Analysis of the inhibitors of apoptosis identifies BIRC3 as a facilitator of malignant progression in glioma.
ABSTRACT: Gliomas, the most common primary brain tumor in humans, include a spectrum of disease. High-grade gliomas (HGG), such as glioblastoma, may arise from low-grade gliomas (LGG) that have a more indolent course. The process of malignant transformation (MT) of LGG to HGG is poorly understood but likely involves the activation of signaling programs that suppress apoptosis. We previously showed that Survivin (BIRC5) plays a role in malignant progression of glioma. Here, we investigated the role of the remaining members of the Inhibitors of Apoptosis (IAP) family on promoting MT in glioma. Utilizing expression data from the cancer genome atlas (TCGA), we identified BIRC3 as a key facilitator of MT from LGG to HGG. TCGA HGGs with high expression of BIRC 3 demonstrated a survival disadvantage and expression levels of BIRC3 were also significantly higher in TCGA HGG compared to TCGA LGG cases. We validated our findings from TCGA by using matched human specimens to show that BIRC expression is increased in HGG compared to their precursor LGG lesions. Using a unique murine model of glioma, we show that overexpression of BIRC3 promotes higher grade glioma and significantly reduces tumor-free survival in mice.
Project description:Gliomas are the most prevalent form of primary malignant brain tumor, which currently have no effective treatments. Evidence from human studies has indicated that oral microbiota is closely related to cancers; however, whether oral microbiota plays a role in glioma malignancy remains unclear. The present study aimed to investigate the association between oral microbiota and grade of glioma and examine the relationship between malignancy-related oral microbial features and the isocitrate dehydrogenase 1 (IDH1) mutation in glioma. High-grade glioma (HGG; n=23) patients, low-grade glioma (LGG; n=12) patients, and healthy control (HCs; n=24) participants were recruited for this case-control study. Saliva samples were collected and analyzed for 16S ribosomal RNA (rRNA) sequencing. We found that the shift in oral microbiota β-diversity was associated with high-grade glioma (p=0.01). The phylum Patescibacteria was inversely associated with glioma grade (LGG and HC: p=0.035; HGG and HC: p<0.01). The genera Capnocytophaga (LGG and HC: p=0.043; HGG and HC: p<0.01) and Leptotrichia (LGG and HC: p=0.044; HGG and HC: p<0.01) were inversely associated with glioma grades. The genera Bergeyella and Capnocytophaga were significantly more positively correlated with the IDH1 mutation in gliomas when compared with the IDH1-wild-type group. We further identified five oral microbial features (Capnocytophaga Porphyromonas, Haemophilus, Leptotrichia, and TM7x) that accurately discriminated HGG from LGG (area under the curve [AUC]: 0.63, 95% confidence interval [CI]: 0.44–0.83) and HCs (AUC: 0.79, 95% CI: 0.68–0.92). The functional prediction analysis of oral bacterial communities showed that genes involved in cell adhesion molecules (p<0.001), extracellular matrix molecule-receptor interaction (p<0.001), focal adhesion (p<0.001), and regulation of actin cytoskeleton (p<0.001) were associated with glioma grades, and some microbial gene functions involving lipid metabolism and the adenosine 5'-monophosphate-activated protein kinase signaling pathway were significantly more enriched in IDH1 mutant gliomas than compared with the IDH1-wild-type gliomas. In conclusion, our work revealed oral microbiota features and gene functions that were associated with glioma malignancy and the IDH1 mutation in glioma.
Project description:The role of cerebellum and cerebro-cerebellar system in neural plasticity induced by cerebral gliomas involving language network has long been ignored. Moreover, whether or not the process of reorganization is different in glioma patients with different growth kinetics remains largely unknown. To address this issue, we utilized preoperative structural and resting-state functional MRI data of 78 patients with left cerebral gliomas involving language network areas, including 46 patients with low-grade glioma (LGG, WHO grade II), 32 with high-grade glioma (HGG, WHO grade III/IV), and 44 healthy controls. Spontaneous brain activity, resting-state functional connectivity and gray matter volume alterations of the cerebellum were examined. We found that both LGG and HGG patients exhibited bidirectional alteration of brain activity in language-related cerebellar areas. Brain activity in areas with increased alteration was significantly correlated with the language and MMSE scores. Structurally, LGG patients exhibited greater gray matter volume in regions with increased brain activity, suggesting a structure-function coupled alteration in cerebellum. Furthermore, we observed that cerebellar regions with decreased brain activity exhibited increased functional connectivity with contralesional cerebro-cerebellar system in LGG patients. Together, our findings provide empirical evidence for a vital role of cerebellum and cerebro-cerebellar circuit in neural plasticity following lesional damage to cerebral language network. Moreover, we highlight the possible different reorganizational mechanisms of brain functional connectivity underlying different levels of behavioral impairments in LGG and HGG patients.
Project description:<h4>Objectives</h4>To investigate the value of local image variance (LIV) as a new technique for quantification of hypointense microvascular susceptibility-weighted imaging (SWI) structures at 7 Tesla for preoperative glioma characterization.<h4>Methods</h4>Adult patients with neuroradiologically suspected diffusely infiltrating gliomas were prospectively recruited and 7 Tesla SWI was performed in addition to standard imaging. After tumour segmentation, quantification of intratumoural SWI hypointensities was conducted by the SWI-LIV technique. Following surgery, the histopathological tumour grade and isocitrate dehydrogenase 1 (IDH1)-R132H mutational status was determined and SWI-LIV values were compared between low-grade gliomas (LGG) and high-grade gliomas (HGG), IDH1-R132H negative and positive tumours, as well as gliomas with significant and non-significant contrast-enhancement (CE) on MRI.<h4>Results</h4>In 30 patients, 9 LGG and 21 HGG were diagnosed. The calculation of SWI-LIV values was feasible in all tumours. Significantly higher mean SWI-LIV values were found in HGG compared to LGG (92.7 versus 30.8; p?<?0.0001), IDH1-R132H negative compared to IDH1-R132H positive gliomas (109.9 versus 38.3; p?<?0.0001) and tumours with significant CE compared to non-significant CE (120.1 versus 39.0; p?<?0.0001).<h4>Conclusions</h4>Our data indicate that 7 Tesla SWI-LIV might improve preoperative characterization of diffusely infiltrating gliomas and thus optimize patient management by quantification of hypointense microvascular structures.<h4>Key points</h4>• 7 Tesla local image variance helps to quantify hypointense susceptibility-weighted imaging structures. • SWI-LIV is significantly increased in high-grade and IDH1-R132H negative gliomas. • SWI-LIV is a promising technique for improved preoperative glioma characterization. • Preoperative management of diffusely infiltrating gliomas will be optimized.
Project description:Neoplasms of the central nervous system (CNS) are the most frequently encountered solid tumors of childhood, but are less common in adolescents and young adults (AYA), aged 15-39 years. Gliomas account for 29%-35% of the CNS tumors in AYA, with approximately two-thirds being low-grade glioma (LGG) and the remaining being high-grade glioma (HGG). We review the epidemiology, work-up, and management of LGG and HGG, focusing on the particular issues faced by the AYA population relative to pediatric and adult populations. Visual pathway glioma and brainstem glioma, which represent unique clinical entities, are only briefly discussed. As a general management approach for both LGG and HGG, maximal safe resection should be attempted. AYA with LGG who undergo gross total resection (GTR) may be safely observed. As age increases and the risk factors for recurrence accumulate, adjuvant therapy should be more strongly considered with a strong consideration of advanced radiation techniques such as proton beam therapy to reduce long-term radiation-related toxicity. Recent results also suggest survival advantage for adult patients with the use of adjuvant chemotherapy when radiation is indicated. Whenever possible, AYA patients with HGG should be enrolled in a clinical trial for the benefit of centralized genetic and molecular prognostic review and best clinical care. Chemoradiation should be offered to all World Health Organization grade IV patients with concurrent and adjuvant chemotherapy after maximal safe resection. Younger adolescents with GTR of grade III lesions may consider radiotherapy alone or sequential radiotherapy and chemotherapy if unable to tolerate concurrent treatment. A more comprehensive classification of gliomas integrating pathology and molecular data is emerging, and this integrative strategy offers the potential to be more accurate and reproducible in guiding diagnostic, prognostic, and management decisions.
Project description:Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, including 77 patients with low-grade gliomas (LGG) and 49 patients with high-grade gliomas (HGG). Functional network mapping for language was performed by construction of a multivariate machine learning-based prediction model of individual aphasia quotient (AQ), a summary score that indicates overall severity of language impairment. We found that the AQ scores for HGG patients were significantly lower than those of LGG patients. The prediction accuracy of HGG patients (R<sup>2</sup> = 0.27, permutation P = 0.007) was much higher than that of LGG patients (R<sup>2</sup> = 0.09, permutation P = 0.032). The rsFC regions predictive of LGG's AQ involved the bilateral frontal, temporal, and parietal lobes, subcortical regions, and bilateral cerebro-cerebellar connections, mainly in regions belonging to the canonical language network. The functional network of language processing for HGG patients showed strong dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal lobes. Together, our findings suggested that individual language processing of glioma patients links large-scale, bilateral, cortico-subcortical, and cerebro-cerebellar functional networks with different network reorganizational mechanisms underlying the different levels of language impairments in LGG and HGG patients.
Project description:<h4>Background</h4>Glioma is a type of tumor that develops in the central nerve system, mainly the brain. Alterations of genomic sequence and sequence segments (such as copy number variations or CNV and copy neutral loss of heterozygosities or cnLOH) are thought to be a major determinant of the tumor grade.<h4>Methods</h4>We mapped genomic variations between low-grade and high-grade gliomas (LGG and HGG) in Chinese population based on Illumina's Beadchip and validated the results using real-time qPCR.<h4>Results</h4>AT THE CYTOBAND LEVEL, WE DISCOVERED: (1) unique losses in LGG on 5q, 8p and 11q, and in HGG on 6q, 11p, 13q and 19q; (2) unique gains in the LGG on 1p and in HGG at 5p, 7p, 7q and 20q; and (3) cnLOH in HGG only on 3q, 8q, 10p, 14q, 15q, 17p, 17q, 18q and 21q. Subsequently, we confirmed well-characterized oncogenes among tumor-related loci (such as EGFR and KIT) and detected novel genes that gained chromosome sequences (such as AASS, HYAL4, NDUFA5 and SPAM1) in both LGG and HGG. In addition, we found gains, losses, and cnLOH in several genes, including VN1R2, VN1R4, and ZNF677, in multiple samples. Mapping grade-associated pathways and their related gene ontology (GO) terms, we classified LGG-associated functions as "arachidonic acid metabolism", "DNA binding" and "regulation of DNA-dependent transcription" and the HGG-associated as "neuroactive ligand-receptor interaction", "neuronal cell body" and "defense response to bacterium".<h4>Conclusion</h4>LGG and HGG appear to have different molecular signatures in genomic variations and our results provide invaluable information for the diagnosis and treatment of gliomas in patients with variable duration or diverse tumor differentiation.
Project description:Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II <i>vs</i>. LGG III. These results outperform state-of-the-art methods in classifying LGG II <i>vs</i>. LGG III and offer competitive performance in distinguishing LGG <i>vs</i>. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with <i>IDH</i> mutations.
Project description:Pediatric high-grade gliomas (HGG) are rare aggressive tumors that present a prognostic and therapeutic challenge. Diffuse midline glioma, H3K27M-mutant is a new entity introduced to HGG in the latest WHO classification. In this study we evaluated the presence of H3K27M mutation in 105 tumor samples histologically classified into low-grade gliomas (LGG) (n?=?45), and HGG (n?=?60). Samples were screened for the mutation in histone H3.3 and H3.1 variants to examine its prevalence, prognostic impact, and assess its potential clinical value in limited resource settings. H3K27M mutation was detected in 28 of 105 (26.7%) samples, and its distribution was significantly associated with midline locations (p-value?<?0.0001) and HGG (p-value?=?0.003). Overall and event- free survival (OS and EFS, respectively) of patients with mutant tumors did not differ significantly, neither according to histologic grade (OS p-value?=?0.736, EFS p-value?=?0.75) nor across anatomical sites (OS p-value?=?0.068, EFS p-value?=?0.153). Detection of H3K27M mutation in pediatric gliomas provides more precise risk stratification compared to traditional histopathological techniques. Hence, mutation detection should be pursued in all pediatric gliomas. Meanwhile, focusing on midline LGG can be an alternative in lower-middle-income countries to maximally optimize patients' treatment options.
Project description:<h4>Background</h4>Our aim was to assess the diagnostic performance of intravoxel incoherent motion (IVIM) MR imaging for differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).<h4>Methods</h4>Forty-five patients with diffuse glioma (age 50.9 ± 20.4 y; 26 males, 19 females) were assessed with IVIM imaging using 13 b-values (0-1000 s/mm(2)) at 3T. The perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were calculated by fitting the bi-exponential model. The apparent diffusion coefficient (ADC) was obtained with 2 b-values (0 and 1000 s/mm(2)). Relative cerebral blood volume was measured by the dynamic susceptibility contrast method. Two observers independently measured D, ADC, D*, and f, and these measurements were compared between the LGG group (n = 16) and the HGG group (n = 29).<h4>Results</h4>Both D (1.26 ± 0.37 mm(2)/s in LGG, 0.94 ± 0.19 mm(2)/s in HGG; P < .001) and ADC (1.28 ± 0.35 mm(2)/s in LGG, 1.03 ± 0.19 mm(2)/s in HGG; P < .01) were lower in the HGG group. D was lower than ADC in the LGG (P < .05) and HGG groups (P < .0001). D* was not different between the groups. The f-values were significantly larger in HGG (17.5 ± 6.3%) than in LGG (5.8 ± 3.8%; P < .0001) and correlated with relative cerebral blood volume (r = 0.85; P < .0001). Receiver operating characteristic analyses showed areas under curve of 0.95 with f, 0.78 with D, 0.73 with ADC, and 0.60 with D*.<h4>Conclusion</h4>IVIM imaging is useful in differentiating HGGs from LGGs.
Project description:<h4>Simple Summary</h4> This study investigates brain network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging and graph theory. We demonstrated that low-grade gliomas (LGG) lead to increased efficiency of the surrounding functional network, while high-grade gliomas (HGG) seem to disrupt brain connectivity in remote areas. Tumor location appears to influence the pattern of reorganization, including the recruitment of the contralateral hemisphere. Overall, LGG may show more favorable connectivity changes than HGG. If confirmed by future studies, the ability to discriminate between ‘maladaptive’ (detrimental) and ‘adaptive’ (beneficial) functional reorganization based on graph theory metrics may provide biomarkers to select patients for surgery and monitor recovery. <h4>Abstract</h4> Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients’ clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR p < 0.05). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann–Whitney U-test (p < 0.05) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency p = 0.03) and decreased contralateral connectivity for HGG (degree/cost p = 0.028). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits.