Neuro-oncology最新文献

Background: Atypical teratoid rhabdoid tumors (ATRTs) are highly aggressive pediatric central nervous system tumors defined by the inactivation of the SMARCB1 gene. Despite the identification of three distinct molecular subtypes, each defined by unique clinical and molecular characteristics, no subtype-specific therapeutic strategies are currently available. This highlights an urgent need to deepen our understanding of the cellular heterogeneity and developmental origins of ATRTs.

Methods: We generated a comprehensive single-nucleus transcriptomic atlas of ATRT samples, integrated it with single-nucleus ATAC-seq and spatial transcriptomics data, and validated our findings experimentally using patient-derived ATRT tumoroid models.

Results: Our analyses revealed distinct subtype-specific differentiation trajectories, each resembling different brain progenitor lineages. We identified key transcription factors that appear to drive these developmental pathways. Furthermore, a shared cycling, intermediate precursor cell (IPC)-like cell population, interspersed throughout tumors, was consistently present within all ATRT samples. We demonstrate that these subtype-specific differentiation pathways can be pharmacologically manipulated in patient-derived ATRT tumoroids. By directing tumor cells along their respective subtype-specific trajectories, we were able to induce a shift toward more differentiated, non-proliferative states.

Conclusions: Collectively, our findings show that ATRTs recapitulate fetal brain signaling programs in a subtype-specific manner. This work provides a framework for understanding ATRT heterogeneity and supports the feasibility of maturation-based therapeutic strategies tailored to the molecular subtype of the tumor.

Background: Lung Cancer Leptomeningeal Metastasis (LC-LM) severely impacts patient survival and quality of life, yet current diagnostic methods lack sufficient sensitivity and specificity, particularly for early detection. Cerebrospinal fluid (CSF) metabolomics may reveal specific biomarkers reflecting brain metastasis.

Methods: We performed untargeted metabolomic profiling of CSF samples by high-resolution mass spectrometry (HRMS) in a cohort of 218 participants, including 99 samples from LC-LM (with cancer cells detected in the CSF), 12 samples from the lung cancer parenchymal brain metastases (with no cancer cells detected in the CSF), 27 samples from the control group, 21 samples from the breast cancer LM, 15 samples from patients with LM from other tumors such as melanoma and gastric cancer, and 36 samples from other diseases. Significant metabolites were identified and validated. Subsequently, targeted metabolomics was conducted on serum samples from an independent cohort (n = 233), including 50 LC-LM patients, 150 patients with primary lung cancer (stages I-III), and 33 benign pulmonary nodules.

Results: Untargeted CSF metabolomics revealed a distinct metabolic signature in LC-LM patients. Differential analysis identified metabolites significantly altered in LC-LM, notably elevated lactic acid, N1, N12-diacetylspermine, and altered amino acid metabolites (e.g., L-proline, L-glutamic acid), each demonstrating strong diagnostic accuracy individually, with area under the receiver operating characteristic (ROC) curve (AUC) > 0.90. Machine learning classification models based on CSF metabolite panels achieved perfect diagnostic performance (AUC = 1.00) in distinguishing LC-LM from controls and other groups. Targeted validation of five top metabolites in serum samples confirmed their diagnostic utility, with N1, N12-diacetylspermine achieving an AUC of 0.882, superior to traditional protein biomarkers.

Conclusion: CSF-based metabolomic profiling combined with machine learning offers a highly accurate and minimally invasive diagnostic tool for LC-LM. Serum validation further supports its translational potential, emphasizing its significance in clinical practice for improving early detection and potentially enhancing patient management and outcomes.

Background: Human brain organoids (BOs) are important models for studying early brain development and neurological disorders. While techniques for creating BOs are advancing, they remain developmental structures. Therefore, when human BOs are used to studying glioma-host interactions, the tumor behavior may be influenced by the BO-developmental microenvironment. Here, we describe the maturation of rat brain organoids (rBOs) into fully differentiated BOs and demonstrate their value as a model for studying glioblastoma (GB)-host interactions and their use in testing therapeutic interventions.

Materials and methods: rBOs were obtained from fetal cortical brains on the 18th day of gestation. Transcriptomic, proteomic, and metabolomic analyses determined their differentiation into maturity. Their developmental trajectory was compared to human BOs derived from induced pluripotent stem cells as well as to rat brain development. Tumor-rBO interactions, including invasion parameters and therapeutic interactions, were studied using five human GB models.

Results: The rBOs develop into organized structures with myelinated neurons, oligodendrocytes, synapses, and glial cells, mirroring the rat brain development. GB invasion in rBOs matched those observed in orthotopic xenografts, enabling real-time assessment of invasion metrics: cellular heterogeneity, single-cell invasion speed, and tumor progression. The BOs had a strong impact on GB transcriptional activity and can be used to study therapeutic interventions. The rBO differentiation status influenced GB invasion capacity.

Conclusions: The rBOs serve as an effective target brain structure for studying GB invasion parameters and for evaluating therapeutic interventions. Their rapid development into mature brain tissue makes rBOs a valuable brain avatar system for studying tumor-host interactions.

Background: DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed.

Methods: We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade.

Results: We first demonstrated that all risk groups benefit from gross total resection. All "high-risk, RT sensitive" tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (p = 0.003); after STR, from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (p = 0.004). "Low-risk, RT sensitive cases" (n = 1207, 61.8%) also benefitted from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, p = 0.03), suggesting that RT be considered in these patients. Neither "low-risk RT resistant" (n = 84, 4.3%) nor "high-risk RT resistant" (n = 356, 18.2%) cases benefitted from RT, and the latter group was associated with universally poor outcomes.

Conclusions: We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.

Background: Differentiating progressive disease (PD) from treatment-related effects (TRE) in glioblastoma remains challenging, particularly at single time point evaluations. TRE can occur at any disease stage, and its underlying biology is poorly understood. This study evaluates the clinical feasibility and diagnostic performance of amide proton transfer-weighted (APTw) MRI in this challenge.

Methods: Following the integration of APTw MRI into the routine clinical workflow for brain tumor imaging, we screened a total of 870 scans from 626 patients. APTw signal (voxel-based measurement) was automatically quantified in gadolinium-enhanced T1w and FLAIR regions of interest using a deep learning-based approach for 3D tumor segmentations. PD and TRE were compared using unpaired t-tests, and diagnostic accuracy was assessed via ROC- and logistic regression analysis.

Results: Among 256 MRI scans of 143 patients with glioblastoma, 65 scans showed PD (n = 42) or TRE (n = 23). The median APTw signal was higher in PD (2.23%) vs TRE (1.76%; p = 0.001). ROC analysis showed an area under the curve (AUC) of 0.82. In patients with early PD or TRE (<6 months post-radiotherapy), the AUC increased to 0.93. Anti-angiogenic therapy decreased APTw signal (p < 0.01). Combining APTw MRI with DWI and PWI improved diagnostic accuracy (AUC = 0.90).

Conclusions: APTw MRI is a non-invasive imaging tool that is feasible for clinical routine and aids in differentiation of early progression from pseudoprogression in glioblastoma. Its diagnostic accuracy decreases with application of anti-angiogenic treatment and at later follow-up time points. Highest diagnostic accuracy was found in a multimodal approach combining APTw MRI, PWI and DWI.

Background: Cysteine is a multifunctional amino acid that can be oxidized affecting disulfide bond formation, redox signaling, and protein function. Reactive oxygen species (ROS) and the metabolic environment dictate cysteine uptake and oxidation status - especially in redox sensitive pathways. As many chemotherapeutic agents increase ROS, including the standard for glioblastoma (GBM), temozolomide (TMZ), we hypothesized that TMZ-resistant (TMZ-R) GBM would have increased ROS affecting cysteine reactivity that could be therapeutically targeted.

Methods: Here, to study the metabolic state within drug sensitive and resistant GBM, we used metabolite tracing with 13C-Cyst(e)ine, specialized cysteine reactivity proteomics and CRISPR screening with drug treatments to determine the efficacy of targeting cysteine metabolic pathways with our designer selenium drug in both patient derived cell lines and patient derived xenograft GBM orthotopic models.

Results: We show that TMZ-R have increased cyst(e)ine uptake, cysteine reactivity, and sensitivity to selenium (Se)-containing compounds - which can bind cysteine - in vitro and in vivo. We show that in TMZ-R models selenium compound treatment increases the need for thioredoxin reductases where co-treatment of Se compounds and the thioredoxin inhibitor auranofin significantly improves overall survival in mouse models.

Conclusions: Overall, our findings show a unique metabolic environment in TMZ-R models where designer brain penetrant Se-containing compounds target cysteine reactivity within proteins necessary for cancer cell survival and hold therapeutic potential.

Background: While severe fatigue is common in patients with diffuse glioma, no evidence-based treatment is currently available. The objective of this RCT was to evaluate efficacy of blended cognitive behavioral therapy (bCBT) for severe fatigue.

Methods: Severely fatigued patients (Checklist Individual Strength, fatigue-severity subscale [CIS-fatigue] ≥ 35) with diffuse glioma and stable disease were randomized to 12 weeks of bCBT or a waiting list condition (WLC). The primary endpoint was fatigue severity 2 weeks after intervention. This Bayesian adaptive trial included prespecified interim analyses for efficacy at n = 40, 50, 60, 70, and 80. Secondary outcomes-health-related quality of life (HRQoL), anxiety, future uncertainty and depression-were assessed at 2 and 12 weeks after intervention.

Results: The trial was stopped for efficacy at the first interim analysis. Of 47 patients randomized, 40 patients reached primary endpoint (mean age 53 years, 47% female). The posterior probability that CIS-fatigue scores were lower with bCBT than with WLC was 99.94%, with a large standardized effect size (Cohen's d) of 1.12 [95% CI: 0.43-1.81]. At 2 weeks after intervention, 68% of patients were no longer severely fatigued after bCBT, compared to 24% in WLC. At 12 weeks follow-up, fatigue was still significantly lower in the bCBT group compared to WLC (d = 1.22). bCBT also demonstrated beneficial effects (d = 0.42-1.19) on anxiety, HRQoL, and future uncertainty.

Conclusions: bCBT significantly reduces fatigue and improves anxiety and HRQoL in patients with diffuse glioma. These findings enable evidence-based supportive care strategies for reducing fatigue and enhancing HRQoL in this population.