Esophageal Cancer from Cells to Population: A Multiscale Approach

项目摘要/摘要 食道癌从细胞到群体的多尺度研究 拟议研究的目标是通过以下方式减轻食管腺癌(EAC)的负担 使用尖端内窥镜成像和优化巴雷特食道(BE)患者的监护 结合标准内窥镜技术对肿瘤组织进行先进的表观遗传学分析。至 为了实现这一目标，我们将建立癌症生物学家之间的多学科合作， 流行病学家、临床医生以及计算和数学模型师。这个研究团队将开发一种 多尺度建模框架，可综合和集成从不同来源和 不同的比例尺，为东非共同体的自然历史提供连贯和丰富的描述。 EAC的仿真模型得到了NCI的CISNET(U01 CA152926)的积极支持和来自 巴雷特的食道翻译研究网络(BETRNet，U54 CA163060)将作为基础 用于一种新的生物激励的多尺度食管腺癌模型(Memo)。这种新型号将 从众多规模的数据中获得信息，包括：分子水平DNA甲基化数据、细胞水平 体积激光内窥镜(VLE)数据、患者水平的内窥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的临床疗效 早期发现和预防食管瘤的监测方案。在颁奖结束前 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 为设计更好的战略来控制人口负担提供了一个平台。

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.