Comprehensive Urinary Proteome Profiling Analysis Identifies Diagnosis and Relapse Surveillance Biomarkers for Bladder Cancer.

Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).

Alix-normalized exosomal programmed death-ligand 1 analysis in urine enables precision monitoring of urothelial cancer.

Anti-programmed death-ligand 1 (PD-L1) Ab-based therapies have demonstrated potential for treating metastatic urothelial cancer with high PD-L1 expression. Urinary exosomes are promising biomarkers for liquid biopsy, but urine's high variability requires normalization for accurate analysis. This study proposes using the PD-L1/Alix ratio to normalize exosomal PD-L1 signal intensity with Alix, an internal exosomal protein less susceptible to heterogeneity concerns than surface protein markers. Extracellular vesicles were isolated using ExoDisc and characterized using various methods, including ExoView to analyze tetraspanins, PD-L1, and Alix on individual exosomes. On-disc ELISA was used to evaluate PD-L1 and Alix-normalized PD-L1 in 15 urothelial cancer patients during the initial treatment cycle with Tecentriq. Results showed that Alix signal range was relatively uniform, whereas tetraspanin marker intensity varied for individual exosome particles. On-disc ELISA was more reliable for detecting exosomal PD-L1 expression than standard plate ELISA-based measurement. Using exosomal Alix expression for normalization is a more reliable approach than conventional methods for monitoring patient status. Overall, the study provides a practical and reliable method for detecting exosomal PD-L1 in urine samples from patients with urothelial cancer.

Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagnosis of Bladder Cancer.

Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. However, the majority of the associated studies adopted biomarker-based unimodal analysis to achieve cancer diagnosis, which has high false positives and low precision. Herein, we report a multimodal platform for the high-precision diagnosis of bladder cancer (BCa) through a multispectral 3D DNA machine in combination with a multimodal machine learning (ML) algorithm. The DNA machine was constructed using magnetic microparticles (MNPs) functionalized with aptamers that specifically identify the target of interest, i.e., five protein markers on bladder-cancer-derived urinary EVs (uEVs). The aptamers were hybridized with DNA-stabilized silver nanoclusters (DNA/AgNCs) and a G-quadruplex/hemin complex to form a sensing module. Such a DNA machine ensured multispectral detection of protein markers by fluorescence (FL), inductively coupled plasma mass spectrometry (ICP-MS), and UV-vis absorption (Abs). The obtained data sets then underwent uni- or multimodal ML for BCa diagnosis to compare the analytical performance. In this study, urine samples were obtained from our prospective cohort (n = 45). Our analytical results showed that the 3D DNA machine provided a detection limit of 9.2 × 103 particles mL-1 with a linear range of 4 × 104 to 5 × 107 particles mL-1 for uEVs. Moreover, the multimodal data fusion model exhibited an accuracy of 95.0%, a precision of 93.1%, and a recall rate of 93.2% on average, while those of the three types of unimodal models were no more than 91%. The elevated diagnosis precision by using the present fusion platform offers a perspective approach to diminishing the rate of misdiagnosis and overtreatment of BCa.

Serum D-asparagine concentration adjusted for eGFR could serve as a novel screening tool for urothelial carcinoma.

The sensitivity of currently available screening tools for urothelial carcinoma (UC) remains unsatisfactory particularly at early stages. Hence, we aimed to establish a novel blood-based screening tool for urothelial carcinoma. We measured serum d-amino acid levels in 108 and 192 patients with and without UC individuals in the derivation cohort, and 15 and 25 patients with and without UC in the validation cohort. Serum d-asparagine levels were significantly higher in patients with UC than in those without UC (p < 0.0001). We developed a novel screening equation for the diagnosis of urothelial carcinoma using d-asparagine in serum and estimated the glomerular filtration rate (eGFR). Serum d-asparagine levels adjusted for eGFR exhibited high performance in the diagnosis of UC (AUC-ROC, 0.869; sensitivity, 80.6 %; specificity, 82.7 %), even in early-stage UC (AUC-ROC: 0.859, sensitivity: 83.3 %, specificity: 82.3 %), which were previously misdiagnosed via urinary occult blood or urine cytology. This established strategy combined with urinary occult blood, improves diagnostic ability (sensitivity: 93.7 %, specificity: 70.1 %).

Bladder cancer risk stratification with the Oncuria 10-plex bead-based urinalysis assay using three different Luminex xMAP instrumentation platforms.

BACKGROUND: No single marker of bladder cancer (BC) exists in urine samples with sufficient accuracy for disease diagnosis and treatment monitoring. The multiplex Oncuria BC assay noninvasively quantifies the concentration of 10 protein analytes in voided urine samples to quickly generate a unique molecular profile with proven BC diagnostic and treatment-tracking utility. Test adoption by diagnostic and research laboratories mandates reliably reproducible assay performance across a variety of instrumentation platforms used in different laboratories. METHODS: We compared the performance of the clinically validated Oncuria BC multiplex immunoassay when data output was generated on three different analyzer systems. Voided urine samples from 36 subjects (18 with BC and 18 Controls) were reacted with Oncuria test reagents in three 96-well microtiter plates on Day 1, and consecutively evaluated on the LED/image-based MagPix, and laser/flow-based Luminex 200 and FlexMap 3D (all xMAP instruments from Luminex Corp., Austin, TX) on Day 2. The BC assay uses magnetic bead-based fluorescence technology (xMAP, Multi-analyte profiling; Luminex) to simultaneously quantify 10 protein analytes in urine specimens [i.e., angiogenin (ANG), apolipoprotein E (ApoE), carbonic anhydrase IX (CA9), CXCL8/interleukin-8 (IL-8), matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-10 (MMP-10), serpin A1/alpha-1 anti-trypsin (A1AT), serpin E1/plasminogen activator inhibitor-1 (PAI-1), CD138/syndecan-1 (SDC1), and vascular endothelial growth factor-A (VEGF-A)]. All three analyzers quantify fluorescence signals generated by the Oncuria assay. RESULTS: All three platforms categorized all 10 analytes in identical samples at nearly identical concentrations, with variance across systems typically < 5%. While the most contemporary instrument, the FlexMap 3D, output higher raw fluorescence values than the two comparator systems, standard curve slopes and analyte concentrations determined in urine samples were concordant across all three units. Forty-four percent of BC samples registered ≥ 1 analyte above the highest standard concentration, i.e., A1AT (n = 7/18), IL-8 (n = 5), and/or ANG (n = 2), while only one control sample registered an analyte (A1AT) above the highest standard concentration. CONCLUSION: Multiplex BC assays generate detailed molecular signatures useful for identifying BC, predicting treatment responsiveness, and tracking disease progression and recurrence. The similar performance of the Oncuria assay across three different analyzer systems supports test adaptation by clinical and research laboratories using existing xMAP platforms. TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov as NCT04564781, NCT03193528, NCT03193541, and NCT03193515.

Urinary microbiome dysbiosis is associated with an inflammatory environment and perturbed fatty acids metabolism in the pathogenesis of bladder cancer.

BACKGROUND: Bladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients' outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved. METHODS: A total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared. RESULTS: Comparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00. CONCLUSIONS: Microbiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development.

Hypermethylated TAGMe as a universal-cancer-only methylation marker and its application in diagnosis and recurrence monitoring of urothelial carcinoma.

BACKGROUND: Urothelial carcinoma (UC) is the second most common urological malignancy. Despite numerous molecular markers have been evaluated during the past decades, no urothelial markers for diagnosis and recurrence monitoring have shown consistent clinical utility. METHODS: The methylation level of tissue samples from public database and clinical collected were analyzed. Patients with UC and benign diseases of the urinary system (BUD) were enrolled to establish TAGMe (TAG of Methylation) assessment in a training cohort (n = 567) using restriction enzyme-based bisulfite-free qPCR. The performance of TAGMe assessment was further verified in the validation cohort (n = 198). Urine samples from 57 UC patients undergoing postoperative surveillance were collected monthly for six months after surgery to assess the TAGMe methylation. RESULTS: We identified TAGMe as a potentially novel Universal-Cancer-Only Methylation (UCOM) marker was hypermethylated in multi-type cancers and investigated its application in UC. Restriction enzyme-based bisulfite-free qPCR was used for detection, and the results of which were consistent with gold standard pyrosequencing. Importantly, hypermethylated TAGMe showed excellent sensitivity of 88.9% (95% CI: 81.4-94.1%) and specificity of 90.0% (95% CI: 81.9-95.3%) in efficiently distinguishing UC from BUD patients in urine and also performed well in different clinical scenarios of UC. Moreover, the abnormality of TAGMe as an indicator of recurrence might precede clinical recurrence by three months to one year, which provided an invaluable time window for timely and effective intervention to prevent UC upstaging. CONCLUSION: TAGMe assessment based on a novel single target in urine is effective and easy to perform in UC diagnosis and recurrence monitoring, which may reduce the burden of cystoscopy. Trial registration ChiCTR2100052507. Registered on 30 October 2021.

Detection of bladder cancer cells using quantitative interferometric label-free imaging flow cytometry.

Bladder cancer is one of the most common cancers with a high recurrence rate. Patients undergo mandatory yearly scrutinies, including cystoscopies, which makes bladder cancer highly distressing and costly. Here, we aim to develop a non-invasive, label-free method for the detection of bladder cancer cells in urine samples, which is based on interferometric imaging flow cytometry. Eight urothelial carcinoma and one normal urothelial cell lines, along with red and white blood cells, imaged quantitatively without staining by an interferometric phase microscopy module while flowing in a microfluidic chip, and classified by two machine-learning algorithms, based on deep-learning semantic segmentation convolutional neural network and extreme gradient boosting. Furthermore, urine samples obtained from bladder-cancer patients and healthy volunteers were imaged, and classified by the system. We achieved accuracy and area under the curve (AUC) of 99% and 97% for the cell lines on both machine-learning algorithms. For the real urine samples, the accuracy and AUC were 96% and 96% for the deep-learning algorithm and 95% and 93% for the gradient-boosting algorithm, respectively. By combining label-free interferometric imaging flow cytometry with high-end classification algorithms, we achieved high-performance differentiation between healthy and malignant cells. The proposed technique has the potential to supplant cystoscopy in the bladder cancer surveillance and diagnosis space.

Changes in the Urinary Microbiome After Transurethral Resection of Non-muscle-Invasive Bladder Cancer: Insights from a Prospective Observational Study.

BACKGROUND: This study aimed to characterize the urinary and tumor microbiomes in patients with non-muscle-invasive bladder cancer (NMIBC) before and after transurethral resection of the bladder tumor (TURBT). METHODS: This single-center prospective study included 26 samples from 11 patients with low-grade Ta papillary NMIBC. Urine samples were collected at the index TURBT and at a 1-year follow-up cystoscopy. The metagenomic analysis of bacterial and archaeal populations was performed based on the highly variable V3-V4 region of the 16S rRNA gene. RESULTS: Phylogenetic alpha diversity of the bladder microbiome detected in urine was found to be lower at the 1-year follow-up cystoscopy compared to the time of the index TURBT (p < 0.01). Actinomyces, Candidatus cloacimonas, Sphingobacterium, Sellimonas, Fusobacterium, and Roseobacter were more differentially enriched taxa in urine at the follow-up cystoscopy than at the index TURBT. Beta diversity of urine microbiome significantly changed over time (p < 0.05). Phylogenetic alpha diversity of the microbiome was greater in tumor tissues than in paired urine samples (p<0.01). Sphingomonas, Acinetobacter, Candidatus, and Kocuria were more differentially overrepresented in tumor tissues than in urine. The enrichment of the abundance of Corynebacterium and Anaerococcus species in urine collected at TURBT was observed in patients who experienced recurrence within the follow-up period. CONCLUSIONS: In patients with low-grade NMIBC, the urine microbiome undergoes changes over time after removal of the tumor. The microbiome detected in tumor tissues is more phylogenetically diverse than in paired urine samples collected at TURBT. The interplay between bladder microbiome, tumor microbiome, and their alterations requires further studies to elucidate their predictive value and perhaps therapeutic implications.

Improving detection of carcinoma in situ in bladder cancer: urinary cytology vs the Xpert® BC Monitor.

OBJECTIVE: To investigate and compare the performance of urinary cytology and the Xpert BC Monitor test in the detection of bladder cancer in various clinically significant patient cohorts, including patients with carcinoma in situ (CIS), in a prospective multicentre setting, aiming to identify potential applications in clinical practice. PATIENTS AND METHODS: A total of 756 patients scheduled for transurethral resection of bladder tumour (TURBT) were prospectively screened between July 2018 and December 2020 at six German University Centres. Central urinary cytology and Xpert BC Monitor tests were performed prior to TURBT. The diagnostic performance of urinary cytology and the Xpert BC Monitor was evaluated according to sensitivity (SN), specificity (SC), negative predictive value (NPV) and positive predictive value (PPV). Statistical comparison of urinary cytology and the Xpert BC Monitor was conducted using the McNemar test. RESULTS: Of 756 screened patients, 733 (568 male [78%]; median [interquartile range] age 72 [62-79] years) were included. Bladder cancer was present in 482 patients (65.8%) with 258 (53.5%) high-grade tumours. Overall SN, SC, NPV and PPV were 39%, 93%, 44% and 92% for urinary cytology, and 75%, 69%, 59% and 82% for the Xpert BC Monitor. In patients with CIS (concomitant or solitary), SN, SC, NPV and PPV were 59%, 93%, 87% and 50% for urinary cytology, and 90%, 69%, 95% and 50% for the Xpert BC Monitor. The Xpert BC Monitor missed four tumours (NPV = 98%) in patients with solitary CIS, while potentially avoiding 63.3% of TURBTs in inconclusive or negative cystoscopy and a negative Xpert result. CONCLUSION: Positive urinary cytology may indicate bladder cancer and should be taken seriously. The Xpert BC Monitor may represent a useful diagnostic tool for correctly identifying patients with solitary CIS and unsuspicious or inconclusive cystoscopy.

Pilot study for bladder cancer detection with volatile organic compounds using ion mobility spectrometry: a novel urine-based approach.

PURPOSE: Despite many efforts, no reliable urinary marker system has so far shown the potential to substitute cystoscopy. Measuring volatile organic compounds (VOCs) from urine is a promising alternative. VOCs are metabolic products which can be measured from the headspace of urine samples. Previous studies confirmed that the urine of bladder tumor patients has a different VOC profile than healthy controls. In this pilot study, the feasibility of discriminating VOCs from urine of bladder cancer patients from that of healthy control subjects was investigated. Aim of this study was to investigate whether VOC-based diagnosis of bladder cancer from urine samples is feasible using multicapillary column ion mobility spectrometry (MCC/IMS) and to identify potential molecular correlates to the relevant analytes. METHODS: Headspace measurements of urine samples of 30 patients with confirmed transitional cell carcinoma (TCC) and 30 healthy controls were performed using MCC/IMS. In the results of the measurements, peaks showing significant differences between both groups were identified and implemented into a decision tree with respect to achieve group separation. Molecular correlates were predicted using a pre-defined dataset. RESULTS: Eight peaks with significantly differing intensity were identified, 5 of which were highly significant. Using a six-step decision tree, MCC/IMS showed a sensitivity of 90% and specificity of 100% in group separation. CONCLUSION: VOC-based detection of bladder cancer is feasible. MCC/IMS is a suitable method for urine-based diagnosis and should be further validated. The molecular characteristics and metabolic background of the analytes require further workup.

Bladder Cancer detection by urinary methylation markers GHSR/MAL: a validation study.

PURPOSE: Although cystoscopy is a reliable tool for detecting bladder cancer, it poses a high burden on patients and entails high costs. This highlights the need for non-invasive and cost-effective alternatives. This study aimed to validate a previously developed urinary methylation marker panel containing GHSR and MAL. METHODS: We enrolled 134 patients who underwent cystoscopy because of hematuria, including 63 individuals with primary bladder cancer and 71 with non-malignant findings. Urine samples were self-collected at home and sent via regular mail. Subsequently, DNA was extracted and the hypermethylation of GHSR and MAL was evaluated using quantitative methylation-specific polymerase chain reaction. The performance of methylation markers was assessed using area-under-the-curve (AUC) analysis and sensitivity and specificity based on pre-established cut-off values. RESULTS: Validation of the marker panel GHSR/MAL resulted in an AUC of 0.87 at 79% sensitivity and 80% specificity. Sensitivity was comparable to the previous investigation (P > 0.9), though specificity was significantly lower (P = 0.026). Sensitivity was higher for high-grade tumors compared to low-grade tumors (94% vs. 60%, P = 0.002). CONCLUSION: Validation of the GHSR/MAL methylation marker panel on at home collected urine samples confirms its robust performance for bladder cancer detection in a hematuria population, and underscores the diagnostic potential for future clinical application.

From Detection to Cure - Emerging Roles for Urinary Tumor DNA (utDNA) in Bladder Cancer.

PURPOSE OF REVIEW: This review sought to define the emerging roles of urinary tumor DNA (utDNA) for diagnosis, monitoring, and treatment of bladder cancer. Building from early landmark studies the focus is on recent studies, highlighting how utDNA could aid personalized care. RECENT FINDINGS: Recent research underscores the potential for utDNA to be the premiere biomarker in bladder cancer due to the constant interface between urine and tumor. Many studies find utDNA to be more informative than other biomarkers in bladder cancer, especially in early stages of disease. Points of emphasis include superior sensitivity over traditional urine cytology, broad genomic and epigenetic insights, and the potential for non-invasive, real-time analysis of tumor biology. utDNA shows promise for improving all phases of bladder cancer care, paving the way for personalized treatment strategies. Building from current research, future comprehensive clinical trials will validate utDNA's clinical utility, potentially revolutionizing bladder cancer management.

A Model Based on Automated Urinalysis Parameters for Urothelial Carcinoma Risk Stratification in Suspected Patients.

BACKGROUND: The aim of this study was to develop and validate a risk stratification model for the screening of patients with suspected urothelial carcinoma (UC). METHODS: We enrolled 671 consecutive patients with suspected UC and generated a risk stratification model based on urinary parameters by using an automated urinalysis analyzer (Sysmex UN-9000). All patients received urine cytology examination from January 1, 2019, to October 31, 2022. RESULTS: Out of the 671 patients, 191 (28.5%) were ultimately diagnosed with UC. The four features associated with the presence of malignancy on multivariable analysis can be summarized by using the mnemonic UC-PAAS: UC, protein vs. creatinine ratio (P/C), age, atypical cells (Atyp.C), and small round epithelial cell (SRC). Major criteria include Atyp.C ≥ 0.1/µL (2 points) and age ≥ 65 years (2 points); minor criteria include SRC ≥ 2.7/µL (1 point) and abnormal P/C results (1 point). The model evidenced good discrimination (area under the curve = 0.802, 95% confidence interval [0.756, 0.848]) in the training group. A UC-PAAS cutoff of more than 4 points identified a high-risk population, of whom 37 of 59 (62.7%) had UC; the negative predictive value was 0.867. The validation group yielded similar findings. CONCLUSIONS: We present a urinalysis-based screening model, the UC-PAAS, that may serve as an accessory clinical tool for the evaluation of patients with suspected UC, because the model identifies patients at higher risk who require closer follow-up than others or additional examinations.

Computer-assisted urine cytology: Faster, cheaper, better?

Recent advancements in computer-assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes.

Cytomorphological characteristics of low-grade papillary urothelial carcinoma in voided urine samples: Distinction from benign and high-grade papillary urothelial carcinoma.

OBJECTIVE: Given its frequent recurrence and the potential for high-grade transformation, accurate diagnosis of low-grade papillary urothelial carcinoma (LGPUC) in urine cytology is clinically important. We attempted to identify cytomorphologic features in urine samples, which could be helpful for the identification of LGPUC. METHODS: We conducted a retrospective review of voided urine specimens collected from patients with histopathologic diagnoses of LGPUC. Their cytomorphological features were compared with those from patients with benign conditions and high-grade papillary urothelial carcinoma (HGPUC). RESULTS: A total of 115 voided urine specimens were evaluated, including 30 benign, 41 LGPUC, and 44 HGPUC cases. In LGPUC, 18 cases (44%) were diagnosed as atypical, a proportion significantly higher than that observed in benign cases (4 cases, 13%), while the remaining 23 cases (56%) were diagnosed as negative. LGPUC urine samples tended to have higher cellularity than benign cases, but the difference was not statistically significant. Three cytological features, namely nuclear enlargement, higher nuclear-to-cytoplasmic (N/C) ratio, and presence of small cell clusters, were statistically more prevalent in LGPUC compared to benign cases, although the changes were relatively subtle. In contrast, cytomorphological distinction between LGPUC and HGPUC was evident, as high cellularity, nuclear enlargement, hyperchromasia, high N/C ratio, irregular nuclear membrane, and apoptosis were significantly more prevalent in HGPUC cases. CONCLUSIONS: Several cytomorphologic features in voided urine samples were more prevalent in cases with LGPUC, albeit not observed in all instances. Since these alterations were relatively subtle, meticulous attention to these cytomorphologic details is crucial to suggest the possibility of LGPUC.

Dysbiosis in Human Urinary Microbiota May Differentiate Patients with a Bladder Cancer.

Recent interest in noninvasive diagnostic approaches has highlighted the potential of urinary microbiota as a novel biomarker for bladder cancer. This study investigated the urinary microbiota of 30 bladder cancer patients and 32 healthy controls using a specific NGS protocol that sequences eight hypervariable regions of the 16S rRNA gene, providing detailed insights into urinary microbiota composition. The relative abundance of microbial compositions in urine samples from cancer patients and healthy controls was analyzed across various taxonomic levels. No notable differences were highlighted at the phylum, class, order, and family levels. At the genus level, 53% of detected genera were represented in either cancer patients or healthy controls. Microbial diversity was significantly lower in cancer patients. The differential analysis identified five genera, Rhodanobacter, Cutibacterium, Alloscardovia, Moryella, and Anaeroglobus, that were significantly more abundant in cancer patients. Notably, Rhodanobacter was present in 20 cancer samples but absent in healthy controls. Conversely, 40 genera, including Lactobacillus, Propionibacterium, and Bifidobacterium, exhibited reduced abundance in cancer patients. These findings suggest that some genera may serve as potential biomarkers for bladder cancer, highlighting the need for further research to explore their roles in disease pathogenesis and their potential applications in diagnostics and therapeutics.

[A real-world study of the clinical application of the Paris system for reporting urinary cytology in cancer hospital].

Objectives: To evaluate the clinical value of the Paris system for reporting urinary cytology (TPS) in the diagnosis of urothelial carcinoma (UC). Methods: A total of 1 744 cytological diagnostic records (from 751 cases) were collected retrospectively. All specimens were voided urines and histopathology as the gold standard. The sensitivity and specificity of urinary cytological diagnosis of UC and risk of high grade malignant (ROHM) in each diagnostic category were compared. Results: There were 360 cases with histopathology. The percentage of negative for high-grade urothelial carcinoma (NHGUC) was 30.1% (226/751), atypical urothelial cells (AUC) was 29.8% (224/751), suspicious for high-grade urothelial carcinoma (SHGUC) was 16.8% (126/751), high grade urothelial carcinoma (HGUC) was 21.2% (159/751), and non-urothelial malignancy (NUM) was 2.1% (16/751). The histpathologic ROHM corresponding to each cytological diagnosis category were 27.3% for NHGUC, 32.7% for AUC, 74.7% for SHGUC, 96.6% for HGUC and 100.0% for NUM, respectively. ROHM of SHGUC was significantly higher than that of AUC group, and the difference between the two groups was statistically significant (P＜0.001). ROHM of HGUC group was significantly higher than that of SHGUC group, and the difference was statistically significant (P＜0.001). With SHGUC as the cut-off value, the sensitivity and specificity of cytological diagnosis of HGUC were 76.7% (165/215) and 85.7% (18/21), and with HGUC as the cut-off value, the sensitivity and specificity of cytological diagnosis of HGUC were 53.0% (114/215) and 100.0% (21/21), respectively. Conclusions: Urine cytology has high sensitivity and specificity in the diagnosis of HGUC. The malignant risk of TPS varies with different diagnosis category. The high malignant risk population in cancer hospital leads to the relatively high malignant proportion and ROHM in each diagnosis category. Urinary cytology TPS reporting system is helpful to clinical management and has good clinical application value.

[What contribution can make artificial intelligence to urinary cytology?] / Quel apport de l'intelligence artificielle en cytologie urinaire ?

Urinary cytology using the Paris system is still the method of choice for screening high-grade urothelial carcinomas. However, the use of the objective criteria described in this terminology shows a lack of inter- and intra-observer reproducibility. Moreover, if its sensitivity is excellent on instrumented urine, it remains insufficient on voided urine samples. Urinary cytology appears to be an excellent model for the application of artificial intelligence to improve performance, since the objective criteria of the Paris system are defined at cellular level, and the resulting diagnostic approach is presented in a highly "algorithmic" way. Nevertheless, there is no commercially available morphological diagnostic aid, and very few predictive devices are still undergoing clinical validation. The analysis of different systems using artificial intelligence in urinary cytology rises clear prospects for mutual contributions.

[Urinary cytology for the detection of urothelial lesions]. / Rôle de la cytologie urinaire dans le dépistage des lésions urothéliales.

Urine cytology is a long-used technique for the detection of high grade neoplastic urothelial lesions. Since 2016, «The Paris System¼ classification has revolutionized this field by introducing a standardized terminology widely adopted by cytopathologists and urologists. In this article, we explain this classification and discuss its impact on the clinical management of patients with urothelial lesions, as well as its role in the secondary prevention of these lesions.

La cytologie urinaire est une technique utilisée depuis longtemps dans la détection des lésions urothéliales tumorales de haut grade. Depuis 2016, la classification «The Paris System¼ a révolutionné ce domaine en introduisant une terminologie standardisée largement adoptée par les cytopathologistes et les urologues. Dans cet article, nous expliquons cette classification et discutons de son impact sur la prise en charge clinique des lésions urothéliales, ainsi que son rôle dans la prévention secondaire de ces lésions.