Imaging Guided Genomics of Malignant Transformation

描述(申请人提供)：这个项目将使用新的定量成像方法来引导活检到脑肿瘤的生物不同区域进行有针对性的外显子组和转录组分析。我们的目标是确定导致低级别胶质瘤(LGG)向高级别胶质瘤(HGG)恶性转化(MT)的自然进化和治疗诱导的突变。MT与非常差的存活率有关，但MT的机制尚不清楚，也不知道切除LGG后的化疗如何改变肿瘤的自然演变过程。我们从外显子组和RNA测序(RNA-seq)获得的大量初步数据表明，替莫唑胺(TMZ)治疗和未治疗患者的突变进化可能截然不同，这种常用的化疗药物本身可能会诱导反复发生的促进转化的驱动程序突变，这些突变会聚在共同的信号通路上。与传统的基因组研究不同，成像引导基因组学可以通过将突变与体内侵袭性肿瘤生长的区域联系起来，丰富对驱动MT的突变的检测。在这里，我们建议用两种互补的方法来询问接受TMZ治疗和未接受治疗的患者MT的遗传基础。在目标1中，我们将使用外显子和RNA-seq来比较来自每个患者的四个肿瘤活检组织中的外显子突变和表达谱，其中两个具有MT的特征，根据新的生理/代谢成像参数预测并随后经组织分析证实。这将从单个手术时间点提供对MT的有针对性的评估。在目标2中，我们将使用从LGG过渡到HGG之前和之后从同一个人那里纵向收集的样本。我们将比较这第二组受试者的突变和表达谱，这些受试者拥有(I)可追溯获得的LGG组织和(Ii)图像引导的组织样本，这些组织样本是作为这项资助的一部分获得的，并显示出向HGG的转化。这些配对样本将允许直接评估个体患者随着时间的推移突变的演变。基因组学与先进成像技术的结合、大型独立肿瘤中突变频率的验证、候选者的实验分析以及最新的计算分析有望丰富识别驱动MT的突变并区分自然进化的 TMZ诱导的突变。因此，这些研究可以通过确定应禁忌化疗的LGG患者，以及确定与MT相关的常见和有针对性的突变来影响患者的管理。

DESCRIPTION (provided by applicant): This project will use novel quantitative imaging methods to guide biopsies to biologically distinct regions of brain tumors for targeted exome and transcriptome analysis. Our goal is to identify naturally evolving and treatment-induced mutations that drive malignant transformation (MT) of low grade glioma (LGG) to high grade glioma (HGG). MT is associated with very poor survival, but the mechanisms underlying MT are unknown, and it is not known how chemotherapy following resection of LGG might alter the natural course of tumor evolution. Our substantial preliminary data from exome and RNA sequencing (RNA-seq) suggests that evolution of mutations can differ dramatically in temozolomide (TMZ) treated and non-treated patients, and that this commonly used chemotherapeutic agent itself may induce recurring transformation-promoting driver mutations that converge on common signaling pathways. In contrast to traditional genomic studies, imaging guided genomics could enrich the detection of mutations that drive MT by linking mutations to regions of aggressive tumor growth in vivo. Here we propose to interrogate the genetic underpinnings of MT in TMZ-treated and untreated patients with two complementary approaches. In aim 1, we will use exome and RNA-seq to compare exon mutations and expression profiles among four tumor biopsies from each patient, two with and two without characteristics of MT as predicted by novel physiologic/metabolic imaging parameters and subsequently confirmed by tissue analyses. This will provide a focused assessment of MT from a single surgical time point. In aim 2, we will use longitudinally collected samples from the same individual before and after transition from LGG to HGG. We will compare the mutation and expression profiles within this second set of subjects who have (i) LGG tissue available retrospectively and (ii) image guided tissue samples that were obtained as part of this grant and that demonstrate transformation to HGG. These paired samples will allow a direct assessment of evolution of mutations in individual patients over time. The integration of genomics with advanced imaging, validation of mutation frequency in large, independent set of tumors, experimental assays of candidates, and up-to-date computational analyses are expected to enrich for the identification of mutations that drive MT and to distinguish naturally evolving from TMZ-induced mutations. These studies could therefore impact patient management by identifying LGG patients for which chemotherapy should be contraindicated, and by identifying common and targetable mutations associated with MT.