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New Insights into Breast Cancer Management: From Tumorigenesis to Personalized...
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New Insights into Breast Cancer Management: From Tumorigenesis to Personalized Treatments—2nd Edition

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3278

Special Issue Editors

Dr. Rita De Sanctis

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Guest Editor
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20090 Milan, Italy
Interests: breast cancer; neoadjuvant chemotherapy; pathological complete response; prognosis; predictive factors; microbiome; radiomics; headache disorders; quality of life; nomograms
Special Issues, Collections and Topics in MDPI journals
Dr. Paola Tiberio

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Guest Editor
Medical Oncology and Hematology Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
Interests: breast cancer; neoadjuvant chemotherapy; supportive therapies; biomarkers; liquid biopsy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer is a very heterogeneous disease with multifactorial etiopathogenesis. In the last few decades, a deeper understanding of the molecular mechanisms underlying breast cancer development and progression has led to the development of novel accurate prognostic analyses and new effective drugs, thus leading to increased overall survival for breast cancer patients. Nevertheless, breast cancer remains the second leading cause of death in women globally. Thus, there is still a need to better understand the complexity of breast cancer and the interaction between the neoplasm and its microenvironment, as well as the immune system, to prevent disease development and to guide personalized treatments. Moreover, since individual response to therapy and long-term prognosis remain highly unpredictable, the current context requires identifying biomarkers that accurately forecast the response to therapy and identify patients who will not benefit from standard regimens. On the other hand, since the number of breast cancer survivors is continuously increasing, understanding the complexity of breast cancer survivorship is essential for adequate patient management.

Dr. Paola Tiberio
Dr. Rita De Sanctis
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • breast cancer
  • diagnosis
  • prognosis
  • treatment response prediction
  • biomarkers
  • risk factors
  • new therapies

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Published Papers (3 papers)

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Research

21 pages, 3114 KB  
Article
Proliferative Tumor States and Immunogenic Ecosystems Predict Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer
by Yuan Teng, Huan Li, Lin Cheng, Yingming Jiang, Hua Jiang and Yu Liu
Biomedicines 2026, 14(3), 643; https://doi.org/10.3390/biomedicines14030643 - 12 Mar 2026
Viewed by 697
Abstract
Background: Triple-negative breast cancer lacks established targeted therapies, and only a subset of patients achieves a pathologic complete response to neoadjuvant chemotherapy. We aimed to integrate bulk cohorts with an exploratory single-cell multi-omic dataset from only five patients to identify tumor and immune-related [...] Read more.
Background: Triple-negative breast cancer lacks established targeted therapies, and only a subset of patients achieves a pathologic complete response to neoadjuvant chemotherapy. We aimed to integrate bulk cohorts with an exploratory single-cell multi-omic dataset from only five patients to identify tumor and immune-related features associated with chemotherapy response. Methods: Bulk analyses were performed in two public breast cancer cohorts (GSE76275 and GSE25065) to compare triple-negative versus non-triple-negative tumors and to relate pretreatment transcriptional and inferred immune infiltration patterns to neoadjuvant chemotherapy response. Separately, in a hypothesis-generating single-cell cohort of five triple-negative breast cancers (n = 5; four responders, one non-responder), we performed single-cell RNA sequencing, T cell and B cell receptor sequencing, single-cell ATAC sequencing, and glycosylation tag profiling. Results: In bulk data, triple-negative tumors showed a loss of luminal estrogen receptor-associated programs, higher proliferation, and CIBERSORT-estimated enrichment of myeloid-associated immune fractions compared with non-triple-negative tumors. Chemotherapy response was associated with modest transcriptional shifts and inferred immune composition differences in triple-negative tumors and more pronounced epithelial, stromal, and inflamed immune changes in non-triple-negative disease. Single-cell data suggested that responder tumors were enriched for T and natural killer cells, antigen-presenting myeloid cells, expanded and diverse T and B cell clonotypes, and immune-associated glycosylation signals, whereas the non-responder sample was dominated by epithelial and fibroblast compartments with secretory, adhesion, and potential immune evasion programs. Checkpoint-related analyses reflected expression patterns and predicted ligand–receptor communication, nominating TIGIT–NECTIN2 as a candidate axis for further investigation. Conclusions: Integrating public bulk cohorts with exploratory single-cell multi-omics supports a model in which chemotherapy sensitivity in triple-negative breast cancer is linked to inflamed, antigen-presenting microenvironments and adaptable antitumor immunity, whereas resistance is associated with stromal and tumor dominance. These candidate biomarkers and pathways require validation in larger independent cohorts, and clinical translation is premature given the exploratory single-cell cohort. Full article
(This article belongs to the Special Issue New Insights into Breast Cancer Management: From Tumorigenesis to Personalized Treatments—2nd Edition)
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18 pages, 2367 KB  
Article
Machine Learning Models Utilizing Oxidative Stress Biomarkers for Breast Cancer Prediction: Efficacy and Limitations in Sentinel Lymph Node Metastasis Detection
by José Manuel Martínez-Ramírez, Cristina Cueto-Ureña, María Jesús Ramírez-Expósito and José Manuel Martínez-Martos
Biomedicines 2025, 13(12), 3107; https://doi.org/10.3390/biomedicines13123107 - 17 Dec 2025
Viewed by 1020
Abstract
Objective: This study aimed to apply the Random Forest machine learning model using oxidative stress biomarkers to classify breast cancer status and assess sentinel lymph node (SLN) metastasis, a pathology of high incidence and mortality that represents a major public health challenge. Methods: [...] Read more.
Objective: This study aimed to apply the Random Forest machine learning model using oxidative stress biomarkers to classify breast cancer status and assess sentinel lymph node (SLN) metastasis, a pathology of high incidence and mortality that represents a major public health challenge. Methods: The breast cancer classification cohort included 188 women with infiltrating ductal carcinoma and 78 healthy volunteers. For SLN metastasis assessment, a subset of 29 women with metastases and 57 controls (n = 86) was used. Data preprocessing and the SMOTE technique were applied to balance the classes in the metastasis set, achieving a perfect balance of 171 examples (57 per class). Random Forest model with a leave-one-out validation strategy was employed and oxidative stress biomarkers (e.g., lipid peroxidation, total antioxidant capacity, superoxide dismutase, catalase, glutathione peroxidase) were used. Results: The model achieved high accuracy (0.996) in classifying breast cancer, representing a substantial improvement over current screening methods such as mammography. In contrast, its performance in detecting SLN metastases was more limited (accuracy = 0.854), likely reflecting the inherent complexity and heterogeneity of the metastatic process. Moreover, these estimates derive from a retrospective case–control cohort and should not be viewed as a substitute for, or a direct comparison with, population-based mammography screening, which would require dedicated prospective validation. Conclusions: The findings underscore the model’s robust performance in distinguishing women with breast cancer from healthy volunteers, but highlight significant gaps in its ability to diagnose metastatic disease. Future research should integrate additional biomarkers, longitudinal data, and explainable artificial intelligence (XAI) methods to improve clinical interpretability and accuracy in metastasis prediction, moving towards precision medicine. Full article
(This article belongs to the Special Issue New Insights into Breast Cancer Management: From Tumorigenesis to Personalized Treatments—2nd Edition)
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12 pages, 1178 KB  
Article
Systemic Immune Profiling Reveals Candidate Biomarkers in Luminal A Breast Cancer: A Comparative Pilot Study
by Tânia Moura, Olga Caramelo, Isabel Silva, Sandra Silva, Paula Laranjeira and Artur Paiva
Biomedicines 2025, 13(11), 2787; https://doi.org/10.3390/biomedicines13112787 - 14 Nov 2025
Viewed by 1054
Abstract
Background: Luminal A breast cancer, the most common molecular subtype, is typically associated with a favorable prognosis. However, its systemic immune landscape remains largely uncharacterized. Methods: In this study, we used high-dimensional flow cytometry to characterize peripheral immune alterations in 13 patients with [...] Read more.
Background: Luminal A breast cancer, the most common molecular subtype, is typically associated with a favorable prognosis. However, its systemic immune landscape remains largely uncharacterized. Methods: In this study, we used high-dimensional flow cytometry to characterize peripheral immune alterations in 13 patients with luminal A breast cancer compared to 14 age-matched healthy female controls. A total of 254 immune subsets were analyzed, including 23 innate populations and 231 T cell subpopulations, defined by detailed phenotypic and functional markers. Results: The main observations in the luminal A breast cancer group included a significant increase in neutrophils, plasmacytoid dendritic cells (pDCs), and CD4+ follicular T lymphocytes, as well as a reduced percentage of monocytes, conventional type 2 dendritic cells (cDC2), and CD4+CD196+ T cells. Conclusions: Despite being a preliminary study, these findings highlight distinct immune alterations in luminal A breast cancer and support the use of flow cytometry for identifying biomarkers, measurable biological indicators of disease presence, progression, or therapeutic response. Full article
(This article belongs to the Special Issue New Insights into Breast Cancer Management: From Tumorigenesis to Personalized Treatments—2nd Edition)
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