Proteomic Analyses of Serial Prediagnostic PLCO Serum in Cases and Controls to Identify Early Detection Ovarian Cancer Biomarkers Rising in a Substantial Fraction of Cases and Stable in Most Controls

对病例和对照中的系列诊断前 PLCO 血清进行蛋白质组学分析，以识别早期检测卵巢癌生物标志物，这些生物标志物在大部分病例中上升，而在大多数对照中保持稳定

• 批准号: 10703252
• 负责人: Steven J Skates
• 金额: $ 12万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10703252
• 关键词: Acute-Phase Proteins; Age; Algorithms; Behavior; Biological; Biological Assay; Biological Markers; Blood Proteins; Blood specimen; CA-125 Antigen; Cancer Etiology; Cell division; Consumption; Custom; Data; Detection; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early identification; Goals; Growth; High Risk Woman; Inflammation; Investigation; Light; Longitudinal Studies; Malignant Neoplasms; Malignant neoplasm of ovary; Measures; Modeling; Pattern; Performance; Persons; Pilot Projects; Plasma; Plasma Proteins; Probability; Process; Proteins; Proteomics; Resources; Sampling; Screening for Ovarian Cancer; Serum; Signal Transduction; Specificity; Statistical Models; Symptoms; Technology; Test Result; Testing; Time; Validation; Variant; WFDC2 gene; Woman; arm; biobank; biomarker discovery; biomarker identification; blood-based biomarker; cancer biomarkers; cancer diagnosis; candidate marker; cohort; detection sensitivity; early detection biomarkers; longitudinal analysis; mortality; new technology; novel; programs; protein biomarkers; response; screening; tumor; two-dimensional

Project Summary This project aims to discover and validate plasma biomarkers for the early detection of ovarian cancer. A hallmark of cancer is uncontrolled cell division, leading to a doubling time of the tumor. This exponential growth stands in stark contrast to the stable or slowly changing profile of plasma proteins in almost all other diseases or in healthy subjects. This project will leverage this unique hallmark to discover and validate plasma protein biomarkers for the early detection of ovarian cancer. We will discover early detection (ED) plasma protein biomarkers by identifying the proteins that significantly rise over time in an exponential fashion in a substantial fraction of cases and yet remain relatively stable over time in most controls. This requires plasma assays over a large suite of proteins with CVs lower than the protein's biological variation over time which can be as low as a CV of 10%. Furthermore, a low volume requirement is essential for access to precious biospecimens formed from long-term large early detection trials. Olink AB has developed proximity extension assays (PEAs) for a suite of ~1,500 proteins with CVs ranging from 6-12% and with a minimal volume requirement of 3 µL. Applying the Olink proteomic assays to serial pre-diagnostic plasma from subjects in the PLCO who were diagnosed with ovarian cancer during the study (cases n=50) and to serial plasma samples from a 4:1 matched control (n=200) : case (n=50) cohort will provide longitudinal data on ~1,500 plasma proteins from cases and controls by which to identify ED candidate biomarkers. Prior to cancer developing in each case, a biomarker will be stable over time, while after cancer inception the biomarker will rise exponentially reflecting tumor doubling. This behavior is represented by a change-point model in cases while the same biomarker in women without ovarian cancer (controls) will have a flat profile. ED biomarkers will be the proteins which have a change-point in a substantial fraction of cases while remaining stable in most (98%) controls. We will identify the top 20 ED biomarkers where the criteria for inclusion is a combination of fraction of cases, complementarity to proteins already selected, and time of rise with earlier risers having priority. After identification of the 20 ED biomarkers, Olink will develop a custom panel of 20 ED markers with absolute quantification. The custom panel will assay the same PLCO plasma samples as used in discovery. These data will be analyzed with a multivariate longitudinal change-point model to form a multiple marker longitudinal algorithm for ED. This classifier will be locked down. The classifier will be validated by assaying the custom panel of 20 ED biomarkers on an independent PLCO serial plasma sample set, from cases (n=50) and 10:1 matched controls (n=500). From these data the classifier will be assessed for two dimensions of sensitivity for early detection: (i) the number of months prior to detection in PLCO, and (ii) proportion of cases detected, while (iii) maintaining a high specificity goal of 98% - or a false positive rate of 2%. This low false positive rate requires a large number of controls (n=500) for its accurate assessment.

项目总结