Urinary Biomarker Panel Research Articles

Abstract B018: UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma

Abstract Introduction. A bleak prognosis of Pancreatic ductal adenocarcinoma (PDAC) is largely due to late diagnosis and highly accurate, non-invasive diagnostic tests are urgently needed to improve patients’ survival. Our group has previously reported on urinary biomarker panel, comprising of LYVE1, REG1B and TFF1 (1-2), which distinguished controls from PDAC stages I-II with AUC 0.936 and sensitivity/specificity (SN/SP)>85%. We constructed the logistic regression based algorithm for interpretation of the data, PancRISK (3), which enables the stratification of high- risk patients into those with ‘average’ or ‘elevated’ risk of developing PDAC. Recently, we showed that our panel combined with CA19-9 can detect PDAC up to 2 years before clinical diagnosis (4). Aim. Here, we report on the design and interim analysis of the UroPanc Trial (ClinicalTrials.gov NCT04449406), which aims to evaluate the accuracy of the urinary biomarker panel and the affiliated PancRISK. We plan to recruit two cohorts: the first one comprises asymptomatic individuals that have high risk of developing PDAC due to familial history and genetic syndroms - these will be recruited through the EUropean Registry Of familial PAncreatic Cancer and hereditary pancreatitis (EUROPAC) (PI: Prof W Greenhalf and team, Liverpool, UK). The second cohort comprises patients with symptoms suggestive of PDAC – such patients are recruited at gastrointestinal clinics at University College London Hospitals (UCLH, PI: Prof S Pereira and team), and The Royal London Hospital (PI: Dr P Wilson).Methods. The urinary proteins are assayed by commercially available ELISAs. Plasma CA19-9 is measured at The Doctors Laboratory in London, UK using Cobas601E, Roche platform. The statistical analysis and accuracy of the test is measured by c-statistics, SN/SP and positive/negative predictive value. The results are compared to imaging and histopathology records (where available). Human factor and budget impact analysis of the future inclusion of the urine test into the diagnostic and surveillance pathways for PDAC patients is ongoing (PI: Dr M Z Ni and the team, Imperial College London, UK). Results and conclusions. At present, 1067 patients have been recruited, and urine samples from 825 patients have been assayed. An interim analysis has shown promising results with SP of 91% and SN of 86%, and accuracy of cancer detection of 90%. Combined with the health economic analysis, we currently work towards the future implementation of this test into clinical practice, with the potential to significantly improve the current diagnostic pathway for individuals at risk of pancreatic cancer. 1. Radon, TP, et al. Clin Cancer Res, 2015; 21:3512-21. 2. Debernardi, S, et al. PLOSMed, 17(12); e1003489. 3. Blyuss, O, et al. Br J Cancer, 2020; 122:692-6. 4. Debernardi, S, et al. Int J Cancer, 2023; 152:769–80. Citation Format: Silvana Debernardi, Evelyn Kurotova, Nurshad Ali, Mariam Mahamood, Ismita Chhetri, Steve Pereira, Patrick Wilson, Bill Greenhalf, Melody Z Ni, Oleg Blyuss, Tatjana Crnogorac-Jurcevic. UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B018.

P06-04 Advancing bladder cancer detection: unveiling a novel urinary biomarker panel through metabolomics

P06-04 Advancing bladder cancer detection: unveiling a novel urinary biomarker panel through metabolomics

Urinary dipeptidase 1 and trefoil factor 1 are promising biomarkers for early diagnosis of colorectal cancer.

Currently utilized serum tumor markers and fecal immunochemical tests do not have sufficient diagnostic power for colorectal cancer (CRC) due to their low sensitivities. To establish non-invasive urinary protein biomarkers for early CRC diagnosis, we performed stepwise analyses employing urine samples from CRCs and healthy controls (HCs). Among 474 urine samples, 363 age- and sex-matched participants (188 HCs, 175 stage 0-III CRCs) were randomly divided into discovery (16 HCs, 16 CRCs), training (110 HCs, 110 CRCs), and validation (62 HCs, 49 CRCs) cohorts. Of the 23 urinary protein candidates comprehensively identified from mass spectrometry in the discovery cohort, urinary levels of dipeptidase 1 (uDPEP1) and Trefoil factor1 (uTFF1) were the two most significant diagnostic biomarkers for CRC in both training and validation cohorts using enzyme-linked immunosorbent assays. A urinary biomarker panel comprising uDPEP1 and uTFF1 significantly distinguished CRCs from HCs, showing area under the curves of 0.825-0.956 for stage 0-III CRC and 0.792-0.852 for stage 0/I CRC. uDPEP1 and uTFF1 also significantly distinguished colorectal adenoma (CRA) patients from HCs, with uDPEP1 and uTFF1 increasing significantly in the order of HCs, CRA patients, and CRC patients. Moreover, expression levels of DPEP1 and TFF1 were also significantly higher in the serum and tumor tissues of CRC, compared to HCs and normal tissues, respectively. This study established a promising and non-invasive urinary protein biomarker panel, which enables the early detection of CRC with high sensitivity.

Straightforward Creation of Multishell Hollow Hybrids for an Integrated Metabolic Monitoring System in Disease Management.

Multidimensional metabolic analysis has become a new trend in establishing efficient disease monitoring systems, as the constraints associated with relying solely on a single dimension in refined monitoring are increasingly pronounced. Here, coordination polymers are employed as derivative precursors to create multishell hollow hybrids, developing an integrated metabolic monitoring system. Briefly, metabolic fingerprints are extracted from hundreds of serum samples and urine samples, encompassing not only membranous nephropathy but also related diseases, using high-throughput mass spectrometry. With optimized algorithm and initial feature selection, the established combined panel demonstrates enhanced accuracy in both subtype differentiation (over 98.1%) and prognostic monitoring (over 95.6%), even during double blind test. This surpasses the serum biomarker panel (≈90.7% for subtyping, ≈89.7% for prognosis) and urine biomarker panel (≈94.4% for subtyping, ≈76.5% for prognosis). Moreover, after attempting to further refine the marker panel, the blind test maintains equal sensitivity, specificity, and accuracy, showcasing a comprehensive improvement over the single-fluid approach. This underscores the remarkable effectiveness and superiority of the integrated strategy in discriminating between MN and other groups. This work has the potential to significantly advance diagnostic medicine, leading to the establishment of more effective strategies for patient management.

Evaluation of urinary C-reactive protein as an early detection biomarker for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Up to now, no specific screening or diagnostic tests are available for early PDAC detection. As a result, most patients are diagnosed with advanced or metastatic disease, which leads to a poor prognosis. In this study, we aimed to evaluate the diagnostic value of urinary CRP (uCRP) alone and in combination with our previously established urine biomarker panel (REG1B, LYVE1 and TFF1) for early detection of PDAC. A total of 534 urine samples from multiple centres were analysed: 93 from healthy individuals, 265 from patients with benign hepatobiliary diseases and 176 from PDAC patients. The uCRP and the urinary biomarker panel were assessed using commercial ELISA assays, while plasma CA19-9 and blood CRP (bCRP) were measured using Roche Cobas platform. Multiple logistic regression and nonparametric Kruskal-Wallis test were used for statistical analysis. An internal validation approach was applied, and the validated AUC estimators were reported to ensure accuracy. A significant difference was observed in the medians of uCRP between healthy and benign controls and PDAC sample groups (p < 0.001). uCRP levels were not dependent on gender and age, as well as cancer stage. When uCRP was combined with the urinary biomarker panel, it achieved AUCs of 0.878 (95% CI: 0.802-0.931), 0.798 (95% CI: 0.738-0.859) and 0.813 (95% CI: 0.758-0.869) in healthy vs PDAC, benign vs PDAC and healthy and benign vs PDAC sample groups, respectively. However, adding plasma CA19-9 to the urinary biomarker panel yielded a better performance, with AUCs of 0.978 (95% CI: 0.959-0.996), 0.911 (95% CI: 0.873-0.949) and 0.919 (95% CI: 0.883-0.955) in the healthy vs PDAC, benign vs PDAC and healthy and benign vs PDAC comparisons, respectively. In conclusion, we show that measuring CRP in urine is a feasible analytical method, and that uCRP could potentially be a promising biomarker in various diseases including other cancer types.

Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios

BackgroundThere is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides.MethodsUrinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated.ResultsIn the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17–1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47–1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39–1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I).ConclusionThe COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

A Multiplexed Urinary Biomarker Panel Has Potential for Alzheimer's Disease Diagnosis Using Targeted Proteomics and Machine Learning.

As disease-modifying therapies are now available for Alzheimer's disease (AD), accessible, accurate and affordable biomarkers to support diagnosis are urgently needed. We sought to develop a mass spectrometry-based urine test as a high-throughput screening tool for diagnosing AD. We collected urine from a discovery cohort (n = 11) of well-characterised individuals with AD (n = 6) and their asymptomatic, CSF biomarker-negative study partners (n = 5) and used untargeted proteomics for biomarker discovery. Protein biomarkers identified were taken forward to develop a high-throughput, multiplexed and targeted proteomic assay which was tested on an independent cohort (n = 21). The panel of proteins identified are known to be involved in AD pathogenesis. In comparing AD and controls, a panel of proteins including MIEN1, TNFB, VCAM1, REG1B and ABCA7 had a classification accuracy of 86%. These proteins have been previously implicated in AD pathogenesis. This suggests that urine-targeted mass spectrometry has potential utility as a diagnostic screening tool in AD.

P02-02: Evaluation of urinary safety biomarker panel to improve monitoring of preclinical Antisense Oligonucleotide-induced tubular kidney toxicity

P02-02: Evaluation of urinary safety biomarker panel to improve monitoring of preclinical Antisense Oligonucleotide-induced tubular kidney toxicity

Metabolomics analysis of visceral leishmaniasis based on urine of golden hamsters

BackgroundLeishmaniasis is one of the most neglected tropical diseases and is spread mainly in impoverished regions of the world. Although many studies have focused on the host’s response to Leishmania invasion, relatively less is known about the complex processes at the metabolic level, especially the metabolic alterations in the infected hosts.MethodsIn this study, we conducted metabolomics analysis on the urine of golden hamsters in the presence or absence of visceral leishmaniasis (VL) using the ultra-performance liquid chromatography (UPLC) system tandem high-resolution mass spectrometer (HRMS). The metabolic characteristics of urine samples, along with the histopathological change and the parasite burden of liver and spleen tissues, were detected at 4 and 12 weeks post infection (WPI), respectively.ResultsAmino acid metabolism was extensively affected at both stages of VL progression. Meanwhile, there were also distinct metabolic features at different stages. At 4 WPI, the significantly affected metabolic pathways involved alanine, aspartate and glutamate metabolism, the pentose phosphate pathway (PPP), histidine metabolism, tryptophan metabolism and tyrosine metabolism. At 12 WPI, the markedly enriched metabolic pathways were almost concentrated on amino acid metabolism, including tyrosine metabolism, taurine and hypotaurine metabolism and tryptophan metabolism. The dysregulated metabolites and metabolic pathways at 12 WPI were obviously less than those at 4 WPI. In addition, seven metabolites that were dysregulated at both stages through partial least squares-discriminant analysis (PLS-DA) and receiver-operating characteristic (ROC) tests were screened to be of diagnostic potential. The combination of these metabolites as a potential biomarker panel showed satisfactory performance in distinguishing infection groups from control groups as well as among different stages of infection.ConclusionOur findings could provide valuable information for further understanding of the host response to Leishmania infection from the aspect of the urine metabolome. The proposed urine biomarker panel could help in the development of a novel approach for the diagnosis and prognosis of VL.Graphical

Cost-effectiveness of an urinary biomarker panel in combination with MRI for prostate cancer diagnosis

PurposeThe health impact and cost-effectiveness of the biomarker test SelectMDx were evaluated when used in combination with MRI, in two US populations: biopsy naïve men and men with a previous negative biopsy.MethodsUsing a decision model, the current MRI strategy was compared with two SelectMDx strategies: SelectMDx used before MRI to select men for MRI and SelectMDx used after a negative MRI to select men for biopsy. Parameters were informed by the literature most relevant for both populations. Differences in quality-adjusted life years (QALYs) and costs between the current strategy and the SelectMDx strategies were calculated using two different assumptions regarding PCa-specific mortality (SPCG-4 and PIVOT).ResultsIn biopsy naïve men, the use of SelectMDx before MRI results in a gain of 0.004 QALY per patient under the SPCG-4 scenario, and a gain of 0.030 QALY under the PIVOT scenario. The cost savings are $1650 per patient. When used after MRI, SelectMDx results in a QALY gain per patient of 0.004 (SPCG-4), and 0.006 (PIVOT) with $262 in cost savings.In the previous negative population, SelectMDx before MRI results in a QALY gain of 0.006 (SPCG-4) and 0.022 (PIVOT), with $1281 in cost savings per patient. SelectMDx after MRI results in a QALY gain of 0.003 (SPCG-4) and 0.004 (PIVOT) with $193 in cost savings.ConclusionApplication of SelectMDx results in better health outcomes and cost savings. The value of SelectMDx was highest when used before MRI to select patients for MRI and subsequent biopsy.

Reconstructing population exposures to acrylamide from human monitoring data using a pharmacokinetic framework

BackgroundAcrylamide toxicity involves several metabolic pathways. Thus, a panel of blood and urinary biomarkers for the evaluation of acrylamide exposure was deemed appropriate. ObjectiveThe study was designed to evaluate daily acrylamide exposure in US adults via hemoglobin adducts and urinary metabolites using a pharmacokinetic framework. MethodsA cohort of 2798 subjects aged 20–79 was selected from the National Health and Nutrition Examination Survey (NHANES, 2013–2016) for analysis. Three acrylamide biomarkers including hemoglobin adducts of acrylamide in blood and two urine metabolites, N-Acetyl-S-(2-carbamoylethyl)cysteine (AAMA) and N-Acetyl-S-(2-carbamoyl-2-hydroxyethyl)-l-cysteine (GAMA) were used to estimate daily acrylamide exposure using validated pharmacokinetic prediction models. Multivariate regression models were also used to examine key factors in determining estimated acrylamide intake. ResultsThe estimated daily acrylamide exposure varied across the sampled population. Estimated acrylamide daily exposure was comparable among the three different biomarkers (median: 0.4–0.7 μg/kg/d). Cigarette smoking emerged as the leading contributor to the acquired acrylamide dose. Smokers had the highest estimated acrylamide intake (1.20–1.49 μg/kg/d) followed by passive smokers (0.47–0.61) and non-smokers (0.45–0.59). Several covariates, particularly, body mass index and race/ethnicity, played roles in determining estimated exposures. DiscussionEstimated daily acrylamide exposures among US adults using multiple acrylamide biomarkers were similar to populations reported elsewhere providing additional support for using the current approach in assessing acrylamide exposure. This analysis assumes that the biomarkers used indicate intake of acrylamide into the body, which is consistent with the substantial known exposures due to diet and smoking. Although this study did not explicitly evaluate background exposure arising from analytical or internal biochemical factors, these findings suggest that the use of multiple biomarkers may reduce uncertainties regarding the ability of any single biomarker to accurately represent actual systemic exposures to the agent. This study also highlights the value of integrating a pharmacokinetic approach into exposure assessments.

Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks

BackgroundEarly diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically.ResultsExperiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers.ConclusionA new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients.

Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction.

Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples.

MP63-02 PROSPECTIVE MULTI-INSTITUTIONAL VALIDATION OF a 3-GENE mRNA BIOMARKER PANEL FOR DETECTION AND SURVEILLANCE OF NON-MUSCLE INVASIVE BLADDER CANCER

You have accessJournal of UrologyCME1 Apr 2023MP63-02 PROSPECTIVE MULTI-INSTITUTIONAL VALIDATION OF a 3-GENE mRNA BIOMARKER PANEL FOR DETECTION AND SURVEILLANCE OF NON-MUSCLE INVASIVE BLADDER CANCER Zachary Kornberg, Eugene Shkolyar, Kathleen E. Mach, Timothy J. Lee, Vinh LA, Ihna Yoo, Gabriela Rodriguez, Amy D. Dobberfuhl, Eila C. Skinner, John T. Leppert, and Joseph C. Liao Zachary KornbergZachary Kornberg More articles by this author , Eugene ShkolyarEugene Shkolyar More articles by this author , Kathleen E. MachKathleen E. Mach More articles by this author , Timothy J. LeeTimothy J. Lee More articles by this author , Vinh LAVinh LA More articles by this author , Ihna YooIhna Yoo More articles by this author , Gabriela RodriguezGabriela Rodriguez More articles by this author , Amy D. DobberfuhlAmy D. Dobberfuhl More articles by this author , Eila C. SkinnerEila C. Skinner More articles by this author , John T. LeppertJohn T. Leppert More articles by this author , and Joseph C. LiaoJoseph C. Liao More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003321.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Risk stratification for patients with non-muscle invasive bladder cancer (NMIBC) is essential for treatment planning. Urinary biomarkers based on mRNA are a promising non-invasive modality for potential augmentation of current detection and surveillance methods. Previously we reported a 3-gene mRNA panel strongly associated with bladder cancer (BCa) showing promise as a screening and surveillance assay. Here we validate the performance of this assay prospectively in a multi-institution cohort to further demonstrate its potential utility in the management of NMIBC. METHODS: Urine samples were collected from patients undergoing BCa screening and surveillance between 2016 and 2022 at Stanford University and the Veterans Affairs Palo Alto Health Care System. Patients with lesions concerning for bladder cancer on cystoscopy underwent transurethral resection, with stage and grade determined by histopathologic diagnosis. BCa incidence were categorized by AUA risk stratification. Voided urine samples were collected, centrifuged, and RNA was isolated from the pellet. qPCR was performed to evaluate expression of ROBO1, CRH, and IGF2. Assay positivity was evaluated via previously published formula, and sensitivity and specificity for the detection of any BCa, as well as increased-risk disease (AUA intermediate and high-risk NMIBC), were determined. RESULTS: 216 patients provided urine samples at time of diagnostic cystoscopy, and 233 patients provided 587 samples at surveillance cystoscopies. In the detection cohort, 88 new cancers were diagnosed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the biomarker panel for any BCa was 1, 0.43, 0.53, and 1 respectively. In the surveillance cohort 189 recurrences occurred. For detection of any recurrence, the sensitivity, specificity, NPV, and PPV of the biomarker panel were 0.97, 0.24, 0.41, and 0.96, and for increased-risk disease, were 0.98, 0.25, 0.36, and 0.96 respectively. Using this assay, 59 cystoscopies (25%) could have been avoided in the detection group without missing a single incidence of BCa, and 96 cystoscopies (17%) could have been avoided in the surveillance group while missing only 4 increased-risk BCas. CONCLUSIONS: The 3-gene urinary mRNA biomarker panel shows high sensitivity for detection of NMIBC in an extended patient cohort. With further validation in prospective trials, this panel may facilitate biomarker-informed BCa screening and surveillance. Source of Funding: Department of Veterans Affairs BLR&D Merit Review I01 BX004962 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e871 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Zachary Kornberg More articles by this author Eugene Shkolyar More articles by this author Kathleen E. Mach More articles by this author Timothy J. Lee More articles by this author Vinh LA More articles by this author Ihna Yoo More articles by this author Gabriela Rodriguez More articles by this author Amy D. Dobberfuhl More articles by this author Eila C. Skinner More articles by this author John T. Leppert More articles by this author Joseph C. Liao More articles by this author Expand All Advertisement PDF downloadLoading ...

Biomarkers of fibrosis, kidney tissue injury and inflammation may predict severity and outcome of renal ANCA - associated vasculitis.

Activity and chronicity of kidney involvement in ANCA-associated vasculitis (AAV) can be currently reliably evaluated only by kidney biopsy. In this study, we measured a panel of serum and urinary biomarkers collected at the time of kidney biopsy and hypothesized that they could reflect specific histopathological parameters in the biopsy and help to predict prognosis. We examined a cohort of 45 patients with AAV and 10 healthy controls. Biomarker levels (DKK-3, CD163, EGF, PRO-C6 and C3M) were measured in this study by ELISA. Biopsies were scored with a scoring system for AAV (focal x crescentic x sclerotic x mixed class) and interstitial fibrosis was quantified. Levels of urinary DKK-3, CD163, EGF, PRO-C6 and C3M significantly differed among biopsy classes in AAV, with urinary DKK-3 and PRO-C6 levels being highest in the sclerotic class and lowest in the focal class, urinary CD163 levels highest in the crescentic class and urinary C3M levels highest in the focal class. Moreover, the urinary biomarkers were able to discriminate focal biopsy class from the other classes. Urinary DKK-3, EGF, PRO-C6 and C3M levels measured at the time of biopsy were also significantly related to the extent of fibrosis and to the final kidney function at the end of follow-up. This small pilot study suggests that selected urinary biomarkers of fibrosis and inflammation may reflect changes in the kidney biopsy and be prognostic of kidney outcome in patients with AAV.

Development and validation of a urinary microRNA biomarker panel as a tool for early detection of prostate cancer in a Chinese population

Introduction Urinary microRNAs (miRNAs) may serve as promising biomarkers for non-invasive early detection of prostate cancer (PCa). We aimed to identify multi-miRNA urinary biomarker panel for early detection of PCa. Methods Urine samples from 83 PCa patients and 88 healthy control subjects in a Chinese population were collected for miRNA profiling. The absolute expression of 360 unique miRNAs were measured in each sample using a highly sensitive and robust RT-qPCR workflow. Candidate urinary miRNA biomarkers were identified based on differential expression between PCa patients and healthy controls. Multi-miRNA biomarker panels were optimised for detection of PCa using three regression algorithms (Lasso, Stepwise, Exhaustive) to identify an optimal biomarker panel with best detection performance and least number of miRNAs. Results A total of 312 miRNAs were detected in urine samples, 10 candidate urinary miRNA biomarkers differentially expressed between PCa and healthy samples were identified. A panel comprising these 10 miRNAs detected PCa with an area under the curve (AUC) of 0.738. Optimization of multi-miRNA panels resulted in a 6-miRNA biomarker panel (hsa-miR-375, hsa-miR-520d-5p, hsa-miR-199b-5p, hsa-miR-518e-5p, hsa-miR-31-3p and hsa-miR-4306) that had an AUC of 0.750. Conclusion We identified a urinary miRNA biomarker panel for early detection of PCa in a Chinese population.

Combining Urinary Biomarker Panels and Machine Learning for Earlier Detection of Pancreatic Cancer

Combining Urinary Biomarker Panels and Machine Learning for Earlier Detection of Pancreatic Cancer

Identification of Optimal Urinary Biomarkers of Synthetic Cannabinoids BZO-HEXOXIZID, BZO-POXIZID, 5F-BZO-POXIZID, and BZO-CHMOXIZID for Illicit Abuse Monitoring.

The continuous introduction of new synthetic cannabinoid (SC) subtypes and analogues remains a major problem worldwide. Recently, a new "OXIZID" generation of SCs surfaced in seized materials across various countries. Hence, there is an impetus to identify urinary biomarkers of the OXIZIDs to detect their abuse. We adapted our previously reported two-pronged approach to investigate the metabolite profiles and disposition kinetics of 4 OXIZID analogues, namely, BZO-HEXOXIZID (MDA-19), BZO-POXIZID (5C-MDA-19), 5F-BZO-POXIZID (5F-MDA-19), and BZO-CHMOXIZID (CHM-MDA-19). First, bottom-up in vitro incubation experiments comprising metabolite identification, metabolic stability, and reaction phenotyping were performed using human liver microsomes and recombinant human cytochrome P450 enzymes. Second, top-down analysis of authentic urine samples from drug abusers was performed to corroborate the in vitro findings and establish a panel of urinary biomarkers. A total of 42 to 51 metabolites were detected for each OXIZID, and their major metabolic pathways included N-alkyl and phenyl hydroxylation, oxidative defluorination (for 5F-BZO-POXIZID), oxidation to ketone and carboxylate, amide hydrolysis, and N-dealkylation. The OXIZIDs were metabolically unstable, mainly metabolized by cytochromes P3A4, P3A5, and P2C9, and demonstrated mechanism-based inactivation of cytochrome P3A4. Integrating with the results of 4 authentic urine samples, the parent drug and both N-alkyl and phenyl mono-hydroxylated metabolites of each OXIZID were determined as suitable urinary biomarkers. Drug enforcement agencies worldwide may apply these biomarkers in routine monitoring procedures to identify abusers and counter the escalation of OXIZID abuse.

Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis.

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1year before PDAC diagnosis (AUC=0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC=0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

In vitro approaches for the identification and characterization of urinary metabolite biomarkers of synthetic cannabinoid ADB-BUTINACA

To identify the major metabolites and investigate the key disposition kinetics of synthetic cannabinoid ADB-BUTINACA so as to establish the optimal urinary biomarkers for forensic surveillance. Novel synthetic cannabinoids (SCs) continue to evolve and cause widespread intoxications globally. Despite this increasing prevalence of abuse, severe toxicities associated with SC intake coupled with uncertainties in identifying the parent drug in biological samples remain perennial problems undermining forensic management. ADB-BUTINACA is an emerging synthetic cannabinoid that was first identified in Europe in 2019 and entered Singapore's drug scene with a total of 40 and 107 occurrences in 2020 and 2021, respectively. Due to the unavailability of toxicological and metabolic data, there is a need to establish urinary metabolite biomarkers for detection of ADB-BUTINACA consumption and elucidate its biotransformation pathways for rationalizing its toxicological implications. The metabolites of ADB-BUTINACA were identified in human liver microsomes (HLM) using liquid chromatography Orbitrap mass spectrometry analysis. Enzyme-specific inhibitors and recombinant enzymes were adopted for the reaction phenotyping of ADB-BUTINACA. Recombinant enzymes were used to generate a pool of key Phase 1 metabolites in situ and the metabolic stability of these metabolites were determined. By coupling in vitro metabolism and authentic human urine analyses, a panel of urinary metabolite biomarkers for the detection of ADB-BUTINACA abuse was proposed. A total of fifteen metabolites of ADB-BUTINACA were identified with key biotransformations being hydroxylation, N-debutylation, dihydrodiol formation, and oxidative deamination. From the reaction phenotyping studies, it was established that ADB-BUTINACA was rapidly eliminated via CYP3A4-, CYP2C19-, and CYP3A5-mediated metabolism pathways. Three major monohydroxylated metabolites were generated in situ, and they demonstrated greater metabolic stability compared to the parent drug, ADB-BUTINACA. Four urinary biomarker metabolites of ADB-BUTINACA, namely 3 hydroxylated metabolites and 1 oxidative deaminated metabolite, were identified from in vitro metabolite profiling coupled with authentic urinary analyses. The 2-pronged approach of using specific enzyme inhibitors and recombinant enzymes have provided adequate evidence to ascertain the metabolic routes of ADB-BUTINACA. Findings also confirmed that cytochrome P450 (CYP450) is the main class of enzymes contributing to the elimination of ADB-BUTINACA. Through this study, a feasible approach of combining in vitro HLM metabolite profiling with authentic human urine analyses for the comprehensive identification of urinary metabolite biomarkers of ADB-BUTINACA is established. The metabolic profile of ADB-BUTINACA and its key metabolizing enzymes and biotransformation pathways had been characterized in this study. This innovative approach used to identify the biomarkers can be applied to other existing and new prevailing SCs for diagnosing abuse.