Spatial transcriptomics has emerged as a pivotal tool in understanding the complex cellular architecture of brain tumors, particularly in medulloblastoma and diffuse intrinsic pontine glioma (DIPG). In a study by Li, the authors conducted a multi-omics analysis to unveil the spatial heterogeneity within medulloblastoma, revealing that tumor cell populations are organized into distinct geographical regions. The findings indicated that stem-like and cycling cells predominantly reside in stem-like regions, while differentiated populations are more concentrated in mature areas, suggesting a significant spatial organization that may influence tumor behavior and treatment responses (ref: Li doi.org/10.1093/neuonc/). Kordowski's research further explored the spatial dynamics of DIPG, utilizing transcriptomic sequencing to analyze a tumor-infiltrated brainstem. This study identified novel ligand-receptor interactions that mediate the crosstalk between tumor cells and the tumor microenvironment (TME), highlighting the role of spatial context in tumor invasion and progression (ref: Kordowski doi.org/10.1186/s40478-025-01952-x/). Together, these studies underscore the importance of spatial transcriptomics in elucidating the intricate relationships between tumor cells and their microenvironment, paving the way for targeted therapeutic strategies.

Molecular profiling has become essential in identifying therapeutic vulnerabilities in brain tumors, particularly in atypical teratoid/rhabdoid tumors (AT/RT). Pauck's study employed an in vitro pharmacogenomic approach to reveal subtype-specific vulnerabilities in AT/RT, which is characterized by genetic alterations in the SMARCB1 and SMARCA4 genes. The research identified distinct molecular subtypes, including AT/RT-TYR, -SHH, and -MYC, each exhibiting unique responses to various therapeutic agents. Despite advancements in understanding AT/RT biology, the study highlighted that curative treatment options remain limited for certain risk groups, emphasizing the need for personalized therapeutic strategies to improve patient outcomes (ref: Pauck doi.org/10.1016/j.phrs.2025.107660/). This work illustrates the critical intersection of molecular profiling and therapeutic development, suggesting that tailored approaches based on molecular characteristics could enhance treatment efficacy and survival rates in patients with AT/RT.

Advancements in neurometabolic imaging techniques are crucial for the non-invasive assessment of altered neurometabolism in neurological diseases and brain cancer. Weiser introduced a novel deep learning approach, Deep-ER, which enhances Magnetic Resonance Spectroscopic Imaging (MRSI) by significantly reducing reconstruction times while maintaining high-resolution metabolic mapping. This method integrates a physical model within an automated processing pipeline, allowing for efficient and robust imaging that can facilitate clinical applications (ref: Weiser doi.org/10.1016/j.neuroimage.2025.121045/). The ability to obtain high-quality metabolic maps rapidly is particularly beneficial in the context of brain tumors, where timely diagnosis and monitoring of metabolic changes can inform treatment decisions. This innovation represents a significant step forward in the field of neurometabolic imaging, potentially improving the diagnostic and therapeutic landscape for patients with brain tumors.