Imaging Guided Genomics of Malignant Transformation

描述（由申请人提供）：该项目将使用新颖的定量成像方法指导活组织检查到生物学上不同的脑肿瘤区域，进行靶向外显子组和转录组分析。我们的目标是确定驱动低级别胶质瘤（LGG）向高级别胶质瘤（HGG）恶性转化（MT）的自然进化和治疗诱导突变。MT与非常差的生存率相关，但MT的机制尚不清楚，也不清楚LGG切除术后化疗如何改变肿瘤进化的自然过程。我们来自外显子组和RNA测序（RNA-seq）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存在显着差异，并且这种常用的化疗药物本身可能诱导反复出现的转化促进驱动突变，这些突变聚集在共同的信号通路上。与传统的基因组研究相比，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tmz治疗和未治疗患者MT的遗传基础。在目标1中，我们将使用外显子组和RNA-seq来比较每个患者的四个肿瘤活检中的外显子突变和表达谱，其中两个有MT特征，两个没有MT特征，这是由新的生理/代谢成像参数预测的，随后由组织分析证实。这将从单个手术时间点对MT进行集中评估。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体纵向收集的样本。我们将比较第二组受试者的突变和表达谱，这些受试者具有(i)可追溯获得的LGG组织和（ii）作为该资助的一部分获得的图像引导组织样本，并证明了向HGG的转化。这些配对样本将允许对个体患者随时间的突变演变进行直接评估。基因组学与先进的成像技术的整合，对大型独立肿瘤突变频率的验证，候选肿瘤的实验分析，以及最新的计算分析，有望丰富对驱动MT的突变的识别，并区分自然进化

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.