Thomas Gevaert

Bladder pain and dysfunction are common symptoms in patients with urinary tract infections (UTIs), including catheter-associated UTIs (CAUTIs). Underlying mechanisms of chronic bladder pain and recurrent UTIs remain poorly understood, and good translational mice models are sparse. Here, we developed a female mouse suprapubic CAUTI model, and combined in vivo and ex vivo imaging techniques and behavioral assays to longitudinally evaluate the consequences for bladder function, inflammation, abdominal pain, bacterial colonization, and biofilm. Catheter-associated urinary tract infection animals, with an implanted suprapubic bladder catheter infected with the bioluminescent uropathogenic Escherichia coli (UPEC) UTI89-Lux strain, were compared with sham-operated mice (SHAM), mice with a sterile catheter (CACON), and mice infected via transurethral instillation (UTI). Compared to transient infections in UTI mice, CAUTI mice exhibited sustained bacterial infection during >2 weeks, which was associated with bacterial biofilm formation on the catheter inner surface. Compared to CACON mice, CAUTI mice showed a more pronounced increase in suprapubic sensitivity, which was sensitive to antibiotic treatment. Void spot assays and fluoroscopic volumetry further revealed a smaller bladder capacity in both CAUTI and CACON mice, but only in CAUTI mice this was associated with reduced voiding efficiency. This model successfully recapitulates clinically relevant symptoms of CAUTI in patients and demonstrates that sustained bacterial colonization of the catheter directly contributes to bladder hypersensitivity and voiding dysfunction. This novel in vivo research model may be instrumental in the search for new therapies aimed at disrupting biofilms and treatments of bladder-related pain, infections, and dysfunction.

Purpose Quantification of PSMA expression via PSMA PET is well-established, however quantification of PSMA via immunohistochemistry (IHC) is not standardized. Our aim was to determine the most optimal PSMA IHC scoring system to quantify PSMA expression with PSMA PET as reference standard. Methods Primary intermediate- and high-risk prostate cancer patients received an [¹⁸F]PSMA-1007 PET/MRI followed by radical prostatectomy. SUV_max, SUV_mean and K_i of the prostate tumor was determined. Prostate tumors were stained with anti-PSMA antibodies and scored by 2 readers via 10 IHC scoring systems: histochemical score (H-score), immunoreactivity score_{predominant intensity} (IRS_{predominant intensity}), IRS classification_{predominant intensity}, IRS_{mean intensity}, IRS classification_{mean intensity}, Allred score, predominant expression pattern, Shannon diversity index (SDI), percentage negatively stained cells and total percentage positively stained cells. Spearman’s rank correlation coefficients (ρ) were calculated between PET parameters and IHC scoring systems. Interreader agreement for the IHC scoring systems was measured by the intraclass correlation coefficient (ICC). Results Fifty tumors in 46 patients were analysed. H-score had the best correlation with SUV_max (ρ 0.615 p < 0.0001) and SUV_mean (ρ 0.570, p < 0.0001) and the second best correlation with K_i (ρ 0.411, p = 0.0030). SDI had the best correlation with K_i (ρ -0.440, p = 0.0014) and the second best correlation with SUV_max (ρ -0.516, p = 0.0001) and SUV_mean (ρ -0.490, p = 0.0003). A moderate interreader agreement was observed for H-score (ICC 0.663, 95% CI 0.495–0.797) and SDI (ICC 0.546, 95% CI 0.354–0.725). Conclusion H-score had the best correlation with PSMA PET quantification and an acceptable interreader agreement. Therefore, we deem H-score the most optimal PSMA IHC scoring system.

Cytokeratin 5 is a marker of basal molecular subtypes of muscle-invasive bladder cancer (MIBC), which correlates with worse overall survival compared to luminal subtypes. Our observations have not confirmed CK5 as a marker of high-grade (HG) disease in Ta non-muscle-invasive bladder cancer (NMIBC). Therefore, to understand the basal-luminal immunohistochemistry profile in Ta NMIBC, we performed immunohistochemistry for CK5, P40, P63 (basal), GATA3 and CK20 (luminal) and studied the correlation with HG and clinical outcome in 109 patients with Ta NMIBC. HG and low-grade (LG) diseases were scored in each patient. Four different CK5 patterns were evaluated: absent (median 41.3%), normal (72.5%), rising (84.4%) and full thickness (23.9%). The median percentage of GATA3 was 100%. HG disease and CK5 expression and rising CK5 pattern had a significant inverse correlation, whereas HG disease and CK20 expression had a significant positive correlation. We also found a significant inverse correlation between CK5 expression and CK20 expression. Quantitative PCR confirmed that the presence of CK5 correlated with up-regulation of CK5 RNA. None of the markers could differentiate patients with regard to clinical outcome. Our results suggest a role for CK5 and CK20 in differentiating between LG and HG disease in Ta NMIBC.

Five programmed death-1/programmed death-ligand 1 (PD-L1) inhibitors are currently approved for the treatment of locally advanced or metastatic urothelial carcinoma of the bladder and the upper urinary tract. Following the FDA and EMA restrictions of first-line treatment with Atezolizumab and Pembrolizumab in platinum-ineligible patients, immunohistochemical PD-L1 testing is now required. Several emerging issues on antibodies, test platforms and scoring algorithms have raised concerns about the comparability and interchangeability between these assays. In this review, we have focused on the interchangeability of the used algorithms and assays for PD-L1 testing in urothelial carcinoma, on the predictive reliability of PD-L1 testing in urothelial carcinoma and the potential of other new and upcoming biomarkers.

The tumour micro-environment (TME) plays a crucial role in the onset and progression of prostate cancer (PCa). Here we studied the potential of a selected panel of TME-markers to predict clinical recurrence (CLR) in PCa. Patient cohorts were matched for the presence or absence of CLR 5 years post-prostatectomy. Tissue micro-arrays (TMA) were composed with both prostate non-tumour (PNT) and PCa tissue and subsequently processed for immunohistochemistry (IHC). The IHC panel included markers for cancer activated fibroblasts (CAFs), blood vessels and steroid hormone receptors ((SHR): androgen receptor (AR), progesterone receptor (PR) and estrogen receptor (ER)). Stained slides were digitalised, selectively annotated and analysed for percentage of marker expression with standardized and validated image analysis algorithms. A univariable analysis identified several TME markers with significant impact on CR: expression of CD31 (vascular marker) in PNT stroma, expression of alpha smooth muscle actin (αSMA) in PCa stroma, and PR expression ratio between PCa stroma and PNT stroma. A multivariable model, which included CD31 expression (vascular marker) in PNT stroma and PR expression ratio between PCa stroma and PNT stroma, could significantly stratify patients for CLR, with the identification of a low risk and high-risk subgroup. If validated and confirmed in an independent prospective series, this subgroup might have clinical potential for PCa patient stratification.

Objective: To perform an external validation of the Cancer of the Bladder Risk Assessment (COBRA) score for estimating cancer‐specific survival (CSS) after radical cystectomy (RC) in a large bi‐institutional cohort of patients. Patients and Methods: Patients treated with RC and lymph node dissection (LND) between May 1996 and July 2017 were retrieved from the RC databases of Leuven and Turin. Collected variables were age at RC, tumour stage, lymph node (LN) density, neoadjuvant chemotherapy, the extent of LND, and nodal stage. The primary outcome was CSS visualised using Kaplan–Meier plots. Cox proportional hazard models were used to assess the impact of variables on CSS. We performed a pairwise comparison between the COBRA score levels using a log‐rank test corrected by Bonferroni, and developed a simplified COBRA score with three risk categories. To compare models, we assessed concordance indices (C‐indices), receiver operating characteristic curves with area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Finally, we compared both COBRA and simplified COBRA models with the established American Joint Committee on Cancer (AJCC) model. Results: A total of 812 patients were included. All COBRA score variables had a significant impact on CSS in a Cox proportional hazard model. However, pairwise comparison of the COBRA subscores could not differentiate significantly between all COBRA score levels. Based on these findings, we developed a simplified COBRA score by introducing three categories within the following COBRA score ranges: low‐ (0–1) vs intermediate‐ (2–4) vs high‐risk (5–7). A pairwise comparison could discriminate significantly between all COBRA risk categories. When finally comparing COBRA and simplified COBRA models with the AJCC model, AJCC performed better than both. C‐indices, AUCs, calibration plots and DCA for AJCC were all better compared with the original and simplified COBRA models. Conclusion: We performed an external validation of the COBRA score in a large bi‐institutional cohort. We observed that several risk groups had overlapping CSS, demonstrating suboptimal performance of the COBRA score. Therefore, we constructed a simplified model with three COBRA score risk categories. This model resulted in demarcated risk groups with non‐overlapping CSS and good predictive accuracy. However, both COBRA score models were outperformed by the AJCC staging system. Therefore, we conclude that the AJCC staging system should remain the current standard for stratifying patients after RC for CSS.

The recent developments in anti-angiogenic and immunomodulatory drugs show that the tumour micro-environment (TME) becomes increasingly important in cancer research. Here we investigated the correlation between the Gleason score (GS) and the TME by comparing tissue expression profiles of steroid hormone receptors, cancer activated fibroblast (CAF) markers and vessel densities between different GS groups. Therefore, matched patient cohorts were composed for different GS (6-7-8). Tissue micro-arrays with 6 samples/patient were processed for immunohistochemistry. Stained slides were digitised, stroma and epithelium were selectively annotated, and all selected areas were quantitatively analysed for marker expression. The most striking findings were decreased stromal expression levels of several steroid hormone receptors, increased CAF-phenotypes and increased vessel densities in high GS prostate cancer compared to low GS prostate cancer and paired prostate non-tumour tissue. The present data reveal a complex correlation between prostate cancer differentiation and TME components and suggest that different GS can be associated with different possible actionable targets in the TME. The use of standardised digital image analysis tools generated robust and reproducible quantitative data, which is novel and more informative compared to the classic semi-quantitative and observer-dependent visual scoring of immunohistochemistry.