Anti-CD47 antibody is effective for treating malignant pediatric brain tumors without detectable toxicity in patient-derived xenograft models.

Tumor-associated macrophages (TAMs) represent an important cellular subset within the glioblastoma (WHO grade IV) microenvironment and are a potential therapeutic target. TAMs display a continuum of different polarization states between antitumorigenic M1 and protumorigenic M2 phenotypes, with a lower M1/M2 ratio correlating with worse prognosis. Here, we investigated the effect of macrophage polarization on anti-CD47 antibody-mediated phagocytosis of human glioblastoma cells in vitro, as well as the effect of anti-CD47 on the distribution of M1 versus M2 macrophages within human glioblastoma cells grown in mouse xenografts. Bone marrow-derived mouse macrophages and peripheral blood-derived human macrophages were polarized in vitro toward M1 or M2 phenotypes and verified by flow cytometry. Primary human glioblastoma cell lines were offered as targets to mouse and human M1 or M2 polarized macrophages in vitro. The addition of an anti-CD47 monoclonal antibody led to enhanced tumor-cell phagocytosis by mouse and human M1 and M2 macrophages. In both cases, the anti-CD47-induced phagocytosis by M1 was more prominent than that for M2. Dissected tumors from human glioblastoma xenografted within NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ mice and treated with anti-CD47 showed a significant increase of M1 macrophages within the tumor. These data show that anti-CD47 treatment leads to enhanced tumor cell phagocytosis by both M1 and M2 macrophage subtypes with a higher phagocytosis rate by M1 macrophages. Furthermore, these data demonstrate that anti-CD47 treatment alone can shift the phenotype of macrophages toward the M1 subtype in vivo.

Glioblastoma multiforme (GBM) is a highly aggressive malignant brain tumor with fatal outcome. Tumor-associated macrophages and microglia (TAMs) have been found to be major tumor-promoting immune cells in the tumor microenvironment. Hence, modulation and reeducation of tumor-associated macrophages and microglia in GBM is considered a promising antitumor strategy. Resident microglia and invading macrophages have been shown to have distinct origin and function. Whereas yolk sac-derived microglia reside in the brain, blood-derived monocytes invade the central nervous system only under pathological conditions like tumor formation. We recently showed that disruption of the SIRPα-CD47 signaling axis is efficacious against various brain tumors including GBM primarily by inducing tumor phagocytosis. However, most effects are attributed to macrophages recruited from the periphery but the role of the brain resident microglia is unknown. Here, we sought to utilize a model to distinguish resident microglia and peripheral macrophages within the GBM-TAM pool, using orthotopically xenografted, immunodeficient, and syngeneic mouse models with genetically color-coded macrophages ( Ccr2 ^RFP ) and microglia ( Cx3cr1 ^GFP ). We show that even in the absence of phagocytizing macrophages ( Ccr2 ^RFP/RFP ), microglia are effector cells of tumor cell phagocytosis in response to anti-CD47 blockade. Additionally, macrophages and microglia show distinct morphological and transcriptional changes. Importantly, the transcriptional profile of microglia shows less of an inflammatory response which makes them a promising target for clinical applications.

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

MRI-based machine learning designed with sequential decision steps reliably differentiated four unique medulloblastoma molecular subgroups. Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient g...