The Use of Biomarkers in Prostate Cancer Screening and Treatment

Prostate cancer biomarkers: from early diagnosis to precision treatment

Prostate cancer (PCa) is the second most prevalent cancer in men. In 2020, approximately 1,414,259 new cases were reported that accounted for 3,75,324 deaths (Sung et al. in CA 71:209-249, 2021). PCa is often asymptomatic at early stages; hence, routine screening and monitoring based on reliable biomarkers is crucial for early detection and assessment of cancer progression. Early diagnosis of disease is key step in reducing PCa-induced mortality. Biomarkers such as PSA have played vital role in reducing recent PCa deaths. Recent research has identified many other biomarkers and also refined PSA-based tests for non-invasive diagnosis of PCa in patients. Despite progress in screening methods, an important issue that influences treatment is heterogeneity of the cancer in different individuals, necessitating personalized treatment. Currently, focus is to identify biomarkers that can accurately diagnose PCa at early stage, indicate the stage of the disease, metastatic nature and chances of survival based on individual patient profile (Fig. 1). Fig. 1 Graphical abstract.

Prostate Cancer Review: Genetics, Diagnosis, Treatment Options, and Alternative Approaches

Simple Summary

Prostate cancer affects men of all racial and ethnic groups and leads to higher rates of mortality in those belonging to a lower socioeconomic status due to late detection of the disease. There is growing evidence that suggests the contribution of an individual’s genetic profile to prostate cancer. Currently used prostate cancer treatments have serious adverse effects; therefore, new research is focusing on alternative treatment options such as the use of genetic biomarkers for targeted gene therapy, nanotechnology for controlled targeted treatment, and further exploring medicinal plants for new anticancer agents. In this review, we describe the recent advances in prostate cancer research.

Abstract

Prostate cancer is one of the malignancies that affects men and significantly contributes to increased mortality rates in men globally. Patients affected with prostate cancer present with either a localized or advanced disease. In this review, we aim to provide a holistic overview of prostate cancer, including the diagnosis of the disease, mutations leading to the onset and progression of the disease, and treatment options. Prostate cancer diagnoses include a digital rectal examination, prostate-specific antigen analysis, and prostate biopsies. Mutations in certain genes are linked to the onset, progression, and metastasis of the cancer. Treatment for localized prostate cancer encompasses active surveillance, ablative radiotherapy, and radical prostatectomy. Men who relapse or present metastatic prostate cancer receive androgen deprivation therapy (ADT), salvage radiotherapy, and chemotherapy. Currently, available treatment options are more effective when used as combination therapy; however, despite available treatment options, prostate cancer remains to be incurable. There has been ongoing research on finding and identifying other treatment approaches such as the use of traditional medicine, the application of nanotechnologies, and gene therapy to combat prostate cancer, drug resistance, as well as to reduce the adverse effects that come with current treatment options. In this article, we summarize the genes involved in prostate cancer, available treatment options, and current research on alternative treatment options.

A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data

There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, and ‘SoC’ (standard of care) clinical data models, alongside a fully integrated omics-model, deemed ‘ExoSpec’. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77−0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1−3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.

Validation of diagnostic nomograms based on CE-MS urinary biomarkers to detect clinically significant prostate cancer

PURPOSE

Prostate cancer (PCa) is one of the most common cancers and one of the leading causes of death worldwide. Thus, one major issue in PCa research is to accurately distinguish between indolent and clinically significant (csPCa) to reduce overdiagnosis and overtreatment. In this study, we aim to validate the usefulness of diagnostic nomograms (DN) to detect csPCa, based on previously published urinary biomarkers.

METHODS

Capillary electrophoresis/mass spectrometry was employed to validate a previously published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker (BM) model was assessed in diagnostic nomograms including prostate-specific antigen (PSA), PSA density and the risk calculator from the European Randomized Study of Screening. For this purpose, urine samples from 147 PCa patients were collected prior to prostate biopsy and before performing digital rectal examination (DRE). The 19-BM score was estimated via a support vector machine-based software based on the pre-defined cutoff criterion of - 0.07. DNs were subsequently developed to assess added value of integrative diagnostics.

RESULTS

Independent validation of the 19-BM resulted in an 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUC _PSA: 0.64), PSA density (AUC _PSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the rest clinical variables into distinct DN, resulted in improved (AUC range: 0.82-0.88) but not significantly better performances over 19-BM alone.

CONCLUSION

19-BM alone or upon integration with clinical variables into DN, might be useful for detecting csPCa by decreasing the number of biopsies.

The Role and Significance of Bioumoral Markers in Prostate Cancer

The prostate is one of the most clinically accessible internal organs of the genitourinary tract in men. For decades, the only method of screening for prostate cancer (PCa) has been digital rectal examination of 1990s significantly increased the incidence and prevalence of PCa and consequently the morbidity and mortality associated with this disease. In addition, the different types of oncology treatment methods have been linked to specific complications and side effects, which would affect the patient's quality of life. In the first two decades of the 21st century, over-detection and over-treatment of PCa patients has generated enormous costs for health systems, especially in Europe and the United States. The Prostate Specific Antigen (PSA) is still the most common and accessible screening blood test for PCa, but with low sensibility and specificity at lower values (<10 ng/mL). Therefore, in order to avoid unnecessary biopsies, several screening tests (blood, urine, or genetic) have been developed. This review analyzes the most used bioumoral markers for PCa screening and also those that could predict the evolution of metastases of patients diagnosed with PCa.

Exosomes as A Next-Generation Diagnostic and Therapeutic Tool in Prostate Cancer

Extracellular vesicles (EVs) have brought great momentum to the non-invasive liquid biopsy procedure for the detection, characterization, and monitoring of cancer. Despite the common use of PSA (prostate-specific antigen) as a biomarker for prostate cancer, there is an unmet need for a more specific diagnostic tool to detect tumor progression and recurrence. Exosomes, which are EVs that are released from all cells, play a large role in physiology and pathology, including cancer. They are involved in intercellular communication, immune function, and they are present in every bodily fluid studied—making them an excellent window into how cells are operating. With liquid biopsy, EVs can be isolated and analyzed, enabling an insight into a potential therapeutic value, serving as a vehicle for drugs or nucleic acids that have anti-neoplastic effects. The current application of advanced technology also points to higher-sensitivity detection methods that are minimally invasive. In this review, we discuss the current understanding of the significance of exosomes in prostate cancer and the potential diagnostic value of these EVs in disease progression.

Multiplexed Prostate Cancer Companion Diagnostic Devices

Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Since the early 1990s, Prostate-Specific Antigen (PSA) has been a commonly recognized PCa-associated protein biomarker. However, PSA testing has been shown to lack in specificity and sensitivity when needed to diagnose, monitor and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA, also known as multiplexing. If conventional methods such as the enzyme-linked immunosorbent assay (ELISA) are used, multiplexed detection of such protein biomarkers can result in an increase in the required sample volume, in the complexity of the analytical procedures, and in adding to the cost. Using companion diagnostic devices such as biosensors, which can be portable and cost-effective with multiplexing capacities, may address these limitations. This review explores recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms. Some of the novel and potential serum-based PCa protein biomarkers will be discussed in this review. In addition, this review discusses the importance of converting research protocols into multiplex point-of-care testing (xPOCT) devices to be used in near-patient settings, providing a more personalized approach to PCa patients’ diagnostic, surveillance and treatment management.

Tissue-Based Biomarkers for the Risk Stratification of Men With Clinically Localized Prostate Cancer

Risk stratification of men with clinically localized prostate cancer has historically relied on basic clinicopathologic parameters such as prostate specific antigen level, grade group, and clinical stage. However, prostate cancer often behaves in ways that cannot be accurately predicted by these parameters. Thus, recent efforts have focused on developing tissue-based genomic tests that provide greater insights into the risk of a given patient's disease. Multiple tests are now commercially available and provide additional prognostic information at various stages of the care pathway for prostate cancer. Indeed, early evidence suggests that these assays may have a significant impact on patient and physician decision-making. However, the impact of these tests on oncologic outcomes remains less clear. In this review, we highlight recent advances in the use of tissue-based biomarkers in the treatment of prostate cancer and identify the existing evidence supporting their clinical use.

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.

Oxidative stress and redox signaling in CRPC progression: therapeutic potential of clinically-tested Nrf2-activators

Androgen deprivation therapy (ADT) is the mainstay regimen in patients with androgen-dependent prostate cancer (PCa). However, the selection of androgen-independent cancer cells leads to castrate resistant prostate cancer (CRPC). The aggressive phenotype of CRPC cells underscores the need to elucidate mechanisms and therapeutic strategies to suppress CRPC outgrowth. Despite ADT, the activation of androgen receptor (AR) transcription factor continues via crosstalk with parallel signaling pathways. Understanding of how these signaling cascades are initiated and amplified post-ADT is lacking. Hormone deprivation can increase oxidative stress and the resultant reactive oxygen species (ROS) may activate both AR and non-AR signaling. Moreover, ROS-induced inflammatory cytokines may further amplify these redox signaling pathways to augment AR function. However, clinical trials using ROS quenching small molecule antioxidants have not suppressed CRPC progression, suggesting that more potent and persistent suppression of redox signaling in CRPC cells will be needed. The transcription factor Nrf2 increases the expression of numerous antioxidant enzymes and downregulates the function of inflammatory transcription factors, e.g., nuclear factor kappa B. We documented that Nrf2 overexpression can suppress AR-mediated transcription in CRPC cell lines. Furthermore, two Nrf2 activating agents, sulforaphane (a phytochemical) and bardoxolone-methyl (a drug in clinical trial) suppress AR levels and sensitize CRPC cells to anti-androgens. These observations implicate the benefits of potent Nrf2-activators to suppress the lethal signaling cascades that lead to CRPC outgrowth. This review article will address the redox signaling networks that augment AR signaling during PCa progression to CRPC, and the possible utility of Nrf2-activating agents as an adjunct to ADT.

miRNAs as Therapeutic Tools and Biomarkers for Prostate Cancer

Prostate cancer (PCa) is the fifth cause of tumor-related deaths in man worldwide. Despite the considerable improvement in the clinical management of PCa, several limitations emerged both in the screening for early diagnosis and in the medical treatment. The use of prostate-specific antigen (PSA)-based screening resulted in patients’ overtreatment and the standard therapy of patients suffering from locally advanced/metastatic tumors (e.g., radical prostatectomy, radiotherapy, and androgen deprivation therapy) showed time-limited efficacy with patients undergoing progression toward the lethal metastatic castration-resistant PCa (mCRPC). Although valuable alternative therapeutic options have been recently proposed (e.g., docetaxel, cabazitaxel, abiraterone, enzalutamide, and sipuleucel-T), mCRPC remains incurable. Based on this background, there is an urgent need to identify new and more accurate prostate-specific biomarkers for PCa diagnosis and prognosis and to develop innovative medical approaches to counteract mCRPC. In this context, microRNA (miRNAs) emerged as potential biomarkers in prostate tissues and biological fluids and appeared to be promising therapeutic targets/tools for cancer therapy. Here we overview the recent literature and summarize the achievements of using miRNAs as biomarkers and therapeutic targets/tools for fighting PCa.

Prostate cancer biomarkers and multiparametric MRI: is there a role for both in prostate cancer management?

Several advancements have been made in recent years with regards to the detection and evaluation of prostate cancer (PCa). The low specificity of prostate specific antigen (PSA) has left much to be desired in a test, but a boom in novel biomarkers has made screening and surveillance more complicated. Several attempts at identifying a niche for these tests has helped somewhat, but much is still undetermined about the benefit that each test provides. In addition to laboratory tests, advancements in multiparametric magnetic resonance imaging (mpMRI) and PIRADSv.2 scoring have provided significant benefit to the evaluation of PCa. With the widespread use of prostate imaging, it is important to re-evaluate the impact of novel biomarkers in the context of furthering PCa screening and management. In this review, we aim to assess the influence mpMRI has on the role of nine different novel biomarkers in the detection and evaluation of PCa. We performed a review of current peer-reviewed literature to assess this question. Much data has been published on the role of these tests, allowing for their placement into one of three best-fit categories: tests for biopsy-naïve men (Prostate Health Index, Mi Prostate Score, 4K Score); tests for men with prior negative biopsies (ConfirmMDx, Progensa PCA3); and men on active surveillance (OncotypeDx, Prolaris, Decipher). Data on the role of these tests with the use of mpMRI have not been comprehensive and excludes several of the markers. More research is needed to determine the combined impact mpMRI and the novel biomarkers on the evaluation and management of PCa.

BAP1 suppresses prostate cancer progression by deubiquitinating and stabilizing PTEN

Deubiquitinase BAP1 is an important tumor suppressor in several malignancies, but its functions and critical substrates in prostate cancer (PCa) remain unclear. Here, we report that the mRNA and protein expression levels of BAP1 are downregulated in clinical PCa specimens. BAP1 can physically bind to and deubiquitinate PTEN, which inhibits the ubiquitination-mediated degradation of PTEN and thus stabilizes PTEN protein. Ectopically expressed BAP1 in PCa cells increases PTEN protein level and subsequently inhibits the AKT signaling pathway, thus suppressing PCa progression. Conversely, knockdown of BAP1 in PCa cells leads to the decrease in PTEN protein level and the activation of the Akt signaling pathway, therefore promoting malignant transformation and cancer metastasis. However, these can be reversed by the re-expression of PTEN. More importantly, we found that BAP1 protein level positively correlates with PTEN in a substantial fraction of human cancers. These findings demonstrate that BAP1 is an important deubiquitinase of PTEN for its stability and the BAP1-PTEN signaling axis plays a crucial role in tumor suppression.

Analysis of Serum Markers with Regard to Treatment Procedures in Advanced Stage Prostate Cancer Patients

Background

Biomarkers predicting the efficacy of treatment for locally limited prostate cancer are greatly needed. This knowledge could improve the classification of patients for different methods of treatment and enable better recognition of groups with higher risk of biological recurrence. We prospectively assessed serial blood levels of apoptotic biomarkers and correlated them with response to treatment and clinical factors.

Material/Methods

Blood was collected from 25 patients with prostate cancer before and after surgery, 16 healthy volunteers with benign prostatic hyperplasia (BPH), and 14 patients with metastasized disease. Immunoenzymatic methods were used to determine circulating apoptotic and inflammatory mediators, including tumor necrosis factor α (TNF-α), type I receptor (TNFRI), and type II receptor (TNFRII); FAS ligand (FasL); TNF-related apoptosis-inducing ligand (TRIAL); caspase 8 (Cas8); caspase 9 (Cas9); DNA methylation (metDNA); P-selectin; and high-sensitivity C-reactive protein. The total circulating fragments of cell-free DNA (cfDNA) were measured directly in serum.

Results

Peripheral serum prostate-specific antigen increased rapidly together with cfDNA. A negative correlation was noted between tumor volume and TNFRI and TNFRII. Postsurgery P-selectin level was decreased, and metDNA and TNFRII levels were increased. Three comparisons were made between patient groups: surgical vs. BPH; surgical vs. palliative; and palliative vs. BPH. TNFRI, TNFRII, metDNA, P-selectin, Cas8, and FasL were shown to have significant roles.

Conclusions

The study indicated significant roles for cfDNA, both TNF receptors, metDNA, and P-selectin as serum biomarkers in patients with prostate cancer.

Oncologic Outcomes after Localized Prostate Cancer Treatment: Associations with Pretreatment Prostate Magnetic Resonance Imaging Findings

PURPOSE

We investigated whether T2-weighted magnetic resonance imaging findings could improve upon established prognostic indicators of metastatic disease and prostate cancer specific survival.

MATERIALS AND METHODS

For a cohort of 3,406 consecutive men who underwent prostate magnetic resonance imaging before prostatectomy (2,160) or radiotherapy (1,246) between 2001 and 2006, T2-weighted magnetic resonance imaging exams were retrospectively interpreted and categorized as I) no focal suspicious lesion, II) organ confined focal lesion, III) focal lesion with extraprostatic extension or IV) focal lesion with seminal vesicle invasion. Clinical risk was recorded based on European Association of Urology (EAU) guidelines and the Cancer of the Prostate Risk Assessment (CAPRA) scoring system. Survival probabilities and c-indices were estimated using Cox models and inverse probability censoring weights, respectively.

RESULTS

The median followup was 10.8 years (IQR 8.6-13.0). Higher magnetic resonance imaging categories were associated with a higher likelihood of developing metastases (HR 3.5-18.1, p <0.001 for all magnetic resonance imaging categories) and prostate cancer death (HR 3.1-29.7, p <0.001-0.025); these associations were statistically independent of EAU risk categories, CAPRA scores and treatment type (surgery vs radiation). Combining EAU risk or CAPRA scores with magnetic resonance imaging categories significantly improved prognostication of metastases (c-indices: EAU: 0.798, EAU + magnetic resonance imaging: 0.872; CAPRA: 0.808, CAPRA + magnetic resonance imaging: 0.877) and prostate cancer death (c-indices: EAU 0.813, EAU + magnetic resonance imaging: 0.889; CAPRA: 0.814, CAPRA + magnetic resonance imaging: 0.892; p <0.001 for all).

CONCLUSION

Magnetic resonance imaging findings of localized prostate cancer are associated with clinically relevant long-term oncologic outcomes. Combining magnetic resonance imaging and clinicopathological data results in more accurate prognostication, which could facilitate individualized patient management.

Incorporating Prognostic Biomarkers into Risk Assessment Models and TNM Staging for Prostate Cancer

In current practice, prostate cancer staging alone is not sufficient to adequately assess the patient's prognosis and plan the management strategies. Multiple clinicopathological parameters and risk tools for prostate cancer have been developed over the past decades to better characterize the disease and provide an enhanced assessment of prognosis. Herein, we review novel prognostic biomarkers and their integration into risk assessment models for prostate cancer focusing on their capability to help avoid unnecessary imaging studies, biopsies and diagnosis of low risk prostate cancers, to help in the decision-making process between active surveillance and treatment intervention, and to predict recurrence after radical prostatectomy. There is an imperative need of reliable biomarkers to stratify prostate cancer patients that may benefit from different management approaches. The integration of biomarkers panel with risk assessment models appears to improve prostate cancer diagnosis and management. However, integration of novel genomic biomarkers in future prognostic models requires further validation in their clinical efficacy, standardization, and cost-effectiveness in routine application.

Xanthine Oxidase/Dehydrogenase Activity as a Source of Oxidative Stress in Prostate Cancer Tissue

Prostate cancer (PC) is one of the most frequent malignancies. Better biomarkers are constantly wanted, such as those which can help with the prediction of cancer behavior. What is also needed is a marker which may serve as a possible therapeutic target. Oxidative stress (OS), which is a hallmark of cancer, is included in the pathogenesis and progression of PC. We have conducted the present study to determine whether xanthine oxidase/dehydrogenase activity is the source of OS in prostate tissue. We have also determined the concentration of TBA-reactive substances (TBARS) and advanced oxidation protein products (AOPP), as well as the activity of catalase. Xanthine oxidase (XO) activity is significantly higher (p < 0.001) in tumor tissue when compared to the control healthy tissue. The concentration of TBARS (p < 0.001) and AOPP (p < 0.05) are also higher in tumor tissue. Catalase has raised its activity (p < 0.05) versus the control. There is also a strong correlation between XO activity and prostate-specific antigen (PSA) levels in the serum. These results indicate a significant role of XO activity in OS in prostate carcinogenesis, and it could be a possible theranostic biomarker, which can be important for a better understanding of the disease, its evolution, and prognosis. A promising treatment may be using XO inhibitors such as allopurinol as adjuvant therapy.

Circular RNA and its potential as prostate cancer biomarkers

Advancing knowledge of the transcriptome has revealed that circular RNAs (circRNAs) are widely expressed and evolutionarily conserved molecules that may serve relevant biological roles. More interesting is the accumulating evidence which demonstrates the implication of circRNAs in diseases, especially cancers. This revelation has helped to form the rationale for many studies exploring their utility as clinical biomarkers. CircRNAs are highly stable due to their unique structures, exhibit some tissue specificity, and are enriched in exosomes, which facilitate their detection in a range of body fluids. These properties make circRNAs ideal candidates for biomarker development in many diseases. This review will outline the discovery, biogenesis, and proposed functions of circRNAs.

Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review

Prediction of prostate cancer in primary care is typically based upon serum total prostate-specific antigen (tPSA) and digital rectal examination results. However, these tests lack sensitivity and specificity, leading to over-diagnosis of disease and unnecessary, invasive biopsies. Therefore, there is a clinical need for diagnostic tests that can differentiate between benign conditions and early-stage malignant disease in the prostate. In this review, we evaluate research papers published from 2009 to 2019 reporting biomarkers that identified or differentiated benign prostatic hyperplasia (BPH) from prostate cancer. Our review identifies hundreds of potential biomarkers in urine, serum, tissue, and semen proposed as useful targets for differentiating between prostate cancer and BPH patients. However, it is still not apparent which of these candidate biomarkers are most useful, and many will not progress beyond the discovery stage unless they are properly validated for clinical practice. We conclude that this validation will come through the use of multivariate panels which can assess the value of biomarker candidates in combination with clinical parameters as part of a risk prediction calculator. Implementation of such a model will help clinicians stratify patients with prostate cancer symptoms in primary care, with tangible benefits for both the patient and the health service.

Multiparametric MRI Versus SelectMDx Accuracy in the Diagnosis of Clinically Significant PCa in Men Enrolled in Active Surveillance

BACKGROUND/AIM

To evaluate the diagnostic accuracy of the urinary SelectMDx test in the diagnosis of clinically significant prostate cancer (csPCa) in men enrolled in an active surveillance (AS) protocol.

PATIENTS AND METHODS

From July 2015 to July 2018, 125 men with very low-risk PCa were enrolled in the AS protocol; all patients underwent confirmatory transperineal saturation biopsy (SPBx). In the presence of PI-RADS score ≥3, a targeted MRI/TRUS fusion-guided biopsy was added to SPBx. Post-digital rectal examination urine was collected in 45/125 (36%) patients before SPBx; the genetic urine analysis was performed using a biomarker-based risk score model, the SelectMDx, that measured mRNA levels of distal-less homeobox 1 (DLX1) and homeobox C6 (HOXC6).

RESULTS

A total of 9/45 (20%) patients were reclassified as csPCa (7 cases=Grade Group 2; 2 cases=Grade Group 3); sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of mpMRI vs. SelectMDx in the diagnosis of csPCa were equal to 66.6 vs. 55.6%, 87.7 vs. 65.8%, 54.5 vs. 27.8%, 92.3 vs. 87%, 84.9 vs. 70.3%, respectively.

CONCLUSION

SPBx combined with MRI/TRUS fusion biopsy significantly outperformed the diagnostic accuracy of SelectMDx (70.3%) in the diagnosis of csPCa in men enrolled in AS.

Thiol Groups as a Biomarker for the Diagnosis and Prognosis of Prostate Cancer

Oxidative stress (OS) is associated with the onset of prostate cancer (PCa). The aims of this study are to examine whether OS biomarkers may be employed as external validating criteria for the diagnosis PCa. This case-control study recruited 204 subjects, 73 patients with PCa, 67 patients with benign prostate hyperplasia (BPH), and 64 healthy controls (HC) and assayed plasma prostate-specific antigen (PSA), protein thiol (−SH) groups, lipid hydroperoxides, carbonyl proteins (PCB), advanced oxidation protein products (AOPP), and total radical-trapping antioxidant parameter (TRAP). -SH groups were significantly and inversely associated with PSA levels. PCa was characterized by lowered -SH groups and red blood cell TRAP levels, and higher PSA, AOPP and PCB levels as compared with BPH and HC. Support vector machine with 10-fold cross-validation showed that PSA values together with -SH groups, PCB and AOPP yielded a cross-validation accuracy of 96.34% for the differentiation of PCa from BPH and HC. The area under the ROC curve using PSA and -SH differentiating PCa from BPH and controls was 0.945. Moreover, lowered -SH, but not PSA, are associated with PCa metastasis and progression. Inflammatory biomarkers were not associated with PCa or BPH. PCa, its progression and metastatic PCa are characterized by lowered antioxidant defenses, especially lowered thiol groups, and increased oxidative stress toxicity, suggesting that these processes play a key role in the pathophysiology of PCa. An algorithm based on -SH and PSA values may be used to differentiate patients with PCa from those with BPH and controls.

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis

The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application.

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.

Analysis of Differential Expression of microRNAs and Their Target Genes in Prostate Cancer: A Bioinformatics Study on Microarray Gene Expression Data

Early diagnosis of prostate cancer (PCa) as the second most common cancer in men is not associated with precise and specific results. Thus, alternate methods with high specificity and sensitivity are needed for accurate and timely detection of PCa. MicroRNAs regulate the molecular pathways involved in cancer by targeting multiple genes. The aberrant expression of the microRNAs has been reported in different cancer types including PCa. In this bioinformatics study, we studied differential expression profiles of microRNAs and their target genes in four PCa gene expression omnibus (GEO) databases. PCa diagnostic biomarker candidates were investigated using bioinformatics tools for analysis of gene expression data, microRNA target prediction, pathway and GO annotation, as well as ROC curves. The results of this study revealed significant changes in the expression of 14 microRNAs and 40 relevant target genes, which ultimately composed four combination panels (miR- 375+96+663/ miR- 133b+143- 3p + 205/ C2ORF72 + ENTPD5 + GLYAT11/LAMB3 + NTNG2+TSLP) as candidate biomarkers capable to distinguish between PCa tumor samples and normal prostate tissue samples. These biomarkers may be suggested for a more accurate early diagnosis of PCa patients along with current diagnostic tests.

An expanded biomarker panel for the detection of prostate cancer from urine DNA

Background

Prostate cancer diagnosis using the PSA test remains controversial because of overdiagnosis and overtreatment of potentially indolent cancers. There remains a need to increase the diagnostic lead time and to target treatment to patients with significant disease. One possible approach to overcome the limitations of PSA is to screen men for the molecular signature of early PCA, monitor the rate of disease progression and target treatment to patients who are likely to benefit from it. Such an approach requires a large panel of markers that define a molecular clock for PCA. We recently developed a panel of 19 markers for the non-invasive detection of PCA from urine DNA. It raised the possibility that additional methylation markers could be successfully analyzed from urine DNA, a prerequisite for increasing the diagnostic lead time and enabling disease monitoring.

Methods

We developed semi-quantitative polymerase chain reaction assays for 13 additional markers and determined their methylation status in 150 urine DNAs from 94 patients with elevated PSA. Eighty five samples were obtained following DRE and 65 samples were from first void. We combined the data of the 13 new markers with the previously reported 19 markers and calculated the sensitivity, specificity, negative and positive predictive values at every threshold from one to 32 positive markers.

Results

Using 10of32 positive markers as the threshold to recommend a biopsy yields a sensitivity of 81% (95% CI 0.68–0.93) and 93% (95% CI 0.84–1.02) and a specificity of 76% (95% CI 0.63–0.88) and 77% (95% CI 0.63–0.91) from DRE and FV DNA, respectively. The PPV was 71% and 77% and the NPV was 85% and 93% from DRE and FV, respectively.

Conclusions

This study shows that large marker panels can be analyzed from urine DNA without loss of sensitivity or specificity. Using 32 markers improved the stratification of patients undergoing screening for PCA particularly for patients below the 10of32 threshold. The results show the utility of larger biomarker panels for PCA diagnosis and suggest that the development of the panels needed to monitor disease progression could be successfully accomplished.

Electronic supplementary material

The online version of this article (10.1186/s40164-019-0137-x) contains supplementary material, which is available to authorized users.

Assessment of men's risk thresholds to proceed with prostate biopsy for the early detection of prostate cancer

PURPOSE

To delineate the range of "risk thresholds" for prostate biopsy to determine how improved prostate cancer (CaP) risk prediction tools may impact shared decision-making (SDM).

METHODS

We conducted a cross-sectional survey study involving men 45-75 years old attending a multispecialty urology clinic. Data included demographics, personal and family prostate cancer history, and prostate biopsy history. Respondents were presented with a summary of the details, risks, and benefits of prostate biopsy, then asked to indicate the specific risk threshold (% chance) of high-grade CaP at which they would proceed with prostate biopsy.

RESULTS

Of a total of 103 respondents, 18 men (17%) had a personal history of CaP, and 31 (30%) had undergone prostate biopsy. The median risk threshold to proceed with prostate biopsy was 25% (interquartile range 10-50%). Risk thresholds did not vary by race, education, or employment. Personal history of CaP or prostate biopsy was significantly associated with lower mean risk thresholds (19% vs. 32% [P = 0.02] and 23% vs. 33% [P = 0.04], respectively). In the lowest versus highest risk threshold quartiles, there were significantly higher rates of CaP (36% vs. 1%, P = 0.01) and prior prostate biopsy (46% vs. 17%, P < 0.01).

CONCLUSIONS

Men have a wide range of risk thresholds for high-grade CaP to proceed with prostate biopsy. Men with a prior history of CaP or biopsy reported lower risk thresholds, which may reflect their greater concern for this disease. The extent to which refined risk prediction tools will improve SDM warrants further study.

CE-MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Background

Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management.

Methods

Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (n_Sig = 98; n_non-Sig = 445) and a validation set of 280 patients (n_Sig = 48, n_non-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm.

Results

Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set.

Conclusions

This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.

Research status and progress of the RNA or protein biomarkers for prostate cancer

Prostate cancer is a kind of male malignancy. Recently, a large number of studies have reported many potential biomarkers for the diagnosis and prognosis of prostate cancer. In this literature review, we have collected a number of potential biomarkers for prostate cancer reported in the last 5 years. Among them, some are undergoing Phase III clinical trials, and others have been approved by the US Food and Drug Administration. However, most are still in the period of basic research. The review will contribute to future research to find the biomarkers to guide clinicians to make personalized treatment decisions for each prostate cancer patient.

Liquid Biopsy Potential Biomarkers in Prostate Cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide with an incidence of 14.8% and a mortality of 6.6%. Shortcomings in comprehensive medical check-ups in low- and middle-income countries lead to delayed detection of PCa and are causative of high numbers of advanced PCa cases at first diagnosis. The performance of available biomarkers is still insufficient and limited applicability, including logistical and financial burdens, impedes comprehensive implementation into health care systems. There is broad agreement on the need of new biomarkers to improve (i) early detection of PCa, (ii) risk stratification, (iii) prognosis, and (iv) treatment monitoring. This review focuses on liquid biopsy tests distinguishing high-grade significant (Gleason score (GS) ≥ 7) from low-grade indolent PCa. Available biomarkers still lack performance in risk stratification of biopsy naïve patients. However, biomarkers with highly negative predictive values may help to reduce unnecessary biopsies. Risk calculators using integrative scoring systems clearly improve decision-making for invasive prostate biopsy. Emerging biomarkers have the potential to substitute PSA and improve the overall performance of risk calculators. Until then, PSA should be used and may be replaced whenever enough evidence has accumulated for better performance of a new biomarker.

Recent advances in prostate cancer research: large-scale genomic analyses reveal novel driver mutations and DNA repair defects

Prostate cancer (PCa) is a disease of mutated and misregulated genes. However, primary prostate tumors have relatively few mutations, and only three genes ( ERG, PTEN, and SPOP) are recurrently mutated in more than 10% of primary tumors. On the other hand, metastatic castration-resistant tumors have more mutations, but, with the exception of the androgen receptor gene ( AR), no single gene is altered in more than half of tumors. Structural genomic rearrangements are common, including ERG fusions, copy gains involving the MYC locus, and copy losses containing PTEN. Overall, instead of being associated with a single dominant driver event, prostate tumors display various combinations of modifications in oncogenes and tumor suppressors. This review takes a broad look at the recent advances in PCa research, including understanding the genetic alterations that drive the disease and how specific mutations can sensitize tumors to potential therapies. We begin with an overview of the genomic landscape of primary and metastatic PCa, enabled by recent large-scale sequencing efforts. Advances in three-dimensional cell culture techniques and mouse models for PCa are also discussed, and particular emphasis is placed on the benefits of patient-derived xenograft models. We also review research into understanding how ETS fusions (in particular, TMPRSS2-ERG) and SPOP mutations contribute to tumor initiation. Next, we examine the recent findings on the prevalence of germline DNA repair mutations in about 12% of patients with metastatic disease and their potential benefit from the use of poly(ADP-ribose) polymerase (PARP) inhibitors and immune modulation. Lastly, we discuss the recent increased prevalence of AR-negative tumors (neuroendocrine and double-negative) and the current state of immunotherapy in PCa. AR remains the primary clinical target for PCa therapies; however, it does not act alone, and better understanding of supporting mutations may help guide the development of novel therapeutic strategies.