Glioblastoma patient characteristics in the Taiwanese population

000These 30 patients were enrolled between February 2009 and September 2022, with ages spanning from 23 to 66 years, calculated from the date of surgery. The median age was 53 years. Within this cohort, Group A consisted of 17 patients who exhibited a survival of over two years, comprising 10 males and 7 females. Meanwhile, Group B comprised 13 patients who survived less than two years, consisting of 8 males and 5 females. The male-to-female ratio was 60% to 40%.

The maximum diameter and number of tumors were evaluated using preoperative Brain MRI scans with contrast. Furthermore, patients were stratified based on their received treatments, which encompassed TMZ + CCRT (Concurrent Chemoradiotherapy) and TMZ + CCRT+Bevacizumab. All treatments were in accordance with the current GBM treatment guidelines.

Furthermore, the impact of diabetes and hypertension on survival was evaluated at the time of diagnosis using Fisher’s exact test (Table 1). No statistically significant disparities were noted in terms of age, gender, tumor size, or number between the two groups. However, the utilization of Bevacizumab demonstrated a statistically significant correlation with prolonged survival (p = 0.030), indicating a positive association between Bevacizumab use and extended survival [8].

The frequently mutated genes in glioblastoma within the Taiwanese population

We employed NGS target panel techniques to analyze 30 glioblastoma samples. Utilizing the tertiary analysis system, QIAGEN Clinical Insight (QCI), we selected variants categorized as pathogenic or likely pathogenic, and filtered out those with a frequency of less than 3%. The samples were then stratified into two groups based on survival period. The results revealed a spectrum of mutations, encompassing missense mutations, nonsense mutations, frameshift mutations, and indels spanning the promoter, exon, and intron regions. Additionally, we conducted a quantification of the number of patients and the proportion of patients with each mutated gene. A visual representation of gene mutations was generated using Comut (Fig. 1) [10].

Fig. 1: Genetic profiles and survival analysis in Taiwanese glioblastoma patients.

This figure illustrates the genetic profiles of glioblastoma in the Taiwanese population, coupled with an investigation into the survival characteristics of patients. It provides a comprehensive analysis of genetic variations and their potential impact on patient survival outcomes, highlighting the significance of personalized medicine in the treatment of glioblastoma.

Upon closer examination of the heat map (Fig. 1), it is evident that the total mutation count in Group A patients surpasses that in Group B. We conducted Fisher’s exact test to scrutinize each mutated gene in both groups, excluding genes with zero mutations in both groups (such as ATRX, MUTYH, PIK3R1, etc.). Due to the limited sample size, our analysis results indicate that none of the mutations reached statistically significant levels (Table 2). However, there is a noteworthy trend towards a p value of 0.05 for TP53.

Table 2 Mutation Frequencies Determined by Sanger Sequencing.

During the course of this experiment, we observed a higher prevalence of TP53 mutations in Group A. This observation contradicts current Western research, which suggests an association between TP53 mutations and a worsened prognosis in GBM [11].

Characteristics of glioblastoma-associated variants in the genes CHEK2, IDH1, TP53, and TERT promoter in the Taiwanese population

We utilized lollipop plots to visually represent glioblastoma-associated gene mutation sites specific to the Taiwanese population. These mutation sites were categorized into coding and non-coding regions. Within the coding region, a single mutation in the CHEK2 gene was identified as (c.1477 G > A, p.E493K) (Fig. 2A). In the IDH1 gene, a solitary mutation was identified as (c.395 G > A, p.R132H) (Fig. 2B). In the TP53 gene, seven mutations were identified as (c.326 T > C, p.F109S), (c.473 G > A, p.R158H), (c.578 A > G, p.H193R), (c.718 A > G, p.S240G), (c.743 G > A, p.R248Q), (c.817 C > T, p.R273C), and (c.833 C > T, p.P278L) (Fig. 2C). The coding region diagrams were generated using Mutation Mapper [12].

Fig. 2: The lollipop plot.

This figure illustrates amino acid substitutions in the A CHEK2, B IDH1, and C TP53 genes. The gray bar denotes the location of amino acids (aa). The circular lollipop marker indicates the specific site of amino acid substitution, with the height representing the variant count at those positions. Colored boxes represent distinct functional domains. D Schematic diagram of the TERT promoter. The bar marks the upstream regulatory region. The circular lollipop marker shows the specific site of nucleotide substitution. E Schematic diagram of the TP53 RNA splicing site. The bar denotes the TP53 gene in the chromosome 17 region using the GRCh37 reference genome. The circular lollipop marker indicates the specific site of nucleotide substitution.

In the non-coding region, two variants were detected in the TERT promoter regulatory region: (c.-146C > T) and (c.-124C > T) (Fig. 2D). In the TP53 gene, two variants were identified at the RNA splicing site: (c.376-2 A > C) and (c.376-2 A > G) (Fig. 2E). The non-coding region diagrams were prepared using track Viewer [13].

The disparities in glioblastoma patient gene mutations between the Taiwanese and Western populations

This study endeavors to elucidate the distinctions in the genetic mutation profiles of glioblastoma between the Taiwanese population and Western populations. We curated data from the TCGA database and various publications to pinpoint the top 10 mutated genes in glioblastoma. Our observations indicate that mutations in the ATR, KMT2C, TERT, RAD50, and CHEK2 genes are more prevalent in the Taiwanese population, whereas the mutation frequencies of TP53, IDH1, ATRX, NF1, and PIK3R1 are more akin to those in Western populations (Table 3). Additional details regarding these six TCGA projects can be found in the Supplementary Materials.

Table 3 The data presented in this table has been sourced from the public records of cBioPortal, with a specific focus on CNS/brain studies, particularly targeting glioblastoma.Statistical analysis of the effects of IDH1 and TP53 mutations on survival rate and age distribution of patients

Following the NGS results, we conducted a thorough statistical analysis. Due to the limited sample size, none of the mutations yielded statistically significant results. However, we identified the genes IDH1 and TP53 as having potential statistical significance. Regarding IDH1, we observed six patients in Group A and two patients in Group B (Fig. 3A). The age distribution analysis for IDH1 mutations indicated one patient below the age of 55 and seven patients aged 55 or above (Fig. 3B). These findings suggest that mutations in IDH1 among glioblastoma patients in Taiwan are linked with a more favorable prognosis, and the majority of patients with IDH1 mutations are under the age of 55.

Fig. 3: The Bar Chart Statistical Analysis illustrates the Frequency of Genetic Mutations in GBM Patients.

A Count of patients with IDH1 mutation in the two-year survival rate. B Count of patients with IDH1 mutation aged 55 years and below. C Count of patients with TP53 mutation in the two-year survival rate. OS: overall survival.

As for TP53, our investigation revealed seven patients in Group A and one patient in Group B (Fig. 3C). This outcome suggests that mutations in TP53 among glioblastoma patients in Taiwan are associated with a better prognosis.

Based on our NGS analysis data, Table 3 illustrates the heat map. ATR stands out as the most frequently mutated gene, accounting for 83% of all mutations. Following KMT2C, TERT, and CHEK2 mutations are the next most prevalent. For further specifics, please refer to the gene mutation heat map and Table 3.

In our study analyzing the top 10 genetic mutations in the Taiwanese population, we utilized a Cox proportional hazards regression model. This model was adjusted for age, gender, and bevacizumab treatment, as detailed in Table 4. Our findings indicate a notable association between the IDH1 mutation and patient prognosis. Specifically, compared to the wild type, the IDH1 mutation shows a hazard ratio (HR) of 0.31 (95% CI: 0.11–0.83) with a p value of 0.020 in the multivariate analysis. This suggests a significantly lower risk ratio (p < 0.05), indicating that glioblastoma patients with the IDH1 mutation may have a higher survival rate. Our study’s findings reveal that patients harboring IDH1 mutations demonstrate a notably prolonged survival rate. According to the World Health Organization’s 2021 glioma classification protocol, which emphasizes the significance of IDH1 mutations in its hierarchical categorization and integrates histopathological features, including microvascular proliferation and/or necrosis, these patients are classified as ‘Astrocytoma, IDH-mutant, CNS WHO Grade 4’ [14]. This classification demonstrates a comparatively favorable prognosis within the CNS WHO Grade 4 spectrum, aligning with our study’s results. This correlation echoes the trends observed in recent literature [14, 15]. In contrast, the TP53 mutation did not demonstrate statistical significance in the risk ratio. After adjustments, the HR for the mutation group was 0.49 (95% CI: 0.21–1.17), with a p value of 0.107. Despite the decreased HR value, the lack of statistical significance underscores the need for further investigation with larger sample sizes. Other genetic mutations analyzed did not show significant effects in this multivariate analysis.

Table 4 Comparative analysis of hazard ratios for specified gene mutations in univariate and multivariate models.