Browsing by Author "Malats, Nuria (7003898578)"

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. Copyright: © 2020 Debernardi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Purpose: The European Association of Urology (EAU) guidelines for non–muscle-invasive bladder cancer (NMIBC) recommend risk stratification based on clinicopathologic parameters. Our aim was to investigate the added value of biomarkers to improve risk stratification of NMIBC. Experimental Design: We prospectively included 1,239 patients in follow-up for NMIBC in six European countries. Fresh-frozen tumor samples were analyzed for GATA2, TBX2, TBX3, and ZIC4 methylation and FGFR3, TERT, PIK3CA, and RAS mutation status. Cox regression analyses identified markers that were significantly associated with progression to muscle-invasive disease. The progression incidence rate (PIR ¼ rate of progression per 100 patient-years) was calculated for subgroups. Results: In our cohort, 276 patients had a low, 273 an intermediate, and 555 a high risk of tumor progression based on the EAU NMIBC guideline. Fifty-seven patients (4.6%) progressed to muscle-invasive disease. The limited number of progressors in this large cohort compared with older studies is likely due to improved treatment in the past two decades. Overall, wild-type FGFR3 and methylation of GATA2 and TBX3 were significantly associated with progression (HR ¼ 0.34, 2.53, and 2.64, respectively). The PIR for EAU high-risk patients was 4.25. On the basis of FGFR3 mutation status and methylation of GATA2, this cohort could be reclassified into a good class (PIR ¼ 0.86, 26.2% of patients), a moderate class (PIR ¼ 4.32, 49.7%), and a poor class (PIR ¼ 7.66, 24.0%). Conclusions: We conclude that the addition of selected biomarkers to the EAU risk stratification increases its accuracy and identifies a subset of NMIBC patients with a very high risk of progression © 2018 American Association for Cancer Research.