Comprehensive genomic characterization defines human glioblastoma genes and core pathways

SVIP reduces IGFBP-2 expression and inhibits glioblastoma progression via stabilizing PTEN

Glioblastoma (GBM) presents significant challenges due to its invasive nature and genetic heterogeneity. In this study, we investigated the impact of Small VCP/P97-Interacting Protein (SVIP) on GBM progression. Our results revealed elevated expression of Insulin-like Growth Factor Binding Protein 2 (IGFBP-2) and STIP1 homology and U-box containing protein 1 (STUB1), coupled with reduced SVIP levels in GBM samples. Notably, high IGFBP-2 expression correlated with poor prognosis. Mechanistically, SVIP competitively inhibited STUB1, selectively binding to VCP/p97, thereby reducing PTEN degradation. This SVIP-mediated regulation exerted influence on the PTEN/PI3K/AKT/mTOR pathway, leading to the suppression of GBM progression. Co-localization experiments demonstrated that SVIP hindered PTEN ubiquitination and degradation by outcompeting STUB1 for VCP/p97 binding. Moreover, SVIP overexpression resulted in reduced activation of AKT/mTOR signaling and facilitated autophagy. In vivo experiments using a GBM xenograft model substantiated the tumor-suppressive effects of SVIP, evident by suppressed tumor growth, decreased IGFBP-2 expression, and improved survival rates. Collectively, our findings underscore the functional significance of SVIP in GBM progression. By inhibiting STUB1 and stabilizing PTEN, SVIP modulates the expression of IGFBP-2 and attenuates the activation of the PI3K/AKT/mTOR pathway, thereby emerging as a promising therapeutic target for GBM treatment.

RUNX1-PDIA5 Axis Promotes Malignant Progression of Glioblastoma by Regulating CCAR1 Protein Expression

PDIA5 is responsible for modification of disulfide bonds of proteins. However, its impact on the malignant progression of glioblastoma multiforme (GBM) remains unknown. We analyzed the expression and prognostic significance of PDIA5 in cohorts of GBM and clinical samples. The PDIA5 protein was significantly overexpressed in GBM tissues, and higher expression of PDIA5 was statistically associated with a worse prognosis in patients with GBM. Transcriptional data from PDIA5 knockdown GBM cells revealed that downstream regulatory genes of PDIA5 were enriched in malignant regulatory pathways and PDIA5 enhanced the proliferative and invasive abilities of GBM cells. By constructing a PDIA5 CXXC motif mutant plasmid, we found CCAR1 was the vital downstream factor of PDIA5 in regulating GBM malignancy in vitro and in vivo. Additionally, RUNX1 bound to the promoter region of PDIA5 and regulated gene transcription, leading to activation of the PDIA5/CCAR1 regulatory axis in GBM. The RUNX1/PDIA5/CCAR1 axis significantly influenced the malignant behavior of GBM cells. In conclusion, this study comprehensively elucidates the crucial role of PDIA5 in the malignant progression of GBM. Downregulating PDIA5 can mitigate the malignant biological behavior of GBM both in vitro and in vivo, potentially improving the efficacy of treatment for clinical patients with GBM.

Importance of Autophagy Regulation in Glioblastoma with Temozolomide Resistance

Glioblastoma (GBM) is the most aggressive and common malignant and CNS tumor, accounting for 47.7% of total cases. Glioblastoma has an incidence rate of 3.21 cases per 100,000 people. The regulation of autophagy, a conserved cellular process involved in the degradation and recycling of cellular components, has been found to play an important role in GBM pathogenesis and response to therapy. Autophagy plays a dual role in promoting tumor survival and apoptosis, and here we discuss the complex interplay between autophagy and GBM. We summarize the mechanisms underlying autophagy dysregulation in GBM, including PI3K/AKT/mTOR signaling, which is most active in brain tumors, and EGFR and mutant EGFRvIII. We also review potential therapeutic strategies that target autophagy for the treatment of GBM, such as autophagy inhibitors used in combination with the standard of care, TMZ. We discuss our current understanding of how autophagy is involved in TMZ resistance and its role in glioblastoma development and survival.

Modeling glioblastoma tumor progression via CRISPR-engineered brain organoids

Glioblastoma (GBM) is an aggressive form of brain cancer that is highly resistant to therapy due to significant intra-tumoral heterogeneity. The lack of robust in vitro models to study early tumor progression has hindered the development of effective therapies. Here, we develop engineered GBM organoids (eGBOs) harboring GBM subtype-specific oncogenic mutations to investigate the underlying transcriptional regulation of tumor progression. Single-cell and spatial transcriptomic analyses revealed that these mutations disrupt normal neurodevelopment gene regulatory networks resulting in changes in cellular composition and spatial organization. Upon xenotransplantation into immunodeficient mice, eGBOs form tumors that recapitulate the transcriptional and spatial landscape of human GBM samples. Integrative single-cell trajectory analysis of both eGBO-derived tumor cells and patient GBM samples revealed the dynamic gene expression changes in developmental cell states underlying tumor progression. This analysis of eGBOs provides an important validation of engineered cancer organoid models and demonstrates their utility as a model of GBM tumorigenesis for future preclinical development of therapeutics.

Gas-Phase Fractionation Data-Independent Acquisition Analysis of 3D Cocultured Spheroid Tumor Model Reveals Altered Translational Processes and Signaling Using Proteomics

Colorectal cancer (CRC) contains considerable heterogeneity; therefore, models of the disease must also reflect the multifarious components. Compared to traditional 2D models, 3D cellular models, such as tumor spheroids, have the utility to determine the drug efficacy of potential therapeutics. Monoculture spheroids are well-known to recapitulate gene expression, cell signaling, and pathophysiological gradients of avascularized tumors. However, they fail to mimic the stromal cell influence present in CRC, which is known to perturb drug efficacy and is associated with metastatic, late-stage colorectal cancer. This study seeks to develop a cocultured spheroid model using carcinoma and noncancerous fibroblast cells. We characterized the proteomic profile of cocultured spheroids in comparison to monocultured spheroids using data-independent acquisition with gas-phase fractionation. Specifically, we determined that proteomic differences related to translation and mTOR signaling are significantly increased in cocultured spheroids compared to monocultured spheroids. Proteins related to fibroblast function, such as exocytosis of coated vesicles and secretion of growth factors, were significantly differentially expressed in the cocultured spheroids. Finally, we compared the proteomic profiles of both the monocultured and cocultured spheroids against a publicly available data set derived from solid CRC tumors. We found that the proteome of the cocultured spheroids more closely resembles that of the patient samples, indicating their potential as tumor mimics.

Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

A universal multiple instance learning framework for whole slide image analysis

BACKGROUND

The emergence of digital whole slide image (WSI) has driven the development of computational pathology. However, obtaining patch-level annotations is challenging and time-consuming due to the high resolution of WSI, which limits the applicability of fully supervised methods. We aim to address the challenges related to patch-level annotations.

METHODS

We propose a universal framework for weakly supervised WSI analysis based on Multiple Instance Learning (MIL). To achieve effective aggregation of instance features, we design a feature aggregation module from multiple dimensions by considering feature distribution, instances correlation and instance-level evaluation. First, we implement instance-level standardization layer and deep projection unit to improve the separation of instances in the feature space. Then, a self-attention mechanism is employed to explore dependencies between instances. Additionally, an instance-level pseudo-label evaluation method is introduced to enhance the available information during the weak supervision process. Finally, a bag-level classifier is used to obtain preliminary WSI classification results. To achieve even more accurate WSI label predictions, we have designed a key instance selection module that strengthens the learning of local features for instances. Combining the results from both modules leads to an improvement in WSI prediction accuracy.

RESULTS

Experiments conducted on Camelyon16, TCGA-NSCLC, SICAPv2, PANDA and classical MIL benchmark datasets demonstrate that our proposed method achieves a competitive performance compared to some recent methods, with maximum improvement of 14.6 % in terms of classification accuracy.

CONCLUSION

Our method can improve the classification accuracy of whole slide images in a weakly supervised way, and more accurately detect lesion areas.

Targeting drug resistance in glioblastoma (Review)

Glioblastoma (GBM) is the most common malignancy of the central nervous system in adults. The current standard of care includes surgery, radiation therapy, temozolomide; and tumor‑treating fields leads to dismal overall survival. There are far limited treatments upon recurrence. Therapies to date are ineffective as a result of several factors, including the presence of the blood‑brain barrier, blood tumor barrier, glioma stem‑like cells and genetic heterogeneity in GBM. In the present review, the potential mechanisms that lead to treatment resistance in GBM and the measures which have been taken so far to attempt to overcome the resistance were discussed. The complex biology of GBM and lack of comprehensive understanding of the development of therapeutic resistance in GBM demands discovery of novel antigens that are targetable and provide effective therapeutic strategies.

The digital revolution in pathology: Towards a smarter approach to research and treatment

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

A syngeneic spontaneous zebrafish model of tp53-deficient, EGFRvIII, and PI3KCAH1047R-driven glioblastoma reveals inhibitory roles for inflammation during tumor initiation and relapse in vivo

High-throughput vertebrate animal model systems for the study of patient-specific biology and new therapeutic approaches for aggressive brain tumors are currently lacking, and new approaches are urgently needed. Therefore, to build a patient-relevant in vivo model of human glioblastoma, we expressed common oncogenic variants including activated human EGFRvIII and PI3KCAH1047R under the control of the radial glial-specific promoter her4.1 in syngeneic tp53 loss-of-function mutant zebrafish. Robust tumor formation was observed prior to 45 days of life, and tumors had a gene expression signature similar to human glioblastoma of the mesenchymal subtype, with a strong inflammatory component. Within early stage tumor lesions, and in an in vivo and endogenous tumor microenvironment, we visualized infiltration of phagocytic cells, as well as internalization of tumor cells by mpeg1.1:EGFP+ microglia/macrophages, suggesting negative regulatory pressure by pro-inflammatory cell types on tumor growth at early stages of glioblastoma initiation. Furthermore, CRISPR/Cas9-mediated gene targeting of master inflammatory transcription factors irf7 or irf8 led to increased tumor formation in the primary context, while suppression of phagocyte activity led to enhanced tumor cell engraftment following transplantation into otherwise immune-competent zebrafish hosts. Altogether, we developed a genetically relevant model of aggressive human glioblastoma and harnessed the unique advantages of zebrafish including live imaging, high-throughput genetic and chemical manipulations to highlight important tumor-suppressive roles for the innate immune system on glioblastoma initiation, with important future opportunities for therapeutic discovery and optimizations.

The landscape of cancer-associated transcript fusions in adult brain tumors: a longitudinal assessment in 140 patients with cerebral gliomas and brain metastases

Background

Oncogenic fusions of neurotrophic receptor tyrosine kinase NTRK1, NTRK2, or NTRK3 genes have been found in different types of solid tumors. The treatment of patients with TRK fusion cancer with a first-generation TRK inhibitor (such as larotrectinib or entrectinib) is associated with high response rates (>75%), regardless of tumor histology and presence of metastases. Due to the efficacy of TRK inhibitor therapy of larotrectinib and entrectinib, it is clinically important to identify patients accurately and efficiently with TRK fusion cancer. In this retrospective study, we provide unique data on the incidence of oncogenic NTRK gene fusions in patients with brain metastases (BM) and gliomas.

Methods

140 samples fixed and paraffin-embedded tissue (FFPE) of adult patients (59 of gliomas [17 of WHO grade II, 20 of WHO grade III and 22 glioblastomas] and 81 of brain metastasis (BM) of different primary tumors) are analyzed. Identification of NTRK gene fusions is performed using next-generation sequencing (NGS) technology using Focus RNA assay kit (Thermo Fisher Scientific).

Results

We identified an ETV6 (5)::NTRK3 (15) fusion event using targeted next-generation sequencing (NGS) in one of 59 glioma patient with oligodendroglioma-grade II, IDH-mutated and 1p19q co-deleted at incidence of 1.69%. Five additional patients harboring TMPRSS (2)::ERG (4) were identified in pancreatic carcinoma brain metastasis (BM), prostatic carcinoma BM, endometrium BM and oligodendroglioma (grade II), IDH-mutated and 1p19q co-deleted. A FGFR3 (17)::TACC3 (11) fusion was identified in one carcinoma breast BM. Aberrant splicing to produce EGFR exons 2-7 skipping mRNA, and MET exon 14 skipping mRNA were identified in glioblastoma and pancreas carcinoma BM, respectively.

Conclusions

This study provides data on the incidence of NTRK gene fusions in brain tumors, which could strongly support the relevance of innovative clinical trials with specific targeted therapies (larotrectinib, entrectinib) in this population of patients. FGFR3 (17)::TACC3 (11) rearrangement was detected in breast carcinoma BM with the possibility of using some specific targeted therapies and TMPRSS (2)::ERG (4) rearrangements occur in a subset of patients with, prostatic carcinoma BM, endometrium BM, and oligodendroglioma (grade II), IDH-mutated and 1p19q co-deleted, where there are yet no approved ERG-directed therapies.

Early 2-Factor Transcription Factors Associated with Progression and Recurrence in Bevacizumab-Responsive Subtypes of Glioblastoma

The early 2-factor (E2F) family of transcription factors, including E2F1 through 8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment. Using machine learning algorithms, including TreeBagger and deep neural networks, we systematically predicted and validated GBM patient survival terms based on the expression profiles of E2Fs across BVZ-responsive GBM subtypes. Our bioinformatics analyses suggested that a significant increase in E2F8 post-BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, implicating this factor as a candidate mechanism of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. These insights underscore that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers, therapeutic targets, and recommending precision BVZ treatment to individual GBM patients.

On the Boundary of Exploratory Genomics and Translation in Sequential Glioblastoma

OMICS methods brought significant advancements to the understanding of tumor cell biology, which transformed the treatment and prognosis of several cancers. Clinical practice and outcomes, however, changed significantly less in the case of glioblastoma (GBM). In this study, we aimed to assess the utility of whole exome (WES) sequencing in the clinical setting. Ten pairs of formalin-fixed, paraffin-embedded (FFPE) GBM specimens were obtained at onset (GBM-P) and at recurrence (GBM-R). Histopathological and molecular features of all samples supported the diagnosis of GBM based on WHO CNS5. WES data were filtered, applying a strict and custom-made pipeline, and occurrence of oncogenic and likely oncogenic variants in GBM-P, GBM-R or both were identified by using the VarSeq program version 2.5.0 (Golden Helix, Inc.). Characteristics and recurrence of the variants were analyzed in our own cohort and were also compared to those available in the COSMIC database. The lists of oncogenic and likely oncogenic variants corresponded to those identified in other studies. The average number of these variants were 4 and 5 out of all detected 24 and 34 variants in GBM-P and GBM-R samples, respectively. On average, one shared oncogenic/likely oncogenic variant was found in the pairs. We assessed the identified variants' therapeutic significance, also taking into consideration the guidelines by the Association for Molecular Pathology (AMP). Our data support that a thorough WES analysis is suitable for identifying oncogenic and likely oncogenic variants in an individual clinical sample or a small cohort of FFPE glioma specimens, which concur with those of comprehensive research studies. Such analyses also allow us to monitor molecular dynamics of sequential GBM. In addition, careful evaluation of data according to the AMP guideline reveal that though therapeutic applicability of the variants is generally limited in the clinic, such information may be valuable in selected cases, and can support innovative preclinical and clinical trials.

mosaicMPI: a framework for modular data integration across cohorts and -omics modalities

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

Role of artificial intelligence in brain tumour imaging

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.

Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma

Early identification of IDH mutation status is of great significance in clinical therapeutic decision-making in the treatment of glioma. We demonstrate a technological solution to improve the accuracy and reliability of IDH mutation detection by combining MRI-based prediction and a CRISPR-based automatic integrated gene detection system (AIGS). A model was constructed to predict the IDH mutation status using whole slices in MRI scans with a Transformer neural network, and the predictive model achieved accuracies of 0.93, 0.87, and 0.84 using the internal and two external test sets, respectively. Additionally, CRISPR/Cas12a-based AIGS was constructed, and AIGS achieved 100% diagnostic accuracy in terms of IDH detection using both frozen tissue and FFPE samples in one hour. Moreover, the feature attribution of our predictive model was assessed using GradCAM, and the highest correlations with tumor cell percentages in enhancing and IDH-wildtype gliomas were found to have GradCAM importance (0.65 and 0.5, respectively). This MRI-based predictive model could, therefore, guide biopsy for tumor-enriched, which would ensure the veracity and stability of the rapid detection results. The combination of our predictive model and AIGS improved the early determination of IDH mutation status in glioma patients. This combined system of MRI-based prediction and CRISPR/Cas12a-based detection can be used to guide biopsy, resection, and radiation for glioma patients to improve patient outcomes.

Small-molecule Molephantin induces apoptosis and mitophagy flux blockage through ROS production in glioblastoma

Glioblastoma (GBM), one of the most malignant brain tumors in the world, has limited treatment options and a dismal survival rate. Effective and safe disease-modifying drugs for glioblastoma are urgently needed. Here, we identified a small molecule, Molephantin (EM-5), effectively penetrated the blood-brain barrier (BBB) and demonstrated notable antitumor effects against GBM with good safety profiles both in vitro and in vivo. Mechanistically, EM-5 not only inhibits the proliferation and invasion of GBM cells but also induces cell apoptosis through the reactive oxygen species (ROS)-mediated PI3K/Akt/mTOR pathway. Furthermore, EM-5 causes mitochondrial dysfunction and blocks mitophagy flux by impeding the fusion of mitophagosomes with lysosomes. It is noteworthy that EM-5 does not interfere with the initiation of autophagosome formation or lysosomal function. Additionally, the mitophagy flux blockage caused by EM-5 was driven by the accumulation of intracellular ROS. In vivo, EM-5 exhibited significant efficacy in suppressing tumor growth in a xenograft model. Collectively, our findings not only identified EM-5 as a promising, effective, and safe lead compound for treating GBM but also uncovered its underlying mechanisms from the perspective of apoptosis and mitophagy.

Differential Gene Expression in MRI-classified Glioblastoma

Previous characterization of the genome and transcriptome of glioblastoma (GBM) has revealed molecular alterations that potentially drive GBM pathogenesis and heterogeneity 1-6 . These open-resources are evolving, such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Atlas (TCIA) at the National Institute of Health comprising a large cohort of molecular and MRI data. Yet, no report deciphers the link between molecular signatures and MRI-classified GBM. The necessity to re-form molecular and imaging data motivated our computational approach to integrate TCIA and TCGA datasets derived from GBM. We uncovered common and distinct molecular signatures across GBM patients and specific to tumor locations, respectively. Despite heterogeneity in GBM, the top 12 genes from our analysis highlights that the dysregulation of a subset of neurotransmitter receptor or transporter and synaptic activity is common across GBM patients. The coherent layer of imaging and molecular information would help us stratify precision neuro-oncology and treatment options in ways that are not possible through MRI or genomic data alone. Our findings provide molecular targets in the disrupted neurocircuit of GBM, suggesting imbalanced excitation and inhibition. Given the fact that GBM patients exhibit similar symptoms resembling patients with neurodegenerative diseases and seizures, our results supported the hypothesis-GBM in the context of neurological disorders beyond a solely cancerous disease.

Glioblastoma microenvironment-from biology to therapy

Glioblastoma (GBM) is the most aggressive primary brain cancer. These tumors exhibit high intertumoral and intratumoral heterogeneity in neoplastic and nonneoplastic compartments, low lymphocyte infiltration, and high abundance of myeloid subsets that together create a highly protumorigenic immunosuppressive microenvironment. Moreover, heterogeneous GBM cells infiltrate adjacent brain tissue, remodeling the neural microenvironment to foster tumor electrochemical coupling with neurons and metabolic coupling with nonneoplastic astrocytes, thereby driving growth. Here, we review heterogeneity in the GBM microenvironment and its role in low-to-high-grade glioma transition, concluding with a discussion of the challenges of therapeutically targeting the tumor microenvironment and outlining future research opportunities.

A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches

Background

Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood.

Methods

Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution.

Results

We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade.

Conclusion

The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

Selective regulation of chemosensitivity in glioblastoma by phosphatidylinositol 3-kinase beta

Resistance to chemotherapies such as temozolomide is a major hurdle to effectively treat therapy-resistant glioblastoma. This challenge arises from the activation of phosphatidylinositol 3-kinase (PI3K), which makes it an appealing therapeutic target. However, non-selectively blocking PI3K kinases PI3Kα/β/δ/γ has yielded undesired clinical outcomes. It is, therefore, imperative to investigate individual kinases in glioblastoma's chemosensitivity. Here, we report that PI3K kinases were unequally expressed in glioblastoma, with levels of PI3Kβ being the highest. Patients deficient of O6-methylguanine-DNA-methyltransferase (MGMT) and expressing elevated levels of PI3Kβ, defined as MGMT-deficient/PI3Kβ-high, were less responsive to temozolomide and experienced poor prognosis. Consistently, MGMT-deficient/PI3Kβ-high glioblastoma cells were resistant to temozolomide. Perturbation of PI3Kβ, but not other kinases, sensitized MGMT-deficient/PI3Kβ-high glioblastoma cells or tumors to temozolomide. Moreover, PI3Kβ-selective inhibitors and temozolomide synergistically mitigated the growth of glioblastoma stem cells. Our results have demonstrated an essential role of PI3Kβ in chemoresistance, making PI3Kβ-selective blockade an effective chemosensitizer for glioblastoma.

ATM inhibition exploits checkpoint defects and ATM-dependent double strand break repair in TP53-mutant glioblastoma

Determining the balance between DNA double strand break repair (DSBR) pathways is essential for understanding treatment response in cancer. We report a method for simultaneously measuring non-homologous end joining (NHEJ), homologous recombination (HR), and microhomology-mediated end joining (MMEJ). Using this method, we show that patient-derived glioblastoma (GBM) samples with acquired temozolomide (TMZ) resistance display elevated HR and MMEJ activity, suggesting that these pathways contribute to treatment resistance. We screen clinically relevant small molecules for DSBR inhibition with the aim of identifying improved GBM combination therapy regimens. We identify the ATM kinase inhibitor, AZD1390, as a potent dual HR/MMEJ inhibitor that suppresses radiation-induced phosphorylation of DSBR proteins, blocks DSB end resection, and enhances the cytotoxic effects of TMZ in treatment-naïve and treatment-resistant GBMs with TP53 mutation. We further show that a combination of G2/M checkpoint deficiency and reliance upon ATM-dependent DSBR renders TP53 mutant GBMs hypersensitive to TMZ/AZD1390 and radiation/AZD1390 combinations. This report identifies ATM-dependent HR and MMEJ as targetable resistance mechanisms in TP53-mutant GBM and establishes an approach for simultaneously measuring multiple DSBR pathways in treatment selection and oncology research.

Genomic Alterations Correlated to Trastuzumab Resistance and Clinical Outcomes in HER2+/HR- Breast Cancers of Patients Living in Northwestern China

Anti-HER2 therapy has significantly improved the survival rates of patients with HER2+ breast cancer. However, a subset of these patients eventually experience treatment failure, and the underlying genetic mechanisms remain largely unexplored. This underscores the need to investigate the genomic heterogeneity of HER2+ breast cancer. In this study, we focus on HER2+/HR- breast cancer, as it differs from HER2+/HR+ breast cancer in terms of genetic and biological characteristics. We performed gene-targeted genome sequencing on 45 HER2+/HR- breast cancer samples and identified 650 mutations across 268 cancer-related genes. TP53 (71.1%) and PIK3CA (35.6%) were the most frequently mutated genes in our sample. Additionally, ERBB2 (77.8%), CDK12 (42.2%), and MYC (11.1%) exhibited a high frequency of copy number amplifications (CNAs). Comparative analysis with two other HER2+/HR- breast cancer cohorts revealed that our cohort had higher genetic variation rates in ARID1A, PKHD1, PTPN13, FANCA, SETD2, BRCA2, BLM, STAG2, FAT1, TOP2A, POLE, ATM, KMT2B, FGFR4, and EPAS1. Notably, in our cohort, NF1 and ATM mutations were more prevalent in trastuzumab-resistant patients (NF1, p=0.016; ATM, p=0.006) and were associated with primary trastuzumab resistance (NF1, p=0.042; ATM, p=0.021). Moreover, patients with NF1 mutations (p=0.009) and high histological grades (p=0.028) were more likely to experience early relapse. Ultimately, we identified a unique cancer-related gene mutation profile and a subset of genes associated with primary resistance to trastuzumab and RFS in patients with HER2+/HR- breast cancer in Northwest China. These findings could lay the groundwork for future studies aimed at elucidating the mechanisms of resistance to trastuzumab and improving HER2-targeted treatment strategies.

Tracing unknown tumor origins with a biological-pathway-based transformer model

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

Advances in synthetic lethality modalities for glioblastoma multiforme

Glioblastoma multiforme (GBM) is characterized by a high mortality rate, high resistance to cytotoxic chemotherapy, and radiotherapy due to its highly aggressive nature. The pathophysiology of GBM is characterized by multifarious genetic abrasions that deactivate tumor suppressor genes, induce transforming genes, and over-secretion of pro-survival genes, resulting in oncogene sustainability. Synthetic lethality is a destructive process in which the episode of a single genetic consequence is tolerable for cell survival, while co-episodes of multiple genetic consequences lead to cell death. This targeted drug approach, centered on the genetic concept of synthetic lethality, is often selective for DNA repair-deficient GBM cells with restricted toxicity to normal tissues. DNA repair pathways are key modalities in the generation, treatment, and drug resistance of cancers, as DNA damage plays a dual role as a creator of oncogenic mutations and a facilitator of cytotoxic genomic instability. Although several research advances have been made in synthetic lethality modalities for GBM therapy, no review article has summarized these therapeutic modalities. Thus, this review focuses on the innovative advances in synthetic lethality modalities for GBM therapy.

BCL6 is a context-dependent mediator of the glioblastoma response to irradiation therapy

Glioblastoma is a rapidly fatal brain cancer that does not respond to therapy. Previous research showed that the transcriptional repressor protein BCL6 is upregulated by chemo and radiotherapy in glioblastoma, and inhibition of BCL6 enhances the effectiveness of these therapies. Therefore, BCL6 is a promising target to improve the efficacy of current glioblastoma treatment. BCL6 acts as a transcriptional repressor in germinal centre B cells and as an oncogene in lymphoma and other cancers. However, in glioblastoma, BCL6 induced by therapy may not be able to repress transcription. Using a BCL6 inhibitor, the whole proteome response to irradiation was compared with and without BCL6 activity. Acute high dose irradiation caused BCL6 to switch from repressing the DNA damage response to promoting stress response signalling. Rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) enabled comparison of BCL6 partner proteins between untreated and irradiated glioblastoma cells. BCL6 was associated with transcriptional coregulators in untreated glioblastoma including the known partner NCOR2. However, this association was lost in response to acute irradiation, where BCL6 unexpectedly associated with synaptic and plasma membrane proteins. These results reveal the activity of BCL6 under therapy-induced stress is context-dependent, and potentially altered by the intensity of that stress.

Genetic mutation patterns among glioblastoma patients in the Taiwanese population - insights from a single institution retrospective study

This study utilized Next-Generation Sequencing (NGS) to explore genetic determinants of survival duration in Glioblastoma Multiforme (GBM) patients. We categorized 30 primary GBM patients into two groups based on their survival periods: extended survival (over two years, N = 17) and abbreviated survival (under two years, N = 13). For identifying pathogenic or likely pathogenic variants, we leveraged the ClinVar database. The cohort, aged 23 to 66 (median: 53), included 17 patients in Group A (survival >2 years, 10 males, 7 females), and 13 patients in Group B (survival <2 years, 8 males, 5 females), with a 60% to 40% male-to-female ratio. Identified mutations included CHEK2 (c.1477 G > A, p.E493K), IDH1 (c.395 G > A, p.R132H), and TP53 mutations. Non-coding regions exhibited variants in the TERT promoter (c.-146C > T, c.-124C > T) and TP53 RNA splicing site (c.376-2 A > C, c.376-2 A > G). While Group A had more mutations, statistical significance wasn't reached, likely due to sample size. Notably, TP53, and ATR displayed a trend toward significance. Surprisingly, TP53 mutations were more prevalent in Group A, contradicting Western findings on poorer GBM prognosis. In Taiwanese GBM patients, bevacizumab usage is linked to improved survival rates, affirming its safety and effectiveness. EGFR mutations are infrequent, suggesting potential distinctions in carcinogenic pathways. Further research on EGFR mutations and amplifications is essential for refining therapeutic approaches. TP53 mutations are associated with enhanced survival, but their functional implications necessitate detailed exploration. This study pioneers genetic analysis in Taiwanese GBM patients using NGS, advancing our understanding of their genetic landscape.

A prognostic neural epigenetic signature in high-grade glioma

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

Chaperonin-containing TCP-1 subunit genes are potential prognostic biomarkers and are correlated with Th2 cell infiltration in lung adenocarcinoma: An observational study

A family of molecular chaperone complexes called chaperonin-containing T-complex protein 1 (TCP-1) subunit genes (CCTs) aids in the folding of numerous proteins. With regard to lung adenocarcinoma (LUAD), this study provided a thorough understanding of the diagnostic and prognostic use of CCTs. The expression of CCTs in LUAD was evaluated by using databases including UALCAN and the Gene Expression Omnibus. Immunohistochemistry (IHC) was conducted to validate the expression of CCTs in LUAD. The mutation in the CCTs was identified through the cBioPortal database, while promoter methylation was measured by the UALCAN database. The prognostic value of CCTs was evaluated using the PrognoScan analysis. The GEPIA2.0 database was used to measure the prognostic value of CCTs and associated Hub genes. Correlation analysis between CCTs expression in LUAD was based on the GEPIA2.0 database. The ROC curves, clinical correlation analysis, gene ontology, Kyoto Encyclopedia of Genes and Genome analysis, and immune cell infiltration analysis were downloaded from The Cancer Genome Atlas database and then analyzed and visualized using the R language. The STRING database was used for protein-protein interaction analysis. Upregulation of CCTs expression in patients with LUAD indicated advanced diseases and a poor prognosis. ROC curve analysis revealed that the CCTs may serve as diagnostic indicators. The functional enrichment analysis showed that CCTs were involved in the mitosis-mediated cell cycle process. Additionally, 10 hub genes associated with CCTs that were linked to LUAD prognosis and tumor progression were identified. Immune cell infiltration analysis showed that CCTs expression in tumor tissues tends to be related to T helper type 2 cell infiltration. This study revealed that CCTs may serve as valuable biomarkers for the diagnosis and targeted therapy of LUAD.

The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma

Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.

Distinct Capabilities in NAD Metabolism Mediate Resistance to NAMPT Inhibition in Glioblastoma

Glioblastoma (GBM) cells require high levels of nicotinamide adenine dinucleotide (NAD) to fuel metabolic reactions, regulate their cell cycle and support DNA repair in response to chemotherapy and radiation. Inhibition of a key enzyme in NAD biosynthesis, NAMPT, has demonstrated significant anti-neoplastic activity. Here, we sought to characterise NAD biosynthetic pathways in GBM to determine resistance mechanisms to NAD inhibitors. GBM cells were treated with the NAMPT inhibitor FK866 with and without NAD precursors, and were analysed by qPCR, Western blot and proliferation assays (monolayer and spheroid). We also measured changes in the cell cycle, apoptosis, NAD/NADH levels and energy production. We performed orthoptic xenograft experiments in athymic nude mice to test the efficacy of FK866 in combination with temozolomide (TMZ). We show that the expression of key genes involved in NAD biosynthesis is highly variable across GBM tumours. FK866 inhibits proliferation, reduces NAD levels and limits oxidative metabolism, leading to G2/M cell cycle arrest; however, this can be reversed by supplementation with specific NAD precursors. Furthermore, FK866 potentiates the effects of radiation and TMZ in vitro and in vivo. NAMPT inhibitors should be considered for the treatment of GBM, with patients stratified based on their expression of key enzymes in other NAD biosynthetic pathways.

Drug-Induced Reorganisation of Lipid Metabolism Limits the Therapeutic Efficacy of Ponatinib in Glioma Stem Cells

The standard of care for glioblastoma (GBM) involves surgery followed by adjuvant radio- and chemotherapy, but often within months, patients relapse, and this has been linked to glioma stem cells (GSCs), self-renewing cells with increased therapy resistance. The identification of the epidermal growth factor receptor (EGFR) and platelet-derived growth factor receptor (PDGFR) as key players in gliomagenesis inspired the development of inhibitors targeting these tyrosine kinases (TKIs). However, results from clinical trials testing TKIs have been disappointing, and while the role of GSCs in conventional therapy resistance has been extensively studied, less is known about resistance of GSCs to TKIs. In this study, we have used compartmentalised proteomics to analyse the adaptive response of GSCs to ponatinib, a TKI with activity against PDGFR. The analysis of differentially expressed proteins revealed that GSCs respond to ponatinib by broadly rewiring lipid metabolism, involving fatty acid beta-oxidation, cholesterol synthesis, and sphingolipid degradation. Inhibiting each of these metabolic pathways overcame ponatinib adaptation of GSCs, but interrogation of patient data revealed sphingolipid degradation as the most relevant pathway in GBM. Our data highlight that targeting lipid metabolism, and particularly sphingolipid degradation in combinatorial therapies, could improve the outcome of TKI therapies using ponatinib in GBM.

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and the treatment response are typically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immunohistochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In contrast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying the tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for the reliable, high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing insights for preclinical research design.

Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte- like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

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.

Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Efficacy and Safety of Panitumumab in Patients With RAF/RAS-Wild-Type Glioblastoma: Results From the Drug Rediscovery Protocol

BACKGROUND

The prognosis of malignant primary high-grade brain tumors, predominantly glioblastomas, is poor despite intensive multimodality treatment options. In more than 50% of patients with glioblastomas, potentially targetable mutations are present, including rearrangements, altered splicing, and/or focal amplifications of epidermal growth factor receptor (EGFR) by signaling through the RAF/RAS pathway. We studied whether treatment with the clinically available anti-EGFR monoclonal antibody panitumumab provides clinical benefit for patients with RAF/RAS-wild-type (wt) glioblastomas in the Drug Rediscovery Protocol (DRUP).

METHODS

Patients with progression of treatment refractory RAF/RASwt glioblastoma were included for treatment with panitumumab in DRUP when measurable according to RANO criteria. The primary endpoints of this study are clinical benefit (CB: defined as confirmed objective response [OR] or stable disease [SD] ≥ 16 weeks) and safety. Patients were enrolled using a Simon-like 2-stage model, with 8 patients in stage 1 and up to 24 patients in stage 2 if at least 1 in 8 patients had CB in stage 1.

RESULTS

Between 03-2018 and 02-2022, 24 evaluable patients were treated. CB was observed in 5 patients (21%), including 2 patients with partial response (8.3%) and 3 patients with SD ≥ 16 weeks (12.5%). After median follow-up of 15 months, median progression-free survival and overall survival were 1.7 months (95% CI 1.6-2.1 months) and 4.5 months (95% CI 2.9-8.6 months), respectively. No unexpected toxicities were observed.

CONCLUSIONS

Panitumumab treatment provides limited CB in patients with recurrent RAF/RASwt glioblastoma precluding further development of this therapeutic strategy.

PLS-based gene subset augmentation and tumor-specific gene identification

In the study of tumor disease pathogenesis, the identification of genes specifically expressed in disease states is pivotal, yet challenges arise from high-dimensional datasets with limited samples. Conventional gene (feature) selection methods often fall short of capturing the complexity of gene-phenotype and gene-gene interactions, necessitating a more robust analysis method. To address these challenges, a gene subset augmentation strategy is proposed in this paper. Our approach introduces diverse perturbation mechanisms to generate distinct gene subsets. The partial least squares-based multiple gene measurement algorithm considers gene-phenotype and gene-gene correlations, identifying differentially expressed genes, including those with weak signals. The constructed gene networks derived from the augmented subsets unveil regulatory patterns, enabling association analysis to explore gene associations comprehensively. Our algorithm excels in identifying small-sized gene subsets with strong discriminative power, surpassing traditional methods that yield a single gene subset. Unlike conventional approaches, our algorithm reveals a spectrum of different gene subsets and their weakly differentially expressed genes. This nuanced perspective aids in unraveling the molecular characteristics and specific expression patterns of tumor genes. The versatility of our approach not only contributes to the advancement of tumor-specific gene identification but also holds promise for addressing challenges in various fields characterized by high-dimensional datasets and limited samples. The Python implementation is available at http://github.com/wenjieyou/PLSGSA.

Expression of molecular markers and synergistic anticancer effects of chemotherapy with antimicrobial peptides on glioblastoma cells

OBJECTIVE

Glioblastoma multiforme (GBM) is the most aggressive and fatal malignant primary brain tumor. The enhancement of the survival rate for glioma patients remains limited, even with the utilization of a combined treatment approach involving surgery, radiotherapy, and chemotherapy. This study was designed to assess the expression of IDH1, TP53, EGFR, Ki-67, GFAP, H3K27M, MGMT, VEGF, NOS, CD99, and ATRX in glioblastoma tissue from 11 patients. We investigated the anticancer impact and combined effects of cathelicidin (LL-37), protegrin-1 (PG-1), with chemotherapy-temozolomide (TMZ), doxorubicin (DOX), carboplatin (CB), cisplatin (CPL), and etoposide (ETO) in primary GBM cells. In addition, we examined the effect of LL-37, PG-1 on normal human fibroblasts and in the C6/Wistar rat intracerebral glioma model.

METHODS

For this study, 11 cases of glioblastoma were evaluated immunohistochemically for IDH1, TP53, EGFR, Ki-67, GFAP, H3K27M, MGMT, VEGF, NOS, CD99, and ATRX. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay was used to study cells viability and to determine cytotoxic effects of LL-37, PG-1 and their combination with chemotherapy in primary GBM cells. Synergism or antagonism was determined using combination index (CI) method. Finally, we established C6 glioblastoma model in Wistar rats to investigate the antitumor activity.

RESULTS

Peptides showed a strong cytotoxic effect on primary GBM cells in the MTT test (IC50 2-16 and 1-32 μM) compared to chemotherapy. The dual-drug combinations of LL-37 + DOX, LL-37 + CB (CI 0.46-0.75) and PG-1 + DOX, PG-1 + CB, PG-1 + TMZ (CI 0.11-0.77), demonstrated a synergism in primary GBM cells. In rat C6 intracerebral GBM model, survival of rats in experimental group (66.75 ± 12.6 days) was prolonged compared with that in control cohort (26.2 ± 2.66 days, p = 0.0008). After LL-37 treatment, experimental group rats showed significantly lower tumor volumes (31.00 ± 8.8 mm3) and weight (49.4 ± 13.3 mg) compared with control group rats (153.8 ± 43.53 mg, p = 0.038; 82.50 ± 7.60 mm3, respectively).

CONCLUSIONS

The combination of antimicrobial peptides and chemical drugs enhances the cytotoxicity of chemotherapy and exerts synergistic antitumor effects in primary GBM cells. Moreover, in vivo study provided the first evidence that LL-37 could effectively inhibit brain tumor growth in rat C6 intracerebral GBM model. These results suggested a significant strategy for proposing a promising therapy for the treatment of GBM.

3D epigenomics and 3D epigenopathies

Mammalian genomes are intricately compacted to form sophisticated 3-dimensional structures within the tiny nucleus, so called 3D genome folding. Despite their shapes reminiscent of an entangled yarn, the rapid development of molecular and next-generation sequencing technologies (NGS) has revealed that mammalian genomes are highly organized in a hierarchical order that delicately affects transcription activities. An increasing amount of evidence suggests that 3D genome folding is implicated in diseases, giving us a clue on how to identify novel therapeutic approaches. In this review, we will study what 3D genome folding means in epigenetics, what types of 3D genome structures there are, how they are formed, and how the technologies have developed to explore them. We will also discuss the pathological implications of 3D genome folding. Finally, we will discuss how to leverage 3D genome folding and engineering for future studies. [BMB Reports 2024; 57(5): 216-231].

Genomic landscape of glioblastoma without IDH somatic mutation in 42 cases: a comprehensive analysis using RNA sequencing data

PURPOSE

Glioblastoma is a malignant brain tumor with a poor prognosis. Genetic mutations associated with this disease are complex are not fully understood and require further elucidation for the development of new treatments. The purpose of this study was to comprehensively analyze genetic mutations in glioblastomas and evaluate the usefulness of RNA sequencing.

PATIENTS AND METHODS

We analyzed 42 glioblastoma specimens that were resected in routine clinical practice and found wild-type variants of the IDH1 and IDH2 genes. RNA was extracted from frozen specimens and sequenced, and genetic analyses were performed using the CLC Genomics Workbench.

RESULTS

The most common genetic alterations in the 42 glioblastoma specimens were TP53 mutation (28.6%), EGFR splicing variant (16.7%), EGFR mutation (9.5%), and FGFR3 fusion (9.5%). Novel genetic mutations were detected in 8 patients (19%). In 12 cases (28.6%), driver gene mutations were not detected, suggesting an association with PPP1R14A overexpression. Our findings suggest the transcription factors SOX10 and NKX6-2 are potential markers in glioblastoma.

CONCLUSION

RNA sequencing is a promising approach for genotyping glioblastomas because it provides comprehensive information on gene expression and is relatively cost-effective.

Novel Clinical Trials and Approaches in the Management of Glioblastoma

PURPOSE OF REVIEW

The purpose of this review is to discuss a wide variety of novel therapies recently studied or actively undergoing study in patients with glioblastoma. This review also discusses current and future strategies for improving clinical trial design in patients with glioblastoma to maximize efficacy in discovering effective treatments.

RECENT FINDINGS

Over the years, there has been significant expansion in therapy modalities studied in patients with glioblastoma. These therapies include, but are not limited to, targeted molecular therapies, DNA repair pathway targeted therapies, immunotherapies, vaccine therapies, and surgically targeted radiotherapies. Glioblastoma is the most common malignant primary brain tumor in adults and unfortunately remains with poor overall survival following the current standard of care. Given the dismal prognosis, significant clinical and research efforts are ongoing with the goal of improving patient outcomes and enhancing quality and quantity of life utilizing a wide variety of novel therapies.

Integrative analysis of transcriptome and proteome profiles in primary and recurrent glioblastoma

PURPOSE

Glioblastoma (GBM) is the most common and aggressive primary brain tumor characterized by poor prognosis and high recurrence. The underlying molecular mechanism that drives tumor progression and recurrence is unclear. This study is intended to look for molecular and biological changes that play a key role in GBM recurrence.

EXPERIMENTAL DESIGN

An integrative transcriptomic and proteomic analysis was performed on three primary GBM and three recurrent GBM tissues. Omics analyses were conducted using label-free quantitative proteomics and whole transcriptome sequencing.

RESULTS

A significant difference was found between primary GBM and recurrent GBM at the transcriptional level. Similar to other omics studies of cancer, a weak overlap was observed between transcriptome and proteome, and Procollagen C-Endopeptidase Enhancer 2 (PCOLCE2) was observed to be upregulated at mRNA and protein levels. Analysis of public cancer database revealed that high expression of PCOLCE2 is associated with poor prognosis of patients with GBM and that it may be a potential prognostic indicator. Functional and environmental enrichment analyses revealed significantly altered signaling pathways related to energy metabolism, including mitochondrial ATP synthesis-coupled electron transport and oxidative phosphorylation.

CONCLUSIONS AND CLINICAL RELEVANCE

This study provides new insights into the recurrence process of GBM through combined transcriptomic and proteomic analyses, complementing the existing GBM transcriptomic and proteomic data and suggesting that integrated multi-omics analyses may reveal new disease features of GBM.

CRISPR-Cas9 library screening combined with an exosome-targeted delivery system addresses tumorigenesis/TMZ resistance in the mesenchymal subtype of glioblastoma

Rationale: The large-scale genomic analysis classifies glioblastoma (GBM) into three major subtypes, including classical (CL), proneural (PN), and mesenchymal (MES) subtypes. Each of these subtypes exhibits a varying degree of sensitivity to the temozolomide (TMZ) treatment, while the prognosis corresponds to the molecular and genetic characteristics of the tumor cell type. Tumors with MES features are predominantly characterized by the NF1 deletion/alteration, leading to sustained activation of the RAS and PI3K-AKT signaling pathways in GBM and tend to acquire drug resistance, resulting in the worst prognosis compared to other subtypes (PN and CL). Here, we used the CRISPR/Cas9 library screening technique to detect TMZ-related gene targets that might play roles in acquiring drug resistance, using overexpressed KRAS-G12C mutant GBM cell lines. The study identified a key therapeutic strategy to address the chemoresistance against the MES subtype of GBM. Methods: The CRISPR-Cas9 library screening was used to discover genes associated with TMZ resistance in the U87-KRAS (U87-MG which is overexpressed KRAS-G12C mutant) cells. The patient-derived GBM primary cell line TBD0220 was used for experimental validations in vivo and in vitro. Chromatin isolation by RNA purification (ChIRP) and chromatin immunoprecipitation (ChIP) assays were used to elucidate the silencing mechanism of tumor suppressor genes in the MES-GBM subtype. The small-molecule inhibitor EPIC-0412 was obtained through high-throughput screening. Transmission electron microscopy (TEM) was used to characterize the exosomes (Exos) secreted by GBM cells after TMZ treatment. Blood-derived Exos-based targeted delivery of siRNA, TMZ, and EPIC-0412 was optimized to tailor personalized therapy in vivo. Results: Using the genome-wide CRISPR-Cas9 library screening, we found that the ERBIN gene could be epigenetically regulated in the U87-KRAS cells. ERBIN overexpression inhibited the RAS signaling and downstream proliferation and invasion effects of GBM tumor cells. EPIC-0412 treatment inhibited tumor proliferation and EMT progression by upregulating the ERBIN expression both in vitro and in vivo. Genome-wide CRISPR-Cas9 screening also identified RASGRP1(Ras guanine nucleotide-releasing protein 1) and VPS28(Vacuolar protein sorting-associated protein 28) genes as synthetically lethal in response to TMZ treatment in the U87-KRAS cells. We found that RASGRP1 activated the RAS-mediated DDR pathway by promoting the RAS-GTP transformation. VPS28 promoted the Exos secretion and decreased intracellular TMZ concentration in GBM cells. The targeted Exos delivery system encapsulating drugs and siRNAs together showed a powerful therapeutic effect against GBM in vivo. Conclusions: We demonstrate a new mechanism by which ERBIN is epigenetically silenced by the RAS signaling in the MES subtype of GBM. Restoration of the ERBIN expression with EPIC-0412 significantly inhibits the RAS signaling downstream. RASGRP1 and VPS28 genes are associated with the promotion of TMZ resistance through RAS-GDP to RAS-GTP transformation and TMZ efflux, as well. A quadruple combination therapy based on a targeted Exos delivery system demonstrated significantly reduced tumor burden in vivo. Therefore, our study provides new insights and therapeutic approaches for regulating tumor progression and TMZ resistance in the MES-GBM subtype.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

Targeting Integrin α3 Blocks β1 Maturation, Triggers Endoplasmic Reticulum Stress, and Sensitizes Glioblastoma Cells to TRAIL-Mediated Apoptosis

Glioblastoma (GBM) is a devastating brain cancer for which new effective therapies are urgently needed. GBM, after an initial response to current treatment regimens, develops therapeutic resistance, leading to rapid patient demise. Cancer cells exhibit an inherent elevation of endoplasmic reticulum (ER) stress due to uncontrolled growth and an unfavorable microenvironment, including hypoxia and nutrient deprivation. Cancer cells utilize the unfolded protein response (UPR) to maintain ER homeostasis, and failure of this response promotes cell death. In this study, as integrins are upregulated in cancer, we have evaluated the therapeutic potential of individually targeting all αβ1 integrin subunits using RNA interference. We found that GBM cells are uniquely susceptible to silencing of integrin α3. Knockdown of α3-induced proapoptotic markers such as PARP cleavage and caspase 3 and 8 activation. Remarkably, we discovered a non-canonical function for α3 in mediating the maturation of integrin β1. In its absence, generation of full length β1 was reduced, immature β1 accumulated, and the cells underwent elevated ER stress with upregulation of death receptor 5 (DR5) expression. Targeting α3 sensitized TRAIL-resistant GBM cancer cells to TRAIL-mediated apoptosis and led to growth inhibition. Our findings offer key new insights into integrin α3's role in GBM survival via the regulation of ER homeostasis and its value as a therapeutic target.

Promoter hypomethylated PDZK1 acts as a tumorigenic gene in glioma by interacting with AKT1

Glioma is the most frequently diagnosed primary brain tumor and typically has a poor prognosis because of malignant proliferation and invasion. It is urgent to elucidate the mechanisms driving glioma tumorigenesis and develop novel treatments to address this deadly disease. Here, we first revealed that PDZK1 is expressed at high levels in gliomas. Promoter hypomethylation may cause high expression of PDZK1 in glioma. Knockdown of PDZK1 inhibits glioma cell proliferation and invasion in vitro. Mechanistically, further investigations revealed that the loss of PDZK1 expression by siRNA inhibited the activation of the AKT/mTOR signaling pathway, leading to cell cycle arrest and apoptosis. Clinically, high expression of PDZK1 predicts a poorer prognosis for glioma patients than low expression of PDZK1. Overall, our study revealed that PDZK1 acts as a novel oncogene in glioma by binding to AKT1 and maintaining the activation of the AKT/mTOR signaling pathway. Thus, PDZK1 may be a potential therapeutic target for glioma.

Metabolic Roles of HIF1, c-Myc, and p53 in Glioma Cells

The metabolic reprogramming that promotes tumorigenesis in glioblastoma is induced by dynamic alterations in the hypoxic tumor microenvironment, as well as in transcriptional and signaling networks, which result in changes in global genetic expression. The signaling pathways PI3K/AKT/mTOR and RAS/RAF/MEK/ERK stimulate cell metabolism, either directly or indirectly, by modulating the transcriptional factors p53, HIF1, and c-Myc. The overexpression of HIF1 and c-Myc, master regulators of cellular metabolism, is a key contributor to the synthesis of bioenergetic molecules that mediate glioma cell transformation, proliferation, survival, migration, and invasion by modifying the transcription levels of key gene groups involved in metabolism. Meanwhile, the tumor-suppressing protein p53, which negatively regulates HIF1 and c-Myc, is often lost in glioblastoma. Alterations in this triad of transcriptional factors induce a metabolic shift in glioma cells that allows them to adapt and survive changes such as mutations, hypoxia, acidosis, the presence of reactive oxygen species, and nutrient deprivation, by modulating the activity and expression of signaling molecules, enzymes, metabolites, transporters, and regulators involved in glycolysis and glutamine metabolism, the pentose phosphate cycle, the tricarboxylic acid cycle, and oxidative phosphorylation, as well as the synthesis and degradation of fatty acids and nucleic acids. This review summarizes our current knowledge on the role of HIF1, c-Myc, and p53 in the genic regulatory network for metabolism in glioma cells, as well as potential therapeutic inhibitors of these factors.

BCOR::CREBBP fusion in malignant neuroepithelial tumor of CNS expands the spectrum of methylation class CNS tumor with BCOR/BCOR(L1)-fusion

Methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" was recently defined based on methylation profiling and tSNE analysis of a series of 21 neuroepithelial tumors with predominant presence of a BCOR fusion and/or characteristic CNV breakpoints at chromosome 22q12.31 and chromosome Xp11.4. Clear diagnostic criteria are still missing for this tumor type, specially that BCOR/BCOR(L1)-fusion is not a consistent finding in these tumors despite being frequent and that none of the Heidelberger classifier versions is able to clearly identify these cases, in particular tumors with alternative fusions other than those involving BCOR, BCORL1, EP300 and CREBBP. In this study, we introduce a BCOR::CREBBP fusion in an adult patient with a right temporomediobasal tumor, for the first time in association with methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" in addition to 35 cases of CNS neuroepithelial tumors with molecular and histopathological characteristics compatible with "CNS tumor with BCOR/BCOR(L1)-fusion" based on a comprehensive literature review and data mining in the repository of 23 published studies on neuroepithelial brain Tumors including 7207 samples of 6761 patients. Based on our index case and the 35 cases found in the literature, we suggest the archetypical histological and molecular features of "CNS tumor with BCOR/BCOR(L1)-fusion". We also present four adult diffuse glioma cases including GBM, IDH-Wildtype and Astrocytoma, IDH-Mutant with CREBBP fusions and describe the necessity of complementary molecular analysis in "CNS tumor with BCOR/BCOR(L1)-alterations for securing a final diagnosis.