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

  • Zhang, Wei, (PI)

Research project

Description

PROJECT SUMMARY Glioblastoma multiforme (GBM) is the most malignant form of brain tumors with more than 18,000newly diagnosed patients and 13,000 deaths annually in the United States. The prognosis for GBM remainsdismal with a median survival time of GBM patients of 14 to 16 months after diagnosis. A hallmark of malignantGBM is their high heterogeneity within the tumors. This unique characteristic manifests to molecular subtypesof GBM that display unique patterns of pathogenesis, biology, and prognosis. While specific molecular markersare of value in clinical care (e.g., IDH1/2 mutations, 1p/19q co-deletion), significant improvement in prognosticstratification and targeted therapeutics are urgently needed. With the ultimate goal of realizing the full potentialof personalized and precision medicine, we propose to interrogate long non-coding RNAs (lncRNAs) in acohort of clinical GBM specimens. LncRNAs are a class of non-coding RNAs that have emerged as criticalmodulators in various cellular processes through gene regulation. Previous studies including The CancerGenome Atlas (TCGA), though limited by their profiling technique not designed for non-coding RNAs, havesuggested 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 exploratoryproject, we will apply the Ribo-Zero-based transcriptomic sequencing (RNA-seq) to comprehensivelycharacterize lncRNAs in 100 clinical GBM samples that have been collected at the Northwestern UniversityBrain Tumor Tissue Bank (Aim 1a). In contrast to the oligo(dT)-based RNA-seq that was used by previousstudies, including TCGA, the Ribo-Zero-based technique is optimized for non-coding RNA transcripts, thusoffering a great advantage for profiling all potentially functional lncRNAs in GBM. Notably, a novel detectionalgorithm based on machine learning will be developed to provide a more flexible and universal framework oflncRNA detection using RNA-seq. Though restricted to those lncRNAs shared between our GBM data and theoligo(dT)-based TCGA, we will evaluate the tissue-specificity of lncRNAs detected in GBM using TCGA dataon several solid tumors (Aim 1b). After characterizing the landscape of lncRNAs in GBM, we will evaluatewhether lncRNAs are associated with the clinical outcomes of GBM patients, and evaluate the feasibility ofintegrating lncRNAs and gene-level transcripts into a prognostic tool (Aim 2a). This proposal will enable us toemploy these novel biomarkers for the prognosis of GBM as well as future functional studies. In addition, wewill 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 thegeneral research community in this new area (Aim 2b). Finally, the PIs have assembled an outstandingresearch team with significant achievements in relevant research areas and complimentary expertise, thereforeensuring the success of this multidisciplinary and highly innovative project.
StatusActive
Effective start/end date8/1/167/31/18