Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

Metabolic syndrome is a type of multifactorial metabolic disease with the presence of at least three factors: obesity, diabetes mellitus, low high-density lipoprotein, hypertriglyceridemia, and hypertension. Recent studies have shown that metabolic syndrome and its related components exert a significant impact on the initiation, progression, treatment response, and prognosis of breast cancer. Metabolic abnormalities not only increase the disease risk and aggravate tumor progression but also lead to unfavorable treatment responses and more treatment side effects. Moreover, biochemical reactions caused by the imbalance of these metabolic components affect both the host general state and organ-specific tumor microenvironment, resulting in increased rates of recurrence and mortality. Therefore, this review discusses the recent advances in the association of metabolic syndrome and breast cancer, providing potential novel therapeutic targets and intervention strategies to improve breast cancer outcome.

Cancer-associated adipocytes (CAAs), one of the primary stromal components, exhibit intimate crosstalk and release multiple cell factors mediating local and systemic biological effects. However, the role of CAAs in the regulation of systemic immune responses and their potential value in the clinical treatment of triple-negative breast cancer (TNBC) are not well described. Transcriptome sequencing was performed on CAA and normal adipocyte (NA) tissues isolated from surgically resected samples from TNBC patients and healthy controls. Cytokines, including C-X-C motif chemokine ligand 8 (CXCL8, also known as IL-8), secreted from NAs and CAAs were compared by transcriptome sequencing and enzyme-linked immunosorbent assay (ELISA). Proliferation, migration and invasion assays were employed to analyze the role of CAAs and CAA-derived CXCL8 (macrophage inflammatory protein-2 (MIP2) as a functional surrogate in mice). TNBC syngraft models were established to evaluate the curative effect of targeting CXCL8 in combination with anti-PD-1 therapies. Real-time quantitative polymerase chain reaction (RT-qPCR), western blotting (WB), polymerase chain reaction (PCR) array, flow cytometry, immunohistochemistry (IHC), and immunofluorescence (IF) were applied to analyze immune cell infiltration and epithelial–mesenchymal transition (EMT) markers. Specifically, we demonstrated that CAAs and CAA-derived CXCL8 played important roles in tumor growth, EMT, metastasis and tumor immunity suppression. CAA-derived CXCL8 remodeled the tumor immune microenvironment not only by suppressing CD4⁺ T and CD8⁺ T immune cell infiltration but also by upregulating CD274 expression in TNBC. The combination of targeting the CXCL8 pathway and blocking the PD-1 pathway synergistically increased the tumor immune response and inhibited tumor progression. Thus, our results highlight the molecular mechanisms and translational significance of CAAs in tumor progression and immune ecosystem regulatory effects and provide a better understanding of the potential clinical benefit of targeting CAA-derived CXCL8 in antitumor immunity and as a new therapeutic moiety in TNBC.

Background: Characterizing the unique immune microenvironment of each tumor is of great importance for better predicting prognosis and guiding cancer immunotherapy. However, the unique features of the immune microenvironment of triple negative breast cancer (TNBC) compared with other subtypes of breast cancer remain elusive. Therefore, we aimed to depict and compare the immune landscape among TNBC, human epidermal growth factor receptor 2‐positive (HER2 ⁺ ) breast cancer, and luminal‐like breast cancer. Methods: Single‐cell RNA sequencing (scRNA‐seq) was performed on CD45 ⁺ immune cells isolated from human normal breast tissues and primary breast tumors of various subtypes. By analyzing the scRNA‐seq data, immune cell clusters were identified and their proportions as well as transcriptome features were compared among TNBC, human HER2 ⁺ breast cancer, and luminal‐like breast cancer. Pseudotime and cell‐cell communication analyses were also conducted to characterize the immune microenvironment. Results: ScRNA‐seq data of 117,958 immune cells were obtained and 31 immune clusters were identified. A unique immunosuppressive microenvironment in TNBC was decoded as compared to that in HER2 ⁺ or luminal‐like breast cancer, which was characterized by higher proportions of regulatory T cells (Tregs) and exhausted CD8 ⁺ T cells and accompanied by more abundant plasma cells. Tregs and exhausted CD8 ⁺ T cells in TNBC exhibited increased immunosuppression signature and dysfunctional scores. Pseudotime analyses showed that B cells tended to differentiate to plasma cells in TNBC. Cell‐cell communication analyses indicated that these unique features are fostered by the diversified T cell‐B cell crosstalk in TNBC. Based on the T cell‐B cell crosstalk, a prognostic signature was established that could effectively predict the prognosis status for patients with TNBC. Additionally, it was found that TNBC had a higher proportion of cytotoxic natural killer (NK) cells, whereas HER2 ⁺ or luminal‐like breast cancer lost this feature, suggesting that HER2 ⁺ or luminal‐like breast cancer, but not TNBC, may benefit from NK‐based immunotherapy. Conclusions: This study identified a distinct immune feature fostered by T cell‐B cell crosstalk in TNBC, which provides better prognostic information and effective therapeutic targets for breast cancer.

Adipocytes are the main components in breast tissue, and cancer-associated adipocytes (CAAs) are one of the most important components in the tumor microenvironment of breast cancer (BC). Bidirectional regulation was found between CAAs and BC cells. BC facilitates the dedifferentiation of adjacent adipocytes to form CAAs with morphological and biological changes. CAAs increase the secretion of multiple cytokines and adipokines to promote the tumorigenesis, progression, and metastasis of BC by remodeling the extracellular matrix, changing aromatase expression, and metabolic reprogramming, and shaping the tumor immune microenvironment. CAAs are also associated with the therapeutic response of BC and provide potential targets in BC therapy. The present review provides a comprehensive description of the crosstalk between CAAs and BC and discusses the potential strategies to target CAAs to overcome BC treatment resistance.

Background: Estrogen receptor (ER), progesterone receptor (PgR), HER2, and Ki67 have been increasingly evaluated by core needle biopsy (CNB) and are recommended for classifying breast cancer into molecular subtypes. However, the concordance rate between CNB and open excision biopsy (OEB) has not been well documented. Methods: Patients with paired CNB and OEB samples from Oct. 2009 to Feb. 2012 in Ruijin Hospital were included. ER, PgR, HER2, and Ki67 were determined by immunohistochemistry (IHC). Patients with HER2 IHC 2+ were further examined by FISH. Cutoff value for Ki67 high expression was 14%. Molecular subtypes were constructed as follows: Luminal A, Luminal B, Triple Negative, and HER2 positive. Results: There were 298 invasive breast cancer patients analyzed. Concordance rates for ER, PgR, and HER2 were 93.6%, 85.9%, and 96.3%, respectively. Ki67 expression was slightly higher in OEB than in CNB samples (29.3% vs. 26.8%, P = 0.046). Good agreement (κ = 0.658) was demonstrated in evaluating molecular subtypes between CNB and OEB, with a concordance rate of 77.2%. We also used a different Ki67 cutoff value (20%) for determining Luminal A and B subtypes in HR (hormone receptor) +/HER2- diseases and the overall concordance rate was 79.2%. However, using a cut-point of Ki67 either 14% or 20% for both specimens, there will be about 14% of HR+/HER2- specimens that are called Luminal A on CNB and Luminal B on OEB. Conclusion: CNB was accurate in determining ER, PgR, and HER2 status as well as non-Luminal molecular subtypes in invasive breast cancer. Ki67 should be retested on OEB samples in HR+/HER2- patients to accurately distinguish Luminal A from B tumors.