Distant metastasis remains the primary cause of morbidity in patients with breast cancer. Hence, the development of more efficacious strategies and the exploration of potential targets for patients with metastatic breast cancer are urgently needed. The data of six patients with breast cancer brain metastases (BCBrM) from two centres were collected, and a comprehensive landscape of the entire tumour ecosystem was generated through the utilisation of single‐cell RNA sequencing. We utilised the Monocle2 and CellChat algorithms to investigate the interrelationships among each subcluster. In addition, multiple signatures were collected to evaluate key components of the subclusters through multi‐omics methodologies. Finally, we elucidated common expression programs of malignant cells, and experiments were conducted in vitro and in vivo to determine the functions of interleukin enhancer‐binding factor 2 (ILF2), which is a key gene in the metastasis module, in BCBrM progression. We found that subclusters in each major cell type exhibited diverse characteristics. Besides, our study indicated that ILF2 was specifically associated with BCBrM, and experimental validations further demonstrated that ILF2 deficiency hindered BCBrM progression. Our study offers novel perspectives on the heterogeneity of BCBrM and suggests that ILF2 could serve as a promising biomarker or therapeutic target for BCBrM.

Breast cancer is the most prevalent form of malignant tumor that frequently metastasizes to axillary lymph nodes (LNs). Nonetheless, the precise mechanisms underlying alterations in the tumor microenvironment (TME) in LN metastasis in breast cancer remain poorly understood. Single‐cell RNA sequencing of 28 LN samples from 23 patients is performed, and a comprehensive landscape of the entire ecosystem is generated. Ten major cell types are identified, with the subclusters of each major cell type exhibiting diverse characteristics. Furthermore, multiple signatures are collected to evaluate the key components of the subclusters using multi‐omics methodologies. This study finds that myCAFs may hasten LN metastasis, and observed a notable increase in APOE + macrophages and a higher proportion of exhausted CD8 + T cells, contributing to the immunosuppressive TME. Moreover, cancer cells in metastatic lesions exhibited diverse expression patterns linked to proliferation, metastasis, oxidative phosphorylation, hypoxia, and interferon responses. Using multi‐omics approaches and experimental validations, it determines that GLO1 can promote lymphatic angiogenesis, metastasis, and inhibit the proteasomal degradation of GSS , thereby maintaining intracellular glutathione (GSH) and reactive oxygen species (ROS) balance. Collectively, the study offers novel perspectives on the microenvironment remodeling of breast cancer LN metastases, suggesting that GLO1 may be a promising therapeutic target.

Objectives: The study aimed to conduct an in-depth analysis of the influence of PD-L1 status and expression levels and other variables on the effectiveness of immune checkpoint inhibitors (ICIs) in treating breast cancer. Methods: A total of 19 articles, involving 16 trials and 7899 patients, were included in the analysis. The outcomes of interest were odds-ratio (OR) for pathological complete response (pCR) in early breast cancer, and hazard ratio (HR) for progression-free survival (PFS) and overall survival (OS) in advanced breast cancer. Results: In early breast cancer, individuals with PD-L1-positive tumors were more likely to benefit from ICIs than those with PD-L1-negative tumors. Furthermore, patients with PD-L1 positivity in immune cells (IC) had superior outcomes compared to those scoring positively on combined positive score (CPS), with ORs for ICIs benefit being 2.28 for IC-positive patients vs 1.78 for CPS-positive patients. Regarding the impact of breast cancer subtypes on the efficacy of ICIs, our findings indicated that triple-negative breast cancer (TNBC) exhibits the greatest therapeutic response with OR of 1.93, followed by the hormone receptor-positive (HoR+) / human epidermal growth factor receptor 2-negative (HER2−), while the HER2+ was the worst. Additionally, age was identified as a key predictive factor in responding to ICIs. In advanced breast cancer, there was an upward trend in CPS values associated with enhanced ICIs responsiveness, with the predictive value increasing from 12% at a CPS threshold of 10 to 13.6% at 20. Conclusion: The study concluded that the PD-L1 expression scoring system effectively discriminates between patients with breast cancer in terms of the degree of benefit they may attain from ICIs. Patients with little or no PD-L1 expression experienced a diminished therapeutic benefit from ICIs.

Breast cancer brain metastasis (BCBM) is driven by microenvironmental remodeling across three interconnected phases: the primary tumor microenvironment (TME) licenses dissemination, the pre-metastatic niche (PMN) primes the brain, and the established metastatic niche sustains outgrowth through central nervous system (CNS)-resident cell interactions. These phases are individually established; we integrate them through cross-phase signaling axes, exemplified by the proposed IL-6$\rightarrow$extracellular vesicle (EV)$\rightarrow$blood-brain barrier (BBB) disruption cascade, that link tumor-associated macrophage-driven epithelial-mesenchymal transition to PMN conditioning and niche maintenance. We integrate single-cell and spatial transcriptomic insights, discuss brain-specific mechanisms including astrocytic STAT3/connexin 43 signaling, neuronal contributions, and the neurovascular unit, and evaluate emerging therapies with emphasis on antibody-drug conjugates with intracranial activity. Subtype-specific differences between HER2-positive and triple-negative breast cancer brain metastases and key translational barriers are systematically addressed.

BACKGROUND Primary Tumor Resection (PTR) remains controversial among women with stage IV breast cancer. OBJECTIVE This study aims to adopt a machine learning (ML) approach to clarify the disputed correlation of primary tumor resection on stage IV breast cancer survival, aiming to pinpoint patients who stand to gain from PTR treatment with enhanced clinical applicability and predictive precision. METHODS A propensity-score matched analysis of stage IV breast cancer patients in the SEER registry (2000-2020) was conducted, using Cox regression and Kaplan-Meier methods to estimate overall survival (OS) and cancer-specific survival (CSS). Among five ML models, an internally and externally validated ML model was crafted for predicting survival outcomes, with a user-friendly web platform based on the shiny platform for clinical use. RESULTS In a cohort of 13,702 stage IV breast cancer patients, with 5,732 matched subjects, Cox regression analysis showed PTR's positive associations with OS (HR, 0.75; 95% CI, 0.69 to 0.82) and CSS (HR, 0.75; 95% CI, 0.68 to 0.82). Subgroup analysis indicated better survival for patients with tumors ≤5 cm, N2 status (OS, HR, 0.52; 95% CI, 0.44 - 0.63;CSS,HR, 0.52; 95% CI, 0.43 - 0.63), and HER2 overexpression (OS, HR, 0.56; 95% CI, 0.52 - 0.61;CSS,HR, 0.56; 95% CI, 0.46 - 0.67), especially those without systemic treatment. The best outcomes were seen with PTR combined with chemotherapy and radiation (OS, HR, 0.40; 95% CI, 0.37 - 0.44; CSS, HR, 0.03; 95% CI, 0.01 - 0.10). The Support Vector Machine (SVM) model (6-month: AUC=0.935; 1-year: AUC=0.945; 2-year: AUC=0.941; 3-year: AUC=0.921) was identified as the most precise tool for survival prediction, demonstrating high accuracy and consistency across external datasets. Furthermore, a user-friendly web application was created to make the prognostic model more accessible. CONCLUSIONS Patients with tumors ≤5 cm, N2 status, and HER2 overexpression experience improved survival with PTR. Our ML model based on eight clinical indicators predicts survival for stage IV cancer patients, guiding surgical candidacy and clinical decisions. The accompanying online platform facilitates patient assessment for healthcare professionals. CLINICALTRIAL Name of the registry: Chinese Clinical Trial Registry. Unique Identifying number or registration ID: ChiCTR2400089791 Hyperlink to your specific registration (must be publicly accessible and will be checked): https://www.chictr.org.cn/showproj.html?proj=244567