Imaging Guided Genomics of Malignant Transformation

描述（由申请人提供）：该项目将使用新型定量成像方法来引导活检到脑肿瘤的生物学不同区域，用于靶向外显子组和转录组分析。我们的目标是确定自然演变和治疗诱导的突变，驱动恶性转化（MT）的低级别胶质瘤（LGG）的高级别胶质瘤（HGG）。MT与非常差的生存率相关，但MT的潜在机制尚不清楚，也不知道LGG切除后的化疗如何改变肿瘤演变的自然过程。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演变可能存在显着差异，并且这种常用的化疗药物本身可能会诱导聚集在共同信号传导途径上的重复转化促进驱动突变。与传统的基因组学研究相比，成像引导的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来丰富对驱动MT的突变的检测。在这里，我们提出了两种互补的方法来询问MT在TMZ治疗和未治疗患者中的遗传基础。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特征，两个不具有MT特征，这是通过新的生理/代谢成像参数预测的，随后通过组织分析证实。这将从单个手术时间点提供MT的集中评估。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中的突变和表达谱，这些受试者具有（i）回顾性可用的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，并证明转化为HGG。这些配对样本将允许直接评估个体患者随时间的突变演变。基因组学与先进成像的整合、在大的独立肿瘤组中突变频率的验证、候选物的实验测定和最新的计算分析预计将丰富驱动MT的突变的鉴定，并将自然进化的MT与非自然进化的MT区分开来。 TMZ诱导的突变。因此，这些研究可以通过确定化疗禁忌的LGG患者以及通过确定与MT相关的常见和靶向突变来影响患者管理。

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.