Breast Cancer Research最新文献

Background: Cell-free DNA (cfDNA) fragmentomics represents a transformative approach for early breast cancer detection, offering significant potential to improve patient survival through timely intervention. Despite this promise, existing cfDNA-based methods demonstrate inadequate sensitivity for clinical implementation, particularly in early-stage malignancies. There remains an urgent need to develop robust, cost-effective diagnostic strategies integrating cfDNA fragmentomic profiling with advanced machine learning algorithms.

Methods: This research involved a total of 191 participants who did not have cancer and 204 participants diagnosed with breast cancer. The plasma cfDNA samples from the participants underwent profiling through whole-genome sequencing. A variety of cfDNA characteristics and machine learning models were assessed within the training cohort to attain the best model. The evaluation of model performance took place in a separate validation cohort.

Results: An assembled ensemble model that combines three cfDNA characteristics with six machine learning algorithms, developed in the training cohort (cancer: 119; healthy: 112), outperformed all models created from individual feature-algorithm pairs. This composite model demonstrated enhanced sensitivities of 93.3% at a specificity of 94.6% for the training cohort (area under the curve [AUC], 0.983) and 96.5% at 93.7% specificity for the validation cohort (AUC, 0.989) (cancer: 85; healthy: 79). Additionally, our model exhibited sensitivity across various stages, distinct pathological types, and diverse molecular classifications.

Conclusion: We have established a stacked ensemble model using cfDNA fragmentomics features and achieved superior sensitivity for detecting early-stage breast cancer, which could promote early diagnosis and benefit more patients.

Breast fibroepithelial lesions (FELs) comprising fibroadenomas (FAs) and phyllodes tumors (PTs) with varying degrees of malignancy, necessitate tailored surgical approaches. However, preoperative diagnosis of FELs remains challenging and their pathogenesis is not fully elucidated. By integrating methylation and expression data, we revealed substantial molecular deregulation common to FAs and PTs, impacting pathways central to genetic information processing and metabolism. Furthermore, we identified 86 genes exhibiting concurrent differential expression and methylation changes between FAs and PTs, some of which have been implicated in the malignant progression of disease. Subsequently, we constructed two gene-pair signatures: one comprising 158 pairs for distinguishing FAs from PTs, and another with 146 pairs for differentiating benign from malignant PTs. Both signatures achieved AUC exceeding 0.85 in independent surgical and core biopsy datasets. Finally, we identified 99 pathogenic genes exhibiting continuous up-regulation or down-regulation from FAs to malignant PTs. Significant associations were observed between these genes and key cancer-related biological pathways.

Background: Neoadjuvant therapy (NAC) is a standard treatment for breast cancer, yet only some patients gain significant benefit. Identifying those most likely to benefit from NAC is crucial. Single-modality data often overlook patient heterogeneity, so we developed an interpretable, attention-based multimodal full information feature fusion transformer, MuFi, to predict NAC responses by integrating whole slide images (WSI) and magnetic resonance imaging (MRI).

Methods: Data from 567 biopsy-confirmed breast cancer patients from two institutions were retrospectively analyzed, with a training cohort (n = 290), validation cohort (n = 73), and external test cohort (n = 204). Multimodal data included pre-treatment pathology slides, MRI scans, and clinical information. A memory-efficient multimodal model was used to fuse WSIs and MRI, with a transformer capturing interactions between histological patches and MRI features.

Results: MuFi achieved AUCs of 81.9% and 78.5% in discovery and validation cohorts and 79.3% in external testing, outperforming clinical, single-modality and late-fusion-based models. Integrating clinical data (cT and molecular subtype) with MuFi and Feature Re-calibration based Multiple Instance Learning (FRMIL) models further increased AUCs to 90.2%, 81.8%, and 81.6% across the cohorts, indicating enhanced predictive accuracy and generalizability, especially in external testing.

Conclusion: By fusing pathology and radiology features, MuFi improves decision reliability and identifies critical multimodal predictors. This integration framework better captures patient heterogeneity, supporting personalized NAC decision-making through improved accuracy and generalizability.

Purpose: We hypothesize that SFRP2 is a promising target for Triple Negative Breast Cancer (TNBC).

Experimental design: 1. Multiplex immunohistochemistry (IHC) was performed on human TNBC to identify SFRP2 localization in the tumor microenvironment. 2. Tumor associated macrophages (TAMs) were isolated from E0771.LMB breast tumors. TAMs were treated with hSFRP2 mAb (10 µM) or control (10 µM) for 1 h and analyzed by western blot and qRT-PCR for IFN-ϒ. 3 SFRP2 and IFN-ϒ mRNA expression levels were analyzed from the Cancer Genome Atlas (tCGA) for breast cancer patients using least squares-linear regression analysis. 4. PY8119 or E0771.LMB TNBC cells were injected i.v. into mice, and mice were treated with either IgG1 or hSFRP2 mAb every 3 days. Lung metastases were counted after 4 weeks and analyzed by IHC for M1/M2 ratio. 5. MDA-MB-231 TNBC cells were injected into the mammary fat pad, and when tumors were established, mice were treated with IGg1 or hSFRP2 mAb every 3 days i.v. for 79 days and tumor volumes were compared. 6. Wild-type (WT) MDA-MB-231 and doxorubicin-resistant MDA-MB-231 cells were treated with hSFRP2 mAb and apoptosis was compared.

Results: 1) Multiplex IHC on human breast tumors showed that SFRP2 localized to tumor cells (87%), TAMs (90%), and tumor-infiltrating lymphocytes (TILs) (96%) in the microenvironment. 2) TAMs treated with hSFRP2 mAb had an increase in IFN-ϒ mRNA by 2.35 ± 0.08-fold (n = 3, p = 0.02) and protein levels by1.9-fold compared to control. 3). Analysis of 1075 breast cancer patients from TCGA database revealed a significant negative association between SFRP2 mRNA and IFN-ϒ expression (p < 0.0001). 4) hSFRP2 mAb reduced lung metastases in EO771.LMB (n = 15, p < 0.05) and PY8119 (n = 11, p < 0.05) mice with an increase the M1/M2 ratio in lungs (n = 3, p = 0.02). 5) hSFRP2 mAb inhibited MDA-MB-231 growth in vivo by 61% percent (n = 9, p < 0.001). 6) hSFRP2 mAb promoted apoptosis in doxorubicin-resistant cells (n = 6, p < 0.0001).

Conclusions: SFRP2 localizes to tumor, TAMs and TILs in the tumor microenvironment and is negatively associated INF-ƴ in human tumors. hSFRP2 mAb reduces primary and metastatic TNBC growth, increases INF-ƴ from TAMS, boosts the M1/M2 ratio in lung metastases, and induces apoptosis in doxorubicin-resistant cells.

Background: ¹⁸F-FDG PET-CT has emerged as a powerful imaging tool for initial staging and prognosis evaluation of patients with Breast Cancer (BC). However, previous studies are inconsistent on attributing a predictive value to the pathological Complete Response (pCR) determined by PET-CT. Our objective was to assess the association between pCR and ¹⁸F-FDG PET-CT findings in patients with HER2+ BC in neoadjuvant setting.

Methods: We collected data from patients enrolled in the prospective and multicentric French clinical trial NeoTOP (NCT02339532) who underwent ¹⁸F-FDG PET-CT before and after their first course of neoadjuvant treatment (depending on topoisomerase 2-α amplification status: 3 cycles of FEC 100 followed by 3 cycles of Docetaxel + Trastuzumab + Pertuzumab or 6 cycles of Docetaxel + Carboplatin + Trastuzumab + Pertuzumab). PET response was evaluated with visual and quantitative methods, by measuring tumor uptake parameters (SUV and SUL maximal and mean values), then compared to the pCR established according to Chevallier's classification. RESULTS: Out of 86 patients, 45 had fully analysable PET and pathological data. pCR rate was 73.3%. Sensitivity and specificity of PET visual analysis for pCR diagnosis were 14.0-83.0% respectively. SUVmax baseline value was 12.0±7.2 and decreased by 55.0±21.0% after one cycle of treatment. Quantitative PET parameters and their variations were not significantly different between pCR and non-pCR patients (p>0.05 in all cases).

Conclusions: ¹⁸F-FDG PET-CT before and after the first cycle of neoadjuvant treatment does not appear to be an effective tool to predict pCR in patients with HER2+ BC.

Background: HER2-positive breast cancer is a prevalent pathological subtype of breast cancer. Resistance to anti-HER2 targeted therapies remains a significant challenge in treatment. Understanding the role of HER2 in breast cancer progression is essential.

Methods: The proteomics analysis was used to explore the regulated proteins in patients with HER2-positive breast cancer. MTS, EdU staininig, flow cytometry and colony formation assays were used to cell proliferation and apoptosis. Protein expressions and interaction of CAND1 and HER2 were clarified by western blot, immunofluorescence and co-immunoprecipitation experiments. In vivo studies using nude mice demonstrated the role of CAND1 in HER2-positive breast cancer cell growth.

Results: An increase in CAND1 expression, which is associated with poor prognosis in patients with HER2-positive breast cancer. Functionally, CAND1-KD suppresses the growth of HER2-positive breast cancer cells by inducing cell cycle arrest and apoptosis. In vivo, CAND1-KD inhibits tumor growth in xenograft models. Mechanistically, CAND1 expression is positively correlated with HER2 protein levels in breast cancer tissues. CAND1 directly interacts with HER2, stabilizing its protein expression. The E3 ligase CUL7 promotes HER2 ubiquitination and is essential for the interaction between CAND1 and HER2. CAND1-KD enhances CUL7 neddylation, which activates its ligase activity and leads to HER2 ubiquitination. Importantly, HER2 overexpression reverses the proliferation inhibition caused by CAND1 loss both in vitro and in vivo.

Conclusion: In summary, this study highlights the critical role of CAND1 in regulating HER2 ubiquitination and suggests a potential therapeutic strategy for patients with HER2-positive breast cancer.