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Background:
This study analyzed 57 patients with glioblastoma treated at the General University Hospital of Castellon, Spain, focusing on clinical, tumor-specific and genetic factors influencing disease outcome. Variables included age, sex, BMI, extent of surgical resection, and use of radiotherapy or chemotherapy. Tumor characteristics assessed included location, size, proximity to the ventricular system and surgical approach. Genetic mutations in the EGFR, TP53 and CDKN2A genes were also analyzed.
Methods:
Kaplan–Meier survival analysis was used to assess the impact of clinical, tumor-related, treatment, lifestyle and genetic variables on overall survival and progression-free survival, with group differences evaluated using log-rank tests. Given the exploratory nature of the study and the sample size, multivariable modeling was not performed. Patients with IDH1/2-mutant tumors were excluded in accordance with the 2021 World Health Organization (WHO) classification, which no longer defines IDH-mutant grade 4 astrocytomas as glioblastoma.
Results:
A significant finding was the strong association between extent of resection, tumor proximity to the ventricular system and survival: patients with tumors closer to the ventricles had significantly shorter survival, highlighting the critical role of spatial tumor characteristics in glioblastoma outcomes.
Conclusion:
These results suggest that integrating clinical, genetic and spatial tumor data into personalized treatment approaches could improve prognosis.
Highlights-Total tumor resection and absence of contact with the ventricles were associated with longer survival.
-Age, sex, std treatments, genetic mutations, and secondary surgeries showed no effect on survival.
IntroductionGlioblastoma (GB), classified as a grade 4 astrocytoma by the World Health Organization (WHO) (Louis et al., 2021; Obrador et al., 2024), is the most prevalent, aggressive, and lethal primary brain tumor in adults. Known for its rapid growth, high invasiveness, and significant molecular diversity (Louis et al., 2021), it typically arises in the cerebral hemispheres (Price et al., 2024; Thakkar et al., 2014). Despite advances in treatment, including extensive surgical resection, radiotherapy, and temozolomide-based (Valenzuela-Fuenzalida et al., 2024) chemotherapy (Stupp protocol) (Stupp et al., 2005), the median survival remains dismally poor at 12–15 months post-diagnosis. This reflects the urgent need for novel therapies that effectively target the biological and molecular complexity (Davis, 2016; Vehlow and Cordes, 2013).
Glioblastoma exhibits extensive heterogeneity and plasticity at the cytopathological, transcriptional, and genetic levels (Becker et al., 2021; Perrin et al., 2019; Brennan et al., 2013; Kim et al., 2015). Moreover, the blood–brain barrier (BBB) and altered tumor permeability represent additional major obstacles to effective therapy, further limiting drug delivery and treatment efficacy (Ahmed et al., 2023; Davis, 2016; Wu et al., 2021). Within this highly complex microenvironment, glioblastoma stem-like cells (GSCs) have emerged as key drivers of tumor progression, therapeutic resistance, and recurrence (Loras et al., 2023; Stiles and Rowitch, 2008; Safa et al., 2015; Xie et al., 2022; Hu et al., 2023; Singh et al., 2004). GSCs exhibit marked resistance to apoptosis, contribute to angiogenesis and immunosuppression, and play a central role in both radio- and chemo-resistance (Di Nunno et al., 2022; Schaff and Mellinghoff, 2023; Furnari et al., 2007; Atkins et al., 2015). These properties are further reinforced by hypoxic conditions within the tumor microenvironment, which promote the maintenance of stem-like phenotypes and enhance tumor adaptability (Yalamarty et al., 2023; Eckerdt and Platanias, 2023). The persistence of therapy-resistant GSC populations is widely regarded as a major reason why glioblastoma almost invariably recurs following standard treatment (Bao et al., 2006).
Recent advances in genomic profiling have provided deeper insights into the genetic alterations driving GB. IDH1/2 mutations are predominantly found in grade 4 astrocytomas that evolve from lower-grade gliomas (secondary GBs) (Ichimura et al., 2009), which differ molecularly and clinically from primary GBs (Ichimura et al., 2009). Secondary GBs are different from primary GBs because they develop from pre-existing lower-grade gliomas (classified as WHO grades 2 or 3) instead of appearing out of nowhere, like primary GBs do (Delgado-Martín and Medina, 2020). Lower-grade gliomas tend to grow more slowly and have better survival rates initially, but over time, they can pick up additional genetic changes, such as mutations in TP53 or loss of ATRX, which can eventually lead to their progression into secondary GBs (Claus et al., 2015). This distinction between primary and secondary GBs is crucial because it highlights the differences in their molecular and clinical pathways (Mao et al., 2012). In contrast, EGFR amplification, present in approximately 40% of GB cases, is linked to worse outcomes by promoting aggressive tumor behavior through constitutive activation of growth signaling pathways (Verhaak et al., 2010). TP53 mutations, affecting the p53 pathway in 87% of cases, significantly contribute to disease progression, while alterations in CDKN2A, a key cell cycle regulator gene, further impair apoptosis and disrupt cell cycle control (Campbell et al., 2016). RTK/PI3K/PTEN pathway alterations are observed in 88% of GB cases, emphasizing their role in tumourigenesis. Additionally, loss of heterozygosity on chromosome 10 is one of the most common chromosomal abnormalities identified (Fujisawa et al., 2000). These genetic insights not only enhance our understanding of GB biology but also have potential to improve diagnosis, predict outcomes, and inform personalized therapies. For example, IDH mutations offer prognostic value, while EGFR, TP53, and CDKN2A alterations highlight the molecular complexity and therapeutic resistance of GB (Dunn et al., 2012).
In addition to genetic drivers, clinical and lifestyle factors such as age, sex, overall health, smoking, family history, and viral infections may influence prognosis, though their roles in GB remain underexplored. Some studies suggest sex-based survival differences, with longer survival in females (Yang et al., 2013). Lifestyle factors such as smoking, family cancer history, and viral infections (e.g., hepatitis B, COVID-19) may also influence disease progression and treatment response, although their roles in GB remain underexplored (Travers and Litofsky, 2021).
Tumor location relative to brain ventricles has emerged as a critical prognostic factor, though evidence in mixed impact on overall survival (OS) have been inconsistent (Mistry et al., 2020). While GB distance from the subventricular neural stem cell niche has not been correlated with survival (Mistry et al., 2017), a recent meta-analysis reported that GBs in contact with the lateral ventricle are associated with lower survival. This effect may be independent of established survival predictors, emphasizing the clinical relevance of the ventricular-subventricular zone contact in GB biology (Mistry et al., 2017; Travers and Litofsky, 2021). Furthermore, tumor location near the third ventricle and the contrast-enhancing tumor border has been identified as a prognostic factor, particularly in elderly patients (Fyllingen et al., 2021). Understanding these spatial relationships and their biological implications is crucial for devising more effective therapies and improving GB prognosis.
This study aimed to evaluate clinical, surgical and genetic determinants of survival in glioblastoma, with particular emphasis on the spatial relationship between tumor and ventricle.
MethodsStudy design and patient selectionThis retrospective cohort study includes 57 adult patients diagnosed with GB at the Castellon General University Hospital, Castellon, Spain. The recruitment period was from January 2020 to August 2023. Patients were selected based on confirmed GB diagnosis and the availability of complete clinical and genetic data. All results reflect data up to February 12th, 2023, with patient survival information updated to this date.
The study was approved by the Drug Research Ethics Committee (CEIm) of the General Hospital University of Castellon, Spain. In accordance with the 2021 WHO classification of tumors of the central nervous system, patients harboring IDH1 or IDH2 mutations were excluded to ensure a homogeneous cohort of IDH-wildtype glioblastoma.
Baseline clinical, tumor-related, treatment, lifestyle, and genetic characteristics of the cohort are summarized in Table 1.
VariableTotaln57Age, years, median (range)64 (17–81)Sex, n (%)Female27 (47.4%)Male30 (52.6%)BMI, median (IQR)25.35 (IQR 24.22–28.57)Tumor–ventricle distance group, n (%)Close (T1)19 (33.3%)Intermediate (T2)19 (33.3%)Far (T3)19 (33.3%)Tumor volume (cm3), median (IQR)9,216 (IQR 3,600–32,164)Extent of resection, n (%)Biopsy18 (31.6%)Partial resection26 (45.6%)Total resection13 (22.8%)Radiotherapy, n (%)No5 (8.8%)Yes52 (91.2%)Chemotherapy (TMZ STUPP), n (%)No8 (14.0%)Yes49 (86.0%)Death (exitus), n (%)Yes23 (41.1%)No33 (58.9%)*Recurrence, n (%)**23 (40.4%)Smoking status, n (%)No30 (52.6%)Yes27 (47.4%)Family history of cancer, n (%)No36 (63.2%)Yes21 (36.8%)Hepatitis B, n (%)No17 (29.8%)Yes40 (70.2%)Type 2 diabetes, n (%)No27 (47.4%)Yes30 (52.6%)EGFR alteration, n (%)Yes15 (26.3%)No42 (73.7%)TP53 alteration, n (%)Yes17 (29.8%)No40 (70.2%)CDKN2A alteration, n (%)Yes20 (35.1%)No37 (64.9%)Baseline clinical, tumor-related, treatment, lifestyle, and genetic characteristics of the glioblastoma cohort.
Summary of demographic variables, tumor features, treatment modalities, comorbidities, and genetic alterations for the 57 patients included in the study. Continuous variables are reported as median (range or interquartile range, IQR), and categorical variables are shown as number (percentage). Tumor–ventricle distance groups were defined using cohort-specific tertiles (T1: close, T2: intermediate, T3: far). Genetic alterations refer to the presence of somatic mutations or copy number variations detected by targeted next-generation sequencing. *Recurrence data are based on available follow-up during the study period and may be affected by censoring. **Percentages may not sum to 100% due to rounding.
Data collectionFor each patient, we created a database including different factors (Supplementary material):
1. Demographic and Clinical Variables: sex, age at diagnosis, year of birth, BMI (calculated from weight and height), and survival metrics (OS, PFS).
2. Tumor and Treatment Details: tumor location (frontal, parietal, temporal, occipital), tumor subtype, and treatment information including radiotherapy, extent of surgical resection (total, partial or subtotal, biopsy), and chemotherapy regimen (Stupp protocol with temozolomide, PCV regimen, and adjunctive use of Bevacizumab). In addition, the distance between the tumor and the ventricular system was quantified on preoperative contrast-enhanced MRI. The coronal slice showing the maximum tumor extension was selected, and the minimum linear distance between the enhancing tumor margin and the ependymal surface of the lateral ventricle was measured. To ensure consistency and reproducibility, measurements were performed using a standardized anatomical approach. For statistical analyses, tumor–ventricle distance was stratified into three groups based on tertiles of the observed distance distribution to ensure balanced group sizes. Patients were assigned to the closest tertile (T1, “close”), the intermediate tertile (T2, “intermediate”), or the farthest tertile (T3, “far”), according to increasing tumor–ventricle distance. In this cohort, these tertiles corresponded to distances of 0–5.20 mm (T1), 5.2–12.45 mm (T2), and 12.6–41.20 mm (T3); group assignment was based on distribution tertiles rather than predefined absolute distance thresholds.
Extent of resection was determined from early postoperative contrast-enhanced MRI performed within 24 h after surgery, complemented by surgical reports, in order to avoid misinterpretation with postoperative changes. This information was available for all 57 patients.
3. Recurrence Data: the recurrence type, date, time to recurrence, and details of any secondary surgical intervention were collected. Additionally, the distance from the tumor to the nearest ventricle.
Tumor volume was determined preoperatively on contrast-enhanced T1-weighted images, segmenting the entire enhancing lesion. Measurements were confirmed with a neuronavigation system (StealthStation™ S8, Medtronic).
Chemotherapy: the majority of patients received concomitant temozolomide radiotherapy and maintenance temozolomide (EORTC/NCIC regimen, often referred to as the Stupp protocol). Dosing followed standard schedules: 75 mg/m2/day during 42 days of RT, followed by adjuvant 150–200 mg/m2/day for 5 days every 28 days, up to 6 cycles. A minority of patients received PCV chemotherapy or adjuvant bevacizumab at physician discretion in recurrent/high-risk cases.
4. Lifestyle and Medical History: presence of prior cancer, smoking status, history of COVID-19 infection, hepatitis B status, and Type II diabetes. The inclusion of variables such as diabetes or hepatitis B was exploratory, based on previous reports suggesting potential prognostic roles of systemic comorbidities in glioblastoma.
5. Genetic Analysis: NGS was performed in tumor DNA and tumor RNA on a panel of target genes. DNA and RNA extraction was performed automatically using QiAcube extractor (Qiagen, Hilden, Germany) following the manufacturer’s instructions. Massive NGS sequencing was performed with Ion Torrent technology (Ion Torrent™ Genexus™ Integrated Sequencer) from Thermo Fisher Scientific (Waltham, MA, USA). The Oncomine Precision panel - GX5 - Solid Tumor - w3.2.0 DNA and Fusions Panel was used with target regions defined in Target Regions Oncomine precision v3.6.20210407.designed.bed. The genetic sequencing data was analyzed at the Department of Clinical Analysis of the Castellon General University Hospital, following their standard protocol for post-surgery tumor examination.
The bioinformatics platform used was Genexus System. The detected variants have been filtered and visualized with the Ion Reporter Software 5.18 and IGV Integrative Genomics Viewer programs. The detection limit of the technique is VAF > 0.5%. The cancer panel sequenced included the following genes, where two types of genetic mutations were determined: (i) cancer driving mutations in the following genes: IDH1, IDH2, EGFR, TP53, KRAS, HRAS, RET, PTEN, NTRK1, PIK3CA, MAP2K1, and BRAF; and (ii) copy number variations (CNVs) in the following genes EGFR, CDKN2A, FGFR2, PTEN, AR.
Statistical analysisStatistical analyses were performed using SPSS and R (RStudio). Kaplan–Meier survival analysis was performed to assess overall survival (OS) and progression-free survival (PFS) in relation to clinical and treatment variables [sex, radiotherapy, chemotherapy, surgical type (total, partial, biopsy), tumor location, tumor–ventricle distance, and tumor size] as well as genetic factors (presence of somatic mutations in IDH, EGFR, TP53, and CDKN2A). Survival differences between groups were evaluated using log-rank tests.
Progression-free survival (PFS) was defined as the time from surgery to radiological progression on follow-up MRI examinations (1 month, 3 months, and every 6 months thereafter).
Statistical analyses were conducted in RStudio. Non-parametric tests (Kruskal–Wallis and pairwise Wilcoxon tests with Bonferroni correction) were applied to evaluate associations between tumor–ventricle distance, extent of resection, and tumor volume. A two-sided p-value < 0.05 was considered statistically significant. Data visualization was performed using the ggplot2 and ggpubr packages.
To assess the relationship between tumor volume analyzed in MRI images and overall survival, we first performed a linear regression analysis using tumor volume (cm3) as the independent variable and survival in days as the dependent variable. As linear regression does not account for censoring, this analysis was considered exploratory and descriptive only; primary time-to-event comparisons relied on Kaplan–Meier and log-rank tests. A scatter plot with a fitted linear model was generated using the ggplot2 package in R. Tumor volume was also categorized into three groups (small, medium, large) divided in quartiles for comparison of survival distributions also using the ggplot2 package in R.
ResultsUnivariate survival and exploratory analyses of clinical, tumor-related, genetic, and lifestyle variables are summarized in Table 2. Overall survival was primarily influenced by tumor proximity to the ventricular system, whereas no significant associations were observed for demographic, treatment-related, genetic, or lifestyle variables.
VariableGroups (n)StatisticalSummary of univariate survival and exploratory statistical analyses performed in the glioblastoma cohort.
Overall survival (OS) was evaluated using Kaplan–Meier survival curves and compared between groups using log-rank tests. Associations between continuous variables and survival or tumor characteristics were explored using descriptive analyses or linear regression, as appropriate. Non-parametric tests were applied where indicated. A two-sided p-value < 0.05 was considered statistically significant. Bold values indicate statistically significant results (p < 0.05).
Demographic and clinical variablesIn this study, we looked at 57 patients who were diagnosed with GB. The median age at the time of diagnosis was 61.5 years, with ages ranging from 16 to 80 years. Interestingly, there was an almost even split between male and female patients, with a 1:1 ratio. The average BMI (Body Mass Index) of the group was 26.6 kg/m2, with a standard deviation of ±4.2. When it came to survival, the median OS was 13.7 months, ranging from as little as 3 months to as long as 32 months. The median progression-free survival (PFS) was slightly longer, at 12.4 months, with a range of 1–18 months.
Using Kaplan-Meier survival curve, we also analyzed whether demographic factors like sex and age (Figure 1A), or BMI had any impact on survival. From this analysis, no significant differences in survival were observed between sexes (p = 0.36). Figure 1B depicts the age distribution of the patient cohort stratified by sex. Individual data points and median values indicate a largely overlapping age range between male and female patients, suggesting comparable baseline age distributions and supporting the absence of marked sex-related differences in this cohort of distinct clustering by these variables.
Survival and cohort heterogeneity by sex. (A) Kaplan–Meier overall survival (OS) curves for glioblastoma patients stratified by sex: female patients (pink line) and male patients (blue line). (B) Distribution of patient age stratified by sex. Each point represents an individual patient, with horizontal bars indicating the median age. Male and female patients show comparable age distributions, illustrating the demographic heterogeneity of the cohort without evident sex-related differences.
Tumor features and treatment detailsTumor locations in this study varied, but most were found in the frontal and temporal lobes (Figure 2B). Surgical resection emerged as a key factor influencing OS. Nearly all patients received standard radiotherapy (n = 55) and chemotherapy (n = 56, mostly concomitant temozolomide radiotherapy and maintenance temozolomide). When stratifying survival by type of surgery, a trend toward improved outcomes was observed (Figure 2B). Patients who underwent gross total resection showed longer OS, whereas those with subtotal resection had shorter survival, and patients who only underwent biopsy had the poorest outcomes. Although the log-rank test did not reach statistical significance (p = 0.079), the pattern suggests a survival advantage for patients undergoing more extensive resections.
Survival and tumor location related to type of surgery. (A) Kaplan–Meier survival curves according to surgical procedure (biopsy, partial resection, or total resection). A trend toward longer survival was observed for gross total resection compared to partial resection and biopsy (log-rank p = 0.079). (B) Distribution of tumor locations (F, frontal; T, temporal; P, parietal; O, occipital) across the cohort, showing that most lesions were frontal and temporal.
We next examined whether tumor location affected the surgical approach. Frontal and parietal tumors were mainly treated with partial resection or biopsy, while temporal lesions more often allowed total resection. Overall, tumor location appeared to influence the extent of resection, which may partly explain survival variability among patients.
Effect of recurrence, tumor location and distance of the tumor to the ventricle on overall survivalOut of all 57 patients included in the study, tumor recurrence was documented in 25 out of 57 patients (43.9%) during follow-up during the study period. The median time to recurrence was relatively short, at 3.4, indicating that recurrence was frequent and occurred early despite standard treatment. Secondary surgeries were performed in 12 patients; however, these additional procedures did not result in a significant improvement in overall survival, highlighting that re-intervention alone was insufficient to overcome tumor progression.
When tumor location (Figure 2A) in the brain was analyzed in relation to OS, no meaningful correlations were found. This suggests that survival outcomes are not strongly influenced by the specific lobe or region of the brain where the tumor was located.
To standardize the classification of tumor proximity to the ventricular system, we used anatomical measurements from coronal brain sections (Figure 3A). Vectors were drawn from the closest tumor edge to the ependymal lining of the lateral ventricle, providing a reproducible method to quantify distance. This schematic was essential for categorizing patients into distance-based groups for survival analyses and ensured consistency in anatomical interpretation across cases. The proximity of the tumor to the ventricular system (Figures 3B,C) also showed a strong and significant association with survival (p < 0.0012) (Figure 3D). For Kaplan-Meier survival analysis, patients were divided into three groups according to terciles of the measured distance between the tumor and the ventricular system, resulting in three categories: T1 (closest), T2 (middle), and T3 (farthest) in tertiles. Patients in the closest distance group (T1) had the shortest median OS (3.74 months), followed by those in the intermediate group (T2) with a median OS of 17.48 months. Interestingly, 50% of patients in the farthest group (T3) were still alive at the end of the follow-up period (therefore, median OS was not reached for this group during the study period) (Figure 3E).
The distance to the lateral ventricle in relation to the type of surgery and survival. (A) Schematic representation of the standardized measurement of the minimum distance between the tumor border and the lateral ventricle on coronal MRI sections. Arrows indicate vectors from the closest tumor edge to the ventricular ependyma, which were used to classify samples based on their proximity to the ventricular system. (B) fMRI scan showing glioblastoma (GB) location relative to the lateral ventricle in axial view, highlighting tumor proximity to ventricular structures. (C) fMRI scan showing glioblastoma (GB) location relative to the lateral ventricle in coronal view, highlighting tumor proximity to ventricular structures. (D) Kaplan-Meier OS curves of GB patients according to tumor distance tertiles (T1 = closest, blue; T2 = intermediate, purple; T3 = farthest, red), showing significantly reduced survival in patients with tumors closer to the ventricle. (E) Violin plot showing the distribution of tumor distance to the ventricle by surgery type: biopsy (red), partial (purple), and total resection (blue). The plot includes boxplots and individual data points. Statistical analysis using the Kruskal-Wallis test (global p-value shown) followed by pairwise Wilcoxon tests with Bonferroni correction revealed no significant differences.
To determine whether surgical strategy could act as a confounding factor, the relationship between tumor–ventricle distance and extent of resection was evaluated. No significant differences in tumor–ventricle distance were observed across surgery types (biopsy, partial resection, total resection; Kruskal–Wallis test, p = 0.91), indicating that ventricular proximity was independent of the surgical approach and supporting its role as an intrinsic anatomical and biological feature rather than a consequence of surgical strategy.
Survival analysis, tumor size and relation to distance to the ventricleIn Figure 4A, tumor volume is plotted against survival time. Kaplan–Meier analysis of tumor volume and survival did not reveal significant differences between tertile
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