Neuro-oncology advances最新文献

Abstract: BackgroundDiaphanous-related formin 3 (DIAPH3) is a member of the formin family, a group of proteins that regulate actin and microtubule dynamics. During mitosis, DIAPH3 localizes specifically to the centrosome. Its loss destabilizes microtubules and disrupts mitotic spindle polarity, leading to multipolar mitoses and abnormal chromosome segregation, which ultimately causes aneuploidy in daughter cells.

Methods: We investigated DIAPH3 expression in glioma samples-including low-grade and high-grade gliomas-using publicly available datasets (The Cancer Genome Atlas and a single-cell RNA-seq study). We also explored the impact of DIAPH3 expression on aneuploidy in cultured glioblastoma cells.

Results: DIAPH3 expression was specifically increased in grade 4 gliomas. However, its prognostic value did not surpass that of the WHO CNS5 glioma classification. DIAPH3 was predominantly expressed in mitotic cells and showed strong coexpression with genes involved in cell division, particularly those regulating mitotic progression and chromosome segregation. Several transcription factors known to drive proliferation and cancer progression may regulate DIAPH3 expression. In glioblastoma cell lines, we confirmed that DIAPH3 is upregulated during mitosis and that its knockdown increases aneuploidy.

Conclusions: These findings confirm the role of DIAPH3 in chromosome segregation in clinical glioma samples and demonstrate its association with high-grade, poor-prognosis gliomas.

Background: Accurate classification of glioma subtypes is essential for personalized treatment, yet current diagnostic approaches rely on invasive procedures to determine molecular profiles. This study aims to enhance non-invasive glioma classification by integrating metabolic imaging with advanced unsupervised learning.

Methods: Whole-brain 3D Magnetic Resonance Spectroscopic Imaging (MRSI) was performed at 3 Tesla. From 26 scanned patients, 12 gliomas (5 astrocytomas, 5 oligodendrogliomas, 2 glioblastomas) that passed strict quality-control criteria were included for analysis. Spectral decomposition was performed using Global Non-Negative Matrix Underapproximation (G-NMU), and tumor subtype classification was achieved with Uniform Manifold Approximation and Projection (UMAP) followed by K-means clustering.

Results: The proposed framework was able to classify tumor types with an accuracy of 99.65% and an AUC of 99.07. Clear subtype-specific metabolic fingerprints were validated by hierarchical clustering and UMAP embeddings, emphasizing 2HG, serine, and inositol as important classification drivers.

Conclusions: This study demonstrates that whole-brain MRSI spectral decomposition based on G-NMU is a reliable non-invasive method for classifying gliomas. In contrast to spectral fitting on prior-knowledge basis sets, G-NMU accurately separates astrocytoma, oligodendroglioma, and glioblastoma by extracting metabolic features without making assumptions about the tumor metabolic composition. These results suggest that integration of metabolic imaging and unsupervised learning into clinical workflows may improve molecular stratification for noninvasive glioma diagnosis.

Abstract: BackgroundMost children with central nervous system (CNS) tumors reside in low- and middle-income countries (LMICs), with limited availability of trained pediatric neuro-oncologists.

Methods: Using a series of structured interviews of physicians who had served as global mentors or mentees in pediatric oncology, we identified mentorship, leadership, and clinical training as key components necessary to virtually train pediatric oncologists in LMICs to become leading pediatric neuro-oncologists while they remain in their home countries. Thus, the St Jude Global Virtual Pediatric Neuro-oncology Fellowship (VPNOF) was designed to incorporate mentorship with global and loco-regional mentors to aid in each fellow's career and institutional goal setting and clinical training involving virtual tumor boards and didactics and ad-hoc case discussions, enabling fellows to manage patients at their home institution. Fellows traveled to their mentors' institutions twice for four-week clinical rotations.

Results: In 2022 and 2023, eleven fellows were selected, representing 10 LMICs. The 2-year fellowship led to the establishment of multi-disciplinary approaches, increased patient volume, increased use of evidence-based practices, 33 abstract presentations, and publication of four journal articles.

Conclusions: The VPNOF is an innovative approach leveraging global mentorship to train pediatric oncologists in resource-limited settings to become pediatric neuro-oncologists, which has led to the successful implementation of new practice paradigms to improve the quality of care for children with CNS tumors in LMICs.

Abstract: BackgroundEmbryonal tumor with multilayered rosettes (ETMR) is an aggressive pediatric brain tumor that carries a poor prognosis, and there is currently no standard of care. Dysregulated mitochondrial bioenergetics and dynamics have been associated with the progression of diverse cancers. Cardiolipins are mitochondrial-specific lipids, and their fatty acid composition has been shown to regulate mitochondrial structure and function. Despite the known functional significance of cardiolipins, their structure-specific accumulation in relation to mitochondrial phenotypes in ETMR remains ill-defined.

Methods: Spatial lipidomic profiles in patient samples and 3D models were determined using mass spectrometry imaging. Cell proliferation and mitochondrial bioenergetics and dynamics were characterized using immunohistochemistry, transmission electron microscopy, Western blotting, and metabolic assays. LCLAT1 KD was carried out using siRNA.

Results: We detected a structure-specific accumulation of cardiolipins and increased expression of the cardiolipin acyl chain remodeling enzyme, lysocardiolipin acyltransferase 1 (LCLAT1), within proliferating tumor cells in patient samples and the 3D tumorspheres. Orthogonal imaging techniques correlated the structure-specific accumulation of cardiolipin with fragmented mitochondria displaying aberrant cristae structure, altered mitochondrial dynamics, decreased expression of respiratory chain enzymes, and a more glycolytic phenotype. LCLAT1 KD altered cardiolipin profiles, reduced growth and proliferation, decreased Sox2 and N-Myc expression, increased p53 and p21 expression, and increased LIN28A and Dcx expression. Additional therapeutic targeting of the fragmented mitochondrial phenotype identified also resulted in selective inhibition of ETMR growth and viability.

Conclusions: Our findings provide novel insight into ETMR biology based on mitochondrial phenotypes and the fatty acid composition of the multifunctional mitochondrial-specific lipid, cardiolipin.

Background: Differentiating recurrent tumor from post-treatment changes remains a major challenge in glioblastoma (GBM) patients. In this work, we compared the performance of 2 different MR perfusion techniques, dynamic susceptibility contrast (DSC), and arterial spin labeling (ASL) to differentiate recurrent tumor and post-treatment changes from the volume of cellular tumor segmented from combined Deep Learning and multimodal MRI measurements, including multishell diffusion and perfusion.

Methods: In this retrospective study, 137 MRIs from 107 patients with GBM were analyzed. Cellular tumor maps were segmented by 2 radiologists based on imaging, clinical history, and pathology. Multimodal MRI with perfusion and multishell diffusion were inputted into 5 nnU-Net Deep Learning models using either DSC or ASL with combination of multishell diffusion and standard MRI sequences to segment cellular tumor. Models with DSC and ASL were compared using segmentation performance (Dice score) and accuracy to detect recurrent tumor from post-treatment changes (area under the curve [AUC] under the receiver operating characteristic curve).

Results: Segmentation performances were similar in both cases, with a median Dice score of 0.75 (IQR: 0.53-0.84) for ASL and 0.76 (IQR: 0.57-0.84). AUC was 0.88 (CI 0.82-0.94) for ASL and 0.86 (CI, 0.80-0.92) for DSC, and this difference was statistically significant (P < .05, n = 10 000 permutation test). In 11 individual cases, recurring disease was detected with ASL but missed with cerebral blood volume, including recurring tumor in the vicinity of a surgical cavity (n = 5), close to the skull base (n = 1), and adjacent to an Ommaya reservoir (n = 2).

Conclusions: Our results demonstrate the utility of ASL in regions where susceptibility artifacts decrease the quality of DSC images.

Background: Cerebrospinal fluid cell-free DNA (cfDNA) can detect and monitor leptomeningeal disease but has not been previously used to monitor parenchymal lesions.

Methods: Herein, we report our initial experience with CSF cfDNA monitoring for 2 patients with colorectal cancer (CRC) metastases to the thalamus, causing obstructive hydrocephalus.

Results: CSF samples were obtained during ventriculoperitoneal shunt placement, demonstrating high levels of cfDNA in both cases. Several genomic alterations detected in the cfDNA sequencing matched those in the tumor tissue biopsy. Follow-up CSF evaluations after subsequent therapy were used to help adjudicate pseudo-progression versus true progression.

Conclusions: Neither patient developed leptomeningeal disease, demonstrating CSF's utility in evaluating solitary brain metastases in direct contact with a CSF compartment.

Background: Intraoperative Stimulated Raman Histology (SRH) has been reported to be fast and accurate in the assessment of neuro-oncological lesions. However, its application to spinal tumors, especially intradural extramedullary tumors (IDEM), remains underexplored. IDEM primarily include meningiomas and schwannomas, as well as less common entities such as metastases or ependymomas. Given that surgical resection is the primary treatment modality, rapid, artificial intelligence (AI)-driven intraoperative tumor classification based on SRH could enhance surgical decision-making and subsequent management.

Methods: We acquired 232 SRH images from patients with IDEM using the NIO Laser Imaging System (Invenio Imaging Inc.). We categorized images into three diagnostic classes: "Meningioma," "Schwannoma," and "Other." Images were divided into 224 × 224 pixel patches and used to train and test AI-based image classifiers employing CTransPath, ResNet, and Vision Transformer architectures.

Results: Our best-performing model, utilizing the CTransPath architecture, achieved a classification accuracy of 94.3% on the test dataset. Vision Transformer-based models also performed well, exceeding 90% accuracy, while ResNet models attained slightly lower accuracies (79.6%-88.8%). Qualitative analysis indicates that the top-performing model primarily relies on cellular morphology for classification.

Conclusions: Our findings confirm the feasibility and effectiveness of AI-assisted SRH analysis for distinguishing IDEM tumor types. This approach may complement conventional intraoperative neuropathology by providing rapid, reliable, and clinically actionable diagnostic information.

Background: Authenticated preclinical brain tumor models provide unprecedented opportunities to evaluate next-generation treatments. However, some therapies with robust anti-tumor activity in mice fail in human trials, highlighting the need to better prioritize candidates for clinical translation. Herein, we implemented a head-to-head preclinical strategy using a well-characterized murine model of NF1-optic pathway glioma (Nf1 ^OPG).

Methods: Nf1 ^OPG mice were treated with standard of care (SOC; carboplatin), clinically evaluated (everolimus, mirdametinib), and investigational (pexidartinib, HBS-101, lamotrigine) drugs during the period of most rapid tumor growth (6-12 weeks of age). Anti-tumoral efficacy was assessed by proliferation (%Ki67⁺ cells) and optic nerve (ON) volume, while vision-related outcomes were measured using retinal nerve fiber layer (RNFL) thickness and retinal ganglion cell (RGC) determinations. Tumor microenvironment (TME) soluble mediator (Ccl2, Ccl3, Ccl4, Ccl5) and tumor cell marker (NeuN, Gpr17) RNA expression was quantitated by qRT-PCR. Outcomes were compared to carboplatin-treated Nf1 ^OPG, untreated Nf1 ^OPG, and Nf1^+/- mice.

Results: While all agents restored normal tissue architecture, reduced ON proliferation, and decreased TME soluble mediator and tumor cell marker RNA expression, only lamotrigine and mirdametinib also reduced ON volume. Everolimus, lamotrigine, and HBS-101 restored RNFL thickness to wild-type levels, whereas carboplatin showed a trend towards normalization.

Conclusions: This referential preclinical study design affords direct head-to-head comparisons of investigational therapies relative to SOC treatment using clinically meaningful outcomes (OPG growth and RNFL thickness). Using this strategy, lamotrigine emerged as the most promising therapy for limiting tumor progression and vision loss in Nf1-OPG mice, relevant to clinical translation for children with NF1-OPG.

Background: Malignant gliomas are heterogeneous brain tumors with extensive neovascularization. Conventional gradient-echo dynamic susceptibility contrast (GRE-DSC) perfusion MRI may underestimate microvascular alterations. We hypothesized that a novel vascular model (NVM), based on Bayesian voxel-wise transit time distribution analysis, could yield higher perfusion metrics in untreated isocitrate dehydrogenase (IDH)-wild-type glioblastoma compared to standard vendor GRE-DSC algorithms.

Methods: In this retrospective, single-center study, 89 patients with neuropathologically confirmed glioblastoma underwent pretherapeutic GRE-DSC perfusion MRI at 1.5 or 3.0 T. Perfusion maps were generated using both the NVM and default vendor algorithms. Using co-registered T1-post-contrast and T2/FLAIR images, two neuroradiologists independently assessed perfusion conspicuity of color-coded maps for each algorithm and manually performed region-of-interest analyses within visually identified tumor hotspots for quantification. Relative values of cerebral blood flow (rCBF), cerebral blood volume (rCBV), and mean transit time (rMTT) were normalized to contralateral normal-appearing white matter. Nonparametric tests evaluated group differences.

Results: The NVM yielded enhanced hotspot delineation and significantly higher median normalized perfusion values than vendor algorithms (all P < .001), with excellent inter-rater reliability (Cohen's κ and intraclass correlation coefficients ≥0.86). At 3.0 T, NVM-derived rCBV was significantly higher than at 1.5 T (P = .008).

Conclusions: NVM post-processing yielded higher normalized CBF, CBV, and MTT values within tumor hotspots than vendor pipelines, suggesting that Bayesian model-based perfusion analysis may enhance the detection of microvascular changes in glioblastoma. As validation against a gold standard is missing, prospective multicenter studies are warranted to confirm our findings, particularly with regard to treatment monitoring and clinical decision-making.

Brain tumor registries around the world have significantly contributed to the clinical, scientific, and epidemiological understanding of brain tumors. The success of these registries has prompted many other countries to create such resources for their own populations. This narrative review compares the construction, structure, and function of brain tumor registries in the United States, China, Japan, Canada, England, Australia, Austria, Denmark, and Sweden, drawing key learnings from each. Brain tumor registries from three large, medium, and small countries were identified, and their establishment, organizational structure, and primary functions were examined. This analysis found eight key considerations for establishing a national clinical registry: (1) clearly defining the aims and objectives of the registry, (2) assessing the role of supportive legislation, (3) evaluating various registry structures, (4) assessing existing registry infrastructure, (5) weighing the benefits and drawbacks of government involvement, (6) recognizing the role of specialist centers, (7) ensuring futureproofing, and (8) prioritizing comprehensive population coverage. These findings were then applied to the New Zealand context to demonstrate how such learnings can be considered by countries wishing to establish their own registry. This review provides a practical framework for nations seeking to develop similar clinical registries.