Tsvetkov, V. O. | bioRxiv Science

bioRxiv2025

Single-cell spatial analysis of pediatric high-grade glioma reveals a novel population of SPP1+/GPNMB+ myeloid cells with immunosuppressive and tumor-promoting capabilities

BackgroundPediatric-type diffuse high-grade gliomas (pHGG) are a leading cause of pediatric cancer-related mortality. Although immunotherapy offers a promising treatment avenue, clinical responses in pHGG patients remain limited. A detailed understanding of the tumor immune microenvironment (TIME) is essential for advancing immunotherapeutic strategies. MethodsWe performed single-cell spatial analysis integrating cyclical immunofluorescence imaging and Spatial Molecular Imaging to interrogate the proteomic and transcriptomic landscape of pHGG. A tissue microarray comprising 32 diagnostic patient-derived pHGG samples was utilized to map the spatial distribution of immune and tumor cells. ResultsOur analyses reveal that the pHGG TIME is predominantly composed of myeloid cells, including brain-resident microglia and monocyte-derived macrophages, with only few T cells. A significant subset of these myeloid cells express mesenchymal-like genes and are positive for SPP1 and GPNMB. Spatial mapping further demonstrated that SPP1+/GPNMB+ myeloid cells localize in close proximity to mesenchymal-like tumor cells, and negatively correlate with the location and presence of CD8+ T cells. These cells also express genes related to immunosuppression and epithelial-to-mesenchymal transition, indicating their potential role in establishing an immunosuppressive niche. ConclusionsOur findings reveal a distinct immune landscape in pHGG characterized by SPP1+/GPNMB+ myeloid cells which may contribute to the exclusion of CD8+ T cells. This spatially resolved insight identifies these myeloid cells as promising therapeutic targets and provides a rationale for developing novel immunotherapeutic strategies to improve outcomes in pediatric high-grade gliomas.

cancer biology↗

bioRxiv2020

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells, is of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology, has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as SARS-CoV-2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in training data of the models seem to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen vs. cancer peptides. Overall, we demonstrate that accurate and reliable prediction of immunogenic CD8+ T cell targets remains unsolved, thus we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

immunology↗