The inter-tumor heterogeneity of the tumor microenvironment (TME) and how it correlates with clinical profiles and biological characteristics in brainstem gliomas (BSGs) remain unknown, dampening the development of novel therapeutics against BSGs. The TME status was determined with a list of pan-cancer conserved gene expression signatures using a single-sample gene set enrichment analysis (ssGSEA) and was subsequently clustered via consensus clustering. BSGs exhibited a high inter-tumor TME heterogeneity and were classified into four clusters: “immune-enriched, fibrotic”, “immune-enriched, non-fibrotic”, “fibrotic”, and “depleted”. The “fibrotic” cluster had a higher proportion of diffuse intrinsic pontine gliomas (p = 0.041), and “PA-like” tumors were more likely to be “immune-enriched, fibrotic” (p = 0.044). The four TME clusters exhibited distinct overall survival (p < 0.001) and independently impacted BSG outcomes. A four-gene panel as well as a radiomics approach were constructed to identify the TME clusters and achieved high accuracy for determining the classification. Together, BSGs exhibited high inter-tumor heterogeneity in the TME and were classified into four clusters with distinct clinical outcomes and tumor biological properties. The TME classification was accurately identified using a four-gene panel that can potentially be examined with the immunohistochemical method and a non-invasive radiomics method, facilitating its clinical application.

Background Brainstem tumors can cause structural brain changes, but the resulting heterogeneity within wholebrain structure is not well-studied. This study examines cortical thickness to identify patterns of structural alterations and explore underlying biological subtypes and their associations with clinical factors. Materials and Methods This study involved 124 pediatric brainstem tumor patients, aged 4-18 years. Cortical thickness was measured using CAT12 segmentation of 3D T1-weighted structural MRI. A normative model was established using data from 849 healthy children. Deviations in cortical thickness were estimated, and patients were classified into two subtypes using spectral clustering. Clinical statistical analyses were conducted with SPSS 26.0. Results The normative model revealed significant heterogeneity in cortical thickness deviations, which correlated with tumor size and growth patterns. Focal tumors primarily caused negative deviations (t = 3.14, p = 0.02). There was a significant positive correlation between extreme positive deviations and tumor volume (r = 0.214, p = 0.010), and between extreme negative deviations and progression-free survival (r = 0.39, p = 0.008). Two subtypes were identified: Subtype 1, consisting of diffuse tumors with extreme positive deviations, and Subtype 2, consisting of focal tumors with extreme negative deviations. Subtype and tumor growth pattern significantly influenced duration (p < 0.01). The Kaplan-Meier survival curves for Subtype 1 and Subtype 2 demonstrated a significant difference in survival probabilities over time (p = 0.03). Conclusion Overall, this study identifies two major patterns of cortical thickness changes in brainstem tumor patients, enhancing our understanding of their relationship with cortical morphology. The findings suggest that cortical thickness alterations could serve as valuable biomarkers for predicting progression-free survival, which is crucial for clinical assessment and personalized treatment strategies. This research provides new insights into the physiological mechanisms by which brainstem tumors affect brain structure, supporting more precise clinical interventions and efficacy monitoring in the future.

Background Brainstem tumors often cause intractable neurobehavioral issues, which can be a challenge for patients and surgeons. Research on cerebellar changes in these patients is limited, despite symptoms similar to cerebellar injuries. This study aims to investigate cerebellar damage pattern resulting from brainstem tumors and its association with behavioral disorders. Methods This study enrolled 147 children with brainstem tumors. A U-Net-based segmentation algorithm is used to divide their cerebellums into 26 lobules. And these lobules are then used to build a normative model for assessing individual structural deviations. Furthermore, a behavior prediction model is developed using the total outlier count (tOC) index and cerebellar lobule volume as predictive features. Results Over 95% of patients are found to have negative deviations in cerebellar regions, particularly in anterior lobules like Left V. Higher tOC is significantly associated with severe social problems (r = 0.31, p = 0.001) and withdrawal behavior (r = 0.28, p = 0.001). Smaller size of cerebellar regions strongly correlates with more pronounced social problems (r = 0.27, p = 0.007) and withdrawal behavior (r = 0.25, p = 0.015). Notably, lobules Right X, V, IV, VIIB, Left IX, VIII, and X influence social problems, while Left V, Right IV, Vermis VI, and VIII impact withdrawal behavior. Conclusions Our study reveals cerebellar damage patterns in patients with brainstem tumors, emphasizing the role of both anterior and posterior cerebellar lobes in social problems and withdrawal behavior. This research sheds light on the cerebro-brainstem-cerebellar underlying complex behavioral disorders in brainstem tumor patients.

Background Brainstem tumors are rare and result in enduring behavioral issues, posing challenges for patients and surgeons. Despite similarities in symptoms to those resulting from cerebellar injuries, limited research has investigated cerebellar changes in patients with brainstem tumors. Methods Here, we employed U-Net-based segmentation to analyze the cerebellum in 26 regions, assessing individual volume deviations. Additionally, we developed a behavior prediction model using the total outlier count (tOC) index and brain volume as features. Findings Our normative model results revealed that most patients exhibited negative deviation in most cerebellar regions, particularly in lobule Right V, Left I-III, Right X, and Left X. The results found a significant association between a higher tOC and severe social problems (r = 0.31, p = 0.001) and withdrawal behavior (r = 0.28, p = 0.001). Further analysis showed that smaller cerebellar regions correlated strongly with more pronounced social problems (r = 0.27, p = 0.007) and withdrawal behavior (r = 0.25, p = 0.015). Notably, lobules such as Right X, V, IV, and VIIB, along with Left IX, VIII, and X exerted the greatest influence on social problems, whereas regions including Left V, Right IV, and Vermis VI and VIII were most impactful on withdrawal behavior. Interpretation In summary, our study revealed cerebellar atrophy patterns in patients with brainstem tumors, emphasizing the role of both anterior and posterior cerebellar lobes in social problems and withdrawal behavior. This research sheds light on the brain mechanisms underlying complex behavioral disorders in brainstem tumor patients. Funding This study was supported by the STI2030-Major Projects 2021ZD0200201 and Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (grant ID: JYY202X-X). Research in Context Our previous neuropsychological research has demonstrated that patients with brainstem tumors exhibit a wide range of cognitive and behavioral abnormalities, which closely resemble cerebellar cognitive affective syndrome (CCAS). Dines et al. hypothesized, based on cognitive impairment observed in patients with brainstem injury, that the brainstem, as an integral part of the cerebellar-cortex pathway, plays a role in cognitive processing. Consequently, brainstem injury may lead to cerebellar dysfunction. Previous studies have also reported that gangliocytoma located in the brainstem can cause ipsilateral cerebellar atrophy. These findings suggest that cerebellar injury may be present in patients with brainstem tumors. However, no systematic studies have investigated the pattern of cerebellar injury in these patients and its association with cognitive and behavioral disorders. Added value of this study This study revealed the pattern of cerebellar heterogeneity in patients with brainstem tumors. Further, this study provides evidences for the cerebellum’s role beyond motor control, underscoring its involvement in complex social behaviors. This study provides insight into automatic cerebellar segmentation algorithm applied to patients’ dataset. Implications of all the available evidence This study is first to successfully apply machine learning for segmenting deformed cerebellums, providing a reference for future large-sample studies of diseased cerebellum and brainstems. Moreover, this study provides evidence that the brainstem and cerebellum are involved in complex cognitive processing. Future research should focus on developing personalized treatment and rehabilitation programs for patients with brainstem tumors and cerebellar injury to improve their prognosis.