Modeling macrophage-T cell interactions in the breast cancer immune microenvironment: from spatial omics to functional validation

Abstract

Breast cancer immunity depends on more than the number of immune cells in a tumor. It is also shaped by where those cells sit, which neighbors they contact, and what functional states they adopt locally. Tumor-associated macrophages (TAMs) and T cells are a key pairing in this setting. Depending on tissue context, their crosstalk may support cytotoxic immunity, reinforce immune exclusion, promote T-cell exhaustion, or weaken therapeutic response. Spatial technologies now allow these states to be examined in intact tumor sections rather than inferred from dissociated or bulk samples. Antibody-based imaging approaches, including imaging mass cytometry, MIBI, and CODEX, together with high-plex transcriptomic platforms such as MERFISH, Xenium, CosMx, Visium, GeoMx, and related methods, have revealed inflamed, excluded, myeloid-rich, stromal-barrier, and tertiary lymphoid structure-associated niches in breast cancer. However, spatial maps alone cannot establish mechanism. Cells that lie close together may not necessarily interact, and computational tools, including ligand-receptor scoring, graph-based neighborhood modeling, and spatial biomarker prediction, can only prioritize candidate macrophage-T cell programs. Functional validation remains essential. In this mini review, we discuss how spatial omics, computational modeling, organoid and explant cultures, microfluidic models, perturbation assays, and therapeutic testing can be linked to study macrophage-T cell crosstalk. We highlight a practical workflow in which spatial maps generate hypotheses, experimental systems test causality, and post-treatment profiling determines whether candidate interactions are remodeled by therapy.