Triple-negative breast cancer (TNBC) is characterized by high metastatic potential, frequent recurrence and limited targeted regimens. The chemokine network acts as a central orchestrator, reshaping its tumor immune microenvironment (TIME) and determining therapeutic responsiveness. Yet its clinical translation remains hindered by network complexity and insufficient patient stratification. In this review, we systematically synthesize current evidence on how chemokine networks regulate key TIME component (macrophages, T cells, cancer-associated fibroblasts, myeloid-derived suppressor cells, and cancer stem cells) to drive TNBC progression, metastasis, and therapeutic resistance. We also critically evaluate chemokine-targeted interventions, spanning preclinical candidates (small-molecule inhibitors, monoclonal antibodies, miRNA-based therapies, natural products, and nanocarrier systems) to clinical-stage agents. A critical gap emerges from this evaluation: although numerous chemokine axes demonstrate robust preclinical efficacy, most clinical trials have yielded negative or marginal results. These failures are largely attributable to target redundancy, the absence of subtype-specific biomarkers, and suboptimal combination strategies. Based on recent research advances, we propose a three-pillar framework for future therapeutic success: precision subtyping-guided target selection, rational combination regimens (particularly with immune checkpoint inhibitors), and iterative biomarker validation. Overall, this review provides a roadmap for navigating chemokine network complexity, distilling actionable insights for clinical translation, and defining priority research directions to overcome current bottlenecks in TNBC chemokine-targeted therapy.
Wanyu Wang, Linhua Chen, Wei Guo et al.· Frontiers in Immunology· 0 citations
A conceptual framework for developmental hazard assessment is outlined, in which exposure-window characterization, congener-specific toxicokinetics, human-relevant models, multi-omics biomarkers, and PBPK/PBTK modeling are positioned as complementary components rather than as a fully operational assessment system.
Bin Deng, Xuan Xia, Xiaoxiang Sun· Frontiers in Public Health· 0 citations
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