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Cancers
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Advances in the Molecular Biology and Pathology of Breast Cancer
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Advances in the Molecular Biology and Pathology of Breast Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Molecular Cancer Biology".

Deadline for manuscript submissions: closed (23 August 2024) | Viewed by 4230

Special Issue Editor

Prof. Dr. Grace Callagy

E-Mail Website
Guest Editor
Discipline of Pathology, School of Medicine, Lambe Institute for Translational Research, University of Galway, H91 TK33 Galway, Ireland
Interests: breast cancer; biomarker; triple negative breast cancer; molecular biology; pathology

Special Issue Information

Dear Colleagues,

Breast cancer is one of the most prevalent malignancies worldwide, being the most commonly diagnosed cancer and leading cause of cancer-related deaths in women. Over the last two decades, there have been significant advances in our understanding of the molecular biology and pathology underlying breast cancer that have led to improvements in the management and outcomes of this disease. Comprehensive and multi-layered analysis has revealed the molecular and biologic heterogeneity of breast cancer with the recognition of distinct subtypes linked to different aetiological pathways. These efforts have affirmed hormone receptor and HER2 signalling pathways as major biological drivers of breast cancer. Classification systems are evolving that integrate molecular and morphologic information, and complex genomic assays now form part of the diagnostic armamentarium that informs management decisions. More recently, molecular targets have emerged as promising therapeutic options, particularly in the advanced setting. Notwithstanding the tremendous advances made, treatment options remain limited for some subtypes of breast cancer for which the prognosis remains poor. This Special Issue will cover recent advances in the molecular biology and pathology of breast cancer that work towards improving the diagnosis, management and outcomes for patients with the disease. 

Prof. Dr. Grace Callagy
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 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • molecular biology
  • molecular mechanisms
  • biologic heterogeneity

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Published Papers (3 papers)

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Research

13 pages, 2669 KiB  
Article
Evaluation of Grading Estrogen Receptors in Breast Cancer Using Fully Automated Rapid Immunohistochemistry Based on Alternating-Current Electric Field Technology
by Chiaki Kudo, Kaori Terata, Hiroshi Nanjo, Kyoko Nomura, Yuko Hiroshima, Eriko Takahashi, Ayuko Yamaguchi, Hikari Konno, Masaaki Onji, Yuki Wakamatsu, Yoshihiko Kimura, Shinogu Takashima, Akiyuki Wakita, Yusuke Sato, Yoshihiro Minamiya and Kazuhiro Imai
Cancers 2025, 17(3), 363; https://doi.org/10.3390/cancers17030363 - 23 Jan 2025
Viewed by 884
Abstract
Background: Immunohistochemistry (IHC) is crucial for determining cancer treatments. We previously developed a rapid IHC method and have now developed a fully automated rapid IHC stainer (R-Auto). This study aimed to evaluate the clinical reliability of the R-Auto protocol for staining estrogen receptors [...] Read more.
Background: Immunohistochemistry (IHC) is crucial for determining cancer treatments. We previously developed a rapid IHC method and have now developed a fully automated rapid IHC stainer (R-Auto). This study aimed to evaluate the clinical reliability of the R-Auto protocol for staining estrogen receptors (ERs) in breast cancer specimens and evaluate the staining performance. Methods: Between January 2015 and June 2020, 188 surgical specimens collected from breast cancer patients treated at our hospital were evaluated via ER staining using R-Auto, conventional manual IHC, and a commercial autostainer. The specimens were scored using Allred scores, after which the staining results were compared between R-Auto and conventional IHC or the commercial autostainer. Weighted kappa coefficients and AC1 statistics were used to assess the agreement between the methods. Results: The AC1 statistic for comparison between R-Auto and conventional IHC was 0.9490 (0.9139–0.9841), with a 95.7% agreement rate, and that for comparison between R-Auto and the commercial autostainer was 0.9095 (0.8620–0.9570), with a 92.6% agreement. There was, thus, substantial agreement between R-Auto and both conventional IHC and the commercial autostainer. However, R-Auto shortened the time required for IHC from 209 min with conventional IHC to 121 min. Conclusions: R-Auto enables a good staining performance in a shorter time with less effort. Full article
(This article belongs to the Special Issue Advances in the Molecular Biology and Pathology of Breast Cancer)
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12 pages, 1575 KiB  
Article
Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset
by Nabil Adam and Robert Wieder
Cancers 2024, 16(23), 4033; https://doi.org/10.3390/cancers16234033 - 1 Dec 2024
Viewed by 1202
Abstract
Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events. Objective: Our goal is to develop [...] Read more.
Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events. Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables. We previously showed that predictive DL survival modeling of potentially curable stage I–III patients can be improved by combining time-fixed and time-varying covariates. Here, we demonstrate that DL-based predictive survival modeling in stage IV patients, where treatment does not offer a cure, can generate accurate individual survival predictions by considering subsequent lines of potential treatment to guide therapy. This guidance is rarely obtainable in the nearly limitless scenarios of metastatic disease. Design, Setting, and Participants: We applied the SEER-Medicare linked dataset from 1991 to 2016 to investigate 14,312 unique stage IV patients with 1,880,153 entries. We used DeepSurv- and DeepHit-, Nnet-survival- and Cox-Time DL-based predictive models to consider the combination of time-fixed and time-varying covariates at each visit for each patient. We adopted random sampling to divide the input dataset into training, validation, and testing sets. We verified the models’ implementation using the pycox package and fine-tuned the models using the open-source library Amazon SageMaker Python SDK 2.232.2 (software development kit). Our results demonstrated the proof of principle of the models by generating individual patients’ survival curves. Conclusions and Relevance: By extending the survival prediction models to consider stage IV BC patients’ time-fixed and time-varying covariates, we achieved a prediction error below 10%. Based on their circumstance-specific situations, these models can predict survival in individual stage IV patients with high confidence. The models will serve as an important adjunct to treatment decisions in patients with stage IV BC and test what-if scenarios of treatment or no treatment options to optimize therapy for extending patient lives and minimizing adverse events. Full article
(This article belongs to the Special Issue Advances in the Molecular Biology and Pathology of Breast Cancer)
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17 pages, 2875 KiB  
Article
Two Different Immune Profiles Are Identified in Sentinel Lymph Nodes of Early-Stage Breast Cancer
by Joana Martins Ribeiro, João Mendes, Inês Gante, Margarida Figueiredo-Dias, Vânia Almeida, Ana Gomes, Fernando Jesus Regateiro, Frederico Soares Regateiro, Francisco Caramelo and Henriqueta Coimbra Silva
Cancers 2024, 16(16), 2881; https://doi.org/10.3390/cancers16162881 - 19 Aug 2024
Viewed by 1391
Abstract
In the management of early-stage breast cancer (BC), lymph nodes (LNs) are typically characterised using the One-Step Nucleic Acid Amplification (OSNA) assay, a standard procedure for assessing subclinical metastasis in sentinel LNs (SLNs). The pivotal role of LNs in coordinating the immune response [...] Read more.
In the management of early-stage breast cancer (BC), lymph nodes (LNs) are typically characterised using the One-Step Nucleic Acid Amplification (OSNA) assay, a standard procedure for assessing subclinical metastasis in sentinel LNs (SLNs). The pivotal role of LNs in coordinating the immune response against BC is often overlooked. Our aim was to improve prognostic information provided by the OSNA assay and explore immune-related gene signatures in SLNs. The expression of an immune gene panel was analysed in SLNs from 32 patients with Luminal A early-stage BC (cT1-T2 N0). Using an unsupervised approach based on these expression values, this study identified two clusters, regardless of the SLN invasion: one evidencing an adaptive anti-tumoral immune response, characterised by an increase in naive B cells, follicular T helper cells, and activated NK cells; and another with a more undifferentiated response, with an increase in the activated-to-resting dendritic cells (DCs) ratio. Through a protein—protein interaction (PPI) network, we identified seven immunoregulatory hub genes: CD80, CD40, TNF, FCGR3A, CD163, FCGR3B, and CCR2. This study shows that, in Luminal A early-stage BC, SLNs gene expression studies enable the identification of distinct immune profiles that may influence prognosis stratification and highlight key genes that could serve as potential targets for immunotherapy. Full article
(This article belongs to the Special Issue Advances in the Molecular Biology and Pathology of Breast Cancer)
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