Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

Extracellular vesicles (EVs) are shed by many different cell types. Their nucleic acids content offers new opportunities for biomarker research in different solid tumors. The role of EV RNA in prostate cancer (PCa) is still largely unknown. EVs were isolated from different benign and malignant prostate cell lines and blood plasma from patients with PCa (n = 18) and controls with benign prostatic hyperplasia (BPH) (n = 7). Nanoparticle tracking analysis (NTA), Western blot, electron microscopy, and flow cytometry analysis were used for the characterization of EVs. Non-coding RNA expression profiling of PC3 metastatic PCa cells and their EVs was performed by next generation sequencing (NGS). miRNAs differentially expressed in PC3 EVs were validated with qRT-PCR in EVs derived from additional cell lines and patient plasma and from matched tissue samples. 92 miRNAs were enriched and 48 miRNAs were depleted in PC3 EVs compared to PC3 cells, which could be confirmed by qRT-PCR. miR-99b-5p was significantly higher expressed in malignant compared to benign EVs. Furthermore, expression profiling showed miR-10a-5p (p = 0.018) and miR-29b-3p (p = 0.002), but not miR-99b-5p, to be overexpressed in plasma-derived EVs from patients with PCa compared with controls. In the corresponding tissue samples, no significant differences in the miRNA expression could be observed. We thus propose that EV-associated miR-10a-5p and miR-29b-3p could serve as potential new PCa detection markers.

In prostate cancer and other malignancies sensitive and robust biomarkers are lacking or have relevant limitations. Prostate specific antigen (PSA), the only biomarker widely used in prostate cancer, is suffering from low specificity. Exosomes offer new perspectives in the discovery of blood-based biomarkers. Here we present a proof-of principle study for a proteomics-based identification pipeline, implementing existing data sources, to exemplarily identify exosome-based biomarker candidates in prostate cancer. Exosomes from malignant PC3 and benign PNT1A cells and from FBS-containing medium were isolated using sequential ultracentrifugation. Exosome and control samples were analyzed on an LTQ-Orbitrap XL mass spectrometer. Proteomic data is available via ProteomeXchange with identifier PXD003651. We developed a scoring scheme to rank 64 proteins exclusively found in PC3 exosomes, integrating data from four public databases and published mass spectrometry data sets. Among the top candidates, we focused on the tight junction protein claudin 3. Retests under serum-free conditions using immunoblotting and immunogold labeling confirmed the presence of claudin 3 on PC3 exosomes. Claudin 3 levels were determined in the blood plasma of patients with localized (n = 58; 42 with Gleason score 6–7, 16 with Gleason score ≥8) and metastatic prostate cancer (n = 11) compared with patients with benign prostatic hyperplasia (n = 15) and healthy individuals (n = 15) using ELISA, without prior laborious exosome isolation. ANOVA showed different CLDN3 plasma levels in these groups (p = 0.004). CLDN3 levels were higher in patients with Gleason ≥8 tumors compared with patients with benign prostatic hyperplasia (p = 0.012) and Gleason 6–7 tumors (p = 0.029). In patients with localized tumors CLDN3 levels predicted a Gleason score ≥ 8 (AUC = 0.705; p = 0.016) and did not correlate with serum PSA. By using the described workflow claudin 3 was identified and validated as a potential blood-based biomarker in prostate cancer. Furthermore this workflow could serve as a template to be used in other cancer entities.

Purpose: The comprehensive complication index (CCI) is a new tool for reporting the cumulative burden of postoperative complications on a continuous scale. This study validates the CCI for urological surgery and its benefits over the Clavien-Dindo-Classification (Clavien). Material and methods: Data from a prospectively maintained data base of all consecutive patients at a university care-center was analyzed. Complications after radical cystectomy (RC), radical prostatectomy (RP), and partial nephrectomy (PN) were classified using the CCI and Clavien system. Differences in complications between the CCI and the Clavien were assessed and correlation analyses performed. Sample size calculations for hypothetical clinical trials were compared between CCI and Clavien to evaluate whether the CCI would reduce the number of required patients in a clinical trial. Results: 682 patients (172 RC, 297 RP, 213 PN) were analyzed. Overall, 9.4–46.6% of patients had > 1 complication cumulatively assessed with the CCI resulting in an upgrading in the Clavien classification for 2.4–32.4% of patients. Therefore, scores between the systems differed for RC: CCI (mean ± standard deviation) 26.3 ± 20.8 vs. Clavien 20.4 ± 16.7, p < 0.001; PN: CCI 8.4 ± 14.7 vs. Clavien 7.0 ± 11.8, p < 0.001 and RP: CCI 5.8 ± 11.7 vs. Clavien 5.3 ± 10.6, p = 0.102. The CCI was more accurate in predicting LOS after RC than Clavien (p < 0.001). Sample size calculations based in the CCI (for future hypothetical trials) resulted in a reduction of required patients for all procedures (− 25% RC, − 74% PN, − 80% RP). Conclusion: The CCI is more accurate to assess surgical complications and reduces required sample sizes that will facilitate the conduction of clinical trials.

Paracrine and long-range signaling via extracellular vesicles, such as exosomes and microvesicles, is deemed crucial for tumorigenesis, invasion and spread of solid tumors. The ESCRT machinery (endosomal sorting complexes required for transport) and Rab-proteins act as key players in vesicular trafficking and secretion. Yet, their role in prostate cancer (PCa) is unknown. Therefore, this study aimed to elucidate the relevance of these components in PCa. In silico reanalysis of genes with known involvement in vesicular trafficking and secretion in an existing microarray dataset revealed low expression of RAB27A, RAB27B and VPS36 to be predictive for reduced BCR-free survival in patients with localized PCa (p=0.033, 0.025 and 0.005). In the same microarray dataset underexpression of RAB27A, RAB27B and VPS36 was seen in distant metastases (p<0.001; p=0.003; p<0.001). This was consistent in two further microarray datasets. qRT-PCR-validation in two independent cohorts of PCa specimens (n=90) showed low expression of VPS36 in PCa tissue (p=0.023), especially in castration-resistant tumors (p=0.002). In all five datasets there were significant correlations between the expression of at least two of the candidates. Upon knockdown of VPS36 an increase of RAB27A and RAB27B expression, but not vice versa, was observed in both prostate and breast cancer cells (PC3, MDA-MB‑231). In PC3 cell knockdown of RAB27B and VPS36 dramatically reduced colony formation (-52.2%, p<0.001; -71.1%, p<0.001) and, controversial to reports in other tumor entities, increased the release of extracellular particles (+25.3%, p=0.014; +45.6%, p<0.001). Taken together RAB27A, RAB27B and VPS36 are frequently underexpressed in advanced PCa and are inversely correlated with PCa outcome. There seems to be a close relationship in the expression of RAB27A, RAB27B and VPS36, with RAB27A and RAB27B being dependent on VPS36. Changes in colony formation and particle release upon RNAi indicate an involvement in paracrine cell-cell communication.