Esophageal Cancer from Cells to Population: A Multiscale Approach

描述（由申请人提供）：食管癌从细胞到人群：一种多尺度的方法拟议研究的目标是通过使用尖端的内镜成像和先进的肿瘤组织表观遗传谱结合标准内镜技术，优化对巴雷特食管（BE）患者的监测，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师之间建立多学科合作。该研究小组将开发一个多尺度建模框架，综合和整合来自不同来源和不同尺度的数据，以提供EAC自然历史的连贯和信息描述。由NCI的CISNET （U01 CA152926）积极支持的EAC模拟模型和来自Barrett食管转化研究网络（BETRNet, U54 CA163060）的数据将作为新的生物驱动的多尺度食管腺癌模型（MEMo）的基础。这个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作为分析工具来评估BE监测方案在早期食管肿瘤检测和预防中的临床有效性。在奖励期结束时，我们将对东白桦尺蠖的生物和自然历史有更全面的了解，从而为设计更好的策略来控制其种群负担提供平台。

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.