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Drug and Therapy Development for Triple Negative Breast Cancer
The first comprehensive and up-to-date compilation of modern diagnostic and treatment methods for triple negative breast cancer
In Drug and Therapy Development for Triple Negative Breast Cancer, a team of distinguished practitioners delivers an in-depth and authoritative discussion of contemporary methods for treating triple negative breast cancer (TNBC). The editors have included material that covers its molecular causes, initial detection, diagnostic tools, treatment procedures, pharmacology, and new and experimental therapies—including nanotherapeutics and photothermal therapies.
As the first comprehensive compilation of modern treatment methods for TNBC, this reference is an unmatched source of information about current and future treatment approaches, including machine learning methods for earlier detection and more accurate diagnosis. Readers will also find:
- A thorough introduction to HER receptors in breast cancers
- Comprehensive explorations of the etiology and therapy of hormone receptor-positive breast cancer and the early-stage diagnosis of breast cancer
- Application of artificial intelligence to breast cancer diagnosis
- New insights on the role of DNA replication stress and genome instability in breast cancer
Perfect for medicinal and pharmaceutical chemists, Drug and Therapy Development for Triple Negative Breast Cancer will also benefit oncologists and professionals working in the pharmaceutical industry or in hospital settings.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Title Page
Copyright
Preface
Part I: History of Breast Cancer
1 Early‐Stage Diagnosis of Breast Cancer: Amelioration in Approaches
1.1 Introduction
1.2 Imaging Techniques
1.3 Microwave Breast Imaging Methods
1.4 Biomarkers and Biosensors for Breast Cancer Detection
1.5 Conclusion
Acknowledgment
Conflict of Interest
Authors Contribution
References
2 DNA Replication Stress and Genome Instability in Breast Cancer
2.1 Introduction
2.2 Causes of Replication Stress and Genomic Instability
2.3 Molecular Mechanism of Genomic Instability
2.4 Aftermath of Replication Stress on Cell and Its Fate
2.5 Therapeutic Approach
2.6 Conclusion
Abbreviations
References
3 Recent Advancement of Nanotherapeutics to Treat Breast Cancer
3.1 Introduction
3.2 Pathophysiology of Breast Cancer
3.3 Classification of Breast Cancer
3.4 Techniques for Breast Cancer Detection
3.5 Current Breast Cancer Therapies
3.6 Nanotherapeutics for Breast Cancer Treatment and Metastasis
3.7 Polymer‐Based Nanoparticles (PBNPs)
3.8 Inorganic Nanoparticles (IONPs)
3.9 Hydrogels (HGLs) and Microbubbles (MBs)
3.10 Recent Patents of Nanotherapeutics for Breast Cancer Treatment
3.11 Clinical Trials of Nanotherapeutics for Breast Cancer
3.12 Conclusion and Future Perspectives
References
4 HER Receptor in Breast Cancer
4.1 Introduction
4.2 Role of HER Receptors in the Human Body
4.3 HER2 Receptor in Breast Cancer Progression
4.4 Conclusion
Conflict of Interest
References
5 Human Endogenous Retroviruses in Triple‐Negative Breast Cancer
5.1 Introduction
5.2 HERVs in Breast Cancer and TNBC
5.3 TROJAN lncRNA and TNBC
5.4 HERVs and Breast Cancer Treatments
5.5 Conclusion
5.6 Search Strategy
References
Part II: Novel Drug Discovery and Development
6 Development in Drug Repurposing for the Treatment of Acute Leukemia Complicating Metastatic Breast Cancer
6.1 Introduction
6.2 Conclusion
Conflict of Interest
References
7 Novel Pharmaceutical Nanomaterials to Advance the Current Breast Cancer Treatment – Current Trends and Future Perspective
7.1 Introduction
7.2 Graphene‐Based Nanomaterials for Breast Cancer
7.3 Light‐Based Nanotechnology for Breast Cancer
7.4 Green Synthesis of Gold Nanoparticles for Breast Cancer
7.5 Nanocarriers for Gene Therapy and Immunotherapy
7.6 Conclusion and Recommendations
References
Part III: Advanced Technologies in Breast Cancer Therapy
8 Artificial Intelligence‐Driven Decisions in Breast Cancer Diagnosis
8.1 Introduction
8.2 Breast Cancer
8.3 Diagnosis of Breast Cancer
8.4 Artificial Intelligence
8.5 Conclusion
8.6 Future Challenges
References
9 Establishing Nanotechnology‐Based Drug Development for Triple‐Negative Breast Cancer Treatment
9.1 Introduction
9.2 Triple‐Negative Breast Cancer
9.3 Challenges
9.4 Future Perspectives on TNBC Metastasis Therapy
9.5 Conclusion
References
10 Etiology and Therapy of Hormone Receptor‐Positive Breast Cancer
10.1 Introduction
10.2 Etiology
10.3 Human Epidermal Growth Factor‐2 (HER‐2)
10.4 Various Types of Breast Cancer Detected Under Hormone Receptor Breast Cancer
10.5 Detection
10.6 Therapy
10.7 Limitations of Hormone Therapy
10.8 Triple‐Negative Breast Cancer
10.9 Conclusion
References
11 Donor–Acceptor‐Based Heterocyclic Compounds as Chemotherapy and Photothermal Agents in Treatment of Breast Cancer Cell
11.1 Introduction
11.2 Causes for Breast Cancer
11.3 Imaging and Screening of Breast Cancer
11.4 Photothermal Therapy (PTT)
11.5 Acceptor–Donor‐Based Heterocyclic Compounds
11.6 Examples of Organic‐Based Donor–Acceptor
11.7 Polymers‐Based Agents
11.8 Conclusion
References
Part IV: Regulatory, Clinical Aspects and Case Studies
12 An Insight into Drug Regulatory Affairs and the Procedures
12.1 Endpoints of Clinical Trials for the Approval of Cancer Drugs and Biologics
12.2 Statutory and Regulatory Requirements for Effectiveness
12.3 Clinical Trial Design Considerations
12.4 Clinical Trial Analysis Issues
12.5 Use of Pathological Complete Response as an Endpoint to Support Accelerated Approval in Neoadjuvant Treatment of High‐Risk Early‐Stage Breast Cancer
12.6 Developing Treatments for Premenopausal Women with Breast Cancer
12.7 Recommendations by FDA
12.8 What is Right to Try?
12.9 Examples of Drugs Approved for Breast Cancer
References
13 A Comprehensive Review of Some Heat‐Shock Proteins in the Development and Progression of Human Breast Cancer
13.1 Introduction
13.2 Structure‐Functional Features of HSPs
13.3 Conclusion and Future Perspectives
Acknowledgments
References
14 Nanoparticle‐Based Therapeutics for Triple Negative Breast Cancer
14.1 Breast Cancer: State of Research and Practice
14.2 Triple Negative Breast Cancer (TNBC) and Treatment Approaches
14.3 Nanoparticle Therapeutics for TNBC
14.4 Ligands Used to Enhance Nanoparticle Therapeutics in TNBC
14.5 Conclusion
References
15 Current Updates in Breast Cancer Drugs
15.1 Introduction
15.2 Therapeutic Approaches
15.3 Targeted Therapy
15.4 Conclusion
Acknowledgment
Conflict of Interest
Authors Contribution
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Benefits and drawbacks of imaging techniques.
Table 1.2 Comparison of
in vitro
,
ex vivo,
and
in vivo
magnetic resonance sp...
Table 1.3 Dielectric properties of female breast tissue at 3.2 GHz.
Table 1.4 Developments in electrochemical biosensors.
Table 1.5 Developments in optical biosensors.
Chapter 3
Table 3.1 Various ND‐based delivery systems against breast cancer models.
Table 3.2 Nanocarriers employed for clinical trials of breast cancer.
Chapter 5
Table 5.1 Literature search results.
Chapter 6
Table 6.1 Cases of breast cancer detected.
Chapter 7
Table 7.1 Clinically approved and currently undergoing clinical trials nanop...
Chapter 9
Table 9.1 Characteristics of BC subtypes.
Table 9.2 Various signaling pathways responsible for the development and dia...
Table 9.3 Traditional treatment available for TNBC.
Chapter 10
Table 10.1 Genes that can influence the outcome of breast cancer.
Table 10.2 Ongoing clinical trials in TNBC.
Chapter 11
Table 11.1 Few of the photosensitizers that are screened against various bre...
Chapter 13
Table 13.1 Summarizes the sizes and the role of heat‐shock proteins during p...
Chapter 14
Table 14.1 Nanomedicines FDA approved and undergoing clinical trials for bre...
Table 14.2 Summary of clinical trials on nanoparticle‐based therapeutics for...
Table 14.3 Advantages and limitations of lipid‐based nanoparticles in TNBC t...
Chapter 15
Table 15.1 Subtypes of breast cancer.
Table 15.2 Current immunotherapy for breast cancer.
Table 15.3 Angiogenesis inhibitors used in breast cancer.
List of Illustrations
Chapter 1
Figure 1.1 3D versus 2D mammography. Source: Andersson et al. [33]/Reproduce...
Figure 1.2 Samples of ultrasonography breast images dataset. Source: Dhabyan...
Figure 1.3 Methods of microwave breast imaging. The figures on the left show...
Figure 1.4 Electrochemical biosensor for detection of MCF‐7 cells. Source: M...
Figure 1.5 Quantum dot based optical biosensor for detection of MCF‐7 cells....
Chapter 2
Figure 2.1 A normal cell becomes cancerous when chromosomal instability (CIN...
Figure 2.2 When a mutation occurs in genes responsible for normal functionin...
Figure 2.3 A normal cell when suffers from DNA damage or mutation, which giv...
Figure 2.4 Polyamines are synthesized in a cell following this pathway. It i...
Chapter 3
Figure 3.1 Classification of breast cancer.
Chapter 4
Figure 4.1 Types of HER receptors and their functioning. Source: Arteaga et ...
Chapter 5
Figure 5.1
Breast cancer is the most common type of cancer in women in the U
...
Figure 5.2
Immune infiltration in breast cancer tumors.
Source: The data was...
Chapter 6
Figure 6.1 Cases of breast cancer detected.
Chapter 7
Figure 7.1 Inorganic, organic, and carbon‐based nanomaterials currently used...
Figure 7.2 Synthesis of gold nanoparticles.
Chapter 8
Figure 8.1 The relationship between AI, ML, DL, and explainable artificial i...
Chapter 9
Figure 9.1 Role of nanomedicines and molecular pathways in TNBC treatment.
Figure 9.2 Illustration of various signaling pathways involved in the develo...
Figure 9.3 Various nanoformulations utilized for TNBC treatment.
Figure 9.4 Schematic representation of various vaccines under CT for TNBC tr...
Chapter 10
Figure. 10.1 Classification of drugs used in hormone therapy.
Figure. 10.2 Structure of Tamoxifen.
Figure. 10.3 Structure of Anastrozole.
Figure. 10.4 Structure of Exemestane.
Figure. 10.5 Structure of Letrozole.
Figure. 10.6 Structure of Fulversant.
Figure. 10.7 Structure of Palbociclib.
Figure. 10.8 Structure of Abemaciclib.
Figure. 10.9 Structure of Capecitabine.
Figure. 10.10 Structure of Gemcitabine.
Chapter 11
Figure 11.1 Types of breast cancer and different types of triple‐negative ca...
Figure 11.2 Illustration of PTT induced inhibition of cancer growth.
Figure 11.3 Different materials used for photothermal therapy. Source: Adapt...
Figure 11.4 Illustration of mechanism of PDT using Jablonski diagram.
Figure 11.5 Hybrid donor–acceptor polymeric materials for breast cancer trea...
Chapter 13
Figure 13.1 Different types of HSP40/DnaJ and its subdomains.
Figure 13.2 HSP60 structure and its lid.
Figure 13.3 The organizational domains of HSP70, with ATPase domain, substra...
Figure 13.4 Folding processes of the newly synthesized protein in partnershi...
Figure 13.5 The homodimer of HSP90 structure and its domain.
Figure 13.6 Folding of the substrate or client protein presented by HSP70 in...
Chapter 14
Figure 14.1 Structural representation of various nanoparticle systems utiliz...
Chapter 15
Figure 15.1 Novel therapeutic targets for treatment of breast cancer. GF, gr...
Guide
Cover Page
Title Page
Copyright
Preface
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
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Drug and Therapy Development for Triple Negative Breast Cancer
Edited by Pravin Kendrekar Vinayak Adimule Tara Hurst
The Editors
Dr. Pravin KendrekarWhite Collar Food & BeveragesPuneIndia
and
T&T PharmaceuticalsMumbaiIndia
Dr. Vinayak AdimuleAngadi Institute of Technology and ManagementKarnatakaIndia
Dr. Tara HurstUniversity of OxfordOxfordUnited Kingdom
Cover Image: © CI Photos/Shutterstock
All books published by WILEY‐VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.
Library of Congress Card No.: applied for
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Bibliographic information published by the Deutsche Nationalbibliothek
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at <http://dnb.d-nb.de>.
© 2023 WILEY‐VCH GmbH, Boschstraße 12, 69469 Weinheim, Germany
All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.
Print ISBN: 978‐3‐527‐35175‐6
ePDF ISBN: 978‐3‐527‐84118‐9
ePub ISBN: 978‐3‐527‐84117‐2
oBook ISBN: 978‐3‐527‐84116‐5
Preface
The book covers unique topics on the novel development of cancer detection, treatment, and its application. The book describes various tools used in the breast cancer therapy such as use of artificial intelligence, deep learning, and machine learning mammography. The book describes use of prosthetic biomarkers and sophisticated techniques in the early detection and treatment of drug discovery and development for triple‐negative breast cancer. The book also describes the fundamentals of the cancer, especially breast cancer, MTT assay studies, flow cytometer studies, use of advanced markers, biomarkers, chemotherapy methods, advancement in the treatment of chemotherapy, and use of different and novel potency heterocyclic molecules in the effective in vivo and in vitro treatment of cancer. The book and the chapters involved describe novel aspects of the triple‐negative breast cancer from the initial diagnosis tools used, innovations achieved in the process of diagnosis, treatment procedures, and advantages and drawbacks of the current methods of treatment of the breast cancer. Also, the book contains chapters from the eminent doctors, academicians, researchers, and scientists who are working in the field of cancer for many years. Their experience and the novel aspects of the treatment procedures are mentioned elaborately in their contributing chapter. Especially the use of artificial intelligence, deep learning methods, cause of food toxicity, and different approaches for the detection level of the breast cancer have been emphasized in the present coverage of the book. It becomes very essential for the readers to understand all the parameters relating to the treatment, diagnosis, and applications of detection of breast cancer.
Pravin Kendrekar
University of Central Lancashire,
Preston, United Kingdom
Vinayak Adimule
Angadi Institute of Technology and Management,
Karnataka, India
Tara Hurst
University of Oxford, Oxford, United Kingdom
September 2023
Part IHistory of Breast Cancer
1Early‐Stage Diagnosis of Breast Cancer: Amelioration in Approaches
Nidhi Manhas1, Lalita S. Kumar1, and Vinayak Adimule2
1 Indira Gandhi National Open University (IGNOU), School of Sciences, Maidan Garhi, New Delhi, 110068, India
2 Angadi Institute of Technology and Management, Department of Chemistry, Savgaon road, Belagavi, Karnataka, 590009, India
1.1 Introduction
Breast cancer (BC) has become one of the most prevalent malignant tumors in women and is increasing at an alarming rate. Based on the population growth, experts have predicted that by 2050, there will be roughly 3.2 million new cases per year globally [1]. Not only is the number of patients with BC rising all over the world, but also the age of affected patients is tending to be younger [2]. Many factors contribute to these circumstances including age, family history, lifestyle surroundings, and many others [1, 3, 4]. Although the relative risk of BC is inevitable, it is possible to reduce BC mortality rate. Its survival rate largely depends upon the woman's timely access to effective and affordable detection and treatment processes [5]. The World Health Organization (WHO) has also described two different but related approaches to reduce BC, i.e. early diagnosis, which is the recognition of symptomatic cancer at an early stage, and screening, which is the identification of asymptomatic disease in a target population of apparently healthy individuals [6]. In many developing countries, women are unaware about the BC because of which it is detected at later stages [7]. However, there are various organizations working for generating awareness and promoting self‐examination of the breast among women. Such efforts will promote the early detection and will help in reduction of the BC mortality rate.
Even previous research has shown that early BC detection, if combined with appropriate treatment, could greatly reduce BC death rates in the long run. Therefore, detecting BC at an early stage is vital. There are different techniques used for its diagnosis. Presently, mammography (MG), breast ultrasound, and breast magnetic resonance imaging (MRI) examination are the most common diagnostic techniques available for the detection of BC [8, 9]. These procedures necessitate specialized equipment, skilled practitioners, and expert analysis but the cost of detection is significantly high. In comparison to these methods, biosensor detection is far more reliable and affordable [10]. Weaver and Leung have summarized the various definitions and applications of biomarkers in imaging BC [11]. On the one hand, breast tumor indicators are critical in the early detection of BC, the characterization of molecular subgroups, the selection of treatment options, and the assessment of survival [12–14], whereas biosensors, on the other hand, provide substantial benefits over standard tumor marker detection methods in terms of specificity, sensitivity, speed, and cost of detection [15, 16] such as chemiluminescence immunoassay [17], enzyme‐linked immunosorbent assay [18], proteomics [17], molecular biology methods, and liquid biopsy. Several biosensors with improved sensitivity, selectivity, stability, and low cost have been created in the previous decade [19].
In this chapter, many diagnostic methods such as MG, ultrasonography (US), MRI, microwave BC detection techniques, and various biosensors will be discussed. We herein discuss their most recent advances, as well as their benefits and drawbacks. This will aid researchers and those working on BC diagnostic methods in selecting appropriate approaches for properly diagnosing BC in its early stages.
1.2 Imaging Techniques
The use of imaging techniques reveals the anatomy and position of malignant cells and provides clinicians with valuable clinical information. When contrast agents and high‐energy rays are used in imaging procedures, unfortunately, patients may be harmed. As a result, we should discuss different imaging modalities and decide which one is best for BC patients. These techniques mainly include MG, US, MRI, positron emission computed tomography (PET), computed tomography (CT), and single‐photon emission computed tomography (SPECT). The benefits and drawbacks of these imaging techniques are listed in Table 1.1.
PET, CT, and SPECT are not advised for diagnosing BC patients due to their high cost, limited practicability, and radiation damage [20]. However, in some circumstances, such as screening for metastatic BC and the presence of bone and lymphatic metastases, these techniques can be employed as additional diagnostic procedures for diagnosing BC. As a result, we solely discuss MG, US, and MRI, which are the primary modalities for detecting BC. These common imaging procedures will be summarized and evaluated to assist clinicians to serve their patients in a better way.
1.2.1 Mammography (MG)
MG is the primary method used for screening and diagnosing BC, and it aids clinicians in gathering clinical data on BC patients. This method is especially advantageous to women between the ages of 40 and 74. Early MG screening may reduce the death rate of BC patients by 30% to 40%, according to one of the earlier research [21]. However, MG has a significant rate of false‐positive and false‐negative results, especially in individuals with dense breasts (for subjects under 40 years old) [22, 23]. But with time, MG is progressing continuously and has shown good results in terms of diagnostic accuracy, sensitivity, and resolution. Presently, two key diagnostic methods are under practice for detection, i.e. contrast‐enhanced mammography (CEM) and digital breast tomosynthesis (DBT) [24, 25]. CEM has been found to be superior to full‐field digital mammography (FFDM) in terms of diagnostic accuracy and disease extent assessment, and its efficiency is also comparable to that of MRI as well as US [26–28]. When compared to FFDM, DBT also offers good performance in terms of specificity (96.4%, 57 229/59 381% versus 97.5%, 23 427/24 020, P < 0.001) [29]. Computer‐aided detection (CAD) is an artificial intelligence (AI) technique that has improved the sensitivity of the instrument and decreased human errors as well as false‐positive and false‐negative results in detection [30]. The combination of CAD with CEM and DBT can significantly improve the performance of these imaging techniques [31, 32]. In individuals who have no indications or symptoms of BC, a 3D MG is utilized to detect the disease as shown in Figure 1.1. The study demonstrated that combining 3D and traditional MG minimizes the need for extra imaging and has increased the accuracy of MVGG up to 94.3% [34]. Various algorithms have been proposed to enhance the MG images. After many experiments and selecting suitable settings, Montaha et al. suggested the BreastNet18 model, which is based on the fine‐tuned VGG16. The accuracy of the algorithm grew to 98.02% of the proposed model [35]. Such type of research will help the doctors in efficient and accurate diagnosis of BC.
Table 1.1 Benefits and drawbacks of imaging techniques.
Imaging techniques
Advantages
Disadvantages
XRM
Standard for diagnosing BC patients
Suitable as a screening method for BC
Finding mammary gland calcification
Not for people under 40
Not for people with high gland density
No more than twice a year
US
Screening for young women
Noninvasive diagnostic method
Finding mammary gland inflammation
Not for small mass and a typical tissue
Affected by the examining doctor
Definition and resolution are not high
MRI
High sensitivity and specificity to invasive BC
Screening of high‐risk groups such as family history of BC
For patients with breast‐conserving surgery
Not for everyone such as patients with claustrophobia
Not for wide‐scale screening
Not for BC staging
PET
High sensitivity to BC recurrence and metastasis
Helpful for staging of the BC
High sensitivity to small breast tumor
High cost, not recommended as routine screening
Not for patients with hypersensitivity to developer
CT
Supplementary diagnostic method for BC, such as identifying BC with or without intrapulmonary metastases
Not the first choice for diagnosing BC
Radiation damage
Poor spatial resolution and need experienced doctors
SPECT
High resolution, small field of vision
Recommended use when suspects metastasis
Obtaining little clinic information
Not for patients with inflammatory bone lesions and bone proliferative metabolic abnormalities or variations
CT, computed technology, MRI, magnetic resonance imaging, PET, positron emission tomography, SPECT, single‐photon emission computed tomography, US, ultrasonography, XRM, X‐ray mammography.
Figure 1.1 3D versus 2D mammography. Source: Andersson et al. [33]/Reproduced with permission from Springer Nature.
From the above discussion, we can conclude that MG is an essential component of early diagnosis for BC patients because of its several benefits, including rapid screening, high accuracy, low cost, and suitability for promoted use. Despite these benefits, MG is not suitable for everyone. It requires a hazardous contrast agent and X‐ray to perform imaging, cannot be used frequently in a short period of time, and is not suggested for people under the age of 40 [36]. But in the coming years, with significant developments like high resolution, MG will be quite safe. Moreover, advances in AI technology have made it possible to simplify the detection and analysis of BC.
1.2.2 Ultrasonography (US)
US is a technique for evaluating the form and status of tumor tissues, as well as correctly locating lesions. The early grayscale US merely showed whether the tumor existed at the detecting point, and because its resolution was inadequate, it was difficult to discriminate benign and malignant tumors [37, 38]. US images showing normal, benign, and malignant BC are given in Figure 1.2.
The flat photographs of tumors received from the two‐dimensional (2D) US might affect the physician's assessment. Therefore, three‐dimensional (3D) US technology was introduced so that one can have 3D imaging of tumor anatomy and blood vessel distribution in diagnosed patients [40]. The color Doppler ultrasound is one of the 3D techniques of ultrasounds that may vividly display tumor and blood flow information and offer clinicians more useful information, allowing them to discriminate between malignant and benign tumors [41]. Krouskop discovered elastic variations in different tissues laying the theoretical groundwork for constructing elastic US [42]. Furthermore, some studies revealed that employing elastic US to screen suspected diseased tissues considerably enhances the accuracy of diagnosing BC [43, 44]. The elastic US, when paired with 3D US, can diagnose axillary lymphadenopathy and classify the patient's tumor state [45]. Although MG is the best tool for detecting BC calcification, when the calcification is too tiny, it is difficult to identify with MG or normal US; so, MicroPure, a new US image‐processing method, was developed [46]. By analyzing images of multidimensional array and frequency, this method may decrease random noise and produce high‐resolution images and tissue homogeneity [47]. Machado et al. examined ex vivo surgical breast specimens with MicroPure and discovered that MicroPure has a high detection rate for BC microcalcifications, whereas conventional US cannot detect [48].
This technique has significant advantages that include the use of less contrast agents, the absence of high‐energy rays, and the fact that it is suited for people of all ages. US has been suggested as a supplement to MG for women at high risk of BC, pregnant women, and those who are unable to get MG [49]. Furthermore, it involves the use of skilled radiologists, which has a major impact on sensitivity and specificity. Breast ultrasound has a high false‐positive rate while being routinely shown to detect mammographically hidden malignancies. However, using AI, radiologists were able to cut false positive rates by 37.3% and requested biopsies by 27.8% while retaining the same sensitivity level (Area Under the Receiver Operating Characteristic Curve [AUROC]: 0.962 AI, 0.924 ± 0.02 radiologists) [50]. AI has the potential to improve the accuracy, consistency, and efficiency of breast ultrasound diagnosis in the future, which will help doctors achieve more accurate diagnostic results by reducing errors caused by unprofessional judgments.
Figure 1.2 Samples of ultrasonography breast images dataset. Source: Dhabyani et al. [39]/with permission from Elsevier/CC BY 4.0.
1.2.3 Magnetic Resonance Imaging (MRI)
MRI enables the early diagnosis of BC, independent of the patient's age, breast density, or risk status [51]. The common approach for breast imaging is dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI), which focuses on the introduction of contrast agents and displays the malignant vascularity, anatomy, and kinetics of breast tumors [52]. DCE‐MRI has been demonstrated as a screening technique for women with a variety of risk levels, with sensitivity ranging from 81% to 100% [53]. The positive prognostic value of DCE‐MRI is 98%, which is higher than the positive prognostic value of MRI alone, i.e. 77% and the specificity is 97%, according to the experts [54]. Another recently developed method that allows for excellent spatial and temporal resolution is ultrafast DCE‐MRI in which various amplification approaches, such as parallel imaging and compressed sensing, are applied, and its ability to characterize BC aggressiveness and tumor subtypes has been proven [55]. There is another technique called Magnetic Resonance Diffusion Weighted (MRDW). It is also used in BC diagnosis that shows clear movement of water molecules in the body, as for different tissues there exist different water dispersion coefficients. Researchers can detect benign and malignant breast tumors by utilizing MRDW to evaluate apparent diffusion coefficient (ADC) values (which reflect diffusion‐limited effects) of tumors, i.e. ADC values: normal breast group > benign group > malignant group [56, 57]. DWI has the advantage of being a non‐contrast technology with a fast scan time [58]. Moreover, DWI was found to be more accurate than MG in detecting cancer in a sample of asymptomatic women [59].
Magnetic resonance spectroscopy (MRS) is an important method that is used to describe the functional state of malignant, benign, and normal breast tissues in three ways: in vivo, ex vivo, and in vitro. Table 1.2 compares the studies of in vitro, ex vivo, and in vivo MRS and MRI techniques in the diagnosis of BC [60]. MRS is a noninvasive technology that can enhance the rate of BC diagnosis by assessing the risk of BC and leading to BC treatment [61, 62]. Solid‐state MR spectroscopic examination of intact biopsied tissues employing the high‐resolution magic angle spinning (HRMAS) approach was also employed in studies to monitor metabolite levels for breast tumor diagnosis/prognosis [63–68]. High amounts of choline‐containing metabolites (tCho) were found in breast in vivo MRS experiments, indicating the rapid proliferation of malignant tumors [69–76]. Hyperpolarized 13C MRI (HP 13C MRI) has recently been used to investigate abnormal tumor metabolism [77].
Table 1.2 Comparison of in vitro, ex vivo, and in vivo magnetic resonance spectroscopy (MRS) and MRI techniques.
Characteristics
Magnetic resonance spectroscopy (MRS)
MRI
In vitro
Ex vivo
In vivo
Information
Biochemical composition (metabolite detection)
Biochemical composition (metabolite detection)
Biochemical composition (metabolite detection)
Anatomy (structure and morphology), functional
Sample/subject
Tissue extract, biofluids, cell lines, aspirates
Excised tissues/biopsies
Living humans/organisms
Living humans/organisms
Equipment
NMR spectrometer
NMR spectrometer with accessories for HRMAS
Human MRI scanner
Human MRI scanner
Field strength
High field strength 9.4–21.1 T
High field strength 9.4–18.8 T
1.5–7 T
1.5–7 T
Nuclei of interest
1
H,
13
C,
31
P,
23
Na,
19
F
1
H,
13
C
1
H,
31
P,
23
Na,
19
F
13
C hyperpolarized
1
H from fat and water
Data
1D/2D spectra
1D/2D spectra
SVS 1D, SVS‐2D, CSI (MRSI)
Conventional T1, T2‐weighted, DCE‐MRI, diffusion‐weighted, perfusion weighted, MR elastography, fMRI
Advantages
High sensitivity and resolution, detection of a large number of metabolites, easy quantification, easy experimentation
High sensitivity and resolution, detection of a large number of metabolites, quantification not that easy, special experimentation
Organ‐specific metabolite composition, and longitudinal studies
Organ‐specific structural and functional studies, longitudinal studies possible
Limitations
Tissue excision is invasive
Tissue excision is invasive
Low sensitivity and resolution, detection of a small number of metabolites, claustrophobia of patients
Claustrophobia of patients contrast required in some studies
Reproducibility
Lesser than
in vivo
Lesser than
in vivo
High
High
1D, one‐dimensional spectrum; 2D, two‐dimensional spectrum; HRMAS, high‐resolution magic angle spinning; SVS, single voxel spectroscopy; CSI, chemical shift imaging; DCE‐MRI, dynamic contrast‐enhanced magnetic resonance imaging.
Source: Sharma and Jagannathan [60] ©MDPI/Public Domain CC BY 4.0.
Magnetic resonance elastography (MRE) is a type of magnetic resonance technology that uses the transmission of mechanical waves in tissues to offer information on tissue elasticity. MRE's future tendency is to identify preoperative tumors and predict treatment response and metastatic potential of primary tumors [78]. PET/MRI or PET and MRI can reveal soft tissue structures in the breast and chest wall. PET can offer molecular‐level information in vivo, and PET/MRI has a high value in evaluating BC metastasis and can improve the positive predictive rate of patients [6, 79, 80]. Moreover, AI‐enhanced model of 18F‐FDG PET/MRI (18F‐fluorodeoxyglucose positron emission tomography magnetic resonance imaging) has accurately shown the difference between benign and malignant breast lesions [81].
MRI is a supplementary method for diagnosing BC that seems to have a number of advantages. Unfortunately, numerous factors influence the widespread use of MRI, i.e. prolonged imaging time, high price, and the fact that it cannot be performed if the patient's body contains metal material. As a result, future research should focus on lowering the cost of MR procedures and reducing the need for contrast agents, so that they can be used at early stages of the BC. Radiomics is a fast‐developing area that uses AI algorithms to analyze medical scans digitally, allowing for thorough tumor characterization [82–84]. So, radiomics applications should be thoroughly investigated, and there is a need to improve radiologists' grasp of basic principles and the development of standardized and reproducible methods and data exchange for clinical applications.
1.3 Microwave Breast Imaging Methods
The microwave region ranging between 300 MHz and 300 GHz has received the least attention but has sparked a lot of interest in medical imaging in the last two decades. In this, the interaction of electromagnetic signals with the matter is determined by the dielectric properties of the matter, i.e. electric permittivity and conductivity [85]. Microwave imaging (MWI) has evolved as a technology for creating dielectric maps of various body sections. Basically, a MWI system includes an antenna array, a microwave signal transmitter and receiver, and a radio‐frequency switch to switch between the arrays' multiple parts [86]. There are some applications of MWI in brain stroke detection [87], extremity imaging [88, 89], and lung cancer detection [90]. However, in this chapter, we have mainly discussed its application in BC detection. Tumors have high water content when compared to normal cells. This is due to the biology of tumor cells, which retain more fluid than normal cells. The dielectric characteristics of breast tissues are altered by this additional fluid, which is in the form of bound water. Tumors are diagnosed by MWI using scattered or reflected waves emerged from variations in dielectric characteristics between normal and malignant breast tissues [91–93]. Table 1.3 shows the difference in the dielectric properties of the female breast tissue at 3.2 GHz [94].
Table 1.3 Dielectric properties of female breast tissue at 3.2 GHz.
Tissue type
Relative permittivity
Conductivity (mS cm
−1
)
Water content (%)
Fatty tissue
2.8–7.6
0.5–2.9
11–31
Normal tissue
9.8–46
3.7–34
41–76
Benign tissue
15–67
7–49
62–84
Malignant tissue
9–59
2–34
66–79
Source: Adapted from Campbell and Land [94].
Figure 1.3 Methods of microwave breast imaging. The figures on the left show (a) passive versus (b) active approaches. The figures on the right show patient's orientations for (c) planar systems (supine position) versus (d) cylindrical systems (prone position). Source: AlSawaftah et al. [85] ©MDPI/Public Domain CC BY 4.0.
The MWI techniques can be classified into two groups, active MWI and passive MWI. The active MWI techniques are further subcategorized into microwave tomography (MWT) and radar‐based MWI. Active MWI examines the difference in dielectric properties between the healthy and malignant tissues, and passive MWI measures the temperature between the healthy and cancerous tissues using radiometry [95]. The primary distinction between these two approaches is that natural electromagnetic radiation released by living tissues is measured in passive systems, whereas in active systems, an electromagnetic signal from a source is incident on the tissues and the reflected signals are measured (as depicted in Figure 1.3) [85].
1.3.1 Microwave Tomography
MWT is a technique that uses electromagnetic field alterations to obtain 2D slices or pictures of the dielectric characteristics of a sample. Typically, MWT setup includes imaging chamber, which is filled with the matching medium, and to improve the performance of this system matching medium, is selected carefully so that most of the microwave electromagnetic signals can couple with breast tissue. Imaging chamber also has array of antennas surrounding the sample, where each antenna transmits a continuous wave (CW) of single‐ or multifrequency electromagnetic signals. The electromagnetic signals scattered by matching medium and sample because of differences in their dielectric properties are measured by non‐transmitting antennas. After analyzing data using algorithms, 2D images of dielectric properties are created. So, we can say that there are basically three steps to create image, i.e. collecting microwave tomogram, data analyzing, and then image display using MWT [85].
Several theoretical and experimental research studies have been done on the use of this method in BC diagnosis [96–100]. Meaney et al. [99] have done series of experiments to improve the performance of this technique. First multifrequency MWT prototype for breast imaging was set up of a cylindrical array of 16 monopole antennas operating at 300–1000 MHz [96]. This study revealed the relationship between the breast permittivity and radiological breast density. Another experiment with glycerin and water mixture as matching medium was done and found that it helped in the reduction of coupling noises between array elements. The results of these clinical investigations showed that tumors as small as 1 cm in diameter can be diagnosed, indicating that MWT has the ability to detect early‐stage BC [101–103].
In recent research, magnetic nanoparticles and compressive sensing (CS) techniques are used as contrast agents to improve the accuracy, sensitivity, and specificity of MWT in BC detection [104, 105]. The results showed that the CS‐based MWT with 12 antennas and an MWT with 70 antennas provided equal‐quality breast pictures. Thus, we can conclude that CS‐based MWT technique lowered operation costs and data‐gathering time significantly.
1.3.2 Radio‐Based Microwave Imaging
During BC detection, a radio‐based MWI technology exploits reflected waves caused by differences in dielectric characteristics between normal and malignant tumor cells and gives valuable information about the location, size, and characteristics of tumor cells. First radar‐based MW system was proposed by Bridge [106]. In Bridge's method, ultrawideband (UWB) of microwave frequency ranging from 1 to 10 GHz was used to illuminate BC tissue from array of antennas placed at different positions around the breast [107, 108]. Radar‐based MW has more advantages over MWT such as being computationally less expensive, having higher resolution, and having better specificity [108, 109].
As per the developments in radar‐based MW, it is classified into five groups [95]:
Confocal microwave imaging
(
CMI
): Hagness et al., first proposed this approach and used pulsed confocal techniques with time gating to improve tumor identification while decreasing tissue asymmetry and absorption effects
[110]
. Their work involves
finite‐difference time‐domain
(
FDTD
), and its results showed that cancer cells having 2 mm diameter can be detected by the 2D CMI. By utilizing a resistive bowtie antenna and using 3D‐FDTD simulations, Hagness et al. improved the prior design
[111]
. This technique has the potential to produce high‐resolution images, but its limitation is that it can't distinguish between errors and noise.
Multi‐static adaptive
(
MSA
)
system
: The high‐impact radar‐based MWI system was developed in which real aperture array of UWM antennas was used. It consisted of 16 UWB aperture‐coupled stacked‐patch antennas located on the section of hemisphere that were arranged in such a way as to improve the conformation to the curve of the breast [
112
–
114
]. The results of the clinical trials showed that this system was successful in detecting the 4–6 mm diameter of cancerous cells
[113]
. The Breast Care Center in Bristol, UK, conducted a substantial initial trial of the group's 31‐element prototype radar system in 2010. Despite the fact that this technique produced good results, the clinical trial findings were mixed. The results were shown to be irreproducible when performed by different clinicians. This is due to slight patient movements throughout the 90‐second scans, as well as certain ambiguities caused by changes in blood flow and temperature. In order to resolve these flaws, the team developed a 60‐antenna array system to improve the system's immunity to clutter and reduce the scan time to 10 seconds. The results showed increase in the accuracy of the images obtained while also delivering a more convenient and acceptable clinical experience for patients
[85]
.
Tissue‐sensing adaptive radar
(
TSAR
): Fear et al. investigated the use of TSAR in BC imaging [
115
,
116
]. This system required the use of two scans of each breast. The first scan specifies the basic location of the breast volume relative to the tank (containing coupling fluid and antennas) obtained at the antenna after the first reflection. The second scan is done in a sagittal direction, from the nipple to the chest wall, providing data for the tumor detection algorithm
[116]
. The results of these clinical experiments showed that TSAR has the potential to detect and localize tumor with more than 4 mm in diameter
[117]
. However, the huge reflections created by skin, the construction of adequate antennas, and the desire to develop high‐speed electronics for real‐time photography all posed hurdles to this system. Development of appropriate sensors, research of practical implementation challenges, enhancement of imaging algorithms, and testing on breast models are all part of the current work on TSAR [
95
,
118
].
Microwave imaging via space–time
(
MIST
)
beamforming
: This type of technique involves the use of continuous transmission of UWB signals from antenna placed near the breast surface, and the received reflected signals are spatially focused using a space–time beam former. Because of the considerable difference in the dielectric characteristics of normal and malignant tissue, discrete regions of high backscattered energy levels appear in the reconstructed pictures, corresponding to malignant tumors
[119]
. The first MIST system was introduced by Bond et al.
[119]
, resulting in the detection and localization of very small synthetic tumors embedded in breast phantoms
[119]
. Bond et al. also developed a MIST system with implementation of a planar array of 16 horn antennas that transmitted UWB microwave signals from each antenna located close to the breast surface
[119]
. This has resulted in significant improvement in the performance of the UWB‐based MI approach. However, further improvements enabled the system to localize, identify, and resolve multiple tumors [
118
,
119
].
Holographic microwave imaging
(
HMI
): HMI has the ability to provide real‐time images at a substantially cheaper cost than other radar‐based MWI approaches since it does not require expensive ultra‐high‐speed electronics. In this approach, MWI is performed in two stages: recording of a sampled intensity pattern followed by image reconstruction
[120]
. Smith and coworkers presented a near‐field indirect HMI approach that involves capturing the breast intensity and reconstructing the image from that data [
120
,
121
]. However, before this technique can be used in clinical settings, it needs to be validated further.
Wang et al. proposed far‐field HMI in which 3D HMI image was reconstructed using 2D HMI images obtained at different vertical positions with single frequency (12.6 GHz) for early detection of BC [122]. It included one transmitter and an array of 15 receivers placed under the breast phantom. In this system, matching solution medium is not required and air between the antennas and breast phantom. The results of the experiments demonstrated that the suggested 2D HMIA approach could successfully detect tiny tumors with a diameter of less than 5 mm in various places [122]. The other experiment was done by combining the above approach with CS, and results showed that CS‐HMIA has the capability of detecting randomly distributed inclusion of various shapes and sizes using smaller number of sensors and lesser scan times [123]. In a recent development, a multifrequency HMI system has been developed by Wang (2019), who checked the feasibility and effectiveness of the proposed algorithm for breast imaging [124]. According to the studies, the multifrequency HMI system has the ability to be used as a microwave diagnostic technology.
A significant amount of research and development is yet to be done in harnessing the full potential of this technology. The future research should focus on improvement of MWI in medical applications, including better designing of hardware, signal processing methods as well as algorithms for image reconstruction.
1.4 Biomarkers and Biosensors for Breast Cancer Detection
The molecular biotechnology studies are done to analyze specific biomarkers such as nucleic acids, proteins, cells, and tissues of patients, and these studies help in early detection of BC than abovementioned imaging techniques or procedures [20]. However, these cannot replace imaging techniques but can be used as auxiliary method to diagnose BC. The examinations help the clinician analyze BC from the level of nucleic acids, proteins, and cells using these biomarkers and biosensors. First, we will discuss biomarkers and then biosensors.
1.4.1 Biomarkers
1.4.1.1 Nucleic Acids
There are different nucleic acid tumor markers viz, BRCA1, BRCA2, microRNA, circulating tumor DNA (ctDNA), circulating cell‐free DNA (ccfDNA), circulating RNA (circRNA), long noncoding RNAs (lncRNAs), etc. [125]. MicroRNA is a single‐stranded noncoding RNA molecule that has evolved as a significant regulator of BC development, prognosis, and therapeutic response [126]. The study has revealed that MicroRNA has been linked to patients' clinical and biological characteristics and that it can target different genes and alter different pathways. LncRNA is a type of noncoding RNA with a length greater than 200 nt that is produced by RNA polymerase II. The role of lncRNAs in the initiation, development, and metastasis of BC is becoming clearer, and they could represent a new diagnostic marker and therapeutic target for BC [127]. CircRNA is a type of double‐stranded closed RNA that is resistant to RNA exonuclease, exhibits steady expression, and is difficult to disintegrate according to the findings, CircRNA expression was linked to tumor cell proliferation, migration, invasion, and treatment resistance [128, 129]. Thus, it can be explored as diagnostic method.
ccfDNA is extracellular DNA found in plasma or serum, and ctDNA is ccfDNA released into the bloodstream by tumor cells [130, 131]. The studies have revealed that primary tumor cells, circulating tumor cells, and hidden and dominant metastatic tumor cells all release more DNA than normal cells, and ctDNA contains mutations that are exclusive to the parent tumor [132]. As a result, ctDNA has the potential to be used as a tumor marker for BC prediction, diagnosis, and prognosis [131, 133]. However, further research is needed to apply it to clinical diagnosis and treatment [133].
1.4.1.2 Proteins
CD24, CD44, and MUC1 are some types of protein tumor biomarkers. CD24 is a glycosylphosphatidylinositol‐binding glycoprotein [12, 134], which has been discovered to be an anti‐phagocytic signal that protects cancer cells from Siglec‐10‐expressing macrophage attacks. The expression of CD24 has also been linked to BC grading and staging [135]. As a result, CD24‐blocking therapy can significantly improve the therapeutic impact of CD24‐positive cancers [134]. On the other hand, CD44 is a complex transmembrane‐binding glycoprotein that has been linked to a poor prognosis in patients. It is involved in the regulation of numerous critical signaling pathways, including tumor growth, invasion, metastasis, and treatment resistance [135–137].
MUC1 (CA15‐3) is a transmembrane mucin glycoprotein that is found in the majority of epithelial tissues [138]. It was shown to have aberrant profile and glycosylation in 90% of BC cases [139]. MUC1 is also a useful marker for tracking the progression of metastatic BC [140]. Further, serum tumor markers such as CEA, CA19‐9, CA125, CA15‐3, and TPS play a significant role in the diagnosis and treatment of BC [141].
1.4.1.3 Tumor Cells
The term “circulating tumor cells (CTCs)” refers to BC cells that have broken free from the tumor and entered the bloodstream (CTC). CTCs have the ability to regenerate tumor tissue. So such types of tumor cells themselves are tumor markers [141]. Patients with metastatic BC could be tiered and graded to get tailored treatment by measuring the number of CTC cells [142]. CTC cells can also be used to assess BC patients' prognoses and identify whether they are candidates for further radiation therapy after surgery [132, 143].
Apart from nucleic acid, proteins, and tumor cells, exosomes (membrane‐enclosed phospholipid extracellular vesicles) can also be applied in the diagnosis and treatment of cancer due to their high secretion on the surface of cancer cells [144]. From the research findings, it was shown that tumor cells release more exosomes than normal cells, and miRNA‐21 and miRNA‐1246 in exosomes are upregulated in patients' plasma [9]. Therefore, exosomes have become a research hotspot in recent years because of their great diagnostic potential. In addition to exosomes, estrogen receptor (ER) [145], progesterone receptor (PR) [146], and human epidermal growth receptor 2 (HER2) [147] are the most widely used tumor markers in the early diagnosis and treatment of BC. The diagnosis of BC using tumor markers with high specificity and sensitivity requires more research.
1.4.2 Biosensors
A biosensor is a self‐contained, tiny analytical instrument that combines a specific biological system with a physiochemical transducer to detect target molecules by transforming the recognition signal into a detectable output signal [148–152]. When compared to traditional tumor marker detection methods, biosensors offer substantial advantages in terms of specificity, sensitivity, speed, and cost of detection [15, 16]. Biosensors can be divided into electrochemical biosensors, optical biosensors, and other types on the basis of detection principles and signals [19,151–154].
1.4.2.1 Electrochemical Biosensors
Electrochemical biosensors monitor changes in dielectric characteristics, size, shape, and charge distribution when antibody–antigen complexes form on the electrode surface. By sensing the electrochemical reaction on the electrode's surface, it quantitatively detects the analyte and the signal of the electrochemical reaction, which depends on the concentration of analyte [155–157]. These biosensors have been designed to detect a variety of biomolecules, including proteins, antigens, DNA, antibodies, and heavy metal ions, among others. Electrochemical sensors have previously been shown to have great sensitivity and specificity in buffer and serum samples [158]. Figure 1.4 shows the developed electrochemical biosensor for MCF‐7 cells detection [160]. Antibodies against surface proteins of MCF‐7 cells were immobilized on nanoparticle‐assembled electrode to capture MCF‐7 cells at the electrode surface, which increases the interfacial resistance and hence enlarged semicircle in Nyquist plot. Alternatively, cDNA complementary to miRNA can also be immobilized to capture target miRNA released from the cell extracts of MCF‐7 cells [95].
Figure 1.4 Electrochemical biosensor for detection of MCF‐7 cells. Source: Mittal et al. [159]/with permission of Elsevier.
There are some methods through which the detection of electrochemical reaction is done: cyclic voltammetry (CV), differential pulse voltammetry (DPV), square wave voltammetry (SWV), linear sweep voltammetry (LSV), electrochemical impedance spectroscopy (EIS), field‐effect transistor (FET), and other methods [19, 148, 149] as shown in Table 1.4. Recent development of electrochemical nanobiosensors has reduced the cost, simplified the technique, increased sensitivity and specificity, increased reliability, and provided a quick response in BC detection [178, 179]. Further research in this direction would be helpful in early detection of BC.
1.4.2.2 Optical Sensors
Optical biosensors detect the optical change on the surface of sensing layer of the target. Every optical biosensor detects different optical signals such as refractive index, resonance, wavelength, and intensity [16, 180, 181]. As shown in Figure 1.5, the diagnosis of BC cell (MCF‐7) is done using quantum dot optical biosensor. In Figure 1.5, quantum dots are labeled with primary antibodies against MCF‐7 cell surface proteins and subjected to sample containing MCF‐7 cells. Addition of secondary antibody labeled, magnetic beads enables sensors for their magnetic separation to obtain fluorescence emission spectra [95]. Different types of optical biosensors have been developed that include fiber optics, fluorescence, resonant mirror optical, interferometric, and surface plasmon resonance as given in Table 1.5. Recently, these sensors have been developed using surface chemistry and nanotechnology [194].
In addition to electrochemical and optical biosensors, there are two other types of sensors viz, quartz crystal microbalance (QCM) biosensors (which detect mass change of the target) and photoelectochemical (PEC) biosensors (which detect the effect of the targets on the photoelectric characteristics of materials).
QCM biosensor
: The effect of the target on the frequencies of the bulk acoustic waves generated in the piezoelectric quartz crystal is the basis for QCM's sensing mechanism. To achieve the concentration detection of the target, the frequency change of the acoustic wave is connected to the mass change on the chip surface. QCM can detect mass changes on the chip surface at the nanogram level
[195]
.
PEC biosensors
: The photoelectric active material in the PEC sensor is stimulated when light is irradiated, resulting in a photocurrent or photovoltage. The target is captured by the recognition sensor on the surface of the photoelectrically active material, causing the photocurrent or photovoltage to change. When the target's concentration changes, so does the photoelectric signal. Therefore, it sets up relationship between the photoelectric signal and concentration. The detection of tumor markers has been reported using several PEC biosensors
[141]
.
Table 1.4 Developments in electrochemical biosensors.
Electrochemical biosensor
Target
Detection limit
Linear range
References
CV
CA15 EGFR miRNA
30.64 U ml
−1
1 pg ml
−1
155.2 × 10
−20
M
2.0–240 U ml
−1
1 pg ml
−1
to 100 ng ml
−1
2 × 10
−20
to 2 × 10
−12
M
[161]
[162]
[163]
DPV
BRCA1 CA15‐3 BRCA1 miRNA
0.0034 pM 3.34 mU ml
−1
3.01 × 10
−16
M 3.6 fM 8.2 fM (miRNA‐21)
0.01 pM to 1 nM 0.01–1000 U ml
−1
1.0 × 10
−15
to 1.0 × 10
−7
M 0.01–10 pM 0.02–10 pM (miRNA‐21)
[164]
[165]
[166]
[167]
SWV
MUC1 miRNA‐21 miRNA‐155
0.33 pM 18.9 aM (miRNA‐21) 39.6 aM (miRNA‐155)
1.0 pM to 10 μM — 0.1 fM to 10 nM
[168]
—
[169]
LCV
HER2‐ECD HER2 CD44 CD44 positive cell
4.4 ng ml
−1
0.16 ng ml
−1
2.17 pg ml
−1
8 cells ml
−1
15–100 ng ml
−1
7.5–50 ng ml
−1
0.01–100 ng ml
−1
10–106 cells ml
−1
[170]
[171]
—
[172]
EIS
HER2 MCF‐7 cell MUC1 BRCA1
19 fg ml
−1
23 cells ml
−1
2.7 nM 3 fM
0.001–10 ng ml
−1
1 × 10
2
to 1 × 10
5
cells ml
−1
5–115 nM 10 fM to 0.1 μM
[173]
—
[174]
[175]
FET
miRNA‐155 CEA
0.03 fM 10 pg ml
−1
0.1 fM to 10 nM 0.1–100 ng ml
−1
[176]
[177]
Source: Adapted from Hong et al. [141].
Figure 1.5 Quantum dot based optical biosensor for detection of MCF‐7 cells. Source: Mittal et al. [159]/with permission of Elsevier.
Table 1.5 Developments in optical biosensors.
Optical biosensor
Target
Detection limit
Linear range
References
Fluorescence biosensor
CEA miRNA‐21
7.9 pg ml
−1
(Water) 10.7 pg ml
−1
(human serum samples) 0.03 fM
0.03–6 ng ml
−1
(water) 0.03–6 ng ml
−1
(human serum samples) 0.1–125 fM
[182]
[183]
Colorimetric biosensor
BRCA1 BRCA1
10
−18
M 0.34 fM
10
−12
to 10
−18
M 1 fM to 100 pM
[184]
[185]
SPRi
CEA HER2‐positive EXO EXO
0.12 ng ml
−1
8280 exosomes μl
−1
5000 exosomes ml
−1
0.40–20 ng ml
−1
8280–33 100 exosomes μl
−1
—
[186]
[187]
[188]
SERS
miR‐K12‐5‐5p MicroRNA
884 pM —
— —
[189]
[190]
ECL
BRCA1 EXO miRNA‐21
0.71 fM 7.41 × 104 exosomes 3.2 fM
1.0 fM to 0.1 nM 3.4 × 10
5
to 1.7 × 10
8
exosomes ml
−1
0.01–10 000 fM
[191]
[192]
[193]
Source: Adapted from Hong et al. [141].
Both of these are also capable of detecting all types of tumor markers. However, they have trouble identifying multiple targets at the same time, but signal amplification techniques can be used to enhance their detection limits for detecting a single target. The obstacles that biosensors have experienced are mostly due to two factors: detection method and detection equipment. These issues can be overcome by the combination of molecularly imprinted polymers (MIPs) and microfluidic chips with biosensors, and the commercialization of these types of biosensors in the future can change the current trend of diagnosis [196, 197].
1.5 Conclusion
This chapter mainly focused on the most frequent approaches for diagnosing BC. As researchers delve more into imaging technology, they know that a single imaging method is just not enough to meet the requirement of accuracy in BC diagnosis. Thus, combining many imaging modalities will be significant for the emerging approaches [107, 198, 199]. Moreover, developments of AI‐based models will help in improving the positive diagnostic rate for BC and reducing the negative diagnosis rate. Additionally, the development of biosensors would lead to the formation of various BC biomarkers. The combination of imaging sensors and biosensors can get unexpected results. Nevertheless, imaging instruments would still be the routine method for screening BC over the next few years as these can be widely applied. The new BC markers will enable these technologies to achieve more efficiency, speed, sensitivity, and specificity. With the development and application of these approaches in the future, the researchers will be able to not only diagnose BC from multiple perspectives but also monitor the effectiveness of treating BC.
Acknowledgment
All the authors are thankful to the School of Sciences, IGNOU, for their continuous support in using the literature resources.
Conflict of Interest
The authors have no conflict of interest.
Authors Contribution
Dr. Vinayak Adimule conceived the idea; Dr. Lalita S. Kumar and Nidhi Manhas wrote the manuscript. All the authors reviewed the manuscript.
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