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Breast Cancer Research: Charting Future Directions

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A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2492

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Special Issue Editor

Dr. Mohamad Adam Bujang

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Guest Editor

Clinical Research Centre, Sarawak General Hospital, Ministry of Health Malaysia, Kuching, Malaysia
Interests: data analyst; methodologist; precision medicine; quality of life

Special Issue Information

Dear Colleagues,

Breast cancer remains one of the most prevalent and complex malignancies affecting people worldwide, especially women. As our understanding of its molecular, genetic, and environmental underpinnings deepens, so does the need to rethink diagnosis, treatment, survivorship, and prevention strategies.

This Special Issue titled “Breast Cancer Research: Charting Future Directions” brings together cutting-edge studies, innovative methodologies, and forward-looking perspectives that aim to shape the next chapter in breast cancer research. It will highlight key advancements in personalized medicine, biomarkers for early detection, immunotherapy, and patient-reported outcomes.

By fostering dialogue between clinicians and researchers, this Special Issue aims to not only reflect the current state of the field but also inspire new inquiries that will accelerate discovery and improve outcomes for all individuals affected by breast cancer. We hope it will serve as both a resource and a catalyst for researchers committed to advancing the science and management of breast cancer well into the future.

Dr. Mohamad Adam Bujang
Guest Editor

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Keywords

personalized medicine
biomarkers for early detection
immunotherapy
patient-reported outcomes

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Published Papers (4 papers)

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Research

19 pages, 1518 KB

Open AccessArticle

Early MRI-Derived Volumetric Thresholds Predict Response and Guide Personalization in HER2-Positive Breast Cancer: A Retrospective Study

by Hao Yao, Xuyang Qian, Ran Zheng, Xingye Sheng, Jingjing Ding, Mingyu Wang, Xiaoming Zha, Shouju Wang and Jue Wang

Biomedicines 2025, 13(12), 2906; https://doi.org/10.3390/biomedicines13122906 - 27 Nov 2025

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Abstract

Background: Neoadjuvant systemic therapy (NST), whose primary purposes include response assessment and treatment individualization, is a key strategy in the treatment of HER2-positive breast cancer. This study investigated the predictive value of the magnetic resonance imaging (MRI)-derived tumor volume reduction rate (δV1) for the early identification of pathological complete response (pCR) during NST and established clinically applicable δV1 thresholds for patient stratification. Methods: HER2-positive breast cancer patients who received THP (taxane, trastuzumab, pertuzumab) followed by epirubicin/cyclophosphamide (EC) were enrolled. MRI was performed at baseline, after THP, and after EC. Tumor volumes were manually segmented using 3D Slicer, and δV1/δV2 were calculated via Python (version3.13). Longest diameter reduction rates (δL1/δL2) were recorded. pCR (ypT0/is ypN0) was the primary endpoint. Receiver operating characteristic (ROC) analysis determined predictive accuracy, and logistic regression identified independent predictors. Thresholds for δV1 were explored, and subgroup analyses were conducted by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Results: Overall, 59.3% of patients achieved pCR. δV1 demonstrated superior predictive accuracy compared with longest diameter reduction (δL1), with an AUC of 0.745 (95% CI: 0.642–0.847) vs. 0.634 (95% CI: 0.512–0.757). A δV1 cutoff of 0.85 discriminated responders (68.4% vs. 41.4%, p = 0.016), while one of 0.91 represented the optimal predictive threshold. In multivariate analysis, δV1 was independently associated with pCR (OR = 1227.1, 95% CI: 6.86–219,562; p = 0.007), along with HER2 3+ expression (OR = 4.24, 95% CI: 1.26–14.31; p = 0.020). Among HR-positive patients, δV1 < 0.93 identified a subgroup with significantly lower pCR rates (19.0% vs. 81.0%, p < 0.001). Conclusions: δV1 is a reliable and early MRI-based imaging biomarker for predicting pCR in HER2-positive breast cancer. Defining thresholds such as 0.85 and 0.91 supports early therapeutic stratification and may help identify patients who could benefit from anthracycline-containing regimens. Full article

(This article belongs to the Special Issue Breast Cancer Research: Charting Future Directions)

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17 pages, 6213 KB

Open AccessArticle

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Radiomics Features of Voxel-Wise DCE-MRI Time-Intensity-Curve Profile Maps

by Ya Ren, Kexin Chen, Meng Wang, Jie Wen, Sha Feng, Honghong Luo, Cuiju He, Yuan Guo, Dehong Luo, Xin Liu, Dong Liang, Hairong Zheng, Na Zhang and Zhou Liu

Biomedicines 2025, 13(10), 2562; https://doi.org/10.3390/biomedicines13102562 - 21 Oct 2025

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Abstract

Objective: Axillary lymph node (ALN) status in breast cancer is pivotal for guiding treatment and determining prognosis. The study aimed to explore the feasibility and efficacy of a radiomics model using voxel-wise dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-intensity-curve (TIC) profile maps to predict ALN metastasis in breast cancer. Methods: A total of 615 breast cancer patients who underwent preoperative DCE-MRI from October 2018 to February 2024 were retrospectively enrolled and randomly allocated into training (n = 430) and testing (n = 185) sets (7:3 ratio). Based on wash-in rate, wash-out enhancement, and wash-out stability, each voxel within manually segmented 3D lesions that were categorized into 1 of 19 TIC subtypes from the DCE-MRI images. Three feature sets were derived: composition ratio (type-19), radiomics features of TIC subtypes (type-19-radiomics), and radiomics features of third-phase DCE-MRI (phase-3-radiomics). Student’s t-test and the least absolute shrinkage and selection operator (LASSO) was used to select features. Four models (type-19, type-19-radiomics, type-19-combined, and phase-3-radiomics) were constructed by a support vector machine (SVM) to predict ALN status. Model performance was assessed using sensitivity, specificity, accuracy, F1 score, and area under the curve (AUC). Results: The type-19-combined model significantly outperformed the phase-3-radiomics model (AUC = 0.779 vs. 0.698, p < 0.001; 0.674 vs. 0.559) and the type-19 model (AUC = 0.779 vs. 0.541, p < 0.001; 0.674 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. The type-19-radiomics showed significantly better performance than the phase-3-radiomics model (AUC = 0.764 vs. 0.698, p = 0.002; 0.657 vs. 0.559, p = 0.037) and type-19 model (AUC = 0. 764 vs. 0.541, p < 0.001; 0.657 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. Among four models, the type-19-combined model achieved the highest AUC (0.779, 0.674) in cross-validation and testing sets. Conclusions: Radiomics analysis of voxel-wise DCE-MRI TIC profile maps, simultaneously quantifying temporal and spatial hemodynamic heterogeneity, provides an effective, noninvasive method for predicting ALN metastasis in breast cancer. Full article

(This article belongs to the Special Issue Breast Cancer Research: Charting Future Directions)

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20 pages, 15691 KB

Open AccessArticle

Comprehensive Analysis of JCHAIN as a Potential Prognostic Factor for Breast Cancer and an Indicator for Tumor Microenvironment

by Yaqin Shi, Li Lin, Xinyu Zhu, Mengyao Wu, Caihua Xu, Wei Li and Kai Chen

Biomedicines 2025, 13(10), 2366; https://doi.org/10.3390/biomedicines13102366 - 26 Sep 2025

Viewed by 573

Abstract

Background: Breast cancer remains a predominant malignancy among females globally, and the tumor microenvironment (TME) exerts a pivotal role in its progression. Despite notable advancements in diagnostic and therapeutic modalities, resistance to conventional therapies persists as a critical hurdle, underscoring the necessity of exploring TME-related prognostic biomarkers. Methods: To elucidate the role of the TME in breast cancer progression and identify potential prognostic biomarkers, we analyzed RNA-seq data from 1081 breast cancer cases and 99 normal controls to assess tumor-infiltrating immune cells (TICs) and stromal components. Differential gene expression analysis identified genes correlated with ImmuneScore and StromalScore. A protein–protein interaction (PPI) network was constructed, followed by univariate Cox regression to pinpoint survival-associated genes. JCHAIN, significantly linked to survival outcomes, was selected for further investigation. Gene Set Enrichment Analysis (GSEA) and TIC correlation analyses were performed to explore its associations with immune pathways. Additionally, immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF) were performed on 61 clinical samples. Results: High ImmuneScore was associated with improved survival. Joining chain of multimeric IgA and IgM (JCHAIN) expression was notably reduced in tumor tissues, with low expression correlating with poorer prognosis. GSEA highlighted immune-related pathways enriched in high JCHAIN expression groups. TIC analysis revealed positive correlations with CD8+ T cells and M1 macrophages. IHC and mIF validations further confirmed decreased JCHAIN protein expression in tumor tissues, and higher JCHAIN expression was associated with increased M1 macrophage density. Conclusions: JCHAIN serves as a promising prognostic biomarker in breast cancer, reflecting immune activity within the TME, providing valuable insights into immune-stromal interactions and the therapeutic potential of JCHAIN. Full article

(This article belongs to the Special Issue Breast Cancer Research: Charting Future Directions)

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17 pages, 427 KB

Open AccessArticle

The Role of Diastolic Stress Echo and Myocardial Work in Early Detection of Cardiac Dysfunction in Women with Breast Cancer Undergoing Chemotherapy

by Stefanos Sokratous, Michaelia Kyriakou, Elina Khattab, Alexia Alexandraki, Elisavet L. Fotiou, Nektaria Chrysanthou, Paraskevi Papakyriakopoulou, Ioannis Korakianitis, Anastasia Constantinidou and Nikolaos P. E. Kadoglou

Biomedicines 2025, 13(10), 2341; https://doi.org/10.3390/biomedicines13102341 - 25 Sep 2025

Viewed by 626

Abstract

Background: Anthracycline-based chemotherapy, while highly effective for breast cancer, poses a significant risk for chemotherapy-related cardiac dysfunction (CTRCD), mainly determined by left ventricular ejection fraction (LVEF) reduction. Objectives: We aimed to evaluate the diagnostic utility of speckle tracking analysis (STA) and Diastolic Stress Test Echocardiography (DSTE) for the early detection of cardiac dysfunction either CTRCD or heart failure with preserved ejection fraction (HFpEF) in women undergoing chemotherapy for breast cancer and developed exertional dyspnea and/or fatigue during follow-up. Methods: In this prospective case–control study, 133 women receiving anthracycline-based chemotherapy (with or without anti-HER2 therapy) (chemotherapy group-CTG) and 65 age-matched healthy women as the control group (CG) underwent resting echocardiographic assessment, including LVEF, global longitudinal strain (GLS), myocardial work indices, biomarkers assay (NT-proBNP, troponin, galectin-3) and DSTE at baseline. That assessment was repeated after 12 months in CTG. Results: In this prospective case—control study, 133 women receiving anthracycline-based chemotherapy (with or without anti-HER2 therapy) were included. Based on the presence of CTRCD, they were further subdivided into a CTRCD subgroup (n = 37) and a CTRCD-free subgroup (n = 88). At the end of this study, CTG showed worse values of LVEF, GLS, myocardial work indices than baseline and CG (p < 0.05). Subgroup comparison (CTRCD vs. CTRCD-free) showed significant impairment in LVEF (53.60% vs. 62.60%, p < 0.001), GLS (–16.68% vs. −20.31%, p < 0.001), DSTE-derived tricuspid regurgitation maximum velocity (TRVmax) (3.05 vs. 2.31 m/s, p < 0.001) and elevated biomarkers (NT-proBNP: 200.06 vs. 61.49 pg/mL; troponin: 12.42 vs. 3.95 ng/L, p < 0.001) in the former subgroup. Regression analysis identified GLS, NT-proBNP, troponin, and TRVmax as independent predictors of CTRCD. Notably, a subgroup of CTRCD-free patients (n = 16) showed a high probability for HFpEF based on the HFA-PEFF score, with elevated GLS, NT-proBNP and DSTE-derived TRVmax compared to the rest of CTRCD-free patients and the CG (p < 0.001). Conclusions: STA and DSTE significantly outperform conventional LVEF in detecting subclinical cardiac dysfunction among women with breast cancer receiving chemotherapy. The combination of novel echocardiographic techniques and biomarkers may enable the detection of early CTRCD, including the under-estimated presence of HFpEF among breast cancer women with HF symptoms. Full article

(This article belongs to the Special Issue Breast Cancer Research: Charting Future Directions)

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