Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis and high recurrence rates. The complex immune microenvironment of GBM is highly infiltrated by tumor-associated microglia and macrophages (TAM). TAMs are known to be heterogeneous in their functional and metabolic states and can transmit either protumoral or antitumoral signals to glioma cells. Here, we performed bulk RNA sequencing and single-cell RNA sequencing on samples from patients with GBM, which revealed increased ATP synthase expression and oxidative phosphorylation activity in TAMs located in the tumor core relative to the tumor periphery. Both in vitro and in vivo models displayed similar trends of augmented TAM mitochondrial activity, along with elevated mitochondrial fission, glucose uptake, mitochondrial membrane potential, and extracellular ATP (eATP) production by TAMs in the presence of GBM cells. Tumor-secreted factors, including GM-CSF, induced the increase in TAM eATP production. Elevated eATP in the GBM microenvironment promoted glioma growth and invasion by activating the P2X purinoceptor 7 (P2X7R) on glioma cells. Inhibition of the eATP–P2X7R axis attenuated tumor cell viability in vitro and reduced tumor size and prolonged survival in glioma-bearing mouse models. Overall, this study revealed elevated TAM-derived eATP in GBM and provided the basis for targeting the eATP–P2X7R signaling axis as a therapeutic strategy in GBM. Significance: Glioblastoma-mediated metabolic reprogramming in tumor-associated microglia increases ATP secretion that supports cancer cell proliferation and invasion by activating P2X7R, which can be inhibited to attenuate tumor growth.

Immunotherapy is a promising therapeutic domain for the treatment of gliomas. However, clinical trials of various immunotherapeutic modalities have not yielded significant improvements in patient survival. Preclinical models for glioma research should faithfully represent clinically observed features regarding glioma behavior, mutational load, tumor interactions with stromal cells, and immunosuppressive mechanisms. In this review, we dive into the common preclinical models used in glioma immunology, discuss their advantages and disadvantages, and highlight examples of their utilization in translational research.

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.