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

项目摘要 该项目旨在发现和验证用于卵巢癌早期检测的血浆生物标志物。一 癌症的标志是细胞分裂不受控制，导致肿瘤加倍。这种指数增长 与几乎所有其他疾病中稳定或缓慢变化的血浆蛋白谱形成鲜明对比 或在健康受试者中。本项目将利用这一独特的标志来发现和验证血浆蛋白 卵巢癌的早期诊断我们将发现早期检测（艾德）血浆蛋白 生物标志物，通过鉴定随时间以指数方式显著升高的蛋白质， 但在大多数对照组中，随着时间的推移仍保持相对稳定。这需要血浆分析， 随着时间的推移，CV低于蛋白质生物学变异的大量蛋白质， CV为10%。此外，低体积要求对于获得形成的珍贵生物标本至关重要。 长期的大型早期检测试验。Olink AB开发了用于检测DNA的邻近延伸测定法（PEAs）。 一套约1，500种蛋白质，CV范围为6-12%，最小体积要求为 3微升。将Olink蛋白质组学测定应用于PLCO中受试者的系列诊断前血浆， 在研究期间被诊断患有卵巢癌（病例n=50），并与来自4：1的 匹配对照组（n=200）：病例（n=50）队列将提供约1，500种血浆蛋白的纵向数据， 病例和对照，通过其鉴定艾德候选生物标志物。在每种情况下，在癌症发展之前， 生物标志物将随时间稳定，而在癌症开始后，生物标志物将呈指数上升， 肿瘤倍增这种行为由变点模型表示，而在某些情况下， 没有卵巢癌的妇女（对照）将具有平坦的轮廓。艾德生物标志物将是蛋白质， 在相当一部分病例中为变化点，而在大多数（98%）对照中保持稳定。我们将确定 前20个艾德生物标志物，其中纳入标准是病例分数、互补性 已经选择的蛋白质，以及具有优先权的早期蛋白质的上升时间。在识别出20个艾德后 Olink将开发一个20个艾德标记物的定制组，并进行绝对定量。自定义 小组将测定与发现中使用的相同的PLCO血浆样品。这些数据将用 多变量纵向变点模型，以形成ED的多标记纵向算法。 分类器将被锁定。将通过测定20个艾德的定制面板来验证分类器 来自病例（n=50）和10：1匹配对照的独立PLCO系列血浆样本集上的生物标志物 （n=500）。根据这些数据，将针对早期检测的两个维度的灵敏度评估分类器： 在PLCO发现之前的月数，以及（ii）发现的病例比例，同时（iii）保持 98%的高特异性目标-或2%的假阳性率。这种低误报率需要大量的 对照组（n=500）的准确评估。

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.