Multiscale modeling of spatiotemporal evolution in Barrett's esophagus

项目总结 这个项目的实际目标是获得高分辨率的遗传和表观遗传学图谱，以揭示 巴雷特食道(BE)随时间和空间的进化关系和动力学。巴雷特的是 因此，食管腺癌的前驱症状(EAC)患者要接受监测检查，以早期发现 癌症。我们的研究将提供前所未有的分子细节，这是任何 先前对BE癌前进化的研究。重要的是，拟议的实验和分析将定义 患者的“组织系统学”，包括克隆性扩张的显著特征，可预测 正在向未来的EAC迈进。为此，我们将利用一套丰富的连续收集的组织样本和 来自西雅图患者的基因组数据是自然历史队列，包括癌症结局患者和 一组年龄匹配的非癌症患者，在多个时间点进行采样。独一无二的 这项病例对照研究的设计使我们能够识别(Epi)预测疾病进展的遗传标记。 来自先进的多组学平台的数据。将使用计算建模和系统发育学来提取 关于BE何时在患者中出现、特定克隆在BE中传播的速度等难以捉摸但至关重要的信息， 以及这些克隆在组织中的分散程度。最终，我们将使用这些进化量来 在有良好记录的潜在患者群体中预测癌症与非癌症的结果。 该项目的长期目标是评估数据驱动的预测模型的可行性和性能 这可以转化为改善临床护理。值得注意的是，这个项目将量化强健分子的效用。 EAC风险标记物，以改善目前仅依赖于 难以评估和解释。为了促进这一目标，我们将推断的时空动力学在 癌症前期的系统地理重建，并将这些测量嵌入到多尺度模型中 人口中从BE到EAC的进展框架。这种多尺度方法显式地模拟了 患者一生中在细胞水平上的随机克隆扩展，在空间限制内 食管。我们项目的三个具体目标是：1)测量新克隆如何在 Barrett‘s腺体；2)通过量化表观遗传学测量腺体如何在Barrett’s病变中运动和生长 漂移估计巴雷特的组织年龄和构建系统地理来推断巴雷特的克隆是如何在空间上进行的 进化；以及3)将来自多区域Barrett样本的时空测量整合到多尺度 EAC的发展模式。拟议的项目是创新的，因为我们将推断进化参数， 例如来自(Epi)基因组数据的BE中的干细胞替换率和TP53两次失活 时间到了。这项研究具有重要意义，因为它有望提供包含动态信息的预测模型 BE患者EAC进展的生物标志物可能为基于风险的监测提供新的策略。

PROJECT SUMMARY The practical goal of this project is to obtain high-resolution genetic and epigenetic maps that reveal the evolutionary relationships and dynamics over space and time in Barrett’s esophagus (BE). Barrett’s is the precursor to esophageal adenocarcinoma (EAC) therefore patients undergo surveillance exams to detect early cancers. Our study will provide an unprecedented level of molecular detail that has not been achieved in any previous study of pre-cancer evolution in BE. Importantly, the proposed experiments and analyses will define a BE patient’s “tissue phylogeography”, including significant features of clonal expansions that are predictive of BE progressing to future EAC. To this end, we will leverage a rich set of serially collected tissue samples and genomic data from patients in the Seattle BE natural history cohort that includes cancer outcome patients and an age-matched group of patients with non-cancer outcomes, sampled at multiple time points. The unique design of this case-control study enables us to identify (epi)genetic markers prognostic of progression using data from advanced multi-omic platforms. Computational modeling and phylogenetics will be used to extract the elusive but essential information on when BE arises in a patient, how fast particular clones spread in BE, and how dispersive these clones are within the tissue. Ultimately, we will use these evolutionary quantities to forecast outcomes of cancer versus non-cancer in a well-documented prospective patient population. The long-term goal of the project is to assess the feasibility and performance of data-driven predictive models that can be translated to improved clinical care. Notably, this project will quantify the utility of robust molecular markers for EAC risk to improve the current practice of relying solely on histopathologic features that are difficult to assess and interpret. To facilitate this goal, we will parameterize the inferred space-time dynamics in phylogeographic reconstructions of this pre-cancer, and embed these measurements in a multiscale model framework for progression from BE to EAC in a population. This multiscale approach explicitly models the stochastic clonal expansions at the cellular level over a patient’s lifetime, within the spatial constraints of the esophagus. The three specific aims for our project are: 1) Measure how new clones arise and spread within Barrett’s glands; 2) Measure how glands move and grow through the Barrett’s lesion by quantifying epigenetic drift to estimate Barrett’s tissue age and constructing phylogeographies to infer how Barrett’s clones spatially evolve; and 3) Integrate spatiotemporal measurements from multi-region Barrett’s samples into a multiscale model of EAC development. The proposed project is innovative because we will infer evolutionary parameters, such as rates of stem cell replacement and TP53 two-hit inactivation in BE, from (epi)genomic data for the first time. This research is significant because it is expected to provide predictive models that incorporate dynamic biomarkers of EAC progression in BE patients to potentially offer new strategies of risk-based surveillance.