Recent studies have significantly advanced our understanding of the molecular mechanisms underlying glioblastoma (GBM) and the identification of potential biomarkers for diagnosis and treatment. Hoogstrate et al. conducted a comprehensive transcriptome analysis using RNA sequencing on paired primary-recurrent glioblastoma samples, revealing that transcriptional subtypes form an interconnected continuum, which may inform treatment optimization strategies (ref: Hoogstrate doi.org/10.1016/j.ccell.2023.02.019/). In pediatric neuro-oncology, Sturm et al. integrated multiomic data, including DNA methylation profiling and targeted gene sequencing, to improve diagnostic accuracy in CNS tumors, demonstrating that this approach refined diagnostic classifications in 50% of cases and identified relevant genetic alterations in 47% (ref: Sturm doi.org/10.1038/s41591-023-02255-1/). Furthermore, Hollon et al. developed an artificial intelligence-based diagnostic system, DeepGlioma, which enables rapid molecular classification of diffuse gliomas, potentially streamlining clinical decision-making (ref: Hollon doi.org/10.1038/s41591-023-02252-4/). Goenka et al. explored the role of the long noncoding RNA LINC02283 in enhancing PDGF receptor A-mediated signaling, linking it to glioblastoma tumorigenesis, thus highlighting the importance of lncRNAs in GBM (ref: Goenka doi.org/10.1093/neuonc/). Additionally, Chapman et al. focused on optimizing biomarkers for ependymoma diagnosis and stratification, utilizing a range of techniques across multiple laboratories, which underscores the need for accurate molecular subgroup identification in brain tumors (ref: Chapman doi.org/10.1093/neuonc/).

Innovative therapeutic strategies are emerging in neuro-oncology, particularly in the context of personalized medicine and targeted therapies. Remon et al. reported on the EORTC Lung Cancer Group's APPLE trial, where osimertinib treatment based on plasma T790M mutation monitoring demonstrated a progression-free survival rate of 67.2% in patients, highlighting the potential of ctDNA monitoring in guiding therapy (ref: Remon doi.org/10.1016/j.annonc.2023.02.012/). Tylawsky et al. introduced a novel nanocarrier system targeting P-selectin to enhance drug delivery across the blood-brain barrier, which is crucial for treating medulloblastoma, thereby addressing a significant challenge in pediatric brain tumor therapy (ref: Tylawsky doi.org/10.1038/s41563-023-01481-9/). In a different approach, Winters-Stone et al. evaluated the efficacy of tai ji quan versus strength training for fall prevention in older cancer survivors, although no significant differences were observed, indicating the need for further exploration of supportive care interventions (ref: Winters-Stone doi.org/10.1200/JCO.22.01519/). Xie et al. demonstrated that ATM inhibition significantly enhances radiation efficacy across various molecular subgroups of pediatric high-grade glioma, suggesting a promising avenue for improving treatment outcomes in this challenging cohort (ref: Xie doi.org/10.1093/neuonc/).

The tumor microenvironment plays a critical role in shaping immune responses and therapeutic outcomes in neuro-oncology. Friedrich et al. investigated the T cell landscape in multiple myeloma patients undergoing bispecific T cell engager therapy, revealing that pre-existing T cell states influence therapeutic responses, which could inform future immunotherapy strategies (ref: Friedrich doi.org/10.1016/j.ccell.2023.02.008/). Yang et al. explored the effects of polio virotherapy on malignant gliomas, finding that it targets the myeloid infiltrate and activates microglia, which may enhance anti-tumor immunity (ref: Yang doi.org/10.1093/neuonc/). Gu et al. focused on the role of sterol regulatory element-binding protein 2 (SREBP2) in maintaining glioblastoma stem cells, demonstrating its context-specific regulation of cholesterol metabolism, which could be leveraged for therapeutic interventions (ref: Gu doi.org/10.1093/neuonc/). The BIOMECA study by Chapman et al. also emphasizes the importance of accurately identifying molecular subgroups in ependymoma, which is essential for understanding the tumor microenvironment and tailoring immunotherapeutic approaches (ref: Chapman doi.org/10.1093/neuonc/).

Recent genomic and transcriptomic studies have provided valuable insights into pediatric neuro-oncology, highlighting the unique characteristics of childhood cancers. Comitani et al. utilized a multiscale transcriptomics approach to classify childhood cancers, revealing distinct molecular definitions that could enhance diagnostic accuracy and treatment strategies (ref: Comitani doi.org/10.1038/s41591-023-02221-x/). Sturm et al. further contributed to this field by integrating multiomic data to improve diagnostic accuracy in pediatric CNS tumors, demonstrating that this approach can refine classifications and identify relevant genetic alterations in a significant proportion of patients (ref: Sturm doi.org/10.1038/s41591-023-02255-1/). Hollon et al. developed an AI-based system for rapid molecular classification of diffuse gliomas, which could streamline diagnostic processes and facilitate timely treatment decisions (ref: Hollon doi.org/10.1038/s41591-023-02252-4/). Díaz Méndez et al. identified a circulating miRNA signature associated with glioma invasion, which correlated with patient outcomes, suggesting its potential as a biomarker for monitoring disease progression (ref: Díaz Méndez doi.org/10.1186/s13046-023-02639-8/). Sievers et al. characterized pediatric high-grade neuroepithelial tumors with CIC gene fusions, revealing a common DNA methylation signature that may aid in the understanding of tumor biology and treatment responses (ref: Sievers doi.org/10.1038/s41698-023-00372-1/).

Clinical outcomes and patient management strategies in neuro-oncology are evolving, particularly in response to the challenges posed by the COVID-19 pandemic. Hamy et al. examined the immune infiltration and treatment responses in synchronous bilateral breast cancers, finding that tumor subtype influences immune responses and pathologic complete response rates, which could inform treatment approaches (ref: Hamy doi.org/10.1038/s41591-023-02216-8/). Broom et al. discussed the enduring effects of COVID-19 on cancer care, highlighting the disruptions in patient-provider interactions and clinical trial access, which have significant implications for patient management and treatment continuity (ref: Broom doi.org/10.1158/1078-0432.CCR-23-0151/). Rahman et al. emphasized the importance of accessible data collections for improving decision-making in neuro-oncology clinical trials, particularly for rare tumors like glioblastoma, suggesting that external data can enhance trial efficiency (ref: Rahman doi.org/10.1158/1078-0432.CCR-22-3524/). Greenhall et al. investigated the risk of cancer transmission from organ transplants involving donors with primary brain tumors, finding that the risk is lower than previously thought, which could influence transplant protocols (ref: Greenhall doi.org/10.1001/jamasurg.2022.8419/).

Emerging technologies are revolutionizing cancer diagnosis and treatment, particularly in neuro-oncology. Hollon et al. developed an artificial intelligence-based system for the rapid molecular classification of diffuse gliomas, which could significantly expedite diagnostic processes and improve treatment personalization (ref: Hollon doi.org/10.1038/s41591-023-02252-4/). Remon et al. highlighted the use of plasma T790M mutation monitoring in guiding osimertinib treatment for EGFR-mutant non-small-cell lung cancer, demonstrating the potential of liquid biopsies in optimizing therapeutic strategies (ref: Remon doi.org/10.1016/j.annonc.2023.02.012/). Tylawsky et al. introduced a novel nanocarrier system that enhances drug delivery across the blood-brain barrier, addressing a critical challenge in treating pediatric brain tumors (ref: Tylawsky doi.org/10.1038/s41563-023-01481-9/). Winters-Stone et al. evaluated the effectiveness of tai ji quan versus strength training for fall prevention in older cancer survivors, although no significant differences were found, indicating the need for further research into supportive care interventions (ref: Winters-Stone doi.org/10.1200/JCO.22.01519/). These advancements underscore the importance of integrating innovative technologies into clinical practice to enhance patient outcomes.

Research methodologies in neuro-oncology are evolving to enhance the accuracy and efficiency of clinical trials and diagnostic processes. Hamdy et al. conducted a comprehensive analysis of outcomes after monitoring, surgery, or radiotherapy for prostate cancer, revealing that active monitoring had comparable outcomes to surgical and radiotherapy interventions, which may influence treatment decision-making (ref: Hamdy doi.org/10.1056/NEJMoa2214122/). Gottschlich et al. leveraged single-cell transcriptomic data to develop CAR-T cells for acute myeloid leukemia, identifying novel target antigens that could improve treatment efficacy (ref: Gottschlich doi.org/10.1038/s41587-023-01684-0/). Sturm et al. integrated multiomic data to improve diagnostic accuracy in pediatric neuro-oncology, demonstrating that this approach can refine classifications and identify relevant genetic alterations in a significant proportion of patients (ref: Sturm doi.org/10.1038/s41591-023-02255-1/). Additionally, Hollon et al. developed an AI-based system for rapid molecular classification of diffuse gliomas, which could streamline diagnostic processes and facilitate timely treatment decisions (ref: Hollon doi.org/10.1038/s41591-023-02252-4/). Chapman et al. focused on optimizing biomarkers for ependymoma diagnosis and stratification, utilizing a range of techniques across multiple laboratories, which underscores the need for accurate molecular subgroup identification in brain tumors (ref: Chapman doi.org/10.1093/neuonc/).