Use of Circulating MicroRNAs for Early Detection and Risk Assessment for Pancreatic Cancer

在早期、无症状的阶段发现胰腺癌是降低发病率和 这种最致命的疾病的死亡率。MicroRNAs(MiRNAs)调节基因表达并在 癌症发生和癌症进展。2014年，两项基于miRNA的血液诊断指数能够 胰腺癌患者与健康对照组和慢性胰腺炎患者的区别(AUC：0.83至 0.75)。然而，这些指标在胰腺癌早期检测中的作用尚未得到证实。 已评估。最近，我们完成了一项初步研究，我们测量了诊断前血浆中的800个miRNAs 样本取自185例胰腺癌患者和来自前列腺癌、肺癌、 结直肠癌和卵巢癌筛查试验(PLCO)。我们应用了两个已报道的诊断算法 指数，并发现有一个指数在区分胰腺癌患者和 对照组在癌症诊断前2年内(AUC=0.73)，而不是从癌症诊断前的2-5年内(AUC=0.68)。AUC 当选择4个miRNAs时，癌症诊断前2年和2-5年的数值都增加到0.83 来自我们自己的初步研究被用作分类器；并且在&lt；2年内，进一步增加到0.92当 在分类器中加入了CA19-9和已知危险因素。来验证这些非常有希望的发现，并 评估区分/预测miRNAs对癌症提供最多信息的时间窗口 早期检测和/或风险评估，我们建议使用额外的 PLCO资源以及来自四项前瞻性队列研究的资源：南方社区队列 研究(SCCS)、多民族队列研究(MEC)、上海妇女健康研究(SWHS)和 上海男性健康研究(SMHS)。这项研究的具体目标是：(1)评估所有表现出 在我们的先导性研究和一组独立研究的出版物中，与胰腺癌风险显著相关 在341对病例对照中，使用癌症诊断前5年内收集的血浆。(2)评估 在一组独立的924例病例对照中复制的miRNAs与血浆的相关性 按时间窗口(例如，5-9年和10年以上)在癌症诊断前5年收集的样本。(3)至 制定早期检测指数(基于在时间窗口中显示更强关联的miRNAs 更接近诊断)和风险评估(基于显示长期关联的miRNAs，例如5年以上 在癌症诊断之前)使用PLCO数据并使用来自SCCS，SWHS， SMHS和MEC。(4)评估监测复制miRNAs水平长期变化的附加值 用于癌症早期检测，使用来自PLCO病例和对照的重复诊断前血浆样本。 基于miRNA的指数的性能将与基于CA19-9和TIMP1以及已知风险的指数进行比较 各种因素。建立在强大的试点数据和来自包括多个种族的队列的独特预先存在资源的基础上 这项创新的研究具有很大的潜力，可以产生极具影响力和可翻译的结果。

Detecting pancreatic cancer at an early, asymptomatic stage is a top priority for reducing incidence and mortality of this most fatal disease. MicroRNAs (miRNAs) regulate gene expression and play a critical role in carcinogenesis and cancer progression. In 2014, two blood miRNA-based diagnostic indices capable of distinguishing pancreatic cancer patients from healthy controls and chronic pancreatitis patients (AUC: 0.83 to 0.75) were reported. The utility of these indices for pancreatic cancer early detection, however, has not been evaluated. Recently, we completed a pilot study in which we measured 800 miRNAs in pre-diagnostic plasma samples from 185 pancreatic cancer cases and individually-matched controls selected from the Prostate, Lung, Colorectal and Ovary Cancer Screening Trial (PLCO). We applied the algorithms of the two reported diagnostic indices and found that one index is reasonably effective in discriminating pancreatic cancer patients from controls within 2 years (AUC= 0.73) but not from 2- <5 years (AUC=0.68) before cancer diagnosis. The AUC increased to 0.83 for both <2 years and 2- <5 years prior to cancer diagnosis when 4 miRNAs selected from our own pilot study were applied as the classifier; and for <2 years, further increased to 0.92 when CA19-9 and known risk factors were added to the classifier. To validate these highly promising findings and to evaluate the time window during which the discriminative/predictive miRNAs are most informative for cancer early detection and/or risk assessment, we propose a comprehensive and confirmatory study using additional PLCO resources as well as resources from four prospective cohort studies: the Southern Community Cohort Study (SCCS), the Multiethnic Cohort Study (MEC), the Shanghai Women's Health Study (SWHS), and the Shanghai Men's Health Study (SMHS). Specific aims for the study are: (1) To evaluate all miRNAs showing a significant association with pancreatic cancer risk in our pilot study and from publications in an independent set of 341 case-control pairs using plasma collected within 5 years prior to cancer diagnosis. (2) To evaluate the association of miRNAs replicated in Aim 1 in an independent set of 924 case-control pairs with plasma samples collected >5 years prior to cancer diagnosis by time windows (e.g., 5-9 years and 10+ years). (3) To develop indices for early detection (based on miRNAs showing a stronger association in the time window closer to diagnosis) and risk assessment (based on miRNAs showing a long-term association, e.g., 5+ years prior to cancer diagnosis) using the PLCO data and validate them using samples from the SCCS, SWHS, SMHS and MEC. (4) To assess the added value of monitoring secular changes in levels of replicated miRNAs for cancer early detection using repeated pre-diagnosis plasma samples from the PLCO cases and controls. Performance of miRNA-based indices will be compared to those based on CA19-9 and TIMP1 and known risk factors. Built upon strong pilot data and unique pre-existing resources from cohorts that include multiple ethnic populations, this innovative study has significant potential to generate highly impactful and translatable results.