Dissection of Tumor Evolution Using Patient-Derived Xenografts

 描述(由申请人提供)：肿瘤的异质性是开发改进的癌症治疗方法的主要问题。虽然个体化治疗可能经常治疗肿瘤的部分，但亚克隆的不同反应是癌症复发的一个重要原因。到目前为止，准确识别亚克隆及其进化速度一直是一项挑战。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(PDX)，即在小鼠体内移植和进一步研究人类肿瘤，是一种肿瘤可以被解剖然后在可控的时间间隔内传播的模型，使它们成为研究肿瘤亚克隆群体变化的潜在的强大系统。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，我们已经证明，随着异种移植的生长，这些突变的流行率会发生变化。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。我们将通过两个具体目标来实现这一点。在目标1中，我们将对来自两个不同患者的PDX三阴性乳腺癌肿瘤进行空间解剖，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扩增片段进行测序。这些实验将提供丰富的、交叉验证的数据，我们将使用新的计算方法进行分析，以显著提高肿瘤内亚克隆的识别，以及亚克隆突变的速度和在整体肿瘤中患病率的变化。为了证实这些方法，我们将对数百个来自这些肿瘤的细胞进行单细胞测序。在目标2中，我们将对从目标1中相同的两个患者肿瘤中生长的异种移植进行平行研究，但接受标准护理药物治疗。这将使我们能够比较这些亚克隆群体如何在类似于接受治疗的患者肿瘤的条件下进化。如果成功，这项研究将产生一种经过验证的、可推广的研究肿瘤进化的方法，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.