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

在早期、无症状阶段检测胰腺癌是降低发病率和 这是最致命的疾病。microRNAs（miRNAs）调节基因表达，在基因表达调控中发挥关键作用。 致癌作用和癌症进展。2014年，两种基于血液miRNA的诊断指标能够 将胰腺癌患者与健康对照和慢性胰腺炎患者区分开（AUC：0.83至0.85）。 0.75）。然而，这些指标在胰腺癌早期检测中的效用还没有得到证实。 评估。最近，我们完成了一项初步研究，在该研究中，我们测量了诊断前血浆中的800种miRNAs， 来自185例胰腺癌病例和个体匹配的对照的样品选自前列腺，肺， 结肠直肠癌和卵巢癌筛查试验（PLCO）。我们应用了两个报告的诊断算法 指数，并发现一个指数是合理有效的区分胰腺癌患者， 在癌症诊断前2年内（AUC= 0.73），但在癌症诊断前2- <5年（AUC=0.68），的AUC 当选择4种miRNA时，在癌症诊断前<2年和2- <5年均增加至0.83 从我们自己的试点研究中应用作为分类器;<2年，进一步增加到0.92时， CA 19 -9和已知的危险因素被添加到分类器中。为了验证这些非常有希望的发现， 评估区分性/预测性miRNA对癌症信息量最大的时间窗 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）、多种族队列研究（MEC）、上海妇女健康研究（SWHS）和 上海男性健康研究（SMHS）。本研究的具体目的是：（1）评估所有显示出 在我们的初步研究和独立研究的出版物中， 使用癌症诊断前5年内收集的血浆的341个病例对照对。(2)评价 在一组独立的924个病例对照对中，Aim 1中复制的miRNA与血浆的相关性 通过时间窗在癌症诊断前>5年收集的样品（例如，5-9 10+年）。(3)到 开发早期检测的指标（基于在时间窗内显示更强关联的miRNA 更接近诊断）和风险评估（基于显示长期关联的miRNA，例如，5+ years 在癌症诊断之前）使用PLCO数据，并使用来自SCCS，SWHS， SMHS和MEC。(4)评估监测复制的miRNAs水平长期变化的附加价值 使用来自PLCO病例和对照的重复诊断前血浆样品进行癌症早期检测。 基于miRNA的指标的性能将与基于CA 19 -9和TIMP 1以及已知风险的指标进行比较。 因素建立在强大的试点数据和独特的预先存在的资源，从队列，包括多个种族 这项创新研究具有产生高度影响力和可翻译结果的巨大潜力。

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.