ReviewInternational journal of molecular sciences2026
Dynamic Immune Responsiveness in Breast Cancer: Tumor Microenvironment Plasticity, Therapeutic Timing, and Clinical Translation.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
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Abstract
Breast cancer immunotherapy has advanced substantially, yet durable clinical benefit remains limited to selected patient populations, reflecting heterogeneity in tumor-immune interactions across molecular subtypes, disease stages, and treatment contexts. This review frames immune responsiveness as a dynamic manifestation of tumor microenvironmental plasticity rather than a fixed tumor phenotype. We integrate mechanistic evidence showing that anti-tumor immunity is governed by interactions among malignant cells, immune populations, and cancer-associated fibroblasts. We emphasize therapeutic timing where treatment-induced changes in antigen availability, immune priming, stromal architecture, and cellular composition may create transient windows of immune susceptibility. Mechanisms of immune escape, including defective antigen presentation, T-cell dysfunction, suppressive myeloid populations, stromal exclusion, vascular abnormalities, and metabolic constraints, are evaluated as potential therapeutic targets. Emerging approaches incorporating single-cell and spatial profiling, circulating tumor DNA, multiomics, digital pathology, and artificial intelligence offer approaches for longitudinal characterization of evolving immune states, but their clinical utility in guiding immunotherapy remains largely unvalidated. We propose a precision-immunotherapy framework in which treatment selection and sequencing could be informed by these states. This framework complements established treatment approaches and defines testable hypotheses for prospective clinical studies.
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