Multiparametric Longitudinal Profiling of RCAS-tva-Induced PDGFB-Driven Experimental Glioma

Vaccine-based immunotherapy and related preclinical models for glioma

Glioma, the most common primary malignant tumor in the central nervous system (CNS), lacks effective treatments, and >60% of cases are glioblastoma (GBM), the most aggressive form. Despite advances in immunotherapy, GBM remains highly resistant. Approaches that target tumor antigens expedite the development of immunotherapies, including personalized tumor-specific vaccines, patient-specific target selection, dendritic cell (DC) vaccines, and chimeric antigen receptor (CAR) and T cell receptor (TCR) T cells. Recent studies show promising results in treating GBM and lower-grade glioma (LGG), fostering hope for future immunotherapy. This review discusses tumor vaccines against glioma, preclinical models in immunological research, and the role of CD4+ T cells in vaccine-induced antitumor immunity. We also summarize clinical approaches, challenges, and future research for creating more effective vaccines.

Glioblastoma preclinical models: Strengths and weaknesses

Glioblastoma multiforme is a highly malignant brain tumor with significant intra- and intertumoral heterogeneity known for its aggressive nature and poor prognosis. The complex signaling cascade that regulates this heterogeneity makes targeted drug therapy ineffective. The development of an optimal preclinical model is crucial for the comprehension of molecular heterogeneity and enhancing therapeutic efficacy. The ideal model should establish a relationship between various oncogenes and their corresponding responses. This review presents an analysis of preclinical in vivo and in vitro models that have contributed to the advancement of knowledge in model development. The experimental designs utilized in vivo models consisting of both immunodeficient and immunocompetent mice induced with intracranial glioma. The transgenic model was generated using various techniques, like the viral vector delivery system, transposon system, Cre-LoxP model, and CRISPR-Cas9 approaches. The utilization of the patient-derived xenograft model in glioma research is valuable because it closely replicates the human glioma microenvironment, providing evidence of tumor heterogeneity. The utilization of in vitro techniques in the initial stages of research facilitated the comprehension of molecular interactions. However, these techniques are inadequate in reproducing the interactions between cells and extracellular matrix (ECM). As a result, bioengineered 3D-in vitro models, including spheroids, scaffolds, and brain organoids, were developed to cultivate glioma cells in a three-dimensional environment. These models have enabled researchers to understand the influence of ECM on the invasive nature of tumors. Collectively, these preclinical models effectively depict the molecular pathways and facilitate the evaluation of multiple molecules while tailoring drug therapy.

Preoperative growth dynamics of untreated glioblastoma: Description of an exponential growth type, correlating factors, and association with postoperative survival

Background

Little is known about the growth dynamics of untreated glioblastoma and its possible influence on postoperative survival. Our aim was to analyze a possible association of preoperative growth dynamics with postoperative survival.

Methods

We performed a retrospective analysis of all adult patients surgically treated for newly diagnosed glioblastoma at our center between 2010 and 2020. By volumetric analysis of data of patients with availability of ≥3 preoperative sequential MRI, a growth pattern was aimed to be identified. Main inclusion criterion for further analysis was the availability of two preoperative MRI scans with a slice thickness of 1 mm, at least 7 days apart. Individual growth rates were calculated. Association with overall survival (OS) was examined by multivariable.

Results

Out of 749 patients screened, 13 had ≥3 preoperative MRI, 70 had 2 MRI and met the inclusion criteria. A curve estimation regression model showed the best fit for exponential tumor growth. Median tumor volume doubling time (VDT) was 31 days, median specific growth rate (SGR) was 2.2% growth per day. SGR showed negative correlation with tumor size (rho = -0.59, P < .001). Growth rates were dichotomized according to the median SGR.OS was significantly longer in the group with slow growth (log-rank: P = .010). Slower preoperative growth was independently associated with longer overall survival in a multivariable Cox regression model for patients after tumor resection.

Conclusions

Especially small lesions suggestive of glioblastoma showed exponential tumor growth with variable growth rates and a median VDT of 31 days. SGR was significantly associated with OS in patients with tumor resection in our sample.