Liquid biomarkers in active surveillance

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.

Serum Essential Elements and Survival after Cancer Diagnosis

In a prospective study, we measured the associations between three serum elements (Se, Zn and Cu) and the prognosis of 1475 patients with four different types of cancer (breast, prostate, lung and larynx) from University Hospitals in Szczecin, Poland. The elements were measured in serum taken after diagnosis and prior to treatment. Patients were followed from the date of diagnosis until death from any cause or until the last follow-up date (mean years of follow-up: 6.0-9.8 years, according to site). Kaplan-Meier curves were constructed for all cancers combined and for each cancer separately. Age-adjusted hazard ratios (HRs) were estimated using Cox regression. The outcome was all-cause mortality. A Se level in the highest quartile was also associated with a reduced mortality (HR = 0.66; 95%CI 0.49-0.88; p = 0.005) in all-cause mortality for all cancers combined. Zn level in the highest quartile was also associated with reduced mortality (HR = 0.55; 95%CI 0.41-0.75; p = 0.0001). In contrast, a Cu level in the highest quartile was associated with an increase in mortality (HR = 1.91; 95%CI 1.56-2.08; p = 0.0001). Three serum elements-selenium, zinc and copper-are associated with the prognosis of different types of cancer.

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

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

Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer

Simple Summary

In the last decades, the widespread use of PSA as the standard tool for prostate cancer diagnosis led to a high rate of overdiagnosis and overtreatment. More recently, multiparametric magnetic resonance imaging (mpMRI) became part of the diagnostic pathway, and several next-generation PSA-based tests (PHI, PHI density, 4Kscore, STHLM3) have been proposed. The multivariable approach promises to help with a better stratification of PCa patients at initial diagnosis. In this study, we evaluated the performance of the prostate health index (PHI) and mpMRI for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). Our findings suggested that PHI had a better ability than mpMRI to predict positive biopsy, whereas a comparable performance in the identification of pathological aggressive PCa was pointed out. Notably, PHI and PHI density might represent useful biomarkers to recognize high-grade PCa in patients with low or uncertain PI-RADS scores on mpMRI.

Abstract

Widespread use of PSA as the standard tool for prostate cancer (PCa) diagnosis led to a high rate of overdiagnosis and overtreatment. In this study, we evaluated the performance of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). To this end, we prospectively enrolled 196 biopsy-naïve patients who underwent mpMRI. A subgroup of 116 subjects with biopsy-proven PCa underwent surgery. We found that PHI significantly outperformed both PI-RADS score (difference in AUC: 0.14; p < 0.001) and PHI density (difference in AUC: 0.08; p = 0.002) in the ability to predict positive biopsy with a cut-off value of 42.7 as the best threshold. Conversely, comparing the performance in the identification of clinically significant prostate cancer (csPCa) at RP, we found that PHI ≥ 61.68 and PI-RADS score ≥ 4 were able to identify csPCa (Gleason score ≥ 7 (3 + 4)) both alone and added to a base model including age, PSA, fPSA-to-tPSA ratio and prostate volume. In conclusion, PHI had a better ability than PI-RADS score to predict positive biopsy, whereas it had a comparable performance in the identification of pathological csPCa.

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

Simple Summary

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

Abstract

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