Glioma research
The intra- and inter-tumoral heterogeneity of gliomas and the complex tumor microenvironment make accurate glioma treatment challenging. At present, research on gliomas mainly relies on cell lines, stem cell tumor spheres, and xenotransplantation models. The similarity between traditional tumor models and patients with glioma is very low.
Zhang et al. aimed to address the limitations of traditional tumor models by generating patient-derived glioma organoids using two methods that summarized the cell diversity, histological features, gene expression, and mutant profiles of their respective parent tumors and assess the feasibility of organoids for personalized treatment.
They compared the organoids generated using two methods growth analysis, immunohistological analysis, genetic testing, and the establishment of xenograft models.
Both types of organoids exhibited rapid infiltration when transplanted into the brains of adult immunodeficient mice. However, organoids formed using the microtumor method demonstrated more similar cellular characteristics and tissue structures to the parent tumors. Furthermore, the microtumor method allowed for faster culture times and more convenient operational procedures compared to the Matrigel method.
Patient-derived glioma organoids, especially those generated through the microtumor method, present a promising avenue for personalized treatment strategies. Their capacity to faithfully mimic the cellular and molecular characteristics of gliomas provides a valuable platform for elucidating tumor biology and evaluating therapeutic modalities.
The success rates of the Matrigel and microtumor methods were 45.5% and 60.5%, respectively. The microtumor method had a higher success rate, shorter establishment time, more convenient passage and cryopreservation methods, better simulation of the cellular and histological characteristics of the parent tumor, and a high genetic guarantee 1).
In April 2019, Feng et al. downloaded glioma-related publications indexed in PubMed between 1994 and 2018. They used Python to extract the title, publication date, MeSH terms, and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation (LDA) was applied to the abstracts to identify publications' research topics with greater specificity.
They identified and analyzed a total of 52,625 publications. They found that research on prognosis and the treatment of glioblastoma increased the most in terms of volume and rate of publications over the past 25 years. However, publications regarding clinical trials accounted for <5% of all publications considered in this study. The current research landscape covers clinical, pre-clinical, biological, and technical aspects of glioblastoma; at present, researchers appear to be less concerned with glioblastoma's psychological effects or patients' end-of-life care.
Publication of glioma-related research has expanded rapidly over the past 25 years. Common topics include the disease's molecular background, patients' survival, and treatment outcomes; more research needs to be done on the psychological aspects of glioblastoma and end-of-life care 2)