Zhaoshi Bao

Studies of gene rearrangements and the consequent oncogenic fusion proteins have laid the foundation for targeted cancer therapy. To identify oncogenic fusions associated with glioma progression, we catalogued fusion transcripts by RNA-seq of 272 gliomas. Fusion transcripts were more frequently found in high-grade gliomas, in the classical subtype of gliomas, and in gliomas treated with radiation/temozolomide. Sixty-seven in-frame fusion transcripts were identified, including three recurrent fusion transcripts: FGFR3-TACC3, RNF213-SLC26A11, and PTPRZ1-MET (ZM). Interestingly, the ZM fusion was found only in grade III astrocytomas (1/13; 7.7%) or secondary GBMs (sGBMs, 3/20; 15.0%). In an independent cohort of sGBMs, the ZM fusion was found in three of 20 (15%) specimens. Genomic analysis revealed that the fusion arose from translocation events involving introns 3 or 8 of PTPRZ and intron 1 of MET. ZM fusion transcripts were found in GBMs irrespective of isocitrate dehydrogenase 1 (IDH1) mutation status. sGBMs harboring ZM fusion showed higher expression of genes required for PIK3CA signaling and lowered expression of genes that suppressed RB1 or TP53 function. Expression of the ZM fusion was mutually exclusive with EGFR overexpression in sGBMs. Exogenous expression of the ZM fusion in the U87MG glioblastoma line enhanced cell migration and invasion. Clinically, patients afflicted with ZM fusion harboring glioblastomas survived poorly relative to those afflicted with non-ZM-harboring sGBMs (P < 0.001). Our study profiles the shifting RNA landscape of gliomas during progression and reveled ZM as a novel, recurrent fusion transcript in sGBMs.

Defining glioma subtypes based on objective genetic and molecular signatures may allow for a more rational, patient-specific approach to molecularly targeted therapy. However, prior studies attempting to classify glioma subtypes have given conflicting results. We aim to complement and validate the existing molecular classification system on a large number of samples from an East Asian population. A total of 225 samples from Chinese patients was selected for whole genome gene expression profiling. Consensus clustering was applied. Three major groups of gliomas were identified (referred to as G1, G2, and G3). The G1 subgroup correlates with a good clinical outcome, young age, and extremely high frequency of IDH1 mutations. Relative to the G1 subgroup, the G3 subgroup is correlated with a poorer clinical outcome, older age, and a very low rate of mutations in the IDH1 gene. Correlations of the G2 subgroup with respect to clinical outcome, age, and IDH1 mutation fall between the G1 and G3 subgroups. In addition, the G2 subtype was associated with a higher percentage of loss of 1p/19q when compared with G1 and G3 subtypes. Furthermore, our classification scheme was validated on 2 independent datasets derived from the cancer genome atlas (TCGA) and Rembrandt. With use of the TCGA classification system, proneural, neural, and mesenchymal, but not classical subtype, associated gene signatures were clearly defined. In summary, our results reveal that 3 main subtypes stably exist in Chinese patients with glioma. Our classification scheme may reflect the clinical and genetic alterations more clearly. Classical subtype-associated gene signature was not found in our dataset.

Background: Researches on immunotherapy of glioma has been increasing exponentially in recent years. However, autoimmune-like side effects of current immune checkpoint blockade hindered the clinical application of immunotherapy in glioma. The discovery of the TIM-3, a tumor-specific immune checkpoint, has shed a new light on solution of this dilemma. We aimed at investigating the role of TIM-3 at transcriptome level and its relationship with clinical practice in glioma.Methods: A cohort of 325 glioma patients with RNA-seq data from Chinese Glioma Genome Atlas (CGGA project) was analyzed, and the results were well validated in TCGA RNA-seq data of 699 gliomas. R language was used as the main tool for statistical analysis and graphical work.Results: TIM-3 was enriched in glioblastoma (the most malignant glioma) and IDH-wildtype glioma. TIM-3 can act as a potential marker for mesenchymal molecular subtype according to TCGA transcriptional classification scheme in glioma. TIM-3 was closely related to immune functions in glioma, especially T cell mediated immune response to tumor cell and T cell mediated cytotoxicity directed against tumor cell target. Moreover, TIM-3 and PD-L1 played almost exactly the same inflammatory activation functions in glioma. Clinically, high expression of TIM-3 was an independent indicator of poor prognosis.Conclusion: The expression of TIM-3 is closely related to the pathology and molecular pathology of glioma. Meanwhile, in glioma TIM-3 plays a specific role in T cell tumor immune response. Therefore, TIM-3 is a promising target for immunotherapeutic strategies, providing an alternative treatment when glioma gains resistance to antibodies of PD-1/PD-L1.

INTRODUCTION: Gliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas. AIMS: To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades. RESULTS: We used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn) as a discovery set and data from Rembrandt and GSE16011 for validation sets. Data from every single grade were analyzed by the Kaplan-Meier method with a two-sided log-rank test. Univariate Cox regression and linear risk score formula were applied to derive a gene signature with better prognostic performance. We found that patients who had high risk score according to the signature had poor overall survival compared with patients who had low risk score. Highly expressed genes in the high-risk group were analyzed by gene ontology (GO) and gene set variation analysis (GSVA). As a result, the reason for the divisibility of gliomas was likely due to cell life processes and adhesion. CONCLUSION: This 9-gene-signature prediction model provided a more accurate predictor of prognosis that denoted patients with high risk score have poor outcome. Moreover, these risk models based on defined molecular profiles showed the considerable prospect in personalized cancer management.