The tumor microenvironment (TME) of glioblastoma (GBM) is critical in influencing tumor progression and therapeutic responses. Recent studies have highlighted the importance of engineered materials in reconstructing the GBM microenvironment to better understand its dynamics and potential therapeutic targets (ref: Wolf doi.org/10.1038/s41578-019-0135-y/). Additionally, pulsed radiation therapy (PRT) has emerged as a promising treatment modality for newly diagnosed GBM, demonstrating feasibility and effectiveness while preserving neurocognitive function (ref: Almahariq doi.org/10.1093/neuonc/). The integration of synaptic connections between neurons and tumor cells has also been explored, revealing how gliomas can integrate into neuronal circuits, thus opening new avenues for targeted therapies (ref: Venkataramani doi.org/10.1093/neuonc/). Furthermore, single-cell lineage analysis has provided insights into the genetic and epigenetic mechanisms underlying drug resistance in GBM, emphasizing the need for personalized treatment strategies (ref: Eyler doi.org/10.1186/s13059-020-02085-1/). The identification of distinct molecular subgroups within IDH wild-type GBM through integrated pharmaco-proteogenomics has further underscored the heterogeneity of these tumors and their implications for prognosis and treatment (ref: Oh doi.org/10.1038/s41467-020-17139-y/). Lastly, dynamic contrast perfusion MRI has shown potential in predicting early responses to bevacizumab, highlighting the role of advanced imaging techniques in treatment evaluation (ref: Schmainda doi.org/10.1093/neuonc/).

Molecular stratification of glioblastoma has gained traction, particularly through cell lineage-based approaches that reveal unique transcriptional profiles associated with different tumor origins (ref: Wang doi.org/10.1016/j.ccell.2020.06.003/). Genome-wide analyses of high-risk brain cancer pedigrees have identified PDXDC1 as a potential predisposition gene, suggesting genetic factors play a significant role in glioblastoma susceptibility (ref: Cannon-Albright doi.org/10.1093/neuonc/). The development of rabies virus-inspired metal-organic frameworks for targeted drug delivery across the blood-brain barrier represents a novel strategy to enhance therapeutic efficacy in GBM (ref: Qiao doi.org/10.1002/anie.202007474/). Additionally, murine models have demonstrated spatial segregation in tumor evolution, indicating that IDH-wild-type glioblastomas can exhibit distinct growth patterns and genetic divergence (ref: Li doi.org/10.1038/s41467-020-17382-3/). Single-cell RNA sequencing has further elucidated the hierarchical structure of glioblastoma, revealing that cancer stem cells recapitulate normal neurodevelopmental pathways, which may contribute to treatment resistance (ref: Couturier doi.org/10.1038/s41467-020-17186-5/). Finally, the clinical relevance of BRAF mutations in brain tumors has been emphasized, with evidence suggesting that targeted therapies may be beneficial for specific tumor types harboring these mutations (ref: Kowalewski doi.org/10.1007/s11523-020-00735-9/).

Advancements in imaging techniques are crucial for improving the diagnosis and treatment of glioblastoma. Diffusion histology imaging, which combines diffusion basis spectrum imaging with machine learning, has shown promise in accurately detecting and classifying glioblastoma pathology, thereby enhancing surgical planning and treatment evaluation (ref: Ye doi.org/10.1158/1078-0432.CCR-20-0736/). Dynamic contrast perfusion MRI has also been validated as a predictive tool for early responses to bevacizumab in newly diagnosed GBM patients, highlighting its potential in monitoring treatment efficacy (ref: Schmainda doi.org/10.1093/neuonc/). Radiomics analyses have been employed to predict MGMT methylation status in glioblastoma patients, demonstrating the utility of imaging features in guiding treatment decisions (ref: Qian doi.org/10.1016/j.ijrobp.2020.06.073/). Furthermore, multi-scale segmentation techniques using diffusion tensor imaging are being developed to improve the precision of GBM treatment planning, addressing the challenges of tumor delineation in complex imaging datasets (ref: Rahmat doi.org/10.1016/j.compbiomed.2020.103815/). These innovations underscore the critical role of advanced imaging modalities in enhancing the management of glioblastoma.

Immunotherapy has emerged as a promising approach for treating glioblastoma, with dendritic cell vaccines targeting cytomegalovirus showing reproducibility in clinical trials and a significant survival benefit for patients (ref: Batich doi.org/10.1158/1078-0432.CCR-20-1082/). The complexity of the immune landscape in glioblastoma, characterized by high levels of immune suppression from myeloid-derived suppressor cells (MDSCs), poses challenges for effective immunotherapy (ref: Alban doi.org/10.3389/fimmu.2020.01191/). Recent findings indicate that different subsets of MDSCs express distinct receptor profiles, suggesting that targeted therapies could mitigate immune suppression (ref: Alban doi.org/10.3389/fimmu.2020.01191/). Additionally, CD133 mRNA-loaded dendritic cell vaccination has demonstrated efficacy in abrogating glioma stem cell propagation in humanized mouse models, marking a significant step forward in targeting glioma stem cells (ref: Do doi.org/10.1016/j.omto.2020.06.019/). The clinical relevance of BRAF V600E mutations in brain tumors is also being explored, with potential implications for targeted therapies in specific patient populations (ref: Kowalewski doi.org/10.1007/s11523-020-00735-9/). Overall, these studies highlight the evolving landscape of immunotherapy in glioblastoma and the need for tailored approaches to overcome immune evasion.

Understanding drug resistance in glioblastoma is critical for improving treatment outcomes. Recent research has shown that glioblastomas can adapt to therapeutic pressures through genetic and epigenetic alterations, contributing to intratumoral heterogeneity and treatment failure (ref: Eyler doi.org/10.1186/s13059-020-02085-1/). The characterization of IDH-wild-type glioblastomas has revealed distinct evolutionary patterns, with some tumors exhibiting multifocal growth and significant genetic divergence, which complicates treatment strategies (ref: Li doi.org/10.1038/s41467-020-17382-3/). The role of transcription factors such as AHR and MYC in regulating cellular metabolism has also been implicated in glioblastoma proliferation, suggesting that metabolic pathways may be targeted to enhance treatment efficacy (ref: Lafita-Navarro doi.org/10.1074/jbc.AC120.014189/). Furthermore, the identification of the LGMN pseudogene as a promoter of tumor progression highlights the complex regulatory networks involved in glioblastoma biology (ref: Liao doi.org/10.1016/j.canlet.2020.07.012/). These insights into the molecular mechanisms of drug resistance underscore the need for innovative therapeutic approaches to overcome the challenges posed by glioblastoma heterogeneity.

Clinical trials play a pivotal role in advancing treatment strategies for glioblastoma. A phase I study of buparlisib in combination with temozolomide and radiotherapy has provided insights into the safety and tolerability of this regimen, although dose-limiting toxicities were observed (ref: Wen doi.org/10.1136/esmoopen-2020-000673/). The reproducibility of dendritic cell vaccine trials targeting cytomegalovirus has shown promising long-term survival rates, emphasizing the potential of immunotherapy in this aggressive malignancy (ref: Batich doi.org/10.1158/1078-0432.CCR-20-1082/). Additionally, the stratification of glioblastoma based on cell lineage has revealed unique transcriptional profiles that may inform personalized treatment approaches (ref: Wang doi.org/10.1016/j.ccell.2020.06.003/). However, the effects of valproic acid on histone deacetylation in glioblastoma patients did not align with in vitro findings, indicating a gap between laboratory results and clinical outcomes (ref: Berendsen doi.org/10.1093/noajnl/). These findings highlight the complexities of translating preclinical research into effective clinical therapies and the necessity for ongoing clinical investigations to refine treatment protocols.

The dynamics of stem cells and tumor heterogeneity are critical in understanding glioblastoma progression and treatment resistance. Integrated pharmaco-proteogenomics has identified two distinct subgroups within IDH wild-type glioblastoma, revealing significant molecular heterogeneity that complicates patient stratification and treatment (ref: Oh doi.org/10.1038/s41467-020-17139-y/). Genome-wide linkage analysis in high-risk brain cancer pedigrees has pointed to PDXDC1 as a potential predisposition gene, suggesting that genetic factors contribute to tumor heterogeneity (ref: Cannon-Albright doi.org/10.1093/neuonc/). Furthermore, murine models have demonstrated spatial segregation in tumor initiation and evolution, indicating that glioblastomas can develop distinct genetic profiles depending on their location within the brain (ref: Li doi.org/10.1038/s41467-020-17382-3/). The development of a humanized mouse model targeting glioma stem cells has shown promise in abrogating tumor propagation, marking a significant advance in immunotherapy strategies (ref: Do doi.org/10.1016/j.omto.2020.06.019/). These studies underscore the importance of understanding stem cell dynamics and tumor heterogeneity in developing effective therapeutic strategies for glioblastoma.