Breast Cancer : Targets and Therapy最新文献

Purpose: This study was intended to explore the effectiveness of Circuit Weight Training (CWT) on quality of Life, balance, strength, and functional Capacity in female breast cancer patients.

Patients and methods: Fifty female post-mastectomy patients, aged 35 to 50 years, were recruited. They were randomly split into two equal groups: a control group and a study group. Both groups participated in a standardized physiotherapy program thrice weekly for eight weeks, focusing on shoulder mobility exercises under expert supervision. In addition, Group A (study group, n = 25) performed a Circuit Weight Training (CWT) protocol alongside physiotherapy, consisting of two exercise circuits per session with functional and aerobic components, conducted at moderate intensity. Group B (control group, n = 25) received the physiotherapy program without the CWT component. Outcome measures included muscle strength (measured via a Handheld Dynamometer), postural stability and limits of stability (assessed using the BIODEX Balance System SD), functional capacity (evaluated with the 2-Minute Step Test), and health-related quality of life (HRQoL) (assessed via the 12-item Short Form Survey).

Results: The study group indicated statistically significant improvements in muscle strength: 14.98% (middle and lower trapezius), 17.37% (teres major), 19.79% (latissimus dorsi), 13.63% (quadriceps), 15.59% (hamstrings), 19.63% (gluteus maximus), 14.60% (gluteus medius), 14.95% (dorsiflexors), and 12.87% (plantar flexors) (P = 0.001). Improvements were also observed in balance parameters: limits of stability (38.18%), postural stability (47.69%), and single-leg stance (49.67%) (P = 0.001). Functional capacity increased by 36.50% (P = 0.001). Additionally, significant improvements in HRQoL were recorded: 49.82% in the mental section and 50.33% in the physical section of SF-12 (P = 0.001).

Conclusion: Circuit Weight Training significantly enhances postural stability, muscle strength, functional capacity, and HRQoL in postmastectomy breast cancer patients. These findings underscore the value of incorporating structured exercise programs into oncology rehabilitation protocols.

Introduction: In silico methods have significantly transformed the landscape of drug discovery by enabling rapid and cost-effective screening of prospective therapeutic compounds. However, these computational techniques remain limited in their ability to fully predict complex biological behavior, particularly within the constraints of quantum level interactions and simplified receptor-ligand models. As such, validation through experimental data remains critical.

Purpose: This review aims to critically evaluate the correlation between molecular docking predictions specifically Gibbs free energy (ΔG) and in vitro cytotoxicity data (IC₅₀ values) obtained from MCF-7 breast cancer cell studies.

Methodology: A structured methodology was employed, applying predefined inclusion and exclusion criteria to identify studies reporting both in silico molecular docking results and in vitro cytotoxicity data on the MCF-7 cell line, with a focus on compounds targeting breast cancer-related proteins.

Results: Findings demonstrated that, contrary to theoretical expectations, no consistent linear correlation was observed between ΔG values and IC₅₀ across the analyzed compounds and targets. This discrepancy arises from several intertwined factors, including variability in protein expression within cell-based systems, compound-specific characteristics such as permeability and metabolic stability, and methodological limitations of docking approaches that rely on rigid receptor conformations and simplified scoring functions. In addition, the chemical diversity of the evaluated compounds further contributes to the inconsistency of cytotoxic outcomes. Nevertheless, when experimental and computational systems are uniformly controlled, a measurable and meaningful correlation between ΔG and IC₅₀ can be demonstrated.

Conclusion: This review underscores the need to move beyond single parameter docking predictions and adopt integrated strategies that combine computational models with empirical validations. Future studies should emphasize the use of standardized in vitro conditions, rational target selection, and complementary techniques such as molecular dynamics simulations, intracellular exposure assessment, and target engagement validation. These integrative approaches will enhance the predictive power of in silico methods and foster a more reliable foundation for anti-breast cancer drug development.

Purpose: This study aimed to evaluate the feasibility, dosimetric characteristics, and early clinical outcomes of CyberKnife (CK)-based accelerated partial breast irradiation (APBI) using non-invasive skin fiducial marker tracking in an Asian population.

Materials and methods: We retrospectively analyzed 74 female patients diagnosed with early-stage breast cancer who underwent APBI using the CK system between May 2019 and December 2024. Patient selection was based on the modified 2017 American Society for Radiation Oncology (ASTRO) consensus criteria. The total tumor doses were 30 Gy in 5 consecutive daily fractions. Non-invasive skin fiducial markers were used for respiratory motion tracking. Dosimetric parameters were recorded according to European Society for Radiotherapy and Oncology (ESTRO) and Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) recommendations. Primary clinical outcomes, acute and chronic toxicities were evaluated during a median follow-up period of 26.5 months. Predictive factors for toxicity were assessed using receiver operating characteristic (ROC) curve analysis.

Results: A total of 74 patients having a median age of 56 years were included in the study, with a median follow-up period of 26.5 months. Non-invasive skin fiducial markers demonstrated a strong correlation with internal surgical clips, validating their accuracy for motion tracking. The median conformity and homogeneity indexes were 1.15 and 1.20, respectively. Median mean heart doses were 1 Gy (left-sided) and 0.5 Gy (right-sided), while the ipsilateral lung mean dose was 2.34 Gy. Two patients (2.7%) developed ipsilateral breast tumor recurrence. There were no grade ≥2 toxicities or cardiopulmonary events observed. Radiation dermatitis represented the most common acute toxicity (48.6%), whereas breast fibrosis was the most frequent late toxicity (12.2%). Skin D0.03cc >29.45 Gy and PTV-to-breast volume ratio >14.5% were associated with grade 1 dermatitis, while a breast volume <455.2 cm³ and PTV-to-breast volume ratio >28.9% were predictive of breast fibrosis.

Conclusion: By retrospective reviewing, APBI using the CyberKnife system with non-invasive skin fiducial marker tracking is a safe, precise, and effective treatment option for early-stage breast cancer. Although this retrospective study with limited follow-up demonstrated favorable dosimetric outcomes and minimal acute toxicity, further prospective studies with larger cohorts and longer observation are needed to validate these findings.

Background: Luminal breast cancer (BC) is the most common subtype of BC. C-type lectin domain family 3 member A (CLEC3A) has been shown to promote malignant characteristics in BC cells, but the specific mechanisms are not well understood. This study aimed to explore the oncogenic role of CLEC3A in luminal BC and its potential mechanisms.

Methods: Transcriptomic data from GEO and TCGA databases were analyzed to identify differentially expressed genes in luminal BC. Kaplan-Meier curves were used to assess the prognostic value of CLEC3A in luminal BC, and CLEC3A expression was further validated in BC cell lines. Functional assays, including colony formation, wound healing, Transwell, and flow cytometry, were performed following CLEC3A knockdown or overexpression. The impact of CLEC3A on PD-L1 stability was analyzed by co-immunoprecipitation (co-IP) and Western blotting. The influence of CLEC3A on T cell activity was investigated by co-culturing CD8⁺ T cells with BC cells.

Results: CLEC3A expression was significantly upregulated in luminal BC patients and correlated with poor overall survival. In vitro, CLEC3A knockdown suppressed proliferation, migration, invasion and promoted apoptosis, whereas CLEC3A overexpression enhanced these malignant features. CLEC3A also regulated mRNA expression levels of key proliferation-related genes and immune factors, and it regulated the stability of PD-L1 protein in BC cells through ubiquitination. Additionally, CLEC3A knockdown increased tumor cell death and CD8⁺ T cell activity, while overexpression suppressed these responses.

Conclusion: CLEC3A promotes BC progression and immune evasion by regulating PD-L1 stability and inhibiting CD8⁺ T cell function. Targeting CLEC3A may enhance anti-tumor immunity and improve patient outcomes in luminal BC.

Background: Breast cancer is one of the most common malignancies among women worldwide. Neoadjuvant chemotherapy (NAC) has become a standard treatment for locally advanced breast cancer, offering several advantages. However, accurate assessment of axillary lymph node status after NAC is crucial for surgical planning and prognosis. Although the role of ultrasound in axillary staging has been studied, its accuracy in the post-NAC setting remains controversial.

Research gaps: Previous studies have small sample sizes and do not comprehensively analyze factors influencing ultrasound performance. This study aims to evaluate the accuracy of ultrasound in assessing axillary lymph node status after NAC in breast cancer patients and identify clinicopathological factors affecting its performance.

Methodology: This retrospective cohort study analyzed data from 171 breast cancer patients who underwent NAC followed by surgery at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, between January 2015 and December 2019. Ultrasound assessments of axillary lymph nodes were compared with final pathological results. The impact of various clinicopathological factors on ultrasound accuracy was evaluated using univariate and multivariate logistic regression analyses.

Results: The overall accuracy of ultrasound in predicting axillary lymph node status after NAC was 76.2%, with a sensitivity of 68.4% and specificity of 83.7%. Factors significantly affecting ultrasound accuracy included tumor size reduction rate, lymph node cortical thickness change, and tumor biological subtype.

Conclusion: This study shows that ultrasound has moderate accuracy in assessing axillary lymph node status after NAC, but ultrasound alone is not sufficient for definitive assessment, and surgical confirmation is still necessary. The identified significant factors can optimize the use of ultrasound in post-NAC axillary staging.

Purpose: The unlimited proliferation of breast cancer (BC) cells is the basis for recurrence and metastasis. Ambra1 is involved in the regulation of cell proliferation, but its role may be cancer type-dependent, and the underlying mechanisms need further exploration. In addition, it remains unclear whether Ambra1 is involved in regulating the proliferation of BC cells. This study aims to explore the regulatory effect of Ambra1 on the proliferation of BC cells, as well as the underlying mechanisms.

Methods: The effects of Ambra1 on cell proliferation were detected in MCF-7 and MDA-MB-231 cells using CCK-8, EdU, and colony formation assays. The role of Ambra1 in regulating p27 via the Akt-FoxO1 pathway was determined in MCF-7, MDA-MB-231, and 293T cells through Western blotting, qRT-PCR, and co-immunoprecipitation. Subsequently, the role of p27 in Ambra1-mediated regulation of cell proliferation was validated in cell models and xenograft mouse models.

Results: Ambra1 deficiency significantly inhibited the proliferation of BC cells. p27 played a crucial role in this process. Furthermore, Ambra1 regulates the phosphorylation of the Ser256 residue of FoxO1 through Akt, thereby altering the nuclear distribution of FoxO1 and the transcription of p27.

Conclusion: Ambra1 can control the proliferation of BC cells by regulating the Akt-FoxO1-p27 signaling pathway. Therefore, this protein is a potential therapeutic target for BC.

Artificial intelligence (AI), particularly deep learning, is reshaping breast cancer diagnostics in the radiology and pathology fields. This review synthesizes recent advances in mammography, digital breast tomosynthesis (DBT), ultrasound, MRI, and whole-slide imaging, with an emphasis on convolutional neural networks (CNNs), Vision Transformers (ViTs), and generative adversarial networks (GANs). When embedded within established screening and diagnostic workflows, AI systems can enhance lesion detection and triage, as well as reduce interpretive variability. However, performance and generalizability depend on dataset quality, population and vendor heterogeneity, acquisition protocols, and calibrated probability outputs; diminished performance on external datasets and miscalibration remain recurrent risks that require explicit mitigation during development and deployment of these models. Beyond detection and classification, segmentation and risk prediction models increasingly integrate imaging with clinicopathological and, where available, genomic variables to enable individualized risk stratification and follow-up planning. Data generation strategies, including GAN-based augmentation, can partially address data scarcity and class imbalance but require rigorous quality control and bias monitoring. Persistent barriers to clinical adoption include uneven external validation, domain shifts across institutions, variability in reporting standards, limited interpretability, and ethical, privacy, and regulatory constraints. Overall, AI should augment, rather than replace, the role of clinicians. Priorities for responsible integration include multi-site prospective evaluations, transparent and standardized reporting, bias mitigation, robust calibration, and lifecycle monitoring to ensure sustained safety and equity.

Purpose: Breast cancer is one of the most common malignant tumors in women. Advanced patients often experience distant metastasis, among which bone metastasis has a relatively high incidence rate, seriously affecting the quality of life and prognosis of patients. LINC00917 may be related to the prognosis of breast cancer patients. This study aims to explore whether LINC00917 plays a significant role in breast cancer bone metastasis by targeting and regulating the expression of miR-491-5p.

Patients and methods: 254 breast cancer patients were recruited. The levels of LINC00917 were examined by RT-qPCR. Furthermore, the association between LINC00917 expression and patient prognosis was evaluated using Kaplan-Meier curves and Cox regression analysis. An in vitro cell model was established, and CCK-8 and Transwell assays were conducted to explore the role of LINC00917 in breast cancer bone metastasis. Additionally, the interaction among LINC00917, miR-491-5p, and FOXP4 were examined using dual-luciferase reporter assays.

Results: LINC00917 was upregulated in breast cancer bone metastasis and was associated with bad prognosis. Additionally, the knockdown of LINC00917 inhibited the function of breast cancer cells, and suppressed osteoclastogenesis while promoting osteoblast differentiation. Moreover, miR-491-5p inhibition counteracted the effects of LINC00917 knockdown on cell models. Furthermore, FOXP4 may be a target gene of miR-491-5p.

Conclusion: LINC00917 is a potential prognostic indicator for breast cancer bone metastasis. It is proposed that LINC00917 may facilitate the bone metastasis process in breast cancer by modulating the miR-491-5p/FOXP4 axis.

Purpose: This study aimed to develop and validate a predictive model using radiomics features from automatic breast volume scanner (ABVS) and 2D ultrasound images to preoperatively assess Ki-67 expression in breast cancer (BC), thereby supporting personalized clinical treatment planning.

Methods: Data from 426 BC patients who met the inclusion criteria were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed on the clinical ultrasound characteristics to construct a clinical model. Radiomics features were extracted from both the tumor and the sub-regions based on ABVS and 2D images. The silhouette coefficient was used to evaluate clustering performance and determine the optimal number of clusters. Radiomics-based prediction models were developed using four machine learning classifiers: Logistic Regression, ExtraTree, XGBoost, and LightGBM. A combined model was further constructed by integrating radiomics and habitat radiomics features from ABVS and 2D images with relevant clinical factors. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results: In the validation set, the area under the ROC curve (AUC) values of the radiomics model (Rad _{ABVS + 2D} ), the habitat radiomics model (Hab _{ABVS + 2D} ), and the combined radiomics model (Rad-Hab _{ABVS + 2D} ) were 0.603, 0.664, and 0.850, respectively. By integrating independent clinical factors (US-ALNs, T-stage) with the Rad-Hab _{ABVS + 2D} model, a comprehensive model (CM _{Clinical + Rad-Hab} ) was constructed using LightGBM. According to the DeLong test, this model significantly outperformed others in terms of AUC (P < 0.05). The AUC values for the training and validation sets were 0.951 (95% CI: 0.928-0.973) and 0.884 (95% CI: 0.832-0.949), respectively. The calibration curves and DCA of CM _{Clinical + Rad-Hab} demonstrated excellent model calibration and clinical utility.

Conclusion: The CM _{Clinical + Rad-Hab} model developed in this study enables accurate preoperative prediction of Ki-67 expression in BC patients, facilitating personalized and precise treatment strategies.

Background: Lymph node (LN) status is a critical prognostic factor for breast cancer patients undergoing neoadjuvant chemotherapy (NAC). This study aims to develop and validate machine learning models to predict LN response in breast cancer patients with LN metastases.

Methods: Breast cancer patients who received NAC in our hospital were retrospectively analyzed. Clinicopathological data, and MRI imaging were collected. Patients were randomly divided into a training set and a testing set in 7:3 ratio. Radiomic features were extracted from pre-treatment imaging. Random forests and logistic regression were employed alongside Clinical, Clinical-Radiomics and Clinical-Deep-learning-radiomics (Clinical-DLR) in training set. Model performance was evaluated using metrics including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), accuracy and F1-score. Finally, patients were divided into high-risk and low-risk groups according to the model with the best performance.

Results: Overall, 447 patients were enrolled. In the Clinical, Clinical-Radiomics, and Clinical-DLR logistic regression models, the AUC values in the testing set were 0.738, 0.798, and 0.911, respectively. For the random forest models, the AUC values in the testing set were 0.754, 0.801, and 0.921, respectively. Based on the predictions from the Clinical-DLR model, patients can be stratified into high-risk and low-risk groups. The survival outcomes for high-risk patients were significantly worse compared to those of low-risk patients.

Conclusion: The deep learning radiomics offers a promising approach to predict LN status and survival outcome in breast cancer patients undergoing NAC. This could facilitate personalized treatment strategies and improve clinical decision-making.