Computational approach to modeling microbiome landscapes associated with chronic human disease progression.

A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn's disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn's disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn's disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

Association of SNPs in the PAI1 Gene with Disease Recurrence and Clinical Outcome in Bladder Cancer.

PURPOSE: Bladder cancer (BCa) is one of the most common cancer types worldwide and is characterized by a high rate of recurrence. In previous studies, we and others have described the functional influence of plasminogen activator inhibitor-1 (PAI1) in bladder cancer development. While polymorphisms in PAI1 have been associated with increased risk and worsened prognosis in some cancers, the mutational status of PAI1 in human bladder tumors has not been well defined. METHODS: In this study, we evaluated the mutational status of PAI1 in a series of independent cohorts, comprised of a total of 660 subjects. RESULTS: Sequencing analyses identified two clinically relevant 3' untranslated region (UTR) single nucleotide polymorphisms (SNPs) in PAI1 (rs7242; rs1050813). Somatic SNP rs7242 was present in human BCa cohorts (overall incidence of 72%; 62% in Caucasians and 72% in Asians). In contrast, the overall incidence of germline SNP rs1050813 was 18% (39% in Caucasians and 6% in Asians). Furthermore, Caucasian patients with at least one of the described SNPs had worse recurrence-free survival and overall survival (p = 0.03 and p = 0.03, respectively). In vitro functional studies demonstrated that SNP rs7242 increased the anti-apoptotic effect of PAI1, and SNP rs1050813 was related to a loss of contact inhibition associated with cellular proliferation when compared to wild type. CONCLUSION: Further investigation of the prevalence and potential downstream influence of these SNPs in bladder cancer is warranted.

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.

MOTIVATION: Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. RESULTS: To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. AVAILABILITY AND IMPLEMENTATION: An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diagnostic performance of Oncuria™, a urinalysis test for bladder cancer.

BACKGROUND: Due to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with bladder cancer. The identification of multiplex molecular signatures associated with disease has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis and monitoring. METHODS: To evaluate the performance of Oncuria™, a multiplex immunoassay for bladder detection in voided urine samples. The test was evaluated in a multi-institutional cohort of 362 prospectively collected subjects presenting for bladder cancer evaluation. The parallel measurement of 10 biomarkers (A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1 and VEGFA) was performed in an independent clinical laboratory. The ability of the test to identify patients harboring bladder cancer was assessed. Bladder cancer status was confirmed by cystoscopy and tissue biopsy. The association of biomarkers and demographic factors was evaluated using linear discriminant analysis (LDA) and predictive models were derived using supervised learning and cross-validation analyses. Diagnostic performance was assessed using ROC curves. RESULTS: The combination of the 10 biomarkers provided an AUROC 0.93 [95% CI 0.87-0.98], outperforming any single biomarker. The addition of demographic data (age, sex, and race) into a hybrid signature improved the diagnostic performance AUROC 0.95 [95% CI 0.90-1.00]. The hybrid signature achieved an overall sensitivity of 0.93, specificity of 0.93, PPV of 0.65 and NPV of 0.99 for bladder cancer classification. Sensitivity values of the diagnostic panel for high-grade bladder cancer, low-grade bladder cancer, MIBC and NMIBC were 0.94, 0.89, 0.97 and 0.93, respectively. CONCLUSIONS: Urinary levels of a biomarker panel enabled the accurate discrimination of bladder cancer patients and controls. The multiplex Oncuria™ test can achieve the efficient and accurate detection and monitoring of bladder cancer in a non-invasive patient setting.

Computational approach for deriving cancer progression roadmaps from static sample data.

As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy.

Genetic deletion of sphingosine kinase 1 suppresses mouse breast tumor development in an HER2 transgenic model.

Aberrant sphingolipid metabolism has been reported to promote breast cancer progression. Sphingosine kinase 1 (SphK1) is a key metabolic enzyme for the formation of pro-survival S1P from pro-apoptotic ceramide. The role of SphK1 in breast cancer has been well studied in estrogen receptor (ER)-positive breast cancer; however, its role in human epidermal growth factor 2 (HER2)-positive breast cancer remains unclear. Here, we show that genetic deletion of SphK1 significantly reduced mammary tumor development with reduced tumor incidence and multiplicity in the MMTV-neu transgenic mouse model. Gene expression analysis revealed significant reduction of claudin-2 (CLDN2) expression in tumors from SphK1 deficient mice, suggesting that CLDN2 may mediate SphK1's function. It is remarkable that SphK1 deficiency in HER2-positive breast cancer model inhibited tumor formation by the different mechanism from ER-positive breast cancer. In vitro experiments demonstrated that overexpression of SphK1 in ER-/PR-/HER2+ human breast cancer cells enhanced cell proliferation, colony formation, migration and invasion. Furthermore, immunostaining of SphK1 and CLDN2 in HER2-positive human breast tumors revealed a correlation in high-grade disease. Taken together, these findings suggest that SphK1 may play a pivotal role in HER2-positive breast carcinogenesis. Targeting SphK1 may represent a novel approach for HER2-positive breast cancer chemoprevention and/or treatment.

A multiplex immunoassay for the non-invasive detection of bladder cancer.

BACKGROUND: Urine based assays that can non-invasively detect bladder cancer (BCa) have the potential to reduce unnecessary and invasive procedures. The purpose of this study was to develop a multiplex immunoassay that can accurately and simultaneously monitor ten diagnostic urinary protein biomarkers for application as a non-invasive test for BCa detection. METHODS: A custom electrochemiluminescent multiplex assay was constructed (Meso Scale Diagnostics, LLC, Rockville, MD, USA) to detect the following urinary proteins; IL8, MMP9, MMP10, ANG, APOE, SDC1, A1AT, PAI1, CA9 and VEGFA. Voided urine samples from two cohorts were collected prior to cystoscopy and samples were analyzed blinded to the clinical status of the participants. Means (±SD) and receiver operating characteristic (ROC) curve analysis were used to compare assay performance and to assess the diagnostic accuracy of the diagnostic signature. RESULTS: Comparative diagnostic performance analyses revealed an AUROC value of 0.9258 for the multiplex assay and 0.9467 for the combination of the single-target ELISA assays (p = 0.625), so there was no loss of diagnostic utility for the MSD multiplex assay. Analysis of the independent 200-sample cohort using the multiplex assay achieved an overall diagnostic sensitivity of 0.85, specificity of 0.81, positive predictive value 0.82 and negative predictive value 0.84. CONCLUSIONS: It is technically feasible to simultaneously monitor complex urinary diagnostic signatures in a single assay without loss of performance. The described protein-based assay has the potential to be developed for the non-invasive detection of BCa.

A multiplex urinary immunoassay for bladder cancer detection: analysis of a Japanese cohort.

BACKGROUND: Bladder cancer (BCa) is among the most commonly diagnosed malignancies worldwide, and due the high rate of post-operative disease recurrence, it is one of the most prevalent in many countries. The development of non-invasive molecular assays that can accurately detect and monitor BCa would be a major advance, benefiting both patients and healthcare systems. We have previously identified a urinary protein biomarker panel that is being developed for application in at-risk patient cohorts. Here, we investigated the potential utility of the multiplex assay in a Japanese cohort. METHODS: The Japanese study cohort collected from urology clinics at two institutions was comprised of a total of 288 subjects. The protein biomarker panel (IL8, MMP9, MMP10, ANG, APOE, SDC1, A1AT, PAI1, CA9, VEGFA) was monitored in voided urine samples collected prior to cystoscopy using a custom multiplex ELISA assay. The diagnostic performance of the biomarker panel was assessed using receiver operator curves, predictive modeling and descriptive statistics. RESULTS: Urinary biomarker concentrations were significantly elevated in cases versus controls, and in cases with high-grade and muscle-invasive tumors. The AUC for the 10-biomarker assay was 0.892 (95 % confidence interval 0.850-0.934), with an overall diagnostic sensitivity specificity of 0.85 and 0.81, respectively. A predictive model trained on the larger institutional cohort correctly identified 99 % of the cases from the second institution. CONCLUSIONS: Urinary levels of a 10-biomarker panel enabled discrimination of patients with BCa. The multiplex urinary diagnostic assay has the potential to be developed for the non-invasive detection of BCa in at-risk Japanese patients.

Simultaneous multi-analyte urinary protein assay for bladder cancer detection.

BACKGROUND: The ability to accurately measure multiple proteins simultaneously in a single assay has the potential to markedly improve the efficiency of a myriad of clinical assays. Here, we tested the performance of a new, multiplex protein array platform to quantitate three bladder cancer-associated proteins in urine samples. The following analytes, interleukin 8 (IL8), matrix metallopeptidase 9 (MMP9), and vascular endothelial growth factor A (VEGFA) were monitored using Q-plex, a customized multiplex ELISA system from Quansys Biosciences, and individual target commercial ELISA kits. The performance of the two approaches was compared by evaluating the diagnostic accuracy of the biomarker assays in samples from a cohort of 73 subjects of known bladder cancer status. RESULTS: The combination biomarker panel analyses revealed an AUROC value of 0.9476 for the Q-plex assay, and 0.9119 for the combination of the single-target ELISA assays. The Q-plex assay achieved an overall diagnostic sensitivity of 0.93 and specificity of 0.81, and the individual target ELISA assays achieved an overall sensitivity of 0.77 and specificity of 0.91. CONCLUSION: Based on these encouraging preliminary data, we believe that the Q-Plex technology is a viable platform that can be exploited as an efficient, highly accurate tool to quantitate multiplex panels of diagnostic proteins in biologic specimens.

Matrix metalloproteinase-10 promotes tumor progression through regulation of angiogenic and apoptotic pathways in cervical tumors.

BACKGROUND: Cancer invasion and metastasis develops through a series of steps that involve the loss of cell to cell and cell to matrix adhesion, degradation of extracellular matrix and induction of angiogenesis. Different protease systems (e.g., matrix metalloproteinases, MMPs) are involved in these steps. MMP-10, one of the lesser studied MMPs, is limited to epithelial cells and can facilitate tumor cell invasion by targeting collagen, elastin and laminin. Enhanced MMP-10 expression has been linked to poor clinical prognosis in some cancers, however, mechanisms underlying a role for MMP-10 in tumorigenesis and progression remain largely unknown. Here, we report that MMP-10 expression is positively correlated with the invasiveness of human cervical and bladder cancers. METHODS: Using commercial tissue microarray (TMA) of cervical and bladder tissues, MMP-10 immunohistochemical staining was performed. Furthermore using a panel of human cells (HeLa and UROtsa), in vitro and in vivo experiments were performed in which MMP-10 was overexpressed or silenced and we noted phenotypic and genotypic changes. RESULTS: Experimentally, we showed that MMP-10 can regulate tumor cell migration and invasion, and endothelial cell tube formation, and that MMP-10 effects are associated with a resistance to apoptosis. Further investigation revealed that increasing MMP-10 expression stimulates the expression of HIF-1α and MMP-2 (pro-angiogenic factors) and PAI-1 and CXCR2 (pro-metastatic factors), and accordingly, targeting MMP-10 with siRNA in vivo resulted in diminution of xenograft tumor growth with a concomitant reduction of angiogenesis and a stimulation of apoptosis. CONCLUSION: Taken together, our findings show that MMP-10 can play a significant role in tumor growth and progression, and that MMP-10 perturbation may represent a rational strategy for cancer treatment.

Clinical implications in the shift of syndecan-1 expression from the cell membrane to the cytoplasm in bladder cancer.

BACKGROUND: To determine the diagnostic and prognostic capability of urinary and tumoral syndecan-1 (SDC-1) levels in patients with cancer of the urinary bladder. METHODS: SDC-1 levels were quantitated by enzyme-linked immunosorbent assay (ELISA) in 308 subjects (102 cancer subjects and 206 non-cancer subjects) to assess its diagnostic capabilities in voided urine. The performance of SDC-1 was evaluated using the area under the curve of a receiver operating characteristic curve. In addition, immunohistochemical (IHC) staining assessed SDC-1 protein expression in 193 bladder specimens (185 cancer subjects and 8 non-cancer subjects). Outcomes were correlated to SDC-1 levels. RESULTS: Mean urinary levels of SDC-1 did not differ between the cancer subjects and the non-cancer subjects, however, the mean urinary levels of SDC-1 were reduced in high-grade compared to low-grade disease (p < 0.0001), and in muscle invasive bladder cancer (MIBC) compared to non-muscle invasive bladder cancer (NMIBC) (p = 0.005). Correspondingly, preliminary data note a shift from a membranous cellular localization of SDC-1 in normal tissue, low-grade tumors and NMIBC, to a distinctly cytoplasmic localization in high-grade tumors and MIBC was observed in tissue specimens. CONCLUSION: Alone urinary SDC-1 may not be a diagnostic biomarker for bladder cancer, but its urinary levels and cellular localization were associated with the differentiation status of patients with bladder tumors. Further studies are warranted to define the potential role for SDC-1 in bladder cancer progression.

Prostate cancer progression modeling provides insight into dynamic molecular changes associated with progressive disease states.

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multi-dimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from the TCGA study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

Expression of CXCL1 in human endothelial cells induces angiogenesis through the CXCR2 receptor and the ERK1/2 and EGF pathways.

Endothelial cell growth and proliferation are critical for angiogenesis; thus, greater insight into the regulation of pathological angiogenesis is greatly needed. Previous studies have reported on chemokine (C-X-C motif) ligand 1 (CXCL1) expression in epithelial cells and that secretion of CXCL1 from these epithelial cells induces angiogenesis. However, limited reports have demonstrated CXCL1 expression in endothelial cells. In this report, we present data that expand on the role of CXCL1 in human endothelial cells inducing angiogenesis. Specifically, CXCL1 is expressed and secreted from human endothelial cells. Interference of CXCL1 function using neutralizing antibodies resulted in a reduction in endothelial cell migration and viability/proliferation, the latter associated with a decrease in levels of cyclin D and cdk4. In vitro studies revealed that CXCL1 influenced neoangiogenesis through the regulation of epidermal growth factor and ERK1/2. In a xenograft angiogenesis model, interference of CXCL1 function resulted in inhibition of angiogenesis. A better understanding of the role of CXCL1 in the interactions between the endothelial and epithelial components will provide insight into how human tissues use CXCL1 to survive and thrive in a hostile environment.

Multiplex protein signature for the detection of bladder cancer in voided urine samples.

PURPOSE: Accurate urine assays for bladder cancer detection would benefit patients and health care systems. Through extensive genomic and proteomic profiling of urine components we previously identified a panel of 8 biomarkers that can facilitate the detection of bladder cancer in voided urine samples. In this study we confirmed this diagnostic molecular signature in a diverse multicenter cohort. MATERIALS AND METHODS: We performed a case-control, phase II study in which we analyzed voided urine from 102 subjects with bladder cancer and 206 with varying urological disorders. The urinary concentration of 8 biomarkers (IL-8, MMP-9 and 10, PAI-1, VEGF, ANG, CA9 and APOE) was assessed by enzyme-linked immunosorbent assay. Diagnostic performance of the panel of tested biomarkers was evaluated using ROCs and descriptive statistical values, eg sensitivity and specificity. RESULTS: Seven of the 8 urine biomarkers were increased in subjects with bladder cancer relative to those without bladder cancer. The 7 biomarkers were assessed in a new model, which had an AUROC of 0.88 (95% CI 0.84-0.93), and 74% sensitivity and 90% specificity. In contrast, the sensitivity of voided urine cytology and the UroVysion® cytogenetic test in this cohort was 39% and 54%, respectively. Study limitations include analysis performed on banked urine samples and the lack of voided urine cytology and cytogenetic test data on controls. CONCLUSIONS: The study provides further evidence that the reported panel of diagnostic biomarkers can reliably achieve the noninvasive detection of bladder cancer with higher sensitivity than currently available urine based assays.

Chemokine (C-X-C) ligand 1 (CXCL1) protein expression is increased in aggressive bladder cancers.

BACKGROUND: Chemokines, including chemokine (C-X-C motif) ligand 1 (CXCL1), may regulate tumor epithelial-stromal interactions that facilitate tumor growth and invasion. Studies have linked CXCL1 expression to gastric, colon and skin cancers, but limited studies to date have described CXCL1 protein expression in human bladder cancer (BCa). METHODS: CXCL1 protein expression was examined in 152 bladder tissue specimens (142 BCa) by immunohistochemical staining. The expression of CXCL1 was scored by assigning a combined score based on the proportion of cells staining and intensity of staining. CXCL1 expression patterns were correlated with clinicopathological features and follow-up data. RESULTS: CXCL1 protein expression was present in cancerous tissues, but was entirely absent in benign tissue. CXCL1 combined immunostaining score was significantly higher in high-grade tumors relative to low-grade tumors (p = 0.012). Similarly, CXCL1 combined immunostaining score was higher in high stage tumors (T2-T4) than in low stage tumors (Ta-T1) (p < 0.0001). An increase in the combined immunostaining score of CXCL1 was also associated with reduced disease-specific survival. CONCLUSION: To date, this is the largest study describing increased CXCL1 protein expression in more aggressive phenotypes in human BCa. Further studies are warranted to define the role CXCL1 plays in bladder carcinogenesis and progression.

Investigation of CCL18 and A1AT as potential urinary biomarkers for bladder cancer detection.

BACKGROUND: In this study, we further investigated the association of two biomarkers, CCL18 and A1AT, with bladder cancer (BCa) and evaluated the influence of potentially confounding factors in an experimental model. METHODS: In a cohort of 308 subjects (102 with BCa), urinary concentrations of CCL18 and A1AT were assessed by enzyme-linked immunosorbent assay (ELISA). In an experimental model, benign or cancerous cells, in addition to blood, were added to urines from healthy controls and analyzed by ELISA. Lastly, immunohistochemical staining for CCL18 and A1AT in human bladder tumors was performed. RESULTS: Median urinary protein concentrations of CCL18 (52.84 pg/ml vs. 11.13 pg/ml, p < 0.0001) and A1AT (606.4 ng/ml vs. 120.0 ng/ml, p < 0.0001) were significantly elevated in BCa subjects compared to controls. Furthermore, the addition of whole blood to pooled normal urine resulted in a significant increase in both CCL18 and A1AT. IHC staining of bladder tumors revealed CCL18 immunoreactivity in inflammatory cells only, and there was no significant increase in these immunoreactive cells within benign and cancerous tissue and no association with BCa grade nor stage was noted. A1AT immunoreactivity was observed in the cytoplasm of epithelia cells and intensity of immunostaining increased with tumor grade, but not tumor stage. CONCLUSIONS: Further development of A1AT as a diagnostic biomarker for BCa is warranted.

Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks.

BACKGROUND: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. METHODS: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. RESULTS: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

Diagnostic potential of urinary α1-antitrypsin and apolipoprotein E in the detection of bladder cancer.

PURPOSE: The ability to reliably diagnose bladder cancer in voided urine samples would be a major advance. Using high throughput technologies, we identified a panel of bladder cancer associated biomarkers with potential clinical usefulness. In this study we tested 4 potential biomarkers for the noninvasive detection of bladder cancer. MATERIALS AND METHODS: We examined voided urine specimens from 124 patients, including 63 newly diagnosed with bladder cancer and 61 controls. Concentrations of proteins were assessed by enzyme-linked immunosorbent assay, including α1-antitrypsin, apolipoprotein E, osteopontin and pentraxin 3. Data were compared to the results of urinary cytology and the BTA Trak® enzyme-linked immunosorbent assay based bladder cancer detection assay. We used the AUC of ROC curves to compare the usefulness of each biomarker to detect bladder cancer. RESULTS: Urinary levels of α1-antitrypsin, apolipoprotein E and bladder tumor antigen were significantly increased in subjects with bladder cancer. α1-Antitrypsin (AUC 0.9087, 95% CI 0.8555-0.9619) and apolipoprotein E (AUC 0.8987, 95% CI 0.8449-0.9525) were the most accurate biomarkers. The combination of α1-antitrypsin and apolipoprotein E (AUC 0.9399) achieved 91% sensitivity, 89% specificity, and a positive and negative predictive value of 89% and 90%, respectively. Multivariate regression analysis highlighted only apolipoprotein E as an independent predictor of bladder cancer (OR 24.9, 95% CI 4.22-146.7, p = 0.0004). CONCLUSIONS: Alone or in combination, α1-antitrypsin and apolipoprotein E show promise for the noninvasive detection of bladder cancer (OR 24.9, 95% CI 4.22-146.7, p = 0.0004). Larger, prospective studies including more low grade, low stage tumors are needed to confirm these results.

Urinary BTA: indicator of bladder cancer or of hematuria.

BACKGROUND: In this study, we investigated the influence of hematuria on the performance of the bladder tumor antigen (BTA) tests in a clinical cohort and in an experimental model. MATERIALS AND METHODS: Urine samples from a cohort of 126 subjects (64 with BCa and 62 controls) were analyzed by ELISA for hemoglobin and BTA. The experimental model involved the spiking of urine with blood from the same subject, and hemoglobin, red blood cell count, and BTA levels (BTA stat© and BTA-TRAK©). BTA-TRAK© analyses were also performed on serum samples obtained from 40 subjects (20 with confirmed with BCa). RESULTS: In the 126 subject cohort, correlation between hemoglobin and BTA was 0.732. Of the 64 BCa samples, 72 % had a positive BTA assay, but 47 % of controls were also positive. The sensitivity and specificity of BTA to detect BCa was 72 and 53 %, respectively. Hematuria, measured by urinary hemoglobin, was a better indicator of BCa with 75 % sensitivity and 90 % specificity. Spiking of BTA-negative urine samples with as little as 1 µl/10 ml was enough to produce a positive BTA test. High levels of BTA were found equally in the serum of subjects with or without BCa (mean BTA levels 355,159 vs. 332,329 U/ml, respectively). CONCLUSIONS: Rather than detecting a bladder tumor antigen, urinary BTA assays may be measuring serum cFH introduced by bleeding, a common presenting factor in BCa subjects. The presence of hematuria in subjects without malignant disease can result in false-positive BTA assays.

IL-8 as a urinary biomarker for the detection of bladder cancer.

BACKGROUND: Current urine-based assays for bladder cancer (BCa) diagnosis lack accuracy, so the search for improved biomarkers continues. Through genomic and proteomic profiling of urine, we have identified a panel of biomarkers associated with the presence of BCa. In this study, we evaluated the utility of three of these biomarkers, interleukin 8 (IL-8), Matrix metallopeptidase 9 (MMP-9) and Syndecan in the diagnosis of BCa through urinalysis. METHODS: Voided urines from 127 subjects, cancer subjects (n = 64), non-cancer subjects (n = 63) were analyzed. The protein concentrations of IL-8, MMP-9, and Syndecan were assessed by enzyme-linked immunosorbent assay (ELISA). Data were also compared to a commercial ELISA-based BCa detection assay (BTA-Trak©) and urinary cytology. We used the area under the curve of a receiver operating characteristic (AUROC) to compare the performance of each biomarker. RESULTS: Urinary protein concentrations of IL-8, MMP-9 and BTA were significantly elevated in BCa subjects. Of the experimental markers compared to BTA-Trak©, IL-8 was the most prominent marker (AUC; 0.79; 95% confidence interval [CI], 0.72-0.86). Multivariate regression analysis revealed that only IL-8 (OR; 1.51; 95% CI, 1.16-1.97, p = 0.002) was an independent factor for the detection of BCa. CONCLUSIONS: These results suggest that the measurement of IL-8 in voided urinary samples may have utility for urine-based detection of BCa. These findings need to be confirmed in a larger, prospective cohort.