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

鉴定长非编码 RNA 作为异质性胶质母细胞瘤的新型生物标志物

基本信息

批准号：
9321295
负责人：
Shi-Yuan Cheng
金额：
$ 16.57万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9321295
关键词：
19q Achievement Affect Algorithms Area Biological Markers Biology Brain Brain Neoplasms Cancer Patient Catalogs Cell Proliferation Cell physiology Central Nervous System Neoplasms Cessation of life Characteristics Clinical Code Colorectal Neoplasms Combined Modality Therapy Communities Complex Data Detection Development Diagnosis Ensure Epidermal Growth Factor Receptor Exons Female Future Gene Expression Gene Expression Profile Gene Expression Regulation Genes Genetic Transcription Glioblastoma Glioma Goals Heterogeneity Human Internet Length Machine Learning Malignant - descriptor Malignant Neoplasms Malignant neoplasm of brain Measures Messenger RNA Morphologic artifacts Mutation Neoplasm Metastasis Newly Diagnosed Nucleotides Operative Surgical Procedures Outcome Pathogenesis Pathway Analysis Patient-Focused Outcomes Patients Pattern Primary Neoplasm RNA RNA Splicing Regulation Regulator Genes Research Resources Role Sampling Scanning Solid Neoplasm Specificity Specimen Stratification Techniques The Cancer Genome Atlas Time Tissue Banks Tissues Transcript United States Universities Untranslated RNA Validation Work X Inactivation base cancer biomarkers cancer type clinical Diagnosis clinical care cohort differential expression flexibility gene function histone modification indexing innovation molecular marker molecular subtypes multidisciplinary novel novel marker novel therapeutics oligo (dT)outcome forecast peripheral blood personalized medicine precision medicine prognostic prognostic tool public health relevance success targeted treatment transcriptome transcriptome sequencing transcriptomics tumor tumorigenesis

项目摘要

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.

项目概要多形性胶质母细胞瘤 (GBM) 是最恶性的脑肿瘤，有超过 18,000 个美国每年新诊断的患者和 13,000 人死亡。 GBM 的预后仍然 GBM 患者诊断后的中位生存时间为 14 至 16 个月。恶性的标志 GBM 是肿瘤内的高度异质性。这种独特的特征体现在分子亚型上 GBM 显示出独特的发病机制、生物学和预后模式。虽然特定的分子标记在临床治疗中具有价值（例如 IDH1/2 突变、1p/19q 共缺失），显着改善预后迫切需要分层和靶向治疗。最终目标是充分发挥潜力为了实现个性化和精准医疗，我们建议以一种方法来研究长非编码 RNA (lncRNA) 临床 GBM 标本队列。 LncRNA 是一类已成为关键的非编码 RNA 通过基因调节调节各种细胞过程。之前的研究包括癌症基因组图谱 (TCGA) 虽然受到并非为非编码 RNA 设计的分析技术的限制，但研究表明，lncRNA 在人类癌症中含量丰富，并且具有高度的癌症类型特异性。尤其， lncRNA 与大脑功能和神经胶质瘤的发展有关。具体来说，在这个探索性的项目中，我们将应用基于Ribo-Zero的转录组测序（RNA-seq）全面描述西北大学收集的 100 个临床 GBM 样本中的 lncRNA 特征脑肿瘤组织库（目标 1a）。与之前使用的基于oligo(dT)的RNA-seq相比研究，包括 TCGA，基于 Ribo-Zero 的技术针对非编码 RNA 转录本进行了优化，因此为分析 GBM 中所有潜在功能性 lncRNA 提供了巨大的优势。值得注意的是，一种新颖的检测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 检测 lncRNA。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GBM中检测到的lncRNA的组织特异性对几种实体瘤的影响（目标 1b）。在描述了 GBM 中 lncRNA 的情况后，我们将评估 lncRNA是否与GBM患者的临床结果相关，并评估其可行性将 lncRNA 和基因水平转录本整合到预后工具中（目标 2a）。该提案将使我们能够利用这些新颖的生物标志物进行 GBM 的预后以及未来的功能研究。此外，我们将利用共表达网络分析为 GBM 中检测到的 lncRNA 分配功能。一个集成的、将构建基于互联网的目录，以提供 GBM 中的 lncRNA 资源，这将有利于这个新领域的一般研究团体（目标 2b）。最后，PI 们集合了一支出色的队伍研究团队在相关研究领域取得了重大成就并具有互补的专业知识，因此确保这个多学科且高度创新的项目取得成功。