Esophageal Cancer from Cells to Population: A Multiscale Approach

描述(申请人提供)：食道癌从细胞到人群：一种多尺度的方法拟议的研究的目标是通过使用尖端的内窥镜成像和先进的肿瘤组织表观遗传学特征结合标准内窥镜技术来优化对Barrett食道(BE)患者的监测，从而减轻食管腺癌(EAC)的负担。为了实现这一目标，我们将在癌症生物学家、流行病学家、临床医生以及计算和数学建模人员之间建立一个多学科合作。该研究小组将开发一个多尺度建模框架，综合和整合从不同来源和不同尺度产生的数据，以提供对东非共同体自然历史的连贯和信息描述。NCI的CISNET(U01 CA152926)积极支持的EAC模拟模型和Barrett的食道翻译研究网络(BETRNet，U54 CA163060)的数据将作为新的生物激励的多尺度食管腺癌模型(Memo)的基础。这一新模型将由跨越多个尺度的数据提供信息，包括：分子水平的DNA甲基化数据、细胞水平的体积激光内窥镜(VLE)数据、患者水平的内窥镜监测数据和人群水平的癌症登记SEER数据。我们将使用Memo作为一种分析工具来评估BE监测方案在食管瘤早期检测和预防中的临床有效性。到获奖期结束时，我们将对东非地区的生物和自然历史有一个更好和更全面的了解，这为制定更好的战略来控制其人口负担提供了一个平台。

DESCRIPTION (provided by applicant): 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.