Esophageal Cancer from Cells to Population: A Multiscale Approach

项目总结/摘要 食管癌从细胞到群体的多尺度研究 这项研究的目的是通过以下方式减轻食管腺癌（EAC）的负担： 使用最先进的内窥镜成像优化Barrett食管（BE）患者的监测， 肿瘤组织的先进表观遗传分析与标准内窥镜技术相结合。到 为了实现这一目标，我们将在癌症生物学家之间建立多学科合作， 流行病学家、临床医生以及计算和数学建模者。该研究团队将开发一种 多尺度建模框架，综合和集成从不同来源生成的数据， 不同的尺度，以提供一个连贯的和翔实的描述东非共同体的自然历史。 由NCI的CISNET（U01 CA152926）积极支持的EAC模拟模型和来自 巴雷特食管转化研究网络（BETRNet，U54 CA163060）将作为基础 一种新的生物学驱动的多尺度食管腺癌模型（MEMo）。这一新模式将 通过跨越许多尺度的数据来了解信息，包括：分子水平的DNA甲基化数据，细胞水平的 体积激光显微内镜（VLE）数据、患者水平内镜监测数据和人群水平 癌症登记SEER数据。我们将使用MEMo作为分析工具来评估BE的临床有效性 早期食管肿瘤检测和预防的监测方案。在颁奖结束时 在此期间，我们将对生物和自然历史有更好和更全面的了解 东非共同体提供了一个平台，以设计更好的战略，以控制其人口负担。

PROJECT SUMMARY/ABSTRACT Esophageal Cancer from Cells to Population: A Multiscale Approach The goal of the proposed research is to reduce the burden of esophageal adenocarcinoma (EAC) by optimizing surveillance of patients with Barrett's esophagus (BE) using cutting-edge endoscopic imaging and advanced epigenetic profiling of neoplastic tissues in combination with standard endoscopic techniques. To accomplish this goal we will establish a multidisciplinary collaboration between cancer biologists, epidemiologists, clinicians and computational and mathematical modelers. This research team will develop a multiscale modeling framework that synthesizes and integrates data generated from diverse sources and at different scales to provide a coherent and informative portrayal of the natural history of EAC. Simulation models of EAC actively supported by the NCI's CISNET (U01 CA152926) and data from the Barrett's Esophagus Translational Research Network (BETRNet, U54 CA163060) will serve as the foundation for a new biologically-motivated Multiscale Esophageal Adenocarcinoma Model (MEMo). This new model will be informed by data that span numerous scales including: molecular level DNA methylation data, cellular level volumetric laser endomicroscopy (VLE) data, patient level endoscopic surveillance data, and population level cancer registry SEER data. We will use MEMo as an analytic tool to assess the clinical effectiveness of BE surveillance protocols for early esophageal neoplasia detection and prevention. By the end of the award period, we will have an improved and more comprehensive understanding of the biological and natural history of EAC that provides a platform to design better strategies to control its population burden.