Urinary Biomarker Panel Research Articles

Novel urinary protein biomarker panel for early diagnosis of gastric cancer

BackgroundWith the goal of discovering non-invasive biomarkers for early diagnosis of GC, we conducted a case-control study utilising urine samples from individuals with predominantly early GC vs. healthy control (HC).MethodsAmong urine samples from 372 patients, age- and sex-matched 282 patients were randomly divided into three groups: 18 patients in a discovery cohort; 176 patients in a training cohort and 88 patients in a validation cohort.ResultsAmong urinary proteins identified in the comprehensive quantitative proteomics analysis, urinary levels of TFF1 (uTFF1) and ADAM12 (uADAM12) were significantly independent diagnostic biomarkers for GC, in addition to Helicobacter pylori status. A urinary biomarker panel combining uTFF1, uADAM12 and H. pylori significantly distinguished between HC and GC patients in both training and validation cohorts. On the analysis for sex-specific biomarkers, this combination panel demonstrated a good AUC of 0.858 for male GC, whereas another combination panel of uTFF1, uBARD1 and H. pylori also provided a good AUC of 0.893 for female GC. Notably, each panel could distinguish even stage I GC patients from HC patients (AUC = 0.850 for males; AUC = 0.845 for females).ConclusionsNovel urinary protein biomarker panels represent promising non-invasive biomarkers for GC, including early-stage disease.

Evaluation of urinary biomarkers for early detection of acute kidney injury in a rat nephropathy model

IntroductionThe implementation of novel, reliable biomarkers for the early and differential diagnosis of acute kidney injury (AKI) could greatly improve the timely treatment and prevention of disease progression, particularly since the current gold standards for detecting kidney injury such as serum creatinine (SCr) and blood urea nitrogen (BUN) lack sensitivity and specificity. We evaluated novel urinary kidney injury biomarkers focusing on early detection and better prediction of AKI with higher sensitivity and specificity. MethodsIn the rat, urinary biomarkers for kidney injury, i.e. albumin, beta-2-microglobulin (B2M), clusterin, cystatin C, kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), osteopontin (OPN), and total protein (TP), were investigated in an AKI model using different hyperosmolar and high-dose solutions, i.e. mannitol, sucrose, and contrast medium (CM), as acute single insults leading to kidney injury. Additionally, dose-dependency of sucrose was investigated and effects were compared to the sucrose- and iron-containing marketed drug Venofer®. ResultsLevels of excreted urinary biomarkers correlated with severity of AKI, exhibited a dose-dependent response to sucrose treatment, and demonstrated evidence of recovery from kidney injury with transient and reversible changes. The exceptions were KIM-1 and NGAL, which showed later responses following CM and iron-induced renal injury. All biomarkers outperformed plasma creatinine (PCr), BUN, and histopathology, with regard to practicability and/or detection of proximal tubular injury. DiscussionThe use of a panel of urinary kidney injury biomarkers emerged as an early, sensitive, and predictive tool to detect AKI showing enhanced sensitivity compared to current state-of-the-art markers.

A Panel of Urinary Volatile Biomarkers for Differential Diagnosis of Prostate Cancer from Other Urological Cancers.

Our group recently developed a urinary 6-biomarker panel for the diagnosis of prostate cancer (PCa) which has a higher level of accuracy compared to the serum prostate specific antigen (PSA) test. Herein, urine from an independent cohort of PCa patients and cancer-free controls was analyzed to further validate the discriminative power of that panel. Additionally, urine from patients diagnosed with bladder cancer (BC) and renal cancer (RC) were included to evaluate the site-specificity of the panel. Results confirmed the ability of the 6-biomarker panel to discriminate PCa patients from controls, but not from other urological cancers. To overcome this limitation, an untargeted approach was performed to unveil discriminant metabolites among the three cancer types. A 10-biomarker panel comprising the original panel plus four new metabolites was established to discriminate PCa from controls, BC, and RC, with 76% sensitivity, 90% specificity, and 92% accuracy. This improved panel also disclosed better accuracy than serum PSA test and provides the basis for a new non-invasive early detection tool for PCa.

Combined use of GM2AP and TCP1-eta urinary levels predicts recovery from intrinsic acute kidney injury

Deficient recovery from acute kidney injury (AKI) has immediate and long-term health, clinical and economic consequences. Pre-emptive recovery estimation may improve nephrology referral, optimize decision making, enrollment in trials, and provide key information for subsequent clinical handling and follow-up. For this purpose, new biomarkers are needed that predict outcome during the AKI episode. We hypothesized that damage pattern-specific biomarkers are expected to more closely associate to outcome within distinct subpopulations (i.e. those affected by specific pathological processes determining a specific outcome), as biomarker pleiotropy (i.e. associated to phenomena unrelated to AKI) introduced by unselected, heterogeneous populations may blur statistics. A panel of urinary biomarkers was measured in patients with AKI and their capacity to associate to normal or abnormal recovery was studied in the whole cohort or after sub-classification by AKI etiology, namely pre-renal and intrinsic AKI. A combination of urinary GM2AP and TCP1-eta best associates with recovery from AKI, specifically within the sub-population of renal AKI patients. This two-step strategy generates a multidimensional space in which patients with specific characteristics (i.e. renal AKI patients with good or bad prognosis) can be identified based on a collection of biomarkers working serially, applying pathophysiology-driven criteria to estimate AKI recovery, to facilitate pre-emptive and personalized handling.

Urinary Protein Biomarker Panel for the Diagnosis of Antibody-Mediated Rejection in Kidney Transplant Recipients

IntroductionAntibody-mediated rejection (ABMR) impacts kidney allograft outcome. The diagnosis is made based on findings from invasive kidney transplant biopsy specimens. The aim of this study was to identify a noninvasive urinary protein biomarker for ABMR after kidney transplantation.MethodsWe performed a multicenter case-control study to identify a urinary biomarker for ABMR (training cohort, n = 249) and an independent, prospective multicenter cohort study for validation (n = 391). We used concomitant biopsies to classify the samples according to the Banff classification. After untargeted protein identification and quantification, we used a support vector machine to train the model in the training cohort. The primary endpoint was the diagnostic accuracy of the urinary biomarker for ABMR in the validation cohort.ResultsWe identified a set of 10 urinary proteins that accurately discriminated patients with (n = 60) and without (n = 189) ABMR in the training cohort with an area under the curve (AUC) of 0.98 (95% confidence interval [CI], 0.96–1.00). The diagnostic accuracy was maintained in the validation cohort (AUC, 0.88; 95% CI, 0.8–0.93) for discriminating the presence (n = 43) from the absence (n = 348) of ABMR. The negative predictive value of the 10-protein marker set for exclusion of ABMR was 0.99, and the positive predictive value was 0.33. The diagnostic accuracy was independent of the reason for performing the biopsy, time after transplantation, and better than the accuracy of gross proteinuria (AUC, 0.76).ConclusionsWe identified and validated a urinary protein biomarker set that can be used to exclude ABMR.

P0768NOVEL URINARY PROTEIN BIOMARKERS FOR UROTHELIAL CANCER IN PATIENTS WITH CHRONIC KIDNEY DISEASE

Abstract Background and Aims Introduction: Chronic kidney disease (CKD) patients are known to have greater risk of urothelial cancer (UC) than general population. Urine is the most non-invasive examination and is directly linked to UC. Urinalysis and urine cytology are the major screening tools; however, these tests have low sensitivity or specificity in early disease. In this study, we used both normal and Non-UC CKD patients as controls to discover novel and more specific protein biomarkers in urine for early UC diagnosis. Aims To discover novel and more specific urinary protein biomarkers for screening of urothelial cancer (UC) in CKD patients. Method This is a multicenter prospective cohort study, carried out at 10 nationwide hospitals covering different regions of Taiwan from 2013-2018. Subjects were Taiwanese healthy adults (healthy controls, n=210), patients with CKD but without UC (disease controls, n=273) and patients with ongoing UC (n=211). For non-UC patients, urine and blood specimens were collected at the time of the structured interview. For UC patients, urine and blood samples were collected before operation. iTRAQ-LC-MS/MS approach was applied to compare protein expression among normal, CKD and UC cohorts. Results A total of 3838 proteins in urine were identified and 2497 proteins of them can be quantified. By filtering with fold changes, published papers and Human ATLAS, 27 candidates were selected as potential biomarkers and were validated with ELISA assay in a large sample size of urine specimens. Among the 27 candidates, we found BRDT, CYBP, GARS and HDGF were highly-expressed in the UC cohort when compared to normal and CKD cohorts (Figure 1). To increase the diagnostic value of UC, we combined ROC of our newly discovered 4 protein biomarkers. The AUC values of our newly discovered 4-biomarker panel were higher than the AUC values of the published biomarker panel, including the FDA-approved protein biomarkers (Figure 2). Conclusion We discover and validate a highly specific urinary protein biomarker panel (BRDT, GARS, HDGF and CYBP) to distinguish UC from CKD and healthy controls. It may be used for high throughput screening of subjects, such as CKD patients, who are at high risk to develop UC.

Dual Metabolomic Platforms Identified a Novel Urinary Metabolite Signature for Hepatitis B Virus-Infected Patients with Depression.

ObjectiveDepression could make the treatment outcome worse. However, up to now, no objective methods were developed to diagnose depression in hepatitis B virus (HBV)-infected patients. Therefore, the dual metabolomic platforms were used here to identify potential biomarkers for diagnosing HBV-infected patients with depression (dHB).MethodsBoth gas chromatography-mass spectrometry-based and nuclear magnetic resonance-based metabolomic platforms were used to conduct urine metabolic profiling of dHB subjects and HBV-infected patients without depression (HB). Orthogonal partial least-squares discriminant analysis was used to identify the differential metabolites between dHB subjects and HB subjects, and the step-wise logistic regression analysis was used to identify potential biomarkers.ResultsIn total, 21 important metabolites responsible for distinguishing dHB subjects from HB subjects were identified. Meanwhile, seven potential biomarkers (α-ydroxyisobutyric acid, hippuric acid, azelaic acid, isobutyric acid, malonic acid, levulinic acid, and phenylacetylglycine) were viewed as potential biomarkers. The simplified biomarker panel consisting of these seven metabolites had an excellent diagnostic performance in discriminating dHB subjects from HB subjects. Moreover, this panel could yield a higher accuracy in separating dHB subjects from HB subjects than our previous panels (identified by single metabolomic platform) did.ConclusionThese results suggested that the dual metabolomic platforms could yield a better urinary biomarker panel for dHB subjects than any single metabolomic platform did, and our results could be helpful for developing an objective method in future to diagnose depression in HBV-infected patients.

Prospective evaluation of a urinary biomarker panel to detect and predict recurrence of non-muscle-invasive bladder cancer.

To perform a feasibility phase study of a panel of putative protein biomarkers and determine whether it can identify and predict tumor recurrence in patients with non-muscle-invasive bladder cancer (NMIBC) on follow-up. We prospectively analyzed the urine of 152 patients previously treated for NMIBC. Quantitative expression of plasminogen activator inhibitor-1 (PAI-1), DJ-1, apolipoprotein A-I (apoA-1), matrix metallopeptidase-9 (MMP-9), and interleukin-8 (IL-8) was assessed by enzyme-linked immunosorbent assay and compared amongst patients with and without bladder cancer recurrence at urine collection and during 3years of follow-up. Tumor recurrence was confirmed by pathologic analysis. We performed a prediction analysis, excluding patients with recurrence at the start of the study, and assessed the influence of previous use of intravesical BCG on the level of biomarkers. Median follow-up time was 47months (interquartile range 39-50months). Sixteen patients (10.5%) were diagnosed with recurrence at the start of the study, and 21 (15.4%) were diagnosed during the study. Three biomarker proteins (apoA-1, MMP-9, and IL-8) appear to hold diagnostic potential [odds ratio (OR) = 12.9; 95% CI 3.5-47.4]; while, PAI-1 and IL-8 predict recurrence (OR = 4.1; 95% CI 1.4-11.4). Previous use of intravesical BCG did not affect biomarker levels. In the feasibility phase, the panel of urine biomarkers analyzed detected and predicted recurrence of NMIBC and provided reliable results in patients who had previously used intravesical BCG. Validation studies are required to confirm the panel clinical utility.

HIV preexposure prophylaxis with tenofovir disoproxil fumarate/emtricitabine and changes in kidney function and tubular health.

To evaluate the effects of HIV preexposure prophylaxis (PrEP) with tenofovir disoproxial fumurate (TDF)/emtricitabine (FTC) on kidney function and kidney tubular health. The Iniciativa Profilaxis Pre-Exposicion open-label extension (iPrEx-OLE) study enrolled former PrEP trial participants to receive open-label TDF/FTC. This study included 123 iPrEx-OLE participants who demonstrated PrEP adherence. We compared estimated glomerular filtration rate calculated using serum creatinine (eGFRcr), serum cystatin C (eGFRcys), and in combination (eGFRcr-cys), and a panel of 14 urine biomarkers reflecting kidney tubular health before and 6 months after PrEP initiation. At baseline, mean eGFRcr, eGFRcys, and eGFRcr-cys were 108.3, 107.0, and 111.1 ml/min per 1.73 m, respectively. Six months after PrEP initiation, eGFRcr declined by -4% (95% CI: -5.7 to -2.4%), eGFRcys declined by -3.3% (95% CI: -8.3 to 1.9%), and eGFRcr-cys declined by -4.1% (95% CI: -7.5 to -0.7%). From the urine biomarker panel, α1-microglobulin and β2-microglobulin increased by 22.7% (95% CI: 11.8--34.7%) and 14.1% (95% CI: -6.1 to 38.6%), whereas chitinase-3-like 1 protein and monocyte chemoattractant protein-1 decreased by -37.7% (95% CI: -53.0 to -17.3%) and -15.6% (95% CI: -31.6 to 4.2%), respectively. Ten of the 14 urine biomarkers, including albumin, had estimated changes of less than 12% with wide confidence intervals. Six months of PrEP with TDF/FTC was associated with decreases in eGFRcr and eGFRcys. We also observed for the first time changes in flour of 14 urine biomarkers reflecting kidney tubular health. These findings demonstrate that PrEP has direct effects on eGFR and the proximal tubule.

Comparison of serum and urinary biomarker panels with albumin/creatinine ratio in the prediction of renal function decline in type 1 diabetes

Aims/hypothesisWe examined whether candidate biomarkers in serum or urine can improve the prediction of renal disease progression in type 1 diabetes beyond prior eGFR, comparing their performance with urinary albumin/creatinine ratio (ACR).MethodsFrom the population-representative Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) we sampled 50% and 25% of those with starting eGFR below and above 75 ml min−1 [1.73 m]−2, respectively (N = 1629), and with median 5.1 years of follow-up. Multiplexed ELISAs and single molecule array technology were used to measure nine serum biomarkers and 13 urine biomarkers based on our and others’ prior work using large discovery and candidate studies. Associations with final eGFR and with progression to <30 ml min−1 [1.73] m−2, both adjusted for baseline eGFR, were tested using linear and logistic regression models. Parsimonious biomarker panels were identified using a penalised Bayesian approach, and their performance was evaluated through tenfold cross-validation and compared with using urinary ACR and other clinical record data.ResultsSeven serum and seven urine biomarkers were strongly associated with either final eGFR or progression to <30 ml min−1 [1.73 m]−2, adjusting for baseline eGFR and other covariates (all at p<2.3 × 10−3). Of these, associations of four serum biomarkers were independent of ACR for both outcomes. The strongest associations with both final eGFR and progression to <30 ml min−1 [1.73 m]−2 were for serum TNF receptor 1, kidney injury molecule 1, CD27 antigen, α-1-microglobulin and syndecan-1. These serum associations were also significant in normoalbuminuric participants for both outcomes. On top of baseline covariates, the r2 for prediction of final eGFR increased from 0.702 to 0.743 for serum biomarkers, and from 0.702 to 0.721 for ACR alone. The area under the receiver operating characteristic curve for progression to <30 ml min−1 [1.73 m]−2 increased from 0.876 to 0.953 for serum biomarkers, and to 0.911 for ACR alone. Other urinary biomarkers did not outperform ACR.Conclusions/interpretationA parsimonious panel of serum biomarkers easily measurable along with serum creatinine may outperform ACR for predicting renal disease progression in type 1 diabetes, potentially obviating the need for urine testing.

A Panel of Urinary Long Non-coding RNAs Differentiate Bladder Cancer from Urocystitis.

Liquid biopsy is becoming a promising method for non-invasive cancer detection. In several proof-of-concept studies, long non-coding RNAs (lncRNAs) were found to be potential biomarkers for bladder cancer detection. The objective of this study was to discover a panel of cell-free, urinary lncRNAs as liquid biopsy biomarkers to non-invasively differentiate bladder cancer from chronic urocystitis. To this end, we collected urine samples from both bladder cancer patients and urocystitis patients. These samples were divided into discovery group and validation group. In the discovery group, the expression levels of 16 cell-free urinary lncRNAs were measured by qPCR to discover candidate biomarkers. The diagnostic performance of the candidate lncRNA biomarkers was then evaluated, which led to a panel of lncRNA biomarkers for bladder cancer detection. The performance of this panel of biomarkers was further evaluated in the validation group to see if these lncRNA biomarkers could discriminate the bladder cancer patients from urocystitis patients. We found that all of the 16 lncRNAs evaluated in this study demonstrated significant difference (p<0.05) of expression between bladder cancer patients and urocystitis patients. Nine lncRNAs provided decent diagnostic performance with area under the receiver operating characteristic (ROC) curve (AUC) reaching 0.70 or higher. We then selected the top four lncRNAs, namely UCA1-201, HOTAIR, HYMA1 and MALAT1, to form a panel of urinary biomarkers. Using this panel, bladder cancer patients could be discriminated from urocystitis patients, with sensitivity and specificity reaching 95.7% and 94.3%, respectively. Finally, we confirmed the applicability of the four-lncRNA panel in an independent validation study that included 60 bladder cancer patients and 60 urocystitis patients. Our study paves the way for further studies aimed at large-scale clinical tests of developing lncRNA biomarkers in urine for bladder cancer diagnostics.

An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy.

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC (mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772–0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714–0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study.

Population-based screening programs are credited with earlier colorectal cancer diagnoses and treatment initiation, which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory as they are invasive, are resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomic-based biomarker panel for the detection of colorectal cancer that has the potential for global population-based screening. Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 342 participants (colorectal cancer, 171; healthy controls, 171) from two study sites (Canada, United States) were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for colorectal cancer were identified by comparing the metabolomic profiles from colorectal cancer versus controls. Multiple models were constructed leading to a good separation of colorectal cancer from controls. A panel of 17 metabolites was identified as possible biomarkers for colorectal cancer. Using only two of the selected metabolites, namely diacetylspermine and kynurenine, a predictor for detecting colorectal cancer was developed with an AUC of 0.864, a specificity of 80.0%, and a sensitivity of 80.0%. We present a potentially "universal" metabolomic biomarker panel for colorectal cancer independent of cohort clinical features based on a North American population. Further research is needed to confirm the utility of the profile in a prospective, population-based colorectal cancer screening trial. A urinary metabolomic biomarker panel was identified for colorectal cancer with the potential of clinical application.

A novel urinary microRNA biomarker panel for detecting gastric cancer.

Gastric cancer (GC) is one of the most common causes of cancer deaths worldwide; however, reliable and non-invasive screening methods for GC are not established. Therefore, we conducted this study to develop a biomarker for GC detection, consisting of urinary microRNAs (miRNAs). We matched 306 participants by age and sex [153 pairs consisting of patients with GC and healthy controls (HCs)], then randomly divided them across three groups: (1) the discovery cohort (4 pairs); (2) the training cohort (95 pairs); and (3) the validation cohort (54 pairs). There were 22 urinary miRNAs with significantly aberrant expressions between the two groups in the discovery cohort. Upon multivariate analysis of the training cohort, urinary expression levels of miR-6807-5p and miR-6856-5p were significantly independent biomarkers for diagnosis of GC, in addition to Helicobacter pylori (H. pylori) status. A diagnostic panel that combined these 2 miRNAs and H. pylori status distinguished between HC and GC samples with an area under the curve (AUC) = 0.736. In the validation cohort, urinary miR-6807-5p and miR-6856-5p showed significantly higher expression levels in the GC group, and the combination biomarker panel of miR-6807-5p, miR-6856-5p, and H. pylori status also showed excellent performance (AUC = 0.885). In addition, this biomarker panel could distinguish between HC and stage I GC patients with an AUC = 0.748. Urinary expression levels of miR-6807-5p and miR-6856-5p significantly decreased to undetectable level after curative resection of GC. This novel biomarker panel enables early and non-invasive detection of GC.

Coccidioidomycosis Detection Using Targeted Plasma and Urine Metabolic Profiling.

Coccidioidomycosis, also known as Valley fever (VF), is a potentially lethal fungal infection that results in more than 200 deaths per year in the United States. Despite the important role of metabolic processes in the molecular pathogenesis of VF, robust metabolic markers to enable effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of VF are still lacking. We present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying metabolic marker candidates that could enable rapid, highly sensitive, and specific VF detection. Using this targeted approach, 207 plasma metabolites and 231 urinary metabolites from many metabolic pathways of potential biological significance were reliably detected and monitored in 147 samples taken from two groups of subjects (48 VF patients and 99 non-VF controls). The results of our univariate significance testing and multivariate model development informed the construction of a three-metabolite panel of potential plasma biomarkers and a nine-metabolite panel of potential urinary biomarkers. Receiver operating characteristic curves generated based on orthogonal partial least-squares-discriminant analysis models showed excellent classification performance, with 94.4% sensitivity and 97.6% specificity for plasma metabolites. Urine metabolites were less accurate, demonstrating 89.7% sensitivity and 88.1% specificity. Enrichment, pathway, and network analyses revealed significant disturbances in glycine and serine metabolism, in both plasma and urine samples. To the best of our knowledge, this is the first study aiming to discover novel metabolite markers of VF, which could achieve accurate diagnosis within 24 h. The results expand the basic knowledge of the metabolome related to VF and potentially reveal pathways or markers that could be therapeutically targeted. This study also provides a promising basis for the development of larger multisite projects to validate our findings across population groups and further advance the development of better clinical care for VF patients.

Coccidioidomycosis Detection Using Targeted Plasma and Urine Metabolic Profiling

Coccidioidomycosis, or Valley Fever (VF), is a potentially lethal fungal infection that results in more than 200 deaths/year in the United States. Differential diagnosis and timely treatment of VF is made difficult due to presentation of vague symptoms that often mimic other pulmonary conditions. Accurate diagnosis therefore relies on a combination of clinical presentation, serology, radiography, histology and culture. These diagnostic methods are either costly, time‐consuming, invasive, or indeterminate. Previous studies have used MS‐based methods for the accurate diagnosis of similar fungal diseases as well as characterization of host‐fungal interactions. Herein, we present the first targeted plasma and urine liquid chromatography tandem mass spectrometry (LC‐MS/MS) profiling approach for the relatively non‐invasive, rapid and accurate detection of VF.In this targeted approach, 207 plasma metabolites and 231 urine metabolites were reliably detected using LC‐MS/MS. These metabolites, which are representative of more than 35 metabolic pathways of potential biological relevance, were monitored in 59 plasma samples and 88 urine samples acquired from excess clinical specimens taken from two groups of patients (48 VF patients and 99 non‐VF controls) at Mayo Clinic Arizona. Univariate and multivariate statistical analyses were performed to develop predictive plasma and urine biomarker panels. Pathway and enrichment analyses were performed to elucidate potential pathogenic mechanisms and therapeutic targets.Results of our univariate significance testing and multivariate model estimation informed the construction of a 3‐metabolite panel of plasma biomarkers (q &lt; 0.05 and VIP &gt; 2; Fig 1) and a 9‐metabolite panel of urinary biomarkers (q &lt; 0.05 and VIP &gt; 1; Fig 2). Receiver operating characteristic (ROC) curves (Fig 3) generated based on enhanced orthogonal partial least squares‐discriminant analysis (OPLS‐DA) models (Fig 4) showed excellent overall classification performance (99.5%), with 94.4% sensitivity and 97.6% specificity for plasma metabolites. Although urine metabolites were less accurate (92.5%), demonstrating 89.7% sensitivity and 88.1% specificity, they represent a substantial increase in diagnostic accuracy over currently available urinary antigen tests, which only report sensitivity of around 70%. Pathway analysis (Fig 5) and enrichment analysis (Fig 6) ubiquitously showed alterations in metabolic pathways related to fungal virulence, DNA/RNA synthesis and energy utilization, and host immune escape mechanisms. These results potentially reveal pathways or markers that could be targeted therapeutically.This study is the first‐ever MS‐based metabolomics approach for rapid and highly accurate diagnosis of VF.Support or Funding InformationSupport from the College of Health Solutions at Arizona State University, Arizona Biomedical Research Council (ABRC) grant# 16–162513 and American Achievement Rewards for College Scientists (ARCS) Foundation Award is gratefully acknowledged.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

105 - Identification of clinically significant prostate cancer using a novel multiplexed urine biomarker panel

105 - Identification of clinically significant prostate cancer using a novel multiplexed urine biomarker panel

A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity

BackgroundA urine ‘biomarker panel’ comprising alpha-1-acid-glycoprotein, ceruloplasmin, transferrin and lipocalin-like-prostaglandin-D synthase performs to an ‘excellent’ level for lupus nephritis identification in children cross-sectionally. The aim of this study was to assess if this biomarker panel predicts lupus nephritis flare/remission longitudinally. MethodsThe novel urinary biomarker panel was quantified by enzyme linked immunoabsorbant assay in participants of the United Kingdom Juvenile Systemic Lupus Erythematosus (UK JSLE) Cohort Study, the Einstein Lupus Cohort, and the South African Paediatric Lupus Cohort. Monocyte chemoattractant protein-1 and vascular cell adhesion molecule-1 were also quantified in view of evidence from other longitudinal studies. Serial urine samples were collected during routine care with detailed clinical and demographic data. A Markov Multi-State model of state transitions was fitted, with predictive clinical/biomarker factors assessed by a corrected Akaike Information Criterion (AICc) score (the better the model, the lower the AICc score). ResultsThe study included 184 longitudinal observations from 80 patients. The homogeneous multi-state Markov model of lupus nephritis activity AICc score was 147.85. Alpha-1-acid-glycoprotein and ceruloplasmin were identified to be the best predictive factors, reducing the AICc score to 139.81 and 141.40 respectively. Ceruloplasmin was associated with the active-to-inactive transition (hazard ratio 0.60 (95% confidence interval [0.39, 0.93])), and alpha-1-acid-glycoprotein with the inactive-to-active transition (hazard ratio 1.49 (95% confidence interval [1.10, 2.02])). Inputting individual alpha-1-acid-glycoprotein/ceruloplasmin values provides 3, 6 and 12 months probabilities of state transition. ConclusionsAlpha-1-acid-glycoprotein was predictive of active lupus nephritis flare, whereas ceruloplasmin was predictive of remission. The Markov state-space model warrants testing in a prospective clinical trial of lupus nephritis biomarker led monitoring.

Growing international evidence for urinary biomarker panels identifying lupus nephritis in children – verification within the South African Paediatric Lupus Cohort

A urinary biomarker panel including alpha-1-acid-glycoprotein (AGP), lipocalin-like-prostaglandin-D-synthase (LPGDS), transferrin and ceruloplasmin demonstrates an 'excellent' ability for identifying active lupus nephritis in UK/US children. This study aimed to assess whether this panel identifies active lupus nephritis within the South African Paediatric Lupus Cohort. Juvenile-onset-systemic lupus erythematosus (JSLE) patients aged < 19 years at diagnosis and healthy controls were recruited. Patients were categorized as having active lupus nephritis (renal BILAG score; A/B and previous histological confirmation) or inactive lupus nephritis (renal BILAG score: D/E). Urinary biomarkers were quantified by ELISA. Mann-Whitney U-test compared biomarker levels between groups. Binary logistic regression and receiver operating curve analysis assessed biomarker combinations. Twenty-three juvenile-onset-systemic lupus erythematosus patients were recruited with a median age of 13.5 years (interquartile range (IQR) 12.7-14.9) and disease duration of 2.6 years (IQR 1.8-4.0). Eighteen healthy controls had a median age of 11.0 years (IQR 10.0-12.0). AGP, LPGDS, transferrin, ceruloplasmin and VCAM-1 were significantly higher in active than in inactive lupus nephritis patients (corrected p-values, all pc < 0.05), with no difference between inactive lupus nephritis patients and healthy controls (all pc = 1.0). The optimal biomarker combination included AGP, ceruloplasmin, LPGDS and transferrin (area under the curve = 1.0). A urinary biomarker panel comprising AGP, ceruloplasmin, LPGDS and transferrin previously validated within UK/US cohorts also performed excellently within a racially distinct South African cohort which displayed more severe lupus nephritis.

"Mix-to-Go" Silver Colloidal Strategy for Prostate Cancer Molecular Profiling and Risk Prediction.

Molecular profiling via analysis of multiple disease biomarkers is a powerful tool for disease diagnosis and risk prediction. Due to simplicity and minimal instrumentation requirement, colloidal-based colorimetric DNA/RNA assays are attractive for driving molecular profiling toward widespread clinical usage. Still, the reliability and speed of current colorimetric assays need to be further improved upon for eventual clinical use. Herein, we propose a "mix-to-go" colloidal strategy that utilizes the electrostatic attraction between negatively charged target sequences and positively charged silver nanoparticles (AgNPs) to induce aggregation of AgNPs to profile a panel of clinically validated urinary prostate cancer (PCa) RNA biomarkers ( TMPRSS2:ERG, T2:ERG; prostate cancer antigen 3, PCA3; and kallikrein-related peptidase 2, KLK2). Our strategy is unique in inducing a rapid (10 s), unambiguous solution color change in the presence of target sequences, without the additional NP aggregation agents that are used in existing electrostatic-mediated aggregation assays. Our strategy is analytically specific and sensitive for the detection of 105 and 104 target copies by the naked eye and UV-vis spectrophotometry, respectively. Analytical accuracies of our strategy in detecting T2:ERG, PCA3, and KLK2 RNA biomarkers were 95.9%, 97.3%, and 100%, respectively, as validated by quantitative reverse transcription-polymerase chain reaction. To further evaluate clinical molecular profiling performance beyond conventional proof-of-concept demonstration, we tested our colloidal strategy for noninvasive PCa risk prediction of 73 patients, using the urinary RNA biomarker panel comprising of T2:ERG, PCA3, and KLK2. We found that elevated T2:ERG and PCA3 levels were positively associated with high-risk PCa and obtained a corresponding area-under-the-curve values of 0.790 and 0.833 for predicting PCa and high-risk PCa on biopsy, respectively. We believe our "mix-to-go" strategy may serve as a reliable and accessible Ag colloidal-based molecular profiling approach for clinical applications.