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Precision Medicine in Breast Cancer: Challenges and Opportunities in Diagnostic and Therapeutic Purposes

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A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (25 September 2021) | Viewed by 20147

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Special Issue Editor

Dr. Chia-Jung Li

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Guest Editor

Department of Obstetrics and Gynecology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
Interests: mitochondrial medicine; free radical biology; reproduction; cancer biology; chronic diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Breast cancer emergencies have become a rapidly evolving field in medicine over the last ten years. Carcinogenesis is a multiparametric process that involves diverse factors such as genetic, environmental, or aging. Recent research that elucidates the tumor biology and molecular pathways that mediate cancer progression and drug resistance has led to the development of various molecular targeted therapies involving monoclonal antibodies, small molecule receptor tyrosine kinase inhibitors, and agents blocking downstream signaling pathways in breast cancer. Breast cancer has become a prominent example of the success of precision medicine in treating solid tumor malignancies. The first step in this process involves new blood-based diagnostics, which can now noninvasively provide clinically useful information. However, the identification of novel biomarkers that could be used in early diagnosis is urgently needed, especially for guiding initial therapy and predicting relapse or drug resistance following the administration of novel targeted therapies. In this Special Issue, we invite research and review papers in any area of breast cancer, including but not limited to fundamental understanding of oncogenomics and cancer signaling pathways, diagnostic, prognostic, and pharmacogenomic biomarkers, molecular diagnosis by gene expression profiling, molecular targets driving the progression of human breast cancer, cancer drug development on these targets, and clinical trials with new agents and cancer epigenetics.

Dr. Chia-Jung Li
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 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. Journal of Personalized Medicine 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
Molecular heterogeneity
Biomarkers
Signaling pathways
Targeted therapies
Development of novel drugs
Preclinical models

Published Papers (5 papers)

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Research

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17 pages, 2210 KiB

Open AccessArticle

Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction

by Siti Fairuz Mat Radzi, Muhammad Khalis Abdul Karim, M Iqbal Saripan, Mohd Amiruddin Abd Rahman, Iza Nurzawani Che Isa and Mohammad Johari Ibahim

J. Pers. Med. 2021, 11(10), 978; https://doi.org/10.3390/jpm11100978 - 29 Sep 2021

Cited by 24 | Viewed by 3917

Abstract

Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network—multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method’s performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis. Full article

(This article belongs to the Special Issue Precision Medicine in Breast Cancer: Challenges and Opportunities in Diagnostic and Therapeutic Purposes)

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14 pages, 4015 KiB

Open AccessArticle

Integrated Molecular Characterization to Reveal the Association between Kynurenine 3-Monooxygenase Expression and Tumorigenesis in Human Breast Cancers

by Yuk-Wah Tsang, Chi-Hsun Liao, Chiao-Hsu Ke, Chi-Wen Tu and Chen-Si Lin

J. Pers. Med. 2021, 11(10), 948; https://doi.org/10.3390/jpm11100948 - 24 Sep 2021

Cited by 9 | Viewed by 1756

Abstract

Kynurenine 3-monooxygenase (KMO) is overexpressed in several tumors and participates in the progression of breast cancer tumorigenesis, including cancer types such as triple-negative breast cancer (TNBC). This malignant gene is an enzyme in the kynurenine pathway, which is involved in the carcinogenesis of cancer through immune function manipulation. However, it remains unclear whether the role of the KMO contributes to tumorigenesis and immune functions in human breast cancer. In this study, we found that KMO was highly expressed in different types of tumors, especially in invasive ductal breast carcinoma. In addition, KMO expression was positively correlated with the malignant clinical features of patients with breast cancer, such as TNBC and a nodal-positive status, along with patients with a higher Nottingham prognostic index (NPI). Furthermore, the top ten KMO-correlated genes were the chemokines and pro-inflammatory cytokines known to be involved in the progression of various cancers, therefore, KMO may facilitate breast cancers via synergistically regulating inflammatory responses in tumors with these hub genes. Taken together, these findings highlight the tumor-promotion role of KMO in breast cancers and suggest that KMO can serve as a biomarker for prognosis prediction in breast cancer patients. Full article

(This article belongs to the Special Issue Precision Medicine in Breast Cancer: Challenges and Opportunities in Diagnostic and Therapeutic Purposes)

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15 pages, 1756 KiB

Open AccessArticle

Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates

by Gianluca Gatta, Salvatore Cappabianca, Daniele La Forgia, Raffaella Massafra, Annarita Fanizzi, Vincenzo Cuccurullo, Luca Brunese, Alberto Tagliafico and Roberto Grassi

J. Pers. Med. 2021, 11(9), 875; https://doi.org/10.3390/jpm11090875 - 31 Aug 2021

Cited by 13 | Viewed by 2810

Abstract

In our study, we added a three-dimensional automated breast ultrasound (3D ABUS) to mammography to evaluate the performance and cancer detection rate of mammography alone or with the addition of 3D prone ABUS in women with dense breasts. Our prospective observational study was based on the screening of 1165 asymptomatic women with dense breasts who selected independent of risk factors. The results evaluated include the cancers detected between June 2017 and February 2019, and all surveys were subjected to a double reading. Mammography detected four cancers, while mammography combined with a prone Sofia system (3D ABUS) doubled the detection rate, with eight instances of cancer being found. The diagnostic yield difference was 3.4 per 1000. Mammography alone was subjected to a recall rate of 14.5 for 1000 women, while mammography combined with 3D prone ABUS resulted in a recall rate of 26.6 per 1000 women. We also observed an additional 12.1 recalls per 1000 women screened. Integrating full-field digital mammography (FFDM) with 3D prone ABUS in women with high breast density increases and improves breast cancer detection rates in a significant manner, including small and invasive cancers, and it has a tolerable impact on recall rate. Moreover, 3D prone ABUS performance results are comparable with the performance results of the supine 3D ABUS system. Full article

(This article belongs to the Special Issue Precision Medicine in Breast Cancer: Challenges and Opportunities in Diagnostic and Therapeutic Purposes)

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12 pages, 266 KiB

Open AccessArticle

Clinical and Pathological Features of Breast Cancer in Systemic Sclerosis: Results from the Sclero-Breast Study

by Angela Toss, Amelia Spinella, Chrystel Isca, Caterina Vacchi, Guido Ficarra, Luca Fabbiani, Anna Iannone, Luca Magnani, Paola Castrignanò, Pierluca Macripò, Elisa Gasparini, Simonetta Piana, Laura Cortesi, Antonino Maiorana, Carlo Salvarani, Massimo Dominici and Dilia Giuggioli

J. Pers. Med. 2021, 11(6), 580; https://doi.org/10.3390/jpm11060580 - 20 Jun 2021

Cited by 5 | Viewed by 2273

Abstract

Systemic Sclerosis (SSc) is a chronic disease associated with a 1.5-fold increase in cancer risk, including lung cancer, hematological malignancies, and breast cancer (BC). This is a retrospective study aiming to explore the clinical and pathological features of BC developed by SSc patients. A total of 54.5% of patients developed BC before SSc (median interval: 5 years), whereas 45.5% of patients developed BC after SSc (median delay: 8 years). A total of 93.1% of patients were diagnosed with an early stage tumor. Among invasive carcinomas, 70.8% presented with a low Mib1, 8.3% with a tubular histotype, and 42.8% with a Luminal A-like phenotype. A total of 66.6% of patients underwent breast-conserving surgery and 55.5% RT. A total of 40% of patients developed interstitial lung disease after RT and 20% diffuse cutaneous SSc. The cause of death of the six deceased patients was PAH. A significant association was observed between the use of immunosuppressive therapy and diffuse skin extension, negative ACA, positive Anti-Scl-70, and interstitial lung disease, but not BC status. SSc patients developed BC at a good prognosis, suggesting a de-escalation strategy of cancer therapies. In particular, ionizing radiation and chemotherapeuticals should be limited to higher-risk cases. Finally, proper screening is mandatory in order to allow for early cancer detection in SSc patients. Full article

(This article belongs to the Special Issue Precision Medicine in Breast Cancer: Challenges and Opportunities in Diagnostic and Therapeutic Purposes)

Review

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13 pages, 13653 KiB

Open AccessReview

Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review

by Ioana Boca (Bene), Anca Ileana Ciurea, Cristiana Augusta Ciortea and Sorin Marian Dudea

J. Pers. Med. 2021, 11(8), 703; https://doi.org/10.3390/jpm11080703 - 23 Jul 2021

Cited by 29 | Viewed by 7784

Abstract

Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination. Full article

(This article belongs to the Special Issue Precision Medicine in Breast Cancer: Challenges and Opportunities in Diagnostic and Therapeutic Purposes)

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