Prostate cancer reshapes the secreted and extracellular vesicle urinary proteomes.

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Urine Biopsy as Dynamic Biomarker to Enhance Clinical Staging of Bladder Cancer in Radical Cystectomy Candidates.

PURPOSE: There is significant interest in identifying complete responders to neoadjuvant chemotherapy (NAC) before radical cystectomy (RC) to potentially avoid removal of a pathologically benign bladder. However, clinical restaging after NAC is highly inaccurate. The objective of this study was to develop a next-generation sequencing-based molecular assay using urine to enhance clinical staging of patients with bladder cancer. METHODS: Urine samples from 20 and 44 patients with bladder cancer undergoing RC were prospectively collected for retrospective analysis for molecular correlate analysis from two clinical trials, respectively. The first cohort was used to benchmark the assay, and the second was used to determine the performance characteristics of the test as it correlates to responder status as measured by pathologic examination. RESULTS: First, to benchmark the assay, known mutations identified in the tissue (MT) of patients from the Accelerated Methotrexate, Vinblastine, Doxorubicin, Cisplatin trial (ClinicalTrials.gov identifier: NCT01611662, n = 16) and a cohort from University of California-San Francisco (n = 4) were cross referenced against mutation profiles from urine (MU). We then determined the correlation between MU persistence and residual disease in pre-RC urine samples from a second prospective clinical trial (The pT0 trial; ClinicalTrials.gov identifier: NCT02968732). Residual MU status correlated strongly with residual disease status (pT0 trial; n = 44; P = .0092) when MU from urine supernatant and urine pellet were assessed separately and analyzed in tandem. The sensitivity, specificity, PPV, and NPV were 91%, 50%, 86%, and 63% respectively, with an overall accuracy of 82% for this second cohort. CONCLUSION: MU are representative of MT and thus can be used to enhance clinical staging of urothelial carcinoma. Urine biopsy may be used as a reliable tool that can be further developed to identify complete response to NAC in anticipation of safe RC avoidance.

Urinary extracellular vesicle-derived miR-126-3p predicts lymph node invasion in patients with high-risk prostate cancer.

To investigate extracellular vesicles (EVs), biomarkers for predicting lymph node invasion (LNI) in patients with high-risk prostate cancer (HRPCa), plasma, and/or urine samples were prospectively collected from 45 patients with prostate cancer (PCa) and five with benign prostatic hyperplasia (BPH). Small RNA sequencing was performed to identify miRNAs in the EVs. All patients with PCa underwent radical prostatectomy and extended pelvic lymph node dissection. Differentially expressed miRNAs were identified in patients with and without pathologically-verified LNI. The candidate miRNAs were validated in low-risk prostate cancer (LRPCa) and BPH. Four miRNA species (e.g., miR-126-3p) and three miRNA species (e.g., miR-27a-3p) were more abundant in urinary and plasma EVs, respectively, of patients with PCa. None of these miRNA species were shared between urinary and plasma EVs. miR-126-3p was significantly more abundant in patients with HR PCa with LNI than in those without (P = 0.018). miR-126-3p was significantly more abundant in the urinary EVs of patients with HRPCa than in those with LRPCa (P = 0.017) and BPH (P = 0.011). In conclusion, urinary EVs-derived miR-126-3p may serve as a good biomarker for predicting LNI in patients with HRPCa.

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.

Long-Term Changes of Urinary Exosomal Peptide Levels After Thyroidectomy in Patients with Thyroid Cancer: A Prospective Observational Study.

Background: The recurrence rate of thyroid cancer can be as high as 30%. The purpose of this study was to examine changes of urine exosomal peptide levels after thyroidectomy in patients with thyroid cancer to determine if levels can predict the risk of recurrence. Methods: Patients >20 years old as newly diagnosed with papillary thyroid cancer who had received a thyroidectomy were recruited. Urine samples were collected at 12 months after enrollment to the study, and 1 year later. Urine exosomes containing different peptides were identified and compared. Results: A total of 70 patients were enrolled in the study, and were classified by the interval between surgery and enrollment: 42 patients with < 5 years between surgery and enrollment, 14 patients between 5-10 years, and 14 patients longer than 10 years. No recurrence was observed in any patient during the 2 years after enrollment. No significant differences were found in the levels of serum proteins or urine exosomal peptides between groups, or between intervals. Known risk factors for high-risk thyroid cancer had only a mild correlation with serum protein levels and urine exosomal peptides. Conclusion: Our study revealed the long-term basal fluctuation ranges of serum proteins and urine exosomal peptides in patients with thyroid cancer who underwent thyroidectomy. For high-risk patients after thyroidectomy, concentrations of serum proteins or urine exosomal peptides within the ranges may indicate there is a lower risk of thyroid cancer recurrence during long-term follow-up. Trial Registration: ClinicalTrials.gov: NCT03488134.

Deep learning-assisted monitoring of trastuzumab efficacy in HER2-Overexpressing breast cancer via SERS immunoassays of tumor-derived urinary exosomal biomarkers.

Monitoring drug efficacy is significant in the current concept of companion diagnostics in metastatic breast cancer. Trastuzumab, a drug targeting human epidermal growth factor receptor 2 (HER2), is an effective treatment for metastatic breast cancer. However, some patients develop resistance to this therapy; therefore, monitoring its efficacy is essential. Here, we describe a deep learning-assisted monitoring of trastuzumab efficacy based on a surface-enhanced Raman spectroscopy (SERS) immunoassay against HER2-overexpressing mouse urinary exosomes. Individual Raman reporters bearing the desired SERS tag and exosome capture substrate were prepared for the SERS immunoassay; SERS tag signals were collected to prepare deep learning training data. Using this deep learning algorithm, various complicated mixtures of SERS tags were successfully quantified and classified. Exosomal antigen levels of five types of cell-derived exosomes were determined using SERS-deep learning analysis and compared with those obtained via quantitative reverse transcription polymerase chain reaction and western blot analysis. Finally, drug efficacy was monitored via SERS-deep learning analysis using urinary exosomes from trastuzumab-treated mice. Use of this monitoring system should allow proactive responses to any treatment-resistant issues.

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).

Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.

Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis. We tested our approach on two data sets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using Auto-sklearn, surpassed standalone ML algorithms like SVM and k-Nearest Neighbors in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers. The effectiveness of Auto-sklearn is highlighted by its AUC scores of 0.97 for RCC and 0.85 for OC, obtained from the unseen test sets. Importantly, on most of the metrics considered, Auto-sklearn demonstrated a better classification performance, leveraging a mix of algorithms and ensemble techniques. Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1_16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.

Optical biomarker analysis for renal cell carcinoma obtained from preoperative and postoperative patients using ATR-FTIR spectroscopy.

Renal cell carcinoma (RCC) is the most common malignant tumor in the urinary system, accounting for 80 % to 90 % for all renal malignancies. Traditional diagnostic methods like magnetic resonance imaging (MRI) and computed tomography (CT) lack the sensitivity and specificity as they lack specific biomarkers. These limitations impede effective monitoring of tumor recurrence. This study aims to employ Attenuated Total Reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy, an optical technology sensitive to molecular groups, to analyze the potential optical biomarkers in urine and plasma samples from RCC patients pre- and post-surgery. The results reveal distinctive spectral information from both plasma and urine samples. Post-surgery urine spectra exhibit complexity compared to plasma, showing reduced content at 1072 cm-1, 1347 cm-1 and 1654 cm-1 bands, while increased content at 1112 cm-1, 1143 cm-1, 1447 cm-1, 3334 cm-1 and 3420 cm-1 bands. Utilizing machine learning models such as eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), partial least squares (PLS), and artificial neural network (ANN), the study evaluated plasma and urine samples pre- and post-surgery. Remarkably, the XGBoost method excelled in distinguishing between tumor conditions and recovery, achieving an impressive AUC value of 0.99. These results underscore the potential of ATR-FTIR technology in identifying RCC optical biomarkers, with XGBoost showing promise as a valuable screening tool for RCC recurrence diagnosis.

Identification of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising. METHODS: RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models. RESULTS: An initial cluster analysis of RNA-seq expression data showed separation by the subjects' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091). CONCLUSIONS: Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.

Urinary extracellular vesicles-encapsulated miRNA signatures: A new paradigm for urinary bladder cancer diagnosis and classification.

Bladder cancer (BCa) stands as prevalent malignancy of the urinary system globally, especially among men. The clinical classification of BCa into non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for prognosis and treatment decisions. However, challenges persist in current diagnostic methods like Urine cytopathology that shows poor sensitivity therefore compromising on accurately diagnosing and monitoring BCa. In recent years, research has emphasized the importance of identifying urine and blood-based specific biomarkers for BCa that can enable early and precise diagnosis, effective tumor classification, and monitoring. The convenient proximity of urine with the urinary bladder epithelium makes urine a good source of noninvasive biomarkers, in particular urinary EVs because of the packaged existence of tumor-associated molecules. Therefore, the review assesses the potential of urinary extracellular vesicles (uEVs) as noninvasive biomarkers for BCa. We have elaborately reviewed and discussed the research that delves into the role of urinary EVs in the context of BCa diagnosis and classification. Extensive research has been dedicated to investigating differential microRNA (miRNA) expressions, with the goal of establishing distinct, noninvasive biomarkers for BCa. The identification of such biomarkers has the potential to revolutionize early detection, risk stratification, therapeutic interventions, and ultimately, the long-term prognosis of BCa patients. Despite notable advancements, inconsistencies persist in the biomarkers identified, methodologies employed, and study populations. This review meticulously compiles reported miRNA biomarkers, critically assessing the variability and discrepancies observed in existing research. By synthesizing these findings, the article aims to direct future studies toward a more cohesive and dependable approach in BCa biomarker identification, fostering progress in patient care and management.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine.

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Analysis of the relationship between MYCN gene and serum NSE and urinary VMA levels and neuroblastoma pathological features and prognosis.

The purpose of this study was to explore the relationship between the MYCN gene, serum neuron-specific enolase (NSE), urinary vanillylmandelic acid (VMA) levels, and neuroblastoma pathological features and prognosis. Ninety-four children with neuroblastoma treated in the hospital were selected to compare the differences in MYCN gene amplification, serum NSE, and urinary VMA levels in children with different clinicopathological features and prognoses. The proportion of children with MYCN gene copy number ≥10 in INSS stage 3-4 was higher than that of children with INSS stage 1-2 (P < 0.05); the proportion of children with MYCN gene copy number ≥10 in high-risk children in the COG risk stratification was higher than that of children with intermediate and low risk (P < 0.05); the serum NSE of children aged >12 months higher than that of children aged ≤12 months (P < 0.05); serum NSE of children with tumors >500 cm3 higher than that of children with tumors ≤500 cm3 (P < 0.05); serum NSE and urinary VMA of children with INSS staging of stages 3-4 were higher than that of children with stages 1 to 2 (P < 0.05); serum NSE and urinary VMA in children with lymph node metastasis were higher than that of children without lymph node metastasis (P < 0.05); serum NSE of children with MYCN gene copy number ≥10 was higher than that of children without lymph node metastasis (P < 0.05); the proportion of children with MYCN gene copy number ≥10 who died, and the percentages of serum NSE and urinary VMA were higher than those of the surviving children (P < 0.05). MYCN gene amplification and serum NSE and urinary VMA levels were related to the age, tumor size, INSS stage, COG stage, lymph node metastasis, and prognosis of the children with neuroblastoma.

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.

Evaluation of URO17® to improve non-invasive detection of bladder cancer.

BACKGROUND: The gold standard for detecting bladder cancer is cystoscopy with biopsy or transurethral resection confirming histologic diagnosis. URO17® employs a chromogenically labeled monoclonal antibody to keratin 17 (k17), an intermediate filament cytoskeleton molecule associated with bladder, pancreatic, and cervical cancers. Preliminary studies evaluating k17 demonstrated a high sensitivity and specificity for the detection of bladder cancer, supporting the need for further study. OBJECTIVE: To evaluate the sensitivity and specificity of URO17. METHODS: This is a cross-sectional study of participants undergoing urologic procedures between July 6, 2018 and July 17, 2019 at a single institution. Patients undergoing cystectomy, endoscopic bladder and/or upper tract procedure for probable urothelial carcinoma comprised cases; patients undergoing urologic procedures for other reasons comprised the control group (i.e. prostatectomy, nephrectomy, etc.). Voided urine samples were at the time of procedure; a minority of participants underwent multiple resections in the study period, thus, as many as three urine samples were taken from any given participant. Samples were distributed for blinded testing with URO17. Sensitivity and specificity were calculated. RESULTS: In 152 participants and 167 samples, URO17 demonstrated an overall sensitivity of 90% and 92% and a specificity of 88% and 87%, respectively. In 76 participants and 91 samples from patients with suspected urothelial carcinoma, the sensitivity was 90% and 92%, and the specificity was 50% and 54%, respectively. No controls demonstrated a positive URO17 result, and URO17 superseded urine cytology detection of low-grade and high-grade Ta. False positive results were associated with inflamed tissue or urothelial atypia on histology; the large majority had a history of intravesical therapy. CONCLUSION: Limitations include cross-sectional design and convenience sampling. URO17 may improve sensitivity of urine cytology in the detection of urothelial cancer, though further study is required to refine the application of this biomarker in clinical practice.

Universal nanocomposite coating with antifouling and redox capabilities for electrochemical affinity biosensing in complex biological fluids.

Electrochemical affinity biosensors have the potential to facilitate the development of multiplexed point-of-care diagnostics in complex biological fluids. However, their commercial viability has been hindered by challenges such as electrode biofouling and the lack of inherent redox properties. To address this unmet need, we have developed a universal nanocomposite coating which is unique in its ability to not only allow oriented conjugation of the biorecognition element but also specific detection directly in complex biological fluids like serum and urine owing to its built-in antifouling and redox capabilities, thus improving suitability for point of care testing. This multifunctional coating comprises a 3D porous crosslinked bovine serum albumin matrix for oriented conjugation and antifouling properties with embedded graphene nanosheets modified with amino-ferrocene for enhanced conductivity and mediator-free biosensing. The coating showed minimal signal degradation despite prolonged exposure to 1% bovine serum albumin, artificial urine and untreated human serum for up to 30 days. To demonstrate its utility, we fabricated and tested proof-of-concept electrochemical immunosensors for bladder cancer protein biomarkers, specifically interleukin-8 (IL-8) and vascular endothelial growth factor (VEGF). The practical feasibility was highlighted by the excellent sensitivity and specificity observed for IL-8 and VEGF with a limit of detection of 41 pg mL-1 and 67 pg mL-1, respectively. Consequently, this universal nanocomposite-based electrochemical biosensing platform can be extended to the point of care testing of a broad spectrum of biomarkers present in complex biological fluids, thus enabling reliable and early diagnostics.

Enhancing breast cancer screening with urinary biomarkers and Random Forest supervised classification: A comprehensive investigation.

OBJECTIVES: Urinary sex hormones are investigated as potential biomarkers for the early detection of breast cancer, aiming to evaluate their relevance and applicability, in combination with supervised machine-learning data analysis, toward the ultimate goal of extensive screening. METHODS: Sex hormones were determined on urine samples collected from 250 post-menopausal women (65 healthy - 185 with breast cancer, recruited among the clinical patients of Candiolo Cancer Institute FPO-IRCCS (Torino, Italy). Two analytical procedures based on UHPLC-MS/HRMS were developed and comprehensively validated to quantify 20 free and conjugated sex hormones from urine samples. The quantitative data were processed by seven machine learning algorithms. The efficiency of the resulting models was compared. RESULTS: Among the tested models aimed to relate urinary estrogen and androgen levels and the occurrence of breast cancer, Random Forest (RF) proved to underscore all the other supervised classification approaches, including Partial Least Squares - Discriminant Analysis (PLS-DA), in terms of effectiveness and robustness. The final optimized model built on only five biomarkers (testosterone-sulphate, alpha-estradiol, 4-methoxyestradiol, DHEA-sulphate, and epitestosterone-sulphate) achieved an approximate 98% diagnostic accuracy on replicated validation sets. To balance the less-represented population of healthy women, a Synthetic Minority Oversampling TEchnique (SMOTE) data oversampling approach was applied. CONCLUSIONS: By means of tunable hyperparameters optimization, the RF algorithm showed great potential for early breast cancer detection, as it provides clear biomarkers ranking and their relative efficiency, allowing to ground the final diagnostic model on a restricted selection five steroid biomarkers only, as desirable for noninvasive tests with wide screening purposes.

Fragmentation patterns of cell-free DNA and somatic mutations in the urine of metastatic breast cancer patients.

BACKGROUND: Urinary cell-free deoxyribonucleic acid (DNA) (ucfDNA) holds promise as a biomarker; however, its potential remains largely unexplored. We examined the fragmentation pattern of ucfDNA and identified somatic mutations within urine samples from metastatic breast cancer (MBC) patients. METHODS: Urine and blood specimens were collected before treatment from 45 MBC patients and posttreatment urine samples from 16 of the 45 patients at the China National Cancer Center. Somatic mutations and tumor mutational burden (TMB) in the urine and plasma of 10 patients were analyzed by next-generation sequencing (NGS). Fragmentation patterns of cfDNA were displayed using electropherograms. Differences in the extracted amount of cfDNA, length of cfDNA fragments, and TMB between urine and plasma were compared using a Wilcoxon test. RESULTS: The fragmentation patterns of ucfDNA were categorized as follows: (1) profile A (n = 26) containing a short peak (100-200 bp) and a long peak (>1500 bp); (2) profile B (n = 8) containing only a long peak; and (3) profile C (n = 11) containing flat pattern. For profile A patients, the short-peaked ucfDNA circulating in the bloodstream was much shorter compared with plasma cfDNA (149 vs. 171 bp, Wilcoxon test, P = 0.023). The fragmentation patterns in lung metastasis patients exhibited a higher propensity toward profile C ( P = 0.002). After treatment, 87.5% of the patients exhibited consistent fragmentation patterns. The concordance rate for somatic mutations in the plasma and urine was 30%, and the median TMB of urine and plasma was not significantly different. CONCLUSIONS: This study established a fragmentation pattern for ucfDNA and detected somatic mutations in the urine of MBC patients. These results suggest the potential application of ucfDNA as a biomarker for MBC.

Prospective evaluation of the RT-PCR based urinary marker Bladder Epicheck® as a diagnostic tool in upper urinary tract tumor.

BACKGROUND: Upper-tract-urothelial-carcinoma (UTUC) represents 5-10% of all urothelial-neoplasms with increasing incidence in the last decades. Current standard tools for diagnosis of UTUC include cytology, computed tomography (CT) urography and ureterorenoscopy (URS). The aim of this study was to evaluate the impact of Bladder Epicheck® Test as diagnostic tool for UTUC diagnosis and recurrence. METHODS: Overall, 136 urine samples, selective collected from upper-urinary-tract before URS for suspicion of UTUC were analyzed with cytology and Bladder Epicheck® Test. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of both markers were calculated and compared to URS and/or histology as reference. RESULTS: UTUC was detected in 40 cases (33.3%), among them 30 were classified as low-grade (LG) and 10 as high-grade (HG). Overall sensitivity of Bladder Epicheck® for UTUC detection was 65% compared to 42.5% for cytology, increasing to 100% for Bladder Epicheck® and 90% for cytology if considering only HG tumors. Overall specificity of Bladder Epicheck® was 81.2% and of cytology 93.7%. PPV and NPV were 63.4% and 82.2% for Bladder Epicheck® and 77.2% and 76.5% for cytology. Considering an EpiScore cut-off >75, instead of 60, specificity of Bladder Epicheck® improves to 89% and PPV to 74.2%. Limitations include the use of a marker validated only for bladder-cancer and the relatively small number of cases. CONCLUSIONS: Due to its high sensitivity for HG tumors, the Bladder Epicheck® Test can be used in diagnosis and treatment decision-making of UTUC. Furthermore, it could be very useful in follow-up of UTUC, after endoscopic treatment to postpone or avoid unnecessary endoscopic exploration. Even if further studies are needed to validate these findings, Bladder Epicheck® could be a promising clinical tool for detection of UTUC.