Molecular heterogeneity in glioblastoma: potential clinical implications

Targeting inflammation in glioblastoma: An updated review from pathophysiology to novel therapeutic approaches

Glioblastoma (GBM) is a highly aggressive primary malignant brain tumor with a dismal prognosis despite current treatment strategies. Inflammation plays an essential role in GBM pathophysiology, contributing to tumor growth, invasion, immunosuppression, and angiogenesis. As a result, pharmacological intervention with anti-inflammatory drugs has been used as a potential approach for the management of GBM. To provide an overview of the current understanding of GBM pathophysiology, potential therapeutic applications of anti-inflammatory drugs in GBM, conventional treatments of glioblastoma and emerging therapeutic approaches currently under investigation. A narrative review was carried out, scanning publications from 2000 to 2023 on PubMed and Google Scholar. The search was not guided by a set research question or a specific search method but rather focused on the area of interest. Conventional treatments such as surgery, radiotherapy, and chemotherapy have shown some benefits, but their effectiveness is limited by various factors such as tumor heterogeneity and resistance.

PDGF-BB overexpression in p53 null oligodendrocyte progenitors increases H3K27me3 and induces transcriptional changes which favor proliferation

Proneural gliomas are brain tumors characterized by enrichment of oligodendrocyte progenitor cell (OPC) transcripts and genetic alterations. In this study we sought to identify transcriptional and epigenetic differences between OPCs with Trp53 deletion and PDGF-BB overexpression (BB-p53n), which form tumors when transplanted in mouse brains, and those carrying only p53 deletion (p53n), which do not. We used unbiased histone proteomics and RNA-seq analysis on these two genetically modified OPC populations and detected higher levels of H3K27me3 in BB-p53n compared to p53n OPCs. The BB-p53n OPC were characterized by higher levels of transcripts related to proliferation and lower levels of those related to differentiation. Pharmacological inhibition of histone H3K27 trimethylation in BB-p53n OPC reduced cell cycle transcripts and increased the expression of differentiation markers. These data suggest that PDGF-BB overexpression in p53 null OPC results in histone post-translational modifications and consequent transcriptional changes favoring proliferation while halting differentiation, thereby promoting the early stages of transformation.

Extended dosing (12 cycles) vs conventional dosing (6 cycles) of adjuvant temozolomide in adults with newly diagnosed high-grade gliomas: a randomized, single-blind, two-arm, parallel-group controlled trial

Purpose

Maximum safe surgical resection followed by adjuvant chemoradiation and temozolomide chemotherapy is the current standard of care in the management of newly diagnosed high grade glioma. However, there are controversies about the optimal number of adjuvant temozolomide cycles. This study aimed to compare the survival benefits of 12 cycles against 6 cycles of adjuvant temozolomide adults with newly diagnosed high grade gliomas.

Methods

Adult patients with newly diagnosed high grade gliomas, and a Karnofsky performance status>60%, were randomized to receive either 6 cycles or 12 cycles of adjuvant temozolomide. Patients were followed-up for assessment of overall survival (OS) and progression-free survival (PFS) by brain MRI every 3 months within the first year after treatment and then every six months.

Results

A total of 100 patients (6 cycles, 50; 12 cycles, 50) were entered. The rate of treatment completion in 6 cycles and 12 cycles groups were 91.3% and 55.1%, respectively. With a median follow-up of 26 months, the 12-, 24-, 36-, and 48-month OS rates in 6 cycles and 12 cycles groups were 81.3% vs 78.8%, 58.3% vs 49.8%, 47.6% vs 34.1%, and 47.6% vs 31.5%, respectively (p-value=.19). Median OS of 6 cycles and 12 cycles groups were 35 months (95% confidence interval (CI), 11.0 to 58.9) and 23 months (95%CI, 16.9 to 29.0). The 12-, 24-, 36-, and 48- month PFS rates in 6 cycles and 12 cycles groups were 70.8% vs 56.9%, 39.5% and 32.7%, 27.1% vs 28.8%, and 21.1% vs 28.8%, respectively (p=.88). The Median PFS of 6 cycles and 12 cycles groups was 18 months (95% CI, 14.8 to 21.1) and 16 (95% CI, 11.0 to 20.9) months.

Conclusion

Patients with newly diagnosed high grade gliomas treated with adjuvant temozolomide after maximum safe surgical resection and adjuvant chemoradiation do not benefit from extended adjuvant temozolomide beyond 6 cycles.

Trial registration

Prospectively registered with the Iranian Registry of Clinical Trials: IRCT20160706028815N3. Date registered: 18/03/14.

Photothermal Prussian blue nanoparticles generate potent multi-targeted tumor-specific T cells as an adoptive cell therapy

Prussian blue nanoparticle-based photothermal therapy (PBNP-PTT) is an effective tumor treatment capable of eliciting an antitumor immune response. Motivated by the ability of PBNP-PTT to potentiate endogenous immune responses, we recently demonstrated that PBNP-PTT could be used ex vivo to generate tumor-specific T cells against glioblastoma (GBM) cell lines as an adoptive T cell therapy (ATCT). In this study, we further developed this promising T cell development platform. First, we assessed the phenotype and function of T cells generated using PBNP-PTT. We observed that PBNP-PTT facilitated CD8+ T cell expansion from healthy donor PBMCs that secreted IFNγ and TNFα and upregulated CD107a in response to engagement with target U87 cells, suggesting specific antitumor T cell activation and degranulation. Further, CD8+ effector and effector memory T cell populations significantly expanded after co-culture with U87 cells, consistent with tumor-specific effector responses. In orthotopically implanted U87 GBM tumors in vivo, PBNP-PTT-derived T cells effectively reduced U87 tumor growth and generated long-term survival in >80% of tumor-bearing mice by Day 100, compared to 0% of mice treated with PBS, non-specific T cells, or T cells expanded from lysed U87 cells, demonstrating an enhanced antitumor efficacy of this ATCT platform. Finally, we tested the generalizability of our approach by generating T cells targeting medulloblastoma (D556), breast cancer (MDA-MB-231), neuroblastoma (SH-SY5Y), and acute monocytic leukemia (THP-1) cell lines. The resulting T cells secreted IFNγ and exerted increased tumor-specific cytolytic function relative to controls, demonstrating the versatility of PBNP-PTT in generating tumor-specific T cells for ATCT.

MiRNAs from the Dlk1-Dio3 locus and miR-224/452 cluster contribute to glioblastoma tumor heterogeneity

Glioblastoma is one of the most common and aggressive brain tumors and has seen few improvements in patient outcomes. Inter-tumor heterogeneity between tumors of different patients as well as intra-tumor heterogeneity of cells within the same tumor challenge the development of effective drugs. MiRNAs play an essential role throughout the developing brain and regulate many key genes involved in oncogenesis, yet their role in driving many of the processes underlying tumor heterogeneity remains unclear. In this study, we highlight miRNAs from the Dlk1-Dio3 and miR-224/452 clusters which may be expressed cell autonomously and have expression that is associated with cell state genes in glioblastoma, most prominently in neural progenitor-like and mesenchymal-like states respectively. These findings implicate these miRNA clusters as potential regulators of glioblastoma intra-tumoral heterogeneity and may serve as valuable biomarkers for cell state identification.

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.

Biodistribution and pharmacokinetics of [89Zr]-anti-VEGF mAbs using PET in glioblastoma rat models

INTRODUCTION

Glioblastomas present intensive angiogenesis, thus anti-Vascular Endothelial Growth Factor (VEGF) antibodies (mAbs) have been proposed as promising therapies. However, the results of clinical trials reported moderate toxicity and limited effectiveness. This study evaluates the in vivo pharmacokinetics and biodistribution of these mAbs in a growing model of glioblastoma in rats using Positron Emission Tomography (PET).

MATERIAL

&Methods: mAbs were radiolabeled with zirconium-89. Four days after the model induction, animals were injected with 2.33 ± 1.3 MBq of [89Zr]-DFO-bevacizumab (n = 8) or 2.35 ± 0.26 MBq of [89Zr]-DFO-aflibercept (n = 6). PETs were performed at 0H, 48H, 168H, 240H, and 336H post-injection. Tumor induction was confirmed using [18F]-Fluorodeoxyglucose-PET and immunohistochemistry. Radiotracer uptake was estimated in all pre-defined Volumes-of-Interest.

RESULTS

Anti-VEGF mAbs showed 100 % Radiochemical-Purity. [89Zr]-DFO-bevacizumab showed a significantly higher bioavailability in whole-blood. A significant increase in the tumor uptake was detectable at 168H PET with [89Zr]-DFO-bevacizumab meanwhile with [89Zr]-DFO-aflibercept it was only detectable at 336H. [89Zr]-DFO-bevacizumab tumor uptake was significantly higher than that of [89Zr]-DFO-aflibercept in all the scans. Tumor induction was confirmed in all animal models.

CONCLUSION

MAbs detect VEGF-expression in glioblastoma models. Tumors were earlier targeted by Bevacizumab. The lower blood availability of aflibercept resulted in a lower tumor uptake than bevacizumab in all the scans.

Patient-Derived Microphysiological Systems for Precision Medicine

Patient-derived microphysiological systems (P-MPS) have emerged as powerful tools in precision medicine that provide valuable insight into individual patient characteristics. This review discusses the development of P-MPS as an integration of patient-derived samples, including patient-derived cells, organoids, and induced pluripotent stem cells, into well-defined MPSs. Emphasizing the necessity of P-MPS development, its significance as a nonclinical assessment approach that bridges the gap between traditional in vitro models and clinical outcomes is highlighted. Additionally, guidance is provided for engineering approaches to develop microfluidic devices and high-content analysis for P-MPSs, enabling high biological relevance and high-throughput experimentation. The practical implications of the P-MPS are further examined by exploring the clinically relevant outcomes obtained from various types of patient-derived samples. The construction and analysis of these diverse samples within the P-MPS have resulted in physiologically relevant data, paving the way for the development of personalized treatment strategies. This study describes the significance of the P-MPS in precision medicine, as well as its unique capacity to offer valuable insights into individual patient characteristics.

Glioblastoma Pseudoprogression Discrimination Using Multiparametric Magnetic Resonance Imaging, Principal Component Analysis, and Supervised and Unsupervised Machine Learning

BACKGROUND

One of the most frequent phenomena in the follow-up of glioblastoma is pseudoprogression, present in up to half of cases. The clinical usefulness of discriminating this phenomenon through magnetic resonance imaging and nuclear medicine has not yet been standardized; in this study, we used machine learning on multiparametric magnetic resonance imaging to explore discriminators of this phenomenon.

METHODS

For the study, 30 patients diagnosed with IDH wild-type glioblastoma operated on at both study centers in 2011-2020 were selected; 15 patients corresponded to early tumor progression and 15 patients to pseudoprogression. Using unsupervised learning, the number of clusters and tumor segmentation was recorded using gap-stat and k-means method, adjusting to voxel adjacency. In a second phase, a class prediction was carried out with a multinomial logistic regression supervised learning method; the outcome variables were the percentage of assignment, class overrepresentation, and degree of voxel adjacency.

RESULTS

Unsupervised learning of the tumor in its diagnosis shows up to 14 well-differentiated tumor areas. In the supervised learning phase, there is a higher percentage of assigned classes (P < 0.01), less overrepresentation of classes (P < 0.01), and greater adjacency (55% vs. 33%) in cases of true tumor progression compared with pseudoprogression.

CONCLUSIONS

True tumor progression preserves the multidimensional characteristics of the basal tumor at the voxel and region of interest level, resulting in a characteristic differential pattern when supervised learning is used.

Intrinsic and Microenvironmental Drivers of Glioblastoma Invasion

Gliomas are diffusely infiltrating brain tumors whose prognosis is strongly influenced by their extent of invasion into the surrounding brain tissue. While lower-grade gliomas present more circumscribed borders, high-grade gliomas are aggressive tumors with widespread brain infiltration and dissemination. Glioblastoma (GBM) is known for its high invasiveness and association with poor prognosis. Its low survival rate is due to the certainty of its recurrence, caused by microscopic brain infiltration which makes surgical eradication unattainable. New insights into GBM biology at the single-cell level have enabled the identification of mechanisms exploited by glioma cells for brain invasion. In this review, we explore the current understanding of several molecular pathways and mechanisms used by tumor cells to invade normal brain tissue. We address the intrinsic biological drivers of tumor cell invasion, by tackling how tumor cells interact with each other and with the tumor microenvironment (TME). We focus on the recently discovered neuronal niche in the TME, including local as well as distant neurons, contributing to glioma growth and invasion. We then address the mechanisms of invasion promoted by astrocytes and immune cells. Finally, we review the current literature on the therapeutic targeting of the molecular mechanisms of invasion.

Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer

Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.

Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64 vs. 30%, p = 0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI 1.5-12.1; p < 0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.

Normofractionated irradiation and not temozolomide modulates the immunogenic and oncogenic phenotype of human glioblastoma cell lines

PURPOSE

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, with an overall poor prognosis after diagnosis. Conventional treatment includes resection, chemotherapy with temozolomide (TMZ), and concomitant radiotherapy (RT). The recent success of immunotherapy approaches in other tumor entities, particularly with immune checkpoint inhibitors, could not be clinically transferred to GBM treatment so far. Therefore, preclinical analyses of the expression of both immune-suppressive and immune-stimulatory checkpoint molecules following treatment of human glioblastoma cells with RT and/or temozolomide is needed to design feasible radio(chemo)immunotherapy trials for GBM in the future.

METHODS

Five human glioblastoma cell lines (H4, HROG-06, U118, U138, U251) were analyzed regarding their clonogenic survival and cell death forms after chemotherapy (CT) with TMZ and/or normofractionated RT (5 × 2 Gy) via multicolor flow cytometry. Further, the tumor cell surface expression of immune-activating (OX40L, CD137L, CD70, and ICOSL) and immune-suppressive (PD-L1, PD-L2, HVEM) checkpoint molecules and of an oncogenic molecule (EGFR) were measured via multicolor flow cytometry after CT and RT alone or after RCT.

RESULTS

Normofractionated RT and not TMZ was the trigger of induction of predominantly necrosis in the glioblastoma cells. Notably, clonogenicity did not correlate with cell death induction by RT. The basal expression level of immune-suppressive PD-L1, PD-L2, and HVEM varied in the analyzed glioblastoma cells. RT, but not TMZ, resulted in a significant upregulation of PD-L1 and PD-L2 in all tumor cells investigated. Also, the expression of HVEM was increased after RT in most of the GBM cell lines. In contrast, normofractionated RT individually modulated expression of the stimulating immune checkpoint molecules CD70, CD137L, OX40L, and ICOSL1. The oncogenic factor EGFR was significantly increased by irradiation in all examined cell lines, albeit to a different extent. None of the investigated molecules were downregulated after the treatments.

CONCLUSION

Normofractionated radiotherapy modulates the immunogenic as well as the oncogenic phenotype of glioblastoma cells, partly individually. Therefore, not only PD-L1 and PD-L2, but also other immunogenic molecules expressed on the surface of glioblastoma cells could serve as targets for immune checkpoint blockade in combination with RT in the future.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.

Methods

We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.

Results

Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.

Conclusion

We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.

Statement of Significance

This manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.

Enrichment of Deleterious Mutated Genes Involved in Ciliary Function and Histone Modification in Brain Cancer Patient-Derived Xenograft Models

Patient-derived xenograft (PDX) models, which can retain the characteristics of original tumors in an in vivo-mimicking environment, have been developed to identify better treatment options. However, although original tumors and xenograft tissues mostly share oncogenic mutations and global gene expression patterns, their detailed mutation profiles occasionally do not overlap, indicating that selection occurs in the xenograft environment. To understand this mutational alteration in xenografts, we established 13 PDX models derived from 11 brain tumor patients and confirmed their histopathological similarity. Surprisingly, only a limited number of somatic mutations were shared between the original tumor and xenograft tissue. By analyzing deleteriously mutated genes in tumors and xenografts, we found that previously reported brain tumor-related genes were enriched in PDX samples, demonstrating that xenografts are a valuable platform for studying brain tumors. Furthermore, mutated genes involved in cilium movement, microtubule depolymerization, and histone methylation were enriched in PDX samples compared with the original tumors. Even with the limitations of the heterogeneity of clinical lesions with a heterotropic model, our study demonstrates that PDX models can provide more information in genetic analysis using samples with high heterogeneity, such as brain tumors.

JAK3 Inhibition Regulates Stemness and Thereby Controls Glioblastoma Pathogenesis

Glioblastoma multiforme (GBM) is the most deadly brain tumor, effective treatment options for which still remain elusive. The current treatment procedure of maximal resection followed by chemotherapy has proved to be grossly insufficient to prevent disease progression and death. Despite best efforts, the maximum survival post-diagnosis is a mere 1.5 years. Therefore, there is a huge unmet clinical need to find effective therapeutic procedures to prevent the pathogenesis and relapse of GBM. Small-molecule inhibitors of signaling pathways are an attractive option to prevent various types of tumors. However, no effective small-molecule inhibitors have been successful against GBM in clinical trials. Various signaling pathways are altered and an array of signaling molecules, transcription factors (TFs), and epigenetic modifying factors have been implicated in the pathogenesis of GBM. JAK-STAT pathway alteration is an important contributor to GBM pathogenesis and relapse. Many small-molecule inhibitors of JAKs, or STAT TFs, especially JAK2 and STAT3, have been assessed for their anti-tumor activity in GBM. However, no definitive success so far has been achieved. Herein, by using two small-molecule inhibitors of JAK3, we show that they are quite effective in inhibiting GBM cell proliferation and neurosphere formation, downregulating their stemness character, and inducing differentiation into neuronal origin cells. The effect of a single treatment with the drugs, both in a serum-containing differentiation medium and in a proliferation medium containing EGF and FGF, was really strong in limiting GBM cell growth, suggesting a potential therapeutic application for these JAK inhibitors in GBM therapy.

A Narrative Review on CD44's Role in Glioblastoma Invasion, Proliferation, and Tumor Recurrence

High invasiveness is a characteristic of glioblastoma (GBM), making radical resection almost impossible, and thus, resulting in a tumor with inevitable recurrence. GBM recurrence may be caused by glioma stem-like cells (GSCs) that survive many kinds of therapy. GSCs with high expression levels of CD44 are highly invasive and resistant to radio-chemotherapy. CD44 is a multifunctional molecule that promotes the invasion and proliferation of tumor cells via various signaling pathways. Among these, paired pathways reciprocally activate invasion and proliferation under different hypoxic conditions. Severe hypoxia (0.5-2.5% O2) upregulates hypoxia-inducible factor (HIF)-1α, which then activates target genes, including CD44, TGF-β, and cMET, all of which are related to tumor migration and invasion. In contrast, moderate hypoxia (2.5-5% O2) upregulates HIF-2α, which activates target genes, such as vascular endothelial growth factor (VEGF)/VEGFR2, cMYC, and cyclin D1. All these genes are related to tumor proliferation. Oxygen environments around GBM can change before and after tumor resection. Before resection, the oxygen concentration at the tumor periphery is severely hypoxic. In the reparative stage after resection, the resection cavity shows moderate hypoxia. These observations suggest that upregulated CD44 under severe hypoxia may promote the migration and invasion of tumor cells. Conversely, when tumor resection leads to moderate hypoxia, upregulated HIF-2α activates HIF-2α target genes. The phenotypic transition regulated by CD44, leading to a dichotomy between invasion and proliferation according to hypoxic conditions, may play a crucial role in GBM recurrence.

Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction

BACKGROUND

The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor.

PURPOSE

We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging.

DATA SOURCES

Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science.

STUDY SELECTION

Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria.

DATA ANALYSIS

We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.

DATA SYNTHESIS

Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles.

LIMITATIONS

The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias.

CONCLUSIONS

While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.

Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression

Introduction

Glioblastoma (GBM) is notorious for its clinical and molecular heterogeneity, contributing to therapeutic failure and a grim prognosis. WWOX is one of the tumor suppressor genes important in nervous tissue or related pathologies, which was scarcely investigated in GBM for reliable associations with prognosis or disease progression despite known alterations. Recently, we observed a phenotypic heterogeneity between GBM cell lines (U87MG, T98G, U251MG, DBTRG-05MG), among which the anti-GBM activity of WWOX was generally corresponding, but colony growth and formation were inconsistent in DBTRG-05MG. This prompted us to investigate the molecular landscapes of these cell lines, intending to translate them into the clinical context.

Methods

U87MG/T98G/U251MG/DBTRG-05MG were subjected to high-throughput sequencing, and obtained data were explored via weighted gene co-expression network analysis, differential expression analysis, functional annotation, and network building. Following the identification of the most relevant DBTRG-distinguishing driver genes, data from GBM patients were employed for, e.g., differential expression analysis, survival analysis, and principal component analysis.

Results

Although most driver genes were unique for each cell line, some were inversely regulated in DBTRG-05MG. Alongside driver genes, the differentially-expressed genes were used to build a WWOX-related network depicting protein-protein interactions in U87MG/T98G/U251MG/DBTRG-05MG. This network revealed processes distinctly regulated in DBTRG-05MG, e.g., microglia proliferation or neurofibrillary tangle assembly. POLE4 and HSF2BP were selected as DBTRG-discriminating driver genes based on the gene significance, module membership, and fold-change. Alongside WWOX, POLE4 and HSF2BP expression was used to stratify patients into cell lines-resembling groups that differed in, e.g., prognosis and treatment response. Some differences from a WWOX-related network were certified in patients, revealing genes that clarify clinical outcomes. Presumably, WWOX overexpression in DBTRG-05MG resulted in expression profile change resembling that of patients with inferior prognosis and drug response. Among these patients, WWOX may be inaccessible for its partners and does not manifest its anti-cancer activity, which was proposed in the literature but not regarding glioblastoma or concerning POLE4 and HSF2BP.

Conclusion

Cell lines data enabled the identification of patients among which, despite high expression of WWOX tumor suppressor, no advantageous outcomes were noted due to the cancer-promoting profile ensured by other genes.

Cellular senescence in glioma

INTRODUCTION

Glioma is the most common primary brain tumor and is often associated with treatment resistance and poor prognosis. Standard treatment typically involves radiotherapy and temozolomide-based chemotherapy, both of which induce cellular senescence-a tumor suppression mechanism.

DISCUSSION

Gliomas employ various mechanisms to bypass or escape senescence and remain in a proliferative state. Importantly, senescent cells remain viable and secrete a large number of factors collectively known as the senescence-associated secretory phenotype (SASP) that, paradoxically, also have pro-tumorigenic effects. Furthermore, senescent cells may represent one form of tumor dormancy and play a role in glioma recurrence and progression.

CONCLUSION

In this article, we delineate an overview of senescence in the context of gliomas, including the mechanisms that lead to senescence induction, bypass, and escape. Furthermore, we examine the role of senescent cells in the tumor microenvironment and their role in tumor progression and recurrence. Additionally, we highlight potential therapeutic opportunities for targeting senescence in glioma.

Systematic Review of Photodynamic Therapy in Gliomas

Over the last 20 years, gliomas have made up over 89% of malignant CNS tumor cases in the American population (NIH SEER). Within this, glioblastoma is the most common subtype, comprising 57% of all glioma cases. Being highly aggressive, this deadly disease is known for its high genetic and phenotypic heterogeneity, rendering a complicated disease course. The current standard of care consists of maximally safe tumor resection concurrent with chemoradiotherapy. However, despite advances in technology and therapeutic modalities, rates of disease recurrence are still high and survivability remains low. Given the delicate nature of the tumor location, remaining margins following resection often initiate disease recurrence. Photodynamic therapy (PDT) is a therapeutic modality that, following the administration of a non-toxic photosensitizer, induces tumor-specific anti-cancer effects after localized, wavelength-specific illumination. Its effect against malignant glioma has been studied extensively over the last 30 years, in pre-clinical and clinical trials. Here, we provide a comprehensive review of the three generations of photosensitizers alongside their mechanisms of action, limitations, and future directions.

The Glioblastoma Landscape: Hallmarks of Disease, Therapeutic Resistance, and Treatment Opportunities

Malignant brain tumors are aggressive and difficult to treat. Glioblastoma is the most common and lethal form of primary brain tumor, often found in patients with no genetic predisposition. The median life expectancy for individuals diagnosed with this condition is 6 months to 2 years and there is no known cure. New paradigms in cancer biology implicate a small subset of tumor cells in initiating and sustaining these incurable brain tumors. Here, we discuss the heterogenous nature of glioblastoma and theories behind its capacity for therapy resistance and recurrence. Within the cancer landscape, cancer stem cells are thought to be both tumor initiators and major contributors to tumor heterogeneity and therapy evasion and such cells have been identified in glioblastoma. At the cellular level, disruptions in the delicate balance between differentiation and self-renewal spur transformation and support tumor growth. While rapidly dividing cells are more sensitive to elimination by traditional treatments, glioblastoma stem cells evade these measures through slow division and reversible exit from the cell cycle. At the molecular level, glioblastoma tumor cells exploit several signaling pathways to evade conventional therapies through improved DNA repair mechanisms and a flexible state of senescence. We examine these common evasion techniques while discussing potential molecular approaches to better target these deadly tumors. Equally important, the presented information encourages the idea of augmenting conventional treatments with novel glioblastoma stem cell-directed therapies, as eliminating these harmful progenitors holds great potential to modulate tumor recurrence.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Integrated analysis of single-cell chromatin state and transcriptome identified common vulnerability despite glioblastoma heterogeneity

In 2021, the World Health Organization reclassified glioblastoma, the most common form of adult brain cancer, into isocitrate dehydrogenase (IDH)-wild-type glioblastomas and grade IV IDH mutant (G4 IDHm) astrocytomas. For both tumor types, intratumoral heterogeneity is a key contributor to therapeutic failure. To better define this heterogeneity, genome-wide chromatin accessibility and transcription profiles of clinical samples of glioblastomas and G4 IDHm astrocytomas were analyzed at single-cell resolution. These profiles afforded resolution of intratumoral genetic heterogeneity, including delineation of cell-to-cell variations in distinct cell states, focal gene amplifications, as well as extrachromosomal circular DNAs. Despite differences in IDH mutation status and significant intratumoral heterogeneity, the profiled tumor cells shared a common chromatin structure defined by open regions enriched for nuclear factor 1 transcription factors (NFIA and NFIB). Silencing of NFIA or NFIB suppressed in vitro and in vivo growths of patient-derived glioblastomas and G4 IDHm astrocytoma models. These findings suggest that despite distinct genotypes and cell states, glioblastoma/G4 astrocytoma cells share dependency on core transcriptional programs, yielding an attractive platform for addressing therapeutic challenges associated with intratumoral heterogeneity.

Long-Acting Therapeutic Delivery Systems for the Treatment of Gliomas

Despite the emergence of cutting-edge therapeutic strategies and tremendous progress in research, a complete cure of glioma remains elusive. The heterogenous nature of tumor, immunosuppressive state and presence of blood brain barrier are few of the major obstacles in this regard. Long-acting depot formulations such as injectables and implantables are gaining attention for drug delivery to brain owing to their ease in administration and ability to elute drug locally for extended durations in a controlled manner with minimal toxicity. Hybrid matrices fabricated by incorporating nanoparticulates within such systems help to enhance pharmaceutical advantages. Utilization of long-acting depots as monotherapy or in conjunction with existing strategies rendered significant survival benefits in many preclinical studies and some clinical trials. The discovery of novel targets, immunotherapeutic strategies and alternative drug administration routes are now coupled with several long-acting systems with an ultimate aim to enhance patient survival and prevent glioma recurrences.

Engineered tumor-specific T cells using immunostimulatory photothermal nanoparticles

BACKGROUND

Adoptive T cell therapy (ATCT) has been successful in treating hematological malignancies and is currently under investigation for solid-tumor therapy. In contrast to existing chimeric antigen receptor (CAR) T cell and/or antigen-specific T cell approaches, which require known targets, and responsive to the need for targeting a broad repertoire of antigens in solid tumors, we describe the first use of immunostimulatory photothermal nanoparticles to generate tumor-specific T cells.

METHODS

Specifically, we subject whole tumor cells to Prussian blue nanoparticle-based photothermal therapy (PBNP-PTT) before culturing with dendritic cells (DCs), and subsequent stimulation of T cells. This strategy differs from previous approaches using tumor cell lysates because we use nanoparticles to mediate thermal and immunogenic cell death in tumor cells, rendering them enhanced antigen sources.

RESULTS

In proof-of-concept studies using two glioblastoma (GBM) tumor cell lines, we first demonstrated that when PBNP-PTT was administered at a "thermal dose" targeted to induce the immunogenicity of U87 GBM cells, we effectively expanded U87-specific T cells. Further, we found that DCs cultured ex vivo with PBNP-PTT-treated U87 cells enabled 9- to 30-fold expansion of CD4+ and CD8+ T cells. Upon co-culture with target U87 cells, these T cells secreted interferon-ɣ in a tumor-specific and dose-dependent manner (up to 647-fold over controls). Furthermore, T cells manufactured using PBNP-PTT ex vivo expansion elicited specific cytolytic activity against target U87 cells (donor-dependent 32-93% killing at an effector to target cell (E:T) ratio of 20:1) while sparing normal human astrocytes and peripheral blood mononuclear cells from the same donors. In contrast, T cells generated using U87 cell lysates expanded only 6- to 24-fold and killed 2- to 3-fold less U87 target cells at matched E:T ratios compared with T cell products expanded using the PBNP-PTT approach. These results were reproducible even when a different GBM cell line (SNB19) was used, wherein the PBNP-PTT-mediated approach resulted in a 7- to 39-fold expansion of T cells, which elicited 25-66% killing of the SNB19 cells at an E:T ratio of 20:1, depending on the donor.

CONCLUSIONS

These findings provide proof-of-concept data supporting the use of PBNP-PTT to stimulate and expand tumor-specific T cells ex vivo for potential use as an adoptive T cell therapy approach for the treatment of patients with solid tumors.

Applications and current challenges of chimeric antigen receptor T cells in treating high-grade gliomas in adult and pediatric populations

High-grade gliomas (HGGs) continue to be some of the most devastating diseases in the USA. Despite extensive efforts, the survival of HGG patients has remained relatively stagnant. Chimeric antigen receptor (CAR) T-cell immunotherapy has recently been studied in the context of improving these tumors' clinical outcomes. HGG murine models treated with CAR T cells targeting tumor antigens have shown reduced tumor burden and longer overall survival than models without treatment. Subsequent clinical trials investigating the efficacy of CAR T cells have further shown that this therapy could be safe and might reduce tumor burden. However, there are still many challenges that need to be addressed to optimize the safety and efficacy of CAR T-cell therapy in treating HGG patients.

Prognostic subtypes of thyroid cancer was constructed based on single cell and bulk-RNA sequencing data and verified its authenticity

There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy. We identified six distinct cell types in the THAC microenvironment by analyzing single-cell RNA sequencing (Sc-RNAseq) data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, TampNK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients. In addition, we identified significant differences in the composition of immune cell subsets between high-risk and low-risk patients, which may be responsible for their different prognosis. Through in vitro experiments, we identify that knockdown of NPC2 can significantly promote thyroid cancer cell apoptosis, and NPC2 may be a potential therapeutic target for thyroid cancer. In this study, we developed a well-performing prognostic model based on Sc-RNAseq data, revealing the cellular microenvironment and tumor heterogeneity of thyroid cancer. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Integration of clinical and spatial data to explore lipid metabolism-related genes for predicting prognosis and immune microenvironment in gliomas

Lipid metabolism is crucial to tumor growth and immune microenvironment as well as drug sensitivity in glioma. Identifying prognostic indicators of glioma and elucidating the mechanisms of glioma progression are critical for improving the prognosis of glioma patients. In this study, we investigated the role and prognostic value of metabolism-related genes in glioma by integrative analysis of datasets from GEO, CGGA, and TCGA. Based on clinical data and transcriptome data, we found that the expression pattern of three major pathways related to lipid metabolism is fatty acidhigh-phospholipidhigh-triglyceridelow, which is associated with better prognosis and immune infiltration. The genes involved in these three pathways were used to generate a prognostic model, which showed high stability and efficiency in the test set and validation set. The spatial transcriptome of glioma patients revealed that the microenvironment of the regions with high expression of risk genes CAV1 and SCD is in a state of hypoxia, EMT, and cell cycle arrest, and thus can be used as markers of metabolic reprogramming in the tumor microenvironment. In the high-risk group, M0 macrophages and M1 macrophages were significantly enriched, and the risk score was significantly correlated with gene mutation and methylation of risk genes. We further performed drug sensitivity screening corresponding to different risk genes. This study provided novel insights into the differential immune microenvironment with different expression patterns of metablism-related genes and highlighted the spatial and temporal synergy of tumor progression and metabolic reprogramming.

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 10⁶ C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.

A comparative study of quantitative metrics in chemical exchange saturation transfer imaging for grading gliomas in adults

PURPOSE

To evaluate the performance of different chemical exchange saturation transfer (CEST) metrics for grading gliomas with semiautomatically defined regions of interest (ROIs).

METHODS

Thirty-eight adult subjects were included, including 23 high-grade gliomas and 15 low-grade gliomas confirmed by histopathology. The B0-corrected CEST z-spectra were first calculated with magnetization transfer ratio asymmetry (MTR_asym) analysis at frequency offsets of 3.5, 3, 2.5, 2, 1.5, and 1 ppm to obtain the fit-free metrics and subsequently fitted with three Lorentzian functions denoting direct water saturation (DS), amide proton transfer (APT), and combined semisolid magnetization transfer and nuclear Overhauser enhancement (MT & NOE) effects to derive the fit-based metrics. Wilcoxon rank-sum test was performed to determine if a statistically significant difference was present in the CEST metrics between low- and high-grade gliomas. Receiver operating characteristic (ROC) curves were used to evaluate the differentiation of CEST metrics between low- and high-grade gliomas. Pearson correlation coefficients were employed to evaluate the correlations of CEST metrics.

RESULTS

For the fit-free metrics, the highest areas under the curve (AUCs) of 0.85, 0.88, and 0.88, corresponding to MTR_asym, MTR_normref (normalization by the reference scan), and MTR_Rex (subtraction of inverse z-spectra), respectively, were obtained at 3 ppm across various frequency offsets. In addition, the AUCs generated from the fit-based metrics (0.88-0.90) were higher than those generated from the fit-free metrics at 3 ppm.

CONCLUSION

The results of this preliminary study indicate that fit-free CEST metrics at 3 ppm are superior to the other frequency offsets for grading human brain gliomas. The fit-based metrics manifested improved differentiation between low- and high-grade gliomas compared to the fit-free CEST metrics.

Pioneering models of pediatric brain tumors

Among children and adolescents in the United States (0 to 19 years old), brain and other central nervous system tumors are the second most common types of cancers, surpassed in incidence only by leukemias. Despite significant progress in the diagnosis and treatment modalities, brain cancer remains the leading cause of death in the pediatric population. There is an obvious unfulfilled need to streamline the therapeutic strategies and improve survival for these patients. For that purpose, preclinical models play a pivotal role. Numerous models are currently used in pediatric brain tumor research, including genetically engineered mouse models, patient-derived xenografts and cell lines, and newer models that utilize novel technologies such as genome engineering and organoids. Furthermore, extensive studies by the Children's Brain Tumor Network (CBTN) researchers and others have revealed multiomic landscapes of variable pediatric brain tumors. Combined with such integrative data, these novel technologies have enabled numerous applicable models. Genome engineering, including CRISPR/Cas9, expanded the flexibility of modeling. Models generated through genome engineering enabled studying particular genetic alterations in clean isogenic backgrounds, facilitating the dissection of functional mechanisms of those mutations in tumor biology. Organoids have been applied to study tumor-to-tumor-microenvironment interactions and to address developmental aspects of tumorigenesis, which is essential in some pediatric brain tumors. Other modalities, such as humanized mouse models, could potentially be applied to pediatric brain tumors. In addition to current valuable models, such novel models are anticipated to expedite functional tumor biology study and establish effective therapeutics for pediatric brain tumors.

Glioblastoma Spheroid Invasion through Soft, Brain-Like Matrices Depends on Hyaluronic Acid-CD44 Interactions

Increased secretion of hyaluronic acid (HA), a glycosaminoglycan abundant in the brain extracellular matrix (ECM), correlates with worse clinical outcomes for glioblastoma (GBM) patients. GBM cells aggressively invade the brain parenchyma while encountering spatiotemporal changes in their local ECM, including HA concentration. To investigate how varying HA concentrations affect GBM invasion, patient-derived GBM cells are cultured within a soft, 3D matrix in which HA concentration is precisely varied and cell migration observed. Data demonstrate that HA concentration can determine the invasive activity of patient-derived GBM cells in a biphasic and highly sensitive manner, where the absolute concentration of HA at which cell migration peaked is specific to each patient-derived line. Furthermore, evidence that this response relies on phosphorylated ezrin, which interacts with the intracellular domain of HA-engaged CD44 to effectively link the actin cytoskeleton to the local ECM is provided. Overall, this study highlights CD44-HA binding as a major mediator of GBM cell migration that acts independently of integrins and focal adhesion complexes and suggests that targeting HA-CD44-ezrin interactions represents a promising therapeutic strategy to prevent tumor cell invasion in the brain.

Neoadjuvant immune checkpoint inhibition in the management of glioblastoma: Exploring a new frontier

Brain tumors are one of the leading causes of cancer related death in both the adult and pediatric patient population. Gliomas represent a cohort of brain tumors derived from glial cell lineages which include astrocytomas, oligodendrogliomas and glioblastomas (GBMs). These tumors are known to grow aggressively and have a high lethality with GBM being the most aggressive tumor in this group. Currently, few treatment options exist for GBM outside of surgical resection, radiation therapy and chemotherapy. While these measures have been shown to marginally improve patient survival, patients, especially those diagnosed with GBM, often experience a recurrence of their disease. Following disease recurrence, treatment options become more limited as additional surgical resections can pose life threatening risk to the patient, patients may be ineligible for additional radiation, and the recurrent tumor may be resistant to chemotherapy. Immune checkpoint inhibitors (ICIs) have revolutionized the field of cancer immunotherapy as many patients with cancers residing outside the central nervous system (CNS) have experienced a survival benefit from this treatment modality. It has often been observed that this survival benefit is increased following neoadjuvant administration of immune checkpoint inhibitors as tumor antigen is still present in the patient which enables a more robust anti-tumor immune response. Interestingly, results for ICI-based studies for patients with GBM have been largely disappointing which is a stark contrast from the success this treatment modality has had in non-central nervous system cancers. In this review, we will discuss the various benefits of neoadjuvant immune checkpoint inhibition such as how this approach reduces tumor burden and allows for a greater induction of an anti-tumor immune response. Additionally, we will discuss several non-CNS cancers where neoadjuvant immune checkpoint inhibition has been successful and discuss why we believe this approach may provide a survival benefit for GBM patients. We hope this manuscript will foster future studies aimed at exploring whether this approach may be beneficial for patients diagnosed with GBM.

Discovery of novel thyrointegrin αvβ3 antagonist fb-PMT (NP751) in the management of human glioblastoma multiforme

Background

Thyrointegrin αvβ3 receptors are unique molecular cancer therapeutic targets because of their overexpression on cancer and rapidly dividing blood vessel cells compared and quiescent on normal cells. A macromolecule, TriAzole Tetraiodothyroacetic acid (TAT) conjugated to polyethylene glycol with a lipophilic 4-fluorobenyl group (fb-PMT and NP751), interacts with high affinity (0.21 nM) and specificity with the thyrointegrin αvβ3 receptors on the cell surface without nuclear translocation in contrast to the non-polymer conjugated TAT.

Methods

The following in vitro assays were carried out to evaluate NP751 including binding affinity to different integrins, transthyretin (TTR)-binding affinity, glioblastoma multiforme (GBM) cell adhesion, proliferation assays, nuclear translocations, chorioallantoic membrane model of angiogenesis, and microarray for molecular mechanisms. Additionally, in vivo studies were carried out to evaluate the anticancer efficacy of NP751, its biodistribution, and brain GBM tumor versus plasma levels kinetics.

Results

NP751 demonstrated a broad spectrum of antiangiogenesis and anticancer efficacy in experimental models of angiogenesis and xenografts of human GBM cells. Tumor growth and cancer cells' viability were markedly decreased (by > 90%; P < .001) in fb-PMT-treated U87-luc or 3 different primary human GBM xenograft-bearing mice based on tumor in vivo imaging system (IVIS) imaging and histopathological examination, without relapse upon treatment discontinuation. Additionally, it effectively transports across the blood-brain barrier via its high-affinity binding to plasma TTR with high retention in brain tumors. NP751-induced effects on gene expression support the model of molecular interference at multiple key pathways essential for GBM tumor progression and vascularization.

Conclusions

fb-PMT is a potent thyrointegrin αvβ3 antagonist with potential impact on GBM tumor progression.

Personalised therapeutic approaches to glioblastoma: A systematic review

Introduction

Glioblastoma is the most common and malignant primary brain tumour with median survival of 14.6 months. Personalised medicine aims to improve survival by targeting individualised patient characteristics. However, a major limitation has been application of targeted therapies in a non-personalised manner without biomarker enrichment. This has risked therapies being discounted without fair and rigorous evaluation. The objective was therefore to synthesise the current evidence on survival efficacy of personalised therapies in glioblastoma.

Methods

Studies reporting a survival outcome in human adults with supratentorial glioblastoma were eligible. PRISMA guidelines were followed. MEDLINE, Embase, Scopus, Web of Science and the Cochrane Library were searched to 5th May 2022. Clinicaltrials.gov was searched to 25th May 2022. Reference lists were hand-searched. Duplicate title/abstract screening, data extraction and risk of bias assessments were conducted. A quantitative synthesis is presented.

Results

A total of 102 trials were included: 16 were randomised and 41 studied newly diagnosed patients. Of 5,527 included patients, 59.4% were male and mean age was 53.7 years. More than 20 types of personalised therapy were included: targeted molecular therapies were the most studied (33.3%, 34/102), followed by autologous dendritic cell vaccines (32.4%, 33/102) and autologous tumour vaccines (10.8%, 11/102). There was no consistent evidence for survival efficacy of any personalised therapy.

Conclusion

Personalised glioblastoma therapies remain of unproven survival benefit. Evidence is inconsistent with high risk of bias. Nonetheless, encouraging results in some trials provide reason for optimism. Future focus should address target-enriched trials, combination therapies, longitudinal biomarker monitoring and standardised reporting.

Polymer nanocarriers for targeted local delivery of agents in treating brain tumors

Glioblastoma (GBM), the deadliest brain cancer, presents a multitude of challenges to the development of new therapies. The standard of care has only changed marginally in the past 17 years, and few new chemotherapies have emerged to supplant or effectively combine with temozolomide. Concurrently, new technologies and techniques are being investigated to overcome the pharmacokinetic challenges associated with brain delivery, such as the blood brain barrier (BBB), tissue penetration, diffusion, and clearance in order to allow for potent agents to successful engage in tumor killing. Alternative delivery modalities such as focused ultrasound and convection enhanced delivery allow for the local disruption of the BBB, and the latter in particular has shown promise in achieving broad distribution of agents in the brain. Furthermore, the development of polymeric nanocarriers to encapsulate a variety of cargo, including small molecules, proteins, and nucleic acids, have allowed for formulations that protect and control the release of said cargo to extend its half-life. The combination of local delivery and nanocarriers presents an exciting opportunity to address the limitations of current chemotherapies for GBM toward the goal of improving safety and efficacy of treatment. However, much work remains to establish standard criteria for selection and implementation of these modalities before they can be widely implemented in the clinic. Ultimately, engineering principles and nanotechnology have opened the door to a new wave of research that may soon advance the stagnant state of GBM treatment development.

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

Introduction

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.

Methods and results

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.

Conclusion

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.

Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication

OBJECTIVES

To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified).

METHODS

Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data.

RESULTS

Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812).

CONCLUSION

Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis.

KEY POINTS

• Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance.

Therapy Resistant Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are a heterogenous group of malignancies originating from neuroendocrine cells of the gastrointestinal tract, the incidence of which has been increasing for several decades. While there has been significant progress in the development of therapeutic options for patients with advanced or metastatic disease, these remain limited both in quantity and durability of benefit. This review examines the latest research elucidating the mechanisms of both up-front resistance and the eventual development of resistance to the primary systemic therapeutic options including somatostatin analogues, peptide receptor radionuclide therapy with lutetium Lu 177 dotatate, everolimus, sunitinib, and temozolomide-based chemotherapy. Further, potential strategies for overcoming these mechanisms of resistance are reviewed in addition to a comprehensive review of ongoing and planned clinical trials addressing this important challenge.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Glioblastoma (GBM) is the most common and highly lethal type of brain tumor, with poor survival despite advances in understanding its complexity. After current standard therapeutic treatment, including tumor resection, radiotherapy and concomitant chemotherapy with temozolomide, the median overall survival of patients with this type of tumor is less than 15 months. Thus, there is an urgent need for new insights into GBM molecular characteristics and progress in targeted therapy in order to improve clinical outcomes. The literature data revealed that a number of different signaling pathways are dysregulated in GBM. In this review, we intended to summarize and discuss current literature data and therapeutic modalities focused on targeting dysregulated signaling pathways in GBM. A better understanding of opportunities for targeting signaling pathways that influences malignant behavior of GBM cells might open the way for the development of novel GBM-targeted therapies.

Challenges in the Treatment of Glioblastoma by Chimeric Antigen Receptor T-Cell Immunotherapy and Possible Solutions

Glioblastoma (GBM), one of the most lethal brain cancers in adults, accounts for 48.6% of all malignant primary CNS tumors diagnosed each year. The 5-year survival rate of GBM patients remains less than 10% even after they receive the standard-of-care treatment, including maximal safe resection, adjuvant radiation, and chemotherapy with temozolomide. Therefore, new therapeutic modalities are urgently needed for this deadly cancer. The last decade has witnessed great advances in chimeric antigen receptor T (CAR-T) cell immunotherapy for the treatment of hematological malignancies. Up to now, the US FDA has approved six CAR-T cell products in treating hematopoietic cancers including B-cell acute lymphoblastic leukemia, lymphoma, and multiple myeloma. Meanwhile, the number of clinical trials on CAR-T cell has increased significantly, with more than 80% from China and the United States. With its achievements in liquid cancers, the clinical efficacy of CAR-T cell therapy has also been explored in a variety of solid malignancies that include GBMs. However, attempts to expand CAR-T cell immunotherapy in GBMs have not yet presented promising results in hematopoietic malignancies. Like other solid tumors, CAR-T cell therapies against GBM still face several challenges, such as tumor heterogeneity, tumor immunosuppressive microenvironment, and CAR-T cell persistence. Hence, developing strategies to overcome these challenges will be necessary to accelerate the transition of CAR-T cell immunotherapy against GBMs from bench to bedside.

Tooth Formation as Experimental Model to Study Chemotherapy on Tissue Development: Effect of a Specific Dose of Temozolomide/Veliparib

Background: Chemotherapy treatment of cancer in children can influence formation of normal tissues, leading to irreversible changes in their structure and function. Tooth formation is susceptible to several types of chemotherapy that induce irreversible changes in the structure of enamel, dentin and dental root morphology. These changes can make the teeth more prone to fracture or to caries when they have erupted. Recent studies report successful treatment of brain tumors with the alkylating drug temozolomide (TMZ) in combination with veliparib (VLP) in a glioblastoma in vivo mouse model. Whether these drugs also affect tooth formation is unknown. Aim: In this study the effect of TMZ/VLP on incisor formation was investigated in tissue sections of jaws from mice and compared with mice not treated with these drugs. Materials and method: The following aspects were studied using immunohistochemistry of specific protein markers including: (1) proliferation (by protein expression of proliferation marker Ki67) (2) a protein involved in paracellular ion transport (expression of tight junction (TJ) protein claudin-1) and (3) in transcellular passage of ions across the dental epithelium (expression of Na+, K+ 2Cl- cotransporter/NKCC1). Results: Chemotherapy with TMZ/VLP strongly reduced immunostaining for claudin-1 in distal parts of maturation ameloblasts. No gross changes were found in the treated mice, either in cell proliferation in the dental epithelium at the cervical loop or in the immunostaining pattern for NKCC1 in (non-ameloblastic) dental epithelium. The salivary glands in the treated mice contained strongly reduced immunostaining for NKCC1 in the basolateral membranes of acinar cells. Discussion/Conclusions: Based on the reduction of claudin-1 immunostaining in ameloblasts, TMZ/VLP may potentially influence forming enamel by changes in the structure of TJs structures in maturation ameloblasts, structures that are crucial for the selective passage of ions through the intercellular space between neighboring ameloblasts. The strongly reduced basolateral NKCC1 staining seen in fully-grown salivary glands of TMZ/VLP-treated mice suggests that TMZ/VLF could also influence ion transport in adult saliva by the salivary gland epithelium. This may cause treated children to be more susceptible to caries.

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data

Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI-IMS, thereby compelling researchers to use complementary techniques and resource intensive experimental setups. Despite these strategies, sufficient information could not be extracted because of lack of an optimum data combination strategy/software. Here, we introduce a new open-source software ImShot that aims at identifying peptides obtained in MALDI-IMS. This is achieved by combining information from IMS and shotgun proteomics (LC-MS) measurements of serial sections of the same tissue. The software takes advantage of a two-group comparison to determine the search space of IMS masses after deisotoping the corresponding spectra. Ambiguity in annotations of IMS peptides is eliminated by introduction of a novel scoring system that identifies the most likely parent protein of a detected peptide in the corresponding IMS dataset. Thanks to its modular structure, the software can also handle LC-MS data separately and display interactive enrichment plots and enriched Gene Ontology terms or cellular pathways. The software has been built as a desktop application with a conveniently designed graphic user interface to provide users with a seamless experience in data analysis. ImShot can run on all the three major desktop operating systems and is freely available under Massachusetts Institute of Technology license.

Glioblastoma spheroid growth and chemotherapeutic responses in single and dual-stiffness hydrogels

Glioblastoma (GBM) is the deadliest brain tumor for which there is no cure. Bioengineered GBM models, such as hydrogel-encapsulated spheroids, that capture both cell-cell and cell-matrix interactions could facilitate testing of much needed therapies. Elucidation of specific microenvironment properties on spheroid responsiveness to therapeutics would enhance the usefulness of GBM models as predictive drug screening platforms. Here, GBM spheroids consisting of U87 or patient-derived GBM cells were encapsulated in soft (∼1 kPa), stiff (∼7 kPa), and dual-stiffness polyethylene glycol-based hydrogels, with GBM spheroids seeded at the stiffness interface. Spheroids were cultured for 7 days and examined for viability, size, invasion, laminin expression, hypoxia, proliferation, and response to the chemotherapeutic temozolomide (TMZ). We noted excellent cell viability in all hydrogels, and higher infiltration in soft compared to stiff hydrogels for U87 spheroids. In dual gels spheroids mostly infiltrated away from the stiffness interface with minimal crossing over it and some individual cell migration along the interface. U87 spheroids were equally responsive to TMZ in the soft and stiff hydrogels, but cell viability in the spheroid periphery was higher than the core for stiff hydrogels whereas the opposite was true for soft hydrogels. HIF1A expression was higher in the core of spheroids in the stiff hydrogels, while there was no difference in cell proliferation between spheroids in the stiff vs soft hydrogels. Patient-derived GBM spheroids did not show stiffness-dependent drug responses. U87 cells showed similar laminin expression in soft and stiff hydrogels with higher expression in the spheroid periphery compared to the core. Our results indicate that microenvironment stiffness needs to be considered in bioengineered GBM models including those designed for use in drug screening applications. STATEMENT OF SIGNIFICANCE: Recent work on tumor models engineered for use in drug screening has highlighted the potential of hydrogel-encapsulated spheroids as a simple, yet effective platform that show drug responses similar to native tumors. It has also been shown that substrate stiffness, in vivo and in vitro, affects cancer cell responses to drugs. This is particularly important for glioblastoma (GBM), the deadliest brain cancer, as GBM cells invade by following the stiffer brain structures such as white matter tracks and the perivascular niche. Invading cells have also been associated with higher resistance to chemotherapy. Here we developed GBM spheroid models using soft, stiff and dual-stiffness hydrogels to explore the connection between substrate stiffness, spheroid invasion and drug responsiveness in a controlled environment.

Homogeneity of antibody-drug conjugates critically impacts the therapeutic efficacy in brain tumors

Glioblastoma multiforme (GBM) is the most aggressive and fatal disease of all brain tumor types. Most therapies rarely provide clinically meaningful outcomes in the treatment of GBM. Although antibody-drug conjugates (ADCs) are promising anticancer drugs, no ADCs have been clinically successful for GBM, primarily because of poor blood-brain barrier (BBB) penetration. Here, we report that ADC homogeneity and payload loading rate are critical parameters contributing to this discrepancy. Although both homogeneous and heterogeneous conjugates exhibit comparable in vitro potency and pharmacokinetic profiles, the former shows enhanced payload delivery to brain tumors. Our homogeneous ADCs provide improved antitumor effects and survival benefits in orthotopic brain tumor models. We also demonstrate that overly drug-loaded species in heterogeneous conjugates are particularly poor at crossing the BBB, leading to deteriorated overall brain tumor targeting. Our findings indicate the importance of homogeneous conjugation with optimal payload loading in generating effective ADCs for intractable brain tumors.

Most therapies rarely provide clinically meaningful improvements in glioblastoma multiforme (GBM) treatment. Anami et al. report that intravenous administration of homogeneous antibody-drug conjugates (ADCs) efficiently delivers payloads to brain tumors, leading to substantially improved tumor growth suppression. Their findings provide rational ADC design for effectively treating intractable brain tumors, including GBM.

Tracing the origins of glioblastoma by investigating the role of gliogenic and related neurogenic genes/signaling pathways in GBM development: a systematic review

Background

Glioblastoma is one of the most aggressive tumors. The etiology and the factors determining its onset are not yet entirely known. This study investigates the origins of GBM, and for this purpose, it focuses primarily on developmental gliogenic processes. It also focuses on the impact of the related neurogenic developmental processes in glioblastoma oncogenesis. It also addresses why glial cells are at more risk of tumor development compared to neurons.

Methods

Databases including PubMed, MEDLINE, and Google Scholar were searched for published articles without any date restrictions, involving glioblastoma, gliogenesis, neurogenesis, stemness, neural stem cells, gliogenic signaling and pathways, neurogenic signaling and pathways, and astrocytogenic genes.

Results

The origin of GBM is dependent on dysregulation in multiple genes and pathways that accumulatively converge the cells towards oncogenesis. There are multiple layers of steps in glioblastoma oncogenesis including the failure of cell fate-specific genes to keep the cells differentiated in their specific cell types such as p300, BMP, HOPX, and NRSF/REST. There are genes and signaling pathways that are involved in differentiation and also contribute to GBM such as FGFR3, JAK-STAT, and hey1. The genes that contribute to differentiation processes but also contribute to stemness in GBM include notch, Sox9, Sox4, c-myc gene overrides p300, and then GFAP, leading to upregulation of nestin, SHH, NF-κB, and others. GBM mutations pathologically impact the cell circuitry such as the interaction between Sox2 and JAK-STAT pathway, resulting in GBM development and progression.

Conclusion

Glioblastoma originates when the gene expression of key gliogenic genes and signaling pathways become dysregulated. This study identifies key gliogenic genes having the ability to control oncogenesis in glioblastoma cells, including p300, BMP, PAX6, HOPX, NRSF/REST, LIF, and TGF beta. It also identifies key neurogenic genes having the ability to control oncogenesis including PAX6, neurogenins including Ngn1, NeuroD1, NeuroD4, Numb, NKX6-1 Ebf, Myt1, and ASCL1. This study also postulates how aging contributes to the onset of glioblastoma by dysregulating the gene expression of NF-κB, REST/NRSF, ERK, AKT, EGFR, and others.