Dataset Information

Whole-transcriptome sequencing profiling identifies functional and prognostic signatures in patients with PTPRZ1-MET fusion-negative secondary glioblastoma multiforme.

ABSTRACT: Gliomas are the most common type of primary brain tumor in adults with a high mortality rate. Low-grade gliomas progress to glioblastoma multiforme (GBM) in the majority of cases, forming secondary GBM (sGBM), followed by rapid fatal clinical outcomes. Protein tyrosine phosphatase receptor type Z1 (PTPRZ1)-MET proto-oncogene receptor tyrosine kinase (MET) (ZM) fusion has been identified as a biomarker for sGBM that is involved in glioma progression, but the mechanism of gliomagenesis and pathology of ZM-negative sGBM has remained to be fully elucidated. A whole-transcriptome signature is thus required to improve the outcome prediction for patients with sGBM without ZM fusion. In the present study, whole-transcriptome sequencing on 42 sGBM samples with or without ZM fusion from the Chinese Glioma Genome Atlas database identified mRNAs with differential expression between patients with and without ZM fusion and the most significant survival-associated genes were identified. A 6-gene signature was identified as a novel prognostic model reflecting survival probability in patients with ZM-negative sGBM. Clinical characteristics in patients with a high or low risk score value were analyzed with the Kaplan-Meier method and a two-sided log-rank test. In addition, ZM-negative sGBM patients with a high risk score exhibited an increase in immune cells, NF-?B-induced pathway activation and a decrease in endothelial cells compared with those with a low risk score. The present study demonstrated the potential use of a next-generation sequencing-based cancer gene signature in patients with ZM-negative sGBM, indicating possible clinical therapeutic strategies for further treatment of such patients.

SUBMITTER: Chen BS

PROVIDER: S-EPMC7479526 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Whole-transcriptome sequencing profiling identifies functional and prognostic signatures in patients with PTPRZ1-MET fusion-negative secondary glioblastoma multiforme.

Chen Bao-Shi BS Wang Kuan-Yu KY Yu Shu-Qing SQ Zhang Chuan-Bao CB Li Guan-Zhang GZ Wang Zhi-Liang ZL Bao Zhao-Shi ZS

Oncology letters 20200903 5

Gliomas are the most common type of primary brain tumor in adults with a high mortality rate. Low-grade gliomas progress to glioblastoma multiforme (GBM) in the majority of cases, forming secondary GBM (sGBM), followed by rapid fatal clinical outcomes. Protein tyrosine phosphatase receptor type Z1 (PTPRZ1)-MET proto-oncogene receptor tyrosine kinase (MET) (ZM) fusion has been identified as a biomarker for sGBM that is involved in glioma progression, but the mechanism of gliomagenesis and patholo ...[more]

PMID: 32952656

Similar Datasets

Project description:The aggressive nature of malignant gliomas and their genetic and clinical heterogeneity present a major challenge in their diagnosis and treatment. Development of targeted therapy brought attention on detecting novel gene fusions, since they represent promising therapeutic targets (eg, TRK inhibitors in NTRK fusion-positive tumors). Using targeted next-generation sequencing, we prospectively analyzed 205 primary brain tumors and detected a novel PTPRZ1-ETV1 fusion transcript in 11 of 191 (5.8%) gliomas, including nine glioblastomas, one anaplastic oligodendroglioma and one pilocytic astrocytoma. PTPRZ1-ETV1 fusion was confirmed by RT-PCR followed by Sanger sequencing, and in-silico analysis predicted a potential driver role. The newly detected fusion consists of the PTPRZ1 promoter in frame with the highly conserved DNA-binding domain of ETV1 transcription factor. The ETV1 and PTPRZ1 genes are known oncogenes, involved in processes of tumor development. ETV1 is a member of the ETS family of transcription factors, already known oncogenic drivers in Ewing sarcoma, prostate cancer and gastrointestinal stromal tumors, but not in gliomas. Its overexpression contributes to tumor growth and more aggressive tumor behavior. PTPRZ1 is already considered to be a tumor growth promoting oncogene in gliomas. In 8%-16% of gliomas, PTPRZ1 is fused to the MET oncogene, resulting in a PTPRZ1-MET fusion, which is associated with poorer prognosis but is also a positive predictive biomarker for treatment with kinase inhibitors. In view of the oncogenic role that the two fusion partners, PTPRZ1 and ETV1, exhibit in other malignancies, PTPRZ1-ETV1 fusion might present a novel potential therapeutic target in gliomas. Although histopathological examination of PTPRZ1-ETV1 fusion-positive gliomas did not reveal any specific or unique pathological features, and the follow-up period was too short to assess prognostic value of the fusion, careful monitoring of patients and their response to therapy might provide additional insights into the prognostic and predictive value of this novel fusion.

Project description:Glioblastoma multiforme (GBM) has been recognized as the most lethal type of malignant brain tumor. Despite efforts of the medical and research community, patients' survival remains extremely low. Multi-omic profiles (including DNA sequence, methylation and gene expression) provide rich information about the tumor. These profiles are likely to reveal processes that may be predictive of patient survival. However, the integration of multi-omic profiles, which are high dimensional and heterogeneous in nature, poses great challenges. The goal of this work was to develop models for prediction of survival of GBM patients that can integrate clinical information and multi-omic profiles, using multi-layered Bayesian regressions. We apply the methodology to data from GBM patients from The Cancer Genome Atlas (TCGA, n = 501) to evaluate whether integrating multi-omic profiles (SNP-genotypes, methylation, copy number variants and gene expression) with clinical information (demographics as well as treatments) leads to an improved ability to predict patient survival. The proposed Bayesian models were used to estimate the proportion of variance explained by clinical covariates and omics and to evaluate prediction accuracy in cross validation (using the area under the Receiver Operating Characteristic curve, AUC). Among clinical and demographic covariates, age (AUC = 0.664) and the use of temozolomide (AUC = 0.606) were the most predictive of survival. Among omics, methylation (AUC = 0.623) and gene expression (AUC = 0.593) were more predictive than either SNP (AUC = 0.539) or CNV (AUC = 0.547). While there was a clear association between age and methylation, the integration of age, the use of temozolomide, and either gene expression or methylation led to a substantial increase in AUC in cross-validaton (AUC = 0.718). Finally, among the genes whose methylation was higher in aging brains, we observed a higher enrichment of these genes being also differentially methylated in cancer.

Dataset Information

Whole-transcriptome sequencing profiling identifies functional and prognostic signatures in patients with PTPRZ1-MET fusion-negative secondary glioblastoma multiforme.

Publications

Whole-transcriptome sequencing profiling identifies functional and prognostic signatures in patients with PTPRZ1-MET fusion-negative secondary glioblastoma multiforme.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets