bims-malgli 2024-07-28 papers

Issue of 2024‒07‒28
seven papers selected by
Oltea Sampetrean, Keio University

Clin Cancer Res. 2024 Jul 23.

Mutant IDH modulates suppressive myeloid populations in malignant glioma.

Eric P Grewal, Leland G K Richardson, Jing Sun, Rishab Ramapriyan, Maria Martinez-Lage, Julie J Miller, Bob S Carter, Daniel P Cahill, William T Curry, Bryan D Choi.

PURPOSE: Mutations in the isocitrate dehydrogenase (IDH) genes IDH1 and IDH2 have critical diagnostic and prognostic significance in diffuse gliomas. Neomorphic mutant IDH activity has been previously implicated in T-cell suppression; however, the effects of IDH mutations on intratumoral myeloid populations remain underexplored. Here, we investigate the influence of IDH status on the myeloid compartment using human glioma specimens and preclinical models.EXPERIMENTAL DESIGN: We performed RNA-sequencing and quantitative immunofluorescence on newly diagnosed, treatment-naive IDH-mutant grade 4 astrocytoma and IDH-wildtype glioblastoma (GBM) specimens. We also generated a syngeneic murine model, comparing transcriptomic and cell-level changes in paired isogenic glioma lines that differ only in IDH mutational status.
RESULTS: Among patient samples, IDH-mutant tumors displayed underrepresentation of suppressive myeloid transcriptional signatures, which was confirmed at the cellular level with decreased numbers of intratumoral M2-like macrophages and MDSCs. Introduction of the IDH-mutant enzyme into murine glioma was sufficient to recapitulate the transcriptomic and cellular shifts observed in patient samples.
CONCLUSIONS: We provide transcriptomic and cellular evidence that mutant IDH is associated with a quantitative reduction of suppressive myeloid cells in gliomas and that introduction of the mutant enzyme is sufficient to result in corresponding cellular changes using an in vivo preclinical model. These data advance our understanding of high-grade gliomas by identifying key myeloid cell populations that are reprogrammed by mutant-IDH and may be targetable through therapeutic approaches.

DOI: https://doi.org/10.1158/1078-0432.CCR-24-1056

Neuroscientist. 2024 Jul 26. 10738584241259773

Breaking Down Glioma-Microenvironment Crosstalk.

Raghavskandhan Ramachandran, Alexander F Jeans.

High-grade gliomas (HGGs) are the commonest primary brain cancers. They are characterized by a pattern of aggressive growth and diffuse infiltration of the host brain that severely limits the efficacy of conventional treatments and patient outcomes, which remain generally poor. Recent work has described a suite of mechanisms via which HGGs interact, predominantly bidirectionally, with various cell types in the host brain including neurons, glial cells, immune cells, and vascular elements to drive tumor growth and invasion. These insights have the potential to inspire novel approaches to HGG therapy that are critically needed. This review explores HGG-host brain interactions and considers whether and how they might be exploited for therapeutic gain.

Keywords: glioblastoma; glioma; high-grade glioma; host-glioma interactions; neurologic disease; neurooncology; synapse

DOI: https://doi.org/10.1177/10738584241259773

Elife. 2024 Jul 25. pii: e100824. [Epub ahead of print]13

Modeling glioblastoma.

Ian Lorimer.

Establishing a zebrafish model of a deadly type of brain tumor highlights the role of the immune system in the early stages of the disease.

Keywords: cancer; cancer biology; cancer variant modeling; cell biology; glioblastoma; inflammation; tumor microenvironment; zebrafish

DOI: https://doi.org/10.7554/eLife.100824

Elife. 2024 Jul 25. pii: RP93077. [Epub ahead of print]13

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.

Alex Weiss, Cassandra D'Amata, Bret J Pearson, Madeline N Hayes.

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.

Keywords: cancer biology; cancer variant modeling; cell biology; glioblastoma; inflammation; tumor microenvironment; zebrafish

DOI: https://doi.org/10.7554/eLife.93077

iScience. 2024 Jul 19. 27(7): 110225

METTL1-modulated LSM14A facilitates proliferation and migration in glioblastoma via the stabilization of DDX5.

Changyu Wang, Yan He, Xiang Fang, Danyang Zhang, Jinhai Huang, Shuxin Zhao, Lun Li, Guangyu Li.

Glioblastoma (GBM) is characterized by aggressive growth, invasiveness, and poor prognosis. Elucidating the molecular mechanisms underlying GBM is crucial. This study explores the role of Sm-like protein 14 homolog A (LSM14A) in GBM. Bioinformatics and clinical tissue samples analysis demonstrated that overexpression of LSM14A in GBM correlates with poorer prognosis. CCK8, EdU, colony formation, and transwell assays revealed that LSM14A promotes proliferation, migration, and invasion in GBM in vitro. In vivo mouse xenograft models confirmed the results of the in vitro experiments. The mechanism of LSM14A modulating GBM cell proliferation was investigated using mass spectrometry, co-immunoprecipitation (coIP), protein half-life, and methylated RNA immunoprecipitation (MeRIP) analyses. The findings indicate that during the G1/S phase, LSM14A stabilizes DDX5 in the cytoplasm, regulating CDK4 and P21 levels. Furthermore, METTL1 modulates LSM14A expression via mRNA m7G methylation. Altogether, our work highlights the METTL1-LSM14A-DDX5 pathway as a potential therapeutic target in GBM.

Keywords: Bioinformatics; Cancer; Molecular biology

DOI: https://doi.org/10.1016/j.isci.2024.110225

Sci Rep. 2024 Jul 23. 14(1): 16960

Exploring transcriptomic databases: unraveling circadian gene disruptions in lower grade glioma.

Weiyu Hou, Weiming Hou, Xueming Zhao.

The study explored the role of circadian rhythm genes (CRGs) in lower grade glioma (LGG) development and found that certain genes, such as CRY1, NPAS2, and RORB, were associated with increased or decreased risk of LGG. The study also investigated the correlation between CRGs and immune cell infiltration, revealing a negative association with macrophage infiltration and a positive correlation with B cell and CD8 + T cell infiltration. Additionally, the study identified major mutated CRGs, including PER2, BMAL1, CLOCK, and BMAL2, and their potential interaction with other CNS-associated genes. The study suggests that CRGs play a crucial role in immune response and tumorigenesis in LGG patients and warrants further investigation.

Keywords: Circadian rhythm; DNA methylation; Immune cell infiltration; Lower grade glioma; Survival analysis

DOI: https://doi.org/10.1038/s41598-024-67559-9

Sci Rep. 2024 Jul 26. 14(1): 17195

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.

Raquel Sánchez-Marqués, Vicente García, J Salvador Sánchez.

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms.

Keywords: Class imbalance; Clinical factors; Data-centric machine learning; Feature ranking; Glioma grade; Molecular biomarkers

DOI: https://doi.org/10.1038/s41598-024-68291-0