Dataset Information

Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma.

ABSTRACT:

Purpose

Noninvasive biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) are currently not available. Here, we aimed to identify a set of urine proteins able to distinguish patients with early-stage PDAC from healthy individuals.

Experimental design

Proteomes of 18 urine samples from healthy controls, chronic pancreatitis, and patients with PDAC (six/group) were assayed using GeLC/MS/MS analysis. The selected biomarkers were subsequently validated with ELISA assays using multiple logistic regression applied to a training dataset in a multicenter cohort comprising 488 urine samples.

Results

LYVE-1, REG1A, and TFF1 were selected as candidate biomarkers. When comparing PDAC (n = 192) with healthy (n = 87) urine specimens, the resulting areas under the receiver-operating characteristic curves (AUC) of the panel were 0.89 [95% confidence interval (CI), 0.84-0.94] in the training (70% of the data) and 0.92 (95% CI, 0.86-0.98) in the validation (30% of the data) datasets. When comparing PDAC stage I-II (n = 71) with healthy urine specimens, the panel achieved AUCs of 0.90 (95% CI, 0.84-0.96) and 0.93 (95% CI, 0.84-1.00) in the training and validation datasets, respectively. In PDAC stage I-II and healthy samples with matching plasma CA19.9, the panel achieved a higher AUC of 0.97 (95% CI, 0.94-0.99) than CA19.9 (AUC = 0.88; 95% CI, 0.81-0.95, P = 0.005). Adding plasma CA19.9 to the panel increased the AUC from 0.97 (95% CI, 0.94-0.99) to 0.99 (95% CI, 0.97-1.00, P = 0.04), but did not improve the comparison of stage I-IIA PDAC (n = 17) with healthy urine.

Conclusions

We have established a novel, three-protein biomarker panel that is able to detect patients with early-stage pancreatic cancer in urine specimens.

SUBMITTER: Radon TP

PROVIDER: S-EPMC4539580 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma.

Radon Tomasz P TP Massat Nathalie J NJ Jones Richard R Alrawashdeh Wasfi W Dumartin Laurent L Ennis Darren D Duffy Stephen W SW Kocher Hemant M HM Pereira Stephen P SP Guarner posthumous Luisa L Murta-Nascimento Cristiane C Real Francisco X FX Malats Núria N Neoptolemos John J Costello Eithne E Greenhalf William W Lemoine Nick R NR Crnogorac-Jurcevic Tatjana T

Clinical cancer research : an official journal of the American Association for Cancer Research 20150801 15

<h4>Purpose</h4>Noninvasive biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) are currently not available. Here, we aimed to identify a set of urine proteins able to distinguish patients with early-stage PDAC from healthy individuals.<h4>Experimental design</h4>Proteomes of 18 urine samples from healthy controls, chronic pancreatitis, and patients with PDAC (six/group) were assayed using GeLC/MS/MS analysis. The selected biomarkers were subsequently validated with ELISA a ...[more]

PMID: 26240291

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Project description:BackgroundThe grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC.Methods539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC.ResultsThe signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98-0.99) and sensitivity of 0.99 (95% CI 0.98-1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66-0.91) and sensitivity 0.67 (95% CI 0.50-0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91-0.99), sensitivity 0.86 (95% CI 0.68-1), was attained compared to an AUC of 0.80 (95% CI 0.66-0.93), sensitivity 0.65 (95% CI 0.48-0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92-0.99), sensitivity 0.82 (95% CI 0.64-0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03-0.69) (p = 0.013 and p = 0.0055, respectively).ConclusionOur ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk.

Project description:IntroductionNew biomarkers are urgently needed to detect pancreatic ductal adenocarcinoma (PDAC) at an earlier stage for individualized treatment strategies and to improve outcomes. Autoantibodies (AAbs) in principle make attractive biomarkers as they arise early in disease, report on disease-associated perturbations in cellular proteomes, and are static in response to other common stimuli, yet are measurable in the periphery, potentially well in advance of the onset of clinical symptoms.MethodsHere, we used high-throughput, custom cancer antigen microarrays to identify a clinically relevant autoantibody biomarker combination able to differentially detect PDAC. Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).ResultsCombinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Additionally, differential expression analysis on the samples run on the iOme™ array identified 4 biomarkers (ALX1-GPA33-LIP1-SUB1) upregulated in PDAC against diseased and healthy controls. Identified AAbs were validated in silico using public immunohistochemistry datasets and experimentally using a custom PDAC protein microarray comprising the 11 optimal AAb biomarker panel. The clinical utility of the biomarker panel was tested in an independent cohort comprising 223 PDAC, PC, PRC, colorectal cancer (CRC), and HC samples. Combinatorial ROC curve analysis on the validation data identified the most effective biomarker combination to be CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Subsequently, the specificity of the 11-biomarker panel was validated against other cancers (PDAC vs PC: AUC = 70·3%; PDAC vs CRC: AUC = 84·3%; PDAC vs PRC: AUC = 80·2%) and healthy controls (PDAC vs HC: AUC = 80·9%), confirming that this novel AAb biomarker panel is able to selectively detect PDAC amongst other confounding diseases.ConclusionThis AAb panel may therefore have the potential to form the basis of a novel diagnostic test for PDAC.

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

Dataset Information

Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma.

Purpose

Experimental design

Results

Conclusions

Publications

Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma.

Similar Datasets

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