been conducted by Genome Dx using the Affymetrix Human 1.0 ST Genechip platform. The Sig24 score was calculated for each patient and higher Gleason score was associated with significantly higher Sig24 scores. The association studies for metastatic disease and prostate cancer-specific mortality, however, were only carried out in the Mayo Clinic cohort II. For both clinical endpoints the Sig24 score combined with the clinical model outperformed the clinical model alone of PSA, Gleason score and tumour stage. For metastatic disease the area under the curve for the clinical model alone was 0.69 (0.62–0.77) compared with 0.73 (0.66–0.78) for the clinical model combined with Sig24. For the prostate cancer-specific mortality endpoint, the area under the curve for the clinical model alone was 0.69 (0.67–0.87) compared with 0.74 (0.63–0.85) for the clinical model combined with Sig24. These gene signatures have significant potential for predicting the progression of disease rather than waiting for PSA relapse, which is currently used to identify disease recurrence, with interval to biochemical failure being the best univariate factor predicting prostate cancer mortality and overall survival 6. This would allow the initiation of additional therapies before recurrence and a better outcome for the patient. This concept has been demonstrated in a study in which the use of the Decipher gene signature was shown to improve the identification of patients who could benefit from adjuvant radiotherapy and thus only these patients were targeted for therapy 7. Incorporating these gene signatures in robust clinical assays and integrating them into clinical decision-making is the next essential step in order for these strategies to have an impact on patient outcomes. None declared.

A combination of computer extracted measurements of prostate capsule shape and tumor texture on MRI to predict biochemical recurrence post treatment.

e16554 Background: Prostate cancer (PCa) biochemical recurrence (BCR) occurs in a significant proportion of men after treatment and is associated with increased mortality. Identifying PCa patients at risk of BCR following definitive therapy may help identify patients who might benefit from adjuvant therapy. Multi-parametric MRI (mpMRI) is being increasingly used in pre-treatment staging and risk stratification of PCa. In this work, we sought to explore whether a combination of computer extracted features relating to prostate capsule shape and tumor texture from pre-treatment mpMRI is predictive of BCR post treatment. Methods: This single center and retrospective study included 80 men with PCa who underwent pre-treatment 3T mpMRI and were followed for &gt; 3 years post treatment. These men were grouped into a training set D1 (N = 50, 25 each of BCR+ and BCR-) and an independent validation set D2 (N = 30, 10 BCR+ and 20 BCR-). The prostate capsule and cancer region of interest (ROI) were annotated on mpMRI by a single experienced radiologist. Shape features (curvature and orientation) were extracted from a surface of interest where statistically significant differences were observed between BCR+ and BCR- patients in the training set. Radiomic features for capturing tumor textural patterns (including 1st and 2nd order statistics, Gabor and Haralick) were extracted from within the radiologist annotated cancer ROIs. Features from D1 were used to train random forest classifiers, one each with shape (Cs) and radiomic (CR) features. A fused Bayesian classifier (CR+S) was created by integrating decisions from both Cs and CR. Results: The classifiers Cs, CR and CR+S were evaluated on independent validation set D2, resulting in area under the curve (AUCs) of 0.71, 0.81 and 0.84 respectively. Cs added value in patients who had extra prostatic spread of PCa, but suffered from mpMRI intensity artefacts that affected performance of CR. Conclusions: Integrating prostate capsule shape and tumor radiomic features from pre-treatment mpMRI enabled prediction of PCa BCR after treatment. Multi-site validation is needed to establish the robustness of the approach.

The prostate clinical outlook (PCO) classifier for predicting biochemical recurrences in patients treated by stereotactic body radiation therapy.

e16552 Background: Although prostate cancer risk classifiers have been developed for predicting surgical and radiation therapy outcomes, a classifier suitable for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) remains to be defined. SBRT is delivered in large fractions of highly conformal radiation therapy and such treatments are believed to be radiobiologically more effective in treating prostate cancers. The aim of this study is to develop a new classifier specifically for informing patients electing to undergo prostate cancer treatment with SBRT. Methods: We have studied outcomes of 809 patients treated with SBRT between August 2007 and November 2016 at MedStar-Georgetown University Hospital. A Cox regression to BCR was performed and the Prostate Clinical Outlook (PCO) score was calculated at diagnosis based on age at diagnosis, clinical-radiological staging, pre-treatment PSA and Gleason score. Accuracy of the PCO classifier was assessed with concordance (c)-indexes. The results were also compared to classifications by D’Amico and National Comprehensive Cancer Network (NCCN) recurrence risk groups. Results: PCO total scores range from zero to 156 points. The PCO classifier splits patients into 3 risk-groups with the following 5-year BCR-free survival: for low-risk 98%; for intermediate-risk 95%; for high-risk 86%. Our classifier outperforms D’Amico and NCCN for all of the evaluated end-points, with concordance indices of 74 % versus 64 % and 66%, respectively. Conclusions: The PCO classifier is a potential tool for employing readily available parameters to stratify prostate cancer patients and to predict probabilities of BCR after SBRT.

Racial Variation in the Outcome of Subsequent Prostate Biopsies in Men With an Initial Diagnosis of Atypical Small Acinar Proliferation.

African American (AA) men are known to have more aggressive prostate cancer (PCa) compared with Caucasian American men. We sought to determine predictors of subsequent detection and risk stratification of PCa in a racially diverse group of men with atypical small acinar proliferation (ASAP) on initial prostate biopsy. A retrospective analysis was conducted on data from men with ASAP on initial prostate biopsy who subsequently received confirmatory biopsies between September 2000 and July 2015. Biopsies with more than 3 years between initial and confirmatory biopsies were excluded. Race, age, body mass index, transrectal ultrasound volume, serum prostate-specific antigen (PSA), PSA velocity, PSA density, and elapsed time between biopsies were assessed for predictive value in subsequent PCa diagnosis after an initial finding of ASAP. Of 106 men analyzed, 75 (71%) were AA and 31 (29%) were non-AA. Baseline variables revealed AA men had higher PSA levels, PSA velocity, and PSA density (all P< .05). PCa was diagnosed in subsequent biopsy in 42 (40%) patients without significant racial variation; 30 (40%) AA versus 12 (39%) non-AA. Of the 42 PCa patients, 25 (24%) met Epstein criteria for significant disease without racial variation; 18 (24%) AA versus 7 (23%) non-AA. Only 10 (9%) patients had any component of Gleason 4; 7 (9%) AA versus 3 (10%) non-AA. In multivariate analysis, increasing age, PSA level, and PSA density were significant predictors of PCa. AA men diagnosed with ASAP on initial prostate biopsy do not have increased risk of PCa on confirmatory biopsy compared with non-AA men.

Prostate volume index stratified prostate cancer risk in patients elected to a first random biopsy set.

To investigate prostate volume index (PVI), defined as the ratio of volume of the transitional zone on that of the peripheral zone, as a factor stratifying prostate cancer (PCA) risk in patients elected to a first random biopsy set. The study evaluated 596 patients who were elected to a first random biopsy set because of suspected PCA in a period between September 2010 and September 2015. Prostate volume index was dichotomized to PVI ≤1 vs PVI >1. The multivariate logistic regression model investigated clinical factors with dichotomized PVI associating with PCA. The detection rate of PCA was 49%. The dichotomized PVI >1 stratified PCA risk (odds ratio [OR] 0.455; p<0.0001) beyond age (OR 1.062; p<0.0001), PSA (OR 1.167; p<0.0001), PV (OR 0.957; p<0.0001), and abnormal digital rectal examination (OR 2.094; p<0.0001). The goodness of fit statistics assessed model efficacy. A large cohort of patients elected to a first random biopsy set had PCA risk stratified by dichotomized PVI beyond other clinical independent factors. Confirmatory studies are required.

Performance of a validated urine exosome gene expression assay to predict high-grade prostate cancer utilizing the International Society of Urological Pathology (ISUP) 2014 grading system.

49 Background: Overdetection and overtreatment of indolent prostate cancer (PCa) remains a significant health issue requiring noninvasive assays to guide the prostate biopsy decision process. We demonstrated that a urine exosome gene expression assay (ExoDx P rostate ( I ntelliScore) (EPI) discriminates GS 7 PCa from GS 6 and benign disease, potentially reducing the number of unnecessary biopsies. The International Society of Urological Pathology (ISUP) proposed a prognostic PCa grading system to accurately reflect the biology of PCa; ISUP separates GS 7 PCa into group 2 (GS 3+4) and group 3 (GS 4+3). We sought to evaluate the performance of the EPI test according to the newly proposed ISUP system. Methods: The 519 patient validation cohort was re-annotated using ISUP and assessed benign (B) +/- ISUP 1 and B+ISUP 1+2 (GS 3+3, GS 3+4) from ISUP 3-5 (GS &gt;= 4+3). We used validated / adjusted cut-points to assess performance with the AUC, sensitivity, specificity and NPV. In addition, we investigated the association of the EPI score with the benign biopsies (BB) and ISUP groups in the combined training and test cohort (n=774). Results: Applying the adjusted cut point (EPI 20) on the 519 ISUP cohort discriminated benign biopsies (BB) + ISUP 1 from &gt;/=ISUP 2, 37% of biopsies avoided, NPV of 90%, equivalent to Gleason grading. Utilizing either the validated (15.6) or adjusted cut points on BB+ISUP 1+ 2 vs. &gt;/=ISUP 3 (dominant pattern 4), 26% vs 37% biopsies avoided, with an improved NPV 98%. In the combined training / test cohort, higher EPI scores were significantly associated with ISUP categories. In this analysis EPI in BB vs. all ISUP groups (p&lt;0.0001); BB+ISUP 1 vs. &gt;/= ISUP2 (p&lt;0.0001) and BB+ISUP 1+2 vs. &gt;/=ISUP3 (p&lt;0.0001); supporting accurate discrimination in high grade PCa. Conclusions: The EPI test is a noninvasive, first-catch non-DRE gene expression assay that accurately discriminates low-grade from high-grade PCa in both ISUP as well as Gleason score based grading systems. The test has the potential to reduce the number of unnecessary biopsies and performs equally well in contemporary approaches to PCa stratification.

Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer.

Rapid and reliable diagnosis of prostate cancer (PCa) is highly desirable as current used methods lack specificity. In addition, identification of PCa biomarkers that can classify patients into high- and low-risk groups for disease progression at early stage will improve treatment decision-making. Here, we describe a set of protein-combination panels in urinary extracellular vesicles (EVs), defined by targeted proteomics and immunoblotting techniques that improve early non-invasive detection and stratification of PCa patients.We report a two-protein combination in urinary EVs that classifies benign and PCa patients (ADSV-TGM4), and a combination of five proteins able to significantly distinguish between high- and low-grade PCa patients (CD63-GLPK5-SPHM-PSA-PAPP). Proteins composing the panels were validated by immunohistochemistry assays in tissue microarrays (TMAs) confirming a strong link between the urinary EVs proteome and alterations in PCa tissues. Moreover, ADSV and TGM4 abundance yielded a high diagnostic potential in tissue and promising TGM4 prognostic power. These results suggest that the proteins identified in urinary EVs distinguishing high- and low grade PCa are a reflection of histological changes that may be a consequence of their functional involvement in PCa development. In conclusion, our study resulted in the identification of protein-combination panels present in urinary EVs that exhibit high sensitivity and specificity for PCa detection and patient stratification. Moreover, our study highlights the potential of targeted proteomic approaches–such as selected reaction monitoring (SRM)–as diagnostic assay for liquid biopsies via urinary EVs to improve diagnosis and prognosis of suspected PCa patients.

Risk stratification of prostate cancer 2016

Prostate cancer is a common malignancy in men, but its management is fraught with controversy owing to its variable biologic and clinical behavior. Despite evidence that PSA screening reduces prostate cancer specific metastasis and death, it has not gained acceptance by various health authorities. Nevertheless, recent advances in biomarker development potentially address many of the shortcomings of routine PSA testing alone, including improved specificity for the detection of clinically significant cancer, optimized risk stratification to aid clinical management decisions, and discovery of genetic variants that may guide optimized therapy of advanced disease.

PBX3 is a putative biomarker of aggressive prostate cancer.

There is a great need to identify new and better prognostic and predictive biomarkers to stratify prostate cancer patients for optimal treatment. The aims of this study were to characterize the expression profile of pre-B cell leukemia homeobox (PBX) transcription factors in prostate cancer with an emphasis on investigating whether PBX3 harbours any prognostic value. The expression profile of PBX3 and PBX1 in prostate tissue was determined by immunohistochemical and immunoblot analysis. Furthermore, the expression of PBX3 transcript variants was analyzed by RT-PCR, NanoString Technologies®, and by analyzing RNA sequence data. The potential of PBX3 to predict prognosis, either at mRNA or protein level, was studied in four independent cohorts. PBX3 was mainly expressed in the nucleus of normal prostate basal cells, while it showed cytosolic expression in prostatic intraepithelial neoplasia and cancer cells. We detected four PBX3 transcript variants in prostate tissue. Competing risk regression analysis revealed that high PBX3 expression was associated with slower progression to castration resistant prostate cancer (sub-hazard ratio (SHR) 0.18, 95% CI: 0.081-0.42, p values < 0.001). PBX3 expression had a high predictive accuracy (area under the curve (AUC) = 0.82) when combined with Gleason score and age. Patients undergoing radical prostatectomy, with high levels of PBX3 mRNA, had improved prostate cancer specific survival compared to patients expressing low levels (SHR 0.21, 95% CI: 0.46-0.93, p values < 0.001, and AUC = 0.75). Our findings strongly indicate that PBX3 has potential as a biomarker, both as part of a larger gene panel and as an immunohistochemical marker, for aggressive prostate cancer.