Dataset Information

Detection of bladder cancer using proteomic profiling of urine sediments.

ABSTRACT: We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we identified 473 peaks showing significant differential expression across different categories of paired bladder tumor and adjacent urothelial samples compared to normal urothelium. Then the intensities of those 473 peaks were examined in a training set of voided urine samples. Using this approach, we identified 41 protein peaks that were differentially expressed in both sets of samples. The expression pattern of the 41 protein peaks was used to classify the voided urine samples as malignant or benign. This approach yielded a sensitivity and specificity of 59% and 90%, respectively, on the training set and 80% and 100%, respectively, on the testing set. The proteomic classification rule performed with similar accuracy in low- and high-grade bladder carcinomas. In addition, we used hierarchical clustering with all 473 protein peaks on 65 benign voided urine samples, 88 samples from patients with clinically evident bladder cancer, and 127 samples from patients with a history of bladder cancer to classify the samples into Cluster A or B. The tumors in Cluster B were characterized by clinically aggressive behavior with significantly shorter metastasis-free and disease-specific survival.

SUBMITTER: Majewski T

PROVIDER: S-EPMC3411788 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of bladder cancer using proteomic profiling of urine sediments.

Majewski Tadeusz T Spiess Philippe E PE Bondaruk Jolanta J Black Peter P Clarke Charlotte C Benedict William W Dinney Colin P CP Grossman Herbert Barton HB Tang Kuang S KS Czerniak Bogdan B

PloS one 20120803 8

We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we ident ...[more]

PMID: 22879988

Similar Datasets

Project description:BackgroundChromosome missegregation and the resulting aneuploidy is a common change in neoplasia. The Aurora kinase A (AURKA) gene, which encodes a key regulator of mitosis, is frequently amplified and/or overexpressed in cancer cells, and the level of AURKA amplification is associated with the level of aneuploidy. We examined whether AURKA gene amplification is a biomarker for the detection of bladder cancer.MethodsThe effect of ectopic expression of Aurora kinase A (AURKA) using an adenoviral vector in simian virus 40-immortalized urothelial cells (SV-HUC) on centrosome multiplication and chromosome copy number was measured in vitro by immunofluorescence and fluorescence in situ hybridization (FISH), respectively. The FISH test was also used to examine AURKA gene copy number in exfoliated cells in voided urine samples from 23 patients with bladder cancer and 7 healthy control subjects (training set), generating a model for bladder cancer detection that was subsequently validated in an independent set of voided urine samples from 100 bladder cancer patients and 148 control subjects (92 healthy individuals and 56 patients with benign urologic disorders). An AURKA gene score (the proportion of cells with three or more AURKA signals) was used to produce receiver operating characteristic (ROC) curves and to calculate the specificity and sensitivity of the AURKA FISH test. Differences between mean AURKA scores in different pathogenetic groups of bladder cancer stratified according to histological grade and stage were tested by unpaired Mann-Whitney t tests or one-way Wilcoxon tests. All statistical tests were two-sided.ResultsForced overexpression of AURKA in urothelial cells induced amplification of centrosomes, chromosome missegregation, and aneuploidy, and natural overexpression was detectable in in situ lesions from patients with bladder cancer. The FISH test for the AURKA gene copy number performed on the validation set yielded a specificity of 96.6% (95% confidence interval [CI] = 92.3% to 98.5%) and sensitivity of 87% (95% CI = 79.0% to 92.2%) and an area under the ROC curve of 0.939 (95% CI = 0.906 to 0.971; P < .001).ConclusionOverexpression of AURKA can cause aneuploidy in urothelial cells, and the AURKA gene copy number is a promising biomarker for detection of bladder cancer.

Project description:BACKGROUND:Bladder cancer is among the common human malignancies that show a heavy mutational load and copy number variations of numerous chromosomes, which makes them a target for diagnostic explorations. OBJECTIVE:We aimed to design a multicolor fluorescence in situ hybridization (FISH) test referred to as the quartet test for the detection of bladder cancer in urine. DESIGN, SETTING, AND PARTICIPANTS:We performed genome-wide copy number variation analysis on cohorts from the University of Texas MD Anderson Cancer Center (n=40) and The Cancer Genome Atlas (n=129), and identified the most frequently amplified chromosomal regions. These data were used to select four of the amplified regions to design a multicolor FISH test, referred to as the quartet test. Assay validation was performed on urine samples from 98 patients with bladder cancer: 56 with low-grade papillary, 42 with high-grade invasive disease, and 48 benign controls. INTERVENTION:The quartet test can be used in clinical practice for noninvasive detection of bladder cancer. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:We initially analyzed samples using a fraction of abnormal cell scores and then by the quantitative score, which included not only the proportion of cells with abnormal copy numbers, but also the proportion of cells with numbers of altered copies and degree of amplification. We used receiver operator characteristic (ROC) curves to identify cutoff values for the scores at which performances of sensitivity and specificity were maximized. RESULTS AND LIMITATIONS:The copy number status assessed by probes detected in voided urine reflected the amplification status of the primary tumor. An ROC curve summarizing the proportion of assayed cells with any abnormal copy numbers gave specificity of 93.8% and sensitivity of 78.6% using the proportion of cells with abnormal copy numbers. The quantitative score giving extra weight to cells with multiple simultaneous amplifications provided 95.8% specificity and 76.8% sensitivity. Both percentage of abnormal cells and quantitative scores were highly effective for assessing the grade of the tumor. The full spectrum of potential clinical applications was not explored in the current study, and further validation studies are needed. CONCLUSIONS:The quartet test shows promising specificity and sensitivity results, but it requires validation on a larger multi-institutional cohort of samples. PATIENT SUMMARY:The quartet test can be used for noninvasive detection of bladder cancer in voided urine. It can also be used to assess the grade of the tumor and tumor recurrence as well as post-treatment effects.

Project description:BackgroundThe development of accurate urinary biomarkers for non-invasive and cost-effective detection of primary and recurrent bladder tumours is recognized as one of the major clinical needs in bladder cancer diagnostics. The purposes of this study were (1) to validate the results of a previous technical comparison by determining the diagnostic performance of nine methylation markers in urine pellet compared to full void urine, and (2) to validate the diagnostic performance of the optimal marker panel GHSR/MAL from a previous exploratory study in a preclinical setting.MethodsUrine samples of 108 patients with bladder cancer and 100 age- and gender-matched controls were prospectively collected for methylation analysis. Urinary methylation levels of the markers FAM19A4, GHSR, MAL, miR-129, miR-935, PHACTR3, PRDM14, SST and ZIC1 were determined with quantitative methylation-specific PCR in urine pellet. Area under the curves (AUCs) were determined for individual markers and the marker panel GHSR/MAL. The diagnostic performance of the marker panel GHSR/MAL was evaluated in the total study population and in different subgroups of patients with bladder cancer using the Chi-square test. The diagnostic accuracy was assessed by leave-one-out cross-validation.ResultsAll nine urinary methylation markers (FAM19A4, GHSR, MAL, miR-129, miR-935, PHACTR3, PRDM14, SST and ZIC1) showed significantly higher methylation levels in bladder cancer patients than in controls (p < 0.001). Area under the curves (AUCs) of the nine methylation markers tested in urine pellet were similar to AUCs in full void urine of an independent previous cohort. GHSR/MAL reached an AUC of 0.89 (95% confidence interval [CI] 0.84-0.94), at 80% sensitivity and 93% specificity. Sensitivity of GHSR/MAL increased with higher tumour grades, higher tumour stages, in primary vs. recurrent tumours, and in males vs. females.ConclusionsThis technical validation supports the robustness of DNA methylation analysis in urine pellet and full void urine for the non-invasive detection of bladder cancer. Subsequent preclinical validation confirmed the diagnostic potential of GHSR/MAL. These findings underline the diagnostic potential of the marker panel GHSR/MAL for future bladder cancer diagnostics.

Project description:BackgroundEarly detection of bladder cancer (BCa) offers patients a favorable outcome and avoids the need for cystectomy. Development of an accurate and sensitive noninvasive BCa diagnostic test is imperative. DNA methylation is an early epigenetic event in the development of BCa. Certain specific aberrant methylations could serve as useful biomarkers. The aim of this study was to identify methylation biomarkers for early detection of BCa.MethodsCpG methylation microarray analysis was conducted on primary tumors with varying stages (T1-T4) and paired nontumor tissues from nine BCa patients. Bisulfite-pyrosequencing was performed to confirm the methylation status of candidate genes in tissues and urine sediments (n = 51). Among them, PENK was selected as a potential candidate and validated using an independent set of 169 urine sediments (55 BCa, 25 benign urologic diseases, 8 other urologic cancers, and 81 healthy controls) with a quantitative methylation-specific real time PCR (mePENK-qMSP). All statistical analyses were performed using MedCalc software version 9.3.2.0.ResultsCpG methylation microarray analysis and stepwise validation by bisulfite-pyrosequencing for tissues and urine sediments supported aberrant methylation sites of the PENK gene as potential biomarkers for early detection of BCa. Clinical validation of the mePENK-qMSP test using urine sediment-DNA showed a sensitivity of 86.5% (95% CI: 71.2 - 95.5%), a specificity of 92.5% (95% CI: 85.7 - 96.7%), and an area under ROC of 0.920 (95% CI: 0.863 - 0.959) in detecting Ta high-grade and advanced tumor stages (T1-T4) of BCa patients. Sensitivities for Ta low-grade, Ta high-grade, T1 and T2-T4 were 55.6, 83.3, 88.5, and 100%, respectively. Methylation status of PENK was not correlated with sex, age or stage, while it was associated with the tumor grade of BCa.ConclusionsIn this study, we analyzed the comprehensive patterns of DNA methylation identified that PENK methylation possesses a high potential as a biomarker for urine-based early detection of BCa. Validation of PENK methylation confirms that it could significantly improve the noninvasive detection of BCa.

Dataset Information

Detection of bladder cancer using proteomic profiling of urine sediments.

Publications

Detection of bladder cancer using proteomic profiling of urine sediments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets