Breast Cancer Research最新文献

Background: Luminal B breast cancer (LBBC) is characterized by marked molecular heterogeneity and unfavorable therapeutic outcomes, underscoring the need for concise and biologically interpretable biomarkers that can support reliable prognostic stratification. However, existing biomarker signatures are often redundant, weakly generalizable, and insufficiently validated in multi-omics regulatory consistency.

Methods: Using a minimum prognostic-redundancy feature identification strategy, we identified an immune-related four-gene prognostic biomarker signature and determined its optimal classification scheme. A molecular subtyping model was subsequently developed and externally validated to distinguish two prognostic immune subtypes: the immune-barren type (IBT) and the immune-enriched type (IET). Functional enrichment analysis, tumor immune microenvironment profiling, and multi-omics integrative consistency assessments were performed to elucidate the biological characteristics and regulatory underpinnings of the predicted subtypes. Moreover, the clinical independence, prognostic robustness, and cross-cohort reproducibility of the model were comprehensively evaluated, and independent clinical variables were incorporated to construct an individualized decision-support tool.

Results: The four-gene biomarker signature demonstrated strong prognostic discrimination and subtype classification capability (max AUC = 0.86, P < 0.05). Across extensive multi-source external cohorts (n = 1,841), the molecular subtyping model robustly stratified patients into immune-related subtypes with significantly different survival outcomes and consistently high predictive performance (ACC ≥ 0.93, AUC ≥ 0.97, all P < 0.001), while remaining independent of conventional clinical variables (P < 0.001). Furthermore, the predicted subtypes exhibited concordant immune functional characteristics and multi-omics regulatory consistency (all P < 0.05). In addition, the individualized decision-support tool showed significant prognostic relevance (P = 0.004) and favorable comparative performance relative to previously reported prognostic models (AUC = 0.86).

Conclusion: This immune-related molecular subtyping model, derived from a four-gene prognostic biomarker signature and supported by multi-omics integrative evidence, provides a robust and clinically independent model for prognostic stratification in LBBC. These findings offer a biologically interpretable basis for individualized risk assessment and may facilitate precision-oriented clinical decision support.

Postpartum breast cancer (PPBC), defined as breast cancer diagnosed within 10 years after childbirth, is increasingly recognized as a biologically distinct and clinically aggressive subtype affecting young women worldwide. The postpartum mammary gland undergoes extensive remodelling during involution, characterized by inflammation, extracellular matrix reorganization, and immune suppression, collectively creating a tumor-promoting microenvironment. Recent studies have revealed unique molecular features of PPBC, including alterations in immune composition, stromal activation, and selective pathway enrichment that distinguish it from breast cancers in nulliparous or age-matched parous women. While estrogen signalling remains an important regulatory axis during the postpartum period, its interplay with involution-associated processes and tumor behaviour requires further elucidation. In this review, we summarize current advances in understanding PPBC biology, highlight emerging biomarkers, and discuss evolving therapeutic considerations relevant to this high-risk population. We also provide an overview of ongoing clinical investigations, such as aspirin-based anti-inflammatory trials aimed at targeting postpartum-specific mechanisms that may contribute to tumor initiation or progression. Together, these insights emphasize the need for dedicated translational research and tailored clinical strategies to improve early detection, risk stratification, and outcomes for women diagnosed with PPBC.

Background: Accurate HER2 assessment is essential for breast cancer (BC) treatment, as it directs targeted therapy decisions and predicts patient prognosis. Even though immunohistochemistry (IHC) is widely used, manual scoring is susceptible to variability. This study was conducted to evaluate HER2 expression in HER2-negative breast cancer patients treated with neoadjuvant chemotherapy (NAC), using H-score and ERBB2 mRNA quantification.

Methods: We conducted a retrospective cohort study in breast cancer patients treated with NAC between 2017 and 2023. H-score was evaluated in 506 patients. For gene expression analyses, only tumors with available H-score and Ct ≤ 35 for all candidate reference genes were included, resulting in 173 evaluable cases for ERBB2 mRNA quantification. HER2-zero tumors were further stratified into HER2-null (H-score = 0) and HER2-ultralow (H-score 1-120) groups.

Results: Among all 506 HER2-negative tumors, 54.6% were classified as HER2-low and 45.4% as HER2-zero. Within the HER2-zero subgroup, 47.4% showed measurable HER2 expression (HER2-ultralow). ERBB2 mRNA levels were significantly higher in HER2-low tumors compared to HER2-zero. Among HER2-zero, HER2-ultralow tumors had higher ERBB2 expression than HER2-null. Higher H-scores were associated with increased ERBB2 transcript expression, according to Spearman correlation coefficient (p < 0.05). HER2-null tumors were more frequently triple-negative.

Conclusions: Quantitative assessment by H-score and ERBB2 transcript analysis reveals heterogeneity within HER2-zero tumors, identifying a HER2-ultralow population with potentially distinct clinical behavior. These findings support the integration of continuous HER2 expression metrics into clinical decision-making and trial design, particularly in the era of HER2-targeted antibody-drug conjugates.

Background: Dysregulated lipid metabolism and chemoresistance are key drivers of breast cancer progression. Lectin, mannose-binding 2 (LMAN2) is frequently overexpressed in human breast tumors and functions as an oncogenic driver. However, whether LMAN2 contributes to chemoresistance remains unknown.

Methods: We integrated multi-omics data from 1,085 primary tumors and matched normal tissues (from GEPIA and UALCAN) with functional studies in breast cancer cell lines and a doxorubicin (ADM)-treated nude mouse xenograft model. LMAN2 expression was modulated via siRNA/shRNA-mediated silencing or lentivirus-driven overexpression. Cellular phenotypes-including proliferation, migration, apoptosis, and response to ADM were systematically assessed. RNA-sequencing, untargeted lipidomics, and rescue experiments identified stearoyl-CoA desaturase (SCD) as a critical downstream effector. IC50 shifts and epistasis analysis further validated the role of the LMAN2/SCD axis in chemoresistance.

Results: LMAN2 mRNA was elevated across all molecular subtypes (luminal > HER2 > triple-negative) and predicted poorer overall survival (P = 5 × 10^-4) and progression-free survival (P = 0.018). Silencing LMAN2 reduced clonogenicity by ~ 45% and migration by 37-63%, whereas overexpression increased cell viability by 1.4-1.7-fold and doubled motility. Knockdown of LMAN2 decreased the ADM IC50 by 4-5 fold, abolished macroscopic colony formation, and elevated apoptosis rates from 15 to 18% to 39-41%; these effects were reversed upon LMAN2 overexpression. In vivo, shLMAN2 combined with ADM reduced tumor volume and weight by 72% and 75%, respectively, compared to ADM alone (P < 0.001). Mechanistically, LMAN2 loss downregulated genes involved in "cholesterol homeostasis" and reduced total cellular cholesterol by 24%. SCD emerged as the most significantly downregulated enzyme and fully rescued the phenotypic and chemoresistance effects resulting from LMAN2 modulation. Epistasis experiments confirmed that LMAN2-mediated chemoresistance strictly depends on SCD function.

Conclusions: LMAN2 is a robust prognostic biomarker that promotes breast tumor growth and anthracycline resistance by enabling SCD-dependent lipid desaturation. Therapeutic targeting of the LMAN2/SCD axis represents a promising strategy to overcome chemoresistance in breast cancer.

Background: Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast cancer. Although sentinel lymph node biopsy (SLNB) is less invasive than axillary lymph node dissection, it still exposes node-negative patients to unnecessary surgical risks. Most existing models predicting lymph node status in breast cancer rely on a limited set of features or variables that are not routinely accessible, and their interpretability remains limited. Therefore, developing a comprehensive and explainable model to evaluate the feasibility of omitting SLNB has important clinical significance.

Methods: A retrospective cohort of 1,485 patients trained nine machine learning (ML) algorithms using clinical, imaging, and pathological features to predict sentinel lymph node metastasis (SLNM). An independent prospective cohort of 103 patients served for prospective validation. Multiple imputation enhanced data reliability, and multi-dimensional feature selection simplified models while retaining key information. Model performance was evaluated using AUC, precision, recall, and calibration. SHapley Additive exPlanations (SHAP) were applied to interpret model outputs and quantify feature contributions.

Results: Ten-fold cross-validation showed that the support vector machine (SVM) model achieved the best performance among nine ML algorithms. After optimizing to 26 variables, the internal test achieved an AUC of 0.892 (95% CI: 0.854-0.940), outperforming the clinical model (AUC 0.796, 95% CI: 0.791-0.811). In the prospective cohort, the AUC was 0.775 (95% CI: 0.667-0.865), superior to the baseline radionuclide- and imaging-based model (AUC 0.65, 95% CI: 0.564-0.729). When applying a safety-optimized threshold, the model calibrated well and could spare 58.1% of node-negative patients from SLNB. SHAP analysis identified tumor margin smoothness, AR expression, and nuclear pleomorphism as top predictors. Decision curve analysis supported clinical utility. A Shiny-based web tool enables real-time prediction.

Conclusions: This study developed an SLNM prediction model based on multi-dimensional features available from routine diagnostic examinations. Prospective and exploratory external validation confirmed its clinical applicability. The model's performance in assessing axillary status was close to that of surgical SLNB, providing preliminary evidence to inform potential de-escalation or stratification of surgical approaches in breast cancer management.

Background: Perfluoroalkyl substances (PFASs) are possible carcinogens with widespread population exposure. Mammographic density (MD) is a biomarker of breast cancer risk that may offer important insights into potential mechanisms of breast carcinogenesis. Examining associations between PFAS exposure and MD may elucidate pathways by which PFASs could affect breast cancer risk. However, associations between PFAS exposure and MD remain poorly studied. We sought to evaluate associations of individual PFASs and their chemical mixture with percent mammographic density and dense breast area.

Methods: We analyzed data from 186 postmenopausal, cancer-free women not currently using hormone therapy from the Susan G. Komen Tissue Bank. Percent mammographic density (PMD) and dense breast area (DBA) were measured from digital mammography images taken near the time of blood sample donation. Serum PFAS concentrations were measured using online solid-phase extraction followed by high-pressure liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Associations were examined using multivariable linear regression and Quantile g-computation.

Results: Participants had a mean age of 58.7 years and mean body mass index of 29.6 kg/m², and most were White (72.6%). The vast majority of mammograms were performed within 2 years of blood sample donation (88%). No statistically significant associations were observed with individual PFASs and either PMD or DBA in analyses adjusted for confounding variables. Quantile G-computation analyses did not identify a statistically significant effect of the PFAS mixture on PMD or DBA. Similar results were observed in analyses stratified by body mass index or breastfeeding history.

Conclusions: Overall, our study did not identify strong and significant associations between a panel of major PFASs and MD.

Background: Tumor-infiltrating lymphocytes (TILs) are prognostic biomarkers in breast cancer (BC), particularly in HER2-positive and triple-negative subtypes. Assessment follows the international guidelines, in which pathologists evaluate whole hematoxylin and eosin (H&E)-stained slides while integrating representative regions of the invasive tumor. However, manual region selection can be labor-intensive, subjective, and may introduce variability, particularly across consecutive tissue sections. Automated region of interest (ROI) registration may mitigate this limitation, yet its impact on longitudinal TIL scoring has not been systematically evaluated. Here, we introduce three ROI registration strategies (direct, intermediate, and serial with/without quality control) and present an automated framework validated for consistent TIL scoring and clinical relevance in predicting relapse.

Methods: We analyzed 104 invasive BC cases, each with 12 consecutive H&E slides. A pathologist annotated ROIs on both the first and twelfth slides. We registered these ROIs using the proposed strategies. We then evaluated them with performance metrics, including Intersection over Union (IoU), Dice Similarity Coefficient (DSC), failure rate, and execution time. Two pathologists scored TILs on manual and automated ROIs. We assessed longitudinal consistency between the first manual ROI and the twelfth slide's corresponding ROIs. We also tested whether the association between TIL score and patient relapse outcomes was preserved.

Results: The direct registration strategy achieved the highest geometric accuracy (mean IoU = 0.650, DSC = 0.769), with < 1% failure rate and ∼5.8 s execution time. TIL scores for both manual and automated ROIs closely matched (P = 0.84), showing strong agreement (Spearman's ρ = 0.923, ICC = 0.936, CCC = 0.915, Cohen's κ = 0.786). Longitudinal analyses showed no significant variability across distant sections (P = 0.79 and P = 0.62). TIL concentrations differed significantly between patients with and without relapse, and this association was preserved on both the first and twelfth slides (P < 0.05).

Conclusions: We present the first automated framework for longitudinal ROI registration to support TIL scoring in BC. By reducing manual effort and variability, the framework supports scalable evaluation of immune biomarkers across tissue depth while preserving clinically relevant signals, supporting precision immuno-oncology applications.

Background: Accurate margin assessment remains a challenge in breast-conserving surgery, where inaccuracy leads to excessive removal and compromised aesthetics. Although second near-infrared (NIR-II) fluorescence imaging offers high contrast, there is still a lack of clinically viable NIR-II equipment.

Methods: We develop an innovative NIR-II fluorescence navigation platform based on indocyanine green (ICG) that enables rapid deployment, operation-friendly workflow, and precision-at-hand capability for intraoperative guidance. Thirty-seven patients were recruited with 1 mg/kg ICG injection 45 minutes before surgery. Image quality was evaluated using perception-based image quality evaluator (PIQE) score, while quantitative analyses of fluorescence, including tumor-to-normal tissue ratio (TNR) and signal-to-background ratio (SBR), together with histopathology and a multi-level reader study, were performed to assess tumor contrast and diagnostic performance.

Results: Compared to conventional NIR-I imaging, our NIR-II system demonstrates performance with enhanced spatial resolution and a higher tumor-to-normal tissue ratio (4.383 ± 1.719 versus 3.154 ± 1.072, P < 0.0001) in 37 patients. The intraoperative NIR-II imaging demonstrates superior tumor detection sensitivity [90.63% (75.78%-96.76%) vs. 84.38% (68.25%-93.14%)], with 100% diagnostic accuracy in delineating malignant tumor margins. Ex vivo NIR-II imaging enables real-time, precise visualization of tumor margins and suggested its potential to reduce the volume of excision. (NIR-II-defined volume < NIR-I-defined volume < current resection volume). Through brief training, surgeons can effectively utilize NIR-II imaging, shortening the learning curve for young surgeons in tumor and margin assessment.

Conclusion: This study demonstrates a clinically adaptable NIR-II imaging platform that expands intraoperative visualization without changing surgical workflow or adding operational burden, supporting standardized margin assessment and improved tissue preservation in breast surgery.

Immune infiltration in triple-negative breast cancer (TNBC) is associated with patient prognosis. However, the precise phenotypic classifications of immune infiltration in TNBC remain unclear, and the key regulatory mechanisms underlying this process require further investigation. In this study, we employed transcriptomic and single-cell data analysis, utilizing unsupervised clustering to classify the immune microenvironment of TNBC. We constructed a prognostic model based on immune phenotypes and further validated the regulatory role of key genes in immune cell modulation. Through unsupervised clustering analysis of the GEO and METABRIC databases, we identified two distinct immune infiltration phenotypes in TNBC tumor tissues. Notably, patients with high immune infiltration exhibited significantly prolonged overall survival (OS). Subsequent analysis of tumor tissues from high/low immune infiltration groups revealed five differentially expressed genes (DEGs), which were significantly correlated with patient prognosis: lymphotoxin β (LTB), interferon regulatory factor 8 (IRF8), indoleamine 2,3-dioxygenase 1 (IDO1), integral membrane protein 2A (ITM2A), and lymphocyte cytosolic protein 1 (LCP1). A prognostic model for TNBC was developed based on the five key genes. Furthermore, we demonstrated a significant association between LTB expression and enhanced immune effects in TNBC tissues. Single-cell sequencing data revealed that LTB expression was predominantly localized to immune-infiltrating T cells, which tended to differentiate toward cytotoxic T cells. We also confirmed that LTB expression improved T cell proliferation and enhanced the tumoricidal effect by modulating T cell differentiation pathways. In conclusion, immune infiltration phenotype is a crucial prognostic biomarker for TNBC patients. LTB contributes to enhanced immune infiltration, making as a promising target for immune therapy in TNBC.

Background: Tumour DNA methylation is a potentially valuable marker of breast cancer survival, but previous studies have been limited by relatively small sample sizes. This study aimed to use a large sample size to identify survival-associated DNA methylation markers and develop a methylation-based signature predictive of breast cancer survival.

Methods: We used DNA methylation data from 2,157 breast tumours collected in the Melbourne Collaborative Cohort Study (MCCS) and nine publicly available datasets. An epigenome-wide association study (EWAS) was conducted for five-year overall survival (N = 1,992 cases). Subgroup analyses were carried out by estrogen receptor status. Pathway enrichment analyses were conducted to identify related biological pathways. Elastic net Cox regression was used to develop a signature of survival from DNA methylation data and clinical characteristics, trained on publicly available datasets and tested in the MCCS (N = 425) in addition to the main clinical characteristics. A set of triple-negative tumours (N = 165) with disease-free survival as the outcome was used for additional replication.

Results: We identified 2,535 CpGs showing an association (P < 1 × 10^- 7) with survival in age-adjusted models, including 281 after adjustment for estrogen receptor (ER) status. In ER-positive tumours, there were 389 associations, of which 315 were absent from the primary EWAS. A 228-CpG signature showed a strong association with survival in the MCCS after adjustment for clinical characteristics: per SD, HR = 1.7, 95%CI: 1.2-2.3, P = 0.001, with a C-index of 0.79, compared with a C-index of 0.75 without methylation information (C-index increase: 0.04, 95%CI: 0.01-0.10). The signature was also predictive of disease-free survival beyond clinical characteristics in triple-negative tumours (HR per SD = 1.4, 95% CI: 1.1-1.8).

Conclusion: Our findings revealed numerous CpG sites where tumour DNA methylation was associated with breast cancer survival. A substantial improvement in the accuracy of five-year overall survival prediction was achieved by adding a 228-CpG epigenetic score to the main clinicopathological variables. These results suggest that DNA methylation markers may provide additional prognostic information beyond established clinicopathological factors.