Gene expression network analysis to identify candidate drivers of treatment resistance in glioblastoma multiforme

基因表达网络分析以确定多形性胶质母细胞瘤治疗抵抗的候选驱动因素

• 批准号: 2278139
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2278139
• 关键词: Gene; expression; network; analysis; identify

Glioblastoma (GBM) is an incurable brain cancer. Patients survive, on average, 14 months post-diagnosis, despite receiving surgery and intensive chemoradiation, because 100% of tumours grow back. Our inability to kill these tumours is likely because they consist of numerous distinct cancer cell populations defined by unique DNA and/or RNA profiles which confer some of them with the ability to resist treatment. It is these treatment resistant cells subsets that we must specifically identify, characterise and learn how to kill. To this end, the secondary supervisor's group has collected hundreds of paired primary and recurrent GBMs from patients that received standard treatment, and is molecularly profiling them at the bulk tissue and single cell level. We now plan to fully exploit these datasets using systems biology approaches. The aim of this project is to assess the perturbations across GBM gene expression networks during therapy to highlight candidate molecules responsible for conferring, or facilitating transcriptional reprogramming to, a more treatment resistant state. This will be done using our existing data, and supplemented with that being produced within a global consortium that we are part of.We have high coverage RNA sequencing data from pairs of primary and matched recurrent GBM tumours. These data are strand directional and include mRNA and non-coding transcripts. We have performed denovo transcript discovery and quantified gene expression for ~80,000 known and novel genes, as well as the resulting transcripts. These data can be used to create co-expression networks within primary and recurrent samples separately. The primary supervisor has previously developed an approach for creating gene expression networks from correlations using as many as 60,000 nodes. Such networks will inform us on the regulatory and functional pathways in each tumour group. More importantly, changes in network structure through therapy will highlight candidate master regulators of transcriptional (re)programming in relation to the emergence or increased prevalence of treatment resistant subpopulations. Overlaying these networks with biological data on each node (gene) and the interactions therein will enable us to interpret and identify therapeutically targetable mechanisms to be tested in laboratory and preclinical settings. The objectives are:1. Build and optimise co-expression networks using primary and recurrent GBM gene expression data2. Use network theory to identify perturbations in network structure through treatment3. Layer biological information onto the nodes of the network to identify candidate master regulators of therapy-driven transcriptional (re)programming

胶质母细胞瘤（GBM）是一种无法治愈的脑癌。尽管接受了手术和密集的放化疗，患者平均存活14个月，因为100%的肿瘤会重新生长。我们无法杀死这些肿瘤可能是因为它们由许多不同的癌细胞群组成，这些癌细胞群由独特的DNA和/或RNA谱定义，这些谱赋予其中一些肿瘤抵抗治疗的能力。正是这些耐药细胞亚群，我们必须特别识别，识别和学习如何杀死。为此，二级主管小组从接受标准治疗的患者中收集了数百对原发性和复发性GBM，并在大量组织和单细胞水平上对其进行分子分析。我们现在计划使用系统生物学方法充分利用这些数据集。该项目的目的是评估治疗过程中GBM基因表达网络的扰动，以突出负责赋予或促进转录重编程的候选分子，使其更具治疗抗性。这将使用我们现有的数据来完成，并补充我们参与的全球联盟中产生的数据。我们拥有来自成对原发性和匹配的复发性GBM肿瘤的高覆盖率RNA测序数据。这些数据是链方向的，包括mRNA和非编码转录本。我们已经进行了从头转录本发现和定量基因表达约80，000已知和新的基因，以及所得的成绩单。这些数据可用于分别在原发性和复发性样本中创建共表达网络。主要主管之前已经开发出一种方法，用于使用多达60，000个节点从相关性创建基因表达网络。这样的网络将告知我们每个肿瘤组的调控和功能途径。更重要的是，通过治疗网络结构的变化将突出与耐药亚群的出现或患病率增加相关的转录（重）编程的候选主调节因子。将这些网络与每个节点（基因）上的生物学数据及其相互作用叠加，将使我们能够解释和识别在实验室和临床前环境中测试的治疗靶向机制。目标是：1.使用原发性和复发性GBM基因表达数据构建和优化共表达网络2。使用网络理论通过处理来识别网络结构中的扰动3.将生物信息分层到网络节点上，以识别治疗驱动的转录（重）编程的候选主调节因子