Neuro-oncology最新文献

Background: Recent studies have highlighted bidirectional signalling between tumours and neurons; however, the interactions between tumours and neurons in response to radio-/chemotherapy remain obscure, which hampers the tumour treatment.

Methods: Glioblastoma organoids (GBOs) and primary neuron coculture, targeted metabonomics, RNA pulldown, mass spectrum, co-immunoprecipitation, RNA-sequencing, transcript/protein validations and multi-electrode arrays were performed to analyse neuron-tumour interaction in response to therapy. In vivo validations were conducted in orthotopic mouse models. Diagnostic and prognostic values were evaluated in serum, tissue-microarray as well as TCGA.

Results: GBOs recruited and induced pro-tumour-survival senescent neurons upon radiation/chemotherapeutic treatment. Targeted metabonomics revealed that significantly increased tumour-derived prostaglandin E2 (PGE2) induced neuronal senescence phenotype. Screening of enzymes involved in PGE2 synthesis identified prostaglandin E synthase 3 (PTGES3) as the key enzyme responsible for PGE2 upregulation. Biochemical studies revealed that irradiation or chemotherapeutic drug-triggered asparagine endopeptidase (AEP) specifically cleaved eIF4A1 to produce teIF4A1-C, which dissociated from DDX6 and recruited eIF4A3 and PABPN1 to promote the mRNA stability of PTGES3. Elevated PGE2 reciprocally enhanced AEP expression. Inhibiting PGE2 or AEP reduced neuronal senescence and delayed tumour progression. Strikingly, single-cell analysis further showed that expressions of AEP/PTGES3/EIF4A1 in tumour cells were consistent with senescent neuronal CDKN1A in high-neuronal-connectivity glioblastoma. The serum PGE2 concentration was elevated after radiation and higher in resistant glioblastoma patients. High expression of PTGES3 was associated with a poor prognosis.

Conclusions: Our study revealed that the AEP/PGE2 feedback loop modulates tumour-induced neuronal senescence upon radio-/chemotherapy and highlight the therapeutic value to improve tumour therapy.

Introduction: This study determines the relationship between diffusion and perfusion-based MRI signatures and radio-pathomic maps of tumor pathology in a large, multi-site cohort.

Methods: This study included perfusion imaging from pre-surgical relative cerebral blood volume (rCBV) images from the UPenn-GBM dataset and pre-surgical arterial spin labeling (ASL) imaging from the UCSF-PDGM dataset. Diffusion imaging included fractional anisotropy (FA) estimates derived from diffusion tensor imaging (DTI) for each subject from each institution. A previously validated autopsy-based model was applied to the structural images from each patient to generate quantitative radio-pathomic maps of cell density and extracellular fluid (ECF). Mean cell density, ECF density, FA, rCBV calculated from DSC imaging, and rCBF calculated from ASL were computed for each patient and statistically compared within contrast-enhancement (CE) and the non-enhancing peritumor FLAIR hyperintensity (FH).

Results: Both rCBV and ASL showed positive correlation with cell density within CE (rCBV: R=0.280, p<0.001; ASL: R=0.117, p=0.023). However, both perfusion metrics also showed no association with cell density within the FH region at the group level (rCBV: R=0.0162, p=0.731; ASL: R=-0.020, p=0.652). Negative correlations were observed between FA and ECF density across both CE and FH in both the UPenn-GBM (CE: r = -0.204, p<0.001, FH: r=-0.332, p<0.001) and the UCSF-PDGM (CE:r=-0.179, p<0.001, FH:-0.355, p<0.001). Additionally, a positive ASL-cell density association per subject within FH was associated with worse survival prognosis (HR=5.58, p=0.022).

Discussion: These results suggest that radio-pathomic maps of tumor pathology provide complementary information to other MR signatures that reveal prognostically valuable signatures of the non-enhancing tumor margin.

Background: Despite numerous studies on medulloblastoma (MB) cell heterogeneity, the spatial characteristics of cellular states remain unclear.

Methods: We analyze single-nucleus and spatial transcriptomes and chromatin accessibility from human MB spanning four subgroups, to identify malignant cell populations and describe the spatial evolutionary trajectories. The spatial CNVs patterns and niches were analyzed to investigate the cellular interactions.

Results: Three main malignant cell populations were identified, including progenitor-like, cycling and differentiated populations. Gene signatures of cell populations strongly correlate to clinical outcomes. These tumor cell populations are geographically organized as stem-like and mature regions, highlighting their spatially heterogeneous nature. Progenitor-like and cycling cells are mainly concentrated in stem-like regions, whereas various differentiated populations are primarily distributed in mature regions. By analyzing chromosomal alterations, we find that stem-like region typically harbors a single pattern of CNVs, reflecting high originality and uniformity, which is in stark contrast to mature region exhibiting multiple patterns with a broader range of biological functions. Projecting cellular state program onto spatial sections fully illustrates the evolution from stem-like region to various functional zones in mature region, which is correlated to microenvironmental components along the paths to maintain stemness or promote differentiation. Conclusions. This multi-omics database comprehensively facilitates the understanding of MB spatial evolutionary organization.

Background: Diffuse Midline Glioma (DMG) is a highly aggressive pediatric brain tumor with limited treatment options despite extensive genomic characterization. The aim of this study was to investigate the proteomic landscape of DMG to identify potential therapeutic targets.

Methods: We conducted a comprehensive proteomic analysis using LC-MS3, along with DNA methylation and DNA/RNA sequencing in 55 DMG patients' samples. PTM profiling (phosphoproteome and methylproteome) was conducted in 30 patient samples. We then investigated the effects of modulating key protein targets on protein methylation, protein synthesis, and DMG cell growth in vitro and in vivo.

Results: DMGs exhibited high global protein methylation, with significant enrichment of translation machinery proteins and factors involved in apoptosis regulation. Surprisingly, while targets of key kinases were highly phosphorylated, overall protein phosphorylation was lower in DMG compared to normal brain tissues. Non-histone methyltransferases METTL13 and METTL21B, along with protein kinases PAK2, PRKACA, and AKT1, were identified as key players in DMG methylproteome and phosphoproteome, respectively. METTL13 knockdown led to reduced EEF1A1 protein methylation, a shift in oncoprotein synthesis, and inhibited DMG cell growth in vitro and in vivo.

Conclusions: Our findings highlight the dependency of DMG on methyl-signaling pathways, particularly involving METTL13, which regulates EEF1A1 protein methylation and oncoprotein synthesis. Targeting the non-histone methyltransferases offers a promising therapeutic strategy for DMG. This study underscores the potential of post-translational modifications, specifically methyl-signaling pathways, as novel therapeutic targets for DMG and possibly other currently incurable cancers.

Background: Brainstem gliomas (BSGs) harboring a histone 3 lysine27-to-methionine (H3K27M) mutation represent one of the deadliest brain tumors with a dismal prognosis, as they exhibit a much worse response to therapy compared to the wildtype BSGs. Early non-invasive recognition of the H3K27M mutation is paramount for clinical decision-making in treating BSGs.

Methods: Plasma and urine samples were prospectively collected from BSG patients before biopsy or surgical resection and were chronologically divided into discovery, test, and validation cohorts. Utilizing the discovery and test cohort samples, an untargeted metabolomic strategy was exploited to identify candidate metabolite biomarkers, related to the H3K27M mutation. The candidate biomarkers were validated in the validation cohort with a targeted metabolomic method.

Results: Differential metabolomic profiles were detected between the H3K27M-mutant and wild-type BSGs in both the plasma and urine, the metabolomic changes were more dramatic in urine than in plasma. After rigorous screening for candidate biomarkers and validation with a targeted metabolomic approach, three metabolites, nomilin, Lys-Leu, and hawkinsin, emerged as significantly elevated biomarkers in H3K27M-mutant BSG urine samples. The biomarker panel combining the three metabolites had a diagnostic area under the curve (AUC) of approximately 75%. Furthermore, the biomarker panel improved the prediction accuracy of radiomics/clinical models to an AUC value high as 93.38%.

Conclusions: A urinary metabolite biomarker panel that exhibited high accuracy for non-invasive prediction of the H3K27M mutation status in BSG patients was identified. This panel has the potential to improve the predictive performance of current radiomics models or clinical features.

Background: Spinal hemangioblastomas (sHBs) are rare vascular tumors with significant neurological implications. Their management, particularly in von Hippel-Lindau (VHL) disease, remains challenging due to recurrence and functional decline. Timely identification and intervention are critical for optimal outcomes.

Methods: This international, multicenter retrospective cohort study included 357 patients (199 VHL-associated, 158 sporadic) from 13 neuro-oncological centers. Clinical and imaging data were analyzed to assess progression-free survival (PFS) and functional outcomes using the Modified McCormick Scale (mMCS) at 12 months. Secondary analyses identified factors associated with VHL disease in sHBs.

Results: Complete resection (CR) was achieved in 87.7% of cases, leading to significantly improved PFS at 72 months (sporadic: 95.1%, VHL-associated: 91.1%; HR: 0.18, 95%CI: 0.08-0.4). Multivariable analysis identified predictors of unfavorable outcomes at 12 months: Preoperative mMCS ≥2 (OR: 5.17, p=0.008), intramedullary tumor location (OR: 9.48, p=0.01), and preoperative bleeding (OR: 31.12, p=0.02). Factors independently associated with VHL disease in sHBs included non-cervical tumor location (OR: 2.08, p=0.004), intramedullary growth (OR: 2.39, p<0.001), and age <43 years (OR: 3.24, p<0.001). Functional improvements were observed in most patients, particularly those with sporadic sHBs.

Conclusions: Complete surgical resection is essential for long-term tumor control and favorable functional outcomes in both sporadic and VHL-associated sHBs. Early intervention, particularly in mild symptomatic and progressive cases, before neurological deterioration or hemorrhage, optimizes recovery. This study, the largest of its kind in a multicentric international setting, provides robust evidence to guide the management of both sporadic and VHL-associated sHBs.

Background: Individual-level characteristics underlying population-level variation in glioma risk and outcomes remain incompletely understood. Cancer immunosurveillance, host immunity, and some immunotherapies center on the ability of an individual's immune cells to recognize antigen epitopes presented on MHC molecules. Inter-individual variation in HLA alleles can elicit distinct repertoires of tumor antigen for presentation to immune cells. Therefore, HLA alleles may impact glioma incidence and prognosis.

Methods: HLA class I (HLA-I) alleles were identified using sequencing data from four large glioma cohorts and healthy cohorts, matched on ancestry, and race- and age-matched imputed cohorts developed by the Hardy-Weinberg equilibrium were referred to determine odds ratio incidence estimated by logistic regression. HLA prognostication was quantified by Cox regression.

Results: We analyzed 1,215 cases of glioma patients from non-Hispanic Whites and Asians. The HLA-I allelic frequencies of gliomas generally corresponded to their distribution within each race. However, specific HLA-I alleles were significantly associated with glioma incidence and prognosis, which differ between races but were independent of age and sex. Notably, non-Hispanic White glioma patients exhibited greater HLA homozygosity rates compared with race-matched controls. HLA-C01:02 and HLA-C07:02 displayed opposing effects on glioma prognosis between races. The distinct effects were associated with their capability of presenting specific mutations that appeared at the initial or late phase of glioma progression.

Conclusions: Expression of specific HLA-I alleles are associated with glioma incidence and prognosis within race. HLA-I-homozygosity is a risk factor for glioma in non-Hispanic Whites. These findings may guide development of precision-guided immunotherapies for glioma.

Background: This study proposed a classification system for the interaction between gliomas and white matter tracts, exploring its potential associations with clinical characteristics, tumor pathological subtypes, and patient outcomes.

Methods: Clinical data and diffusion MRI from 360 glioma patients who underwent craniotomy were analyzed. Using automatic fiber tractography, glioma-tract relationships were categorized into three types: displacement, infiltration, and disruption. Double immunohistochemical staining for IDH and MBP was performed on neuronavigation-guided tissue samples to validate the imaging-based classifications. The clinical implications of these classifications on the extent of tumor resection, postoperative motor function, and survival outcomes were evaluated.

Results: Among the patients, 35 (9.7%) were categorized as displacement type, 283 (78.6%) as infiltration type, and 42 (11.7%) as disruption type. Disruption-type tracts were predominantly associated with IDH wild-type gliomas (87.2%), significantly higher than infiltration (28.5%) and displacement types (23.5%) (P < 0.001). Displacement and infiltration types were more common in IDH-mutant gliomas (P < 0.001). Displacement-type tracts were significantly associated with higher rates of complete tumor resection compared to infiltration types (P = 0.015). In corticospinal tract involved cases, displacement-type tumors demonstrated no significant postoperative motor strength changes, whereas infiltration (P < 0.001) and disruption types (P = 0.013) were highly associated with postoperative motor deficits. Histological results aligned with dMRI-based classifications.

Conclusions: This dMRI-based classification of glioma-tract interactions is significantly associated with tumor pathology, resection outcomes, functional prognosis, and survival, providing a valuable tool for personalized and precise surgical planning.

Background: Diffuse midline glioma, characterised by H3K27 alteration (DMG), is the predominant high-grade glioma in children. It commonly originates in the brainstem, yet effective treatments for these patients remain elusive.

Methods: To identify novel therapies for DMG, we conducted high-throughput drug screens (HTS) using biologically active, clinically approved compounds against DMG neurospheres. Multiple primary DMG cultures were utilised in conjunction with in vitro cytotoxicity and clonogenic assays to validate the efficacy of top compounds. Molecularly diverse patient-derived and transgenic DMG orthotopic models were employed to assess therapeutic efficacy alongside pharmacokinetic and immunohistochemical analyses. Mechanistic studies, including RNA sequencing, western blotting, and flow cytometry, were conducted to elucidate the antitumour efficacy of the most promising compound, fenretinide, in DMG cells.

Results: Through HTS, six compounds were identified and validated for their potent cytotoxic activity. However, most of these compounds failed to improve survival in an orthotopic Diffuse Midline Glioma (DMG) model due to limited blood-brain barrier (BBB) penetration. In contrast, fenretinide exhibited effective BBB penetration, significantly enhancing the survival of tumour-bearing animals. Mechanistic studies revealed that fenretinide increased reactive oxygen species (ROS) generation and induced apoptosis by inhibiting PDGFRα. RNA-sequencing further elucidated that fenretinide upregulates the Unfolded Protein Response (UPR) and endoplasmic reticulum (ER) stress pathways while downregulating neurogenesis. The in vivo antitumour efficacy of two fenretinide formulations was demonstrated in PDGFRα-amplified and transgenic DMG models.

Conclusion: This comprehensive study has identified new DMG therapeutic vulnerabilities and highlights fenretinide as a brain-penetrant, anti-DMG agent.

Background: Longitudinal assessment of tumor burden using imaging helps to determine whether there has been a response to treatment both in trial and real-world settings. From a patient and clinical trial perspective alike, the time to develop disease progression, or progression-free survival, is an important endpoint. However, manual longitudinal response assessment is time-consuming and subject to interobserver variability. Automated response assessment techniques based on machine learning (ML) promise to enhance accuracy and reduce reliance on manual measurement. This paper evaluates the quality and performance accuracy of recently published studies.

Methods: Following PRISMA guidelines and the CLAIM checklist, we searched PUBMED, EMBASE, and Web of Science for articles (January 2010-November 2024). Our PROSPERO-registered study (CRD42024496126) focused on adult brain tumor automated treatment response assessment studies using ML methodologies. We determined the extent of development and validation of the tools and employed QUADAS-2 for study appraisal.

Results: Twenty (including seventeen retrospective and three prospective) studies were included. Data extracted included information on the dataset, automated response assessment including pertinent steps within the pipeline (index tests), and reference standards. Only limited conclusions are appropriate given the high bias risk and applicability concerns (particularly regarding reference standards and patient selection), and the low-level evidence. There was insufficient homogenous data for meta-analysis.

Conclusion: The study highlights the potential of ML to improve brain tumor longitudinal treatment response assessment. Interpretation is limited due to study bias and limited evidence of generalizability. Prospective studies with external datasets validating the latest neuro-oncology criteria are now required.