Paul M. Clement

Background/Objectives: Glioblastoma (GBM) remains the most aggressive primary brain tumor in adults, characterized by inevitable recurrence, extensive inter-and intratumoral heterogeneity, and resistance to current therapies. A defining feature of GBM is the dynamic interplay between malignant cells and a diverse tumor microenvironment (TME), which together drive disease progression, therapeutic adaptation, and relapse. Understanding these complex cellular ecosystems has therefore become a major focus of glioblastoma research. Recent advances in spatial omics technologies have transformed our ability to investigate GBM biology directly within intact tissue architectures. Over the past five years, an expanding array of spatial transcriptomic, proteomic, and multi-omic platforms has enabled high-dimensional characterization of cellular states, cell–cell interactions, and tissue niches while preserving spatial context. These approaches have generated unprecedented insights into tumor organization, cellular plasticity, immune landscapes, vascular niches, and treatment-induced ecosystem remodeling. Methods: In this review, we provide an overview of spatial omics applications in glioblastoma research so far. Results: We summarize the technologies employed, the types and numbers of patient samples analyzed, and the major biological and clinical insights generated. We compare the strengths and limitations of different spatial platforms, discuss key considerations for study design and data interpretation, and highlight emerging trends in multimodal and longitudinal analyses. Conclusions: By integrating both technological and biological perspectives, this review serves as a practical resource for researchers seeking to implement spatial omics approaches in glioblastoma studies and to advance precision neuro-oncology.

BackgroundPatients with glioblastoma experience a substantial symptom burden that negatively affects functioning in daily life. Symptoms frequently co-occur and may form symptom clusters, yet the robustness, generalizability, and temporal stability of these clusters remain insufficiently studied. This is particularly relevant given the dynamic disease course and treatment-related effects in glioblastoma.MethodsWe used data from the CODAGLIO 2.0 database, comprising adult patients with newly diagnosed or recurrent glioblastoma from seven randomized controlled trials not included in previous clustering studies. Symptoms were assessed using 18 symptom scales from EORTC QLQ-C30 and QLQ-BN20 questionnaires at baseline and 3-month follow-up. Symptom prevalence and multi-symptom co-occurrence were described. Symptom clusters were identified using Spearman correlations, partial correlations, and hierarchical cluster analysis and compared with previously reported clusters. Cluster stability over time was evaluated using cophenetic correlation, tanglegrams, entanglement, and adjusted Rand index scores.ResultsAmong 1,795 patients with baseline HRQoL data, 1678 patients (94%) reported multi-symptom co-occurrence at baseline and 1045 patients (96%) at follow-up. Fatigue, communication deficits, motor dysfunction, drowsiness, insomnia, and visual disorder were most prevalent. Four clinically meaningful symptom clusters were identified: pain–headache, motor dysfunction–weakness of the legs, fatigue–drowsiness, and a seizure-related cluster. These clusters largely aligned with previously reported findings and showed moderate stability over time (cophenetic correlation 0.84; adjusted Rand index 0.41), with greatest variability observed in seizure-related symptoms.ConclusionSymptom clusters in glioblastoma are robust yet dynamic. Incorporating cluster-based and longitudinal symptom assessment may enhance symptom monitoring and support more personalized supportive care strategies.

Supplementary Figure 5: Copy number variants in the three main glioblastoma groups according to classifier v12.5. A Copy number variants in all patients combined (n = 295), B in RTK1 tumors (n = 89), C in RTK2 tumors (n = 140) and D in MES tumors (n = 66). PDGFRA amplifications are enriched in RTK1 tumors and gains of chromosomes 19 and 20 are enriched in RTK2 tumors.

Supplementary Table 1: Representativeness of study participants.

Supplementary Figure 1: Overview of the clinical trial and biomarker analysis. Out of 596 patients in the EORTC-26101 trial, patient tumor tissue to perform DNA methylation (Meth) and NGS sequencing analysis was available from 380 patients (63.8%). Of these, 99 patients treated with lomustine and 189 patients treated with lomustine and bevacizumab were eligible to detect biomarkers for bevacizumab response.

Supplementary Figure 3: Characteristics of RTK1 tumors in comparison to RTK2 and MES. A Distribution of MGMT promotor methylation in main glioblastoma methylation subgroups. Classifier assignment is based on the v12.5 version (n = 295). Mutational profiles in B RTK1 tumors (n = 89), C RTK2 tumors (n = 140) and D MES tumors (n = 66).

Supplementary Figure 7: Exceptional survivors of the EORTC-26101 study (n = 78).

Supplementary Figure 2: Overall (A) and progression-free (B) survival in the EORTC-26101 clinical study (n = 596) and EORTC-26101 biomarker cohort (n = 380).