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Blocking immunosuppressive neutrophils deters pY696-EZH2-driven brain metastases.

ABSTRACT: The functions of immune cells in brain metastases are unclear because the brain has traditionally been considered "immune privileged." However, we found that a subgroup of immunosuppressive neutrophils is recruited into the brain, enabling brain metastasis development. In brain metastatic cells, enhancer of zeste homolog 2 (EZH2) is highly expressed and phosphorylated at tyrosine-696 (pY696)-EZH2 by nuclear-localized Src tyrosine kinase. Phosphorylation of EZH2 at Y696 changes its binding preference from histone H3 to RNA polymerase II, which consequently switches EZH2's function from a methyltransferase to a transcription factor that increases c-JUN expression. c-Jun up-regulates protumorigenic inflammatory cytokines, including granulocyte colony-stimulating factor (G-CSF), which recruits Arg1⁺- and PD-L1⁺ immunosuppressive neutrophils into the brain to drive metastasis outgrowth. G-CSF-blocking antibodies or immune checkpoint blockade therapies combined with Src inhibitors impeded brain metastasis in multiple mouse models. These findings indicate that pY696-EZH2 can function as a methyltransferase-independent transcription factor to facilitate the brain infiltration of immunosuppressive neutrophils, which could be clinically targeted for brain metastasis treatment.

SUBMITTER: Zhang L

PROVIDER: S-EPMC7948522 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeWhile deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.MethodsWe collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.ResultsDose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).ConclusionThis study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.

Project description:BackgroundMUC16 (CA125) is a commonly used tumor marker for ovarian cancer screening and reported to be an immunosuppressive factor by acting on the sialic acid-binding immunoglobulin-like lectin-9 (Siglec-9) on the surface of natural killer cells (NK cells), B cells, and monocytes. However, the role of MUC16 on neutrophils in the tumor microenvironment remains to be further explored.MethodsThe correlation between the proportion and count of peripheral blood cells, serum inflammatory-related factors and serum MUC16 (CA125) level in patients was constructed based on clinical samples. RNAseq data was obtained from TCGA and sequencing of ovarian cancer tissues, followed by TIMER immune cell infiltration and correlation analysis. Ovarian cancer organoid was constructed to stimulate neutrophils with immunophenotype identification by qPCR and flow cytometry. MUC16 protein stimulation to neutrophils validated the role of MUC16 under the analysis of RNA sequencing and inhibition of NK cytotoxicity in vitro.ResultsThe serum MUC16 level was positively correlated with the proportion and count of peripheral blood neutrophils, neutrophil-to-lymphocyte ratio (NLR) and inflammatory factors IL-6, IL-8, IL-10 and IL-2R. Siglec-9, the receptor of MUC16, was expressed on neutrophils and was positively correlated to neutrophil infiltration in ovarian cancer. After the stimulation of ovarian cancer organoids and MUC16 respectively, the proportions of CD11b+, CD66b+, and ICAM-1+ neutrophils were significantly increased, while the proportion of CXCR4+ neutrophils was slightly decreased, with increasing of of inflammatory factors MMP9, IL-8, OSM, IL-1β, TNF-α, CXCL3, and ROS. RNA-sequencing analysis revealed that inflammatory response, TNFA signaling pathway, and IL6-related pathway were upregulated in MUC16-stimulated neutrophils, accompanied by high expression of immunosuppression-related factors HHLA2, IL-6, TNFRSF9, ADORA2A, CD274 (PD-L1), and IDO1. NK cytotoxicity was decreased when treated by supernanant of MUC16-stimulated neutrophils in vitro.ConclusionMUC16 acted on neutrophils by Siglec-9 leading to an inflammatory and immunosuppressive phenotype in ovarian cancer.

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Blocking immunosuppressive neutrophils deters pY696-EZH2-driven brain metastases.

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