Despite recent advances in cancer immunotherapy, certain tumor types, such as Glioblastomas, are highly resistant due to their tumor microenvironment disabling the anti-tumor immune response. Here we show, by applying an in-silico multidimensional model integrating spatially resolved and single-cell gene expression data of 45,615 immune cells from 12 tumor samples, that a subset of Interleukin-10-releasing HMOX1⁺ myeloid cells, spatially localizing to mesenchymal-like tumor regions, drive T-cell exhaustion and thus contribute to the immunosuppressive tumor microenvironment. These findings are validated using a human ex-vivo neocortical glioblastoma model inoculated with patient derived peripheral T-cells to simulate the immune compartment. This model recapitulates the dysfunctional transformation of tumor infiltrating T-cells. Inhibition of the JAK/STAT pathway rescues T-cell functionality both in our model and in-vivo, providing further evidence of IL-10 release being an important driving force of tumor immune escape. Our results thus show that integrative modelling of single cell and spatial transcriptomics data is a valuable tool to interrogate the tumor immune microenvironment and might contribute to the development of successful immunotherapies.

PURPOSE: Clinical outcomes of patients with CNS lymphomas (CNSLs) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure is challenging. Furthermore, CNSL diagnosis often remains unconfirmed because of contraindications for invasive stereotactic biopsies. Therefore, improved biomarkers are needed to better stratify patients into risk groups, predict treatment response, and noninvasively identify CNSL. PATIENTS AND METHODS: We explored the value of circulating tumor DNA (ctDNA) for early outcome prediction, measurable residual disease monitoring, and surgery-free CNSL identification by applying ultrasensitive targeted next-generation sequencing to a total of 306 tumor, plasma, and CSF specimens from 136 patients with brain cancers, including 92 patients with CNSL. RESULTS: Before therapy, ctDNA was detectable in 78% of plasma and 100% of CSF samples. Patients with positive ctDNA in pretreatment plasma had significantly shorter progression-free survival (PFS, P < .0001, log-rank test) and overall survival (OS, P = .0001, log-rank test). In multivariate analyses including established clinical and radiographic risk factors, pretreatment plasma ctDNA concentrations were independently prognostic of clinical outcomes (PFS HR, 1.4; 95% CI, 1.0 to 1.9; P = .03; OS HR, 1.6; 95% CI, 1.1 to 2.2; P = .006). Moreover, measurable residual disease detection by plasma ctDNA monitoring during treatment identified patients with particularly poor prognosis following curative-intent immunochemotherapy (PFS, P = .0002; OS, P = .004, log-rank test). Finally, we developed a proof-of-principle machine learning approach for biopsy-free CNSL identification from ctDNA, showing sensitivities of 59% (CSF) and 25% (plasma) with high positive predictive value. CONCLUSION: We demonstrate robust and ultrasensitive detection of ctDNA at various disease milestones in CNSL. Our findings highlight the role of ctDNA as a noninvasive biomarker and its potential value for personalized risk stratification and treatment guidance in patients with CNSL. [Media: see text]

Objective: Glioblastomas are the most common primary central nervous system (CNS) tumors. Although modern management strategies have modestly improved overall survival, the prognosis remains dismal, with treatment side effects often impinging on the clinical course. Glioblastomas cause neurological dysfunction by infiltrating CNS tissue and via perifocal oedema formation. The administration of steroids such as dexamethasone is thought to alleviate symptoms by reducing oedema. However, despite its widespread use, the evidence for the administration of dexamethasone is limited and conflicting. Therefore, we aimed to review the current evidence concerning the use and outcomes of dexamethasone in patients with glioblastoma. Methods: We performed a systematic review and meta-analysis according to the PRISMA-P guidelines. We performed a restricted search using the keywords “Dexamethasone” and “Glioblastoma” on PubMed, Web of Science, Cochrane Library, and Academic Search Premier. We included studies reporting on overall survival (OS) and progression-free survival (PFS) in glioblastoma patients receiving higher or lower dexamethasone doses. The risk of bias was assessed using ROBINS-I. We performed a meta-analysis using a random effects model for OS and PFS. Results: Twenty-two retrospective studies were included. Higher doses of dexamethasone were associated with poorer OS (hazard ratio 1.62, confidence interval 1.40–1.88) and PFS (1.49, 1.23–1.81). OS remained worse even when studies corrected for clinical status (1.52, 1.38–1.67). Conclusion: Despite the widespread use of dexamethasone in glioblastoma patients, its use is correlated with worse long-term outcomes. Consequently, Dexamethasone administration should be restricted to selected symptomatic patients. Future prospective studies are crucial to confirm these findings.

Early diagnosis is important to differentiate central nervous system lymphomas (CNSL) from the main differential diagnosis, glioblastoma (GBM), because of different primary treatment modalities for these entities. Due to neurological deficits, diagnostic stereotactic biopsies often need to be performed urgently. In this setting the availability of an intraoperative neuropathological assessment is limited. This study uses AI-based analysis of Stimulated Raman Histology (SRH) to establish a classifier distinguishing CNSL from glioblastoma in an intraoperative setting. We collected 126 intraoperative SRH images from 40 patients diagnosed with CNSL. These SRH images were divided into patches, measuring 224 x 224 pixels each. Additionally, we used a comparative dataset of 87 SRH images from 31 patients with GBM as a control group to train and validate a neural network based on the CTransPath architecture. Two distinct diagnostic categories were established: “Lymphoma” and “Glioblastoma". Our model demonstrated an accuracy rate of 92.5% in distinguishing between lymphoma and glioblastoma. Analysis of our test dataset showed a sensitivity of 84.2% and a specificity of 100% in the detection of CNSL, demonstrating performance comparable to standard intraoperative histopathological analysis. The use of AI-driven analysis of SRH images holds promise for intraoperative tissue examination of stereotactic biopsies with suspected CNSL en par with the current gold standard. This study could improve the management of these cases especially in the emergency setting when conventional intraoperative neuropathological evaluation is unavailable.

The incidence of aneurysmal subarachnoid hemorrhage (aSAH) is well studied. Yet, little is known about the trend of aSAH severity. This systematic review aims to analyze the distribution of aSAH severity over time. We performed a systematic review of the literature according to the PRISMA-P guidelines. We included studies from January 1968 up to December 2022. Studies were included if they either reported the severity of aSAH as single increments of the corresponding 5-point scale or as a binary measure (good grade 1-3, poor grade 4-5) on the Hunt and Hess (HH) or World Federation of Neurosurgical Societies (WFNS) scale. Studies with fewer than 50 patients, (systematic) reviews, and studies including non-aSAH patients were excluded. A total of 2465 publications were identified, of which 214 met the inclusion and exclusion criteria. In total, 102,845 patients with an aSAH were included. Over the last five decades the number of good-grade HH (0.741 fold, p = 0.004) and WFNS (0.749 fold, p < 0.001) has decreased. Vice versa, an increase in number of poor grade HH (2.427 fold, p = 0.004), WFNS (2.289 fold, p < 0.001), as well as HH grade 5 (6.737 fold, p = 0.010), WFNS grade 4 (1.235 fold, p = 0.008) and WFNS grade 5 (8.322 fold, p = 0.031) was observed. This systematic review shows a worldwide 2-3 fold increase of poor grade aSAH patients and an 6-8 fold increase of grade 5 patients, over the last 50 years. Whether this evolution is due to more severe hemorrhage, improvements in neuro-intensive care and prehospital management, or to a change in grading behavior is unknown. This study strongly emphasizes the necessity for an improved grading system to differentiate grade 4 and grade 5 patients for meaningful clinical decision- making.