Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15–20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines. CD markers are surface markers expressed on various cells throughout the body, often associated with immune cells. Cytokines are the effector molecules of the immune system. Together, CD markers and cytokines can serve as useful biomarkers to reflect immune status in patients with GBM. However, there are gaps in the understanding of the intricate interactions between GBM and the peripheral immune system and how these interactions change with standard and immune-modulating treatments. The key to understanding the true nature of these interactions is through multi-omic analysis of tumor progression and treatment response. This review aims to identify potential non-invasive blood-based biomarkers that can contribute to an immune signature through multi-omic approaches, leading to a better understanding of immune involvement in GBM.

Despite attempts at improving survival by employing novel therapies, progression in glioma is nearly universal. Precision biomarkers are critical to advancing outcomes; however, biomarkers for glioma are currently unknown. Most data on which the field can draw for biomarker identification comprise tissue-based analysis requiring the biospecimen to be removed from the tumor. Non-invasive specimen-based precision biomarkers are needed. Mucins are captured in tissue and blood and are increasingly studied in cancer, with several studies exploring their role as biomarkers to detect disease and monitor disease progression. CA125, also known as MUC16, is implemented as a biomarker in the clinic for ovarian cancer. Similarly, several mucins are membrane-bound, facilitating downstream signaling associated with tumor resistance and hallmarks of cancer. Evidence supports mucin expression in glioma cells with relationships to tumor detection, progression, resistance, and patient outcomes. The differential expression of mucins across tissues and organs could also provide a means of attributing signals measured in serum or plasma. In this review, we compiled existing research on mucins as candidate precision biomarkers in glioma, focusing on promising mucins in relationship to glioma and leading to a framework for mucin analysis in biospecimens as well as avenues for validation as data evolve.

Background and Hypothesis:Glioblastoma (GBM) is an aggressive primary malignancy of the CNS with a dismal prognosis (~15-20 months) despite standard of care therapies. The poor prognosis of GBM despite ongoing research may be due to inaccuracies in preclinical models of the disease. Cell lines typically used to study GBM are exposed to ambient air containing 21% oxygen (normoxia compared to physiologic oxygen tension, physoxia, ~5%). In extracranial tumors, exposure to the oxygen in ambient air triggers epigenetic changes that alter cell growth, metabolism, and treatment responsiveness. The aim of this study is to obtain preliminary data on the impact ofphysiologic oxygen tensions on primary glioma cell growth in vitro. Experimental Design:Growth of primary glioma cell lines (GB43, GB10, GB001) and one immortalized cell line (293T HEK) was assessed in normoxia and physoxia. All cell lines were plated at 10,000 cells per well. Cells were harvested and counted in triplicate on days 2, 4, 6, 8, 10, 12. On each day the cells were counted, the media in the remaining wells was changed. This experiment was repeated three times. Wound healing assays with all cell lines were also performed at normoxia and physoxia. Results:Cells line growth curves were plotted and showed consistent exponential growth after counting on day two. From these graphs, the cell doubling time was calculated over a period of four days during which the cells were undergoing exponential growth. Conclusions:Current results indicated that primary glioblastoma cell lines grow at different rates at differing oxygen tensions. In ongoing studies, we are exploring the effect of low oxygen tensions on functional assays such as wound-healing. Future work will assess rates of growth and functional consequence of physoxia in tumor samples never exposed to ambient air to most accurately recapitulate the in-situ environment.

BACKGROUND: Cerebral vasospasm is a rare yet serious complication of transsphenoidal surgery (TSS). While its etiology is incompletely understood, early detection is critical for proper management. OBSERVATIONS: The authors report the case of a 35-year-old female with acromegaly who underwent TSS for resection of somatotroph macroadenoma. The adenoma was completely removed with its pseudocapsule, and no CSF leakage was encountered. Postoperative imaging was notable for blood products in the interpeduncular cistern. A week after surgery, she presented to the clinic with dysarthria, right-sided ataxia, and right eye exotropia. CT angiography identified vasospasm at the basilar tip, with subsequent MRI demonstrating an infarction of the midbrain. The patient was admitted, underwent aggressive fluid resuscitation, and then discharged after vitals had stabilized and ambulation and speech had improved. Incomplete right eye convergence resolved by the 3-month follow-up. LESSONS: The authors present a rare case of cerebral vasospasm following transsphenoidal resection of a pituitary macroadenoma and discuss the effect on midbrain tracts involved in vergence. https://thejns.org/doi/10.3171/CASE25453

Introduction: Primary CNS tumors impact 25,000 individuals annually in the U.S., posing significant health challenges. Research is hindered by fragmented data across electronic health record (EHR) systems and the inefficiency of manual extraction for diagnostic, molecular, and treatment data. Large Language Models (LLMs) present a solution by automating extraction, enhancing efficiency and consistency, and enabling AI-driven insights. Methods: We analyzed 4,974 EHR documents from 256 patients with confirmed glioblastoma (GBM). All patients were treated on NCI NIH IRB (IRB00011862)-approved protocols 00-C-0074, 02C0064, 04C0200, 06C0112, 16-C-0081, and 20-C-0027. Cohort 1 (GBM, n = 109) was used to develop to the pipeline, while Cohorts 2 (various CNS histologies, n = 147) and 3 (GBM lesions, n = 15) served as testing sets. Clinical features extracted with GPT-4o via the NIH NIDAP Text Extraction Program (NTEP) included date of diagnosis, KPS, extent of resection, MGMT and IDH statuses, and radiation therapy start/end dates. Prompts underwent iterations, utilized JavaScript Object Notation (JSON) formatting, and outputs were then compared to the manual ground truth, established through detailed chart review of all 256 patients across three cohorts to extract key clinical variables of interest, allowing for the analysis of clinical feature and document type accuracy. Results: Prompt refinement led to a 30-fold increase in prompt character count, achieving ≥ 95% accuracy for five of seven features in Cohort 1. MGMT accuracy increased from 26% to 99%, and radiation dates increased from 93% to 98%. KPS (82%) and extent of resection (84%) were less accurate. For Cohort 2, MGMT and IDH were extracted with the highest accuracy (87% and 90%, respectively), while Cohort 3 achieved slightly lower but still strong performance (70% and 78%). Across these cohorts, MGMT and IDH were the most accurately extracted biomarkers. Radiation therapy summaries were the most effective documents across all three cohorts based on the extraction rates of clinical features. Conclusion: LLMs can accurately extract clinical features from EHRs with careful document selection and prompt design. This method may support CNS tumor research and have broader clinical applications. Future work will expand feature sets and validate on external datasets.