Breast Cancer : Targets and Therapy最新文献

Introduction: Aberrant acquisition of lipoprotein cholesterol remains a hallmark feature of breast cancer biology. Low- and high-density lipoprotein receptors (LDLR and scavenger receptor class B type 1 (SR-B1)) are often upregulated to facilitate the tumor cells' high demand for cholesterol. To date, few attempts have been made to therapeutically exploit the high activity of lipoprotein receptors in breast cancer cells.

Methods: In the present study, we examined the utility of engineered low-density lipoprotein nanoparticles to deliver the natural anticancer omega-3 fatty acid docosahexaenoic acid (LDL-DHA) across a panel of breast cancer cells.

Results: Our data showed that LDL-DHA nanoparticles were avidly taken up (K_D 28 µg/mL to 1.9 µg/mL) and cytotoxic to all breast cancer subtypes (LD₅₀ 52.2 µM to 4.7µM), with triple negative breast cancer cells showing some of the highest uptake and sensitivity to LDL-DHA. Follow-up receptor knockout studies in MDA-MB-231 cells revealed that LDL nanoparticle uptake is mediated by both LDLR and SR-B1. These receptors were shown to operate concurrently as well as in a compensatory manner to ensure ample uptake of LDL is maintained. Double knockout of LDLR and SR-B1 significantly impeded LDL nanoparticle uptake (<50%) and protected against LDL-DHA cytotoxicity (viability >70%).

Conclusion: In summary, our studies have shown that malignant cell dependence upon cholesterol acquisition can be exploited for lipoprotein-based drug delivery to breast cancer cells. Furthermore, the capacity of LDL nanoparticles to target both LDLR and SR-B1 ensures this as an efficient drug delivery platform against breast cancer cells.

Objective: At present, the potential functions of most circRNAs in breast cancer (BC) have not been fully elucidated. The investigatory research planned to study biological function of circDENND4C in BC and reveal its downstream molecular mechanism.

Methods: A total of fifty pairs of BC tissue and their corresponding normal tissue were obtained. circDENND4C/miR-26a-5p/Human 71 kDa heat shock cognate protein (HSPA8) were assessed through RT-qPCR or Western blot. After transfecting the relevant plasmids, MKN-45 cell proliferation, cell cycle, invasion, and migration were assessed through MTT and colony formation assays, flow cytometry, and Transwell tests. Bioinformatics analysis, RIP and dual luciferase reporting experiments verified the interaction between circDENND4C, miR-26a-5p, and HSPA8.

Results: Increased circDENND4C was found in BC and was related to a poor prognosis in BC patients. HSPA8 was upregulated and miR-26a-5p was downregulated in BC. Functionally, silencing circDENND4C prevented cells from proliferation, invasion, and migration, and induced cell cycle arrest at G0/G1 phase. circDENND4C overexpression had the opposite effect. The effects of circDENND4C overexpression or knockdown in BC cells were counteracted by overexpressing miR-26a-5p or HSPA8, respectively. circDENND4C mediated HSPA8 expression by competitively adsorbing miR-26a-5p.

Conclusion: circDENND4C absorbs miR-26a-5p to target HSPA8, thereby promoting BC progression, which provides a new insight into the mechanism of BC.

Aim: To develop and validate a radiomics-based nomogram using multimodal magnetic resonance imaging (MRI) features to predict HER-2 expression status in breast cancer.

Methods: A total of 320 breast cancer patients were retrospectively selected for this study, with 80 in the HER-2 positive group and 240 in the HER-2 negative group. Pre-treatment multimodal MRI scans, including dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and T2-weighted imaging, were used to extract radiomic features. Multivariate logistic regression was performed to identify independent predictors for HER-2 positivity. A radiomics-based nomogram was constructed and validated using both training and validation sets. The nomogram's performance was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results: Multivariate analysis identified several independent predictors of HER-2 positivity, including type, edge, local skin thickening or depression, and axillary lymph node enlargement. The radiomics-based nomogram demonstrated excellent predictive accuracy with an area under the ROC curve (AUC) of 0.866 in the training set and 0.876 in the validation set. Calibration plots confirmed the model's good consistency, and DCA indicated that the nomogram provides significant clinical benefit across a range of threshold probabilities. In addition, the HER-2 positive group showed significantly higher tumor marker expression and immune cell infiltration, including elevated CD8+ T-cells, M1 macrophages, Tregs, and TAM (p<0.001).

Conclusion: The radiomics-based nomogram developed in this study offers a promising non-invasive tool for predicting HER-2 expression status in breast cancer. By integrating clinical data and advanced MRI features, this model provides accurate predictions, improving personalized treatment strategies. Further validation in larger, multicenter studies is necessary to confirm its generalizability and clinical applicability.

Background: CTBP1, an NADH-dependent transcriptional co-repressor subject to succinylation, may orchestrate both systemic and cell-type-specific effects on tumor development.

Methods: This integrative study represents a novel approach, combining genetic regulation, single-cell expression quantitative trait loci (eQTLs), and imaging radiomics to investigate CTBP1 in breast cancer. We integrated tissue-relevant eQTLs with breast cancer GWAS to evaluate genetically proxied associations for CTBP1. We further explored cell-type-dependent effects using single-cell eQTL resources and examined expression, survival, and imaging correlations as hypothesis-generating evidence.

Results: Tissue-focused analyses suggested an inverse association between CTBP1 expression and breast cancer risk, with consistent directions observed in expression and survival datasets. Single-cell explorations indicated potential cell-type-specific regulation.Imaging correlations with MRI-derived features were modest and exploratory.

Conclusion: Our results prioritise CTBP1 as a context-dependent, tissue- and cell-type-specific candidate in breast cancer. The observational and exploratory components warrant validation in larger cohorts and functional assays. To our knowledge, this is the first integrative study combining GWAS, single-cell eQTL and MRI radiomics to prioritize CTBP1 in breast cancer.

The integration of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with radiomics has emerged as a transformative approach for non-invasive prediction of breast cancer molecular subtypes. This review systematically evaluates methodological innovations, clinical validation milestones, and translational applications: (1) Methodological Advancements: Standardized DCE-MRI protocols combined with multidimensional radiomic features (morphological, textural, and wavelet-transformed parameters) significantly improved discriminative performance for ER, HER2, and triple-negative subtypes. (2) Deep Learning Integration: Multitask predictive models achieved early treatment response assessment and recurrence risk stratification through spatiotemporal heterogeneity analysis. (3) Clinical Validation: Prospective multicenter trials demonstrated that radiomic models showed strong concordance with 21-gene assays and could potentially replace 38% of repeat biopsies. Despite these advancements, challenges persist in data heterogeneity and mechanistic interpretation of radiomic biomarkers. Emerging strategies integrating radiogenomic analyses and organoid validation platforms are establishing new paradigms for precision imaging-guided therapy.

Background: Cardiotoxicity is the most concerning side effect of trastuzumab in HER2-positive metastatic breast cancer. However, data on the delayed cardiac safety of long-term trastuzumab are rare.

Patients and methods: A retrospective review of patients with HER2-positive breast cancer receiving trastuzumab for more than 24 months in a metastatic setting was conducted. Patients with medical records of regular assessment of the left ventricular ejection fraction (LVEF) and serum brain natriuretic peptide (BNP) level were included. The incidence of cardiac events and possible predictive factors were analyzed.

Results: A total of 75 patients were eligible for the current cardiac safety analyses. The median trastuzumab exposure was 50 (range: 29-114) cycles. By the cut-off time, the median follow-up time for cardiotoxicity was 34.0 (range: 26.0-41.9) months. The median timing of occurrence of a decrease in LVEF was 8.3 months after follow-up, mostly occurring within the third year from the initiation of trastuzumab. The cumulative incidence of LVEF decline was 10.7% (n=8) after 24-month trastuzumab. Patients with a history of anthracycline administration had a higher incidence of LVEF reduction (13.0% vs 3.6%, p=0.006). Five patients recovered soon after the cessation of trastuzumab. One patient died from congestive heart failure. BNP elevations were noted in 42.7% of patients, but elevated BNP levels could not identify patients with cardiotoxicity.

Conclusion: Of the patients who had received long-term trastuzumab for more than 24 months, delayed cardiotoxicity was observed in only a small subset, and was reversible in most of them. Elevation of serum BNP could not predict cardiotoxicity. Less frequent LVEF monitoring could be considered during long-term use of trastuzumab.

Purpose: SGO2 maintains the binding of sisters chromatids by protecting the adhesive complex at the centromere, thus ensuring the correct separation of chromosomes during mitosis and meiosis. And this study explored its role in breast cancer (BC) progression.

Methods: SGO2 expression in breast cancer was analyzed utilizing data from TCGA, GTEX, and HPA databases. Our study leveraged clinical data to predict outcomes. Subsequently, we conducted a GSEA enrichment analysis, dove into co-expression profiling, explored immune cell infiltration patterns, examined drug sensitivity, and analyzed single-cell data. To delve deeper, we validated the functional role of SGO2 through in vitro cellular experiments.

Results: Survival analysis demonstrated a notable correlation between heightened SGO2 expression and unfavorable cancer survival rates. Gene set enrichment analysis (GSEA) highlighted the participation of SGO2 in DNA replication. Immunoinfiltration analysis indicated a connection between SGO2 and several immune cell types, with single-cell analysis verifying SGO2 expression across diverse cell populations.

Conclusion: SGO2 may serve as a valuable prognostic biomarker and a potential therapeutic target in breast cancer.

Background: The elongation of telomeres endows cancer cells with the ability of replicative immortality. Nevertheless, the connection between it and breast cancer (BC) has not been clearly defined yet.

Methods: In this study, expression data from multiple BC datasets were analyzed to discover differentially expressed telomere maintenance-related genes (DE-TMRGs). Cox regression analyses were executed to determine prognostic genes, and a risk model was constructed using these genes to predict survival outcomes. Independent prognostic analysis was executed to assess independent prognostic factors. Enrichment analyses were conducted to determine pathways linked to the different risk groups. Immune infiltration levels and immune checkpoint expression were analyzed across risk categories using various algorithms. A regulatory network involving miRNAs and lncRNAs was constructed, and drug sensitivity was assessed. Mendelian randomization (MR) analysis was executed to investigate causal relationships between prognostic genes and BC. Finally, the expression of prognostic genes was further analysed at the single-cell level.

Results: A sum of 81 DE-TMRGs. Through univariate and LASSO regression analyses, five prognostic genes (WT1, TPRXL, RAD54B, JUN, and SEPHS2) were identified to construct a risk model. This model successfully distinguished between high- and low-risk categories with significant differences in survival rates. Immune profiling revealed the high-risk category had increased levels of activated T cells. Eosinophil demonstrated a high correlation with prognostic genes. Furthermore, JUN (OR = 1.1333, P = 0.0085) and RAD54B (OR = 1.0790, P = 0.0015) were identified as causal risk factors for BC. Single-cell RNA sequencing highlighted JUN's widespread distribution across multiple cell types, suggesting its crucial role in tumor progression.

Conclusion: TM-associated risk model based on WT1, TPRXL, RAD54B, JUN, and SEPHS2 could be used to predict prognosis and treatment of BC.

Lymphovascular invasion (LVI) in breast cancer is a pivotal prognostic factor that directly influences patient survival outcomes and guides the formulation of individualized treatment strategies, such as the selection of adjuvant chemotherapy regimens or the extent of surgical intervention. Accurate preoperative assessment of LVI status is therefore indispensable for optimizing clinical decision-making and improving patient management. This review systematically synthesizes the latest advancements in imaging techniques for LVI evaluation in breast cancer, including conventional ultrasound, contrast-enhanced ultrasound (CEUS), elastography, mammography (digital and contrast-enhanced), magnetic resonance imaging (MRI, encompassing DCE-MRI, DWI, and novel functional sequences like IVIM), and artificial intelligence (AI)-assisted diagnostic tools (radiomics and deep learning). A critical analysis of these modalities reveals distinct strengths and limitations: for instance, ultrasound offers broad accessibility and real-time imaging but exhibits lower sensitivity (60-75%) for subtle LVI lesions compared to MRI (sensitivity 78-90%), which provides superior soft tissue contrast but is constrained by higher cost and longer scan times. Radiomics models, while demonstrating promising AUC values (0.74-0.896) in predicting LVI, suffer from inconsistencies in feature extraction protocols and limited external validation. Additionally, this review highlights key knowledge gaps, such as the lack of standardized imaging parameters for LVI assessment across centers and the underrepresentation of rare breast cancer subtypes (eg, inflammatory breast cancer) in existing studies. We emphasize the clinical urgency of addressing these limitations-including reducing false-positive rates caused by benign vascular proliferations and false negatives from obscured lesions-to enhance diagnostic accuracy. Furthermore, we propose targeted future directions, such as the development of multicenter, prospective trials to validate AI-integrated multimodal imaging workflows and the establishment of consensus guidelines for imaging protocol standardization, ultimately aiming to translate technical advancements into improved patient outcomes.

Background: The reverse-sequence endoscopic nipple-sparing mastectomy with immediate prepectoral implant reconstruction (RSEM-IPI) has been described as a safe and feasible procedure for early-stage breast cancer. The aim of this study was to evaluate the learning curve for RSEM-IPI.

Methods: Between January 2023 and February 2024, we collected data on 104 consecutive patients who underwent RSEM-IPI at our hospital (53 for group 1 and 51 for group 2). The surgeons in group 1 completed the endoscopic training and assistance, whereas the surgeons in group 2 did not. The learning curve was analyzed using the cumulative sum (CUSUM) method to assess changes in the operative times across the case sequence.

Results: Among the 281 patients who received breast reconstruction surgeries, a total of 104 consecutive data were recorded and eligible. Both groups had higher rates of postoperative complications including bleeding, nipple-areolar complex (NAC) ischemia, skin ischemia, infection and longer drainage duration during the phrase 1 compared to the phrase 2. Notably, in terms of the most severe postoperative complications, implant expose and loss, both groups exhibited higher rates during the phrase 1. Breast-Q scores for group 1 in phases 1 and 2 were 66 and 84, respectively, while for group 2, they were 67 and 80. As compared with surgeons in group 2, well-trained surgeons in group 1 were more accessible for stable learning curve with fewer training patients (18 patients) and relatively lower rate of postoperative complications (p > 0.05).

Conclusion: The estimated learning curve for RSEM-IPI was achieved after 18 cases in the well-trained surgeon group. The endoscopic training and assistance were critical to master the breast endoscopic surgeries for breast surgeons.