Modeling Neoplastic Progression in Barrett's Esophagus

描述（由申请人提供）：我们的长期目标是了解肿瘤进展的进化动力学，并开发可以预防或延缓癌症的有效干预措施。肿瘤通过克隆进化过程向恶性发展。然而，人们对这种进化的动力知之甚少。我们建议开发一种基于agent的Barrett食管肿瘤进展计算模型，作为研究肿瘤进展动力学的工具，并整合该疾病的遗传、病理、临床和流行病学数据。我们将代表巴雷特上皮细胞作为模型的代理人，这样我们就可以捕获驱动肿瘤进展的遗传多样性和进化动力学。巴雷特食管是一种人类恶性前病变，食管的鳞状内膜被隐窝结构的肠化生所取代。巴雷特食管是唯一已知的食管腺癌的前兆，在西方世界，其发病率的增长速度比任何其他癌症都要快。然而，大多数Barrett食道患者从未发生过癌症，因此迫切需要预测进展风险的方法，并对高危患者进行干预。我们将对我们的模型进行敏感性分析，以确定可能成为癌症风险预测和癌症预防干预目标的最佳生物标志物的模型参数。巴雷特食管肿瘤进展的关键方面尚不清楚，必须进行测量以建立一个全面的疾病预测模型。我们之前已经表明，细胞克隆的遗传多样性，在一个时间点，在巴雷特上皮内预测未来的进展到癌症。这要么是因为遗传多样性在进展过程中增加，要么是因为与低风险患者相比，高风险患者具有较高且恒定的遗传多样性水平。我们已经证明，我们可以使用基于检测单个细胞中244个高度可变微卫星的突变的细胞谱系分析来测量巴雷特食管细胞之间的遗传多样性。我们将在60例巴雷特食管患者中确定遗传多样性如何随时间变化，并使用近似贝叶斯计算将模型参数与这些结果拟合。我们将测试非甾体抗炎药（NSAID）的使用是否与细胞间遗传多样性的减少有关，非甾体抗炎药（NSAID）与巴雷特食道癌症风险的显著降低有关。我们还将在两个时间点测量243例Barrett食管患者的隐窝密度，以1)确定模型中应该模拟的隐窝数量，以代表组织，2)确定隐窝密度是否随时间变化，3)测试隐窝数量或密度是否预测癌症进展，4)测试非甾体抗炎药使用与隐窝密度之间的关系。该项目将提高对巴雷特食管肿瘤进展的理解，并建立一个模型，作为确定有希望的干预靶点和生物标志物开发的预测工具。公共卫生相关性：肿瘤一旦侵入其他器官，就很难治愈。因此，我们现在的重点是在癌症变得无法治愈之前进行预防。我们特别研究了巴雷特食管，这是一种可以发展为食管癌的恶性前病变。这是一种重要的疾病，因为在美国，食管癌的发病率增长速度比其他任何癌症都要快。然而，大多数患有巴雷特食管的患者永远不会发展成癌症，因此有必要了解巴雷特细胞演变成恶性肿瘤的过程，并确定高危患者，以便我们能够将医疗资源和任何干预措施的固有风险集中在他们身上。我们建议开发巴雷特食管恶性肿瘤演变的计算模型，并在巴雷特食管患者的活检中测量这一过程的动态。这些模型将有助于确定良好的生物标志物，用于测量巴雷特食管患者的癌症风险，以及癌症预防的目标。我们的方法应推广到其他恶性前病变。

DESCRIPTION (provided by applicant): Our long-term goals are to understand the evolutionary dynamics of neoplastic progression and to develop effective interventions that can prevent or delay cancer. Neoplasms progress to malignancy through a process of clonal evolution. However, the dynamics of that evolution are poorly understood. We propose to develop an agent-based computational model of neoplastic progression in Barrett's esophagus as a tool to study the dynamics of neoplastic progression and to integrate the genetic, pathological, clinical, and epidemiological data on this disease. We will represent the cells of the Barrett's epithelium as the agents of the model so that we can capture the genetic diversity and evolutionary dynamics that drive neoplastic progression. Barrett's esophagus is a human, pre-malignant condition in which the squamous lining of the esophagus is replaced by a crypt structured intestinal metaplasia. Barrett's esophagus is the only known precursor to esophageal adenocarcinoma, the incidence of which is increasing faster than any other cancer in the Western world. However, most people with Barrett's esophagus never develop cancer, so there is an urgent need for methods to predict risk of progression and intervene in patients at high risk. We will carry out a sensitivity analysis of our model to identify the model parameters that are likely to make the best biomarkers for cancer risk prediction and targets for cancer prevention interventions. Key aspects of neoplastic progression in Barrett's esophagus are unknown and will have to be measured to develop a comprehensive, predictive model of the disease. We have previously shown that the genetic diversity of clones of cells, at a single time point, within Barrett's epithelium is predictive of future progression to cancer. This is either because genetic diversity increases during progression or because high-risk patients have high, constant levels of genetic diversity compared to low-risk patients. We have shown that we can use a cell lineage assay based on detecting mutations in a panel of 244 highly mutable microsatellites in single cells, to measure genetic diversity among cells in Barrett's esophagus. We will determine how genetic diversity changes over time, in 60 well-characterized patients with Barrett's esophagus, and fit the parameters of the model to those results using approximate Bayesian computation. We will test whether or not non-steroidal anti-inflammatory drug (NSAID) use, which is associated with a dramatic reduction in cancer risk in Barrett's esophagus, is associated with a decrease in genetic diversity among cells. We will also measure the density of crypts in a cohort of 243 patients with Barrett's esophagus at two time points to 1) determine the number of crypts that should be simulated in the model in order to represent the tissue, 2) determine if crypt density changes over time, 3) test if the number or density of crypts predicts progression to cancer and 4) test for an association between NSAID use and crypt density. This project will result in an improved understanding of neoplastic progression in Barrett's esophagus and a model that can act as a predictive tool for identifying promising targets for intervention and biomarker development. PUBLIC HEALTH RELEVANCE: Once a tumor has invaded other organs, it is very difficult to cure. Thus, we are now focusing on preventing cancer before it becomes incurable. In particular, we study Barrett's esophagus, a pre-malignant condition that can develop into esophageal cancer. This is an important disease because the incidence of esophageal cancer is increasing faster than any other cancer in the United States. However, most patients with Barrett's esophagus will never develop cancer, so there is a need to understand the process by which Barrett's cells evolve malignancy and identify patients at high risk, so that we can focus our medical resources, and the inherent risks of any interventions, on them. We are proposing to develop computational models of the evolution of malignancy in Barrett's esophagus and to measure the dynamics of that process in biopsies from patients with Barrett's esophagus. These models will help to identify good biomarkers for measuring cancer risk in patients with Barrett's esophagus as well as targets for cancer prevention. Our methods should be generalizable to other pre-malignant conditions.