Literature DB >> 26191234

Gene mutation profiling of primary glioblastoma through multiple tumor biopsy guided by 1H-magnetic resonance spectroscopy.

Chao Tang1, Jun Guo1, Hong Chen1, Cheng-Jun Yao1, Dong-Xiao Zhuang1, Yin Wang1, Wei-Jun Tang2, Guang Ren2, Yu Yao1, Jin-Song Wu1, Ying Mao1, Liang-Fu Zhou1.   

Abstract

Genetic mutation has served as the biomarkers for the diagnosis and treatment of glioblastoma multiforme (GBM). However, intra-tumor heterogeneity may interfere with personalized treatment strategies based on mutation analysis. This study aimed to characterize somatic mutation profiling of GBM. We collected 33 samples from 7 patients with the primary GBM associated with different Choline (Cho) to N-acetylaspartate (NAA) index (CNI) through the frameless proton magnetic resonance spectroscopy (1H-MRS) guided biopsies and investigated multiple somatic mutations profiling using the AmpliSeq cancer hotspot panel V2. We identified 53 missense or nonsense mutations in 27 genes including some novel mutations such as APC and IDH2. The mutations in EGFR, TP53, PTEN, PIK3CA genes were presented with different frequency and the majority of the mutated gene was only shared by 1-2 samples from one patient. Moreover, we found the association of CNI with histological grade, but there was no significant change of CNI in the presence of TP53, EGFR and PTEN mutations. These data suggest that gene mutations constitute a heterogeneous marker for primary GBM which may be independent of intra-tumor morphological phenotypes of GBM; therefore, gene mutation markers could not be determined from a small number of needle biopsies or only confined to the high-grade region.

Entities:  

Keywords:  Mutation profi ling; biopsy; glioblastoma multiforme; heterogeneity; proton magnetic resonance spectroscopy

Mesh:

Substances:

Year:  2015        PMID: 26191234      PMCID: PMC4503105     

Source DB:  PubMed          Journal:  Int J Clin Exp Pathol        ISSN: 1936-2625


  22 in total

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  11 in total

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6.  Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma.

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10.  Identification of Differentially Expressed miRNAs in Appendiceal Mucinous Cystadenocarcinoma from Mucinous Cystadenoma.

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