Abstract B018: UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma

Abstract Introduction. A bleak prognosis of Pancreatic ductal adenocarcinoma (PDAC) is largely due to late diagnosis and highly accurate, non-invasive diagnostic tests are urgently needed to improve patients’ survival. Our group has previously reported on urinary biomarker panel, comprising of LYVE1, REG1B and TFF1 (1-2), which distinguished controls from PDAC stages I-II with AUC 0.936 and sensitivity/specificity (SN/SP)>85%. We constructed the logistic regression based algorithm for interpretation of the data, PancRISK (3), which enables the stratification of high- risk patients into those with ‘average’ or ‘elevated’ risk of developing PDAC. Recently, we showed that our panel combined with CA19-9 can detect PDAC up to 2 years before clinical diagnosis (4). Aim. Here, we report on the design and interim analysis of the UroPanc Trial (ClinicalTrials.gov NCT04449406), which aims to evaluate the accuracy of the urinary biomarker panel and the affiliated PancRISK. We plan to recruit two cohorts: the first one comprises asymptomatic individuals that have high risk of developing PDAC due to familial history and genetic syndroms - these will be recruited through the EUropean Registry Of familial PAncreatic Cancer and hereditary pancreatitis (EUROPAC) (PI: Prof W Greenhalf and team, Liverpool, UK). The second cohort comprises patients with symptoms suggestive of PDAC – such patients are recruited at gastrointestinal clinics at University College London Hospitals (UCLH, PI: Prof S Pereira and team), and The Royal London Hospital (PI: Dr P Wilson).Methods. The urinary proteins are assayed by commercially available ELISAs. Plasma CA19-9 is measured at The Doctors Laboratory in London, UK using Cobas601E, Roche platform. The statistical analysis and accuracy of the test is measured by c-statistics, SN/SP and positive/negative predictive value. The results are compared to imaging and histopathology records (where available). Human factor and budget impact analysis of the future inclusion of the urine test into the diagnostic and surveillance pathways for PDAC patients is ongoing (PI: Dr M Z Ni and the team, Imperial College London, UK). Results and conclusions. At present, 1067 patients have been recruited, and urine samples from 825 patients have been assayed. An interim analysis has shown promising results with SP of 91% and SN of 86%, and accuracy of cancer detection of 90%. Combined with the health economic analysis, we currently work towards the future implementation of this test into clinical practice, with the potential to significantly improve the current diagnostic pathway for individuals at risk of pancreatic cancer. 1. Radon, TP, et al. Clin Cancer Res, 2015; 21:3512-21. 2. Debernardi, S, et al. PLOSMed, 17(12); e1003489. 3. Blyuss, O, et al. Br J Cancer, 2020; 122:692-6. 4. Debernardi, S, et al. Int J Cancer, 2023; 152:769–80. Citation Format: Silvana Debernardi, Evelyn Kurotova, Nurshad Ali, Mariam Mahamood, Ismita Chhetri, Steve Pereira, Patrick Wilson, Bill Greenhalf, Melody Z Ni, Oleg Blyuss, Tatjana Crnogorac-Jurcevic. UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B018.

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Cancer Research
Published
2024-09-15
DOI
https://doi.org/10.1158/1538-7445.pancreatic24-b018
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article

Abstract B018: UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma

Tatjana Crnogorac‐Jurcevic, Bill Greenhalf, Mariam Mahamood, Oleg B. Blyuss et al.
Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

Abstract B018: UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma

Tatjana Crnogorac‐Jurcevic, Bill Greenhalf, Mariam Mahamood, Oleg B. Blyuss, Ismita Chhetri, Silvana Debernardi, Stephen P. Pereira, Nurshad Ali, Melody Ni, Patrick M. Wilson, Evelyn Kurotova
article en

Abstract

Abstract Introduction. A bleak prognosis of Pancreatic ductal adenocarcinoma (PDAC) is largely due to late diagnosis and highly accurate, non-invasive diagnostic tests are urgently needed to improve patients’ survival. Our group has previously reported on urinary biomarker panel, comprising of LYVE1, REG1B and TFF1 (1-2), which distinguished controls from PDAC stages I-II with AUC 0.936 and sensitivity/specificity (SN/SP)>85%. We constructed the logistic regression based algorithm for interpretation of the data, PancRISK (3), which enables the stratification of high- risk patients into those with ‘average’ or ‘elevated’ risk of developing PDAC. Recently, we showed that our panel combined with CA19-9 can detect PDAC up to 2 years before clinical diagnosis (4). Aim. Here, we report on the design and interim analysis of the UroPanc Trial (ClinicalTrials.gov NCT04449406), which aims to evaluate the accuracy of the urinary biomarker panel and the affiliated PancRISK. We plan to recruit two cohorts: the first one comprises asymptomatic individuals that have high risk of developing PDAC due to familial history and genetic syndroms - these will be recruited through the EUropean Registry Of familial PAncreatic Cancer and hereditary pancreatitis (EUROPAC) (PI: Prof W Greenhalf and team, Liverpool, UK). The second cohort comprises patients with symptoms suggestive of PDAC – such patients are recruited at gastrointestinal clinics at University College London Hospitals (UCLH, PI: Prof S Pereira and team), and The Royal London Hospital (PI: Dr P Wilson).Methods. The urinary proteins are assayed by commercially available ELISAs. Plasma CA19-9 is measured at The Doctors Laboratory in London, UK using Cobas601E, Roche platform. The statistical analysis and accuracy of the test is measured by c-statistics, SN/SP and positive/negative predictive value. The results are compared to imaging and histopathology records (where available). Human factor and budget impact analysis of the future inclusion of the urine test into the diagnostic and surveillance pathways for PDAC patients is ongoing (PI: Dr M Z Ni and the team, Imperial College London, UK). Results and conclusions. At present, 1067 patients have been recruited, and urine samples from 825 patients have been assayed. An interim analysis has shown promising results with SP of 91% and SN of 86%, and accuracy of cancer detection of 90%. Combined with the health economic analysis, we currently work towards the future implementation of this test into clinical practice, with the potential to significantly improve the current diagnostic pathway for individuals at risk of pancreatic cancer. 1. Radon, TP, et al. Clin Cancer Res, 2015; 21:3512-21. 2. Debernardi, S, et al. PLOSMed, 17(12); e1003489. 3. Blyuss, O, et al. Br J Cancer, 2020; 122:692-6. 4. Debernardi, S, et al. Int J Cancer, 2023; 152:769–80. Citation Format: Silvana Debernardi, Evelyn Kurotova, Nurshad Ali, Mariam Mahamood, Ismita Chhetri, Steve Pereira, Patrick Wilson, Bill Greenhalf, Melody Z Ni, Oleg Blyuss, Tatjana Crnogorac-Jurcevic. UroPanc: A large prospective observational study for validation of urinary biomarker test for the earlier detection of pancreatic adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B018.

Cancer ResearchVol. 84(17_Supplement_2)
University of Liverpool (GB), Queen Mary University of London (GB), Royal London Hospital (GB), NIHR Leeds In Vitro Diagnostics Co-operative (GB), University College London (GB), Imperial College London (GB)
Openalex Percentile: Top 74%
Radiomics and Machine Learning in Medical Imaging
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