Breast cancer remains the most prevalent malignancy among women worldwide, and its molecular subtypes display marked differences in clinical outcomes and therapeutic responses. Increasing evidence highlights the critical roles of myeloid-derived suppressor cells (MDSCs) within the tumor microenvironment, where they orchestrate immune evasion, promote metastasis, and contribute to therapy resistance. However, most current studies primarily focus on triple-negative breast cancer (TNBC), while systematic insights into the abundance, subset distribution, and functional heterogeneity of MDSCs in Luminal and HER2 + subtypes remain limited. This review synthesizes recent advances on major MDSC programs and highlights subtype-associated differences in their distribution patterns, immunosuppressive mechanisms, and clinical relevance across TNBC, HER2 + , and HR + /Luminal breast cancer. We further discuss how these subtype-linked myeloid states may influence key therapeutic outcomes, including pathological complete response following neoadjuvant therapy, heterogeneity of benefit from immunotherapy-based combinations, and the emergence of treatment resistance. In addition, we summarize emerging single-cell and spatial omics approaches that refine MDSC classification and enable in situ mapping of myeloid–lymphocyte organization. Finally, we outline mechanism-guided therapeutic strategies targeting MDSCs—encompassing recruitment/trafficking blockade, inhibition of suppressive metabolic effector pathways, and myeloid ecosystem remodeling/reprogramming—to support subtype-tailored precision immunotherapy in breast cancer. This review is intended for researchers in tumor immunology and cancer biology, clinicians and translational medicine professionals, as well as graduate students or early-career scholars interested in immunosuppressive cells and breast cancer.
Building similarity graph...
Analyzing shared references across papers
Loading...
Biyao Gong
Lixiang Zheng
PeerJ
Jiangxi University of Traditional Chinese Medicine
Building similarity graph...
Analyzing shared references across papers
Loading...
Gong et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69df2b65e4eeef8a2a6b0629 — DOI: https://doi.org/10.7717/peerj.20937