Validation of a protein panel for the noninvasive detection of recurrent non-muscle invasive bladder cancer

Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends

Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.

Trends in urine biomarker discovery for urothelial bladder cancer: DNA, RNA, or protein?

Urothelial bladder cancer is a complex disease displaying a landscape of heterogenous molecular subtypes, mutation profiles and clinical presentations. Diagnosis and surveillance rely on flexible cystoscopy which has high accuracy, albeit accompanied by a high-cost burden for healthcare providers and discomfort for patients. Advances in "omic" technologies and computational biology have provided insights into the molecular pathogenesis of bladder cancer and provided powerful tools to identify markers for disease detection, risk stratification, and predicting responses to therapy. To date, numerous attempts have been made to discover and validate diagnostic biomarkers that could be deployed as an adjunct to the cystoscopic diagnosis and long-term surveillance of bladder cancer. We report a comprehensive literature analysis using PubMed to assess the changing trends in investigating DNA, RNA, or proteins as diagnostic urinary biomarkers over a period of 5 decades: 1970-2020. A gradual shift has been observed in research away from protein biomarkers to nucleic acids including different classes of RNA, and DNA methylation and mutation markers. Until 2000, publications involving protein biomarker discovery constituted 87% of the total number of research articles with DNA comprising 6% and RNA 7%. Since 2000 the proportion of protein biomarker articles has fallen to 40%, and DNA and RNA studies increased to 32% and 28%, respectively. Clearly research focus, perhaps driven by technological innovation, has shifted from proteins to nucleic acids. We optimistically hypothesise that, following thorough validation, a clinically useful detection test for bladder cancer based on a panel of DNA or RNA markers could become reality within 5-10 years.

Urinary markers in the surveillance of non-muscle invasive bladder cancer. A literature review

BACKGROUND

The surveillance of non-muscle-invasive bladder cancer (NMIBC) is usually performed by cystoscopy and cytology. Until today, no effective urinary biomarker has been used to reduce the morbidity and cost associated with these procedures.

OBJECTIVE

To describe the performance of urinary biomarkers in the surveillance of NMIBC.

EVIDENCE ACQUISITION

on August 1, 2018, a bibliographic search was carried out in Pubmed, Embase and Cochrane Library, limited to the last 10 years, with the terms: bladder cancer, recurrence, detection and urine marker.973 registers were obtained, and 27 publications were selected following the PRISMA recommendations.

EVIDENCE SYNTHESIS

The negative predictive values (NPV) of several assays could reduce the number of cystoscopies in NMIBC surveillance. Six transcription-factor trials had an NPV rate greater than 90%, and one of them can be performed at the control point. Six transcription-factors evaluations describe anticipated diagnosis between 68% and 83% of their "false positives". Two transcription factors and one protein assays proved reduction between 23% and 35% of surveillance cystoscopies. Nowadays, cell-based assays are restricted to reflex test after doubtful cytologies.

CONCLUSION

There are few studies analysing the improvement of the NMIBC surveillance protocols. Several transcription factor assays are more precise and allow anticipatory diagnosis. Currently, there are no comparative studies between alternative surveillance protocols and classic ones.

BCG Therapy of Bladder Cancer Stimulates a Prolonged Release of the Chemoattractant CXCL10 (IP10) in Patient Urine

Background: Intra-vesical instillation of Bacille Calmette–Guérin (BCG), an attenuated strain of Mycobacterium bovis, is an effective therapy for high-grade non-muscle invasive bladder cancer (NMIBC), which provokes a local immune response resulting in 70% of patients free of relapse after three years. Because non-responder patients usually have a bad prognosis, the early identification of treatment failure is crucial. We hypothesized that, if an effective immune response was taking place in the bladder, soluble factors would be released to the urine many days after BCG instillations. Methods: An extensive panel of cytokines and chemokines released into the urine seven days after every BCG instillation was screened in a cohort of NMIBC patients over three years. Results: The determinations of the urinary concentrations of cytokines, chemokines, and creatinine showed that increasing concentrations of C-X-C motif chemokine 10 (CXCL10) also known as interferon-inducible protein 10 (IP10) could be detected during the six-week induction cycle of BCG-treated patients released into the urine by CD14⁺ cells. In vitro, CXCL10 facilitated the recruitment of effector immune cells after the BCG-mediated upregulation of CXCR3 in both T- and natural killer (NK)-cells. Conclusions: The high concentrations of chemokine detected one week after the encounter with mycobacteria suggest that the CXCL10 axis might be related to the intensity of the immune anti-tumor response.

A risk score staging system based on the expression of seven genes predicts the outcome of bladder cancer

Bladder cancer (BLCA) is among the most malignant types of cancer. At present, the prognostic tools available for this disease are insufficient. In the present study, the transcriptome of 1,049 BLCA samples from four datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) were analyzed. By utilizing the RNA-seq data provided by TCGA, a risk score staging system model was built to predict the outcome of patients with BLCA using random forest variable hunting and Cox multivariate regression. A total of 7 genes, including zinc finger protein 230, Bcl2-like 14, AHNAK, transmembrane protein 109, apolipoprotein L2, advanced glycation end-product specific receptor and amine oxidase, copper containing 2 were identified as predicting the survival time of patients with BLCA. The patients with a low risk score had a significantly higher survival rate than those with a high-risk score both in the training and validation datasets. Association analyses between risk score and other clinical information were additionally performed; it was demonstrated that the risk score was significantly associated with pathological stage. A nomogram was plotted to compare risk score and other clinical information. The risk score spanned the greatest range of points, indicating the relative accuracy of risk score. In summary, the risk staging model based on the expression of 7 genes is robust and performs more effectively than other clinical information in predicting a prognosis.

Expression signature of ten genes predicts the survival of patients with estrogen receptor positive-breast cancer that were treated with tamoxifen

Although tamoxifen is the most frequently used drug for the treatment of estrogen receptor positive (ER⁺)-breast cancer (BRCA), its efficacy varies between patients. In the present study, Cox multivariate regression of the relative mRNA expression levels in two microarray-based datasets (GSE17005 and GSE26971) was employed to develop a risk score model to evaluate the outcome of patients with BRCA in the GSE17005 dataset. A total of ten genes were used to develop the prediction model for the survival of tamoxifen-treated patients with breast cancer. The survival time of patients in the low risk score group was significantly longer compared with patients in the high risk score group. This observation was validated in three other datasets (GSE26971, GSE22219 and GSE56884). The prognostic effect of the clinicopathological indicators and the risk score were tested with the 5-year event receiving operating characteristic curve, and the risk score had an improved prognostic value in patients with ER⁺-BRCA with an area under the curve value of 0.733 compared with the factors of age, tumor stage, tumor grade, chemotherapy, lymph invasion and tumor size. The risk score was significantly associated with the tumor-node-metastasis stage and grade, but was independent of age, sex, lymph invasion and tumor size. In summary, the risk model for breast cancer using the expression signature of ten genes may be an important indicator for predicting the survival of patients with ER⁺-breast cancer and treated with tamoxifen.

Investigating Colorimetric Protein Array Assay Schemes for Detection of Recurrence of Bladder Cancer

A colorimetric microarray for the multiplexed detection of recurrence of bladder cancer including protein markers interleukin-8 (IL8), decorin (DCN), and vascular endothelial growth factor (VEGF) was established to enable easy and cheap read-out by a simple office scanner paving the way for quick therapy monitoring at doctors' offices. The chip is based on the principle of a sandwich immunoassay and was optimized prior to multiplexing using IL8 as a model marker. Six different colorimetric assay formats were evaluated using a detection antibody (dAB) labeled with (I) gold (Au) nanoparticles (NPs), (II) carbon NPs, (III) oxidized carbon NPs, and a biotinylated dAB in combination with (IV) neutravidin-carbon, (V) streptavidin (strp)-gold, and (VI) strp-horseradish peroxidase (HRP). Assay Format (III) worked best for NP-based detection and showed a low background while the enzymatic approach, using 3,3',5,5'-tetramethylbenzidine (TMB) substrate, led to the most intense signals with good reproducibility. Both assay formats showed consistent spot morphology as well as detection limits lower than 15 ng/L IL8 and were thus applied for the multiplexed detection of IL8, DCN, and VEGF in synthetic urine. Colorimetric detection in urine (1:3) yields reaction signals and measurement ranges well comparable with detection in the assay buffer, as well as excellent data reproducibility as indicated by the coefficient of variation (CV 5-9%).

Challenges in Developing a Biochip for Intact Histamine Using Commercial Antibodies

This study describes the development and the challenges in the development of an on-chip immunoassay for histamine using commercially available antibodies. Histamine can be used as an indicator of food freshness and quality, but it is also a relevant marker in clinical diagnostics. Due to its low molecular weight, simple structure and thus low immunogenicity production of high specificity and affinity antibodies is difficult. From six commercial anti-histamine antibodies tested, only two bound the histamine free in the solution. A fluorescent on-chip immunoassay for histamine was established with a dynamic range of 8–111 µg/mL using polyclonal anti-histamine antibody H7403 from Sigma (Mendota Heights, MN, USA). The anti-histamine antibodies described and used in published literature are thoroughly reviewed and the quality of commercial antibodies and their traceability and quality issues are highlighted and extensively discussed.

A Chip for Estrogen Receptor Action: Detection of Biomarkers Released by MCF-7 Cells through Estrogenic and Anti-Estrogenic Effects

The fluorescence-based multi-analyte chip platform for the analysis of estrogenic and anti-estrogenic substances is a new in vitro tool for the high throughput screening of environmental samples. In contrast to existing tools, the chip investigates the complex action of xenoestrogens in a human cell model by characterizing protein expression. It allows for the quantification of 10 proteins secreted by MCF-7 cells, representing various biological and pathological endpoints of endocrine action and distinguishing between estrogen- and anti-estrogen-dependent secretion of proteins. Distinct protein secretion patterns of the cancer cell line after exposure to known estrogen receptor agonists ß-estradiol, bisphenol A, genistein, and nonylphenol as well as antagonists fulvestrant and tamoxifen demonstrate the potential of the chip. Stimulation of cells with Interleukin-1ß shifts concentrations of low abundant biomarkers towards the working range of the chip. In the non-stimulated cell culture, Matrix Metalloproteinase 9 (MMP-9) and Vascular Endothelial Growth Factor (VEGF) show differences upon treatment with antagonists and agonists of the estrogen receptor. In stimulated MCF-7 cells challenged with receptor agonists secretion of Monocyte Chemoattractant Protein (MCP-1), Interleukin-6 (IL-6), Rantes, and Interleukin-8 (IL-8) significantly decreases. In parallel, the proliferating effect of endocrine-disrupting substances in MCF-7 cells is assessed in a proliferation assay based on resazurin. Using ethanol as a solvent for test substances increases the background of proliferation and secretion experiments, while using dimethyl sulfoxide (DMSO) does not show any adverse effects. The role of the selected biomarkers in different physiological processes such as cell development, reproduction, cancer, and metabolic syndrome makes the chip an excellent tool for either indicating endocrine-disrupting effects in food and environmental samples, or for screening the effect of xenoestrogens on a cellular and molecular level.

Risk score based on three mRNA expression predicts the survival of bladder cancer

Bladder cancer (BLCA) is one of the most malignant cancers worldwide, and its prognosis varies. 1214 BLCA samples in five different datasets and 2 platforms were enrolled in this study. By utilizing the gene expression in The Cancer Genome Atlas (TCGA) dataset, and another two datasets, in GSE13507 and GSE31684, we constructed a risk score staging system with Cox multivariate regression to evaluate predict the outcome of BLCA patients. Three genes consist of RCOR1, ST3GAL5, and COL10A1 were used to predict the survival of BLCA patients. The patients with low risk score have a better survival rate than those with high risk score, significantly. The survival profiles of another two datasets (GSE13507 and GSE31684), which were used for candidate gene selection, were similar as the training dataset (TCGA). Furthermore, survival prediction effect of risk score staging system in another 2 independent datasets, GSE40875 and E-TABM-4321, were also validated. Compared with other clinical observations, and the risk score performs better in evaluating the survival of BLCA patients. Moreover, the correlation between radiation were also evaluated, and we found that patients have a poor survival in high risk group, regardless of radiation. Gene Set Enrichment Analysis was also implemented to find the difference between high-risk and low-risk groups on biological pathways, and focal adhesion and JAK signaling pathway were significantly enriched. In summary, we developed a risk staging model for BLCA patients with three gene expression. The model is independent from and performs better than other clinical information.

Analytical Protein Microarrays: Advancements Towards Clinical Applications

Protein microarrays represent a powerful technology with the potential to serve as tools for the detection of a broad range of analytes in numerous applications such as diagnostics, drug development, food safety, and environmental monitoring. Key features of analytical protein microarrays include high throughput and relatively low costs due to minimal reagent consumption, multiplexing, fast kinetics and hence measurements, and the possibility of functional integration. So far, especially fundamental studies in molecular and cell biology have been conducted using protein microarrays, while the potential for clinical, notably point-of-care applications is not yet fully utilized. The question arises what features have to be implemented and what improvements have to be made in order to fully exploit the technology. In the past we have identified various obstacles that have to be overcome in order to promote protein microarray technology in the diagnostic field. Issues that need significant improvement to make the technology more attractive for the diagnostic market are for instance: too low sensitivity and deficiency in reproducibility, inadequate analysis time, lack of high-quality antibodies and validated reagents, lack of automation and portable instruments, and cost of instruments necessary for chip production and read-out. The scope of the paper at hand is to review approaches to solve these problems.

Multiplatform Biomarker Discovery for Bladder Cancer Recurrence Diagnosis

Purpose. Nonmuscle invasive bladder cancer (BCa) has a high recurrence rate requiring lifelong surveillance. Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of recurrent bladder cancer. However, no single marker can achieve the required accuracy. The purpose of this study was to select a multiparameter panel, comprising urinary biomarkers and clinical parameters, for BCa recurrence diagnosis. Experimental Design. Candidate biomarkers were measured in urine samples of BCa patients with recurrence and BCa patients without recurrence. A multiplatform strategy was used for marker quantification comprising a multiplexed microarray and an automated platform for ELISA analysis. A multivariate statistical analysis combined the results from both platforms with the collected clinical data. Results. The best performing combination of biomarkers and clinical parameters achieved an AUC value of 0.91, showing better performance than individual parameters. This panel comprises six biomarkers (cadherin-1, IL-8, ErbB2, IL-6, EN2, and VEGF-A) and three clinical parameters (number of past recurrences, number of BCG therapies, and stage at time of diagnosis). Conclusions. The multiparameter panel could be a useful noninvasive tool for BCa surveillance and potentially impact the clinical management of this disease. Validation of results in an independent cohort is warranted.