"Pseudoprogression" following immune checkpoint inhibitors (ICI) in glioblastoma is often considered in case of radiographic progression. To better characterize the frequency of this phenomenon in glioblastoma, we reviewed the imaging response characteristics of a total of 55 patients treated with ICI in the setting of recurrent (n = 45) or newly diagnosed (n = 10) disease. There was no evidence of pseudoprogression related to ICI-monotherapy in the entire cohort.
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: 11C-methionine positron emission tomography is one of the most reliable imaging modalities for -glioblastoma visualization. This investigation aimed to generate an 11C-methionine positron emission tomography-like image, "Gliomap," from contrast-enhanced magnetic resonance imaging via a conditional Generative Adversarial Network (Gliomap-GAN).
Methods: Eighty-one newly diagnosed glioblastoma patients with preoperative contrast-enhanced magnetic resonance imaging and 11C-methionine positron emission tomography were retrospectively collected. T1-weighted, T2-weighted, and Gd-enhanced T1-weighted images were co-registered and intensity normalized, followed by the creation of a contrast-enhancement subtraction map. They were used as source data to train Gliomap-GAN, targeting the corresponding 11C-methionine positron emission tomography image. The training dataset comprised 2459 images augmented to 4918 pairs by mirroring. The test dataset consisted of 593 pairs. Furthermore, an additional five patients with 16 image-guided sampled tissues were used for histological validation of the generated Gliomap.
Results: Gliomaps visually resembled the original 11C-methionine positron emission tomography images. The residual error between Gliomaps and the original images from test datasets was 0.07 ± 0.04 (mean ± SD) in tumor-to-normal tissue ratio. The Sørensen-Dice coefficient between the lesions predicted by Gliomap and 11C-methionine positron emission tomography reached 0.88 ± 0.07 (mean ± SD) at a threshold of tumor-to-normal tissue ratio of 1.5. The absolute values of Gliomap showed a significant positive correlation with tumor cell density (P = .02).
Conclusion: The present research demonstrates that the Gliomap, generated from contrast-enhanced magnetic resonance imaging using generative artificial intelligence, is a promising imaging surrogate for visualizing tumor cell density in newly diagnosed glioblastoma.
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: Glioblastoma (GBM) is an aggressive brain cancer with limited treatment options and high recurrence rates. The blood-brain barrier (BBB) impedes therapeutic delivery for the brain, limiting systemic treatment efficacy. Focused ultrasound (FUS) combined with microbubbles (MBs) can transiently open the BBB (BBBO), enhancing drug delivery and modulating the tumor immune microenvironment (TME). However, the disorganized and leaky vasculature in GBM limits the effectiveness of FUS-mediated BBBO. Vascular normalization using antiangiogenic therapy may enhance both immune modulation and delivery. This study aimed to investigate whether vascular normalization via VEGFR-2 blockade with DC101, alone or in combination with FUS+MBs, improves TME remodeling in a murine GBM model.
Methods: CT2A glioma-bearing mice were treated with DC101, a VEGFR2 inhibitor, either alone or in combination with FUS+MBs. Tumor growth, survival, vessel permeability, immune cell profiling, and adhesion molecule expression were evaluated using immunohistochemistry, flow cytometry, and confocal microscopy.
Results: DC101 monotherapy significantly reduced tumor growth and prolonged survival. It reduced tumor vessel permeability and increased ICAM1 expression on CD31+ endothelial cells, consistent with vascular normalization. DC101 also reduced FOXP3+ regulatory T cells (Tregs) and increased the CD8/Treg ratio, indicating a more immunostimulatory TME. However, the addition of FUS+MBs in this normalized vascular environment did not further alter the immune landscape, suggesting a stable, quiescent TME.
Conclusion: DC101-mediated vascular normalization beneficially remodels the GBM TME and creates a quiescent platform for supporting future FUS-based therapeutic delivery. This combinatorial strategy offers a promising approach to overcoming BBB-related barriers in glioma treatment.
Background: GBM disproportionately affects older adults, who experience worse survival outcomes and reduced tolerance to aggressive therapies. Despite this, most preclinical GBM studies rely on young animal models, limiting insight into how aging influences tumor progression and treatment vulnerability. The aim of this study was to determine how aging alters glioma growth, survival outcomes, and host brain responses.
Methods: We used a syngeneic murine glioma model to compare young (6-7 weeks) and aged (85-86 weeks) mice implanted with SB28 glioma cells. We assessed survival, functional status (nesting behavior, weight loss), whole-brain tumor infiltration, and glial reactivity. Quantitative histology and image registration to the Allen Brain Atlas enabled region-specific tumor and glial burden analyses.
Results: Aged glioma-bearing mice exhibited significantly reduced survival, increased functional impairment (including impaired nesting and weight loss), and broader tumor infiltration, particularly within white matter tracts. Tumor volume alone did not account for these differences; multivariable logistic regression identified age as the only independent predictor of mortality. Aged brains also displayed heightened extratumoral neuroinflammation, especially in regions involved in motivation and cognitive function.
Conclusions: Aging is associated with a brain environment that permits greater glioma infiltration and is further characterized by heightened glial reactivity and reduced functional resilience to tumor burden. These findings underscore the limitations of relying solely on young animal models in GBM research and support incorporating aging as a critical variable. Targeting neuroinflammatory responses in the aged brain may represent a promising adjunct strategy to improve survival and preserve neurological function in older GBM patients.
Abstract: BackgroundGIntratumoral and intertumoral heterogeneity combined with immunosuppressive tumor microenvironments (TME) contribute to the poor outcomes associated with glioblastoma (GBM). Well-characterized immunocompetent models that recapitulate human GBM features are urgently needed to identify targets in the TME and develop novel therapeutics. Here, we used multiomic approaches to characterize syngeneic mouse brain tumor stem cell lines in vitro and in orthotopically engrafted tumors.
Methods: Whole-genome sequencing, transcriptomics, ATAC-sequencing, and imaging mass cytometry were used to characterize syngeneic brain tumor stem cell lines derived from Trp53+/-/Nf1+/- C57Bl6 mice. Mouse and human bulk, single-cell, and spatial sequencing datasets were analyzed for validation. CRISPR/Cas9 and shRNA were used for gene knockdowns. Tumor growth was investigated using orthotopic engraftment in syngeneic C57Bl6 mice.
Results: One of the syngeneic lines, mBT0309, generated tumors with histopathological characteristics of GBM. mBT0309 displayed amplification and high expression of Igf2. Copy number gains at the IGF2 locus were observed in human GBM tumors and stem cell lines. Furthermore, we determined that high IGF2 RNA expression is associated with poor survival in GBM patients. Imaging mass cytometry on mBT0309 tumors showed early infiltration of monocyte-derived macrophages, vascularization, and cell states characteristic of human GBM. Genetic targeting of Igf2 decreased in vitro cell growth, improved survival of engrafted mice, and decreased the percentage of Arginase-1+ macrophages in mBT0309 tumors.
Conclusions: mBT0309 is a valuable syngeneic model for studying immunosuppression and therapeutic resistance in GBM. IGF2 offers promise as a valuable therapeutic target to combat tumor growth and immunosuppression in GBM patients.
Background: Although schwannomas are common and benign, their growth patterns are often hard to predict. Currently, surgery and radiotherapy are the only standard treatments. Since metabolites are the end products of genes and proteins, metabolomics may reveal downstream tumor features in ways that other -omics cannot. Here, we use metabolomic profiling and stable isotope tracing to characterize primary human schwannomas and describe their changes following radiation in patient-derived xenografts.
Methods: Schwannomas collected during surgical resection underwent metabolomic profiling with gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry (N = 44) as well as DNA methylation profiling (N = 29). Large tumors were also implanted subcutaneously in athymic mice as patient-derived xenografts. Mice were randomized to radiation treatment or control 4-6 weeks post-implantation. Xenografts were harvested 72 h after radiation for metabolomic profiling (N = 53). Another group of xenografts (N = 33) was injected with U-13C-glutamine prior to tumor harvest for stable isotope tracing.
Results: The schwannoma metabolome differs from that of Schwann cells, and metabolomics-based clustering of schwannomas resembles DNA methylation-based classification. In xenografts, radiation decreases cellular proliferation and produces small but detectable changes to the tricarboxylic acid (TCA) cycle and nucleotide metabolism. 13C-glutamine tracing shows that schwannomas can produce urea cycle intermediates, TCA cycle intermediates, cytosine monophosphate (CMP), and cytosine triphosphate from glutamine even after radiation. CMP was the only metabolite with altered 13C uptake following radiation.
Conclusions: Schwannomas have distinct metabolic signatures compared to the Schwann cells from which they originate. Schwannoma xenograft metabolism is surprisingly robust to radiotherapy, and xenografts readily incorporate glutamine into the TCA cycle, urea cycle, and pyrimidine synthesis.
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