Molecular Subtypes Of Glioblastoma Research Articles

Feeding the wrath with myelin

Kloosterman and colleagues studied molecular and cellular changes during radiation therapy and disease recurrence across molecular subtypes of glioblastoma. They uncovered a distinct immune–cancer cell metabolic crosstalk during proneural/oligodendrocyte progenitor cell-like to mesenchymal-like transition, wherein macrophages feed on cholesterol-rich myelin debris to provide lipids to mesenchymal tumor cells, thereby fueling glioblastoma growth.

Leveraging multi-omics data to infer regulators of mRNA 3' end processing in glioblastoma.

Alterations in mRNA 3' end processing and polyadenylation are widely implicated in the biology of many cancer types, including glioblastoma (GBM), one the most aggressive tumor types. Although several RNA-binding proteins (RBPs) responsible for alternative polyadenylation (APA) were identified from functional studies in cell lines, their contribution to the APA landscape in tumors in vivo was not thoroughly addressed. In this study we analyzed a large RNA-seq data set of glioblastoma (GBM) samples from The Cancer Genome Atlas (TCGA) to identify APA patterns differentiating the main molecular subtypes of GBM. We superimposed these to RBP footprinting data and to APA events occurring upon depletion of individual RBPs from a large panel tested by the ENCODE Consortium. Our analysis revealed 22 highly concordant and statistically significant RBP-APA associations, whereby changes in RBP expression were accompanied by APA in both TCGA and ENCODE datasets. Among these, we found a previously unknown PTBP1-regulated APA event in the PRRC2B gene and an HNRNPU-regulated event in the SC5D gene. Both of these were further supported by RNA-sequencing data of paired tumor center-periphery GBM samples obtained at the University Hospital of Basel. In addition, we validated the regulation of APA in PRRC2B by PTBP1 in siRNA-knockdown and overexpression experiments followed by RNA-sequencing in two glioblastoma cell lines. The transcriptome analysis workflow that we present here enables the identification of concordant RBP-APA associations in cancers.

Genomics and proteomics to determine novel molecular subtypes and predict the response to immunotherapy and the effect of bevacizumab in glioblastoma

Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

Comprehensive understanding of glioblastoma molecular phenotypes: classification, characteristics, and transition.

Among central nervous system-associated malignancies, glioblastoma (GBM) is the most common and has the highest mortality rate. The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide. In precision medicine, research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity, as well as the refractory nature of GBM toward therapy. Deep understanding of the different molecular expression patterns of GBM subtypes is critical. Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes. The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors. These subtypes also exhibit high plasticity in their regulatory pathways, oncogene expression, tumor microenvironment alterations, and differential responses to standard therapy. Herein, we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype. Furthermore, we review the mesenchymal transition mechanisms of GBM under various regulators.

EcGBMsub: an integrative stacking ensemble model framework based on eccDNA molecular profiling for improving IDH wild-type glioblastoma molecular subtype classification.

IDH wild-type glioblastoma (GBM) intrinsic subtypes have been linked to different molecular landscapes and outcomes. Accurate prediction of molecular subtypes of GBM is very important to guide clinical diagnosis and treatment. Leveraging machine learning technology to improve the subtype classification was considered a robust strategy. Several single machine learning models have been developed to predict survival or stratify patients. An ensemble learning strategy combines several basic learners to boost model performance. However, it still lacked a robust stacking ensemble learning model with high accuracy in clinical practice. Here, we developed a novel integrative stacking ensemble model framework (ecGBMsub) for improving IDH wild-type GBM molecular subtype classification. In the framework, nine single models with the best hyperparameters were fitted based on extrachromosomal circular DNA (eccDNA) molecular profiling. Then, the top five optimal single models were selected as base models. By randomly combining the five optimal base models, 26 different combinations were finally generated. Nine different meta-models with the best hyperparameters were fitted based on the prediction results of 26 different combinations, resulting in 234 different stacked ensemble models. All models in ecGBMsub were comprehensively evaluated and compared. Finally, the stacking ensemble model named "XGBoost.Enet-stacking-Enet" was chosen as the optimal model in the ecGBMsub framework. A user-friendly web tool was developed to facilitate accessibility to the XGBoost.Enet-stacking-Enet models (https://lizesheng20190820.shinyapps.io/ecGBMsub/).

CNSC-18. DIFFERENTIAL EFFECTS OF NEUROTRANSMITTERS ON THE GROWTH OF GLIOBLASTOMA SUBTYPES

Abstract Glioblastomas are aggressive brain tumors associated with the worst patient prognosis. Neurons play critical roles in modulating the growth of glioblastoma. It has been demonstrated that glutamatergic neurons form bona fide synapses with glioblastoma tumor cells that are critical for tumor cell proliferation and migration. It is less clear whether the activity of other neuronal types influences glioblastoma pathophysiology, especially among the different glioblastoma molecular subtypes. Here, we examined the effects of neurotransmitters on the growth of glioblastoma cells characterized as the classical, proneural and mesenchymal subtypes. Patient-derived glioblastoma cell lines were exposed to varying concentrations of different neurotransmitters for 72 hours and assessed for cell viability using the CCK-8 assay. RESULTS: from the CCK8 assay indicate that acetylcholine and dopamine increase glioblastoma cell growth. Consistent with previous findings, we observed an increase in cell viability in all cell lines after being incubated with L-glutamate. The classical subtype has a stronger growth response to L-glutamate compared to the other two subtypes. Transcriptomic data from TCGA showed that the expression of GRIK4 (glutamate ionotropic receptor kainate type subunit 4) is the highest in the classical subtype. By comparing GRIK4 expression with patient survival in TCGA glioblastoma cases stratified by molecular subtypes, we found that high GRIK4 expression correlates with worse prognosis in the classical subtype, but not the proneural or mesenchymal patient groups. Our findings suggest that each glioblastoma subtype may differentially leverage neuromodulatory mechanisms to support their own growth. Ongoing studies are investigating the differential neuronal responses in glioblastoma subtypes using both pharmacological and genetic approaches.

Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics.

The aim of this study was to build a convolutional neural network (CNN)-based prediction model of glioblastoma (GBM) molecular subtype diagnosis and prognosis with multimodal features. In total, 222 GBM patients were included in the training set from Sun Yat-sen University Cancer Center (SYSUCC) and 107 GBM patients were included in the validation set from SYSUCC, Xuanwu Hospital Capital Medical University, and the First Hospital of Jilin University. The multimodal model was trained with MR images (pre- and postcontrast T1-weighted images and T2-weighted images), corresponding MRI impression, and clinical patient information. First, the original images were segmented using the Multimodal Brain Tumor Image Segmentation Benchmark toolkit. Convolutional features were extracted using 3D residual deep neural network (ResNet50) and convolutional 3D (C3D). Radiomic features were extracted using pyradiomics. Report texts were converted to word embedding using word2vec. These three types of features were then integrated to train neural networks. Accuracy, precision, recall, and F1-score were used to evaluate the model performance. The C3D-based model yielded the highest accuracy of 91.11% in the prediction of IDH1 mutation status. Importantly, the addition of semantics improved precision by 11.21% and recall in MGMT promoter methylation status prediction by 14.28%. The areas under the receiver operating characteristic curves of the C3D-based model in the IDH1, ATRX, MGMT, and 1-year prognosis groups were 0.976, 0.953, 0.955, and 0.976, respectively. In external validation, the C3D-based model showed significant improvement in accuracy in the IDH1, ATRX, MGMT, and 1-year prognosis groups, which were 88.30%, 76.67%, 85.71%, and 85.71%, respectively (compared with 3D ResNet50: 83.51%, 66.67%, 82.14%, and 70.79%, respectively). The authors propose a novel multimodal model integrating C3D, radiomics, and semantics, which had a great performance in predicting IDH1, ATRX, and MGMT molecular subtypes and the 1-year prognosis of GBM.

Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis.

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Abstract 3167: The scaffolding protein DLG5 is necessary to maintain Sonic Hedgehog signaling in glioblastoma stem cells and promote tumor growth

Abstract Proteins complexes that maintain the apico-basal polarity of epithelial cells and organize cell-signaling domains are necessary for normal tissue organization and therefore regarded as tumor suppressors. Accordingly, the scaffolding proteins that form the Scribble polarity complex (SCRIB, LLGL1, and DLG members) are usually downregulated in cancer, in particular in highly aggressive or invasive tumors. Because gliomas arise from non-epithelial precursors that are likely motile before transformation, the expression and functions of these scaffolding proteins in malignant brain tumors has remained almost entirely unexplored. Surprisingly, we have found that one member of the DLG family, DLG5, is highly upregulated in malignant gliomas compared to other solid tumors. Indeed, DLG5 was the predominant DLG member found in all molecular subtypes of glioblastoma (GBM). Knockdown of DLG5 reduced the viability, invasiveness, and self-renewal of GBM stem cells belonging to the proneural and mesenchymal molecular subtypes. As a result, intracranial tumors defficient in DLG5 were smaller and less proliferative than controls, resulting in extended animal survival. At the molecular level, DLG5 knockdown decreased the expression of stemness-related genes (SOX2, NANOG, POU5F1) but increased the expression of LLGL1, a component of the Scribble complex that is associated with neural precursor differentiation. These changes were accompanied by downregulation of Sonic Hedgehog (SHh) signaling, both in vitro and in vivo, which drives GBM cell stemness and proliferation. Moreover, DLG5-deficient GBM stem cells became insensitive to exogenous SHh and were unable to upregulate Gli1 or increase cell invasion in presence of added SHh, compared to control cells. We further confirmed that DLG5 downregulation increased the ubiquitination of Gli1-containing complexes as well as Gli1 proteasomal degradation, providing a suitable mechanism for the loss of SHh signaling and tumor stemness. These results describe for the first time the expression of a DLG family member in malignant gliomas and reveal a novel, polarity-independent, pro-tumoral function of DLG5, which differs from its proposed tumor-suppressor role in other solid tumors. Citation Format: Somanath Kundu, Mohan S. Nandhu, Sharon L. Longo, John A. Longo, Shawn Rai, Lawrence S. Chin, Timothy E. Richardson, Mariano S. Viapiano. The scaffolding protein DLG5 is necessary to maintain Sonic Hedgehog signaling in glioblastoma stem cells and promote tumor growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3167.

EPHA2 mediates PDGFA activity and functions together with PDGFRA as prognostic marker and therapeutic target in glioblastoma

Platelet-derived growth subunit A (PDGFA) plays critical roles in development of glioblastoma (GBM) with substantial evidence from TCGA database analyses and in vivo mouse models. So far, only platelet-derived growth receptor α (PDGFRA) has been identified as receptor for PDGFA. However, PDGFA and PDGFRA are categorized into different molecular subtypes of GBM in TCGA_GBM database. Our data herein further showed that activity or expression deficiency of PDGFRA did not effectively block PDGFA activity. Therefore, PDGFRA might be not necessary for PDGFA function.To profile proteins involved in PDGFA function, we performed co-immunoprecipitation (Co-IP) and Mass Spectrum (MS) and delineated the network of PDGFA-associated proteins for the first time. Unexpectedly, the data showed that EPHA2 could be temporally activated by PDGFA even without activation of PDGFRA and AKT. Furthermore, MS, Co-IP, in vitro binding thermodynamics, and proximity ligation assay consistently proved the interaction of EPHA2 and PDGFA. In addition, we observed that high expression of EPHA2 leaded to upregulation of PDGF signaling targets in TCGA_GBM database and clinical GBM samples. Co-upregulation of PDGFRA and EPHA2 leaded to worse patient prognosis and poorer therapeutic effects than other contexts, which might arise from expression elevation of genes related with malignant molecular subtypes and invasive growth. Due to PDGFA-induced EPHA2 activation, blocking PDGFRA by inhibitor could not effectively suppress proliferation of GBM cells, but simultaneous inhibition of both EPHA2 and PDGFRA showed synergetic inhibitory effects on GBM cells in vitro and in vivo. Taken together, our study provided new insights on PDGFA function and revealed EPHA2 as a potential receptor of PDGFA. EPHA2 might contribute to PDGFA signaling transduction in combination with PDGFRA and mediate the resistance of GBM cells to PDGFRA inhibitor. Therefore, combination of inhibitors targeting PDGFRA and EHA2 represented a promising therapeutic strategy for GBM treatment.

The scaffolding protein DLG5 promotes glioblastoma growth by controlling Sonic Hedgehog signaling in tumor stem cells.

Tumor invasion, a hallmark of malignant gliomas, involves reorganization of cell polarity and changes in the expression and distribution of scaffolding proteins associated with polarity complexes. The scaffolding proteins of the DLG family are usually downregulated in invasive tumors and regarded as tumor suppressors. Despite their important role in regulating neurodevelopmental signaling, the expression and functions of DLG proteins have remained almost entirely unexplored in malignant gliomas. Western blot, immunohistochemistry, and analysis of gene expression were used to quantify DLG members in glioma specimens and cancer datasets. Over-expression and knockdown of DLG5, the highest-expressed DLG member in glioblastoma, were used to investigate its effects on tumor stem cells and tumor growth. qRT-PCR, Western blotting, and co-precipitation assays were used to investigate DLG5 signaling mechanisms. DLG5 was upregulated in malignant gliomas compared to other solid tumors, being the predominant DLG member in all glioblastoma molecular subtypes. DLG5 promoted glioblastoma stem cell invasion, viability, and self-renewal. Knockdown of this protein in vivo disrupted tumor formation and extended survival. At the molecular level, DLG5 regulated Sonic Hedgehog (Shh) signaling, making DLG5-deficient cells insensitive to Shh ligand. Loss of DLG5 increased the proteasomal degradation of Gli1, underlying the loss of Shh signaling and tumor stem cell sensitization. The high expression and pro-tumoral functions of DLG5 in glioblastoma, including its dominant regulation of Shh signaling in tumor stem cells, reveal a novel role for this protein that is strikingly different from its proposed tumor-suppressor role in other solid tumors.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents. Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Targeting the HuR Oncogenic Role with a New Class of Cytoplasmic Dimerization Inhibitors.

The development of novel therapeutics that exploit alterations in the activation state of key cellular signaling pathways due to mutations in upstream regulators has generated the field of personalized medicine. These first-generation efforts have focused on actionable mutations identified by deep sequencing of large numbers of tumor samples. We propose that a second-generation opportunity exists by exploiting key downstream "nodes of control" that contribute to oncogenesis and are inappropriately activated due to loss of upstream regulation and microenvironmental influences. The RNA-binding protein HuR represents such a node. Because HuR functionality in cancer cells is dependent on HuR dimerization and its nuclear/cytoplasmic shuttling, we developed a new class of molecules targeting HuR protein dimerization. A structure-activity relationship algorithm enabled development of inhibitors of HuR multimer formation that were soluble, had micromolar activity, and penetrated the blood-brain barrier. These inhibitors were evaluated for activity validation and specificity in a robust cell-based assay of HuR dimerization. SRI-42127, a molecule that met these criteria, inhibited HuR multimer formation across primary patient-derived glioblastoma xenolines (PDGx), leading to arrest of proliferation, induction of apoptosis, and inhibition of colony formation. SRI-42127 had favorable attributes with central nervous system penetration and inhibited tumor growth in mouse models. RNA and protein analysis of SRI-42127-treated PDGx xenolines across glioblastoma molecular subtypes confirmed attenuation of targets upregulated by HuR. These results highlight how focusing on key attributes of HuR that contribute to cancer progression, namely cytoplasmic localization and multimerization, has led to the development of a novel, highly effective inhibitor. SIGNIFICANCE: These findings utilize a cell-based mechanism of action assay with a structure-activity relationship compound development pathway to discover inhibitors that target HuR dimerization, a mechanism required for cancer promotion.

A retrospective analysis of GSE84010: Cell adhesion molecules might contribute to bevacizumab resistance in glioblastoma

Bevacizumab (BEV) is an anti-angiogenesis antibody which has shown favorable therapeutic effects on some solid tumors. However, many clinical trials showed that BEV could only improve PFS instead of OS in glioblastoma (GBM) patients. However, some studies indicate that specific molecular subtypes of GBM could still benefit from combination treatment of BEV and Stupp protocol. Through the subgroup analysis of GSE84010 dataset, we found the neural and proneural subgroup can benefit from the administration of BEV in terms of OS, which is statistically significant. The further KEGG pathway enrichment analysis showed cell adhesion molecules (CAMs) pathway was enriched, and the expression of ITGAM has a predictive value for prognosis. These findings can provide some hints for future administration of BEV in newly diagnosed GBM patients.

Mapping the Intratumoral Heterogeneity in Glioblastomas with Hyperspectral Stimulated Raman Scattering Microscopy.

Recent genomic studies on the glioblastoma (GBM) subtypes (e.g., mesenchymal, proneural, and classical) pave a way for effective clinical treatments of the recurrent brain tumor. However, identification of the GBM subtype is complicated by the intratumoral heterogeneity that results in coexistence of multiple subtypes within the tissue specimen. Here, we present the use of hyperspectral stimulated Raman scattering (SRS) microscopy for rapid, label-free molecular assessment of GBM intratumoral heterogeneity with submicron resolution. We develop a unique label-free Raman imaging diagnostic platform consisting of the spectral focusing hyperspectral SRS imaging of the large-area GBM tissue specimens, SRS images, and spectrum retrieval using the multivariate curve resolution algorithm and subtype classification based on the quadratic support vector machine model for rapid molecular subtyping of GBMs. Both the stain-free SRS histological images and 2D subtype maps can be obtained within 20-30 min which is superior to the days of the conventional single-cell RNA sequencing. While the SRS histology assesses the demyelination status as a new diagnostic feature, the SRS mapping provides a new insight into intratumoral heterogeneity across GBM tissue specimens. We find that the major proportions of the GBM tissues agree with the diagnostic results of the genomic analysis, but nontrivial portions of the remaining SRS image tiles in the specimens are found to belong to other molecular subtypes, implying the substantial degree of GBM heterogeneity. The rapid SRS imaging diagnostic platform developed has shown the ability of unveiling tumor heterogeneity in GBM tissues accurately, which would promote the improvement of the GBM-targeted therapy in near future.

TAMI-28. DIFFERENTIAL MIGRATION MECHANICS AND IMMUNE RESPONSES OF GLIOMA SUBTYPES

Abstract In Glioblastoma (GBM), tumor spreading is driven by tumor cells’ ability to infiltrate healthy brain parenchyma, which prevents complete surgical resection and contributes to tumor recurrence. GBM molecular subtypes, classical, proneural and mesenchymal, were shown to strongly correlate with specific genetic alterations (Mesenchymal: NF1; Classical: EGFRVIII; Proneural: PDGFRA). Here we tested the hypothesis that a key mechanistic difference between GBM molecular subtypes is that proneural cells are slow migrating and mesenchymal cells are fast migrating. Using Sleeping Beauty transposon system, immune-competent murine brain tumors were induced by SV40-LgT antigen in combination with either NRASG12V (NRAS) or PDGFB (PDGF) overexpression. Cross-species transcriptomic analysis revealed NRAS and PDGF-driven tumors correlate with human mesenchymal and proneural GBM, respectively. Similar to human GBM, CD44 expression was higher in NRAS tumors and, consistent with migration simulations of varying CD44 levels, ex vivo brain slice live imaging showed NRAS tumors cells migrate faster than PDGF tumors cells (random motility coefficient = 30µm2/hr vs. 2.5µm2/hr, p < 0.001). Consistent with CD44 function as an adhesion molecule, migration phenotype was independent of the tumor microenvironment. NRAS and human PDX/MES tumor cells were found to migrate faster and have larger cell spread area than PDGF and human PDX/PN tumors cells, respectively, in healthy mouse brain slices. Furthermore, traction force microscopy revealed NRAS tumor cells generate larger traction forces than PDGF tumors cells which further supports our theoretical mechanism driving glioma migration. Despite increased migration, NRAS cohort had better survival than PDGF which was attributed to enhanced antitumoral immune response in NRAS tumors, consistent with increased immune cell infiltration found in human mesenchymal GBM. Overall our work identified a potentially actionable difference in migration mechanics between GBM subtypes and establishes an integrated biophysical modeling and experimental approach to mechanically parameterize and simulate distinct molecular subtypes in preclinical models of cancer.

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy.

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

Improved prognostication of glioblastoma beyond molecular subtyping by transcriptional profiling of the tumor microenvironment.

Glioblastoma (GBM), the most aggressive form of brain cancer, is characterized by a high level of molecular heterogeneity, and infiltration by various immune and stromal cell populations. Important advances have been made in deciphering the microenvironment of GBMs, but its association with existing molecular subtypes and its potential prognostic role remain elusive. We have investigated the abundance of infiltrating immune and stromal cells in silico, from gene expression profiles. Two cohorts, including in‐house normal brain and glioma samples (n = 70) and a large sample set from TCGA (n = 393), were combined into a single exploratory dataset. A third independent cohort (n = 124) was used for validation. Tumors were clustered based on their microenvironment infiltration profiles, and associations with known GBM molecular subtypes and patient outcome were tested a posteriori in a multivariable setting. We identified a subset of GBM samples with significantly higher abundances of most immune and stromal cell populations. This subset showed increased expression of both immune suppressor and immune effector genes compared to other GBMs and was enriched for the mesenchymal molecular subtype. Survival analyses suggested that tumor microenvironment infiltration pattern was an independent prognostic factor for GBM patients. Among all, patients with the mesenchymal subtype with low immune and stromal infiltration had the poorest survival. By combining molecular subtyping with gene expression measures of tumor infiltration, the present work contributes with improving prognostic models in GBM.

Genomic and Phenotypic Characterization of a Broad Panel of Patient-Derived Xenografts Reflects the Diversity of Glioblastoma.

Glioblastoma is the most frequent and lethal primary brain tumor. Development of novel therapies relies on the availability of relevant preclinical models. We have established a panel of 96 glioblastoma patient-derived xenografts (PDX) and undertaken its genomic and phenotypic characterization. PDXs were established from glioblastoma, IDH-wildtype (n = 93), glioblastoma, IDH-mutant (n = 2), diffuse midline glioma, H3 K27M-mutant (n = 1), and both primary (n = 60) and recurrent (n = 34) tumors. Tumor growth rates, histopathology, and treatment response were characterized. Integrated molecular profiling was performed by whole-exome sequencing (WES, n = 83), RNA-sequencing (n = 68), and genome-wide methylation profiling (n = 76). WES data from 24 patient tumors was compared with derivative models. PDXs recapitulate many key phenotypic and molecular features of patient tumors. Orthotopic PDXs show characteristic tumor morphology and invasion patterns, but largely lack microvascular proliferation and necrosis. PDXs capture common and rare molecular drivers, including alterations of TERT, EGFR, PTEN, TP53, BRAF, and IDH1, most at frequencies comparable with human glioblastoma. However, PDGFRA amplification was absent. RNA-sequencing and genome-wide methylation profiling demonstrated broad representation of glioblastoma molecular subtypes. MGMT promoter methylation correlated with increased survival in response to temozolomide. WES of 24 matched patient tumors showed preservation of most genetic driver alterations, including EGFR amplification. However, in four patient-PDX pairs, driver alterations were gained or lost on engraftment, consistent with clonal selection. Our PDX panel captures the molecular heterogeneity of glioblastoma and recapitulates many salient genetic and phenotypic features. All models and genomic data are openly available to investigators.

Extent of resection is important across glioblastoma molecular subtypes.

Extent of resection is important across glioblastoma molecular subtypes.