Detection Of Aggressive Prostate Cancer Research Articles

Transperineal or Transrectal Magnetic Resonance Imaging–targeted Biopsy for Prostate Cancer Detection

Background and objectiveA notable paradigm shift has emerged in the choice of prostate biopsy approach, with a transition from transrectal biopsy (TRBx) to transperineal biopsy (TPBx) driven by the lower risk of severe urinary tract infections. The impact of this change on detection of clinically significant prostate cancer (csPCa) remains a subject of debate. Our aim was to compare the csPCa detection rate of TRBx and TPBx. MethodsPatients who underwent magnetic resonance imaging (MRI)-targeted and systematic biopsies for clinically localized PCa at 15 European referral centers from 2016 to 2023 were included. A propensity score matching (PSM) analysis was performed to minimize selection biases. Logistic regression models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Key findings and limitationsOf 3949 patients who met the study criteria, 2187 underwent TRBx and 1762 underwent TPBx. PSM resulted in 1301 matched pairs for analysis. Patient demographics and tumor characteristics were comparable in the matched cohorts. TPBx versus TRBx was associated with greater detection of csPCa, whether defined as International Society of Urological Pathology grade group ≥2 (51% vs 45%; OR 1.37, 95% CI 1.15–1.63; p = 0.001) or grade group ≥3 (29% vs 23%; OR 1.38, 95% CI 1.13–1.67; p = 0.001). Similar results were found when considering MRI-targeted biopsy alone and after stratifying patients according to tumor location, Prostate Imaging-Reporting and Data System score, and clinical features. Limitations include the retrospective nature of the study and the absence of centralized MRI review. ConclusionsOur findings bolster existing understanding of the additional advantages offered by TPBx. Further randomized trials to fully validate these findings are awaited. Patient summaryWe compared the rate of detection of clinically significant prostate cancer with magnetic resonance imaging (MRI)-guided biopsies in which the sample needle is passed through the perineum or the rectum. Our results suggest that the perineal approach is associated with better detection of aggressive prostate cancer.

Editorial Comment.

No AccessJournal of UrologyAdult Urology4 May 2023Editorial Comment Claire M. de la Calle and Christian P. Pavlovich Claire M. de la CalleClaire M. de la Calle The Brady Urological Institute and Department of Urology, Johns Hopkins School of Medicine, Baltimore, Maryland More articles by this author and Christian P. PavlovichChristian P. Pavlovich The Brady Urological Institute and Department of Urology, Johns Hopkins School of Medicine, Baltimore, Maryland More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003450.01AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, Publish Ahead of Print, pp. REFERENCES 1. Prostate Health Index density outperforms prostate-specific antigen density in the diagnosis of clinically significant prostate cancer in equivocal magnetic resonance imaging of the prostate: a multicenter evaluation. J Urol. 2023; 210(1):000. Google Scholar 2. Combining Prostate Health Index density, magnetic resonance imaging and prior negative biopsy status to improve the detection of clinically significant prostate cancer. BJU Int. 2018; 121(4):619-626. Crossref, Medline, Google Scholar 3. Optimizing patient's selection for prostate biopsy: a single institution experience with multi-parametric MRI and the 4Kscore test for the detection of aggressive prostate cancer. PLoS One. 2018; 13(8):e0201384. Crossref, Medline, Google Scholar 4. Avoiding unnecessary magnetic resonance imaging (MRI) and biopsies: negative and positive predictive value of MRI according to prostate-specific antigen density, 4Kscore and risk calculators. Eur Urol Oncol. 2020; 3(5):700-704. Crossref, Medline, Google Scholar © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Claire M. de la Calle The Brady Urological Institute and Department of Urology, Johns Hopkins School of Medicine, Baltimore, Maryland More articles by this author Christian P. Pavlovich The Brady Urological Institute and Department of Urology, Johns Hopkins School of Medicine, Baltimore, Maryland More articles by this author Expand All Advertisement PDF downloadLoading ...

Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI-algorithm for lesion detection and classification.

Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS. Consecutive patients under AS who received mpMRI (PI-RADSv2.1 protocol) and subsequent MR-guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI-driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa. Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non-csPCa). AI-augmented lesion detection and PI-RADS scoring is a robust tool to detect progression to csPCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence.

LINEAR BIOMARKER COMBINATION FOR CONSTRAINED CLASSIFICATION.

Multiple biomarkers are often combined to improve disease diagnosis. The uniformly optimal combination, i.e., with respect to all reasonable performance metrics, unfortunately requires excessive distributional modeling, to which the estimation can be sensitive. An alternative strategy is rather to pursue local optimality with respect to a specific performance metric. Nevertheless, existing methods may not target clinical utility of the intended medical test, which usually needs to operate above a certain sensitivity or specificity level, or do not have their statistical properties well studied and understood. In this article, we develop and investigate a linear combination method to maximize the clinical utility empirically for such a constrained classification. The combination coefficient is shown to have cube root asymptotics. The convergence rate and limiting distribution of the predictive performance are subsequently established, exhibiting robustness of the method in comparison with others. An algorithm with sound statistical justification is devised for efficient and high-quality computation. Simulations corroborate the theoretical results, and demonstrate good statistical and computational performance. Illustration with a clinical study on aggressive prostate cancer detection is provided.

Abstract 1441: Genetic determinants of PSA levels improve prostate cancer screening

Abstract Prostate-specific antigen (PSA) screening for prostate cancer (PCa) remains controversial due to poor sensitivity and specificity that lead to overdiagnosis and overtreatment. The aim of our study is to characterize genetic determinants of PSA levels in cancer-free men in order to personalize PCa screening. We hypothesize that test accuracy may be improved by accounting for PSA variation that is due to genetic factors and does not reflect PCa. We conducted the largest ever genome-wide association study (GWAS) of PSA in men without PCa (N=95,768; 85,924 predominantly European ancestry) using data from the UK Biobank, BioVU, PLCO, and Kaiser Permanente cohorts. Our GWAS discovered 129 PSA-associated variants (P<5×10-8), 82 of which were novel. A polygenic score (PGSPSA) comprised of these 129 variants was successfully validated in two cancer prevention trials: PCPT (n=5737) and SELECT (n=22,247). PGSPSA explained 7.3% (p=7.0×10-98) of variation in baseline PSA in PCPT and 8.7% (p=7.0×10-476) in SELECT. Importantly, PGSPSA was not associated with PCa status in PCPT (OR=0.98, p=0.71) or SELECT (OR=1.04, p=0.98), which confirms that it reflects benign PSA variation. Potential clinical utility of PSA genetic adjustment was explored by examining reclassification at thresholds used for biopsy referrals in a real-world setting at Kaiser Permanente. We estimated that correction using PGSPSA would have avoided 21.2% of negative biopsies in non-cases. Reclassification below the biopsy referral threshold was also more common in cases, particularly with low-grade disease with Gleason score <7 (7.3% below vs. 2.6% above). Overall, genetic correction of PSA appeared to improve the accuracy of referral decisions, with a Net Reclassification Index of 0.148. Next, we evaluated genetically adjusted PSA in the context of detection of aggressive PCa, defined as Gleason score ≥7, PSA ≥10 ng/mL, T3-T4 stage, and/or distant or nodal metastases. Genetically adjusted baseline PSA was more robustly associated with aggressive PCa than observed PSA and yielded a higher area under the curve (AUC) in PCPT (OR=3.03, p=3.5×10-7; AUC: 0.72 vs. 0.68) and SELECT (OR=3.37, p=3.5×10-11; AUC: 0.78 vs. 0.74) when added to a baseline model with age and trial arm. Furthermore, genetically adjusted PSA provides complementary information to PCa risk variants. In PCPT, a logistic regression model that included genetically corrected PSA and the 269-variant PGSPCa achieved a significantly higher AUC than PGSPCa-269 alone for aggressive PCa (AUC: 0.73 vs. 0.65, p=3.3×10-4) and overall PCa (AUC=0.69 vs. 0.66, p=3.3×10-6). Our work provides evidence that accounting for genetic determinants of PSA has the potential to reduce unnecessary testing and overdiagnosis of low-risk PCa, as well as increase detection of aggressive disease. Larger and more diverse study populations are required to fully characterize the genetic basis of PSA variation and optimize its clinical utility. Citation Format: Linda Kachuri, Rebecca E. Graff, Sonja I. Berndt, Mitchell Machiela, Neal D. Freedman, Stephen J. Chanock, John P. Shelley, Kerry Schaffer, Jonathan D. Mosley, Phyllis J. Goodman, Cathee Till, Ian Thompson, Robert J. Klein, Stephen K. Van Den Eeden, Thomas J. Hoffmann, John S. Witte. Genetic determinants of PSA levels improve prostate cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1441.

Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning.

Simple SummaryIn approximately 50% of prostate cancer patients undergoing surgical treatment, cancer has extended beyond the prostate boundary (i.e., extraprostatic extension). The aim of our study was to expand artificial intelligence (AI) models that identify cancer in the prostate to also identify the cancer that spreads outside the boundary of the prostate. By combining past models with image post-processing steps and clinical decision rules, we built an autonomous approach to detect the extension of the cancer beyond the prostate boundary using prostate MRI. Our study included 123 prostate cancer patients (38 with extraprostatic extension), and our proposed method can detect cancer outside the prostate boundary in more cases than radiologists.The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.

Value of PHI and PHID in the detection of intermediate- and high-risk prostate cancer

Background and aimsPSA testing practice results in a large number of unnecessary prostate biopsies and the overdiagnosis of clinically insignificant prostate cancer (PCa). The aim of our study was to evaluate the value of PHI and PHID for the detection of PCa. Materials and methodsWe measured tPSA, fPSA and p2PSA in 455 patients scheduled for biopsy, including 243 patients with PCa. D’Amico criteria were used to classify these patients in three groups related to risk of progression. Intermediate- and high-risk PCa were considered as aggressive PCa. ResultsThe best area under the curve (AUC) value obtained in the detection of aggressive PCa was achieved for PHI and PHID (0.766 and 0.760, respectively). We found a relationship of the performance of by these tests with the calculated prostate volume or the estimated prostate size by digital rectal exam, obtaining the higher AUC in patients with a small prostate. Thus, the AUC for PHI was 0,843 for patients with small calculated prostate volume and 0,817 for patients with small estimated prostate size. ConclusionsOur results underline that PHI and PHID outperforms the efficacy obtained with tPSA and %fPSA. Substantial differences in their value in relation to prostate volume were found.

MiCheck prostate blood test for aggressive prostate cancer designed for the clinical lab setting.

229 Background: Clinicians want a more accurate diagnostic test to identify patients for prostate biopsy. We have previously described the development of a Luminex based test for aggressive prostate cancer (MiCheck). To facilitate widespread deployment of MiCheck on standard clinical chemistry analysers such as Roche Cobas and Abbott ARCHITECT/Alinity systems, we have performed additional analytical validation and model development using the MiCheck-01 clinical samples. A novel algorithm, termed MiCheck Prostate, was developed to differentiate aggressive prostate cancer (Gleason 3+4 or above) from non-aggressive (GS3+3) or no cancer patients. Methods: Serum protein biomarkers were measured in 317 samples from the MiCheck-01 clinical trial using either Luminex Multiplex kits, Abbott ARCHITECT or Beckmann Coulter systems. Logistic regression models were used to select best analytes, then Monte Carlo cross-validation was applied to avoid overfitting. A final model was selected from the cross-validation models with best test specificity at 95% sensitivity. The MiCheck-01 samples were later all measured on a standard Abbott ARCHITECT system using commercial ARCHITECT IVD tests. The best cross-validated model was developed and the results compared to those obtained from the mixed analyte platforms using ROC curve analysis. Results: The MiCheck Prostate algorithm was developed to maximise discrimination between aggressive prostate cancer and no cancer or non-aggressive cancer. The MiCheck Prostate algorithm reports a percentage risk of aggressive prostate cancer on biopsy. MiCheck Prostate is a logistic regression algorithm that combines three serum protein markers and one clinical factor and was derived using data from the MiCheck-01 trial. The algorithm gave an AUC of 0.82, with a 48% specificity at a 95% sensitivity cutpoint, with a negative predictive value of 94% for GS3+4 and higher cancers. The samples were later re-measured using all ARCHITECT immunoassays. Good correlations of each analyte across different measurement platforms was observed (Pearson’s R > 0.9). The algorithm was revalidated using the new set of analyte values. AUC of 0.82 for the detection of aggressive CaP, and sensitivity of 95%, specificity of 47% were obtained for the best model, showing no statistical difference to the previous best model derived from the mixed platforms. NPV of 94% for GS3+4 or higher cancers was maintained. Conclusions: The MiCheck Prostate test shows high sensitivity, specificity and NPV for the detection of aggressive prostate cancer. It uses analytes widely available on standard clinical chemistry analysers such as Abbott Architect and Roche Cobas, hence is simple to deploy in the laboratory setting. Studies are ongoing to confirm the MiCheck Prostate algorithm performance using additional clinical sample cohorts.

Retraction Note to: Loss of PDEF, a prostate-derived Ets factor is associated with aggressive phenotype of prostate cancer: Regulation of MMP 9 by PDEF

Prostate-derived Ets factor (PDEF) is expressed in tissues of high epithelial content including prostate, although its precise function has not been fully established. Conventional therapies produce a high rate of cure for patients with localized prostate cancer, but there is, at present, no effective treatment for intervention in metastatic prostate cancer. These facts underline the need to develop new approaches for early diagnosis of aggressive prostate cancer patients, and mechanism based anti-metastasis therapies that will improve the outlook for hormone-refractory prostate cancer. In this study we evaluated role of prostate-derived Ets factor (PDEF) in prostate cancer. We observed decreased PDEF expression in prostate cancer cell lines correlated with increased aggressive phenotype, and complete loss of PDEF protein in metastatic prostate cancer cell lines. Loss of PDEF expression was confirmed in high Gleason Grade prostate cancer samples by immuno-histochemical methods. Reintroduction of PDEF profoundly affected cell behavior leading to less invasive phenotypes in three dimensional cultures. In addition, PDEF expressing cells had altered cell morphology, decreased FAK phosphorylation and decreased colony formation, cell migration, and cellular invasiveness. In contrast PDEF knockdown resulted in increased migration and invasion as well as clonogenic activity. Our results also demonstrated that PDEF downregulated MMP9 promoter activity, suppressed MMP9 mRNA expression, and resulted in loss of MMP9 activity in prostate cancer cells. These results suggested that loss of PDEF might be associated with increased MMP9 expression and activity in aggressive prostate cancer. To confirm results we investigated MMP9 expression in clinical samples of prostate cancer. Results of these studies show increased MMP9 expression correlated with advanced Gleason grade. Taken together our results demonstrate decreased PDEF expression and increased MMP9 expression during the transition to aggressive prostate cancer. These studies demonstrate for the first time negative regulation of MMP9 expression by PDEF, and that PDEF expression was lost in aggressive prostate cancer and was inversely associated with MMP9 expression in clinical samples of prostate cancer. Based on these exciting results, we propose that loss of PDEF along with increased MMP9 expression should serve as novel markers for early detection of aggressive prostate cancer.

Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins.

Recently, we have found that two urinary glycoproteins, prostatic acid phosphatase (ACPP) and clusterin (CLU), combined with serum prostate-specific antigen (PSA) can serve as a three-signature panel for detecting aggressive prostate cancer (PCa) based on a quantitative glycoproteomic study. To facilitate the translation of candidates into clinically applicable tests, robust and accurate targeted parallel reaction monitoring (PRM) assays that can be widely adopted in multiple labs were developed in this study. The developed PRM assays for the urinary glycopeptides, FLN*ESYK from ACPP and EDALN*ETR from CLU, demonstrated good repeatability and a sufficient working range covering three to four orders of magnitude, and their performance in differentiating aggressive PCa was assessed by the quantitative analysis of urine specimens collected from 69 nonaggressive (Gleason score = 6) and 73 aggressive (Gleason ≥ 8) PCa patients. When ACPP combined with CLU, the discrimination power was improved from an area under a curve (AUC) of 0.66 to 0.78. By combining ACPP, CLU, and serum PSA to form a three-signature panel, the AUC was further improved to 0.83 (sensitivity: 84.9%, specificity: 66.7%). Since the serum PSA test alone had an AUC of 0.68, our results demonstrated that the new urinary glycopeptide PRM assays can serve as an adjunct to the serum PSA test to achieve better predictive power toward aggressive PCa. In summary, our developed PRM assays for urinary glycopeptides were successfully applied to clinical PCa urine samples with a promising performance in aggressive PCa detection.

Development of a Whole-urine, Multiplexed, Next-generation RNA-sequencing Assay for Early Detection of Aggressive Prostate Cancer

BackgroundDespite biomarker development advances, early detection of aggressive prostate cancer (PCa) remains challenging. We previously developed a clinical-grade urine test (Michigan Prostate Score [MiPS]) for individualized aggressive PCa risk prediction. MiPS combines serum prostate-specific antigen (PSA), the TMPRSS2:ERG (T2:ERG) gene fusion, and PCA3 lncRNA in whole urine after digital rectal examination (DRE). ObjectiveTo improve on MiPS with a novel next-generation sequencing (NGS) multibiomarker urine assay for early detection of aggressive PCa. Design, setting, and participantsPreclinical development and validation of a post-DRE urine RNA NGS assay (Urine Prostate Seq [UPSeq]) assessing 84 PCa transcriptomic biomarkers, including T2:ERG, PCA3, additional PCa fusions/isoforms, mRNAs, lncRNAs, and expressed mutations. Our UPSeq model was trained on 73 patients and validated on a held-out set of 36 patients representing the spectrum of disease (benign to grade group [GG] 5 PCa). Outcome measurements and statistical analysisThe area under the receiver operating characteristic curve (AUC) of UPSeq was compared with PSA, MiPS, and other existing models/biomarkers for predicting GG ≥3 PCa. Results and limitationsUPSeq demonstrated high analytical accuracy and concordance with MiPS, and was able to detect expressed germline HOXB13 and somatic SPOP mutations. In an extreme design cohort (n = 109; benign/GG 1 vs GG ≥3 PCa, stratified to exclude GG 2 cancer in order to capture signal difference between extreme ends of disease), UPSeq showed differential expression for T2:ERG.T1E4 (1.2 vs 78.8 median normalized reads, p < 0.00001) and PCA3 (1024 vs 2521, p = 0.02), additional T2:ERG splice isoforms, and other candidate biomarkers. Using machine learning, we developed a 15-transcript model on the training set (n = 73) that outperformed serum PSA and sequencing-derived MiPS in predicting GG ≥3 PCa in the held-out validation set (n = 36; AUC 0.82 vs 0.69 and 0.69, respectively). ConclusionsThese results support the potential utility of our novel urine-based RNA NGS assay to supplement PSA for improved early detection of aggressive PCa. Patient summaryWe have developed a new urine-based test for the detection of aggressive prostate cancer, which promises improvement upon current biomarker tests.

A panel of selected serum protein biomarkers for the detection of aggressive prostate cancer.

Background: Current PSA-based tests used to detect prostate cancer (PCa) lack sufficient specificity, leading to significant overdetection and overtreatment. Our previous studies showed that serum fucosylated PSA (Fuc-PSA) and soluble TEK receptor tyrosine kinase (Tie-2) had the ability to predict aggressive (AG) PCa. Additional biomarkers are needed to address this significant clinical problem.Methods: A comprehensive Pubmed search followed by multiplex immunoassays identified candidate biomarkers associated with AG PCa. Subsequently, multiplex and lectin-based immunoassays were applied to a case-control set of sera from subjects with AG PCa, low risk PCa, and non-PCa (biopsy negative). These candidate biomarkers were further evaluated for their ability as panels to complement the prostate health index (phi) in detecting AG PCa.Results: When combined through logistic regression, two panel of biomarkers achieved the best performance: 1) phi, Fuc-PSA, SDC1, and GDF-15 for the detection of AG from low risk PCa and 2) phi, Fuc-PSA, SDC1, and Tie-2 for the detection of AG from low risk PCa and non-PCa, with noticeable improvements in ROC analysis over phi alone (AUCs: 0.942 vs 0.872, and 0.934 vs 0.898, respectively). At a fixed sensitivity of 95%, the panels improved specificity with statistical significance in detecting AG from low risk PCa (76.0% vs 56%, p=0.029), and from low risk PCa and non-PCa (78.2% vs 65.5%, p=0.010).Conclusions: Multivariate panels of serum biomarkers identified in this study demonstrated clinically meaningful improvement over the performance of phi, and warrant further clinical validation, which may contribute to the management of PCa.

PD53-01 DEVELOPMENT OF A WHOLE-URINE, MULTIPLEXED, NEXT GENERATION RNA-SEQUENCING ASSAY FOR AGGRESSIVE PROSTATE CANCER EARLY DETECTION

INTRODUCTION AND OBJECTIVE: Despite advances in biomarker development, early detection of aggressive prostate cancer (PCa) remains challenging. We previously developed the Michigan Prostate Score (MiPS) for individualized risk prediction of aggressive prostate cancer. MiPS uses transcription-mediated amplification to quantify expression of TMPRSS2:ERG (T2:ERG) and PCA3 from whole urine obtained after a digital rectal exam (DRE), combined with serum PSA. To improve upon MiPS, herein we describe the pre-clinical development and validation of a targeted next generation RNA sequencing assay (NGS-MiPS). METHODS: We selected patients with available MiPS scores as well as representing a spectrum of disease grade on biopsy (Benign to Grade Group 5). We used 2.5 mL of post-DRE whole urine to asses ∼90 PCa transcriptomic biomarkers: including T2:ERG, PCA3, and additional isoforms of common PCa gene fusions, mRNAs, lncRNAs, and expressed mutations. RESULTS: NGS-MiPS showed a 98% informative sample rate, high technical reproducibility, robustness and concordance with orthogonal methods (TMA and RT-qPCR), and was able to detect an expressed HOXB13 p.G84E variant and SPOP mutations. NGS-MiPS accurately recapitulated clinical MiPS-measured risk scores for presence of PCa or high-grade PCa (Gleason Score >6) on biopsy as determined by clinical MiPS vs. the same model but with NGS-MiPS data. In an extreme design cohort (Benign or Gleason 6 vs. Gleason ≥ 4+3=7 cancer) NGS-MiPS showed expected differences in the levels of T2:ERG T1E4 (p<0.00001) and PCA3 (p=0.02), with additional T2:ERG splice isoforms and other biomarkers also showing significantly different expression between low vs. high grade cancer. We used a machine learning approach trained on a subset of the extreme design cohort (n=73) to generate a 29-transcript model that outperformed MiPS and serum PSA in two validation cohorts: 1. A held-out set from the extreme design cohort n=36, (AUC 0.82 vs. 0.73 and 0.69, respectively); and 2. A separate PCa active surveillance cohort n=45, (AUC 0.66 vs. 0.58 and 0.53, respectively). CONCLUSIONS: These results support the potential utility and continued development of our urine based targeted NGS assay to supplement serum PSA for improved early detection of aggressive prostate cancer. Source of Funding: University of Michigan Prostate Cancer S.P.O.R.E. Grant P50 CA186786-05. AKC was supported by the NIH Training Program in Translational Research T32-GM113900 and by the University of Michigan Precision Health, 2018 Scholars Awards.TMM and SAT have been supported by the A. Alfred Taubman Biomedical Research Institute.

Urinary glycoproteins associated with aggressive prostate cancer

Background: There is an urgent need for the detection of aggressive prostate cancer. Glycoproteins play essential roles in cancer development, while urine is a noninvasive and easily obtainable biological fluid that contains secretory glycoproteins from the urogenital system. Therefore, here we aimed to identify urinary glycoproteins that are capable of differentiating aggressive from non-aggressive prostate cancer.Methods: Quantitative mass spectrometry data of glycopeptides from a discovery cohort comprised of 74 aggressive (Gleason score ≥8) and 68 non-aggressive (Gleason score = 6) prostate cancer urine specimens were acquired via a data independent acquisition approach. The glycopeptides showing distinct expression profiles in aggressive relative to non-aggressive prostate cancer were further evaluated for their performance in distinguishing the two groups either individually or in combination with others using repeated 5-fold cross validation with logistic regression to build predictive models. Predictive models showing good performance from the discovery cohort were further evaluated using a validation cohort.Results: Among the 20 candidate glycoproteins, urinary ACPP outperformed the other candidates. Urinary ACPP can also serve as an adjunct to serum PSA to further improve the discrimination power for aggressive prostate cancer (AUC= 0.82, 95% confidence interval 0.75 to 0.89). A three-signature panel including urinary ACPP, urinary CLU, and serum PSA displayed the ability to distinguish aggressive prostate cancer from non-aggressive prostate cancer with an AUC of 0.86 (95% confidence interval 0.8 to 0.92). Another three-signature panel containing urinary ACPP, urinary LOX, and serum PSA also demonstrated its ability in recognizing aggressive prostate cancer (AUC=0.82, 95% confidence interval 0.75 to 0.9). Moreover, consistent performance was observed from each panel when evaluated using a validation cohort.Conclusion: We have identified glycopeptides of urinary glycoproteins associated with aggressive prostate cancer using a quantitative mass spectrometry-based glycoproteomic approach and demonstrated their potential to serve as noninvasive urinary glycoprotein biomarkers worthy of further validation by a multi-center study.

Abstract 914: Development of a whole-urine, next-generation sequencing-based assay for early detection of aggressive prostate cancer

Abstract Despite advances in biomarker development, early detection of aggressive prostate cancer (PCa) remains challenging. Existing biomarkers show modest improvement over models based on serum prostate specific antigen (PSA). We have previously developed a clinical-grade laboratory-developed test, named MiProstate Score (MiPS), for individualized risk prediction of aggressive prostate cancer. It uses transcription-mediated amplification to quantify the gene-fusion TMPRSS2:ERG (T2:ERG) (T1E4 splice isoform) and the lncRNA PCA3 from whole-urine obtained after a digital rectal exam (DRE), combined with serum PSA. To improve MiPS, we describe here the pre-clinical development and validation of a targeted next generation sequencing assay (NGS-MiPS) using post-DRE urine RNA to asses ~90 PCa transcriptomic biomarkers. These include those in MiPS as well as many isoforms of common PCa gene fusions, mRNA, and lncRNA candidate biomarkers nominated by our large-scale PCa tissue RNAseq and other sources. We have obtained a 98% informative sample rate from 2.5 mL of urine and high technical reproducibility (Pearson r=0.99). Risk scores for having PCa [or high-grade PCa (Gleason Score &amp;gt;6)] on biopsy, as determined by clinical MiPS vs. the clinical MiPS model using NGS data, were highly concordant, Pearson’s r=0.74 (and r=0.81). Urine from patients with benign or Gleason 6 vs. Gleason ≥ 4+3=7 cancer on biopsy (extreme design) showed expected differences in the levels of T2:ERG T1E4 (p=0.00003) and PCA3 (p=0.07), with additional T2:ERG splice isoforms and other biomarkers also being significantly different between low vs. high grade cancer. Feature selection and logistic regression trained in an extreme design cohort (n=73) yielded a 29-transcript model that outperformed MiPS and serum PSA in two validation cohorts: 1. A held-out set from the extreme design cohort n=36, AUC 0.81 vs. 0.76 and 0.63 respectively; 2. A separate active surveillance cohort n=45, AUCs 0.66 vs. 0.56 and 0.53 respectively. These results support the potential utility of our urine based targeted NGS assay to supplement serum PSA for the early detection of aggressive prostate cancer. Citation Format: Andi K. Cani, Kevin Hu, Javed Siddiqui, Sumin Han, Daniel H. Hovelson, Chia-Jen Liu, Simpa S. Salami, Ganesh S. Palapattu, Todd M. Morgan, John T. Wei, Arul M. Chinnaiyan, Scott A. Tomlins. Development of a whole-urine, next-generation sequencing-based assay for early detection of aggressive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 914.

Re: Optimizing Patient's Selection for Prostate Biopsy: A Single Institution Experience with Multi-parametric MRI and the 4Kscore Test for the Detection of Aggressive Prostate Cancer

Re: Optimizing Patient's Selection for Prostate Biopsy: A Single Institution Experience with Multi-parametric MRI and the 4Kscore Test for the Detection of Aggressive Prostate Cancer

Aggressive Prostate Cancer Detection with an 8-Protein Biomarker Panel

Prostate cancer treatment has risk associated with patients’ being overdiagnosed leading to an overtreatment epidemic. In the United States 1 in 9 males are diagnosed with prostate cancer with approximately 10% of these patients having aggressive forms usually requiring surgery. To diagnose these patients, physicians most often rely on the Prostate Specific Antigen (PSA) blood test and the digital rectal exam. PSA is currently the only biomarker approved for prostate cancer diagnostics, but cannot discern cancer from other prostate conditions, such as Benign Prostatic Hyperplasia (BPH) or Prostatitis. Most importantly, PSA cannot identify the aggressive forms of prostate cancer. These inaccuracies lead to unnecessary treatments including surgery of indolent prostate cancer patients that adversely affect the patients’ quality of life. Measurements of small panels of molecular biomarkers in serum holds tremendous potential for cancer diagnostics and personalized therapy. We are evaluating a new protein panel for prostate cancer including PSA, CD-14, ERG, GOLM-1, PEDF-1, IGF-1, VEGF-D and IGFBP-3 that may be indicative of prostate cancer or cancer progression. We designed a microfluidic immunoarray that features miniaturisation, low sample volume, sensitivity adjustable to the sample characteristics, rapid analysis, and low reagent consumption. A protocol was developed for the simultaneous assay of this 8-protein panel in serum that features online capture and detection with a semi-automated multiple protein immunoarray. A nanostructured sensor array with bound capture antibody in the microfluidic device captures the biomarker proteins from the sample, and a sandwich immunoassay is completed following a concurrent injection of target protein standards or sample, biotinylated secondary antibody and polymerized horseradish peroxidase into the detection chamber. Ultra-low limits of detection (LOD) in the sub fg ml-1 range were achieved for the proteins in the panel. Using this strategy, 8-16 proteins can be detected simultaneously within 30 minutes. Measurements of the biomarker panel tested with prostate cancer patient samples and statistical analyses to assess diagnostic utility will be described.

EpiCaPture: A Urine DNA Methylation Test for Early Detection of Aggressive Prostate Cancer.

PurposeLiquid biopsies that noninvasively detect molecular correlates of aggressive prostate cancer (PCa) could be used to triage patients, reducing the burdens of unnecessary invasive prostate biopsy and enabling early detection of high-risk disease. DNA hypermethylation is among the earliest and most frequent aberrations in PCa. We investigated the accuracy of a six-gene DNA methylation panel (Epigenetic Cancer of the Prostate Test in Urine [epiCaPture]) at detecting PCa, high-grade (Gleason score greater than or equal to 8) and high-risk (D’Amico and Cancer of the Prostate Risk Assessment] PCa from urine.Patients and MethodsPrognostic utility of epiCaPture genes was first validated in two independent prostate tissue cohorts. epiCaPture was assessed in a multicenter prospective study of 463 men undergoing prostate biopsy. epiCaPture was performed by quantitative methylation-specific polymerase chain reaction in DNA isolated from prebiopsy urine sediments and evaluated by receiver operating characteristic and decision curves (clinical benefit). The epiCaPture score was developed and validated on a two thirds training set to one third test set.ResultsHigher methylation of epiCaPture genes was significantly associated with increasing aggressiveness in PCa tissues. In urine, area under the receiver operating characteristic curve was 0.64, 0.86, and 0.83 for detecting PCa, high-grade PCa, and high-risk PCa, respectively. Decision curves revealed a net benefit across relevant threshold probabilities. Independent analysis of two epiCaPture genes in the same clinical cohort provided analytical validation. Parallel epiCaPture analysis in urine and matched biopsy cores showed added value of a liquid biopsy.ConclusionepiCaPture is a urine DNA methylation test for high-risk PCa. Its tumor specificity out-performs that of prostate-specific antigen (greater than 3 ng/mL). Used as an adjunct to prostate-specific antigen, epiCaPture could aid patient stratification to determine need for biopsy.

Abstract 2573: Selection of candidate biomarkers for aggressive prostate cancer based on targeted proteomics

Abstract Mass spectrometry based targeted proteomics such as selected reaction monitoring (SRM) provides an effective, antibody-independent strategy for sensitive, specific and multiplexed verification of genomics biomarker candidates at the protein level. In order to identify a panel of proteins with the potential to predict prostate cancer progression, we have selected 52 protein candidates from existing prostate cancer genomics data sets and validated cancer drivers, and performed quantitative proteomics analysis in tissue samples using the highly sensitive PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM approach. PRISM-SRM assays have been developed for the 52 prostate cancer biomarker candidates including: prostate cancer relevant genes and common cancer drivers. One set of 105 formalin-fixed paraffin-embedded (FFPE) whole mount prostate tissue specimens were analyzed using PRISM-SRM with heavy isotope-labeled synthetic peptides as internal standards: 20 primary tumors from patients showing metastatic progression, 37 primary tumors from patients who showed biochemical recurrence (BCR), and 48 primary tumors from patients with no BCR or metastatic progression after more than ten years of follow-up after radical prostatectomy. Overall, PRISM-SRM analyses of the FFPE tissue samples enabled the detection of 42 out of 52 biomarker candidates; in comparison regular LC-SRM without the front-end chromatographic enrichment could detect only 21 of these candidates at the protein level. Kruskal-Wallis test of the PRISM-SRM results provided a statistical evaluation of comparison of relative protein levels among the “no progression”, BCR and “metastatic progression” groups. Prostate differentiation/AR signaling related proteins (FOLH1, PSA and NCOA) or tumor progression (TGFB1, CCND1 and SPRC) were significantly different between the three groups. These promising biomarker candidates for early detection of aggressive prostate cancer are being further evaluated, individually and in panels, in an independent cohort of 234 samples for their potential prognostic applications. In summary, PRISM-SRM provides a highly sensitive method for quantification and rapid screening of promising cancer biomarker candidates defined by multiomics platforms. Citation Format: Yuqian Gao, Hui Wang, Jennifer Cullen, Yongmei Chen, Athena Schepmoes, Gyorgy Petrovics, Thomas Fillmore, Tujin Shi, Wei-Jun Qian, Richard Smith, Sudhir Srivastava, Jacob Kagan, Albert Dobi, Karin Rodland, Shiv Srivastava, Tao Liu. Selection of candidate biomarkers for aggressive prostate cancer based on targeted proteomics [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 2573.

Abstract 2476: Discovery of an androgen-responsive long noncoding RNA that associates with progression of ERG-overexpressing prostate cancers

Abstract Androgen receptor (AR) signaling pathway has an important role in the growth and development of normal prostate, but also in tumorigenesis and progression of prostate cancer (PC). Although the mechanisms of AR signaling have been widely studied and utilized for treatment in advanced PC, the exact role of AR in development of primary PC is unclear. Former studies have found that AR cistrome is reprogrammed during tumorigenesis to bind novel genomic loci by master regulators, including the ETS family transcription factor ERG. While many AR-induced target genes are known, the effect of AR signaling on regulation of long noncoding RNAs (lncRNAs) is poorly understood, especially in the context of PC progression. Previously, we discovered multiple novel PC-associated transcripts (PCATs) to be aberrantly expressed in PC. Here, we evaluated the expression of 39 Tampere PCATs (TPCATs) in 87 radical prostatectomy specimens using high-throughput real-time PCR, and studied their association with time to PSA progression after prostatectomy. Six TPCATs were significantly associated with time to PSA progression, and four of them also associated with extracapsular extension. In addition, we assessed the expression of TPCATs in the TCGA prostate adenocarcinoma cohort, and found many to be correlated with ERG expression. Moreover, publicly available AR ChIP-seq data from PC tumors indicated that several ERG-associated TPCATs had AR-binding sites on their promoters, some of which overlapped with ERG binding sites. Most notably, we found one progression-associated TPCAT that was regulated by AR in an androgen-sensitive manner according to AR siRNA knockdown and DHT stimulation experiments in vitro. The same TPCAT was also highly associated with overexpression of ERG, and further validated to be a highly PC-specific lncRNA that was abundantly expressed in primary PCs. Taken together, these findings give more insight into the role of AR cistrome in the regulation of lncRNAs in primary PC, and introduce a potential novel prognostic marker to be used in early detection of aggressive PC. Citation Format: Annika Kohvakka, Anastasia Shcherban, Kati K. Kivinummi, Matti Annala, Alfonso Urbanucci, Matti Nykter, Tapio Visakorpi. Discovery of an androgen-responsive long noncoding RNA that associates with progression of ERG-overexpressing prostate cancers [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 2476.