Dissection of Tumor Evolution Using Patient-Derived Xenografts

 描述（由申请人提供）：肿瘤异质性是开发改进的癌症治疗的主要问题。虽然个别疗法通常可以治疗肿瘤的部分，但亚克隆的差异反应是癌症复发的重要原因。到目前为止，精确识别亚克隆及其进化速度一直是一个挑战。这是因为缺乏来自患者肿瘤的精细纵向数据，因为匹配的复发或转移通常在时间上远离原始肿瘤。患者来源的异种移植物（PDX），即在小鼠中移植并进一步研究的人类肿瘤，是一种模型，其中肿瘤可以被解剖，然后在可控的时间间隔内繁殖，使其成为研究肿瘤亚克隆群体变化的潜在强大系统。在初步研究中，我们使用了高深度测序来灵敏地检测PDX片段中的体细胞突变。此外，我们已经表明，这些突变的变化，随着异种移植物的生长，患病率。在这项探索性研究中，我们建议测试和应用PDX作为一种改进的系统来量化肿瘤亚克隆群体进化的速率。我们将在两个具体目标下实现这一目标。在目标1中，我们将在空间上解剖来自两个不同患者的PDX三阴性乳腺癌肿瘤，选择性地对来自肿瘤的交错片段进行测序和繁殖，然后在允许它们在体内进化几个月后对繁殖的片段进行测序。这些实验将提供丰富的交叉验证数据，我们将使用新的计算方法进行分析，不仅可以显着提高肿瘤内亚克隆的识别，还可以提高亚克隆突变的速率和大块肿瘤中患病率的变化。为了证实这些方法，我们将对来自这些肿瘤的数百个细胞进行单细胞测序。在目标2中，我们将对从目标1中相同的两名患者肿瘤生长但用标准治疗药物治疗的异种移植物进行平行研究。这将使我们能够比较这些亚克隆群体如何在与治疗的患者肿瘤相似的条件下进化。如果成功，这项研究将产生一个有效的，可推广的方法来研究肿瘤的演变，可应用于各种癌症。

 DESCRIPTION (provided by applicant): Tumor heterogeneity is a major problem for developing improved cancer treatments. Although individual therapies may often treat portions of a tumor, differential response of subclones is an important reason for cancer recurrence. So far, precisely identifying subclones and their rates of evolution has been challenging. This is because of the lack of fine longitudinal data from patient tumors, as matched recurrences or metastases are often temporally distant from the original tumor. Patient-derived xenografts (PDXs), i.e. human tumors engrafted and further studied in mice, are a model in which tumors can be dissected and then propagated for controllable time intervals, making them a potentially powerful system for studying changes in tumor subclonal populations. In preliminary studies we have used high-depth sequencing to sensitively detect somatic mutations in PDX fragments. Moreover, we have shown that these mutations change in prevalence as a xenograft grows. In this exploratory study, we propose to test and apply PDXs as an improved system to quantify rates of tumor subclonal population evolution. We will pursue this in two specific aims. In Aim 1, we will spatially dissect PDX triple negative breast cancer tumors derived from two separate patients, selectively sequence and propagate interlaced fragments from the tumors, and then sequence the propagated fragments after allowing them to evolve in vivo over several months. These experiments will provide rich, cross-validating data that we will analyze with new computational approaches to significantly improve not only identification of subclones within tumors, but also the rates at which subclones mutate and change in prevalence in bulk tumors. To confirm these approaches, we will perform single cell sequencing of hundreds of cells from these tumors. In Aim 2, we will perform parallel studies on xenografts grown from the same two patient tumors as in Aim 1 but treated with standard-of-care drug therapy. This will allow us to compare how these subclonal populations evolve in conditions similar to a treated patient tumor. If successful, this study will yield a validated, generalizable approach for studying tumor evolution that could be applied to a wide variety of cancers.