Image Guided Genome-Epigenome Analysis of Tumor Heterogeneity and Evolution

项目总结（见说明）： 该项目将使用新的定量成像方法来引导活检到原发性和治疗后复发性GBM的生物学不同区域，用于靶向外显子组，表观基因组和转录组分析。我们的目标是确定自然演变和治疗诱导的突变和表突变，促进恶性亚克隆的选择性生长超过石灰。癌症的基因组分析通常在单个时间点进行，并且在不知道其在异质性肿瘤内的原始背景的情况下对整块切除物的单片进行。与这些传统的基因组研究相比，新诊断和复发肿瘤的图像引导方法可以通过将突变和表观突变与体内侵袭性肿瘤生长区域联系起来来丰富肿瘤生长驱动因素的检测。我们将使用创新的代谢和生理成像来识别同一患者体内具有不同增殖和缺氧水平的区域。据我们所知，这将是第一次使用先进的成像技术来指导任何人类肿瘤的基因组或表观基因组分析。在目标1中，我们将鉴定在新诊断的GBM中表现出瘤内异质性的功能突变和表观突变。在目标2中，我们将使用来自治疗的复发性GBM的图像引导组织样本，包括来自个体患者随时间推移的配对样本，鉴定肿瘤进展期间通常获得的功能突变和表突变。我们的初步数据表明，化疗可以有一个深刻的影响恶性亚克隆的选择性生长。来自目标1和2的数据的整合将鉴定新诊断的肿瘤中的亚克隆，所述亚克隆表现出选择性生长以在复发时成为优势克隆，以及涉及交叉遗传和表观遗传机制的顺序双等位基因事件，所述交叉遗传和表观遗传机制有助于其增强的生长潜力。候选驱动程序变更将使用成熟的计算管道进行评估，并将通过实验测试预测的功能效应。因此，这些研究可以通过识别复发的常见驱动因素，定义治疗对肿瘤演变的影响，并将原发性和复发性肿瘤的特征纳入个性化治疗计划来影响患者护理。

PROJECT SUMMARY (See instructions): This project will use novel quantitative imaging methods to guide biopsies to biologically distinct regions of primary and post-treatment recurrent GBM for targeted exome, epigenome and transcriptome analysis. Our goal is to identify naturally evolving and treatment-induced mutations and epimutations that promote the selective outgrowth of malignant subclones over lime. Genomic analysis of cancer is typically conducted at a single time point and on a single piece of the bulk resection without knowledge of its original context within the heterogeneous tumor. In contrast to these traditional genomic studies, an image guided approach to newly diagnosed and recurrent tumors could enrich for the detection of drivers of tumor growth by linking mutations and epimutations to regions of aggressive tumor growth in vivo. We will use innovative metabolic and physiologic imaging to identify regions with different levels of proliferation and hypoxia within the same patient. To our knowledge, this would be the first time that advanced imaging will be used to guide genomic or epigenomic analysis of any human tumor. In Aim 1, we will identify functional mutations and epimutations that exhibit intratumoral heterogeneity within newly diagnosed GBM. In Aim 2, we will identify functional mutations and epimutations commonly acquired during tumor progression using image guided tissue samples from treated, recurrent GBM, including paired samples from individual patients over time. Our preliminary data show that chemotherapy can have a profound effect on selective outgrowth of malignant subclones. The integration of data from Aims 1 and 2 will identify subclones in newly diagnosed tumor that exhibit selective outgrowth to become the dominant clone(s) at recurrence, and the sequential biallelic events involving intersecting genetic and epigenetic mechanisms that contribute to their enhanced growth potential. Candidate driver alterations will be evaluated using a mature computational pipeline, and will experimentally be tested for predicted functional effect. These studies could therefore impact patient care by the identification of common drivers specific to recurrence, defining the influence of therapy on tumor evolution, and incorporating profiles of primary and recurrent tumors into personalized treatment plans.