Urinary biomarkers for the detection of prostate cancer in patients with high-grade prostatic intraepithelial neoplasia

Gene-Transcript Expression in Urine Supernatant and Urine Cell-Sediment Are Different but Equally Useful for Detecting Prostate Cancer

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51-0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20× higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other.

The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis

Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research.

The Percentage of Free PSA and Urinary Markers Distinguish Prostate Cancer from Benign Hyperplasia and Contribute to a More Accurate Indication for Prostate Biopsy

The main advantage of urinary biomarkers is their noninvasive character and the ability to detect multifocal prostate cancer (CaP). We have previously implemented a quadruplex assay of urinary markers into clinical practice (PCA3, AMACR, TRPM8 and MSMB with KLK3 normalization). In this study, we aimed to validate it in a larger cohort with serum PSA 2.5-10 ng/mL and test other selected transcripts and clinical parameters, including the percentage of free prostate-specific antigen (PSA) (% free PSA) and inflammation. In the main cohort of 299 men, we tested the quadruplex transcripts. In a subset of 146 men, we analyzed additional transcripts (CD45, EPCAM, EZH2, Ki67, PA2G4, PSGR, RHOA and TBP). After a prostate massage, the urine was collected, RNA isolated from a cell sediment and qRT-PCR performed. Ct values of KLK3 (i.e., PSA) were strongly correlated with Ct values of other genes which play a role in CaP (i.e., PCA3, AMACR, TRPM8, MSMB and PSGR). AMACR, PCA3, TRPM8 and EZH2 mRNA expression, as well as % free PSA, were significantly different for BPH and CaP. The best combined model (% free PSA plus PCA3 and AMACR) achieved an AUC of 0.728 in the main cohort. In the subset of patients, the best AUC 0.753 was achieved for the combination of PCA3, % free PSA, EPCAM and PSGR. PCA3 mRNA was increased in patients with inflammation, however, this did not affect the stratification of patients indicated for prostate biopsy. In conclusion, the percentage of free PSA and urinary markers contribute to a more accurate indication for prostate biopsy.

Methodology for the at-home collection of urine samples for prostate cancer detection

Urine from patients with prostate cancer (PCa) contains gene transcripts that have been used for PCa diagnosis and prognosis. Historically, patient urine samples have been collected after a digital rectal examination of the prostate, which was thought necessary to boost the levels of prostatic secretions in the urine. We herein describe methodology that allows urine to be collected by patients at home and then posted to a laboratory for analysis. RNA yields and quality were comparable to those for post digital rectal examination urine, and there was improved sensitivity for the detection of TMPRSS2:ERG transcripts by RT-PCR. The At-Home collection protocol has opened up the potential to perform large-scale PCa studies without the inconvenience, cost, discomfort and expense of patients having to visit the clinic.

In Search for risk predictors at the microscopic scenario of a negative biopsy. A systematic review

INTRODUCTION

In cases of persistent suspicion of prostate cancer (PC), repeat prostate biopsies (PB) are frequently performed in spite of their low yield. In the context of a negative PB, there is a microscopic scenario (MS), which we define as the group of recognizable non-neoplastic lesions. While some of these lesions seem to have a protective effect, the existence of others increases the risk of PC detection in posterior PB. The objective of this systematic review is to identify the lesions that may belong to the MS of a negative PB and analyse the current evidence of their association with the risk of detecting PC in subsequent PBs.

EVIDENCE ACQUISITION

Two independent reviewers conducted a literature search on Medline, Embase and Central Cochrane with the following search terms: small acinar proliferation, ASAP, prostatic intraepithelial neoplasia, HGPIN, adjacent small atypical glands, pinatyp, atrophy, proliferative inflammatory atrophy, pia, prostatic inflammation, prostatitis and prostate cancer. 1,015 references were first identified, and 57 original articles were included in the study, following the PRISMA declaration and the PICO selection principles.

EVIDENCE SYNTHESIS

Atypical small acinar proliferation is associated with PC detection in repeat PB with rates ranging between 32 and 48%. High-grade prostatic intraepithelial neoplasia (HGPIN) is related to PC in 13 to 42% of cases. Studies show that HGPIN, when multifocal, is a significant independent risk factor for PC. Prostatic atrophy, inflammatory proliferative atrophy and prostatic inflammation seem to act as protective factors on the detection of PC in repeat PB. On the other hand, the risk of PC detection reduces significantly in male patients with multifocal HGPIN and coexistent PIA.

CONCLUSIONS

The MS of a negative PB may include atypical small acinar proliferation, HGPIN, prostatic atrophy, inflammatory proliferative atrophy and prostatic inflammation lesions, since they all seem to be associated with the risk of PC detection in repeat PB. This review has led us to create the hypothesis that the MS of a negative PB might be a valuable and useful tool when considering repeat PB.

Importance of identification of prostatic adenocarcinoma in urine cytology

BACKGROUND

Prostate carcinoma (PCa) occasionally involves the urethra and/or bladder. In these cases, PCa cells may be detected in urine. The purpose of this study was to describe the salient cytomorphologic, immunocytochemical, and epidemiologic features of PCa cells detected in urine cytology slides via a retrospective case series review.

MATERIALS AND METHODS

We retrospectively identified 28 cases with urine cytology either suspicious or positive for PCa. Clinical and histopathologic data were reviewed.

RESULTS

We identified 23 prostatic acinar adenocarcinomas (PAAs) and 5 prostatic adenocarcinomas with ductal features (PDAs). Urine cytology was the first evidence of disease in 6 (26%) patients with PAA and in 4 (80%) of the patients with PDA. In patients with PAA, 17 had a previous history of PAA, with positive urine cytology in the setting of disease recurrence or persistence within the bladder or urethra. The PAA in urine presented as single or small clusters of atypical cuboidal glandular cells with large, eccentric, round, or oval uniform nuclei containing conspicuous nucleoli, and scant to moderate delicate or granular cytoplasm, whereas the PDA presented as atypical columnar glandular cells in flat nests or 3-dimensional clusters, and with prominent nucleoli.

CONCLUSIONS

Using standard urine cytology, we were able to detect PCa cells in the urine. Although rare, PCa was first diagnosed by urine cytology in select cases, with a higher frequency in patients with PDA. Clinicians should be aware that PCa cells can be identified by urine cytology as this can lead to an earlier diagnosis and treatment.

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

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

Circulating mRNAs and miRNAs as candidate markers for the diagnosis and prognosis of prostate cancer

Circulating nucleic acids are found in free form in body fluids and may serve as minimally invasive tools for cancer diagnosis and prognosis. Only a few studies have investigated the potential application of circulating mRNAs and microRNAs (miRNAs) in prostate cancer (PCa). The Cancer Genome Atlas (TCGA) database was used for an in silico analysis to identify circulating mRNA and miRNA as potential markers of PCa. A total of 2,267 genes and 49 miRNAs were differentially expressed between normal and tumor samples. The prediction analyses of target genes and integrative analysis of mRNA and miRNA expression revealed eleven genes and eight miRNAs which were validated by RT-qPCR in plasma samples from 102 untreated PCa patients and 50 cancer-free individuals. Two genes, OR51E2 and SIM2, and two miRNAs, miR-200c and miR-200b, showed significant association with PCa. Expression levels of these transcripts distinguished PCa patients from controls (67% sensitivity and 75% specificity). PCa patients and controls with prostate-specific antigen (PSA) ≤ 4.0 ng/mL were discriminated based on OR51E2 and SIM2 expression levels. The miR-200c expression showed association with Gleason score and miR-200b, with bone metastasis, bilateral tumor, and PSA > 10.0 ng/mL. The combination of circulating mRNA and miRNA was useful for the diagnosis and prognosis of PCa.

Association between RNASEL, MSR1, and ELAC2 single nucleotide polymorphisms and gene expression in prostate cancer risk

BACKGROUND

There is contradictory evidence of the effects that environmental factors-dietary habits (ingestion rates of red meat, soy products, fish, etc.) and work environment (exposure to metals, pesticides, several toxic products, etc.)-and KLK3, AR, RNASEL, MSR1, and ELAC2 expression patterns have on prostate cancer (PCa). In our study, we investigated the potential association between KLK3, AR, RNASEL, MSR1, and ELAC2 polymorphisms, expression patterns, exposure to environmental factors, and PCa in a Spanish cohort. Blood and fresh tissue samples were collected from 322 subjects with prostate-specific antigen (PSA)>4ng/ml to determine their genotypes (RNASEL, MSR1, and ELAC2) and assess messenger ribonucleic acid expression levels (by quantitative amplification testing).

MAIN FINDINGS

Among clinical parameters, a 63.6% of patients with CC variants in rs11545302 (ELAC2) had PSA>20ng/ml (P = 0.008), and rs486907 (RNASEL), with 52.8% of patients with CT variants with Gleason score>7. Regarding TNM stage, patients with GG variants, rs4792311 (ELAC2) generally had stage 1 tumors. Genetic expression analysis revealed RNASEL (P = 0.007) was underexpressed in PCa tissue, whereas KLK3 (P = 0.041) was overexpressed. As to environmental factors, the intake of dried fruits (P = 0.036) and practice of sports (P = 0.024) revealed an effect in PCa. Moreover, environmental factors were observed to affect gene expression patterns. Thus, RNASEL (P = 0.018) and ELAC2 (P = 0.023) were found to be underexpressed in patients who ate processed foods frequently; MSR1 (P = 0.024) and AR (P = 0.004) were underexpressed in patients who did not practice sports; and KLK3 (P = 0.039; P = 0.046) underexpressed in patients exposed to dust and toxic products.

CONCLUSIONS

This is the first study to analyze the correlation between RNASEL, MSR1, and ELAC2 genotypes and messenger ribonucleic acid expression in PCa. RNASEL and KLK3 show different expression patterns in normal vs. tumor tissue, which supports their reported relevance in human cancer. The results obtained confirm that RNASEL plays a crucial role in PCa. Environmental factors such as exercise, exposure to toxic agents, and intake of processed foods are associated with PCa.