Abstract 3763: Comparative spatial analyses of the tumor immune landscape in different mouse models of glioblastoma

Abstract Background: Despite advances in treatment, glioblastoma (GBM) remains one of the most difficult types of cancer to treat, and the prognosis is poor. The current median survival time for patients with GBM is about 12-15 months, and the five-year survival rate is less than 10%. There is an unmet need for better GBM treatment options, leveraged from relevant experimental models. To develop new therapies, preclinical animal models are important for analyzing the biology of GBM and evaluating the efficacy of novel therapeutic strategies. While a variety of experimental models are used to study GBM, most preclinical investigations involve mice. In this study we utilize a spatial phenotyping application that permits comprehensive characterization and comparison of key proteins in the brain tumor immune microenvironment (TiME), and detailed comparison the TiME between different immune-competent GBM mouse models. Methods: The Phenocycler-Fusion is a fast spatial biology solution that affords ultrahigh-plex single-cell spatial readouts. We used this solution for the whole slide imaging of mouse FFPE tissues and deep immune phenotyping of 40 proteins, comprising immune cell lineages, activation states and checkpoints, as well as biomarkers for tumor, vascular and neural landscapes of various GBM mouse models. Results: Via single cell spatial phenotyping, we isolated spatial signatures within the mouse GBM tissue immune microenvironment. We focused on multiple immune biomarkers, including microglia/myeloid cells that were identified via the combinatorial expression of the key markers CD68, Iba-1, F4/80, Tmem119 and CD11b. The macrophages and their biomarker expression profiles exhibited significant inter- and intra-tumoral heterogeneity within different mouse GBM models, indicative of the heterogeneous and complex biology of GBM. Conclusions: Our work encompasses the development of a custom antibody panel, an imaging workflow, as well as a novel bioinformatic analysis method. Deployment of this workflow on different mouse GBM models allowed us to study cell populations, according to biomarker profiles and spatial distribution. This study provides a deeper characterization of the diverse mouse GBM models to determine the optimal model that most accurately recapitulates the complex TiME of human GBM, including key features such as invasive tumor margins, high vascularity, blood-brain barrier, etc. We anticipate that this approach has enormous potential for a broad range of applications for which biomolecules’ spatial information is important and will deepen our understanding of the GBM TiME. Citation Format: Dmytro Klymyshyn, Niyati Jhaveri, Aditya Pratapa, Nadezhda Nikulina, Tsz H. Tam, Nadine Nelson, Riccardo Dolcetti, Davide Moi, Roberta Mazzieri, Theo Mantamadiotis. Comparative spatial analyses of the tumor immune landscape in different mouse models of glioblastoma abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 3763.