Urinary dipeptidase 1 and trefoil factor 1 are promising biomarkers for early diagnosis of colorectal cancer.

BACKGROUND: Currently utilized serum tumor markers and fecal immunochemical tests do not have sufficient diagnostic power for colorectal cancer (CRC) due to their low sensitivities. To establish non-invasive urinary protein biomarkers for early CRC diagnosis, we performed stepwise analyses employing urine samples from CRCs and healthy controls (HCs). METHODS: Among 474 urine samples, 363 age- and sex-matched participants (188 HCs, 175 stage 0-III CRCs) were randomly divided into discovery (16 HCs, 16 CRCs), training (110 HCs, 110 CRCs), and validation (62 HCs, 49 CRCs) cohorts. RESULTS: Of the 23 urinary protein candidates comprehensively identified from mass spectrometry in the discovery cohort, urinary levels of dipeptidase 1 (uDPEP1) and Trefoil factor1 (uTFF1) were the two most significant diagnostic biomarkers for CRC in both training and validation cohorts using enzyme-linked immunosorbent assays. A urinary biomarker panel comprising uDPEP1 and uTFF1 significantly distinguished CRCs from HCs, showing area under the curves of 0.825-0.956 for stage 0-III CRC and 0.792-0.852 for stage 0/I CRC. uDPEP1 and uTFF1 also significantly distinguished colorectal adenoma (CRA) patients from HCs, with uDPEP1 and uTFF1 increasing significantly in the order of HCs, CRA patients, and CRC patients. Moreover, expression levels of DPEP1 and TFF1 were also significantly higher in the serum and tumor tissues of CRC, compared to HCs and normal tissues, respectively. CONCLUSIONS: This study established a promising and non-invasive urinary protein biomarker panel, which enables the early detection of CRC with high sensitivity.

A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case-control study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with around 9% of patients surviving >5 years. Asymptomatic in its initial stages, PDAC is mostly diagnosed late, when already a locally advanced or metastatic disease, as there are no useful biomarkers for detection in its early stages, when surgery can be curative. We have previously described a promising biomarker panel (LYVE1, REG1A, and TFF1) for earlier detection of PDAC in urine. Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. We also assessed the complementarity of this panel with CA19-9 and explored the daily variation and stability of the biomarkers and their performance in common urinary tract cancers. METHODS AND FINDINGS: Clinical specimens were obtained from multiple centres: Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and University of Belgrade. The biomarker panel was assayed on 590 urine specimens: 183 control samples, 208 benign hepatobiliary disease samples (of which 119 were chronic pancreatitis), and 199 PDAC samples (102 stage I-II and 97 stage III-IV); 50.7% were from female individuals. PDAC samples were collected from patients before treatment. The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal-Wallis tests adjusted for multiple comparisons, and multiple logistic regression. Training and validation datasets for controls and PDAC samples were obtained after random division of the whole available dataset in a 1:1 ratio. The substitution of REG1A with REG1B enhanced the performance of the panel to detect resectable PDAC. In a comparison of controls and PDAC stage I-II samples, the areas under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843-0.957) and 0.926 (95% CI 0.843-1.000) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the training (95% CI 0.903-0.969) and the validation (95% CI 0.888-0.984) datasets for the new panel including REG1B. This improved panel showed both sensitivity (SN) and specificity (SP) to be >85%. Plasma CA19-9 enhanced the performance of this panel in discriminating PDAC I-II patients from controls, with AUC = 0.992 (95% CI 0.983-1.000), SN = 0.963 (95% CI 0.913-1.000), and SP = 0.967 (95% CI 0.924-1.000). We demonstrate that the biomarkers do not show significant daily variation, and that they are stable for up to 5 days at room temperature. The main limitation of our study is the low number of stage I-IIA PDAC samples (n = 27) and lack of samples from individuals with hereditary predisposition to PDAC, for which specimens collected from control individuals were used as a proxy. CONCLUSIONS: We have successfully validated our urinary biomarker panel, which was improved by substituting REG1A with REG1B. At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK score, we demonstrate a clinically applicable risk stratification tool with a binary output for risk of developing PDAC ('elevated' or 'normal'). PancRISK provides a step towards precision surveillance for PDAC patients, which we will test in a prospective clinical study, UroPanc.

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients.

BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.