Brain Tumor Res Treat.  2024 Jan;12(1):23-39. 10.14791/btrt.2023.0036.

Comprehensive Molecular Genetic Analysis in Glioma Patients by Next Generation Sequencing

• Kim T
• Lee A
• Ahn S
• Park JS
• Jeun SS
• Lee YS

• KMID: 2552336
• DOI: http://doi.org/10.14791/btrt.2023.0036

Abstract

Background
Glioma is caused by multiple genomic alterations. The evolving classification of glio- mas emphasizes the significance of molecular testing. Next generation sequencing (NGS) offers the assessment of parallel combinations of multiple genetic alterations and identifying actionable mutations that guide treatment. This study comprehensively analyzed glioma patients using multi-gene NGS panels, providing powerful insights to inform diagnostic classification and targeted therapies.
Methods
We conducted a targeted panel-based NGS analysis on formalin-fixed and paraffin- embedded nucleic acids extracted from a total of 147 glioma patients. These samples underwent amplicon capture-based library preparation and sequenced using the Oncomine Comprehensive Assay platform. The resulting sequencing data were then analyzed using the bioinformatics tools.
Results
A total of 301 mutations, were found in 132 out of 147 tumors (89.8%). These muta- tions were in 68 different genes. In 62 tumor samples (42.2%), copy number variations (CNVs) with gene amplifications occurred in 25 genes. Moreover, 25 tumor samples (17.0%) showed gene fusions in 6 genes and intragenic deletion in a gene. Our analysis identified actionable targets in several genes, including 11 with mutations, 8 with CNVs, and 3 with gene fusions and intragenic deletion. These findings could impact FDA-approved therapies, NCCN guideline-based treatments, and clinical trials.
Conclusion
We analyzed precisely diagnosing the classification of gliomas, detailing the frequency and co-occurrence of genetic alterations and identifying genetic alterations with potential therapeutic targets by NGS-based molecular analysis. The high-throughput NGS analysis is an efficient and powerful tool to comprehensively support molecular testing in neurooncology.

Keyword

Glioma; Next generation sequencing; Genetic variation; Molecular targeted therapy

Figure

• 

  Fig. 1 The mutation frequency for each diagnosis type. A: Frequency of highly mutated 12 genes in 147 patients with glioma. The mutation frequency of highly mutated 9 genes in each pathological diagnosis of gliomas (B: astrocytomas, C: glioblastomas, D: other gliomas).

• 

  Fig. 2 Correlation of detected mutations. A: A combined plot of 64 genes correlated with each other. It shows the combination of 64 genes whose mutations are correlated with each other out of a total of 68 genes detected. The dot is the name of the gene mutation and the circular line matches the frequency of the gene mutation. Different colors represent different genes and the length of each color circle line represents the number of mutations. The curve in the middle of the circle represents the sample and the two ends of the curve are gene mutations that occur simultaneously with each other in the sample. The correlations with other genes are linked by lines, and single gene mutations are matched back to their own genes. B: Correlation heat map between 36 genes with significant correlation for each gene detected by more than one in a total of 68 genes (p<0.05).

• 

  Fig. 3 The distribution of copy number values in 25 genes, ranging from 6 up to 180.

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  Fig. 4 Correlation of detected copy number variables. A: A combined plot of 25 genes correlated with each other. This shows that the combination of CNVs in a total of 25 genes detected correlates with each other. Different colors represent different genes and the length of each color line represents the number of CNVs. The curve in the middle of the circle represents the sample and the two ends of the curve are gene CNVs that occur simultaneously with each other in the sample. Especially CNV does not occur simultaneously in the same tumor, so most curves have the same genes. B: Correlation heat map between 25 genes with significant correlation for each gene (p<0.05). CNVs, copy number variations.

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