Molecular Subtypes Of Glioma Research Articles

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas.

This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes. A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses. T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051). T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

IDH1-mutant metabolite D-2-hydroxyglutarate inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT

The heterogeneous molecular subtypes of gliomas demonstrate varied responses to chemotherapy and distinct prognostic outcomes. Gliomas with Isocitrate dehydrogenase 1 (IDH1) mutation are associated with better outcomes and are more responsive to temozolomide (TMZ) compared to those without IDH1 mutation. IDH1-mutant gliomas elevate D-2-hydroxyglutarate (D-2HG) levels, with potential dual effects on tumor progression. Limited research has explored the potential anti-glioma effects of D-2HG in combination with TMZ. Clinical data from over 2500 glioma patients in our study confirms that those with IDH1 mutations exhibit enhanced responsiveness to TMZ chemotherapy and a significantly better prognosis compared to IDH1 wild-type patients. In subsequent cellular experiments, we found that the IDH1-mutant metabolite D-2HG suppresses Integrin subunit beta 4 (ITGB4) expression, and down-regulate the phosphorylation levels of PI3K and AKT, ultimately inhibiting cell proliferation while promoting apoptosis, thereby improving glioma prognosis. Additionally, we have demonstrated the synergistic effect of D-2HG and TMZ in anti-glioma therapy involved inhibiting the proliferation of glioma cells and promoting apoptosis. Finally, by integrating data from the CGGA and TCGA databases, it was validated that ITGB4 expression was lower in IDH1-mutant gliomas, and patients with lower ITGB4 expression were associated with better prognosis. These findings indicate that ITGB4 may be a promising therapeutic target for gliomas and D-2HG inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT. These findings drive theoretical innovation and research progress in glioma therapy.

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas.

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment. We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes. The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes. The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

GLIOMA-04 BRIDGING THE GAP ON ADULT GLIOMA IMAGING, DIAGNOSIS AND FOLLOW UP IN SUB-SAHARAN AFRICA

Abstract Cerebral Gliomas are the most common and devastating primary brain tumors. Prior to 2016 WHO CNS tumor classification update, the grading of gliomas, mainly relied on histological features, including cellularity, nuclear atypia, mitotic activity, vascularity, and necrosis, observed on light microscopy with the aid of immunohistochemistry. A number of studies confirmed that diffuse gliomas demonstrates different growth pattern, clinical behavior, and prognostication based on their genomic alterations, these findings necessitated incorporation of molecular subtypes in glioma classification, and leads to 2016 WHO CNS tumors Classification update. Introduction of molecular criteria into the classification of gliomas has given rise to interesting, wide-ranging implications regarding glioma management. In the current classification, all diffuse gliomas have been grouped based on their growth pattern, clinical behavior, and specifically sharing of the mutational state of the gene that codes for isocitrate dehydrogenase (IDH) in its isoforms (IDH1 and IDH2). Regardless of grade, the first phase in glioma molecular characterization is IDH testing. Mutations in IDH1 and IDH2 are associated with significant increase in progression free survival and overall survival. Immunohistochemistry, Molecular subtyping, Both FISH and genetic sequencing have significant implications in gliomas management and clinical outcome, yet they are not available in our low resource settings. Advanced MRI techniques (DTI, MR Perfussion, MR Spectroscopy, and Functional MRI) have a significant impact in presurgical planning and follow up of glioma patients after surgery and chemo radiation treatment. Radiographic findings can bridge the gap on accurate glioma diagnosis in sub-Saharan Africa through predicting Glioma Molecular subtypes based on clinical presentation and utilizing conventional MRI as a radio genomic tool. Also to emphasis in the utilization of available advanced MRI techniques (DTI, MR Perfussion, and MR spectroscopy) for presurgical planning and follow up of patients after Glioma treatment.

Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

ObjectivesRadiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes.MethodsPreoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160).ResultsSupport vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone.ConclusionIntensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting.Clinical relevance statementIntensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes.Key Points• MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability.• Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution.• Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.

The PANoptosis-related signature indicates the prognosis and tumor immune infiltration features of gliomas.

Gliomas are the most common primary tumors of the central nervous system, with high heterogeneity and highly variable survival rates. Accurate classification and prognostic assessment are key to the selection of treatment strategies. One hallmark of the tumor is resistance to cell death. PANoptosis, a novel mode of programmed cell death, has been frequently reported to be involved in the innate immunity associated with pathogen infection and played an important role in cancers. However, the intrinsic association of PANoptosis with glioma requires deeper investigation. The genetics and expression of the 17 reported PANoptosome-related genes were analyzed in glioma. Based on these genes, patients were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between clusters, a prognostic model called PANopotic score was constructed after univariate Cox regression, LASSO regression, and multivariate Cox regression. The expression of the 5 genes included in the PANopotic score was also examined by qPCR in our cohort. The prognostic differences, clinical features, TME infiltration status, and immune characteristics between PANoptotic clusters and score groups were compared, some of which even extended to pan-cancer levels. Gene mutations, CNVs and altered gene expression of PANoptosome-related genes exist in gliomas. Two PANoptotic clusters were significantly different in prognosis, clinical features, immune characteristics, and mutation landscapes. The 5 genes included in the PANopotic score had significantly altered expression in glioma samples in our cohort. The high PANoptotic score group was inclined to show an unfavorable prognosis, lower tumor purity, worse molecular genetic signature, and distinct immune characteristics related to immunotherapy. The PANoptotic score was considered as an independent prognostic factor for glioma and showed superior prognostic assessment efficacy over several reported models. PANopotic score was included in the nomogram constructed for the potential clinical prognostic application. The associations of PANoptotic score with prognostic assessment and tumor immune characteristics were also reflected at the pan-cancer level. Molecular subtypes of glioma based on PANoptosome-related genes were proposed and PANoptotic score was constructed with different clinical characteristics of anti-tumor immunity. The potential intrinsic association between PANoptosis and glioma subtypes, prognosis, and immunotherapy was revealed.

Postoperative risk of IDH-mutant glioma-associated seizures and their potential management with IDH-mutant inhibitors.

Seizures are a frequent complication of adult-type diffuse gliomas, and are often difficult to control with medications. Gliomas with mutations in isocitrate dehydrogenase 1 or 2 (IDHmut) are more likely than IDH-wild type (IDHwt) gliomas to cause seizures as part of their initial clinical presentation. However, whether IDHmut is also associated with seizures during the remaining disease course, and whether IDHmut inhibitors can reduce seizure risk, are unclear. Clinical multivariable analyses showed that preoperative seizures, glioma location, extent of resection, and glioma molecular subtype (including IDHmut status) all contributed to postoperative seizure risk in adult-type diffuse glioma patients, and that postoperative seizures were often associated with tumor recurrence. Experimentally, the metabolic product of IDHmut, d-2-hydroxyglutarate, rapidly synchronized neuronal spike firing in a seizure-like manner, but only when non-neoplastic glial cells were present. In vitro and in vivo models recapitulated IDHmut glioma-associated seizures, and IDHmut inhibitors currently being evaluated in glioma clinical trials inhibited seizures in those models, independent of their effects on glioma growth. These data show that postoperative seizure risk in adult-type diffuse gliomas varies in large part by molecular subtype, and that IDHmut inhibitors could play a key role in mitigating such risk in IDHmut glioma patients.

Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.

2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis. This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall. Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research. This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.

A novel signature of cuproptosis-related lncRNAs predicts prognosis in glioma: Evidence from bioinformatic analysis and experiments.

Background: Glioma patients often experience unfavorable outcomes and elevated mortality rates. Our study established a prognostic signature utilizing cuproptosis-associated long non-coding RNAs (CRLs) and identified novel prognostic biomarkers and therapeutic targets for glioma. Methods: The expression profiles and related data of glioma patients were obtained from The Cancer Genome Atlas, an accessible online database. We then constructed a prognostic signature using CRLs and evaluated the prognosis of glioma patients by means of Kaplan-Meier survival curves and receiver operating characteristic curves. A nomogram based on clinical features was employed to predict the individual survival probability of glioma patients. Functional enrichment analysis was conducted to identify crucial CRL-related enriched biological pathways. The role of LEF1-AS1 in glioma was validated in two glioma cell lines (T98 and U251). Results: We developed and validated a prognostic model for glioma with 9 CRLs. Patients with low-risk had a considerably longer overall survival (OS). The prognostic CRL signature may serve independently as an indicator of prognosis for glioma patients. In addition, functional enrichment analysis revealed significant enrichment of multiple immunological pathways. Notable differences were observed between the two risk groups in terms of immune cell infiltration, function, and immune checkpoints. We further identified four drugs based on their different IC50 values from the two risk groups. Subsequently, we discovered two molecular subtypes of glioma (cluster one and cluster two), with the cluster one subtype exhibiting a remarkably longer OS compared to the cluster two subtype. Finally, we observed that inhibition of LEF1-AS1 curbed the proliferation, migration, and invasion of glioma cells. Conclusion: The CRL signatures were confirmed as a reliable prognostic and therapy response indicator for glioma patients. Inhibition of LEF1-AS1 effectively suppressed the growth, migration, and invasion of gliomas; therefore, LEF1-AS1 presents itself as a promising prognostic biomarker and potential therapeutic target for glioma.

Association between altered metabolism and genetic mutations in human glioma.

Molecular markers for classification of gliomas include isocitrate dehydrogenase (IDH) mutations and codeletion of chromosomal arms 1p and 19q (1p/19q). While mutations in IDH enzymes result in the well-characterized production of oncometabolite 2-hydroxyglutarate, dysregulation of other metabolites in IDH tumors is less characterized. Similarly, the effects of 1p/19q codeletion on cellular metabolism are also unclear. This study aimed to quantify changes in tumor metabolites in human glioma tissue as a function of both IDH mutation and 1p/19q codeletion. Deidentified human glioma tissue and associated clinical data were obtained from the Emory University Winship Cancer Institute tissue biobank from 14 patients (WHO grades II, III, and IV; seven female and seven male). Proton (1 H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy data were acquired using a 600 MHz Bruker AVANCE III NMR spectrometer. Metabolite concentrations were calculated using LCModel. Differences in metabolite concentrations as a function of IDH mutation, 1p/19q codeletion, and survival status were determined using Mann-Whitney U tests. Concentrations of alanine, glutamine, and glutamate were significantly lower in glioma tissue with IDH mutations compared to tissue with IDH wildtype. Additionally, glutamate concentration was significantly lower in glioma tissue with 1p/19q codeletion compared to intact 1p/19q. Exploratory analysis revealed alanine concentration varied significantly as a function of survival status. Given the emerging landscape of glioma treatments that target metabolic dysregulation, an improved understanding of altered metabolism in molecular sub-types of gliomas, including those with IDH mutation and 1p/19q codeletion, is an important consideration for treatment stratification and personalized medicine.

Liquid Biopsy of Cerebrospinal Fluid Enables Selective Profiling of Glioma Molecular Subtypes at First Clinical Presentation.

Current glioma diagnostic guidelines call for molecular profiling to stratify patients into prognostic and treatment subgroups. In case the tumor tissue is inaccessible, cerebrospinal fluid (CSF) has been proposed as a reliable tumor DNA source for liquid biopsy. We prospectively investigated the use of CSF for molecular characterization of newly diagnosed gliomas. We recruited two cohorts of newly diagnosed patients with glioma, one (n = 45) providing CSF collected in proximity of the tumor, the other (n = 39) CSF collected by lumbar puncture (LP). Both cohorts provided tumor tissues by surgery concomitant with CSF sampling. DNA samples retrieved from CSF and matched tumors were systematically characterized and compared by comprehensive (NGS, next-generation sequencing) or targeted (ddPCR, droplet digital PCR) methodologies. Conventional and molecular diagnosis outcomes were compared. We report that tumor DNA is abundant in CSF close to the tumor, but scanty and mostly below NGS sensitivity threshold in CSF from LP. Indeed, tumor DNA is mostly released by cells invading liquoral spaces, generating a gradient that attenuates by departing from the tumor. Nevertheless, in >60% of LP CSF samples, tumor DNA is sufficient to assess a selected panel of genetic alterations (IDH and TERT promoter mutations, EGFR amplification, CDKN2A/B deletion: ITEC protocol) and MGMT methylation that, combined with imaging, enable tissue-agnostic identification of main glioma molecular subtypes. This study shows potentialities and limitations of CSF liquid biopsy in achieving molecular characterization of gliomas at first clinical presentation and proposes a protocol to maximize diagnostic information retrievable from CSF DNA.

Correlation between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma(LGG).

Glioma is the most common primary tumor in the brain.Integrin beta 2(ITGB2) is a member of the leukocyte integrin family (leukocyte integrin), participating in lymphocyte recycling and homing, cell adhesion, and cell surface-mediated signal transduction. However, few studies on ITGB2 in gliomas have been reported yet.This study first discussed the relationship between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma. We collected Clinical data and transcription of glioma patients from TCGA, CGGA, and Rembrant datasets to analyze the differential expression of ITGB2 mRNA in glioma tissues and normal tissues. The box polts to evaluated the expression patterns of ITGB2 in different molecular subtypes. Receiver operating characteristic curve (ROC) were used to evaluate and verify the reliability of the model. Kaplan-Meier survival curves to evaluated the relationship between the level of ITGB2 mRNA expression and overall survival (OS). Using cox regression analysis to verify the ability of ITGB2 as an independent predictor of OS in glioma patients. We use TIMER to analyze and visualize the association between immune infiltration levels and a range of variables. The methylation of GBMLGG patients were obtained from the TCGA database through the biological portal. ITGB2 can be a potential marker for mesenchymal molecular subtype gliomas. COX regression analysis shows that ITGB2 is an independent predictive marker of OS in malignant glioma patients. Biological processes show that ITGB2 has involved glioma immune-related activities, especially closely related to B cells, CD4+Tcells, macrophages, neutrophils, and dendritic cells. ITGB2 is negatively regulated by ITGB2 methylation, resulting in low expression in LGG tissues. Low expression of ITGB2 and high methylation indicate good OS in patients with LGG. The ITGB2 methylation risk score (ITMRS) obtained from the ITGB2 methylation CpG site can better predict the OS of LGG patients. We used univariate and multivariate cox regression analysis of methylationsites, used the R language predict function to obtain the risk score of these ITGB2 methylation sites(ITMRS). ITGB2 can be used as a potential marker of mesenchymal molecular subtypes of gliomas and as an independent predictive marker of OS in patients with malignant gliomas. The ITMRS we established can be used as an independent prognostic factor for LGG and provide a new idea for the diagnosis and treatment of LGG.

Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.

The WHO classification since 2016 confirms the importance of integrating molecular diagnosis for prognosis and treatment decisions of adult-type diffuse gliomas. This motivates the development of non-invasive diagnostic methods, in particular MRI, to predict molecular subtypes of gliomas before surgery. At present, this development has been focused on deep-learning (DL)-based predictive models, mainly with conventional MRI (cMRI), despite recent studies suggesting multi-shell diffusion MRI (dMRI) offers complementary information to cMRI for molecular subtyping. The aim of this work is to evaluate the potential benefit of combining cMRI and multi-shell dMRI in DL-based models. A model implemented with deep residual neural networks was chosen as an illustrative example. Using a dataset of 146 patients with gliomas (from grade 2 to 4), the model was trained and evaluated, with nested cross-validation, on pre-operative cMRI, multi-shell dMRI, and a combination of the two for the following classification tasks: (i) IDH-mutation; (ii) 1p/19q-codeletion; and (iii) three molecular subtypes according to WHO 2021. The results from a subset of 100 patients with lower grades gliomas (2 and 3 according to WHO 2016) demonstrated that combining cMRI and multi-shell dMRI enabled the best performance in predicting IDH mutation and 1p/19q codeletion, achieving an accuracy of 75 ± 9% in predicting the IDH-mutation status, higher than using cMRI and multi-shell dMRI separately (both 70 ± 7%). Similar findings were observed for predicting the 1p/19q-codeletion status, with the accuracy from combining cMRI and multi-shell dMRI (72 ± 4%) higher than from each modality used alone (cMRI: 65 ± 6%; multi-shell dMRI: 66 ± 9%). These findings remain when we considered all 146 patients for predicting the IDH status (combined: 81 ± 5% accuracy; cMRI: 74 ± 5%; multi-shell dMRI: 73 ± 6%) and for the diagnosis of the three molecular subtypes according to WHO 2021 (combined: 60 ± 5%; cMRI: 57 ± 8%; multi-shell dMRI: 56 ± 7%). Together, these findings suggest that combining cMRI and multi-shell dMRI can offer higher accuracy than using each modality alone for predicting the IDH and 1p/19q status and in diagnosing the three molecular subtypes with DL-based models.

Yes-Associated Protein Nuclear Translocation Is Regulated by Epidermal Growth Factor Receptor Activation Through Phosphatase and Tensin Homolog/AKT Axis in Glioblastomas

Gliomas are the most common and lethal primary brain tumors in adults. Glioblastomas, the most frequent and aggressive form of gliomas, represent a therapeutic challenge as no curative treatment exists to date, and the prognosis remains extremely poor. Recently, the transcriptional cofactors Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) belonging to the Hippo pathway have emerged as a major determinant of malignancy in solid tumors, including gliomas. However, the mechanisms involved in its regulation, particularly in brain tumors, remain ill-defined. In glioblastomas, EGFR represents one of the most altered oncogenes affected by chromosomal rearrangements, mutations, amplifications, and overexpression. In this study, we investigated the potential link between epidermal growth factor receptor (EGFR) and the transcriptional cofactors YAP and TAZ by in situ and in vitro approaches. We first studied their activation on tissue microarray, including 137 patients from different glioma molecular subtypes. We observed that YAP and TAZ nuclear location was highly associated with isocitrate dehydrogenase 1/2 (IDH1/2) wild-type glioblastomas and poor patient outcomes. Interestingly, we found an association between EGFR activation and YAP nuclear location in glioblastoma clinical samples, suggesting a link between these 2 markers contrary to its ortholog TAZ. We tested this hypothesis in patient-derived glioblastoma cultures by pharmacologic inhibition of EGFR using gefinitib. We showed an increase of S397-YAP phosphorylation associated with decreased AKT phosphorylation after EGFR inhibition in phosphatase and tensin homolog (PTEN) wild-type cultures, unlike PTEN-mutated cell lines. Finally, we used bpV(HOpic), a potent PTEN inhibitor, to mimic the effect of PTEN mutations. We found that the inhibition of PTEN was sufficient to revert back the effect induced by Gefitinib in PTEN-wild-type cultures. Altogether, to our knowledge, these results show for the first time the regulation of pS397-YAP by the EGFR-AKT axis in a PTEN-dependent manner.

The E3 ubiquitin ligase RBCK1: Implications in the tumor immune microenvironment and antiangiogenic therapy of glioma

E3 ubiquitin ligases (E3s) play a pivotal role in regulating the specificity of protein ubiquitination, and their significant functions as regulators of immune responses against tumors are attracting considerable interest. RBCK1—an RBR E3 ligase—is involved in immune regulation and tumor development. However, the potential effect of RBCK1 on glioma remains enigmatic. In the present study, we performed comprehensive analyses of multilevel data, which disclosed distribution characteristics of RBCK1 in pan-cancer, especially in glioma. Functional roles of RBCK1 were further confirmed using immunohistochemistry, cell biological assays, and xenograft experiments. Aberrant ascending of RBCK1 in multiple types of cancer was found to remodel the immunosuppressive microenvironment of glioma by regulating immunomodulators, cancer immunity cycles, and immune cell infiltration. Notably, the MES-like/RBCK1High cell population, a unique subset of cells in the microenvironment, suppressed T cell-mediated cell killing in glioma. Elevated expression levels of RBCK1 suggested a glioma subtype characterized by immunosuppression and hypo-responsiveness to immunotherapy but manifesting surprisingly increased responses to anti-angiogenic therapy. In conclusion, anti-RBCK1 target therapy might be beneficial for glioma treatment. Moreover, RBCK1 assisted in predicting molecular subtypes of glioma and response rates of patients to different clinical treatments, which could guide personalized therapy.

Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review.

The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.

A Longitudinal Study to Evaluate Biomarkers in Brain Tumour Patients

Abstract AIMS To monitor brain tumour patient blood for predictive and prognostic biomarkers, in a prospective longitudinal study. Continuing the research of Dr. Abel the results may be used to predict tumour recurrence and progression from low- to high-grade. The results may be useful in the clinical management of malignant glioma patients. METHOD A patented panel of biomarkers will be investigated using FFPE tissue and blood components from adult brain tumour patients in the North West region. Biomarker expression will be analysed in different molecular sub-types of glioma, grouped by IDH and methylation status. An evaluation will be conducted to determine if reference ranges can be created to distinguish between high-and low-grade tumours. Spectroscopic analysis of blood samples will be tested to determine if cancerous and non-cancerous can be differentiated from each other. There will be testing of selected miRNA’s for up and down regulation patterns. Sample Intervals: Initial diagnosis, 24 hours post-surgery, pre-therapy, post-radiotherapy, after adjuvant therapy, at 3 then 6 monthly post therapy, at recurrence or suspected recurrence and at intervals during further therapy and follow up. RESULTS There are no results to report at this point in the study time line. Testing to start in April 2022. CONCLUSION The potential impact of research would work towards lower patient risk and less invasive procedures. If used as a screening tool could allow high NHS savings. There could be an impact on patient experience and survival if recurrence could be detected [NICE guideline NG99] recommendation for research.

Regulation of TORC1 by MAPK Signaling Determines Sensitivity and Acquired Resistance to Trametinib in Pediatric BRAFV600E Brain Tumor Models.

We investigated why three patient-derived xenograft (PDX) childhood BRAFV600E-mutant brain tumor models are highly sensitive to trametinib. Mechanisms of acquired resistance selected in situ, and approaches to prevent resistance were also examined, which may translate to both low-grade glioma (LGG) molecular subtypes. Sensitivity to trametinib [MEK inhibitor (MEKi)] alone or in combination with rapamycin (TORC1 inhibitor), was evaluated in pediatric PDX models. The effect of combined treatment of trametinib with rapamycin on development of trametinib resistance in vivo was examined. PDX tissue and tumor cells from trametinib-resistant xenografts were characterized. In pediatric models TORC1 is activated through ERK-mediated inactivation of the tuberous sclerosis complex (TSC): consequently inhibition of MEK also suppressed TORC1 signaling. Trametinib-induced tumor regression correlated with dual inhibition of MAPK/TORC1 signaling, and decoupling TORC1 regulation from BRAF/MAPK control conferred trametinib resistance. In mice, acquired resistance to trametinib developed within three cycles of therapy in all three PDX models. Resistance to trametinib developed in situ is tumor-cell-intrinsic and the mechanism was tumor line specific. Rapamycin retarded or blocked development of resistance. In these three pediatric BRAF-mutant brain tumors, TORC1 signaling is controlled by the MAPK cascade. Trametinib suppressed both MAPK/TORC1 pathways leading to tumor regression. While low-dose intermittent rapamycin to enhance inhibition of TORC1 only modestly enhanced the antitumor activity of trametinib, it prevented or retarded development of trametinib resistance, suggesting future therapeutic approaches using rapamycin analogs in combination with MEKis that may be therapeutically beneficial in both KIAA1549::BRAF- and BRAFV600E-driven gliomas.

CXCL14 Promotes a Robust Brain Tumor-Associated Immune Response in Glioma.

The immunosuppressive tumor microenvironment present in the majority of diffuse glioma limits therapeutic response to immunotherapy. As the determinants of the glioma-associated immune response are relatively poorly understood, the study of glioma with more robust tumor-associated immune responses may be particularly useful to identify novel immunomodulatory factors that can promote T-cell effector function in glioma. We used multiplex immune-profiling, proteomic profiling, and gene expression analysis to define the tumor-associated immune response in two molecular subtypes of glioma and identify factors that may modulate this response. We then used patient-derived glioma cultures and an immunocompetent murine model for malignant glioma to analyze the ability of tumor-intrinsic factors to promote a CD8+ T-cell response. As compared with isocitrate dehydrogenase (IDH)-mutant astrocytoma, MAPK-activated pleomorphic xanthoastrocytoma (PXA) harbored increased numbers of activated cytotoxic CD8+ T cells and Iba1+ microglia/macrophages, increased MHC class I expression, enrichment of genes associated with antigen presentation and processing, and increased tumor cell secretion of the chemokine CXCL14. CXCL14 promoted activated CD8+ T-cell chemotaxis in vitro, recruited tumor-infiltrating CD8+ T cells in vivo, and prolonged overall survival in a cytotoxic T-cell-dependent manner. The immunomodulatory molecule B7-H3 was also highly expressed in PXA. We identify the MAPK-activated lower grade astrocytoma PXA as having an immune-rich tumor microenvironment and suggest this tumor may be particularly vulnerable to immunotherapeutic modulation. We also identify CXCL14 as an important determinant of the glioma-associated immune microenvironment, sufficient to promote an antitumor CD8+ T-cell response.

Telomerase Reverse Transcriptase Promoter Mutations in A Cohort Of Adult Gliomas - Clinicopathological Correlates.

Introduction: Gliomas were previously classified histologically, although now the latest WHO classification incorporates several molecular markers to classify these. Detection of TERT promoter mutations is assuming increased importance due to its relevance to prognostication. : The aim of this study was to determine the frequency of TERT promoter mutations, association of TERT promoter mutations with other molecular alterations and to assess the role of TERT promoter mutations in overall survival and progression-free survival in relation to histological and molecular glioma subtypes. This study analyzed a cohort of 107 adult patients with diffuse gliomas, WHO grades II and III and glioblastoma, by immunohistochemistry for IDH and ATRX mutations, FISH for 1p/19q co-deletions and PCR sequencing for TERT promoter mutation. Further, five glioma molecular sub-groups were derived using three molecular alteration and included the sub-groups with: i) IDH mutations only, ii) IDH and TERT mutations only, iii) IDH and 1p/19q co-deletion only, iv) Triple negative, and v) Triple positive. IDH mutations and 1p/19q co-deletions were individually and significantly associated with an improved progression free (P = 0.001 and P = 0.002, respectively) and overall survival (P = 0.000 and P = 0.005, respectively) in the present cohort of gliomas. TERT promoter mutations occurred frequently in anaplastic oligodendrogliomas (94%), oligodendrogliomas (87.5%) and glioblastomas (54%). Sub-division into molecular sub-groups showed that the triple-positive tumors carried the best prognosis, followed by IDH only, triple negative and finally the TERT mutation only tumors (P < 0.000). : This indicates that sub-classification using these molecular markers separates tumors into prognostically relevant categories.