The tumor microenvironment plays a crucial role in determining response totreatment. This involves a series of interconnected changes in the cellularlandscape, spatial organization, and extracellular matrix composition. How-ever, assessing these alterations simultaneously is challenging from a spatialperspective, due to the limitations of current high-dimensional imagingtechniques and the extent of intratumoral heterogeneity over large lesionareas. In this study, we introduce a spatial proteomic workflow termedHyperplexed Immunofluorescence Imaging (HIFI) that overcomes these lim-itations. HIFI allows for the simultaneous analysis of > 45 markers in fragiletissue sections at high magnification, using a cost-effective high-throughputworkflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyzethe microenvironment response to radiation therapy in a preclinical model ofglioblastoma, and compare this response to a mouse model of breast-to-brainmetastasis. Here we show that glioblastomas undergo extensive spatial reor-ganization of immune cell populations and structural architecture in responseto treatment, while brain metastases show no comparable reorganization. Ourintegrated spatial analyses reveal highly divergent responses to radiationtherapy between brain tumor models, despite equivalent radiotherapy benefit.