Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging

Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model

Glioblastoma (GBM) stands as the most prevalent and lethal malignant brain tumor, characterized by its highly infiltrative nature. This study aimed to identify additional MRI and metabolomic biomarkers of GBM and its impact on healthy tissue using an advanced-stage C6 glioma rat model. Wistar rats underwent a stereotactic injection of C6 cells (GBM group, n = 10) or cell medium (sham group, n = 4). A multiparametric MRI, including anatomical T2W and T1W images, relaxometry maps (T2, T2*, and T1), the magnetization transfer ratio (MTR), and diffusion tensor imaging (DTI), was performed. Additionally, ex vivo magnetic resonance spectroscopy (MRS) HRMAS spectra were acquired. The MRI analysis revealed significant differences in the T2 maps, T1 maps, MTR, and mean diffusivity parameters between the GBM tumor and the rest of the studied regions, which were the contralateral areas of the GBM rats and both regions of the sham rats (the ipsilateral and contralateral). The ex vivo spectra revealed markers of neuronal loss, apoptosis, and higher glucose uptake by the tumor. Notably, the myo-inositol and phosphocholine levels were elevated in both the tumor and the contralateral regions of the GBM rats compared to the sham rats, suggesting the effects of the tumor on the healthy tissue. The MRI parameters related to inflammation, cellularity, and tissue integrity, along with MRS-detected metabolites, serve as potential biomarkers for the tumor evolution, treatment response, and impact on healthy tissue. These techniques can be potent tools for evaluating new drugs and treatment targets.

Association of ADC of hyperintense lesions on FLAIR images with TERT promoter mutation status in glioblastoma IDH wild type

Background

Although mutations in telomerase reverse transcriptase (TERT) promoter (TERTp) are the most common alterations in glioblastoma (GBM), predicting TERTp mutation status by preoperative imaging is difficult. We determined whether tumour-surrounding hyperintense lesions on fluid-attenuated inversion recovery (FLAIR) were superior to those of contrast-enhanced lesions (CELs) in assessing TERTp mutation status using magnetic resonance imaging (MRI).

Methods

This retrospective study included 114 consecutive patients with primary isocitrate dehydrogenase (IDH)-wild-type GBM. The apparent diffusion coefficient (ADC) and volume of CELs and FLAIR hyperintense lesions (FHLs) were determined, and the correlation between MRI features and TERTp mutation status was analyzed. In a subset of cases, FHLs were histopathologically analyzed to determine the correlation between tumor cell density and ADC.

Results

TERTp mutations were present in 77 (67.5%) patients. The minimum ADC of FHLs was significantly lower in the TERTp-mutant group than in the TERTp-wild-type group (mean, 958.9 × 10-3 and 1092.1 × 10-3 mm2/s, respectively, P < 0.01). However, other MRI features, such as CEL and FHL volumes, minimum ADC of CELs, and FHL/CEL ratio, were not significantly different between the two groups. Histopathologic analysis indicated high tumor cell density in FHLs with low ADC.

Conclusion

The ADC of FHLs was significantly lower in IDH-wild-type GBM with TERTp mutations, suggesting that determining the ADC of FHLs on preoperative MRI might be helpful in predicting TERTp mutation status and surgical planning.

White Matter Tract Density Index Prediction Model of Overall Survival in Glioblastoma

Importance

The prognosis of overall survival (OS) in patients with glioblastoma (GBM) may depend on the underlying structural connectivity of the brain.

Objective

To examine the association between white matter tracts affected by GBM and patients' OS by means of a new tract density index (TDI).

Design, Setting, and Participants

This prognostic study in patients with a histopathologic diagnosis of GBM examined a discovery cohort of 112 patients who underwent surgery between February 1, 2015, and November 30, 2020 (follow-up to May 31, 2023), in Italy and 70 patients in a replicative cohort (n = 70) who underwent surgery between September 1, 2012, and November 30, 2015 (follow-up to May 31, 2023), in Germany. Statistical analyses were performed from June 1, 2021, to May 31, 2023. Thirteen and 12 patients were excluded from the discovery and the replicative sets, respectively, because of magnetic resonance imaging artifacts.

Exposure

The density of white matter tracts encompassing GBM.

Main Outcomes and Measures

Correlation, linear regression, Cox proportional hazards regression, Kaplan-Meier, and prediction analysis were used to assess the association between the TDI and OS. Results were compared with common prognostic factors of GBM, including age, performance status, O6-methylguanine-DNA methyltransferase methylation, and extent of surgery.

Results

In the discovery cohort (n = 99; mean [SD] age, 62.2 [11.5] years; 29 female [29.3%]; 70 male [70.7%]), the TDI was significantly correlated with OS (r = -0.34; P < .001). This association was more stable compared with other prognostic factors. The TDI showed a significant regression pattern (Cox: hazard ratio, 0.28 [95% CI, 0.02-0.55; P = .04]; linear: t = -2.366; P = .02). and a significant Kaplan-Meier stratification of patients as having lower or higher OS based on the TDI (log-rank test = 4.52; P = .03). Results were confirmed in the replicative cohort (n = 58; mean [SD] age, 58.5 [11.1] years, 14 female [24.1%]; 44 male [75.9%]). High (24-month cutoff) and low (18-month cutoff) OS was predicted based on the TDI computed in the discovery cohort (accuracy = 87%).

Conclusions and Relevance

In this study, GBMs encompassing regions with low white matter tract density were associated with longer OS. These findings indicate that the TDI is a reliable presurgical outcome predictor that may be considered in clinical trials and clinical practice. These findings support a framework in which the outcome of GBM depends on the patient's brain organization.

Glioblastoma modeling with 3D organoids: progress and challenges

Glioblastoma (GBM) is the most aggressive adult primary brain tumor with nearly universal treatment resistance and recurrence. The mainstay of therapy remains maximal safe surgical resection followed by concurrent radiation therapy and temozolomide chemotherapy. Despite intensive investigation, alternative treatment options, such as immunotherapy or targeted molecular therapy, have yielded limited success to achieve long-term remission. This difficulty is partly due to the lack of pre-clinical models that fully recapitulate the intratumoral and intertumoral heterogeneity of GBM and the complex tumor microenvironment. Recently, GBM 3D organoids originating from resected patient tumors, genetic manipulation of induced pluripotent stem cell (iPSC)-derived brain organoids and bio-printing or fusion with non-malignant tissues have emerged as novel culture systems to portray the biology of GBM. Here, we highlight several methodologies for generating GBM organoids and discuss insights gained using such organoid models compared to classic modeling approaches using cell lines and xenografts. We also outline limitations of current GBM 3D organoids, most notably the difficulty retaining the tumor microenvironment, and discuss current efforts for improvements. Finally, we propose potential applications of organoid models for a deeper mechanistic understanding of GBM and therapeutic development.

Validation of multiparametric MRI based prediction model in identification of pseudoprogression in glioblastomas

BACKGROUND

Accurate differentiation of pseudoprogression (PsP) from tumor progression (TP) in glioblastomas (GBMs) is essential for appropriate clinical management and prognostication of these patients. In the present study, we sought to validate the findings of our previously developed multiparametric MRI model in a new cohort of GBM patients treated with standard therapy in identifying PsP cases.

METHODS

Fifty-six GBM patients demonstrating enhancing lesions within 6 months after completion of concurrent chemo-radiotherapy (CCRT) underwent anatomical imaging, diffusion and perfusion MRI on a 3 T magnet. Subsequently, patients were classified as TP + mixed tumor (n = 37) and PsP (n = 19). When tumor specimens were available from repeat surgery, histopathologic findings were used to identify TP + mixed tumor (> 25% malignant features; n = 34) or PsP (< 25% malignant features; n = 16). In case of non-availability of tumor specimens, ≥ 2 consecutive conventional MRIs using mRANO criteria were used to determine TP + mixed tumor (n = 3) or PsP (n = 3). The multiparametric MRI-based prediction model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI derived parameters from contrast enhancing regions. In the next step, PP values were used to characterize each lesion as PsP or TP+ mixed tumor. The lesions were considered as PsP if the PP value was < 50% and TP+ mixed tumor if the PP value was ≥ 50%. Pearson test was used to determine the concordance correlation coefficient between PP values and histopathology/mRANO criteria. The area under ROC curve (AUC) was used as a quantitative measure for assessing the discriminatory accuracy of the prediction model in identifying PsP and TP+ mixed tumor.

RESULTS

Multiparametric MRI model correctly predicted PsP in 95% (18/19) and TP+ mixed tumor in 57% of cases (21/37) with an overall concordance rate of 70% (39/56) with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.56; p < 0.001). The ROC analyses revealed an accuracy of 75.7% in distinguishing PsP from TP+ mixed tumor. Leave-one-out cross-validation test revealed that 73.2% of cases were correctly classified as PsP and TP + mixed tumor.

CONCLUSIONS

Our multiparametric MRI based prediction model may be helpful in identifying PsP in GBM patients.

Inference of glioblastoma migration and proliferation rates using single time-point images

Cancer cell migration is a driving mechanism of invasion in solid malignant tumors. Anti-migratory treatments provide an alternative approach for managing disease progression. However, we currently lack scalable screening methods for identifying novel anti-migratory drugs. To this end, we develop a method that can estimate cell motility from single end-point images in vitro by estimating differences in the spatial distribution of cells and inferring proliferation and diffusion parameters using agent-based modeling and approximate Bayesian computation. To test the power of our method, we use it to investigate drug responses in a collection of 41 patient-derived glioblastoma cell cultures, identifying migration-associated pathways and drugs with potent anti-migratory effects. We validate our method and result in both in silico and in vitro using time-lapse imaging. Our proposed method applies to standard drug screen experiments, with no change needed, and emerges as a scalable approach to screen for anti-migratory drugs.

Valtrate, an iridoid compound in Valeriana, elicits anti-glioblastoma activity through inhibition of the PDGFRA/MEK/ERK signaling pathway

BACKGROUND

Valtrate, a natural compound isolated from the root of Valeriana, exhibits antitumor activity in many cancers through different mechanisms. However, its efficacy for the treatment of glioblastoma (GBM), a tumor type with a poor prognosis, has not yet been rigorously investigated.

METHODS

GBM cell lines were treated with valtrate and CCK-8, colony formation and EdU assays, flow cytometry, and transwell, 3D tumor spheroid invasion and GBM-brain organoid co-culture invasion assays were performed to assess properties of proliferation, viability, apoptosis and invasion/migration. RNA sequencing analysis on valtrate-treated cells was performed to identify putative target genes underlying the antitumor activity of the drug in GBM cells. Western blot analysis, immunofluorescence and immunohistochemistry were performed to evaluate protein levels in valtrate-treated cell lines and in samples obtained from orthotopic xenografts. A specific activator of extracellular signal-regulated kinase (ERK) was used to identify the pathways mediating the effect.

RESULTS

Valtrate significantly inhibited the proliferation of GBM cells in vitro by inducing mitochondrial apoptosis and suppressed invasion and migration of GBM cells by inhibiting levels of proteins associated with epithelial mesenchymal transition (EMT). RNA sequencing analysis of valtrate-treated GBM cells revealed platelet-derived growth factor receptor A (PDGFRA) as a potential target downregulated by the drug. Analysis of PDGFRA protein and downstream mediators demonstrated that valtrate inhibited PDGFRA/MEK/ERK signaling. Finally, treatment of tumor-bearing nude mice with valtrate led to decreased tumor volume (fivefold difference at day 28) and enhanced survival (day 27 vs day 36, control vs valtrate-treated) relative to controls.

CONCLUSIONS

Taken together, our study demonstrated that the natural product valtrate elicits antitumor activity in GBM cells through targeting PDGFRA and thus provides a candidate therapeutic compound for the treatment of GBM.

Secreted phosphoprotein 1 promotes angiogenesis of glioblastoma through upregulating PSMA expression via transcription factor HIF-1&alpha;

Glioblastoma multiforme (GBM) is a highly vascularized malignant brain tumor. Our previous study showed that prostate-specific membrane antigen (PSMA) promotes angiogenesis of GBM. However, the specific mechanism underlying GBM-induced PSMA upregulation remains unclear. In this study, we demonstrate that the GBM-secreted cytokine phosphoprotein 1 (SPP1) can regulate the expression of PSMA in human umbilical vein endothelial cells (HUVECs). Our mechanistic study further reveals that SPP1 regulates the expression of PSMA through the transcription factor HIF1α. Moreover, SPP1 promotes HUVEC migration and tube formation. In addition, HIF1α knockdown reduces the expression of PSMA in HUVECs and blocks the ability of SPP1 to promote HUVEC migration and tube formation. We further confirm that SPP1 is abundantly expressed in GBM, is associated with poor prognosis, and has high clinical diagnostic value with considerable sensitivity and specificity. Collectively, our findings identify that the GBM-secreted cytokine SPP1 upregulates PSMA expression in endothelial cells via the transcription factor HIF1α, providing insight into the angiogenic process and promising candidates for targeted GBM therapy.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

Peptide-based targeted cancer therapeutics: design, synthesis and biological evaluation

Cancer is the leading cause for human mortality together with cardiovascular diseases. Abl (Abelson) tyrosine kinases play a fundamental role in transducing various signals that control proliferation, survival, migration and invasion in several cancers such as Chronic Myeloid Leukemia (CML), breast cancer and brain cancer. For these reasons Abl tyrosine kinases are considered important biological targets in drug discovery. In this study a series of lysine-based oligopeptides with expected Abl inhibitory activity were designed resembling the binding of FDA-approved drugs (i.e. of Imatinib and Nilotinib), synthesized, purified by High Performance Liquid Chromatography (HPLC), analyzed by mass spectrometry (MS) and biologically tested in vitro in CML (AR-230 and K-562), breast cancers (MDA-MB 231 and MDA-MB 468) and glioblastoma cell lines (U87 and U118). The solid-phase peptide synthesis (SPPS) by Fmoc (9-fluorenylmethoxycarbonyl) chemistry was used to synthesize target compounds. AutoDock Vina was applied for simulation binding to Abl. The biological activities were measured evaluating cytotoxic effect, induction of apoptosis and inhibition of cancer cells migration. The new peptides exhibited different concentration-dependent antiproliferative effect against the tumor cell lines after 72 h treatment. The most promising results were obtained with the U87 glioblastoma cell line where a significant reduction of the migration ability was detected with one compound (H-Lys¹-Lys²-Lys³-NH₂).

Signaling Pathways Regulating the Expression of the Glioblastoma Invasion Factor TENM1

Glioblastoma (GBM) is one of the most aggressive cancers, with dismal prognosis despite continuous efforts to improve treatment. Poor prognosis is mostly due to the invasive nature of GBM. Thus, most research has focused on studying the molecular players involved in GBM cell migration and invasion of the surrounding parenchyma, trying to identify effective therapeutic targets against this lethal cancer. Our laboratory discovered the implication of TENM1, also known as ODZ1, in GBM cell migration in vitro and in tumor invasion using different in vivo models. Moreover, we investigated the microenvironmental stimuli that promote the expression of TENM1 in GBM cells and found that macrophage-secreted IL-6 and the extracellular matrix component fibronectin upregulated TENM1 through activation of Stat3. We also described that hypoxia, a common feature of GBM tumors, was able to induce TENM1 by both an epigenetic mechanism and a HIF2α-mediated transcriptional pathway. The fact that TENM1 is a convergence point for various cancer-related signaling pathways might give us a new therapeutic opportunity for GBM treatment. Here, we briefly review the findings described so far about the mechanisms that control the expression of the GBM invasion factor TENM1.

XRN2 Is Required for Cell Motility and Invasion in Glioblastomas

One of the major obstacles in treating brain cancers, particularly glioblastoma multiforme, is the occurrence of secondary tumor lesions that arise in areas of the brain and are inoperable while obtaining resistance to current therapeutic agents. Thus, gaining a better understanding of the cellular factors that regulate glioblastoma multiforme cellular movement is imperative. In our study, we demonstrate that the 5'-3' exoribonuclease XRN2 is important to the invasive nature of glioblastoma. A loss of XRN2 decreases cellular speed, displacement, and movement through a matrix of established glioblastoma multiforme cell lines. Additionally, a loss of XRN2 abolishes tumor formation in orthotopic mouse xenograft implanted with G55 glioblastoma multiforme cells. One reason for these observations is that loss of XRN2 disrupts the expression profile of several cellular factors that are important for tumor invasion in glioblastoma multiforme cells. Importantly, XRN2 mRNA and protein levels are elevated in glioblastoma multiforme patient samples. Elevation in XRN2 mRNA also correlates with poor overall patient survival. These data demonstrate that XRN2 is an important cellular factor regulating one of the major obstacles in treating glioblastomas and is a potential molecular target that can greatly enhance patient survival.

State-of-the-art techniques using pre-operative brain MRI scans for survival prediction of glioblastoma multiforme patients and future research directions

Objective

Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment.

Methods

This study aims at examining the recent methodologies developed using automated learning and radiomics to automate the process of SP. Automated techniques use pre-operative raw magnetic resonance imaging (MRI) scans and clinical data related to GBM patients. All SP methods submitted for the multimodal brain tumour segmentation (BraTS) challenge are examined to extract the generic workflow for SP.

Results

The maximum accuracies achieved by 21 state-of-the-art different SP techniques reviewed in this study are 65.5 and 61.7% using the validation and testing subsets of the BraTS dataset, respectively. The comparisons based on segmentation architectures, SP models, training parameters and hardware configurations have been made.

Conclusion

The limited accuracies achieved in the literature led us to review the various automated methodologies and evaluation metrics to find out the research gaps and other findings related to the survival prognosis of GBM patients so that these accuracies can be improved in future. Finally, the paper provides the most promising future research directions to improve the performance of automated SP techniques and increase their clinical relevance.

HIF2α Upregulates the Migration Factor ODZ1 under Hypoxia in Glioblastoma Stem Cells

Background: Glioblastoma (GBM) remains a major clinical challenge due to its invasive capacity, resistance to treatment, and recurrence. We have previously shown that ODZ1 contributes to glioblastoma invasion and that ODZ1 mRNA levels can be upregulated by epigenetic mechanisms in response to hypoxia. Herein, we have further studied the transcriptional regulation of ODZ1 in GBM stem cells (GSCs) under hypoxic conditions and analyzed whether HIF2α has any role in this regulation. Methods: We performed the experiments in three primary GSC cell lines established from tumor specimens. GSCs were cultured under hypoxia, treated with HIF regulators (DMOG, chetomin), or transfected with specific siRNAs, and the expression levels of ODZ1 and HIF2α were analyzed. In addition, the response of the ODZ1 promoter cloned into a luciferase reporter plasmid to the activation of HIF was also studied. Results: The upregulation of both mRNA and protein levels of HIF2α under hypoxia conditions correlated with the expression of ODZ1 mRNA. Moreover, the knockdown of HIF2α by siRNAs downregulated the expression of ODZ1. We found, in the ODZ1 promoter, a HIF consensus binding site (GCGTG) 1358 bp from the transcription start site (TSS) and a HIF-like site (CCGTG) 826 bp from the TSS. Luciferase assays revealed that the stabilization of HIF by DMOG resulted in the increased activity of the ODZ1 promoter. Conclusions: Our data indicate that the HIF2α-mediated upregulation of ODZ1 helps strengthen the transcriptional control of this migration factor under hypoxia in glioblastoma stem cells. The discovery of this novel transcriptional pathway identifies new targets to develop strategies that may avoid GBM tumor invasion and recurrence.

Diagnostic Implications of White Matter Tract Involvement by Intra-axial Brain Tumors

Introduction

Diffusion tensor imaging (DTI) is being increasingly used during brain tumor surgery. However, there is limited data available on its diagnostic and prognostic value. Our objective was to assess the pattern of involvement of white matter tracts (WMTs) by intra-axial brain tumors on DTI. Secondary objectives were to evaluate implications of involvement of WMT on surgical resection, and the post-operative functional outcome.

Methods

This was a retrospective study of consecutive patients, who underwent DTI-guided surgery for brain tumors. The involvement of WMTs by tumors on DTI was assessed by a radiologist (who was blind to the pathology) using the Witwer classification. The pathology was reported by histopathologists using the World Health Organization brain tumor classification. Karnofsky Performance Status Scale (KPS) was used for assessing patients’ neurological status at admission, and at follow-up.

Results

Forty-five (58.4%) out of 77 tumors reviewed caused infiltration of WMTs, whereas only 22 (28.6%) tumors caused displacement of WMTs (p= 0.040). Among 32 cases of astrocytoma, the involvement of WMTs was influenced by the grade of tumor (p= 0.012), as high-grade tumors caused infiltration (19; 59.4%), unlike low-grade tumors that commonly caused displacement (2; 50%). Oligodendrogliomas caused infiltration/disruption of WMTs in most cases, irrespective of the grade (19 out of 25 cases; 76%). At the last follow-up, 27 (35.1%) patients showed improvement in KPS and 14 (18.2%) reported deterioration, while there was no change observed in 36 (46.8%) patients. The infiltration of WMTs was associated with a poor functional outcome.

Conclusions

High-grade astrocytomas mostly cause infiltration of WMTs, unlike oligodendrogliomas, which often infiltrate WMTs, irrespective of the tumor grade. The infiltration of WMTs is associated with a poor functional outcome at follow-ups.

Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma

Glioblastoma (GBM) is the most common malignant tumor of the central nervous system with poor prognosis. Although the field of immunotherapy in glioma is developing rapidly, glioblastoma is still prone to recurrence under strong immune intervention. The major challenges in the process of immunotherapy are evaluating the curative effect, accurately distinguishing between treatment-related reactions and tumor recurrence, and providing guidance for clinical decision-making. Since the conventional magnetic resonance imaging (MRI) is usually difficult to distinguish between pseudoprogression and the true tumor progression, many studies have used various advanced imaging techniques to evaluate treatment-related responses. Meanwhile, criteria for efficacy evaluation of immunotherapy are constantly updated and improved. A standard imaging scheme to evaluate immunotherapeutic response will benefit patients finally. This review mainly summarizes the application status and future trend of several advanced imaging techniques in evaluating the efficacy of GBM immunotherapy.

Advanced Magnetic Resonance Imaging in Pediatric Glioblastomas

The shortly upcoming 5th edition of the World Health Organization Classification of Tumors of the Central Nervous System is bringing extensive changes in the terminology of diffuse high-grade gliomas (DHGGs). Previously "glioblastoma," as a descriptive entity, could have been applied to classify some tumors from the family of pediatric or adult DHGGs. However, now the term "glioblastoma" has been divested and is no longer applied to tumors in the family of pediatric types of DHGGs. As an entity, glioblastoma remains, however, in the family of adult types of diffuse gliomas under the insignia of "glioblastoma, IDH-wildtype." Of note, glioblastomas still can be detected in children when glioblastoma, IDH-wildtype is found in this population, despite being much more common in adults. Despite the separation from the family of pediatric types of DHGGs, what was previously labeled as "pediatric glioblastomas" still remains with novel labels and as new entities. As a result of advances in molecular biology, most of the previously called "pediatric glioblastomas" are now classified in one of the four family members of pediatric types of DHGGs. In this review, the term glioblastoma is still apocryphally employed mainly due to its historical relevance and the paucity of recent literature dealing with the recently described new entities. Therefore, "glioblastoma" is used here as an umbrella term in the attempt to encompass multiple entities such as astrocytoma, IDH-mutant (grade 4); glioblastoma, IDH-wildtype; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and high grade infant-type hemispheric glioma. Glioblastomas are highly aggressive neoplasms. They may arise anywhere in the developing central nervous system, including the spinal cord. Signs and symptoms are non-specific, typically of short duration, and usually derived from increased intracranial pressure or seizure. Localized symptoms may also occur. The standard of care of "pediatric glioblastomas" is not well-established, typically composed of surgery with maximal safe tumor resection. Subsequent chemoradiation is recommended if the patient is older than 3 years. If younger than 3 years, surgery is followed by chemotherapy. In general, "pediatric glioblastomas" also have a poor prognosis despite surgery and adjuvant therapy. Magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of glioblastomas. In addition to the typical conventional MRI features, i.e., highly heterogeneous invasive masses with indistinct borders, mass effect on surrounding structures, and a variable degree of enhancement, the lesions may show restricted diffusion in the solid components, hemorrhage, and increased perfusion, reflecting increased vascularity and angiogenesis. In addition, magnetic resonance spectroscopy has proven helpful in pre- and postsurgical evaluation. Lastly, we will refer to new MRI techniques, which have already been applied in evaluating adult glioblastomas, with promising results, yet not widely utilized in children.

Chemoattraction of Neoplastic Glial Cells with CXCL10, CCL2 and CCL11 as a Paradigm for a Promising Therapeutic Approach for Primary Brain Tumors

Chemoattraction is a normal and essential process, but it can also be involved in tumorigenesis. This phenomenon plays a key role in glioblastoma (GBM). The GBM tumor cells are extremely difficult to eradicate, due to their strong capacity to migrate into the brain parenchyma. Consequently, a complete resection of the tumor is rarely a possibility, and recurrence is inevitable. To overcome this problem, we proposed to exploit this behavior by using three chemoattractants: CXCL10, CCL2 and CCL11, released by a biodegradable hydrogel (GlioGel) to produce a migration of tumor cells toward a therapeutic trap. To investigate this hypothesis, the agarose drop assay was used to test the chemoattraction capacity of these three chemokines on murine F98 and human U87MG cell lines. We then studied the potency of this approach in vivo in the well-established syngeneic F98-Fischer glioma-bearing rat model using GlioGel containing different mixtures of the chemoattractants. In vitro assays resulted in an invasive cell rate 2-fold higher when chemokines were present in the environment. In vivo experiments demonstrated the capacity of these specific chemoattractants to strongly attract neoplastic glioblastoma cells. The use of this strong locomotion ability to our end is a promising avenue in the establishment of a new therapeutic approach in the treatment of primary brain tumors.

A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours

Multi-parametric tissue characterisation is demonstrated using a 4-minute protocol based on diffusion trace acquisitions. Three diffusion regimes are covered simultaneously: pseudo-perfusion, Gaussian, and non-Gaussian diffusion. The clinical utility of this method for fast multi-parametric mapping for brain tumours is explored. A cohort of 17 brain tumour patients was measured on a 3T hybrid MR-PET scanner with a standard clinical MRI protocol, to which the proposed multi-parametric diffusion protocol was subsequently added. For comparison purposes, standard perfusion and a full diffusion kurtosis protocol were acquired. Simultaneous amino-acid (¹⁸F-FET) PET enabled the identification of active tumour tissue. The metrics derived from the proposed protocol included perfusion fraction, pseudo-diffusivity, apparent diffusivity, and apparent kurtosis. These metrics were compared to the corresponding metrics from the dedicated acquisitions: cerebral blood volume and flow, mean diffusivity and mean kurtosis. Simulations were carried out to assess the influence of fitting methods and noise levels on the estimation of the parameters. The diffusion and kurtosis metrics obtained from the proposed protocol show strong to very strong correlations with those derived from the conventional protocol. However, a bias towards lower values was observed. The pseudo-perfusion parameters showed very weak to weak correlations compared to their perfusion counterparts. In conclusion, we introduce a clinically applicable protocol for measuring multiple parameters and demonstrate its relevance to pathological tissue characterisation.

Radiomics and radiogenomics in gliomas: a contemporary update

The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

CUX1 Facilitates the Development of Oncogenic Properties Via Activating Wnt/β-Catenin Signaling Pathway in Glioma

Background: Homeobox cut like 1 (CUX1), which often presents aberrated expression in many cancer cells, exerts a crucial role in tumorigenesis. Evidence describing CUX1 in gliomagenesis is scarce, and the effects of CUX1 on the Wnt/β-catenin pathway have not been reported. Our study aimed to explore the biological functions and molecular mechanisms involved in CUX1 activity in glioma.

Methods: Datasets for bioinformatics analysis were obtained from the GEO, TCGA, CGGA, GTEX and CCLE databases. qRT-PCR, western blotting (WB), and immunohistochemistry (IHC) assays were used to investigate the expression patterns of CUX1 among glioma and brain tissues. CUX1 knockdown and overexpression vectors were transfected into glioma cell lines, the CCK-8, clone formation assay, wound healing, Transwell assay, and flow cytometry were performed to detect changes in cell viability, invasiveness, and the cell cycle. WB and immunofluorescence (IF) assays were used to explore changes in cell cycle-related and Wnt/β-catenin signaling protein levels.

Results: Overexpression of CUX1 was identified in glioma tissues, and especially in glioblastoma (GBM), when compared to normal controls and correlated with poor prognosis. In comparison with untreated cells, TJ905 glioma cells overexpressing CUX1 showed higher proliferation and invasion abilities and S phase cell-cycle arrest, while the knockdown of CUX1 suppressed cell invasive ability and induced G1 phase arrest. Active Wnt/β-catenin signaling was enriched and clustered in a CUX1-associated GSEA/GSVA analysis. IF and WB assays indicated that CUX1 regulated the distribution of Axin2/β-catenin in glioma cells and regulated the expression of proteins downstream of the Wnt/β-catenin signaling pathway, suggesting that CUX1 served as an upstream positive regulator of the Wnt/β-catenin pathway. Finally, the knockdown of Axin2 or β-catenin could reverse the tumor-promoting effects caused by CUX1 overexpression, suggesting that CUX1 induced gliomagenesis and malignant phenotype by activating the Wnt/β-catenin signaling pathway.

Conclusion: Our data suggested that the transcription factor CUX1 could be a novel therapeutic target for glioma with gene therapy.

Characterization of Distinctive In Vivo Metabolism between Enhancing and Non-Enhancing Gliomas Using Hyperpolarized Carbon-13 MRI

The development of hyperpolarized carbon-13 (¹³C) metabolic MRI has enabled the sensitive and noninvasive assessment of real-time in vivo metabolism in tumors. Although several studies have explored the feasibility of using hyperpolarized ¹³C metabolic imaging for neuro-oncology applications, most of these studies utilized high-grade enhancing tumors, and little is known about hyperpolarized ¹³C metabolic features of a non-enhancing tumor. In this study, ¹³C MR spectroscopic imaging with hyperpolarized [1-¹³C]pyruvate was applied for the differential characterization of metabolic profiles between enhancing and non-enhancing gliomas using rodent models of glioblastoma and a diffuse midline glioma. Distinct metabolic profiles were found between the enhancing and non-enhancing tumors, as well as their contralateral normal-appearing brain tissues. The preliminary results from this study suggest that the characterization of metabolic patterns from hyperpolarized ¹³C imaging between non-enhancing and enhancing tumors may be beneficial not only for understanding distinct metabolic features between the two lesions, but also for providing a basis for understanding ¹³C metabolic processes in ongoing clinical trials with neuro-oncology patients using this technology.

The Ependymal Region Prevents Glioblastoma From Penetrating Into the Ventricle via a Nonmechanical Force

Background

Intraventricular penetration is rare in glioblastoma (GBM). Whether the ependymal region including the ependyma and subventricular zone (SVZ) can prevent GBM invasion remains unclear.

Methods

Magnetic resonance imaging (MRI) and haematoxylin–eosin (HE) staining were performed to evaluate the size and anatomical locations of GBM. Binary logistic regression analysis was used to assess the correlation between tumor-ependyma contact, ventricle penetration and clinical characteristics. Cell migration and invasion were assessed via Transwell assays and an orthotopic transplantation model.

Results

Among 357 patients with GBM, the majority (66%) showed ependymal region contact, and 34 patients (10%) showed ventricle penetration of GBM. GBM cells were spread along the ependyma in the orthotopic transplantation model. The longest tumor diameter was an independent risk factor for GBM-ependymal region contact, as demonstrated by univariate (OR = 1.706, p < 0.0001) and multivariate logistic regression analyses (OR = 1.767, p < 0.0001), but was not associated with ventricle penetration. Cerebrospinal fluid (CSF) could significantly induce tumor cell migration (p < 0.0001), and GBM could grow in CSF. Compared with those from the cortex, cells from the ependymal region attenuated the invasion of C6 whether cocultured with C6 or mixed with Matrigel (p = 0.0054 and p = 0.0488). Immunofluorescence analysis shows a thin gap with GFAP expression delimiting the tumor and ependymal region.

Conclusion

The ependymal region might restrict GBM cells from entering the ventricle via a non-mechanical force. Further studies in this area may reveal mechanisms that occur in GBM patients and may enable the design of new therapeutic strategies.

Adhesion G protein-coupled receptors in glioblastoma

Background

Members of the adhesion family of G protein-coupled receptors (GPCRs) have received attention for their roles in health and disease, including cancer. Over the past decade, several members of the family have been implicated in the pathogenesis of glioblastoma.

Methods

Here, we discuss the basic biology of adhesion GPCRs and review in detail specific members of the receptor family with known functions in glioblastoma. Finally, we discuss the potential use of adhesion GPCRs as novel treatment targets in neuro-oncology.

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

BACKGROUND

Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.

METHODS

Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival.

RESULTS

Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82-0.98) compared to 0.91 (IQR, 0.83-0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63-0.95) compared to 0.81 (IQR, 0.69-0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74-0.98) compared to 0.83 (IQR, 0.78-0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47-0.97) compared to 0.67 (IQR, 0.42-0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67-13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06-32.12], corrected p = 0.011) were independently associated with overall survival.

CONCLUSION

Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.

mTOR Modulates Intercellular Signals for Enlargement and Infiltration in Glioblastoma Multiforme

Simple Summary

Glioblastoma multiforme (GBM) is the most aggressive and lethal primary brain tumor. Emerging evidence indicate the multi-faceted role of extracellular vesicles (EVs) in GBM growth and proliferation. In fact, GBM-derived EVs can alter the phenotype of GBM-associated parenchymal cells; thus, promoting tumor growth, angiogenesis, and immune evasion. Remarkably, among several pathways that are frequently deregulated in GBM, mammalian Target of Rapamycin (mTOR) up-regulation, and subsequent autophagy (ATG) depression are considered hallmarks of GBM. In fact, mTOR-dependent ATG inhibition strongly correlates with the presence of EVs, which in turn promotes glioblastoma cancer stem cells (GSCs) self-renewal, proliferation, and infiltration. ATG and exosome release are reciprocally regulated. In detail, a failure in ATG enhances exosomal release. Therefore, strategies aimed at targeting on mTOR-dependent extracellular vesicles could be a promising approach for GBM prevention and treatment.

Abstract

Recently, exosomal release has been related to the acquisition of a malignant phenotype in glioblastoma cancer stem cells (GSCs). Remarkably, intriguing reports demonstrate that GSC-derived extracellular vesicles (EVs) contribute to glioblastoma multiforme (GBM) tumorigenesis via multiple pathways by regulating tumor growth, infiltration, and immune invasion. In fact, GSCs release tumor-promoting macrovesicles that can disseminate as paracrine factors to induce phenotypic alterations in glioma-associated parenchymal cells. In this way, GBM can actively recruit different stromal cells, which, in turn, may participate in tumor microenvironment (TME) remodeling and, thus, alter tumor progression. Vice versa, parenchymal cells can transfer their protein and genetic contents to GSCs by EVs; thus, promoting GSCs tumorigenicity. Moreover, GBM was shown to hijack EV-mediated cell-to-cell communication for self-maintenance. The present review examines the role of the mammalian Target of Rapamycin (mTOR) pathway in altering EVs/exosome-based cell-to-cell communication, thus modulating GBM infiltration and volume growth. In fact, exosomes have been implicated in GSC niche maintenance trough the modulation of GSCs stem cell-like properties, thus, affecting GBM infiltration and relapse. The present manuscript will focus on how EVs, and mostly exosomes, may act on GSCs and neighbor non tumorigenic stromal cells to modify their expression and translational profile, while making the TME surrounding the GSC niche more favorable for GBM growth and infiltration. Novel insights into the mTOR-dependent mechanisms regulating EV-mediated intercellular communication within GBM TME hold promising directions for future therapeutic applications.

Multi-scale segmentation in GBM treatment using diffusion tensor imaging

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps

OBJECTIVES

Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through p and q maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed p and q maps in GBM patients in order to inform construction of radiotherapy target volumes.

METHODS

Chan-Vese level set model is applied to segment the p map using the q map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their q map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges.

RESULTS

The expansion of tumour boundary from q map to p map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth p map and the level set automatic segmentation.

CONCLUSION

Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand q map to p map. We have provided initial validation of this technique against manual contours performed by experienced clinicians.

ADVANCES IN KNOWLEDGE

This novel automated technique to generate p maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.

Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma

Glioblastoma is the most common and most malignant primary brain tumor. Despite aggressive multimodal treatment, its prognosis remains poor. Even with continuous developments in MRI, which has provided us with newer insights into the diagnosis and understanding of tumor biology, response assessment in the posttherapy setting remains challenging. We believe that the integration of additional information from advanced neuroimaging techniques can further improve the diagnostic accuracy of conventional MRI. In this article, we review the utility of advanced neuroimaging techniques such as diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging, proton magnetic resonance spectroscopy, and chemical exchange saturation transfer in characterizing and evaluating treatment response in patients with glioblastoma. We will also discuss the existing challenges and limitations of using these techniques in clinical settings and possible solutions to avoiding pitfalls in study design, data acquisition, and analysis for future studies. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:978-997.

Tumor Cell Invasion in Glioblastoma

Glioblastoma (GBM) is a particularly devastating tumor with a median survival of about 16 months. Recent research has revealed novel insights into the outstanding heterogeneity of this type of brain cancer. However, all GBM subtypes share the hallmark feature of aggressive invasion into the surrounding tissue. Invasive glioblastoma cells escape surgery and focal therapies and thus represent a major obstacle for curative therapy. This review aims to provide a comprehensive understanding of glioma invasion mechanisms with respect to tumor-cell-intrinsic properties as well as cues provided by the microenvironment. We discuss genetic programs that may influence the dissemination and plasticity of GBM cells as well as their different invasion patterns. We also review how tumor cells shape their microenvironment and how, vice versa, components of the extracellular matrix and factors from non-neoplastic cells influence tumor cell motility. We further discuss different research platforms for modeling invasion. Finally, we highlight the importance of accounting for the complex interplay between tumor cell invasion and treatment resistance in glioblastoma when considering new therapeutic approaches.

Olfactory function as an independent prognostic factor in glioblastoma

OBJECTIVE

To determine the role of olfactory function in patients with glioblastoma multiforme (GBM) as a prognostic clinical measure.

METHODS

In a prospective case-control study, olfactory testing was performed in 73 patients with primary GBM at baseline during first-line treatment and at later follow-ups. An age-matched control cohort consisted of 49 patients with neurologic diseases, excluding those known to affect olfactory function per se. Depending on the olfactory testing score, patients were allotted to a hyposmia group (HG) or normosmia group (NG). MRI analysis was performed to assess whether tumor location affects olfactory pathways.

RESULTS

Patients with GBM had olfactory dysfunction significantly more often compared to the control cohort (p = 0.003). Tumor location could not explain this finding since no relevant difference in MRI-based olfactory pathway involvement was found between HG and NG (p = 0.131). Patients with olfactory dysfunction had significantly worse overall survival (OS) and progression-free survival (PFS) compared to those without dysfunction (median OS 20.9 vs 40.6 months, p = 0.035; median PFS, 9 vs 19 months, p = 0.022). Multivariate analysis in patients without MRI-based involvement of olfactory pathways confirmed olfaction is an independent prognostic factor for OS (hazard ratio [HR] 0.43; p = 0.042) and PFS (HR 0.51; p = 0.049).

CONCLUSION

This pilot study provides the first indication that olfactory dysfunction is frequently observed in GBM and may be associated with worse survival outcome in GBM. However, validation of these results in an independent cohort is needed.

Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

Rationale and Objectives: Controversy still exists on the diagnosability of diffusion tensor imaging (DTI) for breast lesions characterization across published studies. The clinical guideline of DTI used in the breast has not been established. This meta-analysis aims to pool relevant evidences and evaluate the diagnostic performance of DTI in the differential diagnosis of malignant and benign breast lesions.

Materials and Methods: The studies that assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed, and Cochrane Library between January 2010 and September 2019. Standardized mean differences and 95% confidence intervals of fractional anisotropy (FA), mean diffusivity (MD), and three diffusion eigenvalues (λ1, λ2, and λ3) were calculated using Review Manager 5.2. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated with a bivariate model. Publication bias and heterogeneity between studies were also assessed using Stata 12.0.

Results: Sixteen eligible studies incorporating 1,636 patients were included. The standardized mean differences indicated that breast cancers had a significantly higher FA but lower MD, λ1, λ2, and λ3 than those of benign lesions (all P < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significantly lower MD value than that of ductal carcinoma in situ (DCIS) (P = 0.02). λ1 showed the best diagnostic accuracy with pooled sensitivity, specificity, and AUC of 93%, 92%, and 0.97, followed by MD (AUC = 0.92, sensitivity = 87%, specificity = 83%) and FA (AUC = 0.76, sensitivity = 70%, specificity = 70%) in the differential diagnosis of breast lesions.

Conclusion: DTI with multiple quantitative parameters was adequate to differentiate breast cancers from benign lesions based on their biological characteristics. MD can further distinguish IBC from DCIS. The parameters, especially λ1 and MD, should attract our attention in clinical practice.