Modeling Neoplastic Progression in Barrett's Esophagus

描述(申请人提供)：我们的长期目标是了解肿瘤进展的进化动态，并开发能够预防或延缓癌症的有效干预措施。肿瘤通过克隆进化的过程向恶性肿瘤发展。然而，人们对这种演变的动力知之甚少。我们建议开发一个基于试剂的Barrett‘s食道肿瘤进展的计算模型，作为一种工具来研究肿瘤进展的动力学，并整合这种疾病的遗传、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，这样我们就可以捕捉到驱动肿瘤进展的遗传多样性和进化动力学。Barrett‘s食道是一种人类的癌前病变，食道的鳞状衬里被隐窝结构的肠化生取代。巴雷特食道是唯一已知的食管腺癌的先兆，其发病率的增长速度比西方世界的任何其他癌症都快。然而，大多数患有巴雷特食管症的人从未患上癌症，因此迫切需要方法来预测进展风险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。Barrett‘s食道肿瘤进展的关键方面尚不清楚，必须进行测量，以开发一种全面的、可预测的疾病模型。我们之前已经证明，在单个时间点，巴雷特上皮内细胞克隆的遗传多样性可以预测未来的癌症进展。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，高危患者具有高、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细胞中244个高度可变的微卫星的突变的细胞谱系分析来测量Barrett‘s食道细胞之间的遗传多样性。我们将确定60名特征良好的Barrett‘s食道患者的遗传多样性随时间的变化，并使用近似贝叶斯计算将模型参数与这些结果相匹配。我们将测试非类固醇抗炎药(NSAID)的使用是否与细胞间遗传多样性的减少有关，NSAID与Barrett‘s食道癌风险的显著降低有关。我们还将在两个时间点测量243名Barrett食道患者的隐窝密度，以1)确定为了代表组织而应在模型中模拟的隐窝数量，2)确定隐窝密度是否随时间变化，3)测试隐窝的数量或密度是否预测癌症的进展，以及4)测试非甾体抗炎药的使用与隐窝密度之间的关联。该项目将改善对巴雷特食道肿瘤进展的了解，并建立一个可作为预测工具的模型，以确定有希望的干预和生物标记物开发的目标。

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.