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.

项目摘要 胰腺囊性肿瘤（PCN）代表了人群中常见的偶然发现。分支线 最常见发现的PCN的导管内乳头状肿瘤（IPMN）有风险 恶性肿瘤在诊断后的15年内接近15％。识别现有指南的执行 早期癌症很差，并且导致外科手术过度治疗和早期诊断的错过的机会。 需要有效筛选生物标志物来准确区分需要关闭的高风险PCN 低风险病变的监视，几乎没有机会进步。 在此提案中，我们将测试并验证三种新型血液生物标志物和1个囊肿生物标志物 PCN患者的早期PDAC检测。拟议的标记在学术和 行业环境，在初步研究中表现出希望，具有足够高的敏感性和特异性 进一步评估。我们将结合预期的填充，回顾性盲评评估 （探测）严格测试这些生物标志物的标准，并从三个样品中收集样品 队列：1）I/II期PDAC和对照； 2）接受手术切除的PCN患者； 3）患者 PCN≥2.5cm或主胰管≥5毫米，在监视下，串行成像和样品收集。我们 将单独测试生物标志物的性能，作为多变量模型的一部分 彼此，以及种系测试和临床实验室值的附加信息（CA19-9，HBA1C），以及 具有特定的PDAC风险因素（吸烟状况，饮酒，糖尿病，胰腺炎病史）。 解决此顽固问题的研究策略的关键组成部分包括：1）严格测试几个 通过独特的合作 行业和学术伙伴； 2）测试用于早期检测的高级囊肿分析的新型平台 PDAC的性能以及其与基于血液的生物标志物的比较； 3）大量回顾性和 使用探针设计询问的前瞻性样品，具有生物标志物验证的统计严格； 4） 从已建立的胰腺癌早期检测（先前）财团中利用的资源 用血液和囊肿的流体收集种系基因测试，临床和实验室数据的收集 根据PCDC协议进行生物采样； 6）收集了一大批被识别的合作伴侣胰腺 图像（MRI/MRCP，CT和EUS）和在基于云的基于云的平台上进行数字化病理幻灯片 使用人工智能和机器学习策略的协作机会，以及7）多模式数据 集成模型开发。纵向生物测量将与PCDC共享，以支持 签名队列和去识别的存储图像（MRI，EUS，数字病理学）将用于 协作联盟的工作。