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Cancers
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Biomarkers in Breast Cancer: Recent Advances and Challenges
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Biomarkers in Breast Cancer: Recent Advances and Challenges

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 7228

Special Issue Editors

Dr. Yihong Wang

E-Mail Website
Guest Editor
Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Rhode Island Hospital and Lifespan Medical Center, 593 Eddy Street, Providence, RI 02903, USA
Interests: anatomical pathology; breast pathology; clinical pathology; molecular diagnostics; surgical pathology
Prof. Dr. Evgeny Yakirevich

E-Mail Website
Guest Editor
Department of Pathology Rhode Island Hospital, Brown University Medical School, 593 Eddy St, APC-12, Providence, RI 02903, USA
Interests: renal cancer; gastrointestinal cancer; liver cancer; breast cancer; molecular pathology; comprehensive genomic analysis; immunohistochemistry

Special Issue Information

Dear Colleagues, 

Breast biomarkers are a rapidly evolving area involving oncologists, radiologists, pathologists, and researchers in the field. Biomarkers guide decisions regarding adjuvant endocrine therapy and chemotherapy in early-stage breast cancer and systemic therapy in metastatic breast cancer.

Traditional biomarkers, such as ER, PR, HER2, and Ki-67, continue to evolve, adapting to personalized breast cancer treatments. The recent monarch E clinical trial prospectively investigated Ki-67 as a cyclin-dependent kinase inhibitor (CDKI) biomarker, promoting the increased clinical demand for ki-67 testing. Recent clinical trials have demonstrated promising results in HER2 “low” breast cancers using next-generation HER2-targeting drugs, suggesting that HER2 IHC scoring should be redesigned and validated.

Novel biomarkers, such as tumor-infiltrating lymphocytes (TILs) and programmed cell death receptor ligand-1 (PD-L1), have emerged as clinically relevant and highly reproducible biomarkers. Standardizing PD-L1 testing has become an urgent issue in recommending patients who may benefit from immune checkpoint inhibitors. Several other immune-related markers, such as a subset of TILs, natural killers (NKs), tumor-associated macrophages (TAMs), and dendritic cells (DCs), are under intense investigation. Comprehensive genomic profiling, liquid biopsy, radiomics, machine learning, artificial intelligence analysis, and digital and computational pathology have emerged as novel predictive and prognostic tools. This Special Issue aims to highlight the state-of-the-art biomarkers for precision breast cancer medicine.

We are pleased to invite you to present the most 'hot topics' concerning current breast biomarker research, including describing the latest developments, applications, and controversies around breast biomarkers such as ER, PR, HER2, AR, TILs, Ki-67, TILs, and PD-L1, and multiomics computational or panel biomarkers; and discussing the predictive/prognostic biomarker potentials at the genomic, transcriptomic, and proteomic level and future perspectives.

This Special Issue aims to include the latest breast biomarkers, such as ER, PR, HER2, TILs, Ki-67, and PD-L1; several prognostic/predictive biomarkers at the genomic, transcriptomic, and proteomic level; predictive or prognostic breast cancer biomarkers in preclinical or clinical translational, multiomics computation studies related to biomarkers, and breast cancer treatments, in the form of original research articles and reviews.

We look forward to receiving your contributions.

Dr. Yihong Wang
Prof. Dr. Evgeny Yakirevich
Guest Editors

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 short 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 biomarkers
  • ER, PR, and HER2
  • Ki-67
  • TILs
  • PD-L1
  • comprehensive genomic profiling
  • liquid biopsy
  • machine learning
  • whole-slide imaging
  • radiomics

Published Papers (4 papers)

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Research

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16 pages, 2239 KiB  
Article
An Innovative Non-Linear Prediction Model for Clinical Benefit in Women with Newly Diagnosed Breast Cancer Using Baseline FDG-PET/CT and Clinical Data
by Ken Kudura, Nando Ritz, Arnoud J. Templeton, Tim Kutzker, Martin H. K. Hoffmann, Kwadwo Antwi, Daniel R. Zwahlen, Michael C. Kreissl and Robert Foerster
Cancers 2023, 15(22), 5476; https://doi.org/10.3390/cancers15225476 - 20 Nov 2023
Viewed by 878
Abstract
Objectives: We aimed to develop a novel non-linear statistical model integrating primary tumor features on baseline [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT), molecular subtype, and clinical data for treatment benefit prediction in women with newly diagnosed breast cancer using innovative statistical [...] Read more.
Objectives: We aimed to develop a novel non-linear statistical model integrating primary tumor features on baseline [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT), molecular subtype, and clinical data for treatment benefit prediction in women with newly diagnosed breast cancer using innovative statistical techniques, as opposed to conventional methodological approaches. Methods: In this single-center retrospective study, we conducted a comprehensive assessment of women newly diagnosed with breast cancer who had undergone a FDG-PET/CT scan for staging prior to treatment. Primary tumor (PT) volume, maximum and mean standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured on PET/CT. Clinical data including clinical staging (TNM) but also PT anatomical site, histology, receptor status, proliferation index, and molecular subtype were obtained from the medical records. Overall survival (OS), progression-free survival (PFS), and clinical benefit (CB) were assessed as endpoints. A logistic generalized additive model was chosen as the statistical approach to assess the impact of all listed variables on CB. Results: 70 women with newly diagnosed breast cancer (mean age 63.3 ± 15.4 years) were included. The most common location of breast cancer was the upper outer quadrant (40.0%) in the left breast (52.9%). An invasive ductal adenocarcinoma (88.6%) with a high tumor proliferation index (mean ki-67 expression 35.1 ± 24.5%) and molecular subtype B (51.4%) was by far the most detected breast tumor. Most PTs displayed on hybrid imaging a greater volume (12.8 ± 30.4 cm3) with hypermetabolism (mean ± SD of PT maximum SUVmax, SUVmean, MTV, and TLG, respectively: 8.1 ± 7.2, 4.9 ± 4.4, 12.7 ± 30.4, and 47.4 ± 80.2). Higher PT volume (p < 0.01), SUVmax (p = 0.04), SUVmean (p = 0.03), and MTV (<0.01) significantly compromised CB. A considerable majority of patients survived throughout this period (92.8%), while five women died (7.2%). In fact, the OS was 31.7 ± 14.2 months and PFS was 30.2 ± 14.1 months. A multivariate prediction model for CB with excellent accuracy could be developed using age, body mass index (BMI), T, M, PT TLG, and PT volume as predictive parameters. PT volume and PT TLG demonstrated a significant influence on CB in lower ranges; however, beyond a specific cutoff value (respectively, 29.52 cm3 for PT volume and 161.95 cm3 for PT TLG), their impact on CB only reached negligible levels. Ultimately, the absence of distant metastasis M displayed a strong positive impact on CB far ahead of the tumor size T (standardized average estimate 0.88 vs. 0.4). Conclusions: Our results emphasized the pivotal role played by FDG-PET/CT prior to treatment in forecasting treatment outcomes in women newly diagnosed with breast cancer. Nevertheless, careful consideration is required when selecting the methodological approach, as our innovative statistical techniques unveiled non-linear influences of predictive biomarkers on treatment benefit, highlighting also the importance of early breast cancer diagnosis. Full article
(This article belongs to the Special Issue Biomarkers in Breast Cancer: Recent Advances and Challenges)
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12 pages, 2412 KiB  
Communication
Granzyme B Expression in the Tumor Microenvironment as a Prognostic Biomarker for Patients with Triple-Negative Breast Cancer
by Kimihisa Mizoguchi, Hitomi Kawaji, Masaya Kai, Takafumi Morisaki, Saori Hayashi, Yuka Takao, Mai Yamada, Akiko Shimazaki, Tomofumi Osako, Nobuyuki Arima, Masayuki Okido, Yoshinao Oda, Masafumi Nakamura and Makoto Kubo
Cancers 2023, 15(18), 4456; https://doi.org/10.3390/cancers15184456 - 07 Sep 2023
Viewed by 1065
Abstract
Tumor-infiltrating lymphocytes in the tumor microenvironment are important in the treatment of triple-negative breast cancer (TNBC). Cytotoxic T cells produce cytokines and cytotoxic factors, such as perforin and granzyme, which induce apoptosis by damaging target cells. To identify biomarkers of these cells, we [...] Read more.
Tumor-infiltrating lymphocytes in the tumor microenvironment are important in the treatment of triple-negative breast cancer (TNBC). Cytotoxic T cells produce cytokines and cytotoxic factors, such as perforin and granzyme, which induce apoptosis by damaging target cells. To identify biomarkers of these cells, we investigated granzyme B (GZMB) in the tumor microenvironment as a biomarker of treatment response and prognosis in 230 patients with primary TNBC who underwent surgery without preoperative chemotherapy between January 2004 and December 2014. Programmed cell death ligand 1 (PD-L1) positivity was defined as a composite positive score ≥10 based on the PD-L1 immunostaining of tumor cells and immune cells. GZMB-high was defined as positivity in ≥1% of tumor-infiltrating lymphocytes (TILs). Among the 230 TNBC patients, 117 (50.9%) had CD8-positive infiltrating tumors. In the PD-L1-positive group, a Kaplan–Meier analysis showed that GZMB-high TNBC patients had better recurrence-free survival (RFS) and overall survival (OS) than GZMB-low patients and that OS was significantly longer (RFS: p = 0.0220, OS: p = 0.0254). A multivariate analysis also showed significantly better OS in PD-L1- and GZMB-high patients (hazard ratio: 0.25 (95% IC: 0.07–0.88), p = 0.03). Our findings indicate that GZMB is a useful prognostic biomarker in PD-L1-positive TNBC patients. Full article
(This article belongs to the Special Issue Biomarkers in Breast Cancer: Recent Advances and Challenges)
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Review

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12 pages, 657 KiB  
Review
An Emerging Role for Sigma Receptor 1 in Personalized Treatment of Breast Cancer
by Taylor S. Robinson and Mahasin A. Osman
Cancers 2023, 15(13), 3464; https://doi.org/10.3390/cancers15133464 - 02 Jul 2023
Cited by 1 | Viewed by 1720
Abstract
Despite the major progress in treating breast cancer, recurrence remains a problem and types such as triple-negative breast cancer still lack targeted medicine. The orphan Sigma receptor1 (SigmaR1) has emerged as a target in breast cancer, but its mechanism of action is unclear [...] Read more.
Despite the major progress in treating breast cancer, recurrence remains a problem and types such as triple-negative breast cancer still lack targeted medicine. The orphan Sigma receptor1 (SigmaR1) has emerged as a target in breast cancer, but its mechanism of action is unclear and hinders clinical utility. SigmaR1 is widely expressed in organ tissues and localized to various sub-cellular compartments, particularly the endoplasmic reticulum (ER), the mitochondrial-associated membranes (MAMs) and the nuclear envelope. As such, it involves diverse cellular functions, including protein quality control/ER stress, calcium signaling, cholesterol homeostasis, mitochondrial integrity and energy metabolism. Consequently, SigmaR1 has been implicated in a number of cancers and degenerative diseases and thus has been intensively pursued as a therapeutic target. Because SigmaR1 binds a number of structurally unrelated ligands, it presents an excellent context-dependent therapeutic target. Here, we review its role in breast cancer and the current therapies that have been considered based on its known functions. As SigmaR1 is not classified as an oncoprotein, we propose a model in which it serves as an oligomerization adaptor in key cellular pathways, which may help illuminate its association with variable diseases and pave the way for clinical utility in personalized medicine. Full article
(This article belongs to the Special Issue Biomarkers in Breast Cancer: Recent Advances and Challenges)
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12 pages, 1034 KiB  
Review
HER2 Intratumoral Heterogeneity in Breast Cancer, an Evolving Concept
by Yanjun Hou, Hiroaki Nitta and Zaibo Li
Cancers 2023, 15(10), 2664; https://doi.org/10.3390/cancers15102664 - 09 May 2023
Cited by 9 | Viewed by 2847
Abstract
Amplification and/or overexpression of human epidermal growth factor receptor 2 (HER2) in breast cancer is associated with an adverse prognosis. The introduction of anti-HER2 targeted therapy has dramatically improved the clinical outcomes of patients with HER2-positive breast cancer. Unfortunately, a significant number of [...] Read more.
Amplification and/or overexpression of human epidermal growth factor receptor 2 (HER2) in breast cancer is associated with an adverse prognosis. The introduction of anti-HER2 targeted therapy has dramatically improved the clinical outcomes of patients with HER2-positive breast cancer. Unfortunately, a significant number of patients eventually relapse and develop distant metastasis. HER2 intratumoral heterogeneity (ITH) has been reported to be associated with poor prognosis in patients with anti-HER2 targeted therapies and was proposed to be a potential mechanism for anti-HER2 resistance. In this review, we described the current definition, common types of HER2 ITH in breast cancer, the challenge in interpretation of HER2 status in cases showing ITH and the clinical applications of anti-HER2 agents in breast cancer showing heterogeneous HER2 expression. Digital image analysis has emerged as an objective and reproducible scoring method and its role in the assessment of HER2 status with ITH remains to be demonstrated. Full article
(This article belongs to the Special Issue Biomarkers in Breast Cancer: Recent Advances and Challenges)
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