Three new serum markers for prostate cancer detection within a percent free PSA-based artificial neural network

Increased Density of Growth Differentiation Factor-15+ Immunoreactive M1/M2 Macrophages in Prostate Cancer of Different Gleason Scores Compared with Benign Prostate Hyperplasia

Simple Summary

Prostate cancer (PCa) is the second most diagnosed cancer and cause of death in men worldwide. The main challenge is to discover biomarkers for malignancy to guide the physician towards optimized diagnosis and therapy. There is recent evidence that growth differentiation factor-15 (GDF-15) is elevated in cancer patients. Therefore, we aimed to decipher GDF-15+ cell types and their density in biopsies of human PCa patients with Gleason score (GS)6–9 and benign prostate hyperplasia (BPH). Here we show that the density of GDF-15+ cells, mainly identified as interstitial macrophages (MΦ), was higher in GS6–9 than in BPH, and, thus, GDF-15 is intended to differentiate patients with high GS vs. BPH, as well as GS6 vs. GS7 (or even with higher malignancy). Some GDF-15+ MΦ showed a transepithelial migration into the glandular lumen and, thus, might be used for measurement in urine/semen. Taken together, GDF-15 is proposed as a novel tool to diagnose PCa vs. BPH or malignancy (GS6 vs. higher GS) and as a potential target for anti-tumor therapy. GDF-15 in seminal plasma and/or urine could be utilized as a non-invasive biomarker of PCa as compared to BPH.

Abstract

Although growth differentiation factor-15 (GDF-15) is highly expressed in PCa, its role in the development and progression of PCa is unclear. The present study aims to determine the density of GDF-15+ cells and immune cells (M1-/M2 macrophages [MΦ], lymphocytes) in PCa of different Gleason scores (GS) compared to BPH. Immunohistochemistry and double immunofluorescence were performed on paraffin-embedded human PCa and BPH biopsies with antibodies directed against GDF-15, CD68 (M1 MΦ), CD163 (M2 MΦ), CD4, CD8, CD19 (T /B lymphocytes), or PD-L1. PGP9.5 served as a marker for innervation and neuroendocrine cells. GDF-15+ cell density was higher in all GS than in BPH. CD68+ MΦ density in GS9 and CD163+ MΦ exceeded that in BPH. GDF-15+ cell density correlated significantly positively with CD68+ or CD163+ MΦ density in extratumoral areas. Double immunoreactive GDF-15+/CD68+ cells were found as transepithelial migrating MΦ. Stromal CD68+ MΦ lacked GDF-15+. The area of PGP9.5+ innervation was higher in GS9 than in BPH. PGP9.5+ cells, occasionally copositive for GDF-15+, also occurred in the glandular epithelium. In GS6, but not in BPH, GDF-15+, PD-L1+, and CD68+ cells were found in epithelium within luminal excrescences. The degree of extra-/intra-tumoral GDF-15 increases in M1/M2Φ is proposed to be useful to stratify progredient malignancy of PCa. GDF-15 is a potential target for anti-tumor therapy.

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.

Levels of plasma glycan-binding auto-IgG biomarkers improve the accuracy of prostate cancer diagnosis

Strategies to improve the early diagnosis of prostate cancer will provide opportunities for earlier intervention. The blood-based prostate-specific antigen (PSA) assay is widely used for prostate cancer diagnosis but specificity of the assay is not satisfactory. An algorithm based on serum levels of PSA combined with other serum biomarkers may significantly improve prostate cancer diagnosis. Plasma glycan-binding IgG/IgM studies suggested that glycan patterns differ between normal and tumor cells. We hypothesize that in prostate cancer glycoproteins or glycolipids are secreted from tumor tissues into the blood and induce auto-immunoglobulin (Ig) production. A 24-glycan microarray and a 5-glycan subarray were developed using plasma samples obtained from 35 prostate cancer patients and 54 healthy subjects to identify glycan-binding auto-IgGs. Neu5Acα2-8Neu5Acα2-8Neu5Acα (G81)-binding auto-IgG was higher in prostate cancer samples and, when levels of G81-binding auto-IgG and growth differentiation factor-15 (GDF-15 or NAG-1) were combined with levels of PSA, the prediction rate of prostate cancer increased from 78.2% to 86.2% than with PSA levels alone. The G81 glycan-binding auto-IgG fraction was isolated from plasma samples using G81 glycan-affinity chromatography and identified by N-terminal sequencing of the 50 kDa heavy chain variable region of the IgG. G81 glycan-binding 25 kDa fibroblast growth factor-1 (FGF1) fragment was also identified by N-terminal sequencing. Our results demonstrated that a multiplex diagnostic combining G81 glycan-binding auto-IgG, GDF-15/NAG-1 and PSA (≥ 2.1 ng PSA/ml for cancer) increased the specificity of prostate cancer diagnosis by 8%. The multiplex assessment could improve the early diagnosis of prostate cancer thereby allowing the prompt delivery of prostate cancer treatment.

Relevance of MIC-1 in the Era of PSA as a Serum Based Predictor of Prostate Cancer: A Critical Evaluation

To reduce the ambiguity of contradictory observations in different studies regarding the expression level of Macrophage Inhibitory Cytokine-1 (MIC-1) in serum in prostate cancer (PC), benign prostatic hyperplasia (BPH) and healthy controls (HC), we designed this double-blind study. The study comprises 240 sera from PC, BPH and HC subjects. The expression level of MIC-1 in PC, BPH and HC were appraised using Western blot (WB) and ELISA based approach. WB and ELISA appraisal reveals that the expression level of MIC-1 is significantly higher in PC than in HC or BPH subjects. Regression analysis revealed a significant correlation between MIC-1 vs. PSA (r = 0.09; p < 0.001) and MIC-1 vs. GS (r = 0.7; p < 0.001). ROC analysis using discriminant predicted probability revealed that the MIC-1 was better than PSA. Moreover, the combination of MIC-1 and PSA was allowing 99.1% AUC for the differentiation of BPH + PC from HC, 97.9% AUC for differentiation of BPH from HC, 98.6% AUC for differentiation of PC from HC, and 96.7% AUC for the differentiation of PC from BPH. The augmented expression of MIC-1 in PC compared to BPH and HC subjects is in concurrent of the over-expression of MIC-1 in PC reports and confiscates the contradictory findings of other studies.

Macrophage inhibitory cytokine-1/growth differentiation factor-15 as a predictor of colonic neoplasia

BACKGROUND

Serum macrophage inhibitory cytokine-1 (MIC-1/GDF15) concentration has been associated with colonic adenomas and carcinoma.

AIMS

To determine whether circulating MIC-1/GDF15 serum concentrations are higher in the presence of adenomas and whether the level decreases after excision.

METHODS

Patients were recruited prospectively from a single centre and stratified into five groups: no polyps (NP); hyperplastic polyps (HP); sessile serrated ademona (SSA); adenomas (AP); and colorectal carcinoma (CRC). Blood samples were collected immediately before and 4 weeks after colonoscopy. MIC-1/GDF15 serum levels were quantified using ELISA.

RESULTS

Participants (n=301) were stratified as: NP; n=116 (52%), HP; n=37 (12%), SSA; n=19 (7%), AP; n=68 (23%); and CRC; n=3 (1%). Patients were excluded from the study due to nondiagnostic pathology (n=9, 3%) and exclusion criteria (n=20, 6%). In the 272 remaining subjects (M=149; F=123), age (P=.005), history of colonic polyps (P=.003) and family history of colonic polyps (P=.002) were associated with presence of adenomas. Baseline median MIC-1/GDF15 serum levels increased significantly from NP 609 (460-797) pg/mL, HP 582 (466-852) pg/mL, SSA 561 (446-837) pg/mL to AP 723 (602-1122) pg/mL and CRC 1107 (897-1107) pg/mL; (P<.001). In the pre- and postpolypectomy paired adenoma samples median MIC-1/GDF15 reduced significantly from 722 (603-1164) pg/mL to 685 (561-944) pg/mL (P=.002). A ROC analysis for serum MIC-1/GDF15 to identify adenomatous polyps indicated an area under the curve of 0.71.

CONCLUSIONS

Our data suggest that serum MIC-1/GDF15 has the diagnostic characteristics to increase the detection of colonic neoplasia and improve screening.

The establishment and evaluation of a new model for the prediction of prostate cancer

To develop a new prostate cancer predictor (PCP) model using the combination of total prostate-specific antigen (tPSA), free PSA (fPSA), and complexed PSA (cPSA).The diagnoses of all the included patients were confirmed pathologically in Daping Hospital between December 1, 2011 and December 1, 2014. There were 54 PCa cases and 579 benign prostatic hyperplasia (BPH) cases with tPSA levels of 2 to 10 ng/mL, and 48 PCa cases and 147 BPH cases with tPSA levels of 10 to 20 ng/mL. Logistic regression and receiver operating characteristic curve (ROC) analyses were employed to compare the value of PCP (PCP = tPSA / fPSA × √cPSA) with tPSA, fPSA, the ratio of fPSA to tPSA (%fPSA), and cPSA for the differential diagnosis of PCa and BPH. Meanwhile, bootstrapping analysis was used to calculate the distribution and confidence intervals (CIs) for the area under the curve (AUC), and Hosmer-Lemeshow tests were used to calculate P values.When tPSA levels were 2 to 10 ng/mL, the AUC of PCP (0.680) was significantly higher than that of tPSA (0.588), fPSA (0.571), %fPSA (0.675), and cPSA (0.613). When the sensitivity for the diagnosis of PCa was 90.7%, the specificity of PCP (22.8%) was higher than that of tPSA (11.1%), fPSA (11.2%), %fPSA (17.4%), and cPSA (15.5%). When tPSA levels were 10 to 20 ng/mL, the AUC of PCP (0.686) was significantly higher than that of tPSA (0.603), fPSA (0.643), %fPSA (0.679), and cPSA (0.647). When the sensitivity for the diagnosis of PCa was 91.7%, the specificity of PCP (29.3%) was higher than that of tPSA (10.9%), fPSA (10.2%), %fPSA (23.1%), and cPSA (18.4%).PCP is a novel model for the prediction of PCa; it has more predictive value than tPSA, fPSA, %fPSA, and cPSA when tPSA levels are 2 to 20 ng/mL.

Effects of hexanic extract of Serenoa repens (Permixon® 160 mg) on inflammation biomarkers in the treatment of lower urinary tract symptoms related to benign prostatic hyperplasia

BACKGROUND

Chronic prostatic inflammation (CPI) could be a cause of symptomatic or complicated benign prostatic hyperplasia (BPH). In previous in vitro and in vivo studies, Hexanic Extract of Serenoa repens (HESr) namely Permixon(®) has demonstrated potent anti-inflammatory properties. With the aim to provide new insight onto HESr anti-inflammatory properties in human we explore its effect on CPI biomarkers in men with lower urinary tract symptoms (LUTS) related to BPH using a non-invasive method and investigate links between biomarkers and clinical symptoms.

METHODS

An international, randomized, double-blind, parallel-group, tamsulosin-controlled study was carried out in 206 men with BPH-related LUTS. Patients received oral daily HESr 320mg or tamsulosin 0.4 mg during 3 months. The first urine stream after digital rectal examination (DRE) was collected at Day 1 and Day 90 and mRNA was extracted from prostatic epithelial cells desquaming in the lumen of the glands and seminal plasma fluid after DRE. mRNA quantification of the 29 most significant published inflammation markers in BPH and protein detection in urine was performed.

RESULTS

At D90, a decrease in mean gene expression was observed for 65.4% of the markers detected in the HESr group versus 46.2% in the tamsulosin group. In the 15 most frequently expressed genes, this difference was higher (80% vs. 33% respectively). Three proteins (MCP-1/CCL2, IP-10/CXCL10, and MIF) were detected. At D90, a decrease in the number of patients who expressed MCP-1/CCL2 and IP-10/CXCL10 was observed only in the HESr group. Moreover, MIF expression was significantly reduced by HESr compared with tamsulosin (P = 0.007). Finally, in contrast to tamsulosin, the subgroup of patients treated by HESr and who over expressed MIF at baseline, had a higher response to the International Prostate Symptom Score (I-PSS) than those who did not over express this protein (mean I-PSS change: -6.4 vs. -4.5 respectively). As the study is exploratory, results should be confirmed in a powered clinical study.

CONCLUSIONS

These results showed for the first time at clinical level the anti-inflammatory properties of HESr, already indicated in BPH-related LUTS. Thus, HESr could be of interest to prevent unfavourable evolution in patients with CPI.

Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score

Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always <0.0001) alone. The GS predicted the BCR better than PSA (P = 0.0001), but there was no difference between the ANN and LR models (P = 0.39). Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS ≥7.

Macrophage inhibitory cytokine 1 biomarker serum immunoassay in combination with PSA is a more specific diagnostic tool for detection of prostate cancer

Background

Prostate cancer (PCa) is the most common malignancy among men in the United States. Though highly sensitive, the often-used prostate-specific antigen (PSA) test has low specificity which leads to overdiagnosis and overtreatment of PCa. This paper presents results of a retrospective study that indicates that testing for macrophage inhibitory cytokine 1 (MIC-1) concentration along with the PSA assay could provide much improved specificity to the assay.

Methods

The MIC-1 serum level was determined by a novel p-Chip-based immunoassay run on 70 retrospective samples. The assay was configured on p-Chips, small integrated circuits (IC) capable of storing in their electronic memories a serial number to identify the molecular probe immobilized on its surface. The distribution of MIC-1 and pre-determined PSA concentrations were displayed in a 2D plot and the predictive power of the dual MIC-1/PSA assay was analyzed.

Results

MIC-1 concentration in serum was elevated in PCa patients (1.44 ng/ml) compared to normal and biopsy-negative individuals (0.93 ng/ml and 0.88 ng/ml, respectively). In addition, the MIC-1 level was correlated with the progression of PCa. The area under the receiver operator curve (AUC-ROC) was 0.81 providing an assay sensitivity of 83.3% and specificity of 60.7% by using a cutoff of 0.494 for the logistic regression value of MIC-1 and PSA. Another approach, by defining high-frequency PCa zones in a two-dimensional plot, resulted in assay sensitivity of 78.6% and specificity of 89.3%.

Conclusions

The analysis based on correlation of MIC-1 and PSA concentrations in serum with the patient PCa status improved the specificity of PCa diagnosis without compromising the high sensitivity of the PSA test alone and has potential for PCa prognosis for patient therapy strategies.

Prostate-specific antigen and other serum and urine markers in prostate cancer

Prostate-specific antigen (PSA) is one of the most widely used tumor markers, and strongly correlates with the risk of harboring from prostate cancer (PCa). This risk is visible already several years in advance but PSA has severe limitations for PCa detection with its low specificity and low negative predictive value. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved Prostate Health Index (phi) shows improved specificity over percent free and total PSA. Other serum kallikreins or sarcosine in serum or urine show more diverging data. In urine, the FDA-approved prostate cancer gene 3 (PCA3) has also proven its utility in the detection and management of early PCa. However, some aspects on its correlation with aggressiveness and the low sensitivity at very high values have to be re-examined. The detection of a fusion of the androgen regulated TMPRSS2 gene with the ERG oncogene (from the ETS family), which acts as transcription factor gene, in tissue of ~50% of all PCa patients was one milestone in PCa research. When combining the urinary assays for TMPRSS2:ERG and PCA3, an improved accuracy for PCa detection is visible. PCA3 and phi as the best available PCa biomarkers show an equal performance in direct comparisons.

Artificial neural networks and prostate cancer--tools for diagnosis and management

Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

Mn-doped ZnSe d-dots-based α-methylacyl-CoA racemase probe for human prostate cancer cell imaging

In this paper, we report the successful use of non-cadmium-based Mn-doped ZnSe d-dots (Mn/ZnSe) as highly efficient and nontoxic optical probes for human prostate cancer cells imaging. Mn/ZnSe d-dots are directly prepared in aqueous solution. The α-methylacyl-CoA racemase (AMACR) is overexpressed in prostate cancers; the presence of antibodies specific for AMACR is more sensitive and specific than serum prostate specific antigen levels in distinguishing patients with prostate cancers. Mn/ZnSe d-dots were linked to anti-AMACR to form Mn/ZnSe d-dots-anti-AMACR bioconjugates for the direct prostate cancer cell imaging. 3-(4,5-Dimethylthiazol-2-yl)-2 and 5-diphenyl tetrazolium bromide assay demonstrated that Mn/ZnSe d-dots exhibited favorable cytocompatibility to LNCaP cells with high concentration (1 mM) and long-time incubation (24 h). Furthermore, cellular imaging results demonstrated that Mn/ZnSe d-dots were remarkably efficacious for high-specificity cell imaging. The antibody-mediated delivery of the bioconjugates was further confirmed by the observation of no fluorescence signals in vitro targeting in nonprostate-cancer-based cell lines which are negative for AMACR. Mn/ZnSe d-dots as non-cadmium-based safe and efficient optical imaging nanoprobes could therefore be used for targeting imaging and treatment of cancers in the early stage.

The TGF-β superfamily cytokine, MIC-1/GDF15: a pleotrophic cytokine with roles in inflammation, cancer and metabolism

Macrophage inhibitory cytokine-1 (MIC-1/GDF15) is associated with cardiovascular disease, inflammation, body weight regulation and cancer. Its serum levels facilitate the diagnosis and prognosis of cancer and vascular disease. Furthermore, its serum levels are a powerful predictor of all-cause mortality, suggesting a fundamental role in biological processes associated with ageing. In cancer, the data available suggest that MIC-1/GDF15 is antitumorigenic, but this may not always be the case as disease progresses. Cancer promoting effects of MIC-1/GDF15 may be due, in part, to effects on antitumour immunity. This is suggested by the anti-inflammatory and immunosuppressive properties of MIC-1/GDF15 in animal models of atherosclerosis and rheumatoid arthritis. Furthermore, in late-stage cancer, large amounts of MIC-1/GDF15 in the circulation suppress appetite and mediate cancer anorexia/cachexia, which can be reversed by monoclonal antibodies in animals. Available data suggest MIC-1/GDF15 may be an important molecule mediating the interplay between cancer, obesity and chronic inflammation.

Macrophage migration inhibitory factor (MIF): a promising biomarker

Macrophage migration inhibitory factor (MIF) is an immunoregulatory cytokine, the effect of which on arresting random immune cell movement was recognized several decades ago. Despite its historic name, MIF also has a direct chemokine-like function and promotes cell recruitment. Multiple clinical studies have indicated the utility of MIF as a biomarker for different diseases that have an inflammatory component; these include systemic infections and sepsis, autoimmune diseases, cancer, and metabolic disorders such as type 2 diabetes and obesity. The identification of functional promoter polymorphisms in the MIF gene (MIF) and their association with the susceptibility or severity of different diseases has not only served to validate MIF's role in disease development but also opened the possibility of using MIF genotype information to better predict risk and outcome. In this article, we review the clinical data of MIF and discuss its potential as a biomarker for different disease applications.

Internal validation of an artificial neural network for prostate biopsy outcome

OBJECTIVES

To carry out an internal validation of the retrospectively trained artificial neural network (ANN) 'ProstataClass'.

METHODS

A prospectively collected database of 393 patients undergoing 8-12 core prostate biopsy was analyzed. Data of these patients were applied to the online available ANN 'ProstataClass' using the Elecsys total prostate-specific antigen (tPSA) and free PSA (fPSA) assays. Beside the internal validation of the ANN 'ProstataClass' an additional ANN (named as ANN internal validation: ANNiv) only using the 393 prospective patient data was evaluated. The new ANN model was constructed with the MATLAB Neural Network Toolbox. Diagnostic accuracy was evaluated by receiver operator characteristic (ROC) curves comparing the areas under the ROC curves (AUC) and specificities at 90% and 95% sensitivity.

RESULTS

Within a tPSA range of 1.0-22.8 ng/mL, 229 men (58.3%) had prostate cancer (PCa). tPSA, %fPSA and the number of positive digital rectal examinations (DRE) differed significantly from the cohort of patients of the ANN 'ProstataClass', whereas age and prostate volume were comparable. AUCs for tPSA, %fPSA and the ANN 'ProstataClass' were 0.527, 0.726 and 0.747 (P = 0.085 between %fPSA and ANN). The AUC of the ANNiv (0.754) was significantly better compared with %fPSA (P = 0.021), whereas the AUC of two ANN models built on external cohorts (0.726 and 0.729) showed no differences to %fPSA and the other ANN models.

CONCLUSIONS

Significant differences of DRE status and %fPSA medians decrease the power of the 'ProstataClass' ANN in the internal validation cohort. The effect of retrospective data evaluation the 'ProstataClass' cohort and prospective fPSA measurement may be responsible for %fPSA differences. All ANN models built with different PSA and fPSA assays performed equally if applied to the two cohorts.

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases

BACKGROUND

The aim of this study was to combine the new automated Access [-2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural network (ANN) or logistic regression (LR) model to enhance discrimination between patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and to detect aggressive PCa.

METHODS

Sera from 311 PCa patients and 275 NEM patients were measured with the p2PSA, total PSA (tPSA) and free PSA (fPSA) assays on Access immunoassay technology (Beckman Coulter, Fullerton, CA) within the 0-30 ng/ml tPSA range. Four hundred seventy-five patients (264 PCa, 211 NEM) had a tPSA of 2-10 ng/ml. LR models and leave-one-out (LOO) ANN models with Bayesian regularization by using tPSA, %fPSA, p2PSA/fPSA (%p2PSA), age and prostate volume were constructed and compared by receiver-operating characteristic (ROC) curve analysis.

RESULTS

The ANN and LR model each utilizing %p2PSA, %fPSA, tPSA and age, but without prostate volume, reached the highest AUCs (0.85 and 0.84) and best specificities (ANN: 62.1% and 45.5%; LR: 53.1% and 41.2%) compared with tPSA (22.7% and 11.4%) and %fPSA (45.5% and 26.1%) at 90% and 95% sensitivity. The %p2PSA furthermore distinguished better than tPSA and %fPSA between pT2 and pT3, and Gleason sum <7 and >or=7 PCa.

CONCLUSIONS

The automated p2PSA assay offers a new tool to improve PCa detection, and especially aggressive PCa detection. Incorporation of %p2PSA into an ANN and LR model further enhances the diagnostic accuracy to differentiate between malignant and non-malignant prostate diseases.

Screening for prostate cancer in 2008 II: the importance of molecular subforms of prostate-specific antigen and tissue kallikreins

CONTEXT

Over the past decades, prostate-specific antigen (PSA), its isoforms, and other members of the tissue kallikrein family have been of continuous interest with regard to early detection and screening for prostate cancer (PCa).

OBJECTIVE

This review strives to give an overview of the possible clinical utilities of these markers, focused on early diagnostics and PCa screening.

EVIDENCE ACQUISITION

Using the Medline database, a literature search was performed on the role of molecular subforms of PSA and other members of the tissue kallikrein family in PCa detection.

EVIDENCE SYNTHESIS

With respect to PSA isoforms, only the combination of the various truncated forms (pPSA) shows additional value over total PSA (tPSA) and free PSA (fPSA) in PCa detection within the range of 2-10 ng/ml tPSA. At a high sensitivity for PCa, the specificity of the ratio of pPSA to fPSA (%pPSA) is, in general, better than that of the ratio of fPSA to tPSA (%fPSA), with a gain of 5-11%. The (-2)pPSA, (-4)pPSA, (-5)pPSA, (-7)pPSA, and benign PSA (BPSA) isoforms generally show no additional value over either pPSA or the existing parameters of tPSA and fPSA. Of the other members of the tissue kallikrein family, most studies on human kallikrein 2 (hK2) show an additional value of the ratio of hK2 to fPSA (%hK2) over %fPSA alone in PCa prediction. Other tissue kallikreins cannot be recommended for diagnosing PCa, due to the lack of additional value over tPSA or fPSA or to insufficient research. Regarding a prognostic role, the value of PSA subforms as well as of other members of the tissue kallikrein family is limited with regard to existing parameters.

CONCLUSIONS

pPSA and hK2 are able to improve PCa diagnosis in the range of 4-10 ng/ml tPSA over the existing variables tPSA and fPSA.

Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity

Background

To validate an artificial neural network (ANN) based on the combination of PSA velocity (PSAV) with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa) and benign prostate hyperplasia (BPH).

Methods

The study comprised 199 patients with PCa (n = 49) or BPH (n = 150) with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV) calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values.

Results

71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65%) and increasing values are less frequently (11% vs. 21%) if the ANNV is used instead of the PSAV.

Conclusion

PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11–17% of unnecessary prostate biopsies in known BPH patients.

Utility of kallikrein-related peptidases (KLKs) as cancer biomarkers

BACKGROUND

The human kallikrein-related peptidase (KLK) family consists of 15 highly conserved serine proteases, which are encoded by the largest uninterrupted cluster of protease genes in the human genome. To date, several members of the family have been reported as potential cancer biomarkers. Although primarily known for their biomarker value in prostate, ovarian, and breast cancers, more recent data suggest analogous roles of KLKs in several other cancers, including gastrointestinal, head and neck, lung, and brain malignancies. Among the proposed KLK cancer biomarkers, prostate-specific antigen (also known as KLK3) is the most widely recognized member in urologic oncology.

CONTENT

Despite substantial progress in the understanding of the biomarker utility of individual KLKs, the current challenge lies in devising biomarker panels to increase the accuracy of prognosis, prediction of therapy, and diagnosis. To date, multiparametric KLK panels have been proposed for prostate, ovarian, and lung cancers. In addition to their biomarker utility, emerging evidence has revealed a number of critical functional roles for KLKs in the pathogenesis of cancer and their potential use as therapeutic targets.

SUMMARY

KLKs have biomarker utility in many cancer types but individually lack sufficient specificity or sensitivity to be used in clinical practice; however, groups of KLKs and other candidate biomarkers may offer improved performance.

An artificial neural network for five different assay systems of prostate-specific antigen in prostate cancer diagnostics

OBJECTIVE

To compare separate prostate-specific antigen (PSA) assay-specific artificial neural networks (ANN) for discrimination between patients with prostate cancer (PCa) and no evidence of malignancy (NEM).

PATIENTS AND METHODS

In 780 patients (455 with PCa, 325 with NEM) we measured total PSA (tPSA) and free PSA (fPSA) with five different assays: from Abbott (AxSYM), Beckman Coulter (Access), DPC (Immulite 2000), and Roche (Elecsys 2010) and with tPSA and complexed PSA (cPSA) assays from Bayer (ADVIA Centaur). ANN models were developed with five input factors: tPSA, percentage free/total PSA (%fPSA), age, prostate volume and digital rectal examination status for each assay separately to examine two tPSA ranges of 0-10 and 10-27 ng/mL.

RESULTS

Compared with the median tPSA concentrations (range from 4.9 [Bayer] to 6.11 ng/mL [DPC]) and especially the median %fPSA values (range from 11.2 [DPC] to 17.4%[Abbott], for tPSA 0-10 ng/mL), the areas under the receiver operating characteristic curves (AUC) for all calculated ANN models did not significantly differ from each other. The AUC were: 0.894 (Abbott), 0.89 (Bayer), 0.895 (Beckman), 0.882 (DPC) and 0.892 (Roche). At 95% sensitivity the specificities were without significant differences, whereas the individual absolute ANN outputs differed markedly.

CONCLUSIONS

Despite only slight differences, PSA assay-specific ANN models should be used to optimize the ANN outcome to reduce the number of unnecessary prostate biopsies. We further developed the ANN named 'ProstataClass' to provide clinicians with an easy to use tool in making their decision about follow-up testing.

Association of extent and aggressiveness of inflammation with serum PSA levels and PSA density in asymptomatic patients

OBJECTIVES

The relationship between serum prostate-specific antigen (PSA) levels and histologic prostatic inflammation is controversial. Previous studies were performed using either the intensity or extent of inflammation for grading, with a relatively lower number of specimens. In our study, the inflammation was categorized more profoundly, using both the intensity and the extent of inflammation, to determine the influence of prostatic inflammation on serum PSA levels, percent free PSA (%fPSA), and PSA density (PSAD).

METHODS

The study included 115 patients who underwent transrectal ultrasound-guided prostate biopsy. To categorize the inflammation, a grading method that included the intensity and extent of inflammation was used. The extent and aggressiveness of inflammation were analyzed. The patients were divided into groups using five grades for the extent and four grades for the aggressiveness of inflammation. The serum PSA levels, fPSA levels, %fPSA, and PSAD in each group were compared.

RESULTS

The extent of inflammation grade correlated positively with the serum PSA level (r = 0.423, P <0.001) and PSAD (r = 0.319, P = 0.001). However, a negative correlation was found between the extent of inflammation grade and %fPSA (r = -0.268, P = 0.015). The aggressiveness of inflammation grade correlated positively with the serum PSA level (r = 0.386, P <0.001) and PSAD (r = 0.341, P = 0.001) and negatively with %fPSA (r = -0.289, P = 0.03).

CONCLUSIONS

If the elevation of serum PSA is thought to be caused by histologic inflammation, it might prevent unnecessary repeated biopsies.

PSA and new biomarkers within multivariate models to improve early detection of prostate cancer

This review gives an overview of the use of prostate-specific antigen (PSA) and percent free-PSA (%fPSA)-based artificial neural networks (ANNs) and logistic regression models (LR) to reduce unnecessary prostate biopsies. There is a clear advantage in including clinical data such as age, digital rectal examination and transrectal ultrasound (TRUS) variables like prostate volume and PSA density as additional factors to tPSA and %fPSA within ANNs and LR models. There is also positive impact of tPSA and fPSA assays on the outcome of ANNs. New markers provide additional value within ANNs but to prove their clinical usefulness further testing is necessary.