Open Data for Differential Network Analysis in Glioma

Precision Population Cancer Medicine in Brain Tumors: A Potential Roadmap to Improve Outcomes and Strategize the Steps to Bring Interdisciplinary Interventions

Brain tumors, a significant health burden, rank as the second leading cause of cancer among adolescents and young adults and the eighth most common cancer in older adults. Despite treatment advances, outcomes for many brain tumor types, especially glioblastoma multiforme (GBM), remain poor. Precision population cancer medicine (PPCM) offers promising avenues for improving outcomes in brain tumor management. This comprehensive review delves into the current landscape of brain tumor diagnosis and treatment, with a primary focus on the potential of PPCM to enhance care. The review explores several key areas where PPCM approaches show promise. In genetics and molecular biology, the genetic heterogeneity of brain tumors poses challenges and opportunities for targeted therapies. Understanding genetic patterns can guide treatment strategies and improve prognostication. Epigenetic modifications are crucial in brain tumor development and progression. Deoxyribonucleic acid (DNA) methylation patterns, particularly of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter, serve as essential biomarkers for treatment response and prognosis in GBM. Targeting epigenetic mechanisms could lead to novel therapeutic approaches. Non-invasive liquid biopsy techniques show potential for diagnosis, monitoring, and prognostication in brain tumors. Analysis of circulating tumor DNA and microRNAs may provide valuable information about tumor characteristics and treatment response. Advanced imaging techniques, including radiomics and radiogenomics, combined with artificial intelligence (AI) algorithms, are enhancing tumor detection, characterization, and treatment planning. These technologies can contribute to more personalized treatment approaches. In addition, emerging nanotherapeutic platforms, which involve the use of nanoparticles to deliver drugs directly to tumors, and theranostic approaches, which combine therapy and diagnostics in a single platform, offer new possibilities for targeted drug delivery and real-time treatment monitoring in brain tumors. The review also addresses socioeconomic and demographic factors influencing brain tumor incidence and outcomes. It highlights the stark disparities in care access and survival rates among different racial and ethnic groups, emphasizing the urgent need for PPCM strategies to address these inequities. Challenges in implementing PPCM for brain tumors include the blood-brain barrier, which limits drug delivery, and the need for more extensive clinical trials to validate new approaches. The authors stress the importance of interdisciplinary collaboration and data sharing to advance the field, making the audience feel united and part of a larger team. While PPCM holds great promise, the review emphasizes that it should complement, not replace, population-level interventions and standard-of-care treatments. The authors advocate for a balanced approach that leverages cutting-edge personalized strategies while ensuring broad access to effective treatments. In conclusion, PPCM represents a powerful tool in the fight against brain tumors, offering the potential for more targeted, effective, and less toxic treatments. However, realizing its full potential will require ongoing research, clinical validation, and policy interactions to address disparities in care access.

Computational pathology-based weakly supervised prediction model for MGMT promoter methylation status in glioblastoma

Introduction

The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) is closely related to the treatment and prognosis of glioblastoma. However, there are currently some challenges in detecting the methylation status of MGMT promoters. The hematoxylin and eosin (H&E)-stained histopathological slides have always been the gold standard for tumor diagnosis.

Methods

In this study, based on the TCGA database and H&E-stained Whole slide images (WSI) of Beijing Tiantan Hospital, we constructed a weakly supervised prediction model of MGMT promoter methylation status in glioblastoma by using two Transformer structure models.

Results

The accuracy scores of this model in the TCGA dataset and our independent dataset were 0.79 (AUC = 0.86) and 0.76 (AUC = 0.83), respectively.

Conclusion

The model demonstrates effective prediction of MGMT promoter methylation status in glioblastoma and exhibits some degree of generalization capability. At the same time, our study also shows that adding Patches automatic screening module to the computational pathology research framework of glioma can significantly improve the model effect.

Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains

PURPOSE

To develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities.

MATERIALS AND METHODS

In this study, a novel method based on three-dimensional deep autoencoder network is proposed to automatically detect and segment various brain abnormalities without being trained on any abnormal samples. A total of 578 normal T2w MR volumes without obvious abnormalities were used for model training and validation. The proposed 3D autoencoder was evaluated on two different datasets (BraTs dataset and in-house dataset) containing T2w volumes from patients with glioblastoma, multiple sclerosis and cerebral infarction. Lesions detection and segmentation performance were reported as AUC, precision-recall curve, sensitivity, and Dice score.

RESULTS

In anomaly detection, AUCs for three typical lesions were as follows: glioblastoma, 0.844; multiple sclerosis, 0.858; cerebral infarction, 0.807. In anomaly segmentation, the mean Dice for glioblastomas was 0.462. The proposed network also has the ability to generate an anomaly heatmap for visualization purpose.

CONCLUSION

Our proposed method was able to automatically detect various brain anomalies such as glioblastoma, multiple sclerosis, and cerebral infarction. This work suggests that unsupervised anomaly detection is a powerful approach to detect arbitrary brain abnormalities without labeled samples. It has the potential to support diagnostic workflow in radiology as an automated tool for computer-aided image analysis.

Identification of hub genes and biological pathways in glioma via integrated bioinformatics analysis

OBJECTIVE

Glioma is the most common intracranial primary malignancy, but its pathogenesis remains unclear.

METHODS

We integrated four eligible glioma microarray datasets from the gene expression omnibus database using the robust rank aggregation method to identify a group of significantly differently expressed genes (DEGs) between glioma and normal samples. We used these DEGs to explore key genes closely associated with glioma survival through weighted gene co-expression network analysis. We then constructed validations of prognosis and survival analyses for the key genes via multiple databases. We also explored their potential biological functions using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA).

RESULTS

We selected DLGAP5, CDCA8, NCAPH, and CCNB2, as four genes that were abnormally up-regulated in glioma samples, for verification. They showed high levels of isocitrate dehydrogenase gene mutation and tumor grades, as well as good prognostic and diagnostic value for glioma. Their methylation levels were generally lower in glioma samples. GSEA and GSVA analyses suggested the genes were closely involved with glioma proliferation.

CONCLUSION

These findings provide new insights into the pathogenesis of glioma. The hub genes have the potential to be used as diagnostic and therapeutic markers.

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma

BACKGROUND

Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age.

METHODS

In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers.

RESULTS

Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification.

CONCLUSIONS

We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

Decreased Expression of ZNF554 in Gliomas is Associated with the Activation of Tumor Pathways and Shorter Patient Survival

Zinc finger protein 554 (ZNF554), a member of the Krüppel-associated box domain zinc finger protein subfamily, is predominantly expressed in the brain and placenta in humans. Recently, we unveiled that ZNF554 regulates trophoblast invasion during placentation and its decreased expression leads to the early pathogenesis of preeclampsia. Since ZNF proteins are immensely implicated in the development of several tumors including malignant tumors of the brain, here we explored the pathological role of ZNF554 in gliomas. We examined the expression of ZNF554 at mRNA and protein levels in normal brain and gliomas, and then we searched for genome-wide transcriptomic changes in U87 glioblastoma cells transiently overexpressing ZNF554. Immunohistochemistry of brain tissues in our cohort (n = 62) and analysis of large TCGA RNA-Seq data (n = 687) of control, oligodendroglioma, and astrocytoma tissues both revealed decreased expression of ZNF554 towards higher glioma grades. Furthermore, low ZNF554 expression was associated with shorter survival of grade III and IV astrocytoma patients. Overexpression of ZNF554 in U87 cells resulted in differential expression, mostly downregulation of 899 genes. The "PI3K-Akt signaling pathway", known to be activated during glioma development, was the most impacted among 116 dysregulated pathways. Most affected pathways were cancer-related and/or immune-related. Congruently, cell proliferation was decreased and cell cycle was arrested in ZNF554-transfected glioma cells. These data collectively suggest that ZNF554 is a potential tumor suppressor and its decreased expression may lead to the loss of oncogene suppression, activation of tumor pathways, and shorter survival of patients with malignant glioma.

Hypoxia Inducible Factors' Signaling in Pediatric High-Grade Gliomas: Role, Modelization and Innovative Targeted Approaches

The brain tumor microenvironment has recently become a major challenge in all pediatric cancers, but especially in brain tumors like high-grade gliomas. Hypoxia is one of the extrinsic tumor features that interacts with tumor cells, but also with the blood-brain barrier and all normal brain cells. It is the result of a dramatic proliferation and expansion of tumor cells that deprive the tissues of oxygen inflow. However, cancer cells, especially tumor stem cells, can endure extreme hypoxic conditions by rescheduling various genes' expression involved in cell proliferation, metabolism and angiogenesis and thus, promote tumor expansion, therapeutic resistance and metabolic adaptation. This cellular adaptation implies Hypoxia-Inducible Factors (HIF), namely HIF-1α and HIF-2α. In pediatric high-grade gliomas (pHGGs), several questions remained open on hypoxia-specific role in normal brain during gliomagenesis and pHGG progression, as well how to model it in preclinical studies and how it might be counteracted with targeted therapies. Therefore, this review aims to gather various data about this key extrinsic tumor factor in pHGGs.