Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.8
4.4
Journals
Cancers
Special Issues
Breast Cancer Research and Treatment
cancers-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Cancers Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Breast Cancer Research and Treatment

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Tumor Microenvironment".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1845

Special Issue Editor

Dr. Huaitian Liu

E-Mail Website
Guest Editor
Center for Cancer Research (CCR), National Cancer Institute (NCI), Bethesda, MD 20892, USA
Interests: cancer genomics; genetics; epidemiology; bioinformatics

Special Issue Information

Dear Colleagues,

Breast cancer is classified into four molecular subtypes: luminal A, luminal B, triple-negative breast cancer (TNBC), and human epidermal growth factor receptor type 2 (HER2)-positive. The tumor microenvironment plays a critical role in modulating the aggressiveness and differentiation of malignant cells. Advances in single-cell genomics have transformed our ability to analyze the cellular, transcriptional, and epigenetic heterogeneity of human tumors with unprecedented resolution. This Special Issue will highlight recent progress in our understanding of the tumor and immune microenvironments, as well as identifying relevant biomarkers and therapeutic targets, paving the way for breast cancer to become a curable disease.

Dr. Huaitian Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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
  • tumor microenvironment
  • immune microenvironment
  • intratumoral heterogeneity
  • immunotherapy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 1983 KB  
Article
Federated Learning Architecture for 3D Breast Cancer Image Classification
by Amel Ali Alhussan, Wiem Nhidi, Imen Filali, Faten Benhmida and Ridha Ejbali
Cancers 2025, 17(21), 3450; https://doi.org/10.3390/cancers17213450 - 28 Oct 2025
Viewed by 695
Abstract
Backgrouds: Breast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of [...] Read more.
Backgrouds: Breast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of image interpretation, which makes automated detection methods increasingly vital. Methods: In this study, we propose an advanced approach that leverages 3D mammographic imaging and integrates Federated Learning (FL) to enable decentralized, privacy-preserving model training across multiple institutions. To evaluate the effectiveness of this approach, we assess various machine learning models, including Convolutional Neural Networks (CNNs), Transfer Learning architectures (VGG16, VGG19, ResNet50), and AutoEncoders (AEs), using 3D mammographic data. Results: Our results indicate that the CNN model achieves an accuracy of 97.30%, which improves slightly to 97.37% when the model is combined with Federated Learning, highlighting both the predictive performance and privacy-preserving advantages of our method. In contrast, Transfer Learning models and AutoEncoders exhibit lower accuracies that range from 48.83% to 89.24%, revealing their limitations in the context of this specific task. Conclusions: These findings underscore the effectiveness of the CNN-FL framework as a robust tool for breast cancer detection, showing that this approach offers a promising balance between diagnostic accuracy and data security—two critical factors in medical imaging. Full article
(This article belongs to the Special Issue Breast Cancer Research and Treatment)
►▼ Show Figures

Figure 1

Review

Jump to: Research

38 pages, 2128 KB  
Review
Antibody–Drug Conjugates and Beyond: Next-Generation Targeted Therapies for Breast Cancer
by Adil Farooq Wali, Mohamed El-Tanani, Sirajunisa Talath, Syed Arman Rabbani, Imran Rashid Rangraze, Shakta Mani Satyam, Ashot Avagimyan, Karolina Hoffmann, Ioannis Ilias, Sorina Ispas, Maggio Viviana, Anna Paczkowska and Manfredi Rizzo
Cancers 2025, 17(24), 3943; https://doi.org/10.3390/cancers17243943 - 10 Dec 2025
Viewed by 261
Abstract
Breast cancer is the most common cancer and the most important cause of cancer-related death in females worldwide. Antibody–drug conjugates (ADCs) represent a novel class of targeted therapies that combine the precision of monoclonal antibodies with the potent cell-killing activity of cytotoxic drugs. [...] Read more.
Breast cancer is the most common cancer and the most important cause of cancer-related death in females worldwide. Antibody–drug conjugates (ADCs) represent a novel class of targeted therapies that combine the precision of monoclonal antibodies with the potent cell-killing activity of cytotoxic drugs. This review highlights recent mechanistic, technological, and clinical developments of ADCs in breast cancer, including next-generation ADCs beyond those that target HER2 (human epidermal growth factor receptor 2). Authors performed a systematic literature study for ADCs and their structural features, including their components (antibody, linker, and payload) and their therapeutic efficacy. A frame of preclinical research findings and clinical evidence integration of HER2-targeted therapy outcomes in HER2-positive, HER2-low, and triple-negative breast cancer (TNBC) subtypes were presented. Clinical studies of antibody–drug conjugates such as trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), and sacituzumab govitecan have demonstrated significant improvements in progression-free survival and overall survival across diverse breast cancer patient populations. ADCs offer unique advantages in breast cancer therapy by combining the precision of targeted antibodies with the potency of chemotherapy drugs. This allows them to selectively kill cancer cells, overcome resistance, reduce toxicity to healthy tissues, and expand treatment options for difficult subtypes like HER2-low and triple-negative breast cancer. Unlike previous reviews focusing on HER2-targeted ADCs, herein we review exciting ADCs targeting HER3 HER3 (human epidermal growth factor receptor 3) and Nectin-4, as well as the implications of bispecific and immune-stimulatory ADCs in the clinic. Additionally, it features mechanism-based innovations and novel trial data that revolutionize ADC applications in the HER2-low as well as the triple-negative breast cancer subtypes. The advent of ADC is changing precision oncology in breast cancer. With a new design and indications evolving, they are an attractive avenue for bypassing resistance and reducing toxicity and ultimately improving patient outcomes in the molecular subtypes. The present review summarizes recent advancements in antibody–drug conjugates (ADCs) and emerging targeted therapeutic strategies for breast cancer. It covers mechanistic insights, linker–payload innovations, receptor-based targeting approaches, clinical trial progress, and next-generation modalities that extend beyond HER2-directed ADCs. Current challenges, safety profiles, and future opportunities in engineering more selective and effective ADC platforms are also discussed. Full article
(This article belongs to the Special Issue Breast Cancer Research and Treatment)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop