Fluctuation in prostate cancer gene 3 (PCA3) score in men undergoing first or repeat prostate biopsies

Observational study comparing the accuracy/variability between the ERSPC and the PCPT risk calculators for the prediction of significant prostate cancer in patients with PSA <10 ng/mL

INTRODUCTION

Risk calculators (RCs) are easy-to-use tools considering available clinical variables that could help to select those patients with risk of prostate cancer (PCa) who should undergo a prostate biopsy.

OBJECTIVE

To perform a comparison for the prediction of significant PCa (SigPCa) between the European Randomised Study of Screening for PCa (ERSPC) and the PCa Prevention Trial (PCPT) RCs in patients with prostate-specific antigen (PSA) between 3 and 10 ng/mL through an evaluation of the accuracy/variability between two consecutive PSA values.

SETTING

An observational study in a major university hospital in the south of Spain.

METHODS AND PARTICIPANTS

An observational study was performed in patients who underwent a prostate biopsy. SigPCa probabilities were calculated with the two PSA measures using ERSPC3/4+digital rectal examination and PCPT v2+free PSA RCs. The prediction of SigPCa was determined by the area under the receiver operating characteristic curve (AUC). Calibration, discrimination and decision curve analysis were studied. The variability between both RCs' agreement was compared using Cohen's kappa coefficient.

RESULTS

510 patients were analysed (87 diagnosed with SigPCa). The median PSA values were 5.3 and 5 ng/mL for PSA1 and PSA2, respectively. Both RCs overestimated the risk in the case of high-risk probabilities. Discriminative ability for SigPCa was similar between models with an AUC=0.73 (0.68-0.79) for ERSPC-RC versus 0.73 (0.67-0.79) for PCPT-RC. ERSPC-RC showed less variability than PCPT-RC, with a constant agreement (k=0.7-0.8) for usual range of clinical decision-making. Remarkably, a higher number of biopsies would be avoided using the ERSPC-RC, but more SigPCa would be missed along all the risk probabilities.

CONCLUSIONS

Both RCs performed similar in the prediction of SigPCa. However, ERSPC-RC seems to be more stable for intraindividual PSA variations.

Plasma ghrelin O-acyltransferase (GOAT) enzyme levels: A novel non-invasive diagnosis tool for patients with significant prostate cancer

Early detection of PCa faces severe limitations as PSA displays poor‐specificity/sensitivity. As we recently demonstrated that plasma ghrelin O‐acyltransferase (GOAT)‐enzyme is significantly elevated in PCa‐patients compared with healthy‐controls, using a limited patients‐cohort, we aimed to further explore the potential of GOAT to improve PCa diagnosis using an ample patients‐cohort (n = 312) and defining subgroups (i.e. significant PCa/metastatic patients, etc.) that could benefit from this biomarker. Plasma GOAT‐levels were evaluated by ELISA in patients with (n = 183) and without (n = 129) PCa. Gleason Score ≥ 7 was considered clinically significant PCa. GOAT‐levels were higher in PCa patients vs control patients, and in those with significant PCa vs non‐significant PCa. GOAT‐levels association with the diagnoses of significant PCa was independent from traditional clinical variables (i.e. PSA/age/DRE). Remarkably, GOAT outperformed PSA in patients with PSA‐levels ranging 3‐20 ng/mL for the significant PCa diagnosis [GOAT‐AUC = 0.612 (0.531‐0.693) vs PSA‐AUC = 0.494 (0.407‐0.580)]. A panel of key variables including GOAT/age/DRE/testosterone also outperformed the same panel but with PSA [AUC = 0.720 (0.710‐0.730) vs AUC = 0.705 (0.695‐0.716), respectively]. Notably, GOAT‐levels could also represent a novel predictive biomarker of aggressiveness, as its levels are positively associated with Gleason Score and the presence of metastasis at the time of diagnoses. Altogether, our data reveal that GOAT‐levels can be used as a non‐invasive biomarker for significant PCa diagnosis in patients at risk of PCa (with PSA: 3‐20 ng/mL).

The role of prostate cancer antigen 3 (PCA3) in prostate cancer detection

INTRODUCTION

The prostate cancer antigen 3 (PCA3) score has been the first urine assay to obtain the Food and Drug Administration approval for guiding decisions regarding additional biopsies. Different aspects of this urinary assay (diagnostic performance, prognostic value, cost/benefit balance, integration with other molecular and imaging modalities) have now been well evaluated. Areas covered: This expert review will summarize current achievements and future perspectives provided by this urine biomarker. Expert commentary: The clinical benefit of the PCA3 score, in addition to the other established factors has been demonstrated before regarding biopsy decision making in men with persistent risk of prostate cancer. Its potential prognostic value also suggests its usefulness in selecting low risk patients for active surveillance protocols, however future daily-practice changing studies are needed. Economics assessment and additional value compared with other biomolecular and imaging modalities are still under investigation.

The role of biomarkers in the assessment of prostate cancer risk prior to prostate biopsy: which markers matter and how should they be used?

Prostate cancer (PCa) screening has been substantially influenced by the clinical implementation of serum prostate-specific antigen (PSA). In this context, improvement of early PCa detection and stage migration as well as reduced PCa mortality were achieved, and up-to-date PSA represents the gold standard biomarker of PCa diagnosis together with clinical findings. Nonetheless, PSA shows weakness in discriminating between malign and benign prostatic disease or indolent and aggressive cancers. As a result, the expansion of PSA screening is extensively debated with regard to overdetection and ultimately overtreatment, keeping in mind that PCa is still the third leading cause of cancer-specific mortality in the Western male population. Consequently, today's task is to increase the accuracy of PCa detection and furthermore to allow stratification for indolent PCa that might permit active surveillance and to filter out aggressive cancers necessitating treatment. Thus, novel biomarkers, especially in combination with approved clinical risk factors (e.g., age or family history of PCa), within multivariable prediction models carry the potential to improve many aspects of PCa diagnosis and to enable risk classification in clinical practice. Multivariable models lead to superior accuracy for PCa prediction instead of the use of a single risk factor. The aim of this article was to present an overview of known risk factors for PCa together with new promising blood- and urine-based biomarkers and their application within risk models that may allow risk stratification for PCa prior to prostate biopsy. Risk models may optimize PCa detection and classification with regard to improved PCa risk assessment and avoidance of unnecessary prostate biopsies.