Identification of Long Non-coding RNAs as Novel Biomarkers for Heterogeneous Glioblastomas

项目摘要 多形性胶质母细胞瘤（GBM）是最恶性的脑肿瘤， 美国每年有13,000例新诊断患者和13,000例死亡。GBM的预后仍然是 诊断后GBM患者的中位生存时间为14至16个月。恶性肿瘤的标志 GBM是其在肿瘤内的高度异质性。这种独特的特征表现为分子亚型 显示独特的发病机制、生物学和预后模式。而特定的分子标记 在临床护理中具有价值（例如，IDH 1/2突变，1 p/19 q共缺失），预后显著改善 迫切需要分层和靶向治疗。最终目标是充分发挥 个性化和精准医疗，我们建议询问长非编码RNA（lncRNA）， 临床GBM样本队列。lncRNA是一类非编码RNA， 通过基因调控在各种细胞过程中起调节作用。以前的研究包括癌症 基因组图谱（TCGA），虽然受到其不为非编码RNA设计的分析技术的限制， 提示lncRNA在人类癌症中是丰富的并且是高度癌症类型特异性的。特别是， lncRNA与脑功能和胶质瘤的发展有关。具体而言，在这一探索性的 项目，我们将应用基于Ribo-Zero的转录组测序（RNA-seq）， 在西北大学收集的100个临床GBM样本中表征lncRNA 脑肿瘤组织库（Aim 1a）。与之前的研究中使用的基于寡聚（dT）的RNA-seq相比， 研究，包括TCGA，基于Ribo-Zero的技术针对非编码RNA转录物进行了优化，因此 为GBM中所有潜在功能性lncRNA的谱分析提供了巨大的优势。值得注意的是， 将开发基于机器学习的算法，以提供更灵活和通用的框架， 使用RNA-seq的lncRNA检测尽管仅限于我们的GBM数据和 基于oligo（dT）的TCGA，我们将使用TCGA数据评估在GBM中检测到的lncRNA的组织特异性 几种实体瘤（目标1b）。在描述GBM中lncRNA的景观后，我们将评估 lncRNA是否与GBM患者的临床结局相关，并评估 将lncRNA和基因水平转录物整合到预后工具中（Aim 2a）。这项建议将使我们能够 将这些新的生物标志物用于GBM的预后以及未来的功能研究。另外我们 将利用共表达网络分析来为GBM中检测到的lncRNA分配功能。一个综合的， 将构建基于互联网的目录，以提供GBM中lncRNA的资源， 在这个新领域的一般研究社区（目标2b）。最后，私家侦探们召集了一个杰出的 研究团队在相关研究领域取得了重大成就，因此 确保这一多学科和高度创新的项目取得成功。

PROJECT SUMMARY Glioblastoma multiforme (GBM) is the most malignant form of brain tumors with more than 18,000 newly diagnosed patients and 13,000 deaths annually in the United States. The prognosis for GBM remains dismal with a median survival time of GBM patients of 14 to 16 months after diagnosis. A hallmark of malignant GBM is their high heterogeneity within the tumors. This unique characteristic manifests to molecular subtypes of GBM that display unique patterns of pathogenesis, biology, and prognosis. While specific molecular markers are of value in clinical care (e.g., IDH1/2 mutations, 1p/19q co-deletion), significant improvement in prognostic stratification and targeted therapeutics are urgently needed. With the ultimate goal of realizing the full potential of personalized and precision medicine, we propose to interrogate long non-coding RNAs (lncRNAs) in a cohort of clinical GBM specimens. LncRNAs are a class of non-coding RNAs that have emerged as critical modulators in various cellular processes through gene regulation. Previous studies including The Cancer Genome Atlas (TCGA), though limited by their profiling technique not designed for non-coding RNAs, have suggested that lncRNAs are abundant in human cancers and are highly cancer-type-specific. In particular, lncRNAs have been implicated in brain function and glioma development. Specifically, in this exploratory project, we will apply the Ribo-Zero-based transcriptomic sequencing (RNA-seq) to comprehensively characterize lncRNAs in 100 clinical GBM samples that have been collected at the Northwestern University Brain Tumor Tissue Bank (Aim 1a). In contrast to the oligo(dT)-based RNA-seq that was used by previous studies, including TCGA, the Ribo-Zero-based technique is optimized for non-coding RNA transcripts, thus offering a great advantage for profiling all potentially functional lncRNAs in GBM. Notably, a novel detection algorithm based on machine learning will be developed to provide a more flexible and universal framework of lncRNA detection using RNA-seq. Though restricted to those lncRNAs shared between our GBM data and the oligo(dT)-based TCGA, we will evaluate the tissue-specificity of lncRNAs detected in GBM using TCGA data on several solid tumors (Aim 1b). After characterizing the landscape of lncRNAs in GBM, we will evaluate whether lncRNAs are associated with the clinical outcomes of GBM patients, and evaluate the feasibility of integrating lncRNAs and gene-level transcripts into a prognostic tool (Aim 2a). This proposal will enable us to employ these novel biomarkers for the prognosis of GBM as well as future functional studies. In addition, we will utilize co-expression network analysis to assign functions to the detected lncRNAs in GBM. An integrated, internet-based catalog will be constructed to provide a resource of lncRNAs in GBM that will benefit the general research community in this new area (Aim 2b). Finally, the PIs have assembled an outstanding research team with significant achievements in relevant research areas and complimentary expertise, therefore ensuring the success of this multidisciplinary and highly innovative project.