Image Guided Genome-Epigenome Analysis of Tumor Heterogeneity and Evolution

项目总结(见说明)： 这个项目将使用新的定量成像方法来引导活检组织到初级和治疗后复发的GBM的生物学上不同的区域，以进行靶向外显组、表观基因组和转录组分析。我们的目标是识别自然进化和治疗诱导的突变和表型突变，这些突变和表型可以促进石灰中恶性亚克隆的选择性生长。癌症的基因组分析通常是在单个时间点上进行的，并且是在大块切除的单个部分上进行的，而不知道它在异质肿瘤中的原始背景。与这些传统的基因组研究相比，图像引导的新诊断和复发肿瘤的方法可以通过将突变和表型突变与体内侵袭性肿瘤生长区域联系起来，丰富检测肿瘤生长驱动因素的方法。我们将使用创新的代谢和生理成像来识别同一患者体内不同程度的增殖和缺氧区域。据我们所知，这将是第一次先进的成像将被用于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，我们将确定在新诊断的GBM中表现出肿瘤内异质性的功能突变和表观突变。在目标2中，我们将使用图像引导的组织样本，包括随时间推移来自个体患者的配对样本，识别在肿瘤进展过程中通常获得的功能突变和表型突变。我们的初步数据表明，化疗可以对恶性亚克隆的选择性生长产生深远的影响。整合AIMS 1和AIMS 2的数据将识别新诊断的肿瘤中表现出选择性生长并在复发时成为主导克隆的亚克隆(S)，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，这些事件有助于它们增强生长潜力。候选司机的改装将使用成熟的计算管道进行评估，并将对预测的功能效果进行实验测试。因此，这些研究可以通过确定特定于复发的共同驱动因素，确定治疗对肿瘤演变的影响，以及将原发和复发肿瘤的概况纳入个性化治疗计划，来影响患者的护理。

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.