Biomarker Validation in Pancreatic Cystic Neoplasms

胰腺囊性肿瘤的生物标志物验证

• 批准号: 10722347
• 负责人: DIANE M SIMEONE
• 金额: $ 89.83万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722347
• 关键词: Acceleration; Address; Adult; Alcohol consumption; Artificial Intelligence; Biological Markers; Blinded; Blood; Breakthrough device; CA-19-9 Antigen; Cancer Detection; Clinical; Clinical Data; Collaborations; Collection; Cyst; Cyst Fluid; Data; Data Collection; Dedications; Development; Diabetes Mellitus; Diagnosis; Disease; Duct (organ) structure; Early Diagnosis; Early identification; Evaluation; Excision; Funding; Future; Genetic; Glycosylated hemoglobin A; Goals; Guidelines; High grade dysplasia; Image; Incidental Findings; Individual; Industry; Infrastructure; Laboratories; Lesion; Link; Longitudinal cohort study; Machine Learning; Magnetic Resonance Imaging; Main pancreatic duct; Malignant Neoplasms; Malignant neoplasm of pancreas; Modeling; Mucinous Neoplasm; Neoplasm Metastasis; Operative Surgical Procedures; Pancreatic Ductal Adenocarcinoma; Pancreatic cystic neoplasia; Pancreatitis; Papillary; Pathology; Patients; Performance; Population; Prevalence; Prospective cohort; Protocols documentation; Recording of previous events; Research; Research Personnel; Resources; Retrospective cohort; Risk; Risk Factors; Sampling; Science; Screening for cancer; Sensitivity and Specificity; Slide; Smoking Status; Standardization; Symptoms; Technology; Testing; Unresectable; biomarker panel; biomarker performance; biomarker signature; biomarker validation; blood-based biomarker; cloud based; cloud platform; cohort; combinatorial; data integration; data resource; design; early detection biomarkers; genetic testing; high risk; high risk population; improved; learning strategy; member; model development; multimodal data; novel; overtreatment; pancreas imaging; performance tests; prospective; radiological imaging; risk stratification; sample collection; screening; serial imaging; tumor progression

Project Summary Pancreatic cystic neoplasm (PCN) represents a common incidental finding in the population. Branch-duct intraductal papillary mucinous neoplasms (IPMN), the most common incidentally discovered PCN, have a risk of malignancy approaching 15%within 15 years of diagnosis. The performance of existing guidelines for identifying early cancer is poor, and results in both surgical overtreatment and missed opportunities for early diagnosis. Effective screening biomarkers are needed to accurately differentiate high-risk PCN that require close surveillance from low risk lesions with little chance to progress. In this proposal, we will test and validate of three novel blood-based biomarkers and one cyst fluid biomarker for the detection of early PDAC in patients with PCN. The proposed markers, developed in both academic and industry settings, show promise in preliminary studies, with sensitivity and specificity sufficiently high to warrant further evaluation. We will incorporate prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) standards to rigorously test the performance of these biomarkers with samples collected from three cohorts: 1) stage I/II PDAC and controls; 2) Patients with PCN who undergo surgical resection; 3) Patients with PCN ≥ 2.5 cm or main pancreatic duct ≥ 5 mm under surveillance with serial imaging and sample collection. We will test performance of the biomarkers individually, and as part of multi-variable models in combination with each other, with the added information of germline testing and clinical laboratory values (CA19 -9, HbA1c), and with specific PDAC risk factors (smoking status, alcohol consumption, diabetes, pancreatitis history). Key components of our research strategy to tackle this recalcitrant problem include: 1) rigorous testing of several promising blood-based biomarkers, individually and in combination, through a unique collaboration between industry and academic partners; 2) testing of a novel platform for advanced cyst fluid analysis for early detection of PDAC and comparison of its performance to blood-based biomarkers; 3) a large number of retrospective and prospective samples interrogated using the PRoBE design, with statistical rigor for biomarker validation; 4) resources leveraged from the established Pancreatic Cancer Early Detection (PRECEDE) Consortium including standardized collection of germline genetic testing, clinical, and laboratory data with blood and cyst fluid biosampling in accordance with PCDC protocols; 6) collection of a large set of de-identified partnering pancreatic images (MRI/MRCP, CT and EUS) and digitized pathology slides on a funded cloud-based platform for collaborative opportunities using artificial intelligence and machine learning strategies, and 7) multimodal data integration for model development. Longitudinal biospecimens will be shared with the PCDC to support the Signature Cohorts, and de-identified stored images (MRI, EUS, digitized pathology) will be available for collaborative consortium efforts.

项目总结