Modeling Neoplastic Progression in Barrett's Esophagus

描述（由申请人提供）：与大多数癌前肿瘤一样，巴雷特食管（BE）的主要临床问题之一是预测哪些患者可能进展为癌症（即，风险分层）。一旦我们确定了高风险患者，我们就可以证明 干预措施，以防止癌症的发展。然而，肿瘤通过体细胞进化的基本随机过程进展为癌症，导致在空间和时间上的克隆异质性。最近的癌症基因组测序结果表明，许多不同的突变组合可以产生癌症。所有这些都使得开发可靠的 和准确的突变组进行风险分层。作为进化生物学的领导者， 癌症和肿瘤进展的基于药物的建模，获得来自世界上最好的BE前瞻性队列的组织样本和数据，我们处于独特的地位，可以研究BE中肿瘤进展的进化动力学，并将我们的结果转化为临床。我们的初步结果表明，通过测量体细胞进化的参数，而不是其产品，我们可以开发出强大的生物标志物的风险分层，可能普遍适用于大多数，如果不是所有的肿瘤。具体来说，我们已经表明，遗传多样性的措施，这是自然选择的燃料，预测进展BE。我们还证明，我们可以测量体细胞突变率，在体内，和非甾体抗炎药，这似乎是预防癌症的BE，与一个数量级的减少，在大多数患者的突变率。这种新的进展动力学的初步观点揭示了3个惊喜，这对BE的风险分层和癌症预防很重要：（1）活检水平的突变率显然非常低：每个细胞谱系每年约1个染色体病变。目标1将测试隐窝内的突变率是否高于活检中观察到的B，无论是在进展为癌症的患者还是那些没有进展的患者中。(2)绝大多数病变发生在第一次内镜检查之前，这通常伴随着开始使用强效抑酸药物，如质子泵抑制剂（PPI）。目的2将使用观察性交叉研究设计，以检测PPI对活检和隐窝水平突变率的影响。这将是PPI首次用于癌症预防机制的测试。(3)目前的理论假定肿瘤进展通过一系列（例如，~20）克隆扩增。然而，我们只观察到一个这样的扩张，在156例患者年的监测活检采样的基础上。目标3将开发方法，以更精细的分辨率检测来自两个大型独立队列BE细胞学刷检的单细胞中的克隆扩增，并测试克隆扩增的检测是否预测进展。我们还将使用这些队列作为独立的验证研究，以测试细胞水平的遗传多样性是否预测进展。对于每个目标，我们使用计算模型来生成替代假设下的预期数据，并确定癌症预防的体细胞进化过程中最敏感的方面。

DESCRIPTION (provided by applicant): One of the main clinical problems in Barrett's esophagus (BE), like most pre-malignant neoplasms, is predicting which patients are likely to progress to cancer (i.e., risk stratification). Once we identify high risk patients, we can justify interventions to prevent development of cancer. However, neoplasms progress to cancer through a fundamentally stochastic process of somatic evolution leading to clonal heterogeneity in both space and time. Recent cancer genome sequencing results show that many different combinations of mutations can generate a cancer. All of this makes it difficult to develop reliable and accurate panels of mutations for risk stratification. As leaders in the evolutionary biology of cancer and agent-based modeling of neoplastic progression, with access to tissue samples and data from the world's best prospective cohorts of BE, we are in a unique position to study the evolutionary dynamics of neoplastic progression in BE and translate our results to the clinic. Our preliminary results show that by measuring the parameters of somatic evolution, rather than its products, we can develop robust biomarkers for risk stratification that may be universally applicable to most, if not all neoplasms. Specifically, we have shown that measures of genetic diversity, which is the fuel of natural selection, predict progression in BE. We also demonstrated that we can measure the somatic mutation rate, in vivo, and that non-steroidal anti-inflammatory drugs, which appear to prevent cancer in BE, were associated with an order of magnitude decrease in that mutation rate in most patients. This novel initial view of the dynamics of progression revealed 3 surprises that are important for risk stratification and cancer prevention in BE: (1) The mutation rate at the biopsy level is apparently very low: ~1 chromosomal lesion per year, per cell lineage. Aim 1 will test if the mutation rate within crypts is higher than can b observed in biopsies, both in patients that progressed to cancer, and those that did not. (2) The vast majority of lesions occur prior to the first endoscopy, which is often concomitant with the start of powerful acid suppressive medications such as proton-pump inhibitors (PPIs). Aim 2 will use an observational cross-over study design to test the effects of PPIs on mutation rate at the biopsy and crypt levels. This would be the first time that PPIs would be tested for a cancer prevention mechanism. (3) Current theory posits that neoplastic progression proceeds through a series of (e.g., ~20) clonal expansions. Yet, we only observed one such expansion in 156 patient years of surveillance based on biopsy sampling. Aim 3 will develop methods to detect clonal expansions in the much finer resolution available in single cells from cytology brushings of BE from two large, independent cohorts, and test if the detection of a clonal expansion predicts progression. We will also use these cohorts as an independent validation study to test if cell level genetic diversity predicts progression. For each aim, we use computational models to generate the expected data under alternative hypotheses and to identify the most sensitive aspects of the somatic evolutionary process for cancer prevention.