Prostate cancer biomarkers: Are we hitting the mark?

Genomics in active surveillance and post-prostatectomy patients: A review of when and how to use effectively

PURPOSE OF REVIEW

Prostate cancer (PCa) represents a significant health burden globally, ranking as the most diagnosed cancer among men and a leading cause of cancer-related mortality. Conventional treatment methods such as radiation therapy or radical prostatectomy have significant side effects which often impact quality of life. As our understanding of the natural history and progression of PCa has evolved, so has the evolution of management options.

RECENT FINDINGS

Active surveillance (AS) has become an increasingly favored approach to the management of very low, low, and properly selected favorable intermediate risk PCa. AS permits ongoing observation and postpones intervention until definitive treatment is required. There are, however, challenges with selecting patients for AS, which further emphasizes the need for more precise tools to better risk stratify patients and choose candidates more accurately. Tissue-based biomarkers, such as ProMark, Prolaris, GPS (formerly Oncotype DX), and Decipher, are valuable because they improve the accuracy of patient selection for AS and offer important information on the prognosis and severity of disease. By enabling patients to be categorized according to their risk profiles, these biomarkers help physicians and patients make better informed treatment choices and lower the possibility of overtreatment. Even with their potential, further standardization and validation of these biomarkers is required to guarantee their broad clinical utility. Active surveillance has emerged as a preferred strategy for managing low-risk prostate cancer, and tissue-based biomarkers play a crucial role in refining patient selection and risk stratification. Standardization and validation of these biomarkers are essential to ensure their widespread clinical use and optimize patient outcomes.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Investigating Nutritional and Inflammatory Status as Predictive Biomarkers in Oligoreccurent Prostate Cancer-A RADIOSA Trial Preliminary Analysis

(1) Background: In the RADIOSA phase II randomized clinical trial (NCT03940235), the biology task entails the identification of predictive and prognostic biomarkers in the context of oligorecurrent, castration-sensitive prostate cancer in order to distinguish polymetastatic from oligometastatic disease. This may lay the groundwork for personalized treatments for those patients who could really benefit from metastasis-directed therapies. (2) Methods: Oligorecurrent PCa pts with three or fewer bone or lymph nodal localizations were randomized 1:1 to receive SBRT alone (arm A) or SBRT + 6 months of ADT (arm B). Common serum-derived biomarkers were collected at baseline, and at 3 months after RT. The prognostic nutritional index, an immune and nutrition-based prognostic score, and the controlling nutritional status (CONUT) score, a scoring system for evaluating patient's nutritional status, were calculated in accordance with the body of available literature. As inflammatory indicators, neutrophil-lymphocyte ratio (NLR) and the NLR-albumin ratio (NLRAR) were assessed. Changes in these parameters between baseline and the 3-month timepoint were evaluated both in absolute and relative values. Changes in these parameters between baseline and the 3-month timepoint were evaluated. Significant differences in the trend of these parameters were assessed using the non-parametric Wilcoxon rank-sum test. A network analysis to analyze the relationships between different features stratifying patients according to the arm of study and site of metastases was performed. (3) Results: The current analysis comprised 88 patients (45 arm A, SBRT only, and 43 arm B, SBRT + ADT). When patients were stratified by ADT administration, cholesterol values showed an increasing trend in the group receiving ADT (p = 0.005) which was no longer significant at 1 year. When patients were stratified by site of metastases (52 lymph nodal, 29 bone localizations), the value of NLR was found to be increased in patients with bone localizations (p < 0.05). In addition, the network analysis showed that BMI and NRI are strongly and directly linked for patients at baseline and that this correlation is no longer found at three months. Finally, when patients were divided according to time from surgery to oligorecurrence (enrollment) the patients with a longer time (>6.7 years) showed an increase in CONUT score from baseline. All the other nutritional and inflammatory scores or parameters investigated in the present analysis showed no statistically significant differences at baseline, three months, 1 year, and in absolute change. (4) Conclusions: The nutritional and inflammatory parameters do not seem to represent valuable candidates for possible use in clinical decision making in our cohort of patients and a reliable biological characterization of the oligometastatic state in prostate cancer still seems far from being achieved. Ongoing molecular analysis will show if there is a role of mutational landscape in the definition of the oligometastatic state.

Evaluating the Role of Morphological Parameters in the Prostate Transition Zone in PHI-Based Predictive Models for Detecting Gray Zone Prostate Cancer

Background

The early detection of clinically significant prostate cancer (csPCa) through the integration of multidimensional parameters presents a promising avenue for improving survival outcomes for this fatal disease. This study aimed to assess the contribution of prostate transition zone (TZ) to predictive models based on the prostate health index (PHI), with the goal of enhancing early detection of csPCa in the prostate-specific antigen (PSA) gray zone.

Methods

In this observational cross-sectional study, a total of 177 PSA gray zone patients (total prostate-specific antigen [tPSA] level ranging from 4.0 to 10.0 ng/mL) were recruited and received PHI detections from August 2020 to March 2022. Prostatic morphologies especially the TZ morphological parameters were measured by transrectal ultrasound (TRUS).

Results

Univariable logistic regression indicated prostatic morphological parameters including total prostate volume (PV) indexes and transitional zone volume indexes were all associated with csPCa (P < .05), while the multivariable analysis demonstrated that C-reactive protein (CRP), PHI, PHI density (PHID), and PHI transition zone density (PHI-TZD) were the 4 independent risk factors. The receiver-operating characteristic (ROC) curve analysis suggested that integrated predictive models (PHID, PHI-TZD) yield area under the curves (AUCs) of 0.9135 and 0.9105 in csPCa prediction, which shows a relatively satisfactory predictive capability compared with other predictors. Moreover, the PHI-TZD outperformed PHID by avoiding 30 patients' unnecessary biopsies while maintaining 74.36% specificity at a sensitivity of 90%. Decision-curve analysis (DCA) confirmed the comparable performance of the multivariable full-risk prediction models, without the inclusion of the net benefit, thereby highlighting the superior diagnostic efficacy of PHID and PHI-TZD in comparison with other diagnostic models, in both univariable and multivariable models.

Conclusion

Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.

Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

Identification and Validation of FGF-Related Prognostic Signatures in Prostate Cancer

Background

FGF signaling is critical to controlling various cancers. Nevertheless, the functions of FGF-related genes in PCa are still unknown.

Objective

The objective of this study is to build a FGF-related signature that was capable of accurately predicting PCa survival and prognosis for BCR.

Methods

The univariate and multivariate Cox regression, infiltrating immune cells, LASSO, and GSEA analyses were carried out to build a prognostic model.

Results

A FGF-related signature that consists of PIK3CA and SOS1 was developed for the purpose of predicting PCa prognosis, and all patients were categorized into low- and high-risk groups. In comparison to the low-risk group, high-risk score patients had poorer BCR survival. This signature's predictive power has been investigated utilizing the AUC of the ROC curves. The risk score has been shown to be an independent prognostic factor by multivariate analysis. The four enriched pathways of the high-risk group were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of PCa, including focal adhesion, TGF-β signaling pathway, adherens junction, and ECM receptor interaction. The high-risk groups had considerably higher levels of immune status and tumor immune cell infiltration, suggesting a more favorable response to immune checkpoint inhibitors. IHC found that the expression of the two FGF-related genes in the predictive signature was extremely different in PCa tissues.

Conclusion

To summarize, our FGF-related risk signature may effectively predict and diagnose PCa, indicating that in PCa patients, they are potential therapeutic targets and promising prognostic biomarkers.

Follow-Up Biomarkers in the Evolution of Prostate Cancer, Levels of S100A4 as a Detector in Plasma

The management and screening of prostate cancer (PC) is still the main problem in clinical practice. In this study, we investigated the role of aggressiveness genetic markers for PC stratification. We analyzed 201 plasma samples from PC patients and controls by digital PCR. For selection and validation, 26 formalin-fixed paraffin-embedded tissues, 12 fresh tissues, and 24 plasma samples were characterized by RNA-Seq, immunochemistry, immunofluorescence, Western blot, and extracellular-vesicles analyses. We identified three novel non-invasive biomarkers; all with an increased expression pattern in patients (PCA3: p = 0.002, S100A4: p ≤ 0.0001 and MRC2: p = 0.005). S100A4 presents the most informative AUC (area under the curve) (0.735). Combination of S100A4, MRC2, and PCA3 increases the discriminatory power between patients and controls and between different more and less aggressive stages (AUC = 0.761, p ≤ 0.0001). However, although a sensitivity of 97.47% in PCA3 and a specificity of 90.32% in S100A4 was reached, the detection signal level could be variable in some analyses owing to tumor heterogeneity. This is the first time that the role of S100A4 and MRC2 has been described in PC aggressiveness. Moreover, the combination of S100A4, MRC2, and PCA3 has never been described as a non-invasive biomarker for PC screening and aggressiveness.

Advancing Traditional Prostate-specific Antigen Kinetics in the Detection of Prostate Cancer: A Machine Learning Model

BACKGROUND

Prostate-specific antigen (PSA) kinetics, defined as the change in PSA over time, may be of use as a predictor of prostate cancer. PSA kinetics can be assessed as the PSA velocity, which is traditionally evaluated dichotomously and classified as abnormal if greater than either 0.35 or 0.75 ng/ml/yr. Machine learning models may provide additional benefit in assessing risk using PSA kinetics instead of PSA velocity.

OBJECTIVE

To improve the utility of PSA kinetics by constructing a generalizable, universal machine learning model.

DESIGN, SETTING, AND PARTICIPANTS

Data were obtained from the PLCO and PCPT trials and from a contemporary Australian cohort. PSA data were interpolated using a modified Gaussian process. A machine learning model based on a two-headed approach was designed, in which the multivariable input was fed into a one-dimensional ResNet18 model.

OUTCOME MEASURES AND STATISTICAL ANALYSIS

The model performance was assessed compared to PSA levels and PSA velocity in terms of area under the receiver operator characteristic curve (AUC).

RESULTS AND LIMITATIONS

A total of 10719 patients were included in the analysis. In tests on a validation set of the complete database to diagnose grade group ≥2, the AUC was 0.886 (95% confidence interval [CI] 0.870-0.902) for the machine learning model, compared to 0.807 (95% CI 0.796-0.819) for PSA and 0.627 (95% CI 0.607-0.648) for PSA velocity.

CONCLUSIONS

Machine learning models can be used to augment the diagnostic utility of PSA kinetics in the diagnosis of prostate cancer. We demonstrated significant improvements in accuracy compared to the traditional approaches of PSA velocity and PSA thresholds.

PATIENT SUMMARY

Prostate cancer diagnosis is limited by the diagnostic accuracy of the prostate-specific antigen (PSA) blood test. Advances in techniques such as machine learning algorithms can greatly improve the diagnostic accuracy of prostate cancer screening without additional costs or tests.

Prostate health index (PHI) as a reliable biomarker for prostate cancer: a systematic review and meta-analysis

OBJECTIVES

Prostate cancer (PCa) represents the second most common solid cancer in men worldwide. In the last decades, the prostate health index (PHI) emerged as a reliable biomarker for detecting PCa and differentiating between non-aggressive and aggressive forms. However, before introducing it in clinical practice, more evidence is required. Thus, we performed a systematic review and meta-analysis for assessing the diagnostic performance of PHI for PCa and for detecting clinically significant PCa (csPCa).

METHODS

Relevant publications were identified by a systematic literature search on PubMed and Web of Science from inception to January 11, 2022.

RESULTS

Sixty studies, including 14,255 individuals, met the inclusion criteria for our meta-analysis. The pooled sensitivity and specificity of PHI for PCa detection was 0.791 (95%CI 0.739-0.834) and 0.625 (95%CI 0.560-0.686), respectively. The pooled sensitivity and specificity of PHI for csPCa detection was 0.874 (95%CI 0.803-0.923) and 0.569 (95%CI 0.458-0.674), respectively. Additionally, the diagnostic odds ratio was 6.302 and 9.206, respectively, for PCa and csPCa detection, suggesting moderate to good effectiveness of PHI as a diagnostic test.

CONCLUSIONS

PHI has a high accuracy for detecting PCa and discriminating between aggressive and non-aggressive PCa. Thus, it could be useful as a biomarker in predicting patients harbouring more aggressive cancer and guiding biopsy decisions.

Combined miR-486 and GP88 (Progranulin) Serum Levels Are Suggested as Supportive Biomarkers for Therapy Decision in Elderly Prostate Cancer Patients

Our study aimed to assess the applicability of miR-486 in combination with soluble GP88 protein as a diagnostic and/or predictive biomarker for prostate cancer (PCa) patients. miR-486 and GP88 levels in serum samples from 136 patients undergoing MRI-guided biopsy of the prostate were assessed by qRT−PCR and ELISA, respectively. Of these, 86 patients received a histologically confirmed diagnosis of PCa. Neither marker showed an association with the diagnosis of cancer. PCa patients were separated based on (i) treatment into patients with active surveillance or patients with any type of curative treatment and (ii) age into elderly (>68 years) patients and younger patients (≤68 years). In elderly patients (N = 41) with the intention of curative treatment at optimized cut-off values, significantly higher GP88 levels (p = 0.018) and lower miR-486 levels (p = 0.014) were observed. The total PSA level and ISUP biopsy grade were used in a baseline model for predicting definitive therapy. The baseline model exhibited an area under the curve (AUC) of 0.783 (p = 0.005). The addition of the serum biomarkers miR-486 and GP88 to the baseline model yielded an improved model with an AUC of 0.808 (p = 0.002). Altogether, combined miR-486 and GP88 serum levels are associated with and are therefore suggested as supportive biomarkers for therapy decisions, particularly in elderly PCa patients.

Usefulness of the prostate health index in predicting the presence and aggressiveness of prostate cancer among Korean men: a prospective observational study

Background

We aimed to evaluate the usefulness of the Beckman Coulter prostate health index (PHI) and to compare it with total prostate-specific antigen (PSA) levels and related derivatives in predicting the presence and aggressiveness of prostate cancer (PCa) in the Korean population.

Methods

A total of 140 men who underwent their first prostate biopsy for suspected PCa were included in this prospective observational study. The diagnostic performance of total PSA, free PSA, %free PSA, [–2] proPSA (p2PSA), %p2PSA, and PHI in detecting and predicting the aggressiveness of PCa was estimated using the receiver operating characteristic curve (ROC) and logistic multivariate regression analyses.

Results

Of 140 patients, PCa was detected in 63 (45%) of participants, and 48 (76.2%) of them had significant cancer with a Gleason score (GS) ≥ 7. In the whole group, the area under the curve (AUC) for ROC analysis of tPSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI were 0.63, 0.57, 0.69, 0.69, 0.72, and 0.76, respectively, and the AUC was significantly greater in the PHI group than in the tPSA group (p = 0.005). For PCa with GS ≥ 7, the AUCs for tPSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI were 0.62, 0.58, 0.41, 0.79, 0.86, and 0.87, respectively, and the AUC was significantly greater in the PHI group than in the tPSA group (p < 0.001). In the subgroup with tPSA 4–10 ng/mL, both %p2PSA and PHI were strong independent predictors for PCa (p = 0.007, p = 0.006) and significantly improved the predictive accuracy of a base multivariable model, including age, tPSA, fPSA and %fPSA, using multivariate logistic regression analysis. (p = 0.054, p = 0.048). Additionally, at a cutoff PHI value > 33.4, 22.9% (32/140) of biopsies could be avoided without missing any cases of aggressive cancer.

Conclusions

This study shows that %p2PSA and PHI are superior to total PSA and %fPSA in predicting the presence and aggressiveness (GS ≥ 7) of PCa among Korean men. Using PHI, a significant proportion of unnecessary biopsies can be avoided.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-021-00897-2.

A Novel Urine Exosomal lncRNA Assay to Improve the Detection of Prostate Cancer at Initial Biopsy: A Retrospective Multicenter Diagnostic Feasibility Study

Simple Summary

Prostate cancer (PCa) is the second most common malignancy in males globally. Although PSA screening is a milestone in PCa detection, it also causes overdiagnosis and subsequent overtreatment. Therefore, it is imperative to find an optimal replacement or supplement for PSA testing to increase the detection rate of clinically significant PCa as well as reduce unnecessary biopsies. Here, we aimed at developing and validating a novel noninvasive urinary exosome-based post-DRE lncRNA assay to diagnose PCa and clinically significant PCa at initial prostate biopsy. We found that the lncRNA assay had a significant clinical value in diagnosing PCa and clinically significant PCa compared to the current clinical parameters. These results suggest that this novel lncRNA assay developed in this study could be a valuable biomarker to increase the detection rate of clinically significant PCa as well as reduce unnecessary biopsies.

Abstract

Purpose: This study aimed at developing and validating a novel noninvasive urinary exosome-based post-DRE (digital rectal examination) lncRNA assay to diagnose PCa (prostate cancer) and clinically significant PCa (Gleason score ≥ 7) from the initial prostate biopsy. Methods: A total of 602 urine samples from eligible participants were collected. The expression levels of urinary exosomal PCA3 (prostate cancer antigen 3) and MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) were detected by qPCR (quantitative real-time PCR). Receiver operating characteristic (ROC) analysis was applied to evaluate the diagnostic performance of PCA3, MALAT1 and the lncRNA assay. A decision curve analysis (DCA) and waterfall plots were used to assess the clinical value of the lncRNA assay. Results: Urinary exosomal PCA3 and MALAT1 were overexpressed in PCa and clinically significant PCa (p < 0.001). The lncRNA assay combining PCA3 and MALAT1 had a better diagnostic performance (AUC 0.828) than the current clinical parameters in detecting PCa. More importantly, the lncRNA assay yielded an AUC of 0.831 to detect clinically significant PCa, which is much higher than that of the current clinical parameters. The lncRNA assay was superior to PSA, f/tPSA and the base model for detecting PCa and clinically significant PCa, with a higher net benefit for almost all threshold probabilities. At the cutoff value of 95% sensitivity, the lncRNA assay could avoid 24.2% unnecessary biopsies while only missing 1.2% of the cases of clinically significant PCa. Conclusion: We developed and validated a novel noninvasive post-DRE urine-based lncRNA assay that presented good diagnostic power and clinical utility for the early diagnosis of PCa and high-grade PCa.

The use of molecular biomarker tests: an interview study with healthcare providers about a molecular biomarker test for prostate cancer

Aim: Investigate why healthcare providers are not always willing to use molecular biomarker tests, even though they promise to personalize disease diagnosis and treatment. Materials & methods: We interviewed 20 Dutch urological healthcare providers to ascertain why they used or did not use SelectMDx, a biomarker test for prostate cancer. Results: Whether and how it was used differed from the developers' expectations, because users and nonusers disagreed about its perceived advantages; the scientific and clinical evidence; the advantages of MRI; and the value of PCA3 testing. Financial issues and the absence of SelectMDx in professional guidelines and hospital care pathways also hampered its use. Conclusion: Eliciting users' and nonusers' views is important to better understand how biomarker tests can be embedded in clinical practice.

Mapping genetic variability in mature miRNAs and miRNA binding sites in prostate cancer

MicroRNAs (miRNAs) regulate diverse cancer hallmarks through sequence-specific regulation of gene expression, so genetic variability in their seed sequences or target sites could be responsible for cancer initiation or progression. While several efforts have been made to predict the locations of single nucleotide variants (SNVs) at miRNA target sites and associate them with cancer risk and susceptibility, there have been few direct assessments of SNVs in both mature miRNAs and their target sites to assess their impact on miRNA function in cancers. Using genome-wide target capture of miRNAs and miRNA-binding sites followed by deep sequencing in prostate cancer cell lines, here we identified prostate cancer-specific SNVs in mature miRNAs and their target binding sites. SNV rs9860655 in the mature sequence of miR-570 was not present in benign prostate hyperplasia (BPH) tissue or cell lines but was detectable in clinical prostate cancer tissue samples and adjacent normal tissue. SLC45A3 (prostein), a putative oncogene target of miR-1178, was highly upregulated in PC3 cells harboring an miR-1178 seed sequence SNV. Finally, systematic assessment of losses and gains of miRNA targets through 3'UTR SNVs revealed SNV-associated changes in target oncogene and tumor suppressor gene expression that might be associated with prostate carcinogenesis. Further work is required to systematically assess the functional effects of miRNA SNVs.

Biomarkers for prostate cancer: prostate-specific antigen and beyond

In recent years, several new biomarkers supplementing the role of prostate-specific antigen (PSA) have become available for men with prostate cancer. Although widely used in an ad hoc manner, the role of PSA in screening asymptomatic men for prostate cancer is controversial. Several expert panels, however, have recently recommended limited PSA screening following informed consent in average-risk men, aged 55-69 years. As a screening test for prostate cancer however, PSA has limited specificity and leads to overdiagnosis which in turn results in overtreatment. To increase specificity and reduce the number of unnecessary biopsies, biomarkers such as percent free PSA, prostate health index (PHI) or the 4K score may be used, while Progensa PCA3 may be measured to reduce the number of repeat biopsies in men with a previously negative biopsy. In addition to its role in screening, PSA is also widely used in the management of patients with diagnosed prostate cancer such as in surveillance following diagnosis, monitoring response to therapy and in combination with both clinical and histological criteria in risk stratification for recurrence. For determining aggressiveness and predicting outcome, especially in low- or intermediate-risk men, tissue-based multigene tests such as Decipher, Oncotype DX (Prostate), Prolaris and ProMark, may be used. Emerging therapy predictive biomarkers include AR-V7 for predicting lack of response to specific anti-androgens (enzalutamide, abiraterone), BRAC1/2 mutations for predicting benefit from PARP inhibitor and PORTOS for predicting benefit from radiotherapy. With the increased availability of multiple biomarkers, personalised treatment for men with prostate cancer is finally on the horizon.

Review of novel tissue-based biomarkers for prostate cancer: towards personalised and targeted medicine

Background:

Prostate cancer is the most commonly diagnosed cancer in men and responsible for about 10% of all cancer mortality in both Canadian and American men. Currently, serum PSA level is the most commonly used test for the detection of prostate cancer, though the levels can also be elevated in benign conditions, has limited specificity and has a high rate of overdiagnosis and treatment of indolent disease. Consequently, in recent years, several investigations have been conducted to identify novel cancer biomarkers capable of both effective screening and diagnosis, as well as assisting to shift the diagnostic and treatment paradigm of prostate cancer towards more patient-specific and targeted medicine. The goal of this narrative review paper is to describe eleven novel and promising tissue-based biomarkers for prostate cancer capable to account for individual patient variabilities and have the potential for risk assessment, early detection and diagnosis, identification of patients who will benefit from a particular treatment and monitoring patient response to treatment.

Materials and methods:

We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on tissue-based biomarkers for screening and early diagnosis, treatment and monitoring of prostate cancer.

Conclusions:

Emerging prostate cancer biomarkers have the potential to guide clinical decision-making since they have the potential to detect the disease early, measure the risk of developing the disease and the risk of progression, provide accurate information of patient response to a specific treatment and are capable of informing clinicians about the likely outcome of a cancer diagnosis independent of the treatment received. Therefore, the future holds promise for personalised and targeted medicine from prevention to diagnosis and treatment that considers the individual patient’s variabilities in the management of prostate cancer.

Clinical implications of genomic evaluations for prostate cancer risk stratification, screening, and treatment: a narrative review

New classification systems based on molecular features have been introduced to improve precision medicine for prostate cancer (PCa). This review covers the increasing risk of PCa and the differences in response to targeted therapy that are related to specific gene variations. We believe that genomic evaluations will be useful for guiding PCa risk stratification, screening, and treatment. We searched the PubMed and MEDLINE databases for articles related to genomic testing for PCa that were published in 2020 or earlier. There is increasing evidence that germline mutations in DNA repair genes, such as BRCA1/2 or ATM, are closely related to the development and aggressiveness of PCa. Targeted prostate-specific antigen screening based on the presence of germline alterations in DNA repair genes is recommend to achieve an early diagnosis of PCa. In cases of localized PCa, even if it has a favorable risk classification, patients under active surveillance with these gene alterations are likely to develop aggressive PCa. Thus, active treatment may be preferable to active surveillance for these patients. In cases of metastatic castration–resistant PCa, BRCA1/2 and DNA mismatch repair genes may be useful biomarkers for predicting the response to androgen receptor–targeting agents, poly (ADP-ribose) polymerase inhibitors, platinum chemotherapy, prostate-specific membrane antigen–targeted therapy, immunotherapy, and radium-223. Genomic evaluations may allow for risk stratification of patients with PCa based on their molecular features, which may help guide precision medicine for treating PCa.

Expression of AR-V7 (Androgen Receptor Variant 7) Protein in Granular Cytoplasmic Structures Is an Independent Prognostic Factor in Prostate Cancer Patients

Simple Summary

The expression of the androgen receptor (AR) and its splice variant AR-V7 is crucial for prostate cancer (PCa) biology. An immunohistochemical staining was performed on a tissue microarray with specimens from 410 PCa patients. AR staining, neither in the nucleus nor in the cytoplasm was associated with prognosis. AR-V7 staining of the general cytoplasm was associated with a shorter relapse free survival (RFS), whereas AR-V7 staining of cytoplasmic granules was associated with a longer RFS. Further subgroup stratification for AR-V7 granular staining revealed it as an independent prognostic factor in younger patients (age ≤ 65), patients with negative CK20 staining and patients with perineural invasion. Altogether, AR-V7 protein detected in granular cytoplasmic structures is an independent prognostic factor for RFS in PCa patients.

Abstract

Prostate cancer (PCa) is the second most common cancer, causing morbidity and mortality among men world-wide. The expression of the androgen receptor (AR) and its splice variants is a crucial factor of prostate cancer biology that has not been comprehensively studied in PCa tumors. The aim of this study was to characterize the protein expression of the AR and its splice variant, AR-V7, and their subcellular distributions in PCa by immunohistochemistry and to correlate the results to the clinicopathological data and prognosis. Immunohistochemical staining for AR and AR-V7 was performed on a tissue microarray (TMA) with specimens from 410 PCa patients using an immunoreactive score (IRS) or only the percentage of AR-V7 staining in cytoplasmic granules. Nuclear or cytoplasmic AR staining was not associated with prognosis. AR-V7 staining was only occasionally observed in the nucleus. However, AR-V7 staining in the cytoplasm or in cytoplasmic granules was associated with relapse-free survival (RFS). AR-V7 staining of the cytoplasm was associated with a shorter RFS, whereas AR-V7 staining of cytoplasmic granules was associated with a longer RFS. In a multivariate Cox’s regression analysis, only negative (<5%) AR-V7 staining of cytoplasmic granules remained an independent prognostic factor for RFS (HR = 5.3; p = 0.006). In a further subgroup analysis by multivariate Cox’s regression analysis, AR-V7 was an independent prognostic factor in the following groups: age ≤ 65 (HR = 9.7; p = 0.029), negative CK20 staining (HR = 7.0; p = 0.008), and positive perineural invasion (HR = 3.7; p = 0.034). Altogether, AR-V7 protein in granular cytoplasmic structures is an independent prognostic factor for RFS in PCa patients.

Clinical implication of prognostic and predictive biomarkers for castration-resistant prostate cancer: a systematic review

Background

Diagnosis of metastatic castrate resistant prostate cancer (mCRPC) with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment. There are a number of prognostic biomarkers for CRPC, but there are no validated predictive biomarkers to guide in clinical decision-making. Specific biomarkers are needed that enable to understand the natural history and complex biology of this heterogeneous malignancy, identify early response to treatment outcomes and to identify the population of men most likely to benefit from the treatment. In this systematic review, we discuss the existing literature for the role of biomarkers in CRPC and how they aid in the prognosis, treatment selection and survival outcomes.

Methods

We performed a literature search on PubMed and EMBASE databases from January 2015 through February 2020 in accordance to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Articles were assessed to identify relevant observational studies and randomized controlled trials regarding biomarkers which aid in identifying progression to mCRPC as well as predictive biomarkers which help in treatment selection.

Results

We identified 3640 number of hits of which 58 articles were found to be relevant. Here we addressed biomarkers in the context of prognosis, prediction and patient selection of therapy. These biomarkers were found to be effective as prognostic or predictive factors under variety of conditions. The higher levels for all these biomarkers were associated with shorter median OS and sometimes PFS. Lower amounts of biomarkers in serum or urine were associated with prolonged survival outcomes, longer time to CRPC development or CRPC progression and longer median follow-up irrespective of any therapy.

Conclusion

We observed that the biomarkers included in our study predicted clinically relevant survival outcomes and treatment exposure. Though the current biomarkers are prognostic when measured prior to initiating treatment, not all are validated as predictive markers in post treatment setting. A greater understanding of biomarkers in CRPC is need of the hour for development of more personalized approach to maximize benefit and minimize harm in men with CRPC.

PSA-based machine learning model improves prostate cancer risk stratification in a screening population

CONTEXT

The majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and biochemical adjuncts to PSA may improve prostate cancer risk stratification. We aimed to develop a contemporary, accurate and cost-effective model based on objective measures to improve the accuracy of prostate cancer risk stratification.

METHODS

Data were collated from a local institution and combined with patient data retrieved from the Prostate, Lung, Colorectal and Ovarian Cancer screening Trial (PLCO) database. Using a dataset of 4548 patients, a machine learning model was developed and trained using PSA, free-PSA, age and free-PSA to total PSA (FTR) ratio.

RESULTS

The model was trained on a dataset involving 3638 patients and was then tested on a separate set of 910 patients. The model improved prediction for prostate cancer (AUC 0.72) compared to PSA alone (AUC 0.63), age (AUC 0.52), free-PSA (AUC 0.50) and FTR alone (AUC 0.65). When an operating point is chosen such that the sensitivity of the model is 80% the specificity of the model is 45.3%. The benefit in AUC secondary to the model was related to sample size, with AUC of 0.64 observed when a subset of the cohort was assessed.

CONCLUSIONS

Development of a dense neural network model improved the diagnostic accuracy in screening for prostate cancer. These results demonstrate an additional utility of machine learning methods in prostate cancer risk stratification when using biochemical parameters.

ProstaTrend-A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is the most prevalent solid cancer among men in Western Countries. The clinical behavior of localized PCa is highly variable. Some cancers are aggressive leading to death, while others can even be monitored safely. Hence, there is a high clinical need for precise biomarkers for identification of aggressive disease in addition to established clinical parameters.

OBJECTIVE

To develop an RNA expression-based score for the prediction of PCa prognosis that facilitates clinical decision making.

DESIGN, SETTING, AND PARTICIPANTS

We assessed 233 tissue specimens of PCa patients with long-term follow-up data from fresh-frozen radical prostatectomies (RPs), from formalin-fixed and paraffin-embedded RP specimens and biopsies by transcriptome-wide next-generation sequencing and customized expression microarrays.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We applied Cox proportional hazard models to the cohorts from different platforms and specimen types. Evidence from these models was combined by fixed-effect meta-analysis to identify genes predictive of the time to death of disease (DoD). Genes were combined by a weighted median approach into a prognostic score called ProstaTrend and transferred for the prediction of biochemical recurrence (BCR) after RP in an independent cohort of The Cancer Genome Atlas (TCGA).

RESULTS AND LIMITATIONS

ProstaTrend comprising ∼1400 genes was significantly associated with DoD in the training cohort of PCa patients treated by RP (leave-one-out cross-validation, Cox regression: p=2e-09) and with BCR in the TCGA validation cohort (Cox regression: p=3e-06). The prognostic impact persisted after multivariable Cox regression analysis adjusting for Gleason grading group (GG) ≥3 and resection status (p=0.001; DoD, training cohort) and for GG≥3, pathological stage ≥T3, and resection state (p=0.037; BCR, validation cohort).

CONCLUSIONS

ProstaTrend is a transcriptome-based score that predicts DoD and BCR in cohorts of PCa patients treated with RP.

PATIENT SUMMARY

ProstaTrend provides molecular patient risk stratification after radical prostatectomy.

MiRNA-Based Inspired Approach in Diagnosis of Prostate Cancer

Prostate cancer is one of the most encountered cancer diseases in men worldwide and in consequence it requires the improvement of therapeutic strategies. For the clinical diagnosis, the standard approach is represented by solid biopsy. From a surgical point of view, this technique represents an invasive procedure that may imply several postoperative complications. To overcome these impediments, many trends are focusing on developing liquid biopsy assays and on implementing them in clinical practice. Liquid samples (blood, urine) are rich in analytes, especially in transcriptomic information provided by genetic markers. Additionally, molecular characterization regarding microRNAs content reveals outstanding prospects in understanding cancer progression mechanisms. Moreover, these analytes have great potential for prostate cancer early detection, more accurate prostate cancer staging and also for decision making respecting therapy schemes. However, there are still questionable topics and more research is needed to standardize liquid biopsy-based techniques.

Potential Salivary mRNA Biomarkers for Early Detection of Oral Cancer

We evaluated potential biomarkers in human whole saliva for the early diagnosis of oral squamous cell carcinoma (OSCC). We selected 30 candidate genes with relevance to cancer from recent reports in PubMed. Saliva samples were obtained from 34 non-tumor control and 33 OSCC patients. Real-time PCR was performed, and mRNA levels were compared. Normalized mRNA levels of six genes (NGFI-A binding protein 2 (NAB2), cytochrome P450, family 27, subfamily A, polypeptide 1 (CYP27A1), nuclear pore complex interacting protein family, member B4 (NPIPB4), monoamine oxidase B (MAOB), sialic acid acetyltransferase (SIAE), and collagen, type III, alpha 1 (COL3A1)) were significantly lower in saliva of OSCC patients. Receiver operating characteristics (ROC) analysis was used to individually evaluate the predictive power of the potential biomarkers for OSCC diagnosis. The area under the curve (AUC) values were evaluated for the OSCC vs. non-tumor groups via univariate ROC analyses, as well as multivariate ROC analyses of combinations of multiple potential biomarkers. The combination of CYP27A1 + SIAE showed a favorable AUC value of 0.84. When we divided saliva samples into two groups according to age using a 60-year cut-off, with OSCC patients and controls evaluated together, the AUC of MAOB-NAB2 was more predictive of OSCC in the under-60 group (AUC, 0.91; sensitivity, 0.92; and specificity, 0.86) than any other gene combination. These results are expected to aid the early diagnosis of OSCC, especially in patients under 60 years of age. While more studies with larger numbers of patients are necessary, our result suggest that salivary mRNA would be a potent biomarker for early OSCC diagnosis.

Preoperative prostate health index and %p2PSA as the significant biomarkers of postoperative pathological outcomes of prostate cancer in Korean males: A prospective multi-institutional study

Purpose

To evaluate the clinical utility of percentage of serum prostate-specific antigen (proPSA) to free PSA (%p2PSA) and the prostate health index (PHI) for predicting aggressive pathological outcomes of radical prostatectomy (RP) in Korean males.

Materials and Methods

This prospective observational multicenter study included 160 Korean males who consecutively underwent RP. The predictive utility of preoperative %p2PSA and PHI for predicting the following pathological outcomes of RP including pT3 disease, pathologic Gleason sum ≥7, and Gleason sum upgrading was investigated using multivariate and decision-curve analyses.

Results

The PHI and %p2PSA levels were significantly higher in patients with pT3 disease, pathologic Gleason sum ≥7, and Gleason sum upgrading. On univariate analysis, PHI was an accurate predictor of pT3 disease, pathologic Gleason sum ≥7, and Gleason sum upgrading. Multivariate and decision curve analyses revealed that inclusion of PHI to a base multivariate model including total PSA, percentage free PSA, PSA density, percentage of positive biopsy core, biopsy Gleason sum, and clinical stage factors significantly increased its predictive accuracy; %p2PSA showed a similar result. However, PHI was a more valuable predictor of pathological outcomes of RP.

Conclusions

This study revealed PHI and %p2PSA as preoperative biomarkers of pathological outcomes in Korean males who underwent RP for prostate cancer.

Expression of GP88 (Progranulin) Protein Is an Independent Prognostic Factor in Prostate Cancer Patients

Prostate cancer, the second most common cancer, is still a major cause of morbidity and mortality among men worldwide. The expression of the survival and proliferation factor progranulin (GP88) has not yet been comprehensively studied in PCa tumors. The aim of this study was to characterize GP88 protein expression in PCa by immunohistochemistry and to correlate the findings to the clinico-pathological data and prognosis. Immunohistochemical staining for GP88 was performed by TMA with samples from 442 PCa patients using an immunoreactive score (IRS). Altogether, 233 cases (52.7%) with negative GP88 staining (IRS < 2) and 209 cases (47.3%) with positive GP88 staining (IRS ≥ 2) were analyzed. A significant positive correlation was found for the GP88 IRS with the PSA value at prostatectomy and the cytoplasmic cytokeratin 20 IRS, whereas it was negatively associated with follow-up times. The association of GP88 staining with prognosis was further studied by survival analyses (Kaplan-Meier, univariate and multivariate Cox's regression analysis). Increased GP88 protein expression appeared as an independent prognostic factor for overall, disease-specific and relapse-free survival in all PCa patients. Interestingly, in the subgroup of younger PCa patients (≤65 years), GP88 positivity was associated with a 3.8-fold (p = 0.004), a 6.0-fold (p = 0.008) and a 3.7-fold (p = 0.003) increased risk for death, disease-specific death and occurrence of a relapse, respectively. In the PCa subgroup with negative CK20 staining, GP88 positivity was associated with a 1.8-fold (p = 0.018) and a 2.8-fold increased risk for death and disease-specific death (p = 0.028). Altogether, GP88 protein positivity appears to be an independent prognostic factor for PCa patients.

Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence

BACKGROUND

As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression.

METHODS

We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes.

RESULTS

After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays.

CONCLUSIONS

Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.

Plasma MicroRNA as a novel diagnostic

MicroRNAs (miRNAs) are small, single-stranded, endogenous, non-coding RNAs necessary for proper gene expression. Their mechanism of action controls translation by base-pairing with target messenger RNA (mRNAs) thus leading to translation blockage or mRNA degradation. Many studies have shown that miRNAs play pivotal roles in cancer, cardiovascular disease and neurodegenerative disorders. The lack of blood-derived biomarkers and those markers of poor specificity and sensitivity significantly impact the ability to diagnose in general and at early disease stage specifically. As such, new, non-invasive and quantifiable biomarkers are needed. As post-transcriptional regulators of gene expression, miRNAs have been confirmed to be notably stable in cells, tissues and body fluids. These and other advantages make miRNAs ideal candidates as potential biomarkers and early experimental findings support this finding. This review examines the use of miRNAs as biomarkers in cancer, neurodegenerative, cardiovascular and liver disease and viral infection.

A critical appraisal of biomarkers in prostate cancer

PURPOSE

A number of urine and blood-based biomarker tests have been described for prostate cancer, although to date there has only been a limited exploration of the methodology behind the validation studies that underpin these tests.

METHODS

In this review, a selection of commercially available urine and blood-based biomarker tests for prostate cancer are described, and the underlying key validation studies for each test are critically appraised using the Standards for Reporting Diagnostic Accuracy (STARD) 2015 statement.

RESULTS

The ExoDx Prostate Intelliscore, SelectMDx, Progensa PCA3, Mi-Prostate Score, 4K Score, and Prostate Health Index (PHI) tests were reviewed. Most of the validation studies supporting these tests perform exploratory analyses to determine cut-off values in a post hoc manner, comprise cohorts that are primarily Caucasian, report receiver operating characteristic curves that combine the biomarker's result with established clinical nomograms and are based on a reference standard (prostate biopsy) that lacks central pathology review. Deficiencies in STARD reporting guidelines include frequent failure to provide a published study protocol, prospective study registration in a registry, a flow diagram, justification for sample size determination, a discussion of adverse events with testing, and information on how missing or indeterminate test results should be managed.

CONCLUSIONS

Key validation studies that support many commercially available urine and blood-based biomarkers for prostate cancers have deficiencies in transparency based on STARD reporting guidelines, and limitations in methodology must be considered when deciding when these tests should be applied in clinical practice.

Tissue microarray analysis delineate potential prognostic role of Annexin A7 in prostate cancer progression

Background

Annexin A7 (ANXA7) is a member of the multifunctional calcium or phospholipid-binding annexin gene family. While low levels of ANXA7 are associated with aggressive types of cancer, the clinical impact of ANXA7 in prostate cancer remains unclear. Tissue microarrays (TMA) have revealed several new molecular markers in human tumors. Herein, we have identified the prognostic impact of ANXA7 in a prostate cancer using a tissue microarray containing 637 different specimens.

Methods

The patients were diagnosed with prostate cancer and long-term follow-up information on progression (median 5.3 years), tumor-specific and overall survival data (median 5.9 years) were available. Expression of Ki67, Bcl-2, p53, CD-10 (neutral endopeptidase), syndecan-1 (CD-138) and ANXA7 were analyzed by immunohistochemistry.

Results

A bimodal distribution of ANXA7 was observed. Tumors expressing either high or no ANXA7 were found to be associated with poor prognosis. However, ANXA7 at an optimal level, in between high and no ANXA7 expression, had a better prognosis. This correlated with low Ki67, Bcl-2, p53 and high syndecan-1 which are known predictors of early recurrence. At Gleason grade 3, ANXA7 is an independent predictor of poor overall survival with a p-value of 0.003. Neoadjuvant hormonal therapy, which is known to be associated with overexpression of Bcl-2 and inhibition of Ki67 LI and CD-10, was found to be associated with under-expression of ANXA7.

Conclusions

The results of this TMA study identified ANXA7 as a new prognostic factor and indicates a bimodal correlation to tumor progression.

Long noncoding RNA gastric cancer-associated transcript 3 plays oncogenic roles in glioma through sponging miR-3127-5p

Evidence is emerging that long noncoding RNAs (lncRNAs) play vital roles in tumorigenesis. LncRNA gastric cancer-associated transcript 3 (GACAT3) is reported to participate in the development of breast cancer, colorectal cancer, nonsmall cell lung cancer, and gastric cancer. However, whether it is implicated in glioma has not been elucidated. Here, we found that GACAT3 level was aberrantly elevated in glioma tissues and cell lines. Higher GACAT3 expression predicted lower survival rate. Knockdown of GACAT3 suppressed the proliferation, colony formation, migration, and invasion but promoting apoptosis in glioma cells. Next, we determined that GACAT3 contributes to glioma progression through inhibiting microRNA (miR)-3127-5p. Subsequently, ELAVL1 was identified as a direct target of miR-3127-5p by bioinformatics analysis and luciferase reporter assay. Moreover, we confirmed that GACAT3 promoted ELAVL1 expression through sponging miR-3127-5p, leading to glioma progression. Taken together, our study elucidated that GACAT3/miR-3127-5p/ELAVL1 signaling regulates glioma development and might be a promising therapeutic target.

The Role of Immunohistochemical Analysis as a Tool for the Diagnosis, Prognostic Evaluation and Treatment of Prostate Cancer: A Systematic Review of the Literature

Background: Prostate cancer (PCa) is a heterogeneous disease that lends itself toward numerous therapeutic options depending on its risk stratification. One of the greatest challenges in PCa urologic practice is to select patients who should be referred for biopsy and, for those patients who are diagnosed with cancer, to differentiate between patients with indolent disease from those with an unfavorable prognosis and, to determine ideal patient management and avoid unnecessary interventions. Accordingly, there is a growing body of literature reporting immunohistochemical studies with the objective of determining a prostate cancer prognosis. Among the most frequent biomarkers studied are Ki-67, p53, PTEN, MYC, and ERG. Based on these findings, we systematically reviewed articles that assessed the role of these main prognostic markers in prostate cancer.

Methods: Consistent with PRISMA guidelines, we performed a systematic literature search throughout the Web of Science and PubMed Medline databases. We considered all types of studies evaluating the role of Ki-67, p53, PTEN, MYC, and ERG immunohistochemical analysis in prostate cancer until July 2017.

Results: We identified 361 articles, 44 of which were summarized in this review. Diagnostically, no single immunohistochemical marker was able to define a tumor as benign or malignant. Prognostically, Ki-67, p53, and MYC were related to the tumor grade given by Gleason score and to the tumor stage (higher levels related to higher tumor grade). Furthermore, Ki-67 was also related to higher PSA levels, shorter disease-free intervals and shorter tumor-specific survival; the latter was also related to p53. The loss of PTEN protein expression showed a higher association with biochemical recurrence and with a worse prognosis, beyond that predicted by the Gleason score and tumor stage. ERG staining also showed a strong association with biochemical recurrence.

Conclusion: There are several studies relating immunohistochemical markers with clinical-laboratorial outcomes in prostate cancer, the most frequent being Ki-67, p53, ERG, PTEN, and MYC. However, none of these markers have been validated by literary consensus to be routinely applied in medical practice.

Are localized prostate cancer biomarkers useful in the clinical practice?

Prostate cancer presents itself in a heterogeneous way with both aggressive and indolent forms. Despite the controversy surrounding its use, prostate-specific antigen screening ultimately leads to a greater number of diagnosed patients. One of the biggest challenges in clinical practice is to select the right patients for biopsy and, among diagnosed patients, to differentiate tumors with an indolent course from those with an unfavorable prognosis, in order to determine the best therapeutic decision for each case, avoiding unnecessary interventions. Currently, several types of biomarkers are available for clinical use in patients with prostate cancer, which include blood-based (prostate-specific antigen, Prostate Health Index®, 4K score®); urine sample-based (PCA3, SelectMDx®, ExoDx Prostate IntelliScore®); and biopsy, transurethral resection, or radical prostatectomy tissue-based (ConfirmMDx®, Oncotype®, Prolaris®, Decipher®). The aim of this review is to provide an overview of the current state of evidence and to highlight recent advances in the evaluation and diagnosis of prostate cancer, with emphasis on biomarkers related to diagnosis and to prognostic evaluation of localized prostate cancer.

The Use of Magnetic Resonance Imaging in the Prostate Cancer Primary Diagnostic Pathway: Is It Ready for Primetime?

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly utilized in the prostate cancer diagnostic landscape over the last five years. The majority of the literature has focused on its use in men with a previous negative biopsy. However, over time, clinicians have begun using mpMRI in the work-up of men being considered for primary biopsy and subsequently data characterizing its diagnostic performance in this setting is emerging. This review comprehensively assesses the utility of mpMRI in the primary biopsy setting.

The Use of Magnetic Resonance Imaging in the Prostate Cancer Primary Diagnostic Pathway: Is It Ready for Primetime?

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly utilized in the prostate cancer diagnostic landscape over the last five years. The majority of the literature has focused on its use in men with a previous negative biopsy. However, over time, clinicians have begun using mpMRI in the work-up of men being considered for primary biopsy and subsequently data characterizing its diagnostic performance in this setting is emerging. This review comprehensively assesses the utility of mpMRI in the primary biopsy setting.

Urinary markers aiding in the detection and risk stratification of prostate cancer

The purpose of this review is to highlight the role of existing and promising urinary biomarkers for the detection and prognostication of prostate cancer (PCa). A number of novel urinary biomarkers have been introduced into the clinical space, which in combination with clinical variables, have demonstrated an increased ability to select patients for biopsy and identify men at risk of harboring clinically significant PCa. Though a number of assays require further validation, initial data is promising and forthcoming results will ultimately determine their clinical utility and commercial availability. For the past 30 years, first-line screening for PCa has relied on measurement of serum prostate-specific antigen (PSA) levels and the results from a digital rectal exam (DRE). A large body of evidence from the last 3 decades indicates that these screening methods are problematic, and often inadequate for detecting clinically significant PCa. Extensive efforts have recently been made to identify and commercialize novel PCa biomarkers for more effective detection of PCa, either alone or in combination with current screening methods. This review article highlights problems with current screening standards, and discusses 6 urinary biomarker assays in terms of their ability to detect and risk-stratify PCa: prostate cancer antigen 3 (PCA3), TMPRSS2-ERG, second chromosome locus associated with prostate-1 (SChLAP1), ExoDx, SelectMDx, and Michigan Prostate Score (MiPS).

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

We report here numerous novel genes and multiple new signatures which robustly predict prostate cancer (PC) recurrence. We extracted 696 differentially expressed genes relative to a reported PC signature from the TCGA dataset (n = 492) and built a 15‐gene signature (SigMuc1NW) using Elastic‐net with 10‐fold cross‐validation through analyzing their expressions at 1.5 standard deviation/SD below and 2 SD above a population mean. SigMuc1NW predicts biochemical recurrence (BCR) following surgery with 56.4% sensitivity, 72.6% specificity, and 63.24 median months disease free (MMDF) (P = 1.12e‐12). The prediction accuracy is improved with the use of SigMuc1NW's cutpoint (P = 3e‐15) and is further enhanced (sensitivity 67%, specificity 75.7%, MMDF 45.2, P = 0) when all 15 genes were analyzed through their cutpoints instead of their SDs. These genes individually associate with BCR using either SD or cutpoint as the cutoff points. Eight of 15 genes are individual risk factors after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. Eleven of 15 genes are novel to PC. SigMuc1NW discriminates BCR with time‐dependent AUC (tAUC) values of 76.6% at 11.5 months (76.6%–11.5 m), 73.8%‐22.3 m, 78.5%‐32.1 m, and 76.4%–48.4 m. SigMuc1NW is correlated with adverse features of PC, high Gleason scores (odds ratio/OR 1.48, P < 2e‐16), and advanced tumor stages (OR 1.33, P = 4.37e‐13). SigMuc1NW remains an independent risk factor of BCR (HR 2.44, 95% CI 1.53–3.87, P = 1.62e‐4) after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. In an independent PC (MSKCC) cohort (n = 140), these 15 genes were altered in PC vs normal tissue, metastatic PCs vs primary PCs, and recurrent PCs vs nonrecurrent PCs. Importantly, a 10‐gene subsignature SigMuc1NW1 predicts BCR in MSKCC (P = 3.11e‐15) and TCGA (P = 3.13e‐12); SigMuc1NW1 discriminates BCR at 18.4 m with tAUC as 82.5%. Collectively, our analyses support SigMuc1NW as a novel and robust signature in predicting BCR of PC.

Prostate Cancers Detected by Magnetic Resonance Imaging–Targeted Biopsies Have a Higher Percentage of Gleason Pattern 4 Component and Are Less Likely to Be Upgraded in Radical Prostatectomies

Context.—

In Gleason score (GS) 7 prostate cancers, the quantity of Gleason pattern 4 (GP 4) is an important prognostic factor and influences treatment decisions. Magnetic resonance imaging (MRI)–targeted biopsy has been increasingly used in clinical practice.

Objective.—

To investigate whether MRI-targeted biopsy may detect GS 7 prostate cancer with greater GP 4 quantity, and whether it improves biopsy/radical prostatectomy GS concordance.

Design.—

A total of 243 patients with paired standard and MRI-targeted biopsies with cancer in either standard or targeted or both were studied, 65 of whom had subsequent radical prostatectomy. The biopsy findings, including GS and tumor volume, were correlated with the radical prostatectomy findings.

Results.—

More prostate cancers detected by MRI-targeted biopsy were GS 7 or higher. Mean GP 4 percentage in GS 7 cancers was 31.0% ± 29.3% by MRI-targeted biopsy versus 25.1% ± 29.5% by standard biopsy. A total of 122 of 218 (56.0%) and 96 of 217 (44.2%) prostate cancers diagnosed on targeted biopsy and standard biopsy, respectively, had a GP 4 of 10% or greater (P = .01). Gleason upgrading was seen in 12 of 59 cases (20.3%) from MRI-targeted biopsy and in 24 of 57 cases (42.1%) from standard biopsy (P = .01). Gleason upgrading correlated with the biopsy cancer volume inversely and GP 4 of 30% or less in standard biopsy. Such correlation was not found in MRI-targeted biopsy.

Conclusions.—

Magnetic resonance imaging–targeted biopsy may detect more aggressive prostate cancers and reduce the risk of Gleason upgrading in radical prostatectomy. This study supports a potential role for MRI-targeted biopsy in the workup of prostate cancer and inclusion of percentage of GP 4 in prostate biopsy reports.

A Plasma Biomarker Panel of Four MicroRNAs for the Diagnosis of Prostate Cancer

Prostate cancer is diagnosed in over 1 million men every year globally, yet current diagnostic modalities are inadequate for identification of significant cancer and more reliable early diagnostic biomarkers are necessary for improved clinical management of prostate cancer patients. MicroRNAs (miRNAs) modulate important cellular processes/pathways contributing to cancer and are stably present in body fluids. In this study we profiled 372 cancer-associated miRNAs in plasma collected before (~60% patients) and after/during commencement of treatment (~40% patients), from age-matched prostate cancer patients and healthy controls, and observed elevated levels of 4 miRNAs - miR-4289, miR-326, miR-152-3p and miR-98-5p, which were validated in an independent cohort. The miRNA panel was able to differentiate between prostate cancer patients and controls (AUC = 0.88). Analysis of published miRNA transcriptomic data from clinical samples demonstrated low expression of miR-152-3p in tumour compared to adjacent non-malignant tissues. Overexpression of miR-152-3p increased proliferation and migration of prostate cancer cells, suggesting a role for this miRNA in prostate cancer pathogenesis, a concept that was supported by pathway analysis of predicted miR-152-3p target genes. In summary, a four miRNA panel, including miR-152-3p which likely targets genes with key roles in prostate cancer pathogenesis, has the potential to improve early prostate cancer diagnosis.

Obtaining high quality transcriptome data from formalin-fixed, paraffin-embedded diagnostic prostate tumor specimens

Prognostic genomic biomarkers that can be measured at diagnosis to aid choice of treatment options are unavailable for most common cancers. This is due in part to the poor quality and quantity of available diagnostic specimens for discovery research and to limitations in genomic technologies. Recent technical advances now enable high-density molecular analyses using suboptimal biological specimens. Here we describe the optimization of a transcriptome-specific protocol for use with formalin-fixed, paraffin-embedded (FFPE) diagnostic prostate cancer (PrCa) specimens. We applied the Ion AmpliSeq Transcriptome Human Gene Expression Kit (AmpliSeq Kit) to RNA samples extracted from 36 tumor-enriched and 16 adjacent normal tissues (ADJ_NT) from 37 FFPE PrCa specimens over a series of eight pilot studies, incorporating protocol modifications from Pilots 2 to 5. Data quality were measured by (1) the total number of mapped reads; (2) the percentage of reads that mapped to AmpliSeq target regions (OnTarget%); (3) the percentage of genes on the AmpliSeq panel with a read count ≥10 (TargetsDetected%); and (4) comparing the gene read-count distribution of the prostate tissue samples with the median gene read-count distribution of cell line-derived RNA samples. Modifications incorporated into Pilot study 5 provided gene expression data equivalent to cell line-derived RNA samples. These modifications included the use of freshly cut slides for macrodissection; increased tissue section thickness (8 µm); RNA extraction using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (ThermoFisher); 18 target amplification cycles; and processing six samples per Ion PI chip. This protocol will facilitate the discovery of prognostic biomarkers for cancer by allowing researchers to exploit previously underutilized diagnostic FFPE specimens.

Prostate irradiation with focal dose escalation to the intraprostatic dominant nodule: a systematic review

Radiation therapy (RT) is a curative treatment option for localized prostate cancer. Prostate irradiation with focal dose escalation to the intraprostatic dominant nodule (IDN) is an emerging treatment option that involves the prophylactic irradiation of the whole prostate while increasing RT doses to the visible prostatic tumor. Because of the lack of large multicentre trials, a systematic review was performed in an attempt to get an overview on the feasibility and efficacy of focal dose escalation to the IDN.

A bibliographic search for articles in English, which were listed in MEDLINE from 2000 to 2016 to identify publications on RT with focal directed boost to the IDN, was performed. The review was completed following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

Twenty-two articles describing 1,378 patients treated with RT using focal boost were identified and fulfilled the selection criteria. Intensity-modulated radiation therapy (IMRT) was used in 720 patients (52.3%), volumetric modulated arc therapy was used in 45 patients (3.3%), stereotactic body radiation therapy (SBRT) in 113 patients (8.2%), and low–dose rate and high–dose rate brachytherapy (BT) were used in 305 patients (22.1%) and 195 patients (14.1%), respectively. Use of androgen deprivation therapy varied substantially among series. Biochemical disease-free survival at 5 years was reported for a cohort of 812 (58.9%) patients. The combined median biochemical disease-free survival for this group of patients was 85% (range: 78.8–100%; 95% confidence interval: 77.1–82.7%).

The average occurrence of grade III or worse gastrointestinal and genitourinary late toxicity was, respectively, 2.5% and 3.1% for intensity-modulated RT boost, 10% and 6% for stereotactic body RT, 6% and 2% for low–dose rate BT, and 4% and 4.3% for high–dose rate BT.

This review shows encouraging results for focal dose escalation to the IDN with acceptable short- to medium-term side effects and biochemical disease control rates. However, owing to the heterogeneity of patient population and the short follow-up, the results should be interpreted with caution. Considering that the clinical endpoint in the studies was biochemical recurrence, the use and duration of androgen deprivation therapy administration should be carefully considered before driving definitive conclusions. Randomized trials with long-term follow-up are needed before this technique can be generally recommended.

Diagnostic Performance of %[-2]proPSA and Prostate Health Index for Prostate Cancer: Prospective, Multi-institutional Study

BACKGROUND

We evaluated the clinical performance of [-2]proPSA (p2PSA) and its derivatives in predicting the presence and aggressiveness of prostate cancer (PCa) in Korean men.

METHODS

A total of 246 men with total prostate-specific antigen (tPSA) ≥ 3.5 ng/mL who underwent their first prostate biopsy were included in this prospective, multicenter, observational study. Diagnostic accuracy of tPSA, free-to-total PSA ratio (%fPSA), p2PSA, %p2PSA, and the Beckman Coulter prostate health index (PHI) was assessed by receiver operating characteristic curve analyses and logistic regression analyses.

RESULTS

Overall, PCa was detected in 125 (50.8%) subjects. In men with tPSA 3.5-10 ng/mL, the detection rate of PCa was 39.4% (61/155). In this group, PHI and %p2PSA were the most accurate predictors of PCa and significantly outperformed tPSA and %fPSA; area under the curve for tPSA, %fPSA, %p2PSA, and PHI was 0.56, 0.69, 0.74, and 0.76, respectively. PHI was also the strongest predictor of PCa with Gleason score ≥ 7.

CONCLUSION

This study demonstrates the superior clinical performance of %p2PSA and PHI in predicting the presence and aggressiveness of PCa in Korean men. The %p2PSA and PHI appear to improve detection of PCa and provide prognostic information.

Prospective performance of the Prostate Health Index in prostate cancer detection in the first prostate biopsy of men with a total prostatic specific antigen of 4-10 ng/mL and negative digital rectal examination

Background

The disadvantage of using total serum prostatic specific antigen (PSA) test for detection of prostate cancer is that it has a low specificity. The low specificity of total PSA (tPSA) test leads to unnecessary prostate biopsies. In this prospective study, we assessed the serum tPSA, free PSA, p2PSA, and the Prostate Health Index (PHI) in the detection of prostate cancer in men with a tPSA of 4–10 ng/mL and a negative digital rectal examination (DRE).

Materials and methods

101 male outpatients with a serum PSA of 4–10 ng/mL and nonsuspicious DRE for prostate cancer who underwent first transrectal ultrasound with a prostate biopsy were recruited. A blood sample to enable tPSA, free PSA, and p2PSA levels to be calculated was drawn before the prostate biopsy. The diagnosis and detection of high-grade cancer are correlated with the blood sample.

Results

Sixteen patients were positive for prostate cancer. All had significantly higher serum 2pPSA and PHI levels than patients with no cancer. A PHI level at 90% sensitivity (cutoff of 34.14) demonstrated a higher area under the receiver operating characteristic curve and more specificity in diagnosis and detection of high-grade prostate cancer than other tests.

Conclusions

The PHI in men with a PSA level of 4–10 ng/mL with negative DRE increased specificity in the detection of prostate cancer. This test is useful in discriminating between patients with or without cancer and also enables the detection of high-grade cancer avoiding unnecessary biopsies.

New biomarkers for diagnosis and prognosis of localized prostate cancer

The diagnostics and management of localized prostate cancer is complicated because of cancer heterogeneity and differentiated progression in various subgroups of patients. As a prostate cancer biomarker, FDA-approved detection assay for serum prostate specific antigen (PSA) and its derivatives are not potent enough to diagnose prostate cancer, especially high-grade disease (Gleason ≥7). To date, a collection of new biomarkers was developed. Some of these markers are superior for primary screening while others are particularly helpful for cancer risk stratification, detection of high-grade cancer, and prediction of adverse events. Two of those markers such as proPSA (a part of the Prostate Health Index (PHI)) and prostate specific antigen 3 (PCA3) (a part of the PCA3 Progensa test) were recently approved by FDA for clinical use. Other markers are not PDA-approved yet but are available from Clinical Laboratory Improvement Amendment (CLIA)-certified clinical laboratories. In this review, we characterize diagnostic performance of these markers and their diagnostic and prognostic utility for prostate cancer.

Prostate Magnetic Resonance Imaging Provides Limited Incremental Value Over the Memorial Sloan Kettering Cancer Center Preradical Prostatectomy Nomogram

OBJECTIVE

To examine the incremental value of prostate magnetic resonance imaging (MRI) when used in combination with the currently available preoperative risk stratification tool, the Memorial Sloan Kettering Cancer Center (MSKCC) preradical prostatectomy nomogram.

MATERIALS AND METHODS

We reviewed our institutional database of prostate MRI performed before radical prostatectomy between December 2014 and March 2016 (n = 236). We generated a logistic regression model based on observed final pathology results and the MSKCC nomogram predictions for organ-confined disease, extracapsular extension (ECE), seminal vesicle invasion, and lymph node involvement (LNI) ("MSKCC only"). We then generated a combined regression model incorporating both the MSKCC nomogram prediction with the degree of prostate MRI suspicion ("MSKCC + MRI"). Receiver operating characteristic curves were generated, and the area under the curves (AUCs) were compared.

RESULTS

When independently examining the MSKCC nomogram predicted risk and the degree of prostate MRI suspicion, MRI was a predictor for ECE (odds ratio 2.8, P <.01) and LNI (odds ratio 5.6, P = .01). When examining the "MSKCC + MRI" and "MSKCC only" models, the incremental benefit in risk discrimination from the combined model ("MSKCC + MRI") was not significant for organ-confined disease, ECE, seminal vesicle invasion, or LNI (ΔAUC +0.03, P = .10; ΔAUC +0.03, P = .08; ΔAUC 0.63, P = .63; ΔAUC +0.04, P = .42; respectively).

CONCLUSION

A combined model with prostate MRI and the MSKCC nomogram provides no additional risk discrimination over the MSKCC nomogram-based model alone. Evaluation of prostate MRI as a predictive tool should be performed in combination with, not independent of, these clinical risk stratification models.

Evaluation of saliva and plasma cytokine biomarkers in patients with oral squamous cell carcinoma

The aim of this study was to investigate potential biomarkers in human saliva and plasma to aid in the early diagnosis of oral squamous cell carcinoma (OSCC). Saliva and plasma samples obtained from OSCC patients (n=41) and non-oral cancer patients (n=24) were analyzed by Luminex Bead-based Multiplex Assay. Data were analyzed using the non-parametric Mann-Whitney U-test, Kruskal-Wallis test, and receiver operating characteristics curve (ROC) to evaluate the predictive power of 14 biomarkers individually for OSCC diagnosis. The plasma level of IP-10 in early OSCC differed significantly from that in controls. Among the salivary biomarkers, IL-1β, IL-6, IL-8, MIP-1β, eotaxin and IFN-γ and TNF-α showed significant differences between OSCC patients and controls. With respect to carcinogenesis, significant differences in plasma levels of eotaxin, G-CSF, and IL-6 were found between OSCC stages III/IV and OSCC stages I/II. The area under the curve (AUC) for OSCC vs. control was greater than 0.7 for plasma IP-10 and saliva IL-1β, IL-6, IL-8, and TNF-α. The study findings indicate that salivary biomarkers may serve a useful role as a complementary adjunct for the early detection of oral OSCC. With regard to the evaluation of tumour progression, plasma eotaxin, G-CSF, and IL-6 may help in the detection of advanced OSCC. However, the correlation between saliva and plasma biomarkers in OSCC was weak.