Precision Medicine [1st ed.] 9781071609033, 9781071609040

This book serves to introduce the technologies and applications of precision medicine. Beginning with a section on emerg

575 38 4MB

English Pages XI, 226 [220] Year 2020

Report DMCA / Copyright


Polecaj historie

Precision Medicine [1st ed.]
 9781071609033, 9781071609040

Table of contents :
Front Matter ....Pages i-xi
Front Matter ....Pages 1-1
T Cell Receptor Repertoire Sequencing (Huixin Lin, Yonggang Peng, Xiangbin Chen, Yuebin Liang, Geng Tian, Jialiang Yang)....Pages 3-12
Nanopore Sequencing and Its Clinical Applications (Xue Sun, Lei Song, Wenjuan Yang, Lili Zhang, Meng Liu, Xiaoshuang Li et al.)....Pages 13-32
The Clinical Significance of Microsatellite Instability in Precision Treatment (Zhenyu Huang, Xiaojian Chen, Chenying Liu, Long Cui)....Pages 33-38
Applications of Network Analysis in Biomedicine (Steven Wang, Tao Huang)....Pages 39-50
Front Matter ....Pages 51-51
Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era (Jing Yan, Zhuan Liu, Shengfang Du, Jing Li, Li Ma, Linjing Li)....Pages 53-61
Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer (Lin Chen, Nan Jiang, Yuxiang Wu)....Pages 63-73
Recent Advances in DNA Repair Pathway and Its Application in Personalized Care of Metastatic Castration-Resistant Prostate Cancer (mCRPC) (Chenyang Xu, Shanhua Mao, Haowen Jiang)....Pages 75-89
Methylation in Lung Cancer: A Brief Review (Chang Gu, Chang Chen)....Pages 91-97
Epstein–Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review (Fen Xue, Xiayun He)....Pages 99-107
A Review on Cancer of Unknown Primary Origin: The Role of Molecular Biomarkers in the Identification of Unknown Primary Origin (Na Yan, Yanxiang Zhang, Xuejie Guo, Dawei Yuan, Geng Tian, Jialiang Yang)....Pages 109-119
Front Matter ....Pages 121-121
DNA Methylation in Atrial Fibrillation and Its Potential Role in Precision Medicine (Mengwei Lv, Wen Ge, Zhi Li, Chao Wang, Yangyang Zhang)....Pages 123-131
PCSK9 Inhibition and Atherosclerosis: Current Therapeutic Option and Prospection (Pratik Pandey, Cuimei Zhao, Ban Liu)....Pages 133-143
Precise Drug Sequential Therapy Can Improve the Cardioversion Rate of Atrial Fibrillation with Valvular Disease after Radiofrequency Ablation (Tao Li, Yongjun Qian)....Pages 145-159
Precision Medicine and Dilated Cardiomyopathy (Xiang Li, Wenyan Zhu)....Pages 161-171
Research Progress in Pathogenesis of Total Anomalous Pulmonary Venous Connection (Xin Shi, Yanan Lu, Kun Sun)....Pages 173-178
Front Matter ....Pages 179-179
Airway Inflammation Biomarker for Precise Management of Neutrophil-Predominant COPD (Xue Liang, Ting Liu, Zhiming Zhang, Ziyu Yu)....Pages 181-191
Genome Variation and Precision Medicine in Systemic Lupus Erythematosus (Ru Yang, Yaqi Hu, Lin Bo)....Pages 193-203
Front Matter ....Pages 205-205
Precision Medicine in Tissue Engineering on Bone (Bingkun Zhao, Qian Peng, Rong Zhou, Haixia Liu, Shengcai Qi, Raorao Wang)....Pages 207-215
Progress of Clinical Application for Ex Vivo Lung Perfusion (EVLP) in Lung Transplantation (Chang Gu, Xufeng Pan, Jianxin Shi)....Pages 217-224
Back Matter ....Pages 225-226

Citation preview

Methods in Molecular Biology 2204

Tao Huang Editor

Precision Medicine




Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes:

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Precision Medicine Edited by

Tao Huang Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

Editor Tao Huang Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Shanghai, China

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-0903-3 ISBN 978-1-0716-0904-0 (eBook) © Springer Science+Business Media, LLC, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface With the development of omics technologies, especially next-generation sequencing, disease progression can be investigated in an unprecedented way. The multi-omics approaches reveal the essence of disease pathology and make precision diagnosis and therapy possible. Precision medicine will transform the medical practice fundamentally. In these 19 chapters, we will introduce the technologies and applications of precision medicine. The first four chapters present emerging experimental and bioinformatics technologies that are widely used in precision medicine, such as T cell receptor repertoire sequencing, nanopore sequencing, microsatellite instability, and network analysis. The T cell receptor repertoire sequencing can help monitoring immune responses and predicting the prognosis of disease. Nanopore sequencing as third-generation sequencing has great potential for point-of-care testing (POCT) due to its fast turn-around time, portable and real-time data analysis. Microsatellite instability is a key indicator for predicting response to anti-PD-1 inhibitors. Network analysis can integrate multi-omics big data and reveal the molecular mechanisms underlying complex diseases. The next six chapters are focused on cancer studies. There have many successful applications of precision medicine in cancers. Breast cancer, prostate cancer, lung cancer, nasopharyngeal carcinoma, and cancer of unknown primary origin are discussed. Biomedical imaging, methylation, immunohistochemistry, and gene expression can all be used for cancer diagnosis and treatment optimization. Even cancer of unknown primary origin can be traced based on their molecular characteristics. Cardiovascular diseases (CVDs) are the most common diseases and cause many deaths each year widely. There are five chapters for cardiovascular diseases, such as atrial fibrillation (AF), atherosclerosis, dilated cardiomyopathy, and total anomalous pulmonary venous connection (TAPVC). DNA methylation has potential value in being biomarkers and underlying the diagnosis and prognosis of atrial fibrillation. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays an important role in atherosclerosis and shows therapeutic potentials. The next two chapters are focused on other complex diseases beside cancers and cardiovascular diseases, such as chronic obstructive pulmonary disease (COPD) and systemic lupus erythematosus (SLE). Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality in the world. Airway inflammation biomarkers can facilitate precise manage of neutrophil-predominant COPD. Systemic lupus erythematosus (SLE) is a complex autoimmune disease which faces difficulties in treatment. Stratification of SLE patients based on genetic profiling will enable us to make more effective and precise choices for treatment plans. The last two chapters introduce the latest engineering and surgical developments in precision medicine. Scientists seek various engineering approaches, such as 3D printing, to harness stem cells, scaffolds, growth factors, and the extracellular matrix to promise enhanced and more reliable bone formation. Ex Vivo Lung Perfusion (EVLP) is a technique for extending lung preservation time and repairing lung injury in the field of lung transplantation. EVLP can increase the number of lungs that meet the transplant criteria and, to some extent, alleviate the current shortage of donor lungs.




As mentioned above, this book covers most perspectives of precision medicine, from basic research to clinical surgeries, from cancer to cardiovascular disease, from sequencing technology to big data analysis. It is hoped that this book can broaden the horizons for researchers, engineers, and clinicians and accelerate the interdisciplinary precision medicine research and applications. Shanghai, China

Tao Huang

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .



1 T Cell Receptor Repertoire Sequencing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huixin Lin, Yonggang Peng, Xiangbin Chen, Yuebin Liang, Geng Tian, and Jialiang Yang 2 Nanopore Sequencing and Its Clinical Applications . . . . . . . . . . . . . . . . . . . . . . . . . Xue Sun, Lei Song, Wenjuan Yang, Lili Zhang, Meng Liu, Xiaoshuang Li, Geng Tian, and Weiwei Wang 3 The Clinical Significance of Microsatellite Instability in Precision Treatment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhenyu Huang, Xiaojian Chen, Chenying Liu, and Long Cui 4 Applications of Network Analysis in Biomedicine . . . . . . . . . . . . . . . . . . . . . . . . . . . Steven Wang and Tao Huang


v ix



33 39


5 Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Yan, Zhuan Liu, Shengfang Du, Jing Li, Li Ma, and Linjing Li 6 Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lin Chen, Nan Jiang, and Yuxiang Wu 7 Recent Advances in DNA Repair Pathway and Its Application in Personalized Care of Metastatic Castration-Resistant Prostate Cancer (mCRPC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chenyang Xu, Shanhua Mao, and Haowen Jiang 8 Methylation in Lung Cancer: A Brief Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chang Gu and Chang Chen 9 Epstein–Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fen Xue and Xiayun He




75 91





A Review on Cancer of Unknown Primary Origin: The Role of Molecular Biomarkers in the Identification of Unknown Primary Origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Na Yan, Yanxiang Zhang, Xuejie Guo, Dawei Yuan, Geng Tian, and Jialiang Yang




14 15

DNA Methylation in Atrial Fibrillation and Its Potential Role in Precision Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mengwei Lv, Wen Ge, Zhi Li, Chao Wang, and Yangyang Zhang PCSK9 Inhibition and Atherosclerosis: Current Therapeutic Option and Prospection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pratik Pandey, Cuimei Zhao, and Ban Liu Precise Drug Sequential Therapy Can Improve the Cardioversion Rate of Atrial Fibrillation with Valvular Disease after Radiofrequency Ablation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Li and Yongjun Qian Precision Medicine and Dilated Cardiomyopathy . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang Li and Wenyan Zhu Research Progress in Pathogenesis of Total Anomalous Pulmonary Venous Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Shi, Yanan Lu, and Kun Sun






145 161



Airway Inflammation Biomarker for Precise Management of Neutrophil-Predominant COPD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Xue Liang, Ting Liu, Zhiming Zhang, and Ziyu Yu Genome Variation and Precision Medicine in Systemic Lupus Erythematosus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Ru Yang, Yaqi Hu, and Lin Bo




Precision Medicine in Tissue Engineering on Bone . . . . . . . . . . . . . . . . . . . . . . . . . 207 Bingkun Zhao, Qian Peng, Rong Zhou, Haixia Liu, Shengcai Qi, and Raorao Wang Progress of Clinical Application for Ex Vivo Lung Perfusion (EVLP) in Lung Transplantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Chang Gu, Xufeng Pan, and Jianxin Shi

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


Contributors LIN BO • Department of Rheumatology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China CHANG CHEN • Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China LIN CHEN • Department of Kinesiology, Jianghan University, Wuhan, China XIANGBIN CHEN • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China XIAOJIAN CHEN • Department of Colorectal and Anal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China LONG CUI • Department of Colorectal and Anal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China SHENGFANG DU • Department of Anesthesiology, The Second Hospital of Lanzhou University, Lanzhou, People’s Republic of China WEN GE • Department of Thoracic and Cardiovascular Surgery, Shuguang Hospital, Shanghai University of TCM, Shanghai, China CHANG GU • Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China; Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China XUEJIE GUO • Geneis (Beijing) Co., Ltd., Chaoyang District Beijing, People’s Republic of China XIAYUN HE • Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Shanghai, China TAO HUANG • Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China ZHENYU HUANG • Department of Colorectal and Anal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China YAQI HU • Department of Rheumatology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China HAOWEN JIANG • Department of Urology, Huashan Hospital, Fudan University, Shanghai, China NAN JIANG • Department of General Surgery, First Hospital of Tsinghua University, Beijing, China XUE LIANG • The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China; Key Laboratory of Molecular Target & Clinical Pharmacology, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, People’s Republic of China; State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, People’s Republic of China YUEBIN LIANG • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China JING LI • Department of Clinical Laboratory Center, The Second Hospital of Lanzhou University, Lanzhou, China




LINJING LI • Department of Clinical Laboratory Center, The Second Hospital of Lanzhou University, Lanzhou, China HUIXIN LIN • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China TAO LI • Department of Cardiovascular Surgery, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China BAN LIU • Department of Cardiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China CHENYING LIU • Department of Colorectal and Anal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China HAIXIA LIU • Department of Stomatology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China MENG LIU • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China TING LIU • The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China ZHUAN LIU • Department of Clinical Laboratory Center, The Second Hospital of Lanzhou University, Lanzhou, China XIANG LI • Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China XIAOSHUANG LI • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China ZHI LI • Department of Cardiovascular Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China YANAN LU • Department of Pediatric Cardiology, Xin Hua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China MENGWEI LV • Shanghai East Hospital of Clinical Medicine College, Nanjing Medical University, Shanghai, China; Department of Cardiovascular Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China LI MA • Department of Clinical Laboratory Center, The Second Hospital of Lanzhou University, Lanzhou, China SHANHUA MAO • Department of Urology, Huashan Hospital, Fudan University, Shanghai, China PRATIK PANDEY • Department of Cardiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China XUFENG PAN • Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China QIAN PENG • Department of Stomatology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China YONGGANG PENG • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China YONGJUN QIAN • Department of Cardiovascular Surgery, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China SHENGCAI QI • Department of Stomatology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China JIANXIN SHI • Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China XIN SHI • Department of Pediatric Cardiology, Xin Hua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China LEI SONG • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China



KUN SUN • Department of Pediatric Cardiology, Xin Hua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China XUE SUN • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China GENG TIAN • Geneis (Beijing) Co., Ltd., Chaoyang District Beijing, People’s Republic of China CHAO WANG • Tongji University School of Medicine, Shanghai, China RAORAO WANG • Department of Stomatology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China STEVEN WANG • Department of Biological Sciences, Columbia University, New York, NY, USA WEIWEI WANG • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China YUXIANG WU • Department of Kinesiology, Jianghan University, Wuhan, China CHENYANG XU • Department of Urology, Huashan Hospital, Fudan University, Shanghai, China FEN XUE • Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Shanghai, China JIALIANG YANG • Geneis (Beijing) Co., Ltd., Chaoyang District Beijing, People’s Republic of China RU YANG • Department of Rheumatology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China WENJUAN YANG • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China JING YAN • Department of Clinical Laboratory Center, The Second Hospital of Lanzhou University, Lanzhou, China NA YAN • Geneis (Beijing) Co., Ltd., Chaoyang District Beijing, People’s Republic of China DAWEI YUAN • Geneis (Beijing) Co., Ltd., Chaoyang District Beijing, People’s Republic of China ZIYU YU • The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China LILI ZHANG • Geneis (Beijing) Co., Ltd., Beijing, People’s Republic of China YANGYANG ZHANG • Department of Cardiovascular Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China YANXIANG ZHANG • Geneis (Beijing) Co., Ltd., Chaoyang District Beijing, People’s Republic of China ZHIMING ZHANG • The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China BINGKUN ZHAO • Department of Stomatology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China CUIMEI ZHAO • Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China RONG ZHOU • Department of Stomatology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China WENYAN ZHU • Department of Oncology, Chongqing (CHN.USA) Hygeia Hospital, Chongqing, China; Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, China

Part I Emerging Experimental and Bioinformatics Technologies

Chapter 1 T Cell Receptor Repertoire Sequencing Huixin Lin, Yonggang Peng, Xiangbin Chen, Yuebin Liang, Geng Tian, and Jialiang Yang Abstract The status of T cell receptors (TCRs) repertoire is associated with the occurrence and progress of various diseases and can be used in monitoring the immune responses, predicting the prognosis of disease and other medical fields. High-throughput sequencing promotes the studying in TCR repertoire. The chapter focuses on the whole process of TCR profiling, including DNA extraction, library construction, high-throughput sequencing, and how to analyze data. Key words T cell receptor repertoire, Library construction, High-throughput sequencing, Data analysis


Introduction T lymphocytes play essential roles in the adaptive immune system. A unique T cell receptor (TCR) is expressed on the surface of each individual T lymphocyte. The interaction of TCR with the antigenmajor histocompatibility complex (MHC) molecules is the basis of T lymphocytes recognizing antigen [1]. TCRs are highly variable heterodimer molecules consisting of either a combination of alpha and beta chains (αβ TCR), the major TCR, or a combination of gamma and delta chains (γδ TCR) [2]. The extreme diversity results from randomized combinations of DNA during T cell development, which contain distinct variable diversity, joining (V(D)J) gene segments, and deletion and/or insertion of nucleotides at the junctions of these segments [3]. The hypermutation leads to such strong combinatorial and junctional diversity that the resulted TCRs are able to recognize a marvelous variety of antigens. The sum of all TCRs is termed the TCR repertoire or TCR profile. Researchers are becoming more and more interested in determining the TCR repertoire status for its changing greatly with the occurrence or progression of various diseases [4–7]. The main difficulty in studying the TCR profile is its extreme diversity.

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Huixin Lin et al.

After DNA random combining, the diversity is up to a mathematical prediction of 1015 different TCR αβ molecules. Even with thymic selection, an estimate of about 2  107 TCR αβ molecules will be remained [2]. However, the latest high-throughput sequencing methods can help us face this big challenge. The whole process of TCR profiling includes DNA/RNA extraction, library construction, high-throughput sequencing, and data analysis. Both genomic DNA (gDNA) and RNA can be used as the starting materials for TCR sequencing. Library construction approaches contain multiplex PCR [8], target enrichment [9], and 50 RACE cDNA synthesis plus nested PCR [10]. The choices of starting materials and library construction methods depend on the purposes of studying. In the real world, qualified FFPE DNA samples are relatively easier to be obtained. And given the diversity of the target and the convenience of PCR amplification, multiplex PCR is the most popular approach to construct library. Here, we show the methods of the TCR sequencing, including gDNA extraction from FFPE samples, multiplex PCR for library construction, and the bioinformatics analysis of data.


Materials Unless indicated, the reagents with analytical grade and ultrapure water are necessary to prepare the following solutions.

2.1 Genomic DNA Isolation from FFPE Samples

QIAamp DNA FFPE Tissue Kit (QIAGEN, Hilden, Germany) or other similar products on the market are recommended to isolate genomic DNA from FFPE tissue samples. Besides, the reagents and equipment which need to be supplied by user are listed as below: 1. Xylene. 2. Ethanol (96–100%). 3. 1.5 mL or 2 mL microcentrifuge tubes. 4. Pipet tips (pipet tips with aerosol barriers recommended). 5. Thermomixer, heated orbital incubator, heating block, or water bath capable of incubation at 90  C. 6. Microcentrifuge with rotor for 2 mL tubes. 7. Vortex mixer. 8. Fluorimetry (Qubit) and spectrophotometry (NanoDrop).

2.2 TCRβ CDR3 Library Construction

After amplification and purification of the TCRβ-CDR3 targeted region, ABclonal Rapid DNA Lib Prep Kit (ABclonal, Boston, USA) or other similar products are recommended to construct TCRβ CDR3 library. Besides, the reagents and equipment which need to be supplied by user are listed as below (see Note 1):

T Cell Receptor Repertoire Sequencing


1. Thermocycler. 2. Multiplex adapters compatible with Illumina® platforms. 3. Ethanol. 4. Nuclease-free water. 5. PCR strip tubes or plates. 6. Magnetic stand. 7. Agencourt™ AMPure XP bead (stored at 4  C). 8. Pipettes and multichannel pipettes. 9. Aerosol-resistant pipette tips. 10. Microcentrifuge. 11. Vortex mixer. 12. Agilent Bioanalyzer. 2.3

Data Analysis

The following software are used to analyze the sequencing data. 1. Raw data processing: Cutadapt. 2. Analysis of clean data: MixCR. 3. Characteristics of TCR results: tcR.



3.1 Genomic DNA Isolation from FFPE Samples

Recommend using QIAamp DNA FFPE Tissue Kit (QIAGEN, Hilden, Germany) or other similar products to isolate genomic DNA from FFPE tissue samples. The following are the main steps to isolate genomic DNA according to the standard procedure of QIAamp DNA FFPE Tissue Kit (QIAGEN, Hilden, Germany) (see Note 2). 1. Trim excess paraffin off the sample block using a scalpel (see Note 3). 2. Immediately place up to 8 sections (5–10 μm thick) into a 1.5 mL microcentrifuge tube, and 1 mL xylene was added to the tube. Then close the lid and vortex thoroughly for 10 s. 3. Centrifuge at 20,000  g for 2 min. 4. Discard the supernatant by pipetting, but do not remove any of the pellet. 5. Add 1 mL ethanol (96–100%) to the tube and vortex thoroughly. 6. Centrifuge at 20,000  g for 2 min. 7. Discard the supernatant by pipetting, but do not remove any of the pellet. 8. Open the tube and place it at room temperature until all residual ethanol has evaporated.


Huixin Lin et al.

9. Resuspend the pellet with 180 μL Buffer ATL. Add 20 μL proteinase K to the pellet and fully mix by vortexing. 10. Incubate at 56  C until the sample has been lysed completely. (This process usually needs about 1 h.) 11. Then transfer the tube to 90  C and continue to incubate for 1 h (see Note 4). 12. Briefly centrifuge to collect all drops from the lid. 13. Add 200 μL Buffer AL to the tube and mix vigorously by vortexing. Then add 200 μL ethanol (96–100%) and mix vigorously again. 14. Briefly centrifuge to collect all drops from the lid. 15. Transfer all the lysate to the QIAamp MinElute column (in a 2 mL collection tube) without wetting the rim, then close the lid and centrifuge at 6000  g for 1 min. Place the column in a new 2 mL collection tube and the old collection tube containing the flow-through should be discarded. 16. Carefully open the lid of the column and add 500 μL Buffer AW1 without wetting the rim, then close the lid and centrifuge at 6000  g for 1 min. Place the column in a new 2 mL collection tube, and the old collection tube containing the flow-through should be discarded. 17. Carefully open the lid of the column and add 500 μL Buffer AW2 without wetting the rim, then close the lid and centrifuge at 6000  g for 1 min. Place the column in a new 2 mL collection tube, and the old collection tube containing the flow-through should be discarded. 18. Centrifuge at 20,000  g for 3 min to dry the membrane completely. 19. Place the column in a new 1.5 mL microcentrifuge tube. Carefully open the lid of the column, then add 20–100 μL Buffer ATE to the center of the membrane of the column. 20. Close the lid and incubate at room temperature for 1 min. Centrifuge at 20,000  g for 1 min. 21. Qubit dsDNA assay and NanoDrop are used to assess the concentration and purity of the isolated genomic DNA, respectively. 3.2 TCR CDR3 Library Construction 3.2.1 Target Region Amplification by Multiplex PCR

Use primers for the J alleles of the TCR β chains together with a mix of primers for all known V alleles to amplify the TCR across the CDR3 region by multiplex PCR. 1. Add components including 25 μL Platinum™ Multiplex PCR Master Mix, 16 μL Forward Primers (10 μM), 4.8 μL Reverse Primers (10 μM), 100 ng genomic DNA, and appropriate volume of water ensuring 50 μL total reaction volume to EP tube.

T Cell Receptor Repertoire Sequencing


2. Place the EP tube on thermocycler (95  C for 2 min; 30 cycles of 98  C for 30 s, 64.3  C for 30 s, 72  C for 20 s; 72  C for 5 min; 4  C hold). 3. Add 50 μL (ratio 1.0) of Agencourt™ AMPure XP beads to reaction tube, mix thoroughly by pipetting. Incubate at RT for 5 min. 4. Pellet the beads on a magnetic stand at RT for 2 min. 5. Carefully remove and discard the supernatant. 6. Wash the beads with 200 μL fresh 80% ethanol. Pellet the beads on a magnetic stand and carefully remove the ethanol. 7. Repeat step 6 in Subheading 3.2.1 for a total of two washes. 8. Resuspend the magnetic beads in 37 μL of low-EDTA TE buffer. Mix thoroughly by pipetting, and then incubate at RT for 1 min to release the DNA from the beads. 9. Pellet the beads on a magnetic stand at RT for 2 min. Transfer clear supernatant to a new PCR tube. After amplification and purification of the TCRβ-CDR3 targeted region, recommend using ABclonal Rapid DNA Lib Prep Kit (ABclonal, Boston, USA) or other similar products to construct TCRβ CDR3 library. The following are the main steps to construct library according to the standard procedure of ABclonal Rapid DNA Lib Prep Kit (ABclonal, Boston, USA). 3.2.2 End Preparation

1. Prepare end-preparation reaction mix including 10 μL End Prep Buffer, 3 μL End Prep Enzymes, and 37 μL Fragmented DNA in PCR tubes on ice. 2. Mix thoroughly by pipetting. 3. Place the EP tube on thermocycler (20  C for 30 min; 65  C for 30 min; 4  C hold) with a heated lid at 75  C.

3.2.3 Adaptor Ligation

1. Prepare the ligation reaction mix, including 50 μL End Prep Reaction Mix (Subheading 3.2.2), 16.5 μL Ligation MM, 2.5 μL Working Adaptor, and 3 μL Ligase Mix in PCR tubes on ice. 2. Incubate the reaction at 22  C for 15 min in a thermocycler without a heated lid, and then hold at 4  C.

3.2.4 Clean Up Ligated DNA

1. Add 56 μL (ratio 0.8) of Agencourt™ Ampure XP beads and mix well by pipetting. 2. Incubate the mixture at room temperature for 5 min. 3. Pellet the beads on a magnetic stand at RT for 2 min. 4. Carefully remove and discard the supernatant.


Huixin Lin et al.

5. Wash the beads with 200 μL fresh 80% ethanol. Pellet the beads on a magnetic stand and carefully remove the ethanol. 6. Repeat step 5 in Subheading 3.2.4 for a total of two washes. 7. Resuspend the magnetic beads in 21 μL of low-EDTA TE buffer. Mix thoroughly by pipetting, and then incubate at RT for 1 min to release the DNA from the beads. 8. Pellet the beads on a magnetic stand at RT for 2 min. 9. Transfer 20 μL of the supernatant to a new PCR tube. Store the library at 20  C until it is ready for QC, library quantification, or sequencing. 10. Take appropriate amount of library to satisfy the sequencing platform. 3.3

Data Analysis

3.3.1 Clean Data Obtained from Sequencing Data

In general, the sequencing data with Fastq format from the Illumina platform are raw data. Raw reads in sequencing data may contain primer, adaptor, and low-quality reads. It is necessary to perform quality control to obtain clean reads for the further data analysis (see Note 5). Cutadapt is a wide-used sequencing data filtering tool for highthroughput sequencing platform at a certain fault tolerance. It can recognize, cut, and remove the sequences of adapters, primers, and poly-A tails, and other types of unwanted sequence. The following steps are needed for obtaining the clean data with Cutadapt software. 1. Install the Cutadapt using the pip command in the Linux system: Input the command “pip3 install –user –upgrade cutadapt” for installing the software. ( en/stable/). 2. Trim low-quality reads: type the command “cutadapt -q 15, 10 -o output.fastq input.fastq”, then the 50 end will be trimmed with a cutoff of 15, and the 30 end will be trimmed with a cutoff of 10. The parameter “q” means that both the 50 end and the 30 end of each read were trimmed. The parameter “output.fastq” means the output file, and the parameter “input.fastq” refers to the input file. 3. Trim the adapter sequences for paired-end reads: type the command “cutadapt -b ADAPTER_1 -b ADAPTER_2 -o out.1.fastq -p out.2.fastq reads.1.fastq reads.2.fastq”. “b” represents that the sequence for the 5’or 30 (both possible) adapter will be trimmed. “o” refers to output file after trimming the adapter sequence for reads.1.fq; p means that another output file after trimming the adapter sequence for reads.2.fq.

T Cell Receptor Repertoire Sequencing 3.3.2 Analysis of Data Obtained from Amplification Sequencing for T Cell Receptor Repertoire


Primary analysis for immune repertoire usually includes three main processing steps: (a) align sequencing reads to reference V, D, J, and C genes of T-cell receptors; (b) assemble clonotypes using alignments results; (c) export alignment to text format file. MiXCR software was used for primary analysis in this part [11]. 1. Install the MiXCR software Browse the website ( mixcr/) and download latest MiXCR. MiXCR needs JAVA environment, so we download the Java SE Development Kit ( downloads/jdk12-downloads-5295953.html) and install it according to the instruction. After the installation is complete, we then start analysis of the TCR sequence by MiXCR. The detailed procedure for the TCR analysis is as follows: 2. Primary analysis for T cell receptor repertoire We select single analyze amplicon command in MiXCR for T-cell receptor repertoire analysis. The command for this analysis is as follows: java –jar mixcr.jar analyze amplicon

-s --

starting-material --5-end --3-end --adapters input_file1 [input_file2] analysis name

The parameter “species” refers to the organism, and the value “hs” refers to HomoSapiens. Possible values for parameter “starting-material” is “rna” or “dna.” The values for “5end” is “no-v-primers” that means no V gene primers or v-primers refers to V gene single primer/multiple. The values for “3-end” can be j-primers, j-c-intron-primers, c-primers, indicating J gene single primer/multiplex, J-C intron single primer/multiplex, C gene single primer/multiplex, respectively. Two possible values are no-adapters and adapterspresent. Both input files are input_file1 and input_file2. The parameter “analysis name” refers to the output file. After command running is complete, a tab-delimited text file is produced with information about all clonotypes assembled by CDR3 sequence. 3.3.3 Characteristics of TCR Results

After analysis by MiXCR, we get the TCR information that include clone count,clone fraction, target sequences, and so on. In order to obtain the characteristics of CDR3, further data analysis of the immune repertoire is required by the R package “tcR” [12] (see Note 6). It involves the calculation of diversity indices, calculation of V and J gene usage. Totally, it includes the following steps:


Huixin Lin et al.

1. Install R language and load the R package “tcR”: We choose one mirror to download R (https://cran.r-proj, and install it in windows system according to the instructions. Download the R package “tcR” from CRAN mirror ( tcR/index.html) and install the latest release version. Before starting the program, we load the “tcR” packages, the command is as follows: library(tcR)

2. Input data and data manipulation: The input data for tcR are tab-delimited files which is a default output of the MiXCR software. The following command can parse the input files (see Note 7). immdata1 $100,000) and sufficient laboratory facility and more computing resources are required [1].


Identification of Cancer-Related Fusion Genes Using Nanopore Sequencing

3.1 Accurate Identification of Fusion Genes Using Long-Read Nanopore Sequencing

There are about 25,000 different genes in the human genome. In the presence of tumors, genome-level breaks and reassembles often occur, fusion genes are created. In most cases, fusion genes can lead to abnormal transcripts and proteins, or gene expression disorders. Detection of gene fusion events is important for clinical diagnosis and prognosis. Some fusion genes are reported to be drug targets, and patients with such mutation may achieve complete remission. Tests for fusion genes can guide clinicians to develop a personalized therapy and avoid excessive or inadequate treatment. Fusion gene detection plays an important role in the selection of tumor-targeted drugs [9]. Recently, the nanopore sequencing technology is widely accepted on the market and MinION nanopore sequencing instrument from Oxford Nanopore Technologies (ONT) is one of the commercialized products. Its characteristics are single molecule sequencing, long sequencing reading, fast sequencing speed, realtime monitoring of sequencing data, and convenient carrying [10].

Nanopore Sequencing and Its Clinical Applications

3.2 Materials and Methods 3.2.1 Work Flow of Probe-Based Enrichment of Target Sequences


This work flow describes a method of targeted nanopore sequencing by combining protocols of the Ligation Sequencing Kit 1D and the IDT xGen Lockdown. The overall work flow includes steps from DNA fragmentation, target sequence enrichment, to nanopore library construction [11] (Fig. 2). DNA was extracted using QIAamp DNA Blood Mini Kit (Qiagen, Germany) and fragmented to 1–2 kb using fragmentase or ultrasound instrument. DNA library was constructed with the Ligation Sequencing Kit 1D (SQK-LSK108). The enrichment of DNA is using xGen Hybridization and Wash Kit. A customized blocking oligo is designed based on nanopore library adaptors and used in the hybridization [11].

3.2.2 Bioinformatic Tools for Fusion Junction Identification

Traditional NGS sequence alignment software cannot meet the requirements of MinION sequence alignment because the error rate of MinION sequencing data is relatively high, and even adjusting parameters cannot achieve any better alignment rate. MarginAlign is an alignment software, which is optimized based on LAST or BWA mem. MarginAlign can evaluate and correct the source of Minion sequencing errors, thus increasing the alignment efficiency with reference genome [12, 13]. High-confidence alignments ensure accurate identification of gene fusion and rearrangement.


The short reads of NGS affect the accuracy of fusion gene detection. The problem is particularly severe in cancer samples. Based on previous studies, structural variations from long reads with hundreds of copies measured by MinION were more reliable than those from the millions of short reads sequenced by the NGS platform. Currently, the accuracy of MinION sequencing is about 92%. For the discovery of pathogenic bacteria and alternative splicing, such sequencing accuracy can meet the demands, but for clinical tests require higher accuracy [12], with the continuous optimization of sequencing-related chemistry and base calling software, we can expert better sequence quality with longer read length and nanopore sequencing technology will have broader clinical applications.



Applications of Nanopore Sequencing in Human Genetic Diseases

4.1 Improved Diagnostics in Genetic Diseases with Long-Read Nanopore Sequencing

Next-generation sequencing technologies provide rapid and relatively cost-effective genomic sequencing. The technologies have revolutionized the field of human genetics. However, there are still limitations due to the short-read, for instance, making the detection of repetitive regions and large structural variations very challenging. Unlike short-read sequencing platforms, the nanopore sequencing technology can process complete fragments and offer ultra-long-read lengths over 2 Mb, thus provides a more complete


Xue Sun et al.



50 min

End-prep A A

PCR adapter ligation

PCR with rapid attachment primers



Userdefined Capture of probetemplate duplexes onto beads

Elute and PCR with rapid attachment primers


Attachment of rapid 1D sequencing adapters 10 min

Loading Fig. 2 The work flow of probe-based enrichment of target sequences and nanopore library construction. Specific probes were designed to capture long fragments containing gene fusion junction regions. The enriched target fragments were re-amplified and built into nanopore library

Nanopore Sequencing and Its Clinical Applications


view of genetic variation. Researches can benefit from the nanopore technology to carry out a variety of experiments including whole genome and targeted sequencing. For researchers who are interested in specific areas at high depth, the targeted sequencing approach is commonly employed. There are a range of targeted sequencing methodologies which are compatible with nanopore sequencing. Besides typical PCR and probe capture-based enrichment strategies, CRISPR/Cas9-based enrichment method also been developed for long, targeted DNA molecules [14]. 4.2 Long-range PCR-Based Targeted Sequencing

A team of researchers from the UK and USA utilized the Oxford Nanopore MinION in combination with long-range PCR to amplify and sequence the entire ~8 kb gene GBA [15]. Homozygous or biallelic mutations in the GBA gene can cause Gaucher disease (GD), the most common lysosomal storage disorder. PCR and traditional short-read DNA sequencing of GBA gene is complicated by the nearby pseudogenes (with up to 96% homology). The team design and validate a method for sequencing GBA using nanopore long-read sequencing. They extracted DNA, amplified an 8.9 kb sequence, then carried the barcoding step, library preparation, and sequencing. The data analysis work flow is illustrated in Fig. 3. According to the results, the team concluded that the nanopore sequencing technology can detect missense mutations and an exonic deletion in the difficult gene GBA, with the added advantage of phasing (Fig. 4).

4.3 Targeted, Amplification-Free DNA Sequencing Using CRISPR/Cas9

Conventional amplification-based enrichment methods can be limited by base composition (e.g., GC-rich content), bias (e.g., allele bias), and PCR product length. The CRISPR/Cas9 techniques to enrich for specific regions of interest can solve these challenges with preserving the epigenetic modification information at the same time. Timothy Gilpatrick from Johns Hopkins University utilized the CRISPR/Cas9 technique to detect 10/11 genomic loci with a median length of 18 kb by using the Oxford Nanopore MinION


Reads base called and demultiplexed (Albacore now recommended platform). FASTQ file output. Only ‘pass’ reads further analysed.


Reads mapped to reference genome (hg19) using NGMLR. Coverage calculated using bedtools.


SNVs detected using Nanopolish and structural variants detected using Sniffles.


True variants phased using WhatsHap.

Fig. 3 Bioinformatic pipeline designed for identifying SNVs and long Indels in GBA gene


Xue Sun et al.

Fig. 4 Detection and phasing of a 55‐base pair exonic deletion in one sample. Part of the reads are shown (Reference sequence NM_000157.3). The arrows point to eight selected SNVs (red: coding SNVs; blue: noncoding SNVs). Red box in this figure is the deletion. Red-colored reads and blue-colored reads are the different haplotypes

and Flongle [16]. The sequencing depth generated by the two flow cells were 165X and 30X, respectively. Their study showed that the CRISPR/Cas9 technique can simultaneously assessing single nucleotide variants (SNVs), structural variants (SVs), and CpG methylation. Figure 5 shows the schematic of Cas9 enrichment operation. Figure 6 demonstrates the structural variation results.


Detection of Integration Sites of Cancer-Related Viruses

5.1 Higher Detection Rate of Viral Integrations Using Long-Read Nanopore Sequencing

Approximately 15–20% of all cancers worldwide are associated with viral infections. To date, at least five DNA viruses, Epstein–Barr virus (EBV), Human papilloma virus (HPV), Merkel cell polyomavirus (MCV), Kaposi’s sarcoma-associated herpesvirus (KSHV or HHV-8), and Hepatitis B virus (HBV), and three RNA viruses, Hepatitis C virus (HCV), Human T lymphotropic virus type-1 (HTLV-1), and human immunodeficiency virus (HIV) have been shown to contribute to the development of human cancers though this number is likely to increase over time [17–20]. Previous studies have identified viruses in the tissues of cancer patient [21–26]. HPV sequences were detected in nearly all cervical carcinomas as well as in a subset of squamous cell carcinomas of the head and neck; HBV and HCV were reported to be associated with a subset of liver cancers; EBV gene expressed in a subset of gastric cancers. Oncogenic viruses contribute to tumorigenesis by inducing transformation of the infected cells. Viruses may induce sustained disorders of host cell growth and survival. Viral integration may also trigger DNA damage response (DDR) that many viruses need for their replication, and increases host genome instability [17].

Nanopore Sequencing and Its Clinical Applications







Dephosphorylate DNA ends



Introduce cuts with Cas9 P

End-Prep + Adaptor Ligation

Load to Sequencer




motor protein P dA

dA P




Fig. 5 Procedure of the Cas9 enrichment. ROI region of interest

The integration of viral DNA into the host genome is a critical step in their life cycle. Viral DNA integration into the host genome is considered one of the most important risk factors for the development of carcinoma [27–29]. It has been of great interest to understand the implications of integration and to determine whether it is involved in tumor formation [30–35]. The application of next-generation sequencing (NGS) has dramatically enhance our ability to explore the landscape of viral integration of both DNA and RNA viruses [32, 35]. However, there is a particularly challenge in identifying integrations within repeats or with structural rearrangements by using short-read sequencing. The development of long-read sequencing technology has the potential to increase the detection rate of viral integrations in repeat regions. We introduce two target-enrichment methods for identification of viral integration sites with long-read sequencing.


Xue Sun et al. Breast Cancer: 6kb deletion, chr5

GM12878: 72kb deletion, chr5 400 200 100 50 25 12 6 3

GM12878: 69kb deletion, chr6

72 kb

Log2 Coverage

Log2 Coverage


0 105100000 105160000 Genomic Coord (Chr5)

Log2 Coverage

GM12878: 155kb deletion, chr8 400 200 100 50 25 12 6 3

400 200 100 50 25 12 6 3


coverage [0-80]


reads coverage [0-200]

69 kb



coverage [0-80] MCF-7

0 78260000 78320000 Genomic Coord (Chr5)

reads 18 kb 6 kb Breast Cancer: 8kb deletion, chr7

paternal coverage maternal coverage

coverage [0-400] reads


155 kb coverage [0-400] reads


coverage [0-150] MCF-7

0 39400000


39500000 Genomic Coord(Chr8)

20 kb 8 kb

Fig. 6 Structural variation results: (a) There are three deletions in the GM12878 lymphoblast cell line and the reads are segregated into paternal and maternal allele. Yellow triangles are the Cas9 cut site. (b) Two deletions detected in cell lines MDA-MB-231 and MCF-7 except for MCF-10A cell line 5.2 Materials and Methods 5.2.1 Xdrop Technology for Detecting HPV18 Integration Sites

The Xdrop technology is applied for identification of HPV18 integration sites (Fig. 7), and main steps were described in details as follows. 1. PCR and droplet chemicals Carcinoma cell line DNA (New England Biolabs) was diluted with DNase-free water (Gibco) to 0.5 ng per μL prior to use. PCR-mix for 20 μL was set up as Table 4. All reagent provided by Thermo Scientific. The sequences of primers were shown in Table 5. For the primary droplet production 3% fluorosurfactant (RAN Biotechnologies) in Novec HFE-7500 was used as carrier phase. For the secondary droplets, a DE-buffer containing 1.5 Optima buffer (40 mM Tris–HCl, 60 mM Trizma-base, 25 mM (NH4)2SO4, 0.015% Tween 80, 45 mM NaCl) and 3% glycerol was used as carrier phase. 2. Droplet production Double emulsion droplets were produced using a two-step emulsification procedure, initially creating water in oil (W-O) droplets followed by second emulsification to create water-inoil-in-water (W-O-W) droplets.

Nanopore Sequencing and Its Clinical Applications



Sample DNA

1. PCR reagents and DNA

2. Encapsulate

3. PCR detection

4. Sort

5. Collect

6. Amplify DNA

Fig. 7 Overview of Xdrop enrichment work flow. PCR reagents including primers are mixed with sample DNA (1) before being encapsulated in DE droplets (2). Droplet PCR allows fluorescence-based detection of the DNA molecules of interest (3) that are then sorted out on a cell sorter (4). The DNA from the sorted droplets is initially collected (5) and amplified using droplet MDA (6). The orange DNA helixes depicts the target DNA of interest and grey DNA helixes depicts non-target DNA Table 4 Reaction mix for droplet amplification Reagent

Volume (μL)

10 PCR-buffer without detergent


25 mM MgCl2


2 mM dNTP


50% Glycerol


GoTaq polymerase (5 U/μL)


Bovine serum albumin (2 mg/mL)


Forward/reverse primer (10 μM)


TP1 Probe (μM)


0.5 ng/μL DNA






Table 5 Specific primers for target viral sequences Primer

Sequences (50 –30 )

Forward primer


Reverse primer


TP1 probe



Xue Sun et al.

3. Primary droplets (W-O) The initial chip used to prepare the primary emulsion was a 14 μm etch depth hydrophobic “Small Droplet Chip, 14 μm” (Dolomite Microfluidics). Liquids were pushed into the microfluidic chip using MFCS-EZ pressure controller (Fluigent, Germany) applying pressures of 640 mbar on the primary sample (PCR) and 650 mbar to the secondary liquid (Oil). Droplet production was done for about 40 min, processing a total of 40 μL PCR mixture. 4. Secondary emulsions (W-O-W) Immediately following primary production, droplets were collected in PTFE-tubes using a 1 mL syringe (Scientific Glass Engineering, Australia) ensuring an air-free liquid system to pull the droplets into the tube. The tube was then connected to the inlet-position of the 4-way Linear Connector (Dolomite microfluidics, UK connector (Part number 3000024). Droplets were pushed into the chip at 0.25 μL/min using a Legato 110 syringe pump (KD Scientific). During secondary droplet production, spacer oil was applied into the chip using a syringe system identical to that carrying the droplets, delivering oil to space the introduced droplets prior to the second emulsification. Spacer oil was connected to position 2 in the connector using a syringe pump set to deliver a flow of 0.40 μL/min. Double emulsion buffer (DE) was introduced to the chip using a Legato 100 (KD Scientific) single syringe pump applying pressure to a 10 mL syringe (Scientific Glass Engineering, Australia). Pump speed of the DE-buffer was set to 28 μL/ min. Second emulsification was performed for 160 min until all primary droplets had passed the junction of the DE-chip. 5. Droplet sorting and gating Sorting was carried out on a single laser 488 nm, S3e cell sorter (BioRad inc.) using ProSort software (v. 1.3b). Instrument PMTs were adjusted to: FSC ¼ 239, SSC ¼ 261, FL1 ¼ 590, and FL2 ¼ 367. FSC was used as primary threshold and the value was set to 1.00. Gating of positive droplets was performed in three consecutive gating events. First gate was set to discriminate between double emulsion droplets and “other” elements in the carrier buffer. The second gate was used to split the double emulsion droplets from the first gate into fluorescent and non-fluorescent double emulsion droplets. The third gate was applied to ensure that only positive droplets with the expected properties were sorted. Sorting purity was set to “Enrich” and event rate was kept as close to 4000 events/second as possible throughout the experiment. Prior to sorting droplets, 5 μL Tris (10 mM) was placed at the bottom of the 1.5 mL collection tube to avoid disrupting the sorted droplets. Sorting was done for a period of 27 min and a

Nanopore Sequencing and Its Clinical Applications


total of 143 positive double emulsion droplets were sorted. Upon completed sorting, the collection tube was centrifuged at 1000  g for 10 s to collect any liquid from the side of the tube, arising from the splash impact of sorted droplets hitting the liquid surface of inside tube. 6. DNA amplification The collected droplets were coalesced by adding 20 μL of PicoBreak (Sphere Fluidics), mixing, and centrifuging the sample. 3 μL of the resulting aqueous-phase was used as template for a multiple displacement amplification (MDA) reaction. The MDA reaction mix was kindly provided by Samplix. The MDA reaction was emulsified on a x-junction droplet generator chip (ChipShop) using 1% PicoSurf in 7500-Novec oil as carrier phase. The droplet production was driven by air pressure controlled by pressure regulator (Fluigent). The droplets containing the MDA reaction were incubated for 16 h at 30  C followed by 10 min at 65  C to terminate the reaction. 6ul from the MDA reaction was used as template for a second droplet MDA reaction. After each MDA round the emulsions where coalesced using 20 μL PicoBreak. 7. Nanopore sequencing An ONT library was produced from 400 ng of enriched DNA using the Rapid Sequencing (SQK-RAD003) protocol. The library was sequenced on 1 MIN106 flowcell (R9.4) for 17.5 h with subsequent Albacore basecalling (v2.3.4). All was performed using standard settings according to manufacturer’s recommendations. 8. Detection of HPV18 integration sites HPV18 integration sites were identified by mapping all sequence reads to the HPV18 reference genome. The reads mapping to HPV18 were subsequently re-mapped to the human reference genome GRCh38 (hg38). Reads mapping to both genomes were considered HPV18/Chr8 fusion-reads (Fig. 8). 9. Sanger sequencing PCR primers located on each side of the fusion points were designed and used to generate PCR amplicons across the fusion point. These PCR products were Sanger sequenced using one of the PCR primers. 5.2.2 Pooled CRISPR Inverse PCR Sequencing (PCIP-seq) for Sequencing the Integration Sites and Its Associated Provirus

1. Work flow of RCIP-seq DNA isolation was extracted using the Qiagen AllPrep DNA/RNA/miRNA kit. High molecular weight DNA was sheared to ~8 kb using Covaris g-tubes™ (Woburn, MA) or a Megaruptor (Diagenode), and then was used to make the libraries by end-repair using the NEB Next End Repair Module


Xue Sun et al.

Fig. 8 Detection of HPV18 integration sites by Xdrop enrichment and long-read sequencing. (a) Overview of the complete HPV18 genome with genes depicted in grey. Below the three types of integrated HPV18 found in the HeLa genome. The breakpoints are shown with numbered circles. (b) Overview of the HPV18 fusion points and the suggested structure of the integrations identified in the HeLa genome. The positions of the chromosome 8 integration sites refer to the GRCh38 genome assembly. The position of the primers used for enrichment is shown with red P’s. The numbered circles correspond to the numbering in (a)

(New England Biolabs). Intramolecular circularization was incubated by overnight at 16 Cwith T4 DNA Ligase. Remaining linear DNA was removed with Plasmid-Safe-ATP-Dependent DNAse (Epicentre, Madison WI). Guide RNAs and the final oligo sequence were designed using chopchop (http:// and the EnGen™ sgRNA Template Oligo Designer ( #!/sgrna), respectively. Oligos were synthesized by Integrated DNA Technologies (IDT). Oligos were pooled and guide RNAs synthesized with the EnGen sgRNA Synthesis kit, S. pyogenes (New England Biolabs). Selective linearization reactions were carried out with the Cas-9 nuclease, S. pyogenes (New England Biolabs). PCR primers which were tailed to facilitate the addition of Oxford Nanopore indexes in a subsequent PCR reaction were designed using primer3 ( The linearized fragments were amplified with LongAmp Taq DNA Polymerase (New England Biolabs) and a second PCR added the appropriate Oxford Nanopore index. PCR products were verified via gel electrophoresis and quantified on a nanodrop spectrophotometer. Indexed PCR products were multiplexed and Oxford Nanopore libraries performed either using the Ligation Sequencing Kit 1D (SQK-LSK109). The resulting libraries

Nanopore Sequencing and Its Clinical Applications


Fig. 9 Overview of the PCIP-seq method. (a) Simplified outline of method (b) A pool of CRISPR guide-RNAs targets each region, the region is flanked by PCR primers. Guides and primers adjacent to 50 & 30 LTRs are multiplexed. (c) As the region between the PCR primers is not sequenced, we created two sets of guides and primers. Following circularization, the sample is split, with CRISPR-mediated cleavage and PCR occurring separately for each set. After PCR, the products of the two sets of guides and primers are combined for sequencing

were sequenced on Oxford Nanopore MinION R9.4 flow cells, respectively, and basecalled using albacore 2.3.4 according to the manufacturer’s instructions. Only the 1D reads from both flow cell versions were used (Fig. 9a). 2. Bioinformatic pipeline for identification of viral integration sites Reads were mapped with Minimap ( packages/minimap) to the host genome with the viral genome as a separate chromosome (Fig. 9b). In-house R-scripts were used to identify integration sites (IS). Briefly, chimeric reads that partially mapped to at least one extremity of the viral genome were used to extract virus-host junctions and shear sites (Fig. 9c). Junctions within a 200 bp window were clustered together to form an “IS cluster,” compensating for sequencing/mapping errors. The IS retained corresponded to the position supported by the highest number of virus-host junctions in each IS cluster. Clone abundance was estimated based on the number of reads supporting each IS cluster, reads with the same shear site were considered PCR duplicates.



Xue Sun et al.


There is a wide application of target enrichment methods for shortread sequencing technologies, but only few are compatible with the long-read sequencing platforms [36]. In this report, we described two enrichment methods that compatible with nanopore sequencing. The Xdrop technology isolates long DNA fragments by standard laboratory cell sorter (FACS) of double emulsion (DE) droplets [37]. A unique advantage of the Xdrop work flow is that the DNA amplification can be initiated from femtogram amounts of target DNA, compared to other enrichment protocols were nanograms, or even micrograms, of DNA is required. The method described here can conveniently be applied to other targets, where structural information is sought, by design of a simple primer-set. With an efficient droplet production and optimization of the final amplification, the procedure can be completed in less than 24 h. PCIP-seq can be utilized to identify integration sites while also sequencing the associated provirus [38]. For integration site identification, the method was capable of identifying more than ten thousand BLV integration sites in a single sample, using ~4 μg of template DNA. Even in samples with a PVL of 0.66%, it was possible to identify hundreds of integration sites with only 1 μg of DNA as template. The improved performance of PCIP-seq in repetitive regions further highlights its utility, strictly from the standpoint of integration site identification. In addition to its application in research, high-throughput sequencing of virus integration sites has shown promising clinical tool to monitor viral progression, especially in a clinical context to track clonal evolutions in areas with poor biomedical infrastructure. Other potential applications include determining the integration sites and integrity of retroviral vectors, detecting transgenes in genetically modified organisms or identifying on target CRISPR–Cas9-mediated structural rearrangement that could be missed by conventional long-range PCR. Hybridization directly using short DNA- or RNA-probes are also used to pull down the target sequences. Hybridization-based methods can be used to enrich fragments with size up to 10 kb using short probes (500 ng) [39]. There are large variety of techniques used to detect integration sites based on previous studies [33]. Target enrichment can be a highly cost- and time-effective and a promising technology for point-of-care testing to identify the disease-associated variants [40].

Nanopore Sequencing and Its Clinical Applications


References 1. Norris AL, Workman RE, Fan YF et al (2016) Nanopore sequencing detects structural variants in cancer[J]. Cancer Biol Ther 17 (3):246–253 2. Gong L, Wong C, Cheng W et al (2017) Nanopore sequencing reveals high-resolution structural variation in the cancer genome [J]. bioRxiv 3. Sanchisjuan A, Stephens J, French C et al (2018) Complex structural variants in Mendelian disorders: identification and breakpoint resolution using short- and long-read genome sequencing[J]. Genome Med 10(1):1–10 4. 5. Miao HF, Zhou JP, Yang Q et al (2018) Longread sequencing identified a causal structural variant in an exome-negative case and enabled preimplantation genetic diagnosis. Hereditas 155(32) 6. Mitsuhashi S, Frith MC, Mizuguchi T et al (2019) Tandem-genotypes: robust detection of tandem repeat expansions from long DNA reads[J]. Genome Biol 20(1):58 7. Stancu MC, Van Roosmalen MJ, Renkens I et al (2017) Mapping and phasing of structural variation in patient genomes using nanopore sequencing[J]. Nat Commun 8(1):1326–1338 8. Au CH, Dona N, Beca B (2019) K. Ip et al. Rapid detection of chromosomal translocation and precise breakpoint characterization in acute myeloid leukemia by nanopore longread sequencing. Cancer Genet 239:22–25 9. Soda M, Choi YL, Enomoto M et al (2007) Identification of the transforming EML4–ALK fusion gene in non-small-cell lung cancerJ. Nature 448(7153):561–566 10. Jain M, Olsen HE, Paten B et al (2016) The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics communityJ. Genome Biol 17(1):239 11. Nanopore Protocol 1D Sequence capture (SQK-LSK108) 12. Jain M, Fiddes IT, Miga KH et al (2015) Improved data analysis for the MinION nanopore sequencer[J]. Nat Methods 12(4):351 13. marginAlign 14. Oxford Nanopore Technologies. Incorporating sequence capture into library preparation for MinION GridION and PromethION. Online. Available at: https://nanoporetech. com/resource-centre/incorporatingsequence-capture-library-preparation-miniongridion-and-promethion. Accessed 29 August, 2019

15. Leija‐Salazar M et al (2019) Evaluation of the detection of GBA missense mutations and other variants using the Oxford Nanopore MinION. Mol Genet Genomic Med 7(3):e564 16. Gilpatrick T et al (2019) Targeted nanopore sequencing with Cas9 for studies of methylation, structural variants, and mutations. BioRxiv:604173 17. Mesri E, Feitelson MA, Munger K (2014) Human viral oncogenesis: a cancer Hallmarks analysis. Cell Host Microbe 15(3):266–282 18. Plummer M, Martel CD, Vignat J, Ferlay J, Bray F, Franceschi S (2016) Global burden of cancers attributable to infections in 2012: a synthetic analysis. Lancet Glob Health 4(9): e609–e616 19. Tashiro H, Brenner MK (2017) Immunotherapy against cancer-related viruses. Cell Res 27 (1):59–73 20. Tuna M, Amos CI (2017) Next generation sequencing and its applications in HPV-associated cancers. Oncotarget 8 (5):8877 21. Strong MJ, Tina OG, Zhen L, Guorong X, Melody B, Chris P, Kun Z, Taylor CM, Flemington EK (2013) Epstein-Barr virus and human herpesvirus 6 detection in a non-Hodgkin’s diffuse large B-cell lymphoma Cohort by using RNA sequencing. J Virol 87 (23):13059–13062 22. Tang KW, Alaeimahabadi B, Samuelsson T, Lindh M, Larsson E (2013) The landscape of viral expression and host gene fusion and adaptation in human cancer. Nat Commun 4 (2513):2513 23. Network CGA (2014) Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513(7517):202–209 24. Network CGA (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507(7492):315–322 25. Network CGA (2015) Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517(7536):576 26. Cantalupo PG, Katz JP, Pipas JM (2017) Viral sequences in human cancer. Virology 513:208–216 27. Hanahan D, Weinberg R (2011) Hallmarks of cancer: the next generation. Cell 144 (5):646–674 28. Agalioti T, Lomvardas S, Parekh AB, Yie J, Maniatis T, Thanos D, Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100 (1):57–70


Xue Sun et al.

29. Gutschner T, Diederichs S (2012) The hallmarks of cancer. RNA Biol 9(6):703–719 30. Xu M, Zhang WL, Zhu Q, Yao YY, Feng QS, Zhang Z, Peng RJ, Jia WH, He GP, Feng L (2019) Genome-wide profiling of Epstein-Barr virus integration by targeted sequencing in Epstein-Barr virus associated malignancies. Theranostics 9(4):1115 31. Murakami Y, Saigo K, Takashima H, Minami M, Okanoue T, Bre´chot C, Paterlinibre´chot P (2005) Large scaled analysis of hepatitis B virus (HBV) DNA integration in HBV related hepatocellular carcinomas. Gut 54 (8):1162–1168 32. Pett M, Coleman N (2010) Integration of high-risk human papillomavirus: a key event in cervical carcinogenesis? J Pathol 212 (4):356–367 33. Pinatti LM, Walline HM, Carey TE (2018) Human papillomavirus genome integration and head and neck cancer. J Dent Res 97 (6):691–700 34. Zhang J, Huang T, Zhou Y, Cheng A, Yu J, To KF, Kang W (2018) The oncogenic role of Epstein-Barr virus-encoded microRNAs in Epstein-Barr virus-associated gastric carcinoma. J Cell Mol Med 22(1):38–45 35. Symons J, Cameron PU, Lewin SR (2018) HIV integration sites and implications for

maintenance of the reservoir. Curr Opin HIV and AIDS 13(2):152 36. Ameur A, Kloosterman WP, Hestand MS (2019) Single-molecule sequencing: towards clinical applications. Trends Biotechnol 37 (1):72–85 37. Madsen EB, Kvist T, Ho¨ijer I, Ameur A, Mikkelsen MJ (2018) Xdrop: targeted sequencing of long DNA molecules from low input samples using droplet sorting. bioRxiv:409086 38. Artesi M, Hahaut V, Ashrafi F, Marc¸ais A, Hermine O, Griebel P, Arsic N, van der Meer F, Burny A, Bron D et al (2019) Pooled CRISPR Inverse PCR sequencing (PCIP-seq): simultaneous sequencing of retroviral insertion points and the associated provirus in thousands of cells with long reads. bioRxiv:558130 39. Wang M, Beck CR, English AC, Meng Q, Buhay C, Han Y, Doddapaneni HV, Yu F, Boerwinkle E, Lupski JR (2015) PacBioLITS: a large-insert targeted sequencing method for characterization of human diseaseassociated chromosomal structural variations. BMC Genomics 16(1):214 40. Mamanova L, Coffey AJ, Scott CE, et al. (2010) Target-enrichment strategies for nextgeneration sequencing (vol 7, pg 111) [J]. Nat Methods 7(6):479–479

Chapter 3 The Clinical Significance of Microsatellite Instability in Precision Treatment Zhenyu Huang, Xiaojian Chen, Chenying Liu, and Long Cui Abstract The recent years have seen the high heterogeneity of colorectal cancer (CRC) receiving increasing attention and being revealed step by step. Microsatellite instability (MSI), characterized by the dysfunction of mismatch repair gene, plays an important role in the heterogeneity of colorectal cancer. MSI status can be identified by immunohistochemistry for MMR protein such as MLH1, MSH2, PMS2, and MSH6 or PCR-based array for MMR gene. Recent studies have revealed MSI status is the only biomarker that can be used to select patients with high-risk stage II colon cancer for adjuvant chemotherapy. Furthermore, it always indicated better stage-adjusted survival when compared with microsatellite stable (MSS) tumors. For immunotherapy, patients with MSI tumors exhibited significant response to anti-PD-1 inhibitors after the failure to conventional therapy. In this chapter, we discuss the detection methods of MSI, the prognostic value of MSI, and its clinical guiding value in the management of precision therapy. Key words Microsatellite instability, colorectal cancer, microsatellite stable, DNA mismatch repair, immunotherapy


Introduction Recent study has demonstrated colorectal cancer (CRC) as the third most common cancer in males and the second in females [1]. Among these abundant cases, CRC is a heterogeneous and molecularly complex disease [2]. The complexity of CRC leads to different prognosis in patients with CRC, as well as different response to conventional treatment and novel-target therapy, which requires more accurate and individualized treatment towards all kinds of subgroups. In early-staged CRC, including stage I-II, high-risk stage II, stage III which exist with regional lymph node metastases, we all have an agreed account of that deficient on DNA mismatch repair (dMMR) genes is the only biomarker for them to decide whether they can get survival benefits through adjuvant chemotherapy [3, 4]. MSI (microsatellite instability) which is mainly caused by

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Zhenyu Huang et al.

dMMR is beyond doubt that the most valuable genetic characteristic and an integral part of tumor heterogeneity. MSI related earlystaged CRC display high degrees of microsatellite instability as a result of defects in genes involved in the DNA mismatch repair (MMR) pathway such as MLH1, MSH2, MSH6, and PMS2 [5] or a hypermethylation of the MHL1 promoter [6, 7]. Consequently accumulation of high-level mutations leads to cancer susceptibility. In this review, we discuss the detection methods of MSI, the prognostic value of MSI, and its clinical guiding value in the management of precision therapy.


Cause of MSI Microsatellite sequence, a short tandem repeat, is a repeat of a single base or base fragment (1 to 6 bases) usually comprise of 10 to 60 repetitions, they are scattered throughout the genome, mostly in non-coding regions [8]. These extensions constitute frequent hotspots of DNA polymerase slip during DNA replication, resulting in an increase or decrease in repeat nucleotides [5]. Typically, these errors result in unpaired nucleotides that are recognized and excised by the DNA Mismatch Repair System (MMR) which is a group of ribozymes that play a role in DNA replication and then recombined into the affected portion [9]. If the DNA mismatch base repair enzyme is lacking, the accumulation of replication error DNA cannot be repaired in the proliferating cells and result in cancer susceptibility. Missing mismatch repairs can cause abnormalities in the genome to repeat tens to hundreds of nucleotide base short sequences, called microsatellite instability (MSI), which corresponds to microsatellite stability (MSS). The core of the mismatch repair protein consists of two heterogeneous proteins (MSH2/MSH6 or MLH1/PMS2), so inactivation of one or more MMR genes such as MLH1, MLH2, MSH6, and PMS2 can lead to MSI [10].


Detection of MSI As one of the useful prognostic markers in patients with CRC, the detection of microsatellite status is strongly recommended. The common detection methods of microsatellite status are immunohistochemistry for MMR proteins and PCR-based assay for microsatellite markers. These methods identify MSI from different aspects and each has its own advantages and limitations. Immunohistochemistry identifies MSI by assessing the staining intensity of specific MMR protein including MLH1, MSH2, MSH6, and PMS2. If all MMR proteins are present, the microsatellite status is defined as microsatellite stable (MSS). When there is a

The Clinical Significance of Microsatellite Instability in Precision Treatment


loss of MMR protein, a further detection such as PCR or IHC for BRAF mutation is required, which assists in distinguishing MSI from Lynch syndrome [11]. Microsatellite instability detection by PCR was firstly recommended by a National institute workshop in 1997 [12, 13]. This method evaluates the microsatellite status of tumors by amplifying microsatellite repeats including three dinucleotide markers (D5S346, D2S123, and D17S250) [12, 14] and two mononucleotide markers (BAT25 and BAT26) [12]. MSS was defined as no instability at markers as above, where MSI-H represented there are more than one marker exhibiting instability. And MSI-L was used to describe the intermediate phenotype.


MSI: Prognostic Value Microsatellite instability (MSI) phenotype—a defined subgroup of CRC account for approximately 10–15% of patients shows a high mutation rate in the genomic DNA sequence [15]. The identification of MSI has important clinical significance for several reasons, of which the prognostic value is most obvious. Although how MSI status affects prognosis of CRC patients is not completely understood, abundant studies have demonstrated that MSI always represents a better prognosis especially in cases of locally advanced stage II and stage III CRC [16]. Furthermore, patients with the mutations in MLH1 and MSH2 are at a higher risk of CRC and a more frequent CRC monitoring including FOBT and colonoscopy is required [17]. Prophylactic colectomy is considered if necessary. Meanwhile, the detection of MSI contributes to the diagnosis of Lynch syndrome, an inheritable disease. Individuals with Lynch syndrome have significantly higher risks of developing extracolonic malignancies in addition to early onset of CRC [18]. Intensive cancer surveillance has been shown to substantially reduce cancerrelated death in this group of patients.


MSI: Response to Chemotherapy The current 5-FU-based chemotherapy like FOLFOX and FOLFIRI is the recommended option for high-risk stage 2 and stage 3 CRC. However, a series of studies have shown that patients with MSS colorectal cancer benefit more from 5-Fu chemotherapy [19]. If MSI colorectal cancer is compared with untreated MSS colorectal cancer patients, the former has a better prognosis. However, as long as the treatment is received, the situation is different because the majority of patients observed to be affected by chemotherapy are stage III patients, but most patients with MSI colorectal cancer are diagnosed in stage II. There is no complete study to fully


Zhenyu Huang et al.

predict the different response rates of chemotherapy to MSI and MSS colorectal cancer. Sargent et al. shows that in stage II colorectal cancer, MSI patients receiving 5-Fu monotherapy had a worse prognosis (recurrence rate 30%) than MSS patients, while untreated MSI patients had a better prognosis (recurrence rate 15%), which indicated MSI status as a strong predictive biomarker for nonresponse to 5FU-based chemotherapy [4]. A subgroup analysis of MOSAIC study has confirmed adjuvant therapy containing oxaliplatin is of benefit for patients with stage III MSI tumors [20]. Therefore, it is urgent to investigate the inner mechanism of ineffective 5FU-based chemotherapy. Other therapeutic biomarkers are currently being developed, ranging from immunohistochemical analysis to high-end genomic approaches. Tajima et al. revealed the reason may contribute to that the MSH2-MSH6 mismatch repair complex is required for binding 5FU after its incorporation into DNA and for triggering cell death [21]. In the MAVERICC trial, the expression of the excision repair crosscomplementation group 1 (ERCC1) [22] gene is being investigated as a potential predictive marker of resistance to platinum compounds [23]. And in the study of Tabernero, J. et al. detection of mutations in circulating DNA can predict efficacy to regorafenib [24]. Until now, the predictive and guiding value of MSI for chemotherapy efficacy has not been shaken.


MSI: Response to Immunotherapy The existing research has found that almost all tumors with mismatched repair gene defects have high activity of Th1/CTL in the immune microenvironment, and tumor tissues selectively express a variety of immune checkpoint factors including programmed death-1 (PD-1), programmed death-1 ligand 1 (PD-L1), cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), lymphocyte-activation gene (LAG-3), and indoleamine 2,3-dioxygenase (IDO) in order to balance the microenvironment [25, 26]. This is a good explanation for why MSI tumor patients cannot rely on highly active Th1/CTL in the tumor microenvironment for tumor killing. At present, there are clinically relevant inhibitors for the abovementioned immunological checkpoint molecules, and selective application of immunotarget inhibitors for MSI patients may achieve better therapeutic benefit. Compared with traditional chemotherapy, immunotherapy has the characteristics of low toxicity and long-lasting effect. With the success of targeted therapy using antibodies against immune checkpoint, such as PD-1 in various tumors including lung cancer and melanoma, the application and efficacy of immunotherapy in CRC have received more attention [27].

The Clinical Significance of Microsatellite Instability in Precision Treatment


Recently, U.S. Food and Drug Administration (FDA) approval of two PD-1 inhibitors—nivolumab [28](with or without low-dose ipilimumab which is a CTLA-4 blockade [29]) and pembrolizumab for MSIH/dMMR mCRC after progression on chemotherapy [27]. Several single-arm trials have shown impressive response rates with nivolumab in patients with MSI-H and d-MMR colorectal cancers [27, 28, 30–32]. Target molecules for other checkpoint inhibitors such as lymphocyte activation gene-3 (LAG3) [33] and indoleamine 2,3-dioxygenase (IDO) are under investigation. Currently, studies have been conducted on broad spectrum combination of checkpoint inhibitors, even along with traditional chemotherapeutics, to investigate potential survival benefit for MMR proficient patients which were not sensitive to immunotherapy.


Conclusion The incidence of colorectal cancer worldwide is constantly increased, and it has become much clear that CRC is a complex and heterogeneous disease. DMMR/MSI CRC as an important subtype responsible for approximately 15% of them, the molecular target genes that are differentially regulated between MSI and MSS cancers require further clarification. The more vertically refined the molecular subtype classification of CRC, the better individualized and precise treatment for different molecular types of patients, as well as the substantial clinical benefit.

References 1. Siegel RL et al (2017) Colorectal cancer statistics. CA Cancer J Clin 67(3):177–193 2. Athauda A et al (2019) Integrative molecular analysis of colorectal cancer and gastric cancer: what have we learnt?, Cancer Treat Rev. 73:31–40 3. Hutchins G et al (2011) Value of mismatch repair, KRAS, and BRAF mutations in predicting recurrence and benefits from chemotherapy in colorectal cancer. J Clin Oncol 29 (10):1261–1270 4. Sargent DJ et al (2010) Defective mismatch repair as a predictive marker for lack of efficacy of fluorouracil-based adjuvant therapy in colon cancer. J Clin Oncol 28(20):3219 5. Yamamoto H, Imai K (2015) Microsatellite instability: an update. Arch Toxicol 89 (6):899–921 6. Kim KJ et al (2014) Prognostic implications of tumor-infiltrating FoxP3+ regulatory T cells and CD8+ cytotoxic T cells in microsatellite-

unstable gastric cancers. Hum Pathol 45 (2):285–293 7. Yamamoto H et al (2014) An updated review of gastric cancer in the next-generation sequencing era: insights from bench to bedside and vice versa. World J Gastroenterol 20 (14):3927–3937 8. Vilar E, Gruber SB (2010) Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol 7(3):153–162 9. Kim TM, Laird PW, Park PJ (2013) The landscape of microsatellite instability in colorectal and endometrial cancer genomes. Cell 155 (4):858–868 10. Li GM (2013) Decoding the histone code: role of H3K36me3 in mismatch repair and implications for cancer susceptibility and therapy. Cancer Res 73(21):6379–6383 11. Jass JR (2006) Hereditary non-polyposis colorectal cancer: the rise and fall of a confusing


Zhenyu Huang et al.

term. World J Gastroenterol 12 (31):4943–4950 12. Boland CR et al (1998) A National Cancer Institute Workshop on microsatellite instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. Cancer Res 58 (22):5248–5257 13. Rampino N et al (1997) Somatic frameshift mutations in the BAX gene in colon cancers of the microsatellite mutator phenotype. Science 275(5302):967 14. Perucho M, Boland CR et al (1998) A National Cancer Institute workshop on microsatellite instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. Cancer Res 58:5248–5257 15. Ionov Y et al (1993) Ubiquitous somatic mutations in simple repeated sequences reveal a new mechanism for colonic carcinogenesis. Nature 363(6429):558–561 16. Moertel CG et al (1995) Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: a final report. Ann Intern Med 122(5):321–326 17. Koessler T et al (2008) Common variants in mismatch repair genes and risk of colorectal cancer. Gut 57(8):1097–1101 18. Lynch HT et al (2006) Phenotypic and genotypic heterogeneity in the Lynch syndrome: diagnostic, surveillance and management implications. Eur J Hum Genet 14 (4):390–402 19. Smyth EC et al (2017) Mismatch repair deficiency, microsatellite instability, and survival: an exploratory analysis of the medical research council adjuvant gastric infusional chemotherapy (MAGIC) trial. JAMA Oncol 3 (9):1197–1203 20. Andre T et al (2015) Adjuvant fluorouracil, leucovorin, and oxaliplatin in stage II to III colon cancer: updated 10-year survival and outcomes according to BRAF mutation and mismatch repair status of the MOSAIC study. J Clin Oncol 33(35):4176–4187 21. Tajima Y et al (2018) Prevalence and molecular characteristics of defective mismatch repair epithelial ovarian cancer in a Japanese hospitalbased population. Jpn J Clin Oncol 48 (8):728–735 22. Parikh AR et al (2019) MAVERICC, a randomized, biomarker-stratified, phase II study of mFOLFOX6-bevacizumab versus FOLFIRI-

bevacizumab as first-line chemotherapy in metastatic colorectal cancer. Clin Cancer Res 25 (10):2988–2995 23. Li P et al (2013) ERCC1, defective mismatch repair status as predictive biomarkers of survival for stage III colon cancer patients receiving oxaliplatin-based adjuvant chemotherapy. Br J Cancer 108(6):1238–1244 24. Tabernero J et al (2015) Analysis of circulating DNA and protein biomarkers to predict the clinical activity of regorafenib and assess prognosis in patients with metastatic colorectal cancer: a retrospective, exploratory analysis of the CORRECT trial. Lancet Oncol 16 (8):937–948 25. Galon J et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313 (5795):1960–1964 26. Le DA-O et al (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357(6349):409–413 27. Le DT et al (2015) PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 372(26):2509–2520 28. Overman MJ et al (2017) Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instabilityhigh colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study. Lancet Oncol 18(9):1182–1191 29. Omuro A et al (2018) xNivolumab with or without ipilimumab in patients with recurrent glioblastoma: results from exploratory phase I cohorts of CheckMate 143. Neuro-Oncology 20(5):674–686 30. Le DT et al (2016) Programmed death-1 blockade in mismatch repair deficient colorectal cancer. N Engl J Med 34 (15_suppl):103–103 31. Overman MJ et al (2018) Durable clinical benefit with nivolumab plus ipilimumab in DNA mismatch repair-deficient/microsatellite instability-high metastatic colorectal cancer. Clin Oncol 36(8):773–779 32. Andre T et al (2017) Combination of nivolumab (nivo) + ipilimumab (ipi) in the treatment of patients (pts) with deficient DNA mismatch repair (dMMR)/high microsatellite instability (MSI-H) metastatic colorectal cancer (mCRC): CheckMate 142 study. J Clin Oncol 35:3531–3531 33. Anderson AC, Joller N, Kuchroo VK (2016) Lag-3, Tim-3, and TIGIT: Co-inhibitory receptors with specialized functions in immune regulation. Immunity 44(5):989–1004

Chapter 4 Applications of Network Analysis in Biomedicine Steven Wang and Tao Huang Abstract The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug–target identification, and multi-level omics integration. Key words Network analysis, Random walk, Heat diffusion, Network embedding, Multi-omics


Introduction With the growing availability of large amounts of biological information acquired using detection methods such as high-throughput sequencing, methods such as machine learning and network analysis is becoming the state-of-the-art technique in manipulating large-scale datasets [1]. Implementations of network approaches have shown its capabilities of uncovering interacting components within a constructed network, such as protein–protein interactions or disease–gene relationships [2–7]. Depending on the specific case, there exists various methods of constructing these networks based upon different mathematical models, such as the co-expression network, the Bayesian network, and regression-based networks. These networks are not only a mean of data storage, but also the starting point of implementing various inference methods that perform tasks like classification or clustering using machine learning or even deep learning. Based on these networks, topology analysis can be used to uncover important features. We introduce the mathematical concept and several examples of common

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Steven Wang and Tao Huang

techniques, including module identification, network characteristics analysis, shortest path analysis, guilt by association, random walks, and heat diffusion. Furthermore, we give an overview of popular network embedding methods, which reduces the dimension of networks in order to increase computation efficiency and ease of visualization. Using different combinations of the techniques, recent studies have implemented network analysis in wideranging fields from cancer genetics to drug development. Building upon the current success of network analysis in biomedical fields, network approaches have much room to grow, especially by integrating multi-level omics data into integrative networks.


Methods of Constructing Networks

2.1 Co-expression Network Construction

Co-expression networks are most commonly used in genetics, where relationships between different genes can be represented by a co-expression network (GCN). By modelling the rise and fall of genetic expression across samples, interacting genes can be identified, which are often representative of specific pathways or regulator mechanisms. Specifically, the nodes of a GCN represent individual genes and the edges between nodes represent their co-expression relationship [8]. The edges can be binary (weight 0 or 1) to denote the presence of connection or can be a continuous value between 0 and 1 using soft thresholding methods such as the sigmoid function [9]. Namely, co-expression networks calculate the weight l þa with ωij ¼ min k ij, k ijþ1a , where lij ¼ Σaiuauj and ki ¼ Σaiu. The ð i jÞ ij  1 adjacency function calculates aij ¼ sigmoid s ij , α, τ0  αðs ij τ0Þ 1þe using the similarity matrix sij, which is calculated using the absolute value of the Pearson correlation coefficient sij ¼ j cor(i, j) j. A GCN can be constructed using expression profiles of a list of genes, where co-expressed genes show similar transcript levels. These networks are of interest because co-expressed genes are often functionally related or members of the same pathway, opening new means of associating unknown genes with known biological functions and processes. For example, a study by Ma et al. implemented a GCN to investigate Bamboo growth and identified 1896 functional modules associated with photosynthesis, hormone biosynthesis, cell wall biosynthesis, and more [10]. A commonly used tool to construct GCN is WGCNA [11].

2.2 Bayesian Network Construction

In general, Bayesian networks (BNs) are probabilistic graphical models that compute conditional dependence of random variables using Bayesian inference. By translating these dependencies into interconnected nodes, BNs provide a comprehensible and modular framework for representing complex systems [12]. BNs consist of

Applications of Network Analysis in Biomedicine


two components: a network structure in the form of a directed acyclic graph and a conditional probability for each node as defined below [12]. Xi is the individual node and Pa(Xi) is the set of all parents of Xi. A BN is then trained to optimize the parameters, namely the set of conditional probability distributions P(Xi | Pa(Xi)), to produce a network that best matches the training set. Search procedures are often used for efficiency in training, with a notable method being the Sparse Candidate algorithm introduced by Friedman, Nachman, and Pe’er [13]. Commonly used tools include BNT (opensourced) [14] and BayesiaLab (closed source https://www. 2.3 RegressionBased Network Construction


Regression networks model the relationship between nodes using linear models. Namely, a set of parameters a ¼ [a1, a2, ... an] models a linear weight between a node and its parents u ¼ [u1, u2, ... un]. Applications of regression-based networks include GeneReg, developed by Huang et al., which constructed a gene regulatory network modelling expression changes between regulators and target genes [15]. The network optimizes a time delay linear regression model by iteratively adding possible regulators of a target gene under an AIC (Akaike information criterion) model selection criteria [16].

Network Topology Analysis Network topology is general describes the structural arrangement of links and nodes within a network. In biomedicine, networks often model interacting components such as gene–gene, protein– protein, or drug–target interactions. Hence, the topology of a network contains a large amount of information of interest, and its analysis is key to the utilization of network approaches in various biomedical fields. Here, we introduce several important concepts that are associated algorithms adopted by researchers to perform predictive tasks.

3.1 Module Identification

A prominent feature of real-world networks is their modular structures, where the networks are often organized into distinct modules of highly interconnected nodes [17]. Modules are also known as groups, clusters, or communities in certain cases. In biology, modules typically correspond to sets of interacting components, such as protein complexes, metabolic pathways, or a set of interacting genes [18]. Thus, identifying modules can be of significant use in analyzing functional associations in omics studies. A common computational method to identify modules is by locating “hotspots,” which are subnetworks with high aggregate scores. Scores can be calculated and normalized using tools such as


Steven Wang and Tao Huang

jActiveModules [19]. Based on this information, there are many computational algorithms that search for modular structures. In genomics, for example, algorithms like PANOGA [20], dmGWAS [21], and PinnacleZ [22] have been implemented in studies to map gene or PPI networks [23–25]. In the fields of proteomics, on the other hand, tools like CFinder [26], mfinder [27], and FANMOD [28] are available for identifying novel protein modules and network motifs. 3.2 Network Characteristics Analysis

Networks have several attributes and characteristics that are central to its formation and our understanding of it. There are three defining characteristics to network topologies, being average path length (APL), clustering coefficient, and degree distribution. Average path length (APL), defined as the least number of steps between all possible pairs of nodes averaged over a number of nodes, is a measure of how well connected a network is. Clustering coefficient includes two variations, the global clustering coefficient and the local clustering coefficient. The global coefficient provides a measure of the level of clustering in the whole network. Its definition involves the concept of a triplet, which is simply three nodes connected with two (open triplet) or three edges (closed triplet). With that in mind, the global clustering coefficient is defined as the number of closed triplets over the total number of open and closed triplets. The local clustering coefficient, on the other hand, measures the degree of connection within a cluster. It is defined mathematically as the existing number of edges over the possible number of edges. The concept of degree distribution involves the term degree, which is simply the number of connections that a node has. Degree distribution, therefore, captures the degrees of all the nodes in a network and can often be represented in histogram graphs. With that in mind, an important network topology introduced by Watts and Strogatz is the small world network, which have high clustering coefficients, low APL, and small degrees of separation [29]. In other words, most nodes in small world networks can be reached within a small number of steps despite that most nodes are not neighbors. Mathematically, the distance L between two random nodes in a small world network is proportional to the log of the number of nodes N [29]. L  log N An effective metric used is the small-world index (SWI). Lobs and Cobs are observed APL and clustering coefficients, Llatt and Clatt are obtained from a comparison lattice network, and Lrand and Crand are obtained from a comparison random network [30]. SWI ¼

L obs  L latt C obs  C rand  L rand  L latt C latt  C rand

Applications of Network Analysis in Biomedicine


Another type of network topology that has sparked recent interest is the scale-free network, specifically referring to networks whose degree follows a power-law distribution [31]. P ðkÞ  kλ 3.3 Shortest Path Analysis

Shortest path analysis aims to find a path between two nodes that minimizes sum of the weights in weighted networks or steps in unweighted networks. A commonly used algorithm in shortest path analysis is the Dijkstra’s algorithm, which has several variants corresponding to different types of networks and graphs [32, 33]. There are also other algorithms available that are optimized for different types of networks [34, 35].

3.4 Guilt By Association

Guilt by association, in principle, states functional similarities between genes often suggests that they share expression profiles or are protein interaction partners [36]. This is an immensely useful concept in computational biology, where functions are assigned to genes based upon prior expression profiles of known genes and thus making tasks like predicting novel disease genes to be possible [37– 39]. Nevertheless, this principle has also been challenged in current literature, describing the many scenarios where “guilt by association” is ineffective or inapplicable [4, 40].


Random Walk

The theory of random walk can be traced by the formulation of Brownian motion and since then has been widely used to model diffusion and random motion. The simplest and standard form of random walk is an uncorrelated and unbiased model, where movement in completely independent of previous motion. This model, known as the simple isotropic random walk model, is the basis of other random walk models [41]. On the other hand, correlated random walks (CRWs) have directional biases, where each step likely moves in a similar direction as the previous. In a directed and weighted graph, a random walk is also known as a Markov chain. Namely, Markov chains model the stochastic transition between n states using a transition probability matrix. An important use of random walk in networks is to locate “central” nodes that are highly connected. These nodes often reveal important protein interaction, pathways, or important regulator and disease genes in the context of different biological networks. Therefore, random walk can be found widely implemented in current literature [42–46].


Heat Diffusion

One of the important goals of network analysis is to identify communities—sets of internally cohesive nodes that are separated from the remainder of the network. Mathematically, the concept of a community can be represented by the conductance measure, which is the ratio of the number of edges leaving a set of nodes to the


Steven Wang and Tao Huang

number of internal edges [47, 48]. A small conductance set indicates high number of internal edges and few edges leaving the set, hence a community. Two notable methods of finding small conductance sets are the personalized PageRank diffusion and the heat kernel. These algorithms take an estimate of a diffusion and return the set of smallest conductance. Implementations of heat diffusion can be found in genomics, proteomics, and drug design [49–52].


Network Embedding Since modern information networks can contain billions of nodes and edges, it can become computationally inefficient to perform inferences such as classification or clustering with the original scale. Network embedding is a method that aims to reduce the dimensionality of these networks and thus allowing the data to be effectively visualized and used as features for further inference [1, 53]. Important characteristics of good network embedding methods include adaptability of implementing new data, scalability to large-scale networks, low dimensionality, community awareness of similarity between nodes, and continuous representation [53]. We will introduce some popular network embedding methods and their respective features.



In general, word2vec refers to a group of word embedding models that are used to construct linguistic information. Originally developed by researchers at Google, word2vec is a two-layer neural network that take large corpora of text and outputs a vector space where related words are placed in proximity with each other [54]. This capability of analyzing textual data has helped computational biologist address the time-consuming process of curating current and past literature. A group of researchers have developed a machine learning approach to extract gene–disease relations from available literature via word2vec [3]. Furthermore, the word2vec approach of neural embedding have inspired computational biologists to develop tools like Gene2vec in order to model gene–gene interaction networks [55].



Machine learning algorithms, in general, uses multiple independent and discriminating features to perform tasks. Such features are typically manually designed using expert knowledge. However, this process is time-consuming and does not generalize across different tasks. Node2vec is an algorithmic framework developed by Grover and Leskovec that can learn continuous feature representations for nodes in networks [56]. Concretely, node2vec produces features with maximal likelihood of preserving neighborhoods of nodes in a network. These features can then be used in machine learning inference methods to perform link

Applications of Network Analysis in Biomedicine


prediction, which has a range of applications in discovering gene– gene, protein–protein, or drug–target interactions. Node2vec has been shown in multiple studies to be the best performing embedding method to model gene–gene interaction network [57] and disease–gene networks [5]. 4.3


DeepWalk, developed by Perozzi et al., is a network embedding method that takes a network as input and returns a latent representation as output, which can then be used as features to machine learning algorithms [58]. The central idea behind DeepWalk is analogous to that of word2vec, where sentences are used as training data to analyze words. In DeepWalk, short random walks in a network are generated as training data, and features representations of individual nodes are returned. In comparison to other network embedding methods, DeepWalk extracts features well even with small labelled training datasets and is very scalable. Studies have applied DeepWalk and network-based methods to uncover interaction between biological components. A study by Zhang et al. implemented a DeepWalk-based network to predict long noncoding RNA-associated disease [2]. Other works have developed DeepWalk-based networks to investigate drug–target associations [59, 60] and microRNA functions [61].



The LINE network embedding method, introduced by Tang et al., is a scalable algorithm that can be applied to a range of networks while maintaining local and global network structures [62]. Specifically, LINE addresses three major problems faced by network embedding of large-scale networks: preserving first and secondorder proximity, scalability, and compatibility of different types of edges (directed, undirected, and/or weighted). First-order proximity refers to the local pairwise similarity between nodes, while the second-order proximity indicates the structural similarity between neighborhoods [63]. Hence, by combining the two, LINE can preserve both local and global network structure. A study by Zitnik and Lesovec incorporated LINE to construct networks used to predict protein function [64].



The Structural Deep Network Embedding (SDNE) method, proposed by Wang et al., is a network embedding technique that addresses the nonlinear and sparse nature of real-world large networks [63]. Like LINE, SDNE proposes to preserve network structure using two orders of proximity. Furthermore, SDNE differs from LINE in that it jointly optimizes the first- and secondorder proximities. SDNE also adopts a deep structure to capture the nonlinearity of network structures. The authors show that SDNE outperforms LINE using three testing sets [63].




Steven Wang and Tao Huang


Struc2vec is a recent representation learning framework developed by Riberio et al. that takes a slightly different approach [65]. A common problem faced by available network embedding methods is that while nodes with similar functions should have similar latent representations, algorithms often fail to do so when the neighborhoods of these nodes are far apart. This is because the neighborhood concept is inherently defined by proximity within the network. Struc2vec circumvents this limitation by defining structural similarity between nodes independent of their position in the network. Other features of struc2vec include a hierarchical measurement of structural similarity and the generation of random contexts for nodes.

Application of Network Analysis

5.1 Genetic-Based Prediction of Disease

Network analysis has allowed the prediction of disease causative genes to be possible, which can significantly improve the understanding of the genetics basis of various diseases and guide their treatments. A study by Ata et al. implemented an integrative framework, N2VKO, to predict disease genes [6]. The node embeddings were learned from protein–protein interaction (PPI) network using the node2vec learning method, and classification models were built using node embeddings and various biological annotations. This approach has proved to be effective by cross-validating their prediction with literature on the predicted disease genes.

5.2 Drug–Target Identification

As the productivity of modern pharmaceutical research and drug development is slowing down, network approaches is showing promise in overcoming these obstacles [66]. A common approach of integrating network approaches into drug development is using link prediction to simulate drug–target interaction. For example, a study by Lee and Nam constructed three subnetworks for predicting drug–target interaction. Specifically, random walks with restart to calculate affinity scores between nodes and the model was shown to outperform previous guilt by association models [67]. Other literatures also highlight the promise of using network approaches in drug–target identification [50, 59, 60, 68–70]. Alternatively, repurposing and repositioning drugs offer a time- and cost-efficient way to develop treatment. To this end, Zeng et al. developed deepDR, which uses a multi-modal deep autoencoder to learn low-dimensional representations [71]. The authors validate the repurposing capabilities of deepDR using clinical data of Alzheimer’s disease and Parkinson’s disease.

Applications of Network Analysis in Biomedicine

5.3 Multi-omics Data Integration



Applications of network analysis in genomics involves identifying gene–gene interactions, pathways, and disease-causing genes [6, 10, 12, 23, 24, 39, 46, 57, 66, 72]. In proteomics, the most common uses of network analysis are protein–protein interaction (PPI) analysis and inference of protein functions [73–76]. The increasing abundance of omics data poses the prospect of integrating multi-level omics to achieve higher predictive capabilities of network models. Examples of these algorithms include SAMNetWeb [77] and pwOmics [78], which integrate transcriptomic and proteomic data. Other tools, such as MetScape [79], Cytoscape [80], and Grinn [81], integrate metabolomic and genomic information. The integration of multi-level omics data has produced effective algorithms that have wide-ranging fields of implementation. For example, in the study of ovarian cancer, Gevaert et al. developed an algorithm AMARETTO to identify driver genes [82], while Zhang et al. integrated coding genomic and epigenomic data to uncover potential genetic pathways [83].

Conclusions The inherent highly interconnected nature of biological systems and the growing available of various forms of sequencing data has allowed network analysis to become the state-of-the-art technique in modelling the interaction between biological components. The work of many researchers has produced improved algorithms for various tasks including topology analysis, network embedding, and link prediction, all of which are essential to the growing capabilities of network approaches in biomedicine. Using these algorithms, researchers were able to further human knowledge in cancer, omics studies, drug development, and more. In this brief review, we covered the basic underlying concepts and important analysis techniques of network analysis, as well as examples of studies implementing these tools.

References 1. Nelson W et al (2019) To embed or not: network embedding as a paradigm in computational biology. Front Genet 10 2. Zhang H et al (2019) Predicting lncRNAdisease associations using network topological similarity based on deep mining heterogeneous networks. Math Biosci 315:108229 3. Bhasuran B, Natarajan J (2018) Automatic extraction of gene-disease associations from literature using joint ensemble learning. PLoS One 13(7):–e0200699

4. Guo Y et al (2013) Dissecting disease inheritance modes in a three-dimensional protein network challenges the “Guilt-by-Association” principle. Am J Hum Genet 93(1):78–89 5. Peng J, Guan J, Shang X (2019) Predicting Parkinson’s disease genes based on Node2vec and autoencoder. Front Genet 10 6. Ata SK et al (2018) Integrating node embeddings and biological annotations for genes to predict disease-gene associations. BMC Syst Biol 12(S9)


Steven Wang and Tao Huang

7. Robertson KD (2005) DNA methylation and human disease. Nat Rev Genet 6(8):597–610 8. Stuart JM (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249–255 9. Nagrecha S, Lingras PJ, Chawla NV (2013) Comparison of gene co-expression networks and bayesian networks. In: Intelligent information and database systems. Springer, Berlin, Heidelberg 10. Ma X et al (2018) Co-expression gene network analysis and functional module identification in bamboo growth and development. Front Genet 9:574 11. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9(1):559 12. Su C et al (2013) Using Bayesian networks to discover relations between genes, environment, and disease. BioData Mining 6(1):6 13. Friedman N, Nachman I, Pee´r D (1999) Learning bayesian network structure from massive datasets: the “sparse candidate” algorithm. In: Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc. 14. Murphy K (2001) The bayes net toolbox for matlab. Comput Sci Stat 33(2):1024–1034 15. Huang T et al (2010) Using GeneReg to construct time delay gene regulatory networks. BMC Res Notes 3(1):142 16. Sakamoto Y, Ishiguro M, Kitagawa GJD (1986) Akaike information criterion statistics. D. Reidel, The Netherlands, p 81 17. Guimera` R, Sales-Pardo M, Amaral LAN (2007) Module idesdntification in bipartite and directed networks. Phys Rev E 76 (3):036102 18. Nikolayeva I, Guitart Pla O, Schwikowski B (2018) Network module identification—A widespread theoretical bias and best practices. Methods 132:19–25 19. Ideker T et al (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl_1): S233–S240 20. Bakir-Gungor B, Sezerman OU (2011) A new methodology to associate SNPs with human diseases according to their pathway related context. PloS One 6(10):e26277 21. Jia P et al (2010) dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks. Bioinformatics 27(1):95–102 22. Chuang HY et al (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol:3(1)

23. Hormozdiari F et al (2015) The discovery of integrated gene networks for autism and related disorders. Genome Res 25(1):142–154 24. Wang Q et al (2015) EW_dmGWAS: edgeweighted dense module search for genomewide association studies and gene expression profiles. Bioinformatics 31(15):2591–2594 25. Smith CL et al (2014) Identification of a human neonatal immune-metabolic network associated with bacterial infection. Nat Commun 5:4649 26. Adamcsek B et al (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8):1021–1023 27. Kashtan N et al (2004) Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11):1746–1758 28. Wernicke S, Rasche FJB (2006) FANMOD: a tool for fast network motif detection. Bioinformatics 22(9):1152–1153 29. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442 30. Neal Z (2015) Making big communities small: using network science to understand the ecological and behavioral requirements for community social capital. Am J Community Psychol 55(3–4):369–380 31. Baraba´si A-L, Albert R (1999) Emergence of scaling in random networks. Science 286 (5439):509–512 32. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1 (1):269–271 33. Fredman ML, Tarjan RE (1987) Fibonacci heaps and their uses in improved network optimization algorithms. J ACM (JACM) 34 (3):596–615 34. Thorup M (2004) Integer priority queues with decrease key in constant time and the single source shortest paths problem. J Comput Syst Sci 69(3):330–353 35. Gabow HN (1983) Scaling algorithms for network problems. In: 24th Annual Symposium on Foundations of Computer Science (sfcs 1983). IEEE 36. Oliver SJN (2000) Proteomics: guilt-by-association goes global. Nature 403(6770):601 37. Arabidopsis Interactome Mapping Consortium (2011) Evidence for network evolution in an Arabidopsis interactome map. Science 333 (6042):601–607 38. Mukhtar MS et al (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 333(6042):596–601

Applications of Network Analysis in Biomedicine 39. Wu X et al (2008) Network-based global inference of human disease genes. Mol Syst Biol 4 (1) 40. Gillis J, Pavlidis P (2012) “Guilt by Association” is the exception rather than the rule in gene networks. PLOS Computat Biol 8(3): e1002444 41. Blanchard P, Volchenkov D (2011) Fair and biased random walks on undirected graphs and related entropies. Birkh€ause, Boston, Basel 42. Seah CS et al (2017) An enhanced topologically significant directed random walk in cancer classification using gene expression datasets. Saudi J Biol Sci 24(8):1828–1841 43. Roessler S et al (2015) Integrative genomic and transcriptomic characterization of matched primary and metastatic liver and colorectal carcinoma. Int J Biol Sci 11(1):88 44. Seah CS, Kasim S, Mohamad MS (2017) Specific tuning parameter for directed random walk algorithm cancer classification. Interaction 114:1500 45. Suki B, Frey U (2017) A time-varying biased random walk approach to human growth. Sci Rep 7(1):7805 46. Yang K et al (2019) HerGePred: heterogeneous network embedding representation for disease gene prediction. IEEE J Biomed Health Inform 23(4):1805–1815 47. Kloster K, Gleich DF (2014) Heat kernel based community detection. arXiv e-prints 48. Chung F (2007) The heat kernel as the pagerank of a graph. Proc Natl Acad Sci 104 (50):19735–19740 49. Paull EO et al (2013) Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE). Bioinformatics 29 (21):2757–2764 50. Laenen G et al (2013) Finding the targets of a drug by integration of gene expression data with a protein interaction network. Mol BioSyst 9(7):1676–1685 51. Nitsch D et al (2010) Candidate gene prioritization by network analysis of differential expression using machine learning approaches. BMC Bioinformatics 11(1):460 52. Vandin F (2011) et al Discovery of mutated subnetworks associated with clinical data in cancer Pac Symp Biocomputing 2012. 2011, 55–66 53. Chen H et al (2018) A tutorial on network embeddings 54. Mikolov T, et al (2013) Efficient estimation of word representations in vector space


55. Du J et al (2019) Gene2vec: distributed representation of genes based on co-expression. BMC Genomics 20(1):82 56. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. arXiv e-prints 57. Kim M, Baek SH, Song M (2018) Relation extraction for biological pathway construction using node2vec. BMC Bioinformatics 19 (8):206 58. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge discovery and data mining. ACM 59. Zong N, Wong RSN, Ngo V (2019) Tripartite network-based repurposing method using deep learning to compute similarities for drug-target prediction. In: Vanhaelen Q (ed) Computational methods for drug repurposing. Springer New York, New York, NY, pp 317–328 60. Zong N et al (2017) Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations. Bioinformatics 33(15):2337–2344 61. Deng L, Wang J, Zhang J (2019) Predicting gene ontology function of human MicroRNAs by integrating multiple networks. Front Genet 10:3 62. Tang J et al (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee 63. Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: San Francisco, CA, USA. p. 1225–1234 64. Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33(14):i190–i198 65. Ribeiro LF, Saverese PH, Figueiredo DR (2017) struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM 66. Moffat JG et al (2017) Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat Rev Drug Discov 16 (8):531 67. Lee I, Nam H (2018) Identification of drugtarget interaction by a random walk with restart


Steven Wang and Tao Huang

method on an interactome network. BMC Bioinformatics 19(8):208 68. Yildirim M et al (2007) Drug-target network. Nat Biotechnol 25(10):1119–1126 69. Yamanishi Y et al (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26(12):i246–i254 70. Xu Z et al (2017) Seq2seq fingerprint: an unsupervised deep molecular embedding for drug discovery. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM 71. Zeng X et al (2019) deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35(24):5191–5198 72. Tillquist RC, Lladser ME (2019) Low-dimensional representation of genomic sequences. J Math Biol 79(1):1–29 73. Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: International symposium on computer and information sciences. Springer, Berlin, Heidelberg 74. Raghavan UN (2007) Albert R, Kumara S, Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106 75. Rosvall M, Bergstrom CT (2011) Multilevel compression of random walks on networks

reveals hierarchical organization in large integrated systems. PLoS One 6(4):e18209 76. Blondel VD et al (2008) Fast unfolding of communities in large networks. J Stat Mech 10:P10008 77. Gosline SJ, Oh C, Fraenkel EJB (2014) SAMNetWeb: identifying condition-specific networks linking signaling and transcription. Bioinformatics 31(7):1124–1126 78. Wachter A, Beißbarth TJB (2015) pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge. Bioinformatics 31(18):3072–3074 79. Karnovsky A et al (2011) Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28(3):373–380 80. Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13 (11):2498–2504 81. Wanichthanarak K, Fahrmann JF, Grapov DJBI (2015 Sep 7) Genomic, proteomic, and metabolomic data integration strategies. Biomark Insights 10(Suppl 4):1–6 82. Gevaert O et al (2013) Identification of ovarian cancer driver genes by using module network integration of multi-omics data. Interface Focus 3(4):20130013 83. Zhang Q, Burdette JE, Wang J-P (2014) Integrative network analysis of TCGA data for ovarian cancer. BMC Syst Biol 8(1):1338

Part II Precision Medicine of Cancers

Chapter 5 Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era Jing Yan, Zhuan Liu, Shengfang Du, Jing Li, Li Ma, and Linjing Li Abstract Breast cancer is one of the most leading causes of death for women worldwide. According to statistics published by the International Agency for Research on Cancer (IARC), the incidence of breast cancer is on the rise year by year in most parts of the world. The existence of heterogeneity limits the early diagnosis and targeted therapy of breast cancer. Nowadays, precision medicine brings a new perspective to personalized diagnosis and targeted therapy, overcomes the heterogeneity of different patients, and provides an opportunity for screening of high-risk populations. As a clinician, we are committed to using genomic to provide a favorable perspective in the field of breast cancer. The current review describes the recent advances in the understanding of precision medicine for breast cancer in the aspect of the genomics which could be applied to improve our ability to diagnose and treat breast cancer individually and effectively. Key words Breast cancer, Precision medicine, Genomics, Prevention, Treatment


Introduction Breast cancer is not only the most common malignancy in women, but also one of the major causes of death among women all over the world. Despite global mortality rates are falling, breast cancer is the leading cause of death in some underdeveloped areas. Although early screening programs and effective treatments have reduced mortality rate, breast cancer still caused 521,900 deaths in 2012 accounting for 15% of all cancer deaths among women worldwide [1]. Numerous studies suggested that hereditary factor causes less than 10% of breast cancers, and the most common factors are environmental, reproductive, and lifestyle factors. However, up to 25% of inherited breast cancers are linked to genetic mutations in specific genes [2]. Recent years, considerable attention has been paid to breast cancer care, while new advancements in applying genomics and precision medicine to clinical treatment are still facing challenges, and it is worthwhile devoting much effort [3]. Genetic heterogeneity is the most common feature of the

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Jing Yan et al.

breast cancer, which leads to significant differences in phenotype and response to treatment of breast cancer patient and affects prognosis of each individual, thus pose a critical challenges in prediction, diagnosis, and treating breast cancer individually [4, 5]. As we know, the success of human genome sequencing has brought opportunities for knowing the correlated genes with diseases. On the basis of that, precision medicine for breast cancer, which consider the genes patients are born with and the genes or other biomarkers present in the cancer cells, provides strategy for the prediction, diagnosis, treatment, and prevention. With this approach, blood and tumor tissues of patients are collected for analysis genetically. More importantly, precision medicine is based on examining genes of each patient that will guide the clinicians in every aspect [6]. Physicians can treat patients in a personalized way by identifying the genes associated with the development and prognosis of breast cancer [7]. Due to the diversity of drugs, give the right drugs to the right patients at the right time to achieve the best clinical results remains an ongoing challenge. Therefore, with increasing understanding of the driver genes, it is possible to improve the precision diagnosis and treatment of breast cancer.


New Perspective from Genetic Studies The Human Genome Project (HGP) has provided a complete and accurate sequence of the DNA base pairs of the human genome. The first draft of the HGP was completed in 2001 and officially released in 2003. HGP sequenced 99% of the whole human genome and all data were published to support medical research [8]. As we know, the symptoms of breast cancer vary from person to person. One of the most important reasons is single nucleotide polymorphisms (SNPs) among these patients are different. The completion of HGP and the continuous advancement in genetic research will facilitate the individual medicine which contributes to the precision medicine in breast cancer. As development of HGP, the BRCA1 susceptibility gene for breast cancer was found on chromosome 17 in the 1990s, and the sequence of BRCA1 was acquired few years later. Simultaneously, another team of scientists discovered the BRCA2 gene using linkage analysis. They found that a male breast cancer patient of the mammary and ovarian cancer family did not have a mutation at 17q21 (BRCA1), mapped another breast cancer-related gene and named it as BRCA2, which located in 13q12 [9, 10]. In the year 2000, the HGP published the sequence of the BRCA2 gene. Currently, it was well known that mutation of BRCA1/2 gene are associated with inherited breast cancer, which are normally tumor suppressor genes. Over 1500 mutations have been reported in the BRCA1 gene, and the majority of them resulting in missense or non-functional

Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era


proteins. Similarly, it was confirmed that more than 1800 mutations in BRCA2 have been identified including frameshift deletions, nonsense mutations, and insertions which lead to premature truncation of proteins. These events could result in the loss of function of tumor suppressor genes [11]. It was estimated that most of the BRCA1 mutation carriers, and almost half of the BRCA2 mutation carriers will develop breast cancer [12]. More recently, mutations of 20 genes including CHEK2, PALB2, BRIP1, RAD51C, NLRP2, BARD1, FGFR2, TOX3, etc. were published as susceptibility genes of breast cancer which are frequently mutated in general population and involve in developing of breast cancer [13]. For example, CHEK2 plays a vital role in adjustment of p53 and BRCA1 function, and a single DNA building block at nucleotide sequence 1100delC will lead to abnormality, consequently enhance the risk of breast cancer and influence the response to therapy [14, 15]. PALB2 gene was found as important in breast cancer risk as BRCA1 and BRCA2 which can increase the risk 5 to 9 times higher than average [16, 17] Hence, these genes are supposed to as promising mechanism and new markers which could be widely applied in precision medicine. As we mentioned before, breast cancer is a highly heterogeneous tumor with significant individual differences in molecular immune-phenotype, biological behavior, histopathologic morphology, and response to therapy. Such differences were believed to be caused by molecular differences mainly in the abnormal expression of key genes [18]. It was demonstrated that there are at least four genetic subtypes named Luminal A (ER+, Her2-, G1/2), Luminal B/Her2 negative (ER+, Her2-, G3), HER2-enriched (ER-, Her2 +), and basal-like/triple negative (ER-, PR-, Her2-) in breast cancer. Thus, identification of genetic subtypes and evaluated different patterns of metastases in breast cancer is a major demand for diagnosis, evaluation of prognosis, and generation of individualized therapeutic schedule [19, 20]. It was reported that triple-negative patients have a higher rate of distant metastasis and a poorer prognosis than other breast cancer subtypes [21]. Another study using proteomic profiles discovered that different expression of proteins between subtypes were related to energy metabolism, mRNA translation, cell growth, and cell to cell communication [22].The Cancer Genome Atlas(TCGA) Network also associated the profiling of gene expression in each subtype with mutation profiles and DNA copy number variations. For instance, both luminal A and B which are classified as ER+ tumors demonstrated a high frequency of PIK3CA mutations, whereas basal tumors showed a high frequency of TP53 mutations [23].



Jing Yan et al.

Early Diagnosis and Prognostic Assessing Using Sequencing Approach Diagnosis for breast cancer includes tumor biopsies, biomarkers as well as imaging techniques which have yielded valuable information for decades. However, these traditional diagnostic methods have several drawbacks due to their inability to capture the variation in genetic heterogeneity and invasiveness [24]. Nowadays, with the rapid progress in massive parallel sequencing methods known as next-generation sequencing (NGS), gene screening has been used to discover relationships between genomic variations and diseases of interest, which further facilitate the development of precision medicine [25]. Principle of NGS is based on the reading of numerous fragments randomly digested from genomic material repeatedly (ranging from 100 to 10,000 times). This new approach makes it possible for the sequencing of the entire human genome in several hours with low price, which is hard to analyze using the Sanger sequencing method [26]. To have precise information from the sequencing for diagnosis purposes, the data first should be analyzed. However, the most challenge of NGS is the lack of sufficient databases for analysis of incidental mutations as well as short of sufficient bioinformatics software to deal with mass of data generated by sequencing [27]. As the increasing demand for personalized screening, it is more and more significant to include molecular diagnosis into the screening framework for patients [28]. Genetic testing is necessary for patients with a family history of hereditary cancer. Evidence suggests that diagnosing patients with a family history of breast cancer mutations has better psychosocial benefits [29]. Counseling and genetic information can support the patient in making decision and reduce risk [30]. With the development of NGS, it has become possible to measure large numbers of genes with whole genome sequencing (WGS) which facilitate precision diagnose and being used as emerging standard for genetic testing. Even WGS is widely used in tumor profiling frequently, other genetic screenings are also in common use. On the condition that cancer types have been wellstudied, tumor-related genes or loci could be screened instead of sequencing the whole genome. A study showed that other genes besides BRCA1/2 which are associated with breast cancer could be detected using 25-related-genes panel by NGS [31]. Physicians typically provide genetic tests of BRCA1/2 genes for patients with a family history of BRCA gene mutations [32]. Nowadays, BRCA1/2 gene mutation detection has become a part of clinical management in women with a family history in developed countries [33]. Another detection being called “liquid biopsy” involves screening of enriched circulating tumor cells (CTCs) or circulating tumor DNA (ctDNA) in peripheral blood using NGS. Due to the ability

Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era


of invasion and metastasis, breast cancer has a high clinical mortality. An early and accurate detection of metastasis status is of great value. The liquid biopsy is believed to having the ability of higher sensitivity, noninvasive, early tumor diagnosis, and better recurrence monitoring. It can offer the potential significance of “real-time” diagnosis [34]. Except for BRCA genes, high ctDNA mutations of TP53, PIK3CA, and ESR1 were significantly associated with lymph node metastasis, cancer recurrence, and poor overall survival outcomes [35]. Some researchers declared that others substance in peripheral blood of breast cancer patients could also be biomarker of liquid biopsy. Fibronectin on the extracellular vesicles secreted from breast cancer cells have high diagnostic accuracy, sensitivity, and specificity [36] Circulation of microRNAs (miRNA-133, miRNA-195) could also be tested as potential biomarker to identify the metastatic breast cancer [37].


Personalized and Precise Therapy The traditional treatment for breast cancer including radiation, surgery, chemotherapy, and hormone therapy, which often fail to eradicate tumor cells but still damage normal tissue, especially the body’s immune system. Although rapid progress has been made in conventional therapeutics, the treatment is still complicated with development of resistance in cancer cells and advancement in stage due to genetic heterogeneity. When the same treatment is implemented, each patient response to treatment inconsistently. Early study found the 127 gene mutations in a sample of tissue from a 43-year-old woman with breast cancer which is an example of the genetic heterogeneity [38]. Currently, people pay more and more attention to gene therapy of tumor which is also called personalized and precise therapy. The personalized and precise therapy of breast cancer is a unique area owing to its special subtypes and unique driving genes. It becomes more fascinating because the mutations that cause progression of breast cancer also act as specific targets for treatment. Recently, apart from the traditional chemotherapeutic drugs, targeting the precise molecules according to molecular characterization called targeted therapy are also being developed. In breast cancer, genetic profile (ER+, HER2+, and triple negative) is significant to choose special chemotherapeutic agent and individual treatments [39]. It was reached agreement that the combination with Trastuzumab (Herceptin) known as a HER2 inhibitors and pertuzumab (also being called 2C4, trade name Perjeta) which is a monoclonal antibody targeted HER2, as well as docetaxel could be utilized for the treatment of metastatic HER-positive breast cancer. This effective drug combos were also used as a neoadjuvant in early HER2-positive breast cancer [40].


Jing Yan et al.

With the rapid progress of precision medicine, there are numerous new targeted drugs which achieve satisfactory therapeutic effects. For instance, endocrine therapies employing tamoxifen and/or aromatase inhibitors are significant therapeutic schedule for the targeted treatment in hormone-responsive breast cancer [41]. It was well known that breast cancer cells rely on ER signaling to drive tumor growth despite exposure to CDK4/6 inhibitors. Importantly, Elacestrant known as a novel, nonsteroidal combined estrogen receptor modulator (SERM) and estrogen receptor degrader (SERD) can inhibit ER-dependent growth despite of CDK4/6 inhibitor resistance observed such as CDK6 overexpression, upregulated cyclinE1 and E2F1 and Rb loss [42]. More recently, the Food and Drug Administration (FDA) approved Talazoparib (an adenosine diphosphate-ribose polymerase inhibitor) for patients with deleterious germline BRCA-mutated, HER2-negative metastatic breast cancer. Researcher discovered that Talazoparib is shown antitumor effects in patients with advanced breast cancer and germline mutations in BRCA1/BRCA2 which provided a better effects than standard chemotherapy in progression-free survival [43]. YBX1 which is briefly called nuclear expression of Y-box binding protein has close relationship with clinical poor outcomes and drug resistance in breast cancer, and phosphorylated YBX1 (pYBX1) promotes expression of genes facilitate the drug resistance and cell growth. It was confirmed that Everolimus which is an mTORC1 inhibitor and a novel multikinase inhibitor of AKT can suppress the phosphorylation with YBX1 and overcome antiestrogen resistance in vivo and in vitro suggesting that it has potential therapeutic value in treatment of progressive and antiestrogen-resistant breast cancer [44]. Epertinib is an effective inhibitor of HER2, EGFR, and HER4. It was reported that daily oral Epertinib intake combined with trastuzumab, or with trastuzumab plus capecitabine have safety and encouraging antitumor outcome [45]. In addition, there has been some progress in combination therapies. Knudsen et al. combined cyclin-dependent kinase (CDK) 4 and 6 inhibitors with treatment, and found that the combination therapy inhibits tumor recurrence and improves survival in breast cancer patients [46]. Gene therapy is another new technology through that normal or therapeutic genes can be introduced into target cells in a specific way to correct gene mutations. Compared with the traditional treatment model, gene therapy has better targeting and pertinence, and is more suitable for individualized treatment in breast cancer patients. There are several methods of gene therapy, such as gene replacement and gene knockout. Among that, suicide gene therapy has bright prospect in individual therapy although it remains in the research stage [47]. Mohseni et al. used the iC9 suicide gene to induce apoptosis in McF-7 breast cancer cells, and the effect was also shown to block cell cycle in combination with chemotherapy drugs [48].

Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era



Conclusion Precision medicine is a comprehensive approach that take into account variability in genetic makeup, lifestyle of individual, environments et al. for personalized disease prevention, diagnosis, and treatment. In recent years, the availability of large-scale omics data, gene–lifestyle and gene–environment interaction information, big data analytics as well as predictive algorithms have enabled us to develop precision medicine strategies on case-by-case basis. However, it is just the beginning and it still has a long way to go.

Acknowledgements This work was supported by the Internationally Technological Cooperation Project of Gansu Province (18YF1WA117), Scientific Research Project of Gansu Medical and Health Industry (GSWSKY2016-14), and the Fundamental Research Funds for the Central Universities (lzujbky-2017-81). References 1. Torre LA, Bray F, Siegel RL et al (2015) Global cancer statistics, 2012. CA Cancer J Clinic 65 (2):87–108 2. Rojas K, Stuckey A (2016) Breast cancer epidemiology and risk factors. Clin Obstet Gynecol 59(4):651–672 3. Odle TG (2017) Precision medicine in breast cancer. Radiol Technol 88(4):401m–421m 4. Polyak K (2011) Heterogeneity in breast cancer. J Clin Invest 121(10):3786–3788 5. Haukaas TH, Euceda LR, Giskeodegard GF et al (2016) Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes. Cancer Metabol 4:12 6. Kumar-Sinha C, Chinnaiyan AM (2018) Precision oncology in the age of integrative genomics. Nat Biotechnol 36(1):46–60 7. Hayes DF (2016) Considerations for implementation of cancer molecular diagnostics into clinical care. Am Soc Clin Oncol Educ Book 35:292–296 8. Green ED, Watson JD, Collins FS (2015) Human genome project: twenty-five years of big biology. Nature 526(7571):29–31 9. Miki Y, Swensen J, Shattuck-Eidens D et al (1994) A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science (New York, NY) 266(5182):66–71 10. Stratton MR, Ford D, Neuhasen S et al (1994) Familial male breast cancer is not linked to the

BRCA1 locus on chromosome 17q. Nat Genet 7(1):103–107 11. Petrucelli N, Daly MB, Feldman GL (2010) Hereditary breast and ovarian cancer due to mutations in BRCA1 and BRCA2. Genet Med 12(5):245 12. Chen S, Parmigiani G (2007) Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol 25(11):1329–1333 13. Rossing M, Sorensen CS, Ejlertsen B et al (2019) Whole genome sequencing of breast cancer. APMIS 127(5):303–315 14. Mellemkjaer L, Dahl C, Olsen JH et al (2008) Risk for contralateral breast cancer among carriers of the CHEK2*1100delC mutation in the WECARE study. Br J Cancer 98(4):728–733 15. Knappskog S, Chrisanthar R, Lokkevik E et al (2012) Low expression levels of ATM may substitute for CHEK2 /TP53 mutations predicting resistance towards anthracycline and mitomycin chemotherapy in breast cancer. Breast Cancer Res 14(2):R47 16. Weitzel JN, Neuhausen SL, Adamson A et al (2019) Pathogenic and likely pathogenic variants in PALB2, CHEK2, and other known breast cancer susceptibility genes among 1054 BRCA-negative Hispanics with breast cancer. Cancer 125(16):2829–2836 17. Antoniou AC, Casadei S, Heikkinen T et al (2014) Breast-cancer risk in families with


Jing Yan et al.

mutations in PALB2. N Engl J Med 371 (6):497–506 18. Prat A, Perou CM (2009) Mammary development meets cancer genomics. Nat Med 15 (8):842–844 19. Masood S (2016) Breast cancer subtypes: morphologic and biologic characterization. Women’s Health (London, England) 12 (1):103–119 20. Silverstein A, Sood R, Costas-Chavarri A (2016) Breast cancer in Africa: limitations and opportunities for application of genomic medicine. Int J Breast Cancer 2016:4792865 21. Howlader N, Cronin KA, Kurian AW et al (2018) Differences in Breast Cancer Survival by Molecular Subtypes in the United States. Cancer Epidemiol Biomark Prev 27 (6):619–626 22. Tyanova S, Albrechtsen R, Kronqvist P et al (2016) Proteomic maps of breast cancer subtypes. Nat Commun 7:10259 23. Curtis C, Shah SP, Chin SF et al (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403):346–352 24. Marrugo-Ramirez J, Mir M, Samitier J (2018) Blood-based cancer biomarkers in liquid biopsy: a promising non-invasive alternative to tissue biopsy. Int J Mol Sci 19(10) 25. Ng CK, Schultheis AM, Bidard FC et al (2015) Breast cancer genomics from microarrays to massively parallel sequencing: paradigms and new insights. J Natl Cancer Inst 107(5) 26. Gu W, Miller S, Chiu CY (2019) Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol 14:319–338 27. Bao R, Huang L, Andrade J et al (2014) Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing. Cancer Informat 13(Suppl 2):67–82 28. Onega T, Beaber EF, Sprague BL et al (2014) Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for riskbased and preference-based approaches at a population level. Cancer 120(19):2955–2964 29. Meiser B, Quinn VF, Gleeson M et al (2016) When knowledge of a heritable gene mutation comes out of the blue: treatment-focused genetic testing in women newly diagnosed with breast cancer. Eur J Hum Genet 24 (11):1517–1523 30. Metcalfe KA, Dennis CL, Poll A et al (2017) Effect of decision aid for breast cancer prevention on decisional conflict in women with a BRCA1 or BRCA2 mutation: a multisite,

randomized, controlled trial. Genet Med 19 (3):330–336 31. Tung N, Battelli C, Allen B et al (2015) Frequency of mutations in individuals with breast cancer referred for BRCA1 and BRCA2 testing using next-generation sequencing with a 25-gene panel. Cancer 121(1):25–33 32. Sefton P (2017) Testing for BRCA1/2 mutations. JAMA 318(20):2054 33. Alemar B, Gregorio C, Herzog J et al (2017) BRCA1 and BRCA2 mutational profile and prevalence in hereditary breast and ovarian cancer (HBOC) probands from Southern Brazil: are international testing criteria appropriate for this specific population? PLoS One 12(11): e0187630 34. Neumann MH, Bender S, Krahn T et al (2018) ctDNA and CTCs in liquid biopsy–current status and where we need to progress. Comput Struct Biotechnol J 16:190–195 35. Lee JH, Jeong H, Choi JW et al (2018) Liquid biopsy prediction of axillary lymph node metastasis, cancer recurrence, and patient survival in breast cancer: a meta-analysis. Medicine 97 (42):e12862 36. Moon PG, Lee JE, Cho YE et al (2016) Fibronectin on circulating extracellular vesicles as a liquid biopsy to detect breast cancer. Oncotarget 7(26):40189–40199 37. McAnena P, Tanriverdi K, Curran C et al (2019) Circulating microRNAs miR-331 and miR-195 differentiate local luminal a from metastatic breast cancer. BMC Cancer 19 (1):436 38. Mukherjee S (2010) A distorted version of our normal selves. The Emperor of All Maladies:340–355 39. Wang J, Xu B (2019) Targeted therapeutic options and future perspectives for HER2positive breast cancer. Signal Transduct Target Ther 4:34 40. Wuerstlein R, Harbeck N (2017) Neoadjuvant therapy for HER2-positive breast cancer. Rev Recent Clin Trials 12(2):81–92 41. Shagufta AI (2018) Tamoxifen a pioneering drug: an update on the therapeutic potential of tamoxifen derivatives. Eur J Med Chem 143:515–531 42. Patel HK, Tao N, Lee KM et al (2019) Elacestrant (RAD1901) exhibits anti-tumor activity in multiple ER+ breast cancer models resistant to CDK4/6 inhibitors. Breast Cancer Res 21 (1):146 43. Litton JK, Rugo HS, Ettl J et al (2018) Talazoparib in patients with advanced breast cancer and a germline BRCA mutation. N Engl J Med 379(8):753–763

Diagnosis and Treatment of Breast Cancer in the Precision Medicine Era 44. Shibata T, Watari K, Kawahara A et al (2019) Targeting phosphorylation of Y-box binding protein YBX1 by TAS0612 and everolimus in overcoming antiestrogen resistance. Mol Cancer Ther 19(3):882–894 45. Macpherson IR, Spiliopoulou P, Rafii S et al (2019) A phase I/II study of epertinib plus trastuzumab with or without chemotherapy in patients with HER2-positive metastatic breast cancer. Breast Cancer Res 22(1):1 46. Knudsen ES, Witkiewicz AK (2016) Defining the transcriptional and biological response to


CDK4/6 inhibition in relation to ER+/ HER2-breast cancer. Oncotarget 7 (43):69111–69123 47. Duzgunes N (2019) Origins of suicide gene therapy. Methods Mol Biol (Clifton, NJ) 1895:1–9 48. Mohseni-Dargah M, Akbari-Birgani S, Madadi Z et al (2019) Carbon nanotube-delivered iC9 suicide gene therapy for killing breast cancer cells in vitro. Nanomedicine (London, England) 14(8):1033–1047

Chapter 6 Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer Lin Chen, Nan Jiang, and Yuxiang Wu Abstract Breast cancer is the primary malignant tumor that endangers women’s health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer. Key words Breast cancer, Biomedical imaging, radiomics, computer-aided diagnosis, Digital mammography


Introduction Breast cancer is the most common malignancy in women. Over the past half century, many studies in various countries around the world have confirmed that early screening of breast cancer imaging is one of the most effective methods to improve the early diagnosis rate, survival rate, and quality of life [1–4]. The World Health Organization (WHO) has also clearly listed early breast cancer as a curable disease. Early diagnosis and early treatment are the best way to improve the cure rate of breast cancer [3, 5, 6]. The development of mammography, ultrasound, MRI, nuclear medicine, biomedical optics, and computer-aided diagnosis (CAD) technologies has improved the level of early diagnosis of breast cancer. The application of emerging technologies such as electrical impedance, electronic palpation, heat maps, and optical imaging, combined with traditional diagnostic methods, can improve diagnostic

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Lin Chen et al.

accuracy and enable flexibility and simplicity in rapid screening applications [1, 6–9]. Based on a multifunctional nanoprobe that integrates imaging and treatment, it provides effective solutions to clinical problems such as early diagnosis of breast cancer, curative effect evaluation, and relative lag in treatment. This promotes individualized precise diagnosis and treatment of breast cancer significantly.


Mammography After the French doctor Gross developed the molybdenum target anode X-ray machine in 1969, mammography technology developed rapidly [10]. Panoramic digital mammography system was approved by the US FDA in 2000. Computer-aided diagnosis (CAD) was used for breast imaging diagnosis in 2002. Threedimensional mammography technology was used in 2004. Digital mammography (DBT) technology was used for breast examination in 2006 [11, 12]. At present, the mammography technology has become more mature, can clearly display tiny masses and fine calcifications, and can be quasi-deterministic and localized. It is currently recognized as the world’s preferred and most effective method of mammography. Studies have shown that mammography can detect 59% of noninvasive breast cancers with a diameter of less than 1 cm and 53% of invasive breast cancers. However, mammography has certain limitations, and its sensitivity and specificity are affected by breast density. Because the breasts of young women are in the radiation-sensitive period and the breast tissue is dense at this time, it is not easy to detect the lesions. It is generally believed that mammography is not suitable for screening of women under 40 years of age. Clinically, the application of molybdenum target X-ray combined with spiral CT has significant effects in improving the accuracy of patient diagnosis [13–15].


Ultrasound Imaging of Breast Cancer With the development of digital signal processing technology and the use of new ultrasonic imager, the quality of ultrasonic imaging of breast and small organs has been significantly improved, making the diagnosis and identification accuracy of breast cancer significantly improved [16–18]. The optimization of operating conditions of high-frequency linear array probes improves the transverse resolution and the longitudinal resolution and reduces the noise. Tissue harmonic imaging and composite imaging can better show the edge and internal echo structure of breast tumors and improve the detection rate of microcalcification. Three-dimensional ultrasound technology can visually display the shape, internal structure,

Application and Analysis of Biomedical Imaging Technology in Early. . .


and the relationship between the breast mass and the surrounding tissue, can clearly show the invasion degree of the tumor to the surrounding tissue and the shape and distribution of the blood vessels inside the lesion, and can quantitatively evaluate the richness of the blood supply of the tumor [19–21]. The advantage of ultrasound is that the patient has no pain, no radiation damage, especially suitable for the examination of lactation, pregnancy, and young women, experts have suggested that ultrasound as the preferred means of breast cancer screening in China. It is believed that X-ray is more sensitive to detect calcified breast cancer than high-frequency ultrasound, and high-frequency ultrasound is better than X-ray in detecting blood flow signals. Ultrasound imaging and X-ray photography are still the gold combination of mammography, and the use of ultrasound imaging can improve the sensitivity and specificity of mammography. Clinical studies have also confirmed that combined ultrasound and molybdenum photography can significantly improve the early detection rate of breast cancer patients [22].


Magnetic Resonance and Nuclear Medicine in Breast Imaging In 1982, Ross et al. first applied magnetic resonance imaging to the detection of breast lesions [4]. Following the development of the mammary gland surface coil, high contrast, the introduction of paramagnetic contrast agents and fast dynamic enhancement, diffusion-weighted imaging, perfusion-weighted imaging and magnetic resonance (NMR) spectroscopy analysis, fat suppression, and other new technology application in breast imaging, magnetic resonance (NMR) has made great improve signal-to-noise ratio, can be obtained without overlapping images of 3 mm thick, can effectively find small lesions, MRI breast check increasingly brought to the attention of the two aspects of clinical and imaging [4, 23, 24]. Compared with mammography, the detection of lesions by MRI is not affected by gland density and can reflect the characteristics of blood flow inside the lesions. Compared with ultrasound, MRI provides higher spatial resolution images and is not affected by operator dependence. From the perspective of diagnosis, the sensitivity of MRI to early breast cancer, the accuracy of breast cancer staging, and the consistency with the scope of histological lesions are better than X-ray and ultrasound. MRI has unique advantages for breast examination due to its high soft tissue resolution and no radiation. MRI has a sensitivity of 88.4% to 100% for the diagnosis of breast cancer and can detect breast cancer that cannot be detected by clinical examination, mammography, and ultrasound, but has a low specificity of 37% to 97%. In view of the advantages and disadvantages of MRI, the high cost of examination, the lack of standardized operating techniques and standards,


Lin Chen et al.

and the limited detection of microcalcification, MRI is generally not recommended as a screening tool for breast cancer screening above the scale [9, 25, 26]. The principle of nuclear medicine imaging is to inject radioactive tracer drugs into human body. Due to its own physiological characteristics, the drug automatically concentrates on the organ to be tested, and the nuclear medicine imaging device measures the concentration distribution of radioactive drugs in the organ, so as to achieve functional imaging of human tissues. The development of nuclear medicine imaging has gone through several stages: camera, single-photon emission-computed tomography, positron emission-computed tomography (PET), and PET/CT. PET can perform functional imaging at the molecular level with high sensitivity and specificity, but its anatomical structure is unclear. PET/CT imaging can complete both PET and CT examination at the same time. The anatomical image information obtained by CT can help distinguish the physiological uptake of tracer and the uptake of diseased tissue and can detect and locate early breast cancer lesions. PET/CT achieves the same image fusion of PET molecular functional image and CT anatomical image, which can reflect the morphological structure, pathological and physiological changes of the lesion at the same time, and significantly improve the accuracy of diagnosis [27, 28].


Biomedical Optical Imaging Technology Near-infrared (NIR) mammary gland scanning technology is to make use of its absorption characteristics of hemoglobin to form an image, carry on the mammary gland full-field scanning, observation and diagnosis, has the advantages of simplicity, intuition, convenience, and so on, is one of the important means of diagnosing breast cancer, it can obtain the infrared mammary gland image quickly, painless and lossless [29, 30]. As the mainstream diagnosis technology of breast cancer, molybdenum target soft X-ray diagnosis has ray damage to human body, while near-infrared imaging diagnosis is a non-invasive, repeatable diagnostic technology suitable for large-scale screening, but with a high false positive rate, it has experienced a process from emergence to withdrawal in breast cancer screening. In recent years, as people pay more and more attention to molybdenum target X-ray diagnosis of damage and the emergence of new technology, near-infrared optical imaging technology has found its value again [31–35].

5.1 Digital Infrared Thermal Imaging

Early breast cancer and precancerous lesions mostly occur in the stage of functional change, but have not developed into organic lesions. Digital infrared thermal imaging (DITI) is a noninvasive functional examination method, which is of great significance for

Application and Analysis of Biomedical Imaging Technology in Early. . .


the diagnosis and differentiation of breast masses. DITI uses high sensitivity, high speed low temperature infrared camera to detect the body of the infrared thermal radiation and displays temperature field of the human body surface, helping doctors determine the parts of the lesion, the nature of the disease, the extent of the lesion. This technique allows you to measure not only the depth of the heat source in your body but also the shape and size of the heat. Clinical trials have shown higher accuracy and specificity, especially in the diagnosis of breast diseases. DITI reconstruction of the image by using image analysis algorithm and mammary gland images in different colors (red, orange, yellow), according to any suspected regional hotspots (abnormal) can be marked on the breast imaging. The application of thermal tomography in clinical diagnosis has been accepted by western developed countries and has been certified by FDA. Thermal tomography is a functional information supplement for morphologic diagnostic methods such as b-ultrasound, CT and MRI [36, 37]. 5.2 Dynamic Optical Breast Imaging

The blood vessels in breast malignant tumors are tennis-shaped, with the characteristics of large total blood vessel cross section, slow blood flow rate, strong tumor cell metabolism, and large oxygen consumption, and present a special phenomenon of high blood and low oxygen content. Deoxyhemoglobin has a high absorption rate of light with a wavelength of 640 nm, and has strong light absorption sensitivity. Dynamic Optical Breast Imaging (DOBI) is an advanced digital Dynamic functional breast cancer diagnosis device, which is an early breast cancer diagnosis and screening instrument launched by DOBI Medical International [38–40]. It records the changes of blood volume and metabolic rate of new blood vessels in breast tumors in real time by means of slight uniform pressure pulse. Through continuous monitoring (45 s) and quantitative analysis of these two indicators, early breast cancer above 2 mm can be diagnosed. The DOBI shows the region with increased blood volume through morphological images, indicating the presence of lesions. The characteristics of the lesion were determined by the metabolic rate curve. It supports dynamic functional examination, continuous imaging to reflect physiological changes, and preand post-correlation processing of the collected images. DOBI has a specificity of 74%, a sensitivity of 92%, and a diagnostic accuracy of 79%. DOBI has a good correlation between breast cancer diagnosis and MRI, and the diagnosis is 5–8 years earlier than mammography, showing a significant advantage in the early diagnosis of breast cancer. DOBI is not affected by breast density, and is suitable for the screening and diagnosis of all female breast cancers, as well as the efficacy evaluation and condition monitoring after breast cancer treatment [40–43].


Lin Chen et al.

5.3 Blood Oxygen Functional Imaging

Using a combination of 805 nm (or 850 nm) infrared light and 735 nm (or 760 nm) red light, the functional imaging of mammary gland blood oxygen can detect and compare the blood oxygen content of the patient mass with that of the healthy tissue to determine the nature of the mass, taking advantage of the characteristics of the cancer tissue and the different absorption characteristics in the near-infrared region. Blood oxygen functional imaging detects the changes of oxygenated hemoglobin and deoxygenated hemoglobin in the breast tissue, provides the metabolic status of the breast, displays the information of the breast structure and lesions, and realizes the integration of the information of the breast anatomy and the information of the function, which greatly improves the diagnosis level of the breast disease [44–46].

5.4 Near-infrared Fluorescence Imaging Technology

NIR fluorescence imaging technology is a new in vivo imaging method. The wavelength of the fluorescent dye is 650–900 nm, and the absorption rate of nonspecific tissue is very low. Therefore, compared with the traditional fluorescent dye with a wavelength range of 300–500 nm, NIR fluorescence imaging can obtain fewer background signals and clearer images. Targeted fluorescent probes can accumulate in tumor cells in large quantities and enhance the fluorescence signal, so they have the advantage of accurate identification of breast cancer cells. NIR fluorescence imaging cannot only provide a basis for the early diagnosis of tumors in a noninvasive and efficient manner, but also can be used for intraoperative imaging of breast-conserving breast cancer surgery, reducing normal tissue damage, and achieving the purpose of accurate breast cancer resection. At present, the main difficulty in applying NIR fluorescence imaging to breast cancer surgery is the lack of highly selective fluorescent dyes designed for breast cancer cells [47–50].

5.5 Photoacoustic Technology Based on Multifunctional Nanoprobe

Accurate diagnosis and treatment of breast cancer at the molecular and cellular level has always been the focus of early detection and individualized treatment of breast cancer. Multifunctional nanoprobes can deliver targeted ligands, imaging agents, and drugs to the tumor site at the same time by virtue of their versatility, realizing “multimodal diagnosis and treatment“at the gene and molecular level in vivo. Some nanoprobes (NPs) have unique physicochemical properties and can be used as a common tool for “integrated diagnosis and treatment.” The advantages of nano-drug delivery system over traditional drugs also open up a new way for the treatment of triple-negative and drug-resistant breast cancer. Nanoprobes use different materials (such as metals, organics, and semiconductor particles) as platforms to produce structures similar in size to biological macromolecules. The surface of NP is often coated with the hydrophilic polymer polyethylene glycol or functionalized by facultative ionic groups to resist the adsorption of serum proteins. Modified NP can be coupled with a variety of

Application and Analysis of Biomedical Imaging Technology in Early. . .


molecules, including targeted molecules, fluorescent groups, radioisotopes, and therapeutic drugs. Most of the research on NP in breast cancer is in the preclinical stage. NP has enhanced permeability and residence effects through new blood vessels in breast cancer, exuding from the blood vessel pores, while the lymphatic system of the tumor is not perfect and cannot be cleared in time. Therefore, NP can accumulate in the tumor tissues for a long time, laying the foundation for further imaging detection. Np-based breast cancer imaging includes magnetic resonance imaging, CT imaging, radionuclide imaging, and photoacoustic imaging. NP can also play a targeted therapeutic role by coupling chemotherapy drugs or its own therapeutic effects. Although multimodal nanoprobes have some advantages, they still have some limitations. The particle size of multimodal polymer nanoprobe is relatively large, which affects the penetrability of target cells. Multilayer nanoprobes are difficult to be used in clinic because of their complex structure and high cost. There were also significant differences in probe circulation, tumor targeting, imaging, and therapeutic efficacy. In addition, the mechanism of interaction between NP and organism is still lacking, and biotoxicity is difficult to predict [46, 51–55].


Conclusion Radiomics technology can obtain real-time, dynamic, threedimensional, and functional imaging of normal breast and cancer tissue. Through the multi-dimensional, multi-parameter, and multi-mode functional imaging and molecular imaging, we can combine the spatial relationship with the anatomical structure of breast tissue and pathological tissue for comprehensive diagnosis [5, 56, 57]. The application of radiomics technology in breast cancer patients is an emerging translational research topic and is often expected to improve diagnosis and characterization in experimental design. Radiomics characteristics (such as intensity, shape, texture, or wavelet) provide information about the cancer phenotype and tumor microenvironment that is different and complementary to other clinical and treatment-related data or genomic data [58–61]. When radiomics technology-derived data are combined with other relevant data to infer the outcome, an accurate evidence-based and stable clinical decision support system is generated. There are broad methodological differences in single research, so there is considerable room for improvement, such as the use of standardized methods to improve the quality of research. Just as artificial intelligence (AI) requires advanced computing and statistical science, radiomics also requires highly collaborative interdisciplinary, inter-institutional, and international collaboration [62, 63]. Currently, there is still little overlap between radiomics


Lin Chen et al.

and machine learning, but in the near future it will be promising to integrate them into the study of tumor biology, to classify tumors, and to predict response to treatment and prognosis. More careful evaluation of prospective quantitative imaging characteristics related to clinical outcomes is needed to improve the quality of research and further explore the potential value of breast cancerrelated radiomics [62–66]. References 1. Jerome-D’Emilia B, Suplee P, Kushary D (2019) A 10-year evaluation of New Jersey’s national breast and cervical cancer early detection program: comparison of stage at diagnosis in enrollees and nonenrollees. J Women’s Health 29(2):230–236 2. Klijn JG (2010) Early diagnosis of hereditary breast cancer by magnetic resonance imaging: what is realistic? J Clin Oncol 28:1441–1445 3. Milosevic M, Jankovic D, Milenkovic A, Stojanov D (2018) Early diagnosis and detection of breast cancer. Technol Health Care 26:729–759 4. Tseshkovskii MS, Labetskii II (1986) Radiographic methods of breast imaging in the early diagnosis of cancer. Med Radiol 31:66–70 5. Cardoso F, Kyriakides S, Ohno S, PenaultLlorca F, Poortmans P, Rubio IT et al (2019) Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 30:1674 6. Fouladi N, Pourfarzi F, Daneshian A, Alimohammadi S (2018) Mediating factors in early diagnosis of breast cancer: from initial changes in health to breast cancer detection. Asian Pac J Cancer Prev 19:2751–2755 7. Bulut A, Bulut A (2017) Knowledge, attitudes and behaviors of primary health care nurses and midwives in breast cancer early diagnosis applications. Breast Cancer 9:163–169 8. Capelan M, Battisti NML, McLoughlin A, Maidens V, Snuggs N, Slyk P et al (2017) The prevalence of unmet needs in 625 women living beyond a diagnosis of early breast cancer. Br J Cancer 117:1113–1120 9. Jacobs MA, Wolff AC, Macura KJ, Stearns V, Ouwerkerk R, El Khouli R et al (2015) Multiparametric and multimodality functional radiological imaging for breast cancer diagnosis and early treatment response assessment. J Natl Cancer Inst Monogr 2015:40–46 10. Scherer E, Seifert J (1971) Mammography as a method for early diagnosis of female breast cancer. Experiences with a screening test in

7380 apparently healthy women. Dtsch Med Wochenschr 96:1207–1210 11. Allweis TM, Nissan A, Spira RM, SklairLevy M, Freund HR, Peretz T (2003) Screening mammography for early diagnosis of breast cancer: facts, controversies, and the implementation in Israel. Harefuah 142:281–286. 317 12. Kujiraoka Y, Murakami A (2006) Diagnosis of early stage of breast cancer by mammography. Nihon Rinsho Jap J Clin Med 64:486–490 13. Gutnik LA, Castro MC (2016) Does spatial access to mammography have an effect on early stage of breast cancer diagnosis? A county-level analysis for New York State. Breast J 22:127–130 14. Lacombe J, Mange A, Bougnoux AC, Prassas I, Solassol J (2014) A multiparametric serum marker panel as a complementary test to mammography for the diagnosis of node-negative early-stage breast cancer and DCIS in young women. Cancer Epidemiol Biomark Prev 23:1834–1842 15. Nergiz-Eroglu U, Kilic D (2011) Knowledge, attitude and beliefs women attending mammography units have regarding breast cancer and early diagnosis. Asian Pac J Cancer Prev 12:1855–1860 16. Salih A, Webb WM, Bates T (1999) Does open-access mammography and ultrasound delay the diagnosis of breast cancer? Breast 8:129–132 17. Simmons R (2004) Ultrasound in the changing approaches to breast cancer diagnosis and treatment. Breast J 10(Suppl 10):S13–S14 18. Tohno E, Ueno E (2005) Ultrasound (US) diagnosis of nonpalpable breast cancer. Breast Cancer 12:267–271 19. Carlsen J, Ewertsen C, Sletting S, Vejborg I, Schafer FK, Cosgrove D et al (2015) Ultrasound elastography in breast cancer diagnosis. Ultraschall Med 36:550–562. quiz 63-5 20. Moon WK, Huang YS, Lo CM, Huang CS, Bae MS, Kim WH et al (2015) Computer-aided diagnosis for distinguishing between triplenegative breast cancer and fibroadenomas

Application and Analysis of Biomedical Imaging Technology in Early. . . based on ultrasound texture features. Med Phys 42:3024–3035 21. Zhang YN, Wang CJ, Xu Y, Zhu QL, Zhou YD, Zhang J et al (2015) Sensitivity, specificity and accuracy of ultrasound in diagnosis of breast cancer metastasis to the axillary lymph nodes in chinese patients. Ultrasound Med Biol 41:1835–1841 22. Polat AV, Ozturk M, Polat AK, Karabacak U, Bekci T, Murat N (2019) Efficacy of ultrasound and shear wave elastography for the diagnosis of breast cancer-related lymphedema. J Ultrasound Med 39(4):795–803 23. Tozaki M (2008) Diagnosis of breast cancer: MDCT versus MRI. Breast Cancer 15:205–211 24. Morrow M, Waters J, Morris E (2011) MRI for breast cancer screening, diagnosis, and treatment. Lancet 378:1804–1811 25. Kawashima H, Inokuchi M, Furukawa H, Kitamura S (2012) Accuracy for a diagnosis of breast cancer spread using 3.0T MRI. Nihon Rinsho Jap J Clin Med 70(Suppl 7):306–308 26. Banaie M, Soltanian-Zadeh H, Saligheh-Rad HR, Gity M (2018) Spatiotemporal features of DCE-MRI for breast cancer diagnosis. Comput Methods Prog Biomed 155:153–164 27. Brix G, Henze M, Knopp MV, Lucht R, Doll J, Junkermann H et al (2001) Comparison of pharmacokinetic MRI and [18F] fluorodeoxyglucose PET in the diagnosis of breast cancer: initial experience. Eur Radiol 11:2058–2070 28. Leithner D, Horvat JV, Bernard-Davila B, Helbich TH, Ochoa-Albiztegui RE, Martinez DF et al (2019) A multiparametric [(18)F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis. Eur J Nucl Med Mol Imaging 46:1878–1888 29. Markel AL, Vainer BG (2005) Infrared thermography in diagnosis of breast cancer (review of foreign literature). Terapevticheskii Arkhiv 77:57–61 30. Agostini V, Delsanto S, Molinari F, Knaflitz M (2006) Evaluation of feature-based registration in dynamic infrared imaging for breast cancer diagnosis. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 1:953–956 31. Ooi GJ, Fox J, Siu K, Lewis R, Bambery KR, McNaughton D et al (2008) Fourier transform infrared imaging and small angle x-ray scattering as a combined biomolecular approach to


diagnosis of breast cancer. Med Phys 35:2151–2161 32. Khanmohammadi M, Rajabi FH, Garmarudi AB, Mohammadzadeh R, Mohammadzadeh R (2010) Chemometrics assisted investigation of variations in infrared spectra of blood samples obtained from women with breast cancer: a new approach for cancer diagnosis. Eur J Cancer Care 19:352–359 33. Vreugdenburg TD, Willis CD, Mundy L, Hiller JE (2013) A systematic review of elastography, electrical impedance scanning, and digital infrared thermography for breast cancer screening and diagnosis. Breast Cancer Res Treat 137:665–676 34. Gerasimova E, Audit B, Roux SG, Khalil A, Gileva O, Argoul F et al (2014) Waveletbased multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis. Front Physiol 5:176 35. Han F, Shi G, Liang C, Wang L, Li K (2015) A simple and efficient method for breast cancer diagnosis based on infrared thermal imaging. Cell Biochem Biophys 71:491–498 36. Arora N, Martins D, Ruggerio D, Tousimis E, Swistel AJ, Osborne MP et al (2008) Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. Am J Surg 196:523–526 37. Kosus N, Kosus A, Duran M, Simavli S, Turhan N (2010) Comparison of standard mammography with digital mammography and digital infrared thermal imaging for breast cancer screening. J Turkish German Gynecol Assoc 11:152–157 38. Athanasiou A, Vanel D, Balleyguier C, Fournier L, Mathieu MC, Delaloge S et al (2005) Dynamic optical breast imaging: a new technique to visualise breast vessels: comparison with breast MRI and preliminary results. Eur J Radiol 54:72–79 39. Fournier LS, Vanel D, Athanasiou A, Gatzemeier W, Masuykov IV, Padhani AR et al (2009) Dynamic optical breast imaging: a novel technique to detect and characterize tumor vessels. Eur J Radiol 69:43–49 40. Cerussi AE, Tanamai VW, Mehta RS, Hsiang D, Butler J, Tromberg BJ (2010) Frequent optical imaging during breast cancer neoadjuvant chemotherapy reveals dynamic tumor physiology in an individual patient. Acad Radiol 17:1031–1039 41. Flexman ML, Khalil MA, Al Abdi R, Kim HK, Fong CJ, Desperito E et al (2011) Digital optical tomography system for dynamic breast imaging. J Biomed Opt 16:076014


Lin Chen et al.

42. Sajjadi AY, Isakoff SJ, Deng B, Singh B, Wanyo CM, Fang Q et al (2017) Normalization of compression-induced hemodynamics in patients responding to neoadjuvant chemotherapy monitored by dynamic tomographic optical breast imaging (DTOBI). Biomed Opt Express 8:555–569 43. Zimmermann BB, Deng B, Singh B, Martino M, Selb J, Fang Q et al (2017) Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis. J Biomed Opt 22:46008 44. Vaupel P, Fortmeyer HP, Runkel S, Kallinowski F (1987) Blood flow, oxygen consumption, and tissue oxygenation of human breast cancer xenografts in nude rats. Cancer Res 47:3496–3503 45. Vaupel P, Mayer A, Briest S, Hockel M (2005) Hypoxia in breast cancer: role of blood flow, oxygen diffusion distances, and anemia in the development of oxygen depletion. Adv Exp Med Biol 566:333–342 46. Zalev J, Richards LM, Clingman BA, Harris J, Cantu E, Menezes GLG et al (2019) Optoacoustic imaging of relative blood oxygen saturation and total hemoglobin for breast cancer diagnosis. J Biomed Opt 24:1–16 47. Abe K, Zhao L, Periasamy A, Intes X, Barroso M (2013) Non-invasive in vivo imaging of near infrared-labeled transferrin in breast cancer cells and tumors using fluorescence lifetime FRET. PLoS One 8:e80269 48. Verbeek FP, Troyan SL, Mieog JS, Liefers GJ, Moffitt LA, Rosenberg M et al (2014) Nearinfrared fluorescence sentinel lymph node mapping in breast cancer: a multicenter experience. Breast Cancer Res Treat 143:333–342 49. Chi C, Zhang Q, Mao Y, Kou D, Qiu J, Ye J et al (2015) Increased precision of orthotopic and metastatic breast cancer surgery guided by matrix metalloproteinase-activatable nearinfrared fluorescence probes. Sci Rep 5:14197 50. Abbaci M, Conversano A, De Leeuw F, Laplace-Builhe C, Mazouni C (2019) Nearinfrared fluorescence imaging for the prevention and management of breast cancer-related lymphedema: a systematic review. Eur J Surg 45:1778–1786 51. Bhattacharyya K, Goldschmidt BS, Viator JA (2016) Detection and capture of breast cancer cells with photoacoustic flow cytometry. J Biomed Opt 21:87007 52. Wong TTW, Zhang R, Hai P, Zhang C, Pleitez MA, Aft RL et al (2017) Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy. Sci Adv 3: e1602168

53. Yamaga I, Kawaguchi-Sakita N, Asao Y, Matsumoto Y, Yoshikawa A, Fukui T et al (2018) Vascular branching point counts using photoacoustic imaging in the superficial layer of the breast: a potential biomarker for breast cancer. Photo-Dermatology 11:6–13 54. Nyayapathi N, Xia J (2019) Photoacoustic imaging of breast cancer: a mini review of system design and image features. J Biomed Opt 24:1–13 55. Yao D, Wang Y, Zou R, Bian K, Liu P, Shen S et al (2020) Molecular engineered squaraine nanoprobe for NIR-II/photoacoustic imaging and photothermal therapy of metastatic breast cancer. ACS Appl Mater Interfaces 12 (4):4276–4284 56. Yu FH, Wang JX, Ye XH, Deng J, Hang J, Yang B (2019) Ultrasound-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 119:108658 57. Granzier RWY, van Nijnatten TJA, Woodruff HC, Smidt ML, Lobbes MBI (2019) Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: a systematic review. Eur J Radiol 121:108736 58. Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M et al (2019) Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)-positive breast cancer. JAMA Netw Open 2:e192561 59. Antunovic L, De Sanctis R, Cozzi L, Kirienko M, Sagona A, Torrisi R et al (2019) PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 46:1468–1477 60. Acar E, Turgut B, Yigit S, Kaya G (2019) Comparison of the volumetric and radiomics findings of 18F-FDG PET/CT images with immunohistochemical prognostic factors in local/locally advanced breast cancer. Nucl Med Commun 40:764–772 61. Lee SE, Han K, Kwak JY, Lee E, Kim EK (2018) Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 8:13546 62. Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M (2018) A new challenge for radiologists: radiomics in breast cancer. Biomed Res Int 2018:6120703 63. Park H, Lim Y, Ko ES, Cho HH, Lee JE, Han BK et al (2018) Radiomics signature on magnetic resonance imaging: association with

Application and Analysis of Biomedical Imaging Technology in Early. . . disease-free survival in patients with invasive breast cancer. Clin Cancer Res 24:4705–4714 64. Saha A, Yu X, Sahoo D, Mazurowski MA (2017) Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst Appl 87:384–391 65. Drukker K, Edwards A, Doyle C, Papaioannou J, Kulkarni K, Giger ML (2019) Breast MRI radiomics for the pretreatment


prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. J Med Imaging 6:034502 66. Marino MA, Pinker K, Leithner D, Sung J, Avendano D, Morris EA et al (2019) Contrast-enhanced mammography and radiomics analysis for noninvasive breast cancer characterization: initial results. Mol Imaging Biol:1–8

Chapter 7 Recent Advances in DNA Repair Pathway and Its Application in Personalized Care of Metastatic Castration-Resistant Prostate Cancer (mCRPC) Chenyang Xu, Shanhua Mao, and Haowen Jiang Abstract Prostate cancer (PCa) is one of the common malignancies in male adults. In the era of precision medicine, many other novel agents targeting advanced prostate cancer, especially metastatic castration-resistant prostate cancer (mCRPC), are currently being evaluated. Among all these candidate therapies, poly-ADP ribose polymerase (PARP) inhibitors targeting DNA damage response (DDR) pathway has proven improving survival outcomes in clinical trials. In this review, we focus on recent advances in biology and clinical implication of DDR pathway and aim to discuss the latest results in advanced prostate cancer, especially mCRPC. Key words Metastatic castration-resistant prostate cancer, DNA damage response, Prostate-specific antigen, Androgen-deprived treatment, Homologous recombination deficiency


Introduction Prostate cancer (PCa) is one of the common malignancies in male adults. In men, prostate cancer ranks second in incidence and fifth in mortality. It is estimated that there were almost 1.3 million new cases of prostate cancer and 359,000 associated death worldwide [1]. Thanks to the widespread application of prostate-specific antigen (PSA) screening, more patients with low-grade and organconfined tumors have been detected early. Although the incidence of advanced prostate cancer (T3 + NxMx) declined in the past decades, the mortality of advanced prostate cancer patients remained high [2]. Androgen-deprived treatment (ADT) has been widely prescribed to patients with advanced prostate cancer empirically. In the era of precision medicine, many other novel agents targeting advanced prostate cancer, especially metastatic castration-resistant prostate cancer (mCRPC), are currently being evaluated [3–5]. These new options include the CYP17 inhibitor

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Chenyang Xu et al.

abiraterone [6–8], the androgen receptor antagonist enzalutamide [9, 10], the taxane cabazitaxel [11, 12], and immunotherapy such as PD-1/PD-L1 antibody [13, 14]. Among all these candidate therapies, poly-ADP ribose polymerase (PARP) inhibitors targeting DNA damage response (DDR) pathway has proven improving survival outcomes in clinical trials. It is estimated that at least ten percent of advanced PCa patients have germline mutations in DNA repair genes (DRG), and approximately one fifth of the mCRPC patients carry aberrations of DRG [15]. Among these DRG, homologous recombination deficiency (HRD) such as BRCA1/ 2 mutant cells display sensitivity to PARP inhibition due to the synthetic lethality of tumors [16]. In this review, we focus on recent advances in biology and clinical implication of DDR pathway. Germline and somatic mutation of DDR genes and PARP inhibitors have been widely studied in preclinical research and clinical trials. We aim to discuss the latest results in advanced prostate cancer, especially mCRPC.


Evidence Acquisition A literature research for clinical trials and preclinical basic research studies from January 2014 to December 2019 was conducted in PubMed, Web of Science, EMBASE (Ovid), American Society of Clinical Oncology (ASCO) Meeting Summary, Cochrane Database and (NLM). Keywords include “DNA repair,” “homologous recombination,” “BRCA,” “ATM,” “prostate cancer,” “CRPC,” “PARP,” “predictive biomarkers,” “precision medicine.”


Evidence Synthesis

3.1 DNA Damage Response Pathway 3.1.1 Overview

Defective DNA repair causes replicative immortality, which is a common hallmark of cancer [17]. There are two major pathways to repair hazardous DNA double-strand breaks (DSB) in eukaryotic cells, homologous recombination (HR), and nonhomologous end-joining (NHEJ) [18]. Other mechanisms of DDR pathway include base excision repair (BER), nucleotide excision repair (NER), and mismatch repair (MMR), which mainly correct single-strand breaks [19]. HR functions in the S and G2 phases of the cell cycle, while NHEJ functions by ligating broken DNA ends throughout the cell cycle [18]. HR played a central role in response to DSBs repair, and deficiency of HR pathway may confer and increase the risk of several tumors. Important mediators of HR pathway include BRCA1, BRCA2, PALB2, ATM, RAD51, MRE11, CHEK2, and XRCC2/3 [20].

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . .


In classic HR pathway, a single-stranded DNA template was resected from 50 terminal of a DNA break end. The resection of 50 -terminated DNA strand is mediated by RAD51 recombinase and its ancillary factors. BRCA mediator complex (DSS1-BRCA2PALB2-BRCA1-BARD1) function in presynaptic filament formation, replication fork repair, and interstrand cross-link (ICL) removal [20–22]. Mutations of these genes resulted in attenuation of DSB repair capacity of cells. 3.1.2 BRCA1/2 Mutation

The BRCA gene mutation was detected in familial ovarian and breast cancer. The breast cancer type 1 susceptibility protein (BRCA1) comprises of 1863 amino acid residues. It interacts with BARD1 constituting a RING heterodimer to repair chromosome damage with E3 ubiquitin ligase activity. BRCA1-BARD1 is involved in DNA end resection, RAD51 recruitment, synaptic complex assembly, and several other crucial steps of genome repair [23]. In vitro research showed that BRCA1-deficient cells display susceptibility to DNA damaging agents and chromosomal instability [24–26]. The breast cancer type 2 susceptibility protein (BRCA2) comprises of 3418 amino acid residues. Through interaction with RAD51, it demonstrates important function in replication fork maintenance [27]. In breast cancer, germline BRCA mutations resulted in the expression of oncogenes [28]. The lifetime risk of breast cancer increases from 60 to 70% with the presence of gBRCA mutation [29, 30]. In triple-negative breast cancer (TNBC), germline mutations in BRCA1 or BRCA2 occur in approximately 10% of TNBC patients [31]. In epithelial ovarian cancer development, loss of function in either BRCA1 or BRCA2 is an important risk factor. Recent systemic analysis based on over 5000 tumor immunohistochemistry samples reported that a 47.0 percent of ovarian cancers were associated with loss of BRCA1 and 34.5 percent with the loss of BRCA2 [32].

3.1.3 Ataxia-Telangiectasia Mutated Kinase (ATM)

Ataxia-telangiectasia mutated kinase (ATM) is another extensively studied DDR protein. ATM is an important member of phosphatidylinositol 3-kinase-like protein kinase (PIKKs) family [33]. ATM activates checkpoint and DSB repair pathway by phosphorylation. Either germline or induced ATM mutant leads to tumorigenesis and metastasis [34]. According to immunohistochemistry studies of primary and metastatic tumors including gastric, colon, and breast cancer, ATM deficiency is common (approximately 20 percent) and sensitizes PARP inhibitor therapy [35, 36]. Selective ATR inhibitors like AZD6738 are currently in clinical development. The preclinical data supporting the use of ATR inhibitors in synthetic lethality with novel targeted agents such as PARP inhibitors [37, 38].


Chenyang Xu et al.

3.2 DDR Biomarkers for PCa Diagnosis and Prognosis 3.2.1 Germline BRCA Mutation and Hereditary PCa

3.2.2 Somatic Mutations of DRG and PCa

Studies of hereditary PCa reveal its relationship with inherited DRG mutations. Germline BRCA2 mutations increase the risk of PCa by 8.6 folds. The prevalence of germline BRCA2 mutations is estimated to be 5.3% in advanced PCa [39]. The IMPACT study also found a higher incidence of PCa in gBRCA1/2 carriers than noncarriers [40]. In a large-scale retrospective study including 61 BRCA2 and 18 BRCA1-mutated PCa patients, 23 percent of gBRCA1/2 mutation carriers developed metastasis after 5 years of radical treatment, significantly higher than noncarriers ( p ¼ 0.001).Of note, gBRCA1/2 were also associated with more aggressive prostate cancer, higher risk of lymph node, and distant metastasis at diagnosis [41]. Another retrospective case study of 313 patients found that BRCA1/2 and ATM mutation carrier rate was significantly higher in lethal PCa patients (6.07%) than localized PCa patients (1.44%, p ¼ 0.0007). The mutation status of gBRCA was associated with early age at death [42]. A prospective cohort study of germline DDR gene mutation in mCRPC enrolled 419 patients and identified 107 genes in 68 carriers. Cancer-specific survival (CSS) was only a half in gBRCA2 carriers (17.4 months versus 33.2 months, p ¼ 0.027). Multivariable analysis identified gBRCA2 mutations as an independent prognostic factor for CSS (adjusted hazard ratio ¼ 2.11, p ¼ 0.033) [43]. Germline mutations were reported to correlate with higher grade of PCa. Recent study based on two cohorts included 1211 PCa patients characterized relation between PCa grade reclassification and germline BRCA1/2 and ATM mutations. Eleven of 26 patients with mutations experienced grade reclassification (adjusted hazard ratio ¼ 2.74, p ¼ 0.01). Carrier rates for gBRCA2 were significantly higher in the reclassified cohort (2.1%), especially in those reclassified from Gleason score 3 + 3 at diagnosis to Gleason score  4 + 3 (4.1%) [44]. Somatic mutations in DRG are common in sporadic prostate cancer. Approximately 23% of mCRPC patients carry somatic DDR aberrations. BRCA1/2 and ATM gene mutations are the main somatic aberrations with higher frequency in mCRPC compared with primary PCa [45]. For PARP inhibitor therapy, ideal clinical biomarkers that not only predict HRD in DRG wild-type tumors but also refine DRG mutant population to account for phenotypic variants and reversions remained controversial. Germline BRCA mutation has been the most suitable biomarker for PARP inhibitor response although the problems of variants of uncertain significance still exists. Other PCa-associated mutations include BARD1, PALB2, RAD51B/C/ D, ATM, MLH1, MSH2, FANCA, and so on. These are potential biomarkers that correlate with clinical response to PARP inhibitor therapy. Prospective genomic profiling in PCa patients revealed that

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . .


somatic alterations of DDR genes arose early in mCRPC, including ATM, RAD51C, and FANCA [46]. In metastatic PCa, a panel of 20 DRG next-generation sequencing found 11.8% of the patients carried these mutations [39]. Several other studies reported the prevalence of DRG defects in metastatic PCa within the range from 8.8% to 22.7% [45, 47, 48]. In patients with extremely aggressive localized PCa, two somatic mutations in PRKDC which encodes the catalytic subunit of the DNA-dependent protein kinase involved in DNA double-strand break repair and recombination were reported [49]. 3.3 PARP Inhibitors in mCRPC

Poly-ADP ribose polymerases (PARPs) are a family of enzymes function in detecting and harboring other DNA repair proteins to single-strand breaks (SSB). PARP1 and PARP2 repair DNA damage by catalyzing the cleavage of nicotinomide adenine dinucleotide-plus into nicotinamide and ADP-ribosyl action and attaching ADP-ribose to form a complex [50]. In HR pathway, PARP acts as a sensor and recruiting protein like MRE11 to start DNA resection and replication. Theoretically, PARP inhibitor can halt ligation of SSB before replication. Thus, accumulation of DSBs due to defective BRCA1/2, ATM, and other DDR genes would lead to loss of cell viability. Furthermore, when DDR genes are inactivated, alternative repair pathways act in compensation, BER DNA repair for instance. These alternative repair pathways could also be inhibited through PARP [51–55]. Several PARP inhibitors has been tested in various solid tumors, including olaparib, rucaparib, niraparib, veliparib, and talazoparib.

3.3.1 Olaparib

Olaparib has gained U.S. Food and Drug Administration (FDA) in treatment of advanced epithelial ovarian cancer and Her-2-negative metastatic breast cancer with gBRCA mutations. It was also the first PARP inhibitor that received FDA breakthrough therapy in 2016 to treat mCRPC patients carrying BRCA1/2 or ATM mutations [56]. In a phase II clinical trial of olaparib for multiple tumors (NCT01078662), eight mCRPC patients (one BRCA1 mutant carrier and seven BRCA2 mutant carriers) were enrolled. The median progression-free survival (PFS) for all eight patients was 7.2 month. Two patients responded for over 1 year [57]. Another clinical trial of olaparib monotherapy enrolled 50 mCRPC patients. Next-generation sequencing identified 16 patients carried DRG deficiency. The response rate of these patients is higher than the overall response rate (88% vs 33%) [58]. A multicenter, open-label, randomized phase II trial (TOPARP, NCT01682772) recruited 711 mCRPC patients in the UK. Taken together, 98 patients had DDR gene aberrations and were treated with olaparib. In 400 mg cohort, the preliminary result showed a composite response rate of 54.3% (25 of 46; 95%CI 39.0–69.1). In 300 mg cohort, composite


Chenyang Xu et al.

response was confirmed in 39.1% (18 of 46; 95%CI 25.1–54.6) [59]. As a monotherapy, olaparib is well tolerated. Major adverse effects of olaparib were anemia, nausea, fatigue, and thrombocytopenia [58, 60]. Investigation of olaparib monotherapy helped understanding of its efficacy, safety, and degree of PARP inhibition. More clinical trials have been established to look at the response of olaparib plus other cytotoxic drug. Combination of olaparib with abiraterone in mCRPC patients were evaluated in a randomized, double-blind, placebo-controlled phase II trial (NCT01972217) [61]. Altogether 142 patients were equally divided into two groups receiving olaparib plus abiraterone and abiraterone alone. The PFS was significantly better in the experimental group (13.8 months versus 8.2 months, p ¼ 0.034). Meanwhile, olaparib combining with immunotherapy also demonstrated a potential synergic effect [62, 63]. Olaparib and pembrolizumab (anti-PD-L1 antibody) combination therapy (Keynote-365, NCT02861573) was reported to achieve a median overall survival of 14 months and a median radiographic progression-free survival (rPFS) of 5 months. The disease control rate was 32% (12 out of 41). Six patients (15%) got composite response, none of whom had DDR deficiency [64]. Similar efficacy was identified in durvalumab plus olaparib combination therapy (KEYLINK-010, NCT03834519). Median rPFS for all patients is 16.1 months (95% CI: 4.5–16.1 months) with a 12-month rPFS of 51.5% (95% CI: 25.7–72.3%) [65]. Two ongoing phase II and III clinical trials include mCRPC patients and treat them with olaparib monotherapy. Another two studies focus on combination therapy of olaparib and abiraterone. A phase II study (BRCAAway, NCT03012321) include DDR-mutated mCRPC patients and randomly assigned to three arms of olaparib alone, abiraterone alone, and combination therapy. Multicenter, randomized clinical trial (PROpel, NCT03732820) is a phase III trial aiming to recruit over 700 mCRPC patients and compare olaparib versus placebo in combination with abiraterone. Several other current trials examine the efficacy and safety of olaparib with radiation, immunotherapy, and testosterone (Table 1). 3.3.2 Rucaparib

Rucaparib was first approved by FDA for BRCA-mutated advanced ovarian cancer [66]. Its indication was then expended to maintenance treatment of recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer. Rucaparib was tested in a phase II study (TRITON2, NCT02952534). Fifty-two mCRPC patients with DDR mutation that was previously treated with ADT or taxane-based chemotherapy were recruited. The primary outcome confirmed objective response rate and PSA response (over 50% decrease) rate. The latest update demonstrated that 23 patients (51.1%) had PSA response

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . .


Table 1 Current clinical trials of Olaparib for castration-resistant prostate cancer

NCT number

Combined intervention

Primary Phase outcome

Cancer type

Study design

NCT03434158 None

Metastatic castrationresistant prostate cancer after over 6 cycles of docetaxel

Multicenter, single arm

NCT02987543 None

Multicenter, Metastatic randomized, castrationparallel control resistant prostate treated with ADT cancer and evidence of HRD

NCT03012321 Abiraterone

Metastatic castrationresistant prostate cancer with BRCA1/2 or ATM mutation

NCT03732820 Abiraterone

Multicenter, metastatic randomized, castrationdouble-blinded, resistant prostate placebo control cancer (mCRPC) with no prior cytotoxic chemotherapy or new hormonal agents (NHAs)

NCT03317392 Radium Ra 223 Dichloride

Castration-resistant prostate cancer metastatic to the bone

Multicenter, randomized, parallel control treated with olaparib or radiation

1 and MTD and 2 PFS

NCT02893917 Cediranib

Metastatic castrationresistant prostate cancer

Multicenter, randomized, parallel control treated with olaparib



Multicenter, single arm, doseescalation


DLTs and MTD

Metastatic NCT03874884 177LutetiumcastrationProstateresistant prostate Specific cancer Membrane Antigen (177 Lu-PSMA)

Multicenter, randomized











Chenyang Xu et al.

Table 1 (continued)

NCT number

Combined intervention

Primary Phase outcome

Cancer type

Study design

NCT02861573 Pembrolizumab (MK-3475)

Metastatic castrationresistant prostate cancer

3 Multicenter, non-randomized, parallel control treated with pembrolizumab plus enzalutamide or docetaxel

NCT03834519 Pembrolizumab (MK-3475)

Multicenter, Metastatic randomized, castrationparallel control resistant prostate treated with cancer progressed abiraterone or on an androgen enzalutamide receptor signaling inhibitor

NCT02484404 Anti-PD-L1 antibody (MED14736)

Metastatic castrationresistant prostate cancer, advanced ovarian/breast/ colorectal cancer

NCT03787680 AZD6738

NCT03516812 Testosterone



OS and PFS

Multicenter, non-randomized, parallel treated with MED14736 plus cediranib

1 and 2


Metastatic castrationresistant prostate cancer

Multicenter single arm


CR or PR in DNA repair proficient patients

Castration-resistant prostate cancer

Single-center, single arm



rPFS radiographic progress-free survival, MTD maximum tolerated dose, DLT dose limiting toxicities, ORR objective response rate, RR-PSA prostate-specific antigen response rate, CR complete response, PR partial response

and 11 patients (44.0%) had radiographic response [67]. Grades 3–4 anemia and thrombocytopenia were reported, suggesting that toxicity of rucaparib was higher than olaparib. Currently, two phase II studies aim at evaluating efficacy of rucaparib monotherapy in mCRPC patients with HRD, and there are three clinical trials exploring the potential survival benefit of rucaparib in combination with nivolumab (anti-PD1 antibody) or chemotherapy (Table 2). 3.3.3 Niraparib

Niraparib was the third PARP inhibitor that gained FDA approval in 2017. It was the first PARP inhibitor to receive full approval by the U.S. Food and Drug Administration (FDA) for the maintenance treatment of recurrent ovarian cancer, regardless of a patient’s germline or somatic mutational status [68].

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . .


Table 2 Current clinical trials of Rucaparib for castration-resistant prostate cancer

NCT number

Combined intervention

Cancer type

Study design

Primary Phase outcome

NCT02952534 None

Metastatic castration- Multicenter, single arm resistant prostate cancer and evidence of HRD



NCT02975934 None

Metastatic castration- Multicenter, randomized, 3 parallel control treated resistant prostate with abiraterone or cancer and evidence docetaxel of HRD

NCT03572478 Nivolumab

Metastatic castrationresistant prostate cancer and metastatic/ recurrent endometrial cancer

Single-center, randomized, parallel control treated with monotherapy

1 and DLT 2

NCT03338790 Nivolumab

Metastatic castrationresistant prostate cancer

Single-center, non-randomized, parallel control treated with Nivolumab plus arbiraterone or docetaxel



NCT03442556 Docetaxel or Metastatic castrationcarboplatin resistant prostate cancer with BRCA1/2 or ATM mutation

Single-center, single arm




ORR objective response rate, RR-PSA prostate-specific antigen response rate, rPFS radiographic progress-free survival, DLT dose limiting toxicity

Two phase I dose-escalation studies of niraparib monotherapy for advanced solid tumors have reported promising results. Among the 21 mCRPC patients enrolled from the UK and the USA, 9 patients had stable disease for a median duration of 254 days and a decrease in circulating tumor cells were found in 3 patients (NCT00749502) [69]. A phase II study of niraparib (GALAHAD, NCT02854436) evaluated in 123 mCRPC patients with pathogenic mutations of BRCA1/2, ATM, FANCA, PALB2, CHEK2, BRIP1, or HDAC2). Composite and objective response rates were 65% and 38%. In comparison, in 16 patients without BRCA1/ 2 mutations, objective response rate was 11% [70]. Toxicities of niraparib were limited to low-grade adverse effect. Another phase II study is now open to assess the safety of niraparib in patients with mCRPC. Apart from that, three undergoing clinical trials are


Chenyang Xu et al.

Table 3 Current clinical trials of Niraparib for castration-resistant prostate cancer

NCT number

Combined intervention Cancer type

NCT02854436 None

Study design

Metastatic castration-resistant Multicenter, single prostate cancer arm

Primary Phase outcome 2


NCT03748641 Abiraterone Metastatic prostate cancer acetate

Multicenter, randomized, placebo control



NCT03076203 Radium Ra Hormone-resistant prostate 223 cancer metastatic to the dichloride bone

Multicenter, single arm



NCT03431350 Abiraterone Metastatic castration-resistant Multicenter, acetate prostate cancer non-randomized

1 and ORR 2

ORR objective response rate, rPFS radiographic progress-free survival, MTD maximum tolerated dose

recruiting mCRPC patients to examine the effect of combination therapies, including niraparib plus abiraterone acetate and niraparib plus Radium-223 (Table 3). 3.3.4 Veliparib

Veliparib is another potent inhibitor of PARP1 and PARP2 that demonstrated promising anti-PCa activity. Veliparib does not yet have an FDA-approved label; nevertheless, there are currently promising results available in preclinical and early clinical settings [71–74]. Pilot study of 26 mCRPC patients treated with veliparib plus temozolomide (TMZ) reported a median OS of 9.1 months and a median PFS of 2.9 months [75]. Seventy mCRPC patients with BRCA2-mutation were included in a dose-escalation phase I trial, and three patients were recommended with phase II study of veliparib. The objective response rate was reported to be 37% and 66% in phase I and phase II trial [76]. Another phase II randomized trial of veliparib plus abiraterone for mCRPC (NCT01576172) examined the antitumor efficacy of this agent in combination therapy. The PFS was slightly better in combination therapy compared with abiraterone alone, but the result is of no statistical significance (11 months versus 10.1 months, p ¼ 0.95) [77]. Ongoing phase II and phase III trials are testing veliparib with cytotoxic chemotherapy in advanced solid tumors.

3.3.5 Talazoparib

Talazoparib was more potent than the PARP inhibitors mentioned above [78]. It gained FDA approval for gBRCA-mutated HER2negative locally advanced or metastatic breast cancer [79]. Started in 2018, TALAPRO-1 (NCT03148795) was the first phase II randomized clinical trial involving talazoparib in posttaxane mCRPC patients with DDR defects [80]. An initial safety

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . .


Table 4 Current clinical trials of Talazoparib for castration-resistant prostate cancer

NCT number

Combined intervention

Primary Phase outcome

Cancer type

Study design

NCT03148795 None

DDR-positive metastatic castrationresistant prostate cancer

Multicenter, single arm



NCT03395197 Enzalutamide

Metastatic castrationresistant prostate cancer

Multicenter, randomized, double-blind placebo control treated with enzalutamide



2 Multicenter, NCT04052204 Avelumab, Metastatic non-randomized, bempegaldesleukin castrationparallel control (NKTR-214) resistant treated with prostate cancer avelumab plus and locally bempegaldesleukin advanced squamous cell carcinoma of the head and neck NCT03330405 Avelumab

Locally advanced or metastatic solid tumorsa

NCT04019327 Temozolomide

Single-center, single Castrationarm resistant Prostate Cancer

Multicenter, singlearm


1 and DLT, 2 ORR 1 and ORR 2

ORR objective response rate, rPFS radiographic progress-free survival, DLT dose limiting toxicity, RR-PSA prostatespecific antigen response rate a Including non-small cell lung cancer (NSCLC), triple-negative breast cancer (TNBC), hormone receptor-positive (HR +) breast cancer, recurrent platinum-sensitive ovarian cancer, urothelial cancer (UC), and castration-resistant prostate cancer (CRPC)

and efficacy analysis will be performed on 20 patients after over 8 weeks of treatment. Clinical efficacy and safety of talazoparib combined with other therapies is yet to be determined in ongoing clinical studies (Table 4). 3.3.6 Other PARP Inhibitors in Development

Other preclinical PARP inhibitors for patients with advanced solid tumors include pamiparib [81] (NCT03712930; NCT03150810), IMP4297 (NCT03507543), HWH340 (NCT03415659), SC10914 (NCT02940132), NMS-03305293 (NCT04182516), LT-626 [82], etc. The efficacy and toxicity of these drugs remain to be discovered.



Chenyang Xu et al.

Conclusion In summary, more and more evidences reveal that DNA damage response pathway is critical in carcinogenesis of prostate cancer. DRG mutations such as BRCA1/2 and ATM are related with the susceptibility of prostate cancer and tumor aggressiveness. Fortunately, PARP inhibitors targeting these gene aberrations demonstrate its efficacy in advanced prostate cancer to prolong progression-free survival, especially mCRPC. Although yet not available, olaparib offers an exciting new opportunity that opens the gate of precision medicine for patients with mutation profile. In future, large-scale clinical trials will test olaparib either as monotherapy or in combination with other treatments. Evaluation of rucaparib (TRITON3, NCT02975934), niraparib (NCT02854436), and several other PARP inhibitors for mCRPC patients with DRG mutations is also on its way. In the era of precision medicine, targeted mCRPC patients will benefit from these novel therapeutic approaches.

References 1. Bray F, Ferlay J, Soerjomataram I et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424 2. Fitzmaurice C, Abate D, Abbasi N et al (2019) Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol 5 (12):1749–1768 3. Parker C, Lewington V, Shore N et al (2018) Targeted alpha therapy, an emerging class of cancer agents: a review. JAMA Oncol 4 (12):1765–1772 4. Ritch CR, Cookson MS (2016) Advances in the management of castration resistant prostate cancer. BMJ (Clinical Research ed) 355:i4405 5. Nuhn P, De Bono JS, Fizazi K et al (2019) Update on systemic prostate cancer therapies: management of metastatic castration-resistant prostate cancer in the era of precision oncology. Eur Urol 75(1):88–99 6. Almassi N, Reichard C, Li J et al (2018) HSD3B1 and response to a nonsteroidal CYP17A1 inhibitor in castration-resistant prostate cancer. JAMA Oncol 4(4):554–557 7. Miller K, Carles J, Gschwend JE et al (2018) The Phase 3 COU-AA-302 Study of

abiraterone acetate plus prednisone in men with chemotherapy-naı¨ve metastatic castration-resistant prostate cancer: stratified analysis based on pain, prostate-specific antigen, and gleason score. Eur Urol 74(1):17–23 8. Fizazi K, Tran N, Fein L et al (2017) Abiraterone plus prednisone in metastatic, castrationsensitive prostate cancer. N Engl J Med 377 (4):352–360 9. Saito K, Fujii Y (2018) Antitumor activity and safety of enzalutamide after abiraterone acetate: seeking the optimal treatment sequence for castration-resistant prostate cancer patients. Eur Urol 74(1):46–47 10. Anker MS, Lehmann LH, Anker SD (2018) Enzalutamide in castration-resistant prostate cancer. N Engl J Med 379(14):1380 11. Antonarakis ES, Tagawa ST, Galletti G et al (2017) Randomized, noncomparative, phase II trial of early switch from docetaxel to cabazitaxel or vice versa, with integrated biomarker analysis, in men with chemotherapy-naı¨ve, metastatic, castration-resistant prostate cancer. J Clin Oncol 35(28):3181–3188 12. Rouyer M, Oudard S, Joly F et al (2019) Overall and progression-free survival with cabazitaxel in metastatic castration-resistant prostate cancer in routine clinical practice: the FUJI cohort. Br J Cancer 121(12):1001–1008 13. Hansen AR, Massard C, Ott PA et al (2018) Pembrolizumab for advanced prostate

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . . adenocarcinoma: findings of the KEYNOTE028 study. Ann Oncol 29(8):1807–1813 14. Gulley JL, Madan RA, Pachynski R et al (2017) Role of antigen spread and distinctive characteristics of immunotherapy in cancer treatment. J Natl Cancer Inst 109(4):djw261 15. Lang SH, Swift SL, White H et al (2019) A systematic review of the prevalence of DNA damage response gene mutations in prostate cancer. Int J Oncol 55(3):597–616 16. Ashworth A, Lord CJ (2018) Synthetic lethal therapies for cancer: what’s next after PARP inhibitors? Nat Rev Clin Oncol 15(9):564–576 17. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144 (5):646–674 18. Chang HHY, Pannunzio NR, Adachi N et al (2017) Non-homologous DNA end joining and alternative pathways to double-strand break repair. Nat Rev Mol Cell Biol 18 (8):495–506 19. Caldecott KW (2008) Single-strand break repair and genetic disease. Nat Rev Genet 9 (8):619–631 20. Her J, Bunting SF (2018) How cells ensure correct repair of DNA double-strand breaks. J Biol Chem 293(27):10502–10511 21. Schlacher K, Christ N, Siaud N et al (2011) Double-strand break repair-independent role for BRCA2 in blocking stalled replication fork degradation by MRE11. Cell 145(4):529–542 22. Clauson C, Sch€arer OD, Niedernhofer L (2013) Advances in understanding the complex mechanisms of DNA interstrand crosslink repair. Cold Spring Harb Perspect Biol 5 (10):a012732 23. Zhao W, Steinfeld JB, Liang F et al (2017) BRCA1-BARD1 promotes RAD51-mediated homologous DNA pairing. Nature 550 (7676):360–365 24. Rottenberg S, Jaspers JE, Kersbergen A et al (2008) High sensitivity of BRCA1-deficient mammary tumors to the PARP inhibitor AZD2281 alone and in combination with platinum drugs. Proc Natl Acad Sci U S A 105 (44):17079–17084 25. Moynahan ME, Cui TY, Jasin M (2001) Homology-directed dna repair, mitomycin-c resistance, and chromosome stability is restored with correction of a Brca1 mutation. Cancer Res 61(12):4842–4850 26. Alli E, Sharma VB, Hartman AR et al (2011) Enhanced sensitivity to cisplatin and gemcitabine in Brca1-deficient murine mammary epithelial cells. BMC Pharmacol 11:7 27. Jensen RB, Carreira A, Kowalczykowski SC (2010) Purified human BRCA2 stimulates


RAD51-mediated recombination. Nature 467 (7316):678–683 28. Kuchenbaecker KB, Mcguffog L, Barrowdale D et al (2017) Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J Natl Cancer Inst 109(7):djw302 29. Antoniou A, Pharoah PD, Narod S et al (2003) Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet 72(5):1117–1130 30. Mavaddat N, Peock S, Frost D et al (2013) Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE. J Natl Cancer Inst 105 (11):812–822 31. Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70 32. Teixeira LA, Candido Dos Reis FJ (2020) Immunohistochemistry for the detection of BRCA1 and BRCA2 proteins in patients with ovarian cancer: a systematic review. J Clin Pathol 73(4):191–196 33. Li M, Yu X (2013) Function of BRCA1 in the DNA damage response is mediated by ADP-ribosylation. Cancer Cell 23(5):693–704 34. Rezaeian AH, Li CF, Wu CY et al (2017) A hypoxia-responsive TRAF6-ATM-H2AX signalling axis promotes HIF1α activation, tumorigenesis and metastasis. Nat Cell Biol 19(1):38–51 35. Miller RM, Nworu C, Mckee L et al (2020) Development of an immunohistochemical assay to detect the ataxia-telangiectasia mutated (ATM) protein in gastric carcinoma. Appl Immunohistochem Mol Morphol 28 (4):303–310 36. Jette NR, Radhamani S, Arthur G et al (2019) Combined poly-ADP ribose polymerase and ataxia-telangiectasia mutated/Rad3-related inhibition targets ataxia-telangiectasia mutated-deficient lung cancer cells. Br J Cancer 121(7):600–610 37. Foote KM, Nissink JWM, Mcguire T et al (2018) Discovery and characterization of AZD6738, a potent inhibitor of ataxia telangiectasia mutated and Rad3 related (ATR) Kinase with application as an anticancer agent. J Med Chem 61(22):9889–9907 38. Bradbury A, Hall S, Curtin N et al (2019) Targeting ATR as cancer therapy: a new era for synthetic lethality and synergistic combinations? Pharmacol Ther 207:107450


Chenyang Xu et al.

39. Pritchard CC, Mateo J, Walsh MF et al (2016) Inherited DNA-repair gene mutations in men with metastatic prostate cancer. N Engl J Med 375(5):443–453 40. Bancroft EK, Page EC, Castro E et al (2014) Targeted prostate cancer screening in BRCA1 and BRCA2 mutation carriers: results from the initial screening round of the IMPACT study. Eur Urol 66(3):489–499 41. Castro E, Goh C, Olmos D et al (2013) Germline BRCA mutations are associated with higher risk of nodal involvement, distant metastasis, and poor survival outcomes in prostate cancer. J Clin Oncol 31(14):1748–1757 42. Na R, Zheng SL, Han M et al (2017) Germline mutations in ATM and BRCA1/2 distinguish risk for lethal and indolent prostate cancer and are associated with early age at death. Eur Urol 71(5):740–747 43. Castro E, Romero-Laorden N, Del Pozo A et al (2019) PROREPAIR-B: a prospective cohort study of the impact of germline DNA repair mutations on the outcomes of patients with metastatic castration-resistant prostate cancer. J Clin Oncol 37(6):490–503 44. Carter H, Helfand B, Mamawala M et al (2019) Germline mutations in ATM and BRCA1/2 are associated with grade reclassification in men on active surveillance for prostate cancer. Eur Urol 75(5):743–749 45. Robinson D, Van Allen EM, Wu YM et al (2015) Integrative clinical genomics of advanced prostate cancer. Cell 161 (5):1215–1228 46. Abida W, Armenia J, Gopalan A et al (2017) Prospective genomic profiling of prostate cancer across disease states reveals germline and somatic alterations that may affect clinical decision making. JCO Precis Oncol 2017:10 47. Hart SN, Ellingson MS, Schahl K et al (2016) Determining the frequency of pathogenic germline variants from exome sequencing in patients with castrate-resistant prostate cancer. BMJ Open 6(4):e010332 48. Beltran H, Yelensky R, Frampton GM et al (2013) Targeted next-generation sequencing of advanced prostate cancer identifies potential therapeutic targets and disease heterogeneity. Eur Urol 63(5):920–926 49. Grasso CS, Wu YM, Robinson DR et al (2012) The mutational landscape of lethal castrationresistant prostate cancer. Nature 487 (7406):239–243 50. Wl K (2015) PARPs and ADP-ribosylation: 50 years . . . and counting. Mol Cell 58 (6):902–910 51. El-Khamisy SF, Masutani M, Suzuki H et al (2003) A requirement for PARP-1 for the

assembly or stability of XRCC1 nuclear foci at sites of oxidative DNA damage. Nucleic Acids Res 31(19):5526–5533 52. Luo X, Kraus WL (2012) On PAR with PARP: cellular stress signaling through poly (ADP-ribose) and PARP-1. Genes Dev 26 (5):417–432 53. Hegan DC, Lu Y, Stachelek GC et al (2010) Inhibition of poly(ADP-ribose) polymerase down-regulates BRCA1 and RAD51 in a pathway mediated by E2F4 and p130. Proc Natl Acad Sci U S A 107(5):2201–2206 54. Schiewer MJ, Knudsen KE (2014) Transcriptional roles of PARP1 in cancer. Mol Cancer Res 12(8):1069–1080 55. Langelier MF, Eisemann T, Riccio AA et al (2018) PARP family enzymes: regulation and catalysis of the poly(ADP-ribose) posttranslational modification. Curr Opin Struct Biol 53:187–198 56. Helleday T (2016) PARP inhibitor receives FDA breakthrough therapy designation in castration resistant prostate cancer: beyond germline BRCA mutations. Ann Oncol 27 (5):755–757 57. Kaufman B, Ronnie S-F, Schmutzler Rita K et al (2015) Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/ 2 mutation. J Clin Oncol 33(3):244–250 58. Mateo J, Carreira S, Sandhu S et al (2015) DNA-repair defects and olaparib in metastatic prostate cancer. N Engl J Med 373 (18):1697–1708 59. Mateo J, Porta N, Bianchini D et al (2019) Olaparib in patients with metastatic castration-resistant prostate cancer with DNA repair gene aberrations (TOPARP-B): a multicentre, open-label, randomised, phase 2 trial. Lancet Oncol 21(1):162–174 60. Lafargue CJ, Molin GZD, Sood AK et al (2019) Exploring and comparing adverse events between PARP inhibitors. Lancet Oncol 20(1):e15–e28 61. Clarke N, Wiechno P, Alekseev B et al (2018) Olaparib combined with abiraterone in patients with metastatic castration-resistant prostate cancer: a randomised, double-blind, placebocontrolled, phase 2 trial. Lancet Oncol 19 (7):975–986 62. Heyman B, Jamieson C (2019) To PARP or not to PARP?-Toward sensitizing acute myeloid leukemia stem cells to immunotherapy. EMBO J 38(21):e103479 63. Ce´saire M, Thariat J, Cande´ias SM et al (2018) Combining PARP inhibition, radiation, and immunotherapy: a possible strategy to improve the treatment of cancer? Int J Mol Sci 19 (12):3793

Recent Advances in DNA Repair Pathway and Its Application in Personalized. . . 64. Evan Y, Yu MC, Margitta R et al (2019) Keynote-365 cohort a: pembrolizumab (pembro) plus olaparib in docetaxel-pretreated patients (pts) with metastatic castrate-resistant prostate cancer (mCRPC). J Clin Oncol 37 (7_suppl):145 65. Karzai F, Vanderweele D, Madan RA et al (2018) Activity of durvalumab plus olaparib in metastatic castration-resistant prostate cancer in men with and without DNA damage repair mutations. J Immunother Cancer 6 (1):141 66. Sabatucci I, Maltese G, Lepori S et al (2018) Rucaparib: a new treatment option for ovarian cancer. Expert Opin Pharmacother 19 (7):765–771 67. Abida W, Bryce AH, Vogelzang NJ et al (2019) Genomic characteristics of deleterious BRCA1 and BRCA2 alterations and associations with baseline clinical factors in patients with metastatic castration-resistant prostate cancer (mCRPC) enrolled in TRITON2. J Clin Oncol 37(15_suppl):5031 68. Ison G, Howie LJ, Amiri-Kordestani L et al (2018) FDA Approval summary: niraparib for the maintenance treatment of patients with recurrent ovarian cancer in response to platinum-based chemotherapy. Clin Cancer Res 24(17):4066–4071 69. Sandhu SK, Schelman WR, Wilding G et al (2013) The poly(ADP-ribose) polymerase inhibitor niraparib (MK4827) in BRCA mutation carriers and patients with sporadic cancer: a phase 1 dose-escalation trial. Lancet Oncol 14 (9):882–892 70. Smith MR, Kaur SS, Kevin KW et al (2019) Phase II study of niraparib in patients with metastatic castration-resistant prostate cancer (mCRPC) and biallelic DNA-repair gene defects (DRD): preliminary results of GALAHAD. J Clin Oncol 37(7_suppl):202 71. Boussios S, Karihtala P, Moschetta M et al (2020) Veliparib in ovarian cancer: a new synthetically lethal therapeutic approach. Invest New Drugs 38(1):181–193 72. Tuli R, Shiao SL, Nissen N et al (2019) A phase 1 study of veliparib, a PARP-1/2 inhibitor, with gemcitabine and radiotherapy in locally advanced pancreatic cancer. EBioMedicine 40:375–381 73. Owonikoko TK, Dahlberg SE, Sica GL et al (2019) Randomized phase II trial of cisplatin and etoposide in combination with veliparib or


placebo for extensive-stage small-cell lung cancer: ECOG-ACRIN 2511 study. J Clin Oncol 37(3):222–229 74. Loibl S, O’shaughnessy J, Untch M et al (2018) Addition of the PARP inhibitor veliparib plus carboplatin or carboplatin alone to standard neoadjuvant chemotherapy in triplenegative breast cancer (BrighTNess): a randomised, phase 3 trial. Lancet Oncol 19 (4):497–509 75. Hussain M, Anthony CM, Slovin Susan F et al (2012) Pilot study of veliparib (ABT-888) with temozolomide (TMZ) in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC). J Clin Oncol 30(5_suppl):224–224 76. Pahuja S, Joseph AL, Prakash BC et al (2015) Preliminary activity of veliparib (V) in BRCA2mutated metastatic castration-resistant prostate cancer (mCRPC). J Clin Oncol 33 (7_suppl):170 77. Hussain M, Daignault-Newton S, Twardowski PW et al (2018) Targeting androgen receptor and DNA repair in metastatic castrationresistant prostate cancer: results from NCI 9012. J Clin Oncol 36(10):991–999 78. Murai J, Huang SY, Das BB et al (2012) Trapping of PARP1 and PARP2 by clinical PARP inhibitors. Cancer Res 72 (21):5588–5599 79. Hoy SM (2018) Talazoparib: first global approval. Drugs 78(18):1939–1946 80. De Bono JS, Higano Celestia S, Fred S et al (2019) TALAPRO-1: an open-label, response rate phase II study of talazoparib (TALA) in men with DNA damage repair (DDR) defects and metastatic castration-resistant prostate cancer (mCRPC) who previously received taxane-based chemotherapy (CT) and progressed on greater than or equal to one novel hormonal therapy (NHT). J Clin Oncol 37 (7_suppl):TPS342–TPS342 81. Friedlander M, Meniawy T, Markman B et al (2019) Pamiparib in combination with tislelizumab in patients with advanced solid tumours: results from the dose-escalation stage of a multicentre, open-label, phase 1a/b trial. Lancet Oncol 20(9):1306–1315 82. Mcpherson LA, Shen Y, Ford JM (2014) Poly (ADP-ribose) polymerase inhibitor LT-626: Sensitivity correlates with MRE11 mutations and synergizes with platinums and irinotecan in colorectal cancer cells. Cancer Lett 343 (2):217–223

Chapter 8 Methylation in Lung Cancer: A Brief Review Chang Gu and Chang Chen Abstract Despite the introduction of low-dose computed tomography (LDCT) and implementation of lung cancer screening programs, lung cancer still maintains the leading cause of cancer-specific death all around the world in terms of morbidity and mortality. Many studies demonstrated that the methylation status of selected genes may act as prognostic biomarkers for lung cancer patients. Recently, the development of high-throughput sequencing for methylation would help researchers better understand the role of methylation in the tumorigenicity or metastasis of lung cancer. This chapter reviews the progress of DNA methylation in lung cancer. Key words Lung cancer, Methylation, Methylation-specific polymerase chain reaction, Circulating tumor DNA, Epigenetics


Introduction Despite the introduction of low-dose computed tomography (LDCT) and implementation of lung cancer screening programs, lung cancer still maintains the leading cause of cancer-specific death all around the world in terms of morbidity and mortality [1, 2]. The detection of epigenetic changes in lung cancer started much later than that of other genetic abnormalities. In lung cancer patients, the methylation status of many genes, as well as the methylation in blood or even in exfoliative airway epitheliums were investigated. Many studies demonstrated that the methylation status of selected genes may act as prognostic biomarkers for lung cancer patients. Recently, the development of high-throughput sequencing for methylation would help researchers better understand the role of methylation in the tumorigenicity or metastasis of lung cancer. This chapter reviews the progress of DNA methylation in lung cancer.

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020




Chang Gu and Chang Chen

DNA Methylation and Gene Regulation The regulation of transcriptional activity of genes is mainly owing to epigenetic changes. Many chemical modifications of histone proteins, including methylation, acetylation, phosphorylation, ubiquitination and ADP-ribosylation, as well as DNA methylation, act as pivotal regulators by changing the chromatinic structure or affecting the binding of transcription factors to DNA (resulting in gene activation or silencing) [3, 4]. Furthermore, there is increasing evidence that gene silencing is the result of DNA methylation and chemical modifications of histone proteins [5]. Ikegami et al. [6] demonstrated that DNA methylation has direct influence on the methylation and the acetylation state of histone proteins.


DNA Methylation of Genes in Lung Cancer Patients Bisulfite genomic sequencing and methylation-specific polymerase chain reaction (MSP) are recognized as the most frequent methods to identify the status of gene methylation [7, 8]. Besides, with the development of polymerase chain reaction (PCR), quantitative realtime PCR (RT-PCR) has been widely applied [9]. The methods above are on the basis of unchanged structure of methylated cytosines while unmethylated cytosine bases could be converted by sodium bisulfite. Afterwards, specific primers can be applied to detect methylated or unmethylated target sites. The methylation status of many genes has been explored in lung cancer patients, most of whom were non-small cell lung cancer (NSCLC) patients. Genes associated with methylation have been thoroughly probed, including RASSF1A [10], p16 [11], EGLN2 [12], SETDB1 [13], LRRC3B [14], and so on. RASSF1A and p16 play an important role in cell cycle regulation while EGLN2 and LRRC3B DNA methylation and expression interact with hypoxia inducible factor 1A to affect survival of early-stage NSCLC. SETDB1 is crucial for cell membrane recruitment, phosphorylation, and activation of Akt following growth factor stimulation. In primary NSCLC patients, the incidence of gene methylation was proved up to 96% [15]. In small cell lung cancer (SCLC) patients, ZAR1 acts as a novel epigenetically inactivated tumor suppressor [16].


Different Methylation Patterns According to Histology When compared with malignant lung tissue samples, the methylation of most genes was rarely detected in paired non-malignant lung tissue samples, which implies that methylation of

Methylation in Lung Cancer: A Brief Review


cancer-related genes is an important process in tumorigenesis of lung cancer [17]. Toyooka et al. [18] conclude that small cell lung cancer (SCLC), carcinoids, squamous cell carcinomas, and adenocarcinomas of the lung have unique profiles of aberrant methylation. They found the frequency of methylation of RASSF1A was significantly higher in neuroendocrine tumor patients than in NSCLC patients while the frequencies of methylation of CDH13, APC, and p16 were significantly higher in NSCLC patients than in those with neuroendocrine tumor. Besides, the frequencies of methylation of RASSF1A, CDH1, and RARβ were higher in SCLC patients than in bronchial carcinoids. As for squamous cell carcinoma and adenocarcinoma, p16 is more frequent in lung squamous cell carcinoma while adenocarcinoma has high frequency of APC and CDH13.


DNA Methylation Acts as Biomarker in Lung Cancer DNA methylation can be detected not only in tumor tissues, but in blood, sputum, and bronchioloalveolar lavage (BAL) samples, making DNA methylation detection of specific genes act as a trustworthy approach for diagnosis and prognosis in lung cancer patients. Besides, DNA methylation sites can also serve as therapeutic molecule targets. In early days, some questions arose that is there sufficient circulating tumor cell in early-stage cancer? What is the specificity of blood DNA methylation detection for a certain cancer? In the early stages of lung cancer, DNA methylation occurs at a high frequency, so it can be used as a new marker to help early diagnosis and early screening of lung cancer. Methylated DNA fragments can be detected in peripheral blood and bronchial epithelial exfoliated cells, which are all less invasive diagnostic methods, so the detection of DNA methylation is very promising. The most ideal test sample is peripheral blood. Since lung cancer cells release circulating tumor DNA (ctDNA) into peripheral blood by lysis, autocrine, apoptosis, and necrosis, ctDNA with higher expression levels may be found in peripheral blood of lung cancer patients, and they are usually associated with primary lung cancer lesions, having the same type of gene expression. Some hot DNA methylation sites in lung cancer tissues have been confirmed in plasma or serum [19]. Studies have found that the methylation status of p16, DAPK, SFRP1, and KLK10 in circulating blood of NSCLC is significantly different from that in benign lung lesions and normal lung tissues [20, 21]. Therefore, they can be used as new markers that help with the diagnosis of early-stage lung cancer. Some scholars have found that in the peripheral plasma of patients with non-small cell lung cancer, the methylation of p16 gene combined with circulating DNA content can increase the detection


Chang Gu and Chang Chen

sensitivity to 80%, which has significantly better advantage than using single DNA methylation detection. However, everything has its two sides, and peripheral blood testing is no exception. The main disadvantages are: (1) the circulating DNA content in some specimens is low and difficult to detect; and (2) the methylated DNA fragments in peripheral blood are less organ specific. The sputum sample test has certain organ specificity because most of the cells from the respiratory and lung tissues, and DNA methylation sites have been found in sputum samples [22]. The population with the highest incidence of lung cancer are smokers. The patient set has more sputum than the normal subjects due to long-term and heavy smoking, and their sputum samples often can be easily obtained without special induction. Therefore, the sputum sample test is more suitable for the initial screening of lung cancer in this part of the population. One study found that three or more of the MGMT, p16, RASSF1A, DAPK, GATA5, and PAX5β sites were detected having DNA methylation in the sputum samples collected within 1.5 years prior to the diagnosis of lung cancer, the probability of lung cancer in this group of patients will be 6.5 times higher than other patients [23]. The sputum test also has limitations because sputum mainly comes from the central part of the bronchial system and has limited ability to detect peripheral type of lung neoplasms. Bronchial lavage fluid is another alternative study sample. Similar to sputum samples, bronchial lavage fluid may also contain more specific lung cancer cells and release lung cancer DNA fragments. Some scholars have tested DNA methylation in bronchial lavage fluid in patients with non-small cell lung cancer and found that one or more methylation site(s) of RARβ, p16, RASSF1A, and H-cadherin can be detected in most (68%) samples. Another study detected bronchial lavage samples from patients with suspected lung cancer and found that the specificity was 99% and the sensitivity of 53%, when combined with methylation detection at the three sites of p16, APC, and RASSF1. All of the above three detection methods are mildly invasive, and the detection of exhaled breath condensate samples is a completely noninvasive detection method. The detection technology detects biochemical components of the lung and the respiratory tracts, not affecting the pathophysiological processes in the respiratory system [24]. Compared with sputum sample testing and bronchial lavage sample testing, exhaled breath condensate sample testing is simpler, more feasible, and repeatable. At present, this technology can be widely applied in the analysis of intrapulmonary inflammatory response, exploring lung respiratory function, developing lung cancer tumor markers, studying intrapulmonary oxidative and stress responses, and monitoring the incidence of lung cancer. At this stage, the research data on the diagnosis of lung cancer by exhaled condensate is relatively poor and the data on the

Methylation in Lung Cancer: A Brief Review


methylation of target genes in exhaled condensate samples are scarce [25]. Recently, studies have also found that DNA methylation analysis of the oral epithelium may also be a new method for screening lung cancer [26].


DNA Methylation and the Treatment of Lung Cancer DNA methylation is a reversible process, unlike irreversible genetic information changes such as gene deletions or mutations. Therefore, theoretically demethylation therapy on patients with lung cancer or precancerous lesions may restore the function of some tumor suppressor genes, thereby achieving the purpose of treating lung cancer or preventing lung cancer. Studies by Wu F et al. [27] showed that promoter methylation of hMLH1 is involved in the sensitivity of cisplatin in NSCLC and A549/DDP cells. Cisplatin-based adjuvant chemotherapy is more beneficial to NSCLC patients with hMLH1-free methylation. Methylation of hMLH1 may be a biomarker for individualized treatment of non-small cell lung cancer. Unlike classical genetics, epigenetic methylation changes are reversible, and DNMTs are key enzymes in the process. Hypermethylation of the promoter region results in changes in the expression of tumor suppressor genes, which is the mechanism of most tumorigenesis. However, after DNA combines with methylation inhibitors (decitabine, 5-azacytidine, etc.), stable complex is formed with DNMTs. The complex, which in turn causes a decrease in the activity of DNMTs, thereby reduces the methylation rate of the tumor suppressor genome, inducing tumor cells to differentiate into normal cells or causing apoptosis of tumor cells. Therefore, DNMTs and their inhibitors may become research hotspots for the treatment of tumors.


DNA Methylation and Prognosis of Lung Cancer Some scholars have found that the degree of DNA methylation is closely related to TNM staging and distant metastasis of non-small cell lung cancer [28]. The study suggests that epigenetic abnormalities such as DNA methylation may also be associated with progression of lung cancer and may be an indicator for predicting lung cancer. Studies have shown that the methylation status of DLEC1 and hMLH1 genes in NSCLC patients is associated with TNM staging of lung cancer and metastasis of mediastinal lymph nodes, and is an independent prognostic factor for lung cancer [29]. Another scholar pointed out that abnormal methylation of RXRG is a risk factor in smoking non-small cell lung cancer patients, and abnormal methylation of RXRG in non-smoker


Chang Gu and Chang Chen

non-small cell lung cancer patients is a protective factor [30]. A comparative analysis of postoperative recurrence and recurrencefree stage I NSCLC patients showed that the methylation status of CDH13, p16, APC, and RASSF1A in lung cancer tissues and their corresponding lymph node tissues was closely related to the recurrence of the corresponding patients. If methylation of the p16 and CDH13 genes is detected in both tumor and mediastinal lymph nodes, the odds ratio for lung cancer recurrence is 15.5. It is suggested that if DNA methylation is detected in the dissected lymph nodes, even if no metastasis is found in the histology of the lymph nodes, it may indicate that local micrometastasis may have occurred in the lung cancer patients. It can be seen that the hypermethylation of certain genes in NSCLC may confer the ability of tumor cells to metastasize and spread, which may be an important indicator for postoperative recurrence and poor prognosis.


Research Prospects The epigenetics of lung cancer has been studied for nearly two decades, during which time many important effects and mechanisms of lung cancer development have been accumulated. Liquid biopsy of lung cancer is one of the current research hotspots. It is a noninvasive test and its amount of information, especially DNA methylation, has been extensively studied. DNA methylation markers are stable and easy to detect in tissues or body fluids (blood, sputum, etc.) and may play a significant role in the early diagnosis, prognosis, predictive treatment, and drug resistance of lung cancer. However, the current lack of standardization of methylation detection has hindered the development of methylation, so the establishment of standardized schemes is particularly important. Perhaps in the future, liquid biopsy can provide strong support for the clinical work of treating lung cancer and also provide new insights for the treatment of lung cancer.

References 1. Siegel R, Ma J, Zou Z et al (2014) Cancer statistics, 2014. CA Cancer J Clin 64(1):9–29 2. Gu C, Wang R, Pan X et al (2017) Sublobar resection versus lobectomy in patients aged 35 years with stage IA non-small cell lung cancer: a SEER database analysis. J Cancer Res Clin Oncol 143(11):2375–2382 3. Vettese-Dadey M, Grant PA, Hebbes TR et al (1996) Acetylation of histone H4 plays a primary role in enhancing transcription factor binding to nucleosomal DNA in vitro. EMBO J 15(10):2508–2518

4. Shogren-Knaak M, Ishii H, Sun JM et al (2006) Histone H4-K16 acetylation controls chromatin structure and protein interactions. Science 311(5762):844–847 5. Esteller M (2008) Epigenetics in cancer. N Engl J Med 358(11):1148–1159 6. Ikegami K, Ohgane J, Tanaka S et al (2009) Interplay between DNA methylation, histone modification and chromatin remodeling in stem cells and during development. Int J Dev Biol 53(2–3):203–214

Methylation in Lung Cancer: A Brief Review 7. Herman JG, Baylin SB (1998) Methylationspecific PCR. Curr Protoc Hum Genet 16 (1):10.6. 1–10.6. 10 8. Herman JG, Graff JR, Myo¨h€anen S et al (1996) Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci 93(18):9821–9826 9. Campan M, Weisenberger DJ, Trinh B et al (2009) MethyLight[M]//DNA methylation. Humana Press, New York, NY, pp 325–337 10. Hu H, Zhou Y, Zhang M et al (2019) Prognostic value of RASSF1A methylation status in non-small cell lung cancer (NSCLC) patients: A meta-analysis of prospective studies. Biomarkers 24(3):207–216 11. Liu Z, Lin H, Gan Y et al (2019) P16 methylation leads to paclitaxel resistance of advanced non-small cell lung cancer. J Cancer 10 (7):1726 12. Zhang R, Lai L, He J et al (2019) EGLN2 DNA methylation and expression interact with HIF1A to affect survival of early-stage NSCLC. Epigenetics 14(2):118–129 13. Wang G, Long J, Gao Y et al (2019) SETDB1mediated methylation of Akt promotes its K63-linked ubiquitination and activation leading to tumorigenesis. Nat Cell Biol 21(2):214 14. Guo Y, Zhang R, Shen S et al (2018) DNA methylation of LRRC3B: a biomarker for survival of early-stage non-small cell lung cancer patients. Cancer Epidemiol Prev Biomarkers 27(12):1527–1535 15. Heller G, Zielinski CC, Zo¨chbauer-Mu¨ller S (2010) Lung cancer: from single-gene methylation to methylome profiling. Cancer Metastasis Rev 29(1):95–107 16. Richter AM, Kiehl S, Ko¨ger N et al (2017) ZAR1 is a novel epigenetically inactivated tumour suppressor in lung cancer. Clin Epigenetics 9(1):60 17. Heller G, Fong KM, Girard L et al (2006) Expression and methylation pattern of TSLC1 cascade genes in lung carcinomas. Oncogene 25(6):959 18. Toyooka S, Toyooka KO, Maruyama R et al (2001) DNA methylation profiles of lung tumors1. Mol Cancer Ther 1(1):61–67 19. Hsu HS, Chen TP, Hung CH et al (2007) Characterization of a multiple epigenetic marker panel for lung cancer detection and


risk assessment in plasma. Cancer 110 (9):2019–2026 20. Zhang YW, Miao YF, Yi J et al (2010) Transcriptional inactivation of secreted frizzledrelated protein 1 by promoter hypermethylation as a potential biomarker for non-small cell lung cancer. Neoplasma 57(3):228 21. Zhang Y, Song H, Miao Y et al (2010) Frequent transcriptional inactivation of Kallikrein 10 gene by CpG island hypermethylation in non-small cell lung cancer. Cancer Sci 101 (4):934–940 22. Shivapurkar N, Stastny V, Suzuki M et al (2007) Application of a methylation gene panel by quantitative PCR for lung cancers. Cancer Lett 247(1):56–71 23. Belinsky SA, Liechty KC, Gentry FD et al (2006) Promoter hypermethylation of multiple genes in sputum precedes lung cancer incidence in a high-risk cohort. Cancer Res 66 (6):3338–3344 24. Sriram KB, Larsen JE, Yang IA et al (2011) Genomic medicine in non-small cell lung cancer: paving the path to personalized care. Respirology 16(2):257–263 25. Suda K, Tomizawa K, Yatabe Y et al (2011) Lung cancers unrelated to smoking: characterized by single oncogene addiction? Int J Clin Oncol 16(4):294–305 26. Hiroaki H, Narumi Y, Tomohiro K et al (2018) Clinical implication of DNA methylation analysis in the oral epithelium for lung cancer risk stratification. Respirology 23(S2):69–78 27. Wu F, Lu M, Qu L et al (2015) DNA methylation of hMLH1 correlates with the clinical response to cisplatin after a surgical resection in non-small cell lung cancer. Int J Clin Exp Pathol 8(5):5457 28. Qiu X, Zhou Q (2018) P093 DNA methylation in CTC and ctDNA is a promising marker for early detection of lung cancer metastasis. J Thorac Oncol 13(12):S1082 29. Seng TJ, Currey N, Cooper WA et al (2008) DLEC1 and MLH1 promoter methylation are associated with poor prognosis in non-small cell lung carcinoma. Br J Cancer 99(2):375 30. Lee SM, Lee JY, Choi JE et al (2010) Epigenetic inactivation of retinoid X receptor genes in non-small cell lung cancer and the relationship with clinicopathologic features. Cancer Genet Cytogenet 197(1):39–45

Chapter 9 Epstein–Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review Fen Xue and Xiayun He Abstract Epstein–Barr virus (EBV) is a B-lymphocytes herpes virus and can transform B lymphocytes to malignant tumor cells if infected. Nasopharyngeal carcinoma (NPC) is strongly associated with EBV. Circulating EBV DNA in plasma has been recognized as an important biomarker of NPC. Much work has been done to validate the ability of circulating EBV DNA for screening, diagnosis, risk stratification, monitoring, and predicting prognosis. This chapter reviews the clinical progress of circulating cell-free EBV DNA in NPC. Key words Epstein–Barr virus, Nasopharyngeal carcinoma, Circulating cell-free EBV DNA, Reduced-dose radiation, Prognosis


Introduction Although survival benefit was gained with the development of radiological diagnostic techniques and chemoradiotherapy modalities, distant metastasis and local recurrence remain the main failure patterns for nasopharyngeal carcinoma (NPC). As the survival rates for NPC patients decreased from stage I to stage IV, early diagnosis may improve outcomes [1, 2]. Meanwhile, some patients with the same stage also showed significant differences in treatment efficacy and prognosis, which suggests that current anatomy-based stage system is not enough to accurately predict prognosis [3]. Thus, seeking other potential biomarkers for early detection and effective monitoring would be of great importance for NPC patients. Epstein–Barr virus (EBV) is a B-lymphocytes herpes virus and can transform B lymphocytes to malignant tumor cells if infected [4]. NPC is strongly associated with EBV. Since Lo et al. [5] confirmed the feasibility of detecting circulating EBV DNA in plasma, it has been recognized as an important biomarker of NPC. Much work has been done to validate the ability of circulating EBV DNA for screening, diagnosis, risk stratification,

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Fen Xue and Xiayun He

monitoring, and predicting prognosis [6, 7]. This chapter reviews the clinical progress of circulating cell-free EBV DNA in NPC.


EBV DNA for Screening, Diagnosis, and Staging of NPC With deep and concealed location of nasopharynx, clinical examinations like nasopharyngoscopy or magnetic resonance imaging (MRI) may not always detect the mucosal changes of nasopharynx or small lesions in pharyngeal recess. Besides, early NPC is relatively asymptomatic or with nontypical symptoms, so most of the patients with NPC present with locally advanced disease or distant metastasis at diagnosis. The extent of tumor at diagnosis is one of the most important factors affecting survival. Therefore, identification of patients with early-stage NPC through screening could potentially improve treatment outcomes. Over the last few decades, circulating cell-free EBV DNA has been proved as a potential biomarker for screening and diagnosis [1, 8]. According to Lo et al. [5], circulating cell-free EBV DNA could be detectable in the plasma of 96% of NPC patients and 7% of normal individuals by using real-time quantitative PCR. The NPC patients had much higher EBV DNA concentrations than the normal individuals. And in the former group, patients with advanced disease (stage III and IV) had significantly higher circulating EBV DNA levels (about eight times in median levels) than those with early disease (stage I and II). These findings suggest that the liberation of EBV DNA into the blood may reflect the tumor load in NPC patients. Then, were the amounts of circulating EBV DNA released by small tumors still enough for detection? Chan et al. [8] conducted a prospective study to verify its ability in screening asymptomatic early NPC. In their study, nasopharyngoscopy and MRI were implemented for participants with persistently positive EBV DNA in plasma. The results showed a 97.1% of sensitivity and 98.6% of specificity. Compared to a historical cohort, participants with NPC identified by screening had significantly higher proportion of stage I or II disease (71 vs. 20%, P < 0.001) and had superior 3-year progression-free survival (PFS, 97 vs. 70%; hazard ratio, 0.10). Therefore, circulating cell-free EBV DNA may be an ideal biomarker for screening. With early diagnosis, effective treatments could be administrated timely to improve the treatment outcomes and acute/late adverse effects caused by additional chemotherapy could also be avoided. In the latest eighth edition of the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) TNM staging system for NPC, patients present with cervical lymph node metastasis and detectable EBV DNA but without tumor in nasopharynx were defined as stage T0. Many studies have been trying to incorporate circulating EBV DNA into current

Epstein–Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review


TNM staging system to further segregate prognosis in NPC. Using 979 patients as primary cohort and 550 patients as the validation cohort, Guo et al. [3] generated new stage groupings as follows: stage RI (T1N0), RIIA (T2-T3N0 or T1-T3N1, EBV DNA 2000 copies/mL), stage RIIB (T2-T3N0 or T1-T3N1, EBV DNA >2000 copies/mL; T1-T3N2, EBV DNA 2000 copies/ mL), stage RIII (T1-T3N2, EBV DNA >2000 copies/mL; T4N0N2), and stage RIVA (any T and N3), with 5-year PFS rates of 100%, 87.9%, 76.7%, 68.7%, and 50.4% for the validation cohort, respectively. Their new stage groupings showed better prognostic performance than current staging system. Another new staging system was proposed by Lee et al. [9] as follows: RPA-I (T1–T4 N0–N2 & EBV DNA 500 copies/mL at 6 weeks after treatment showed high risk for disease relapse or death compared with those with undetectable level [14]. However, residual posttreatment EBV DNA could also go spontaneous remission during follow-up [15]. Almost three-quarters (72.4%) of the patients with detectable post-treatment EBV DNA experienced a spontaneous remission of EBV DNA. These patients still had higher risk of disease relapse or death than those with undetectable posttreatment EBV DNA level, and patients with persistently detectable EBV DNA had the highest risk. Therefore, EBV DNA assays need to be performed continuously at subsequent follow-up visits, and early test of EBV DNA after treatment may help pick potential high-risk patients for further intensified treatment. For future post-treatment EBV DNA-related clinical trials, the optimal timing of the EBV DNA assays needed to be uniformed in different centers. Currently, the diagnosis of locoregional recurrence or distant metastasis of NPC patients is mainly based on imaging examination and endoscopic biopsy. However, some of the patients may develop mucosal ulcers, local edema, fibrosis, or osteoradionecrosis after radiotherapy, which may lead to the interference of accurate detection of disease failure. Besides, it is hard to detect a recurrent or metastatic lesion with diameter  5 mm with MRI or computed tomography (CT). Therefore, regular EBV DNA measurement is introduced to be a useful adjunct tool to endoscopy and imaging in the surveillance for disease relapse and metastasis, despite the lack of prospective data. Lo et al. [5] were the first to report that the level of EBV DNA (median copy number: 32350 copies/mL) in patients with recurrence was significantly higher than those without (median copy number: zero copies/mL). It was further supported by an imaging examination conducted by Makitie et al. [16], the detection effects of EBV DNA load were consistent with the results of PET/CT in the monitoring of locoregional recurrence or distant metastasis. The meta-analysis including 25 studies also confirmed the value of EBV DNA levels as a diagnostic tool for disease progression, with a very high-level overall accuracy [17]. However, its sensitivity in the detection of locoregional recurrences is relatively lower than the detection of distant failures. With sensitive tools, detection rate for distant metastasis ranged from 86% to 96%, while that for locoregional recurrence varied from 51% to 67% [13]. It was supposed that locoregional fibrosis and vascular occlusion after radiotherapy may affect the EBV DNA released into circulation in patients with locoregional recurrence. Researchers have found that detection of EBV DNA by trans-oral nasopharyngeal brush biopsy was at high sensitivity and specificity for detecting local NPC recurrence [18]. The same brush detection system has

Epstein–Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review


been demonstrated by Lam et al. [19] to be clinical potential for monitoring local recurrence in post-irradiated NPC patients. To further verify its role in detecting salvageable early recurrent NPC, a prospective study is ongoing by measuring the sensitivity and specificity of the combination of EBV and methylation marker genes in body fluids of both plasma and nasopharyngeal brush (NCT03379610).


EBV DNA and the Prognosis of NPC As mentioned above, the circulating EBV DNA concentrations seems to reflect the tumor burden in NPC patients. Researchers [13] summarized studies and found that levels of circulating EBV DNA increased from stage I to IV in NPC patients, with detection rates of 50–86%, 94–95%, 91–100%, and 94–98%, respectively. Therefore, it is reasonable to deduce that circulating EBV DNA would be a potential prognostic marker. Considering the rapid in vivo clearance rate of circulating EBV DNA, tumor load could be reflected in almost real-time. As a result, researchers have investigated the prognostic value of circulating EBV DNA levels measured at different time points (pre-, mid-, and post-treatment). Leung et al. [20] found that pretreatment EBV DNA concentration is more sensitive in predicting prognosis for early-stage NPC than TNM staging. The prognosis is similar between stage II NPC with low pretreatment EBV DNA concentration and stage I NPC, while stage I NPC with high pretreatment EBV DNA concentration showed even worse prognosis than stage III NPC. Among locoregionally advanced NPC patients, Lin et al. [4] reported an inferior overall survival (OS) and relapse-free survival for NPC patients with a higher pretreatment EBV DNA concentration (with a cut-off of 1500 copies/mL). Another study also showed that NPC patients with higher pretreatment EBV DNA levels (with a cut-off of 4000 copies/mL) had a lower rate of 3-year PFS, distant metastasis-free survival (DMFS) and OS rate, the prognostic effects were proved in multivariate analysis [21]. In metastatic NPC, patients with high circulating tumor cells (CTCs, with a cut-off of 12 cells/7.5 mL) and EBV DNA levels (with a cut-off of 10,000 copies/mL) at baseline also showed significantly shorter PFS and OS [22]. These findings suggest that the pretreatment EBV DNA level provides additional prognostic information and is of great significance for the formulation of individualized treatment plan when combined with clinical stage to develop risk stratification. However, the detection method and standard values need to be harmonized in future studies. The dynamic changes of EBV DNA levels during treatment may reflect the half-life of EBV DNA in plasma. Those with short half-life and rapid negative EBV DNA conversion during treatment


Fen Xue and Xiayun He

may indicate that tumors respond well to current treatment. You et al. [22] found that the conversion of pretreatment unfavorable CTCs and EBV DNA (12 cells/7.5 mL, 10,000 copies/mL) to favorable (1 cells/7.5 mL, 4000 copies/mL) after first-line chemotherapy was associated with significantly longer PFS and OS. Other researchers found that detectable EBV DNA levels after two cycles of neoadjuvant chemotherapy were proved to be poor prognostic factors for PFS and DMFS [23]. To further explore the relation between EBV DNA clearance rate and tumor radiosensitivity, a prospective study was implemented in 107 NPC patients receiving radiotherapy/chemoradiotherapy. Results showed that detectable mid-treatment EBV DNA levels (measured at week 4 of radiotherapy) was associated with worse clinical outcome and was the only independent prognostic factor for DMFS and OS [24]. These findings may provide potentials for escalation or de-escalation clinical studies based on molecular response of EBV DNA during treatment. Post-treatment EBV DNA levels were found to have better prognostic effect than pre- or mid-treatment EBV DNA levels in previous meta-analyses [13, 25]. It was further validated by Qu et al. [6] that the risk of metastasis or mortality for patients with high post-treatment EBV DNA levels was five- to six-fold higher than those with low post-treatment EBV DNA levels. And the prognostic effect of post-treatment EBV DNA levels was two- to three-fold higher than pre- or mid-treatment EBV DNA levels. Most studies supposed that any detectable levels of post-treatment EBV DNA reflected residual tumor or subclinical metastasis and had a worse DMFS and OS [4, 7]. Wang et al. [26] found that almost all patients with persistent EBV DNA levels after systemic treatment developed recurrence or metastasis. Therefore, consolidation therapy should be considered for NPC patients with positive post-treatment EBV DNA levels.


EBV DNA and the Treatment of NPC Considering the importance of EBV DNA in screening, staging, prognosis, and surveillance, its value in guiding treatment of NPC has becomes a hot research. Twu et al. [27] retrospectively compared the clinical outcomes of 85 NPC patients with or without adjuvant chemotherapy (all with persistently detectable EBV DNA after 1 week of radiotherapy). Those received adjuvant chemotherapy showed reduced distant failure and improved OS. However, the results of the first clinical trial using EBV DNA stratified therapy were disappointing. In the Hong Kong NPC Study Group 0502 trial (NCT00370890) [28], adjuvant chemotherapy did not show survival benefits in patients with detectable EBV DNA at 6–8 weeks after radiotherapy. It was supposed that the timing of EBV DNA

Epstein–Barr Virus DNA in Nasopharyngeal Carcinoma: A Brief Review


screening and adjuvant chemotherapy may be too late, or some patients may resistant to cisplatin-based adjuvant chemotherapy after cisplatin-based concurrent chemotherapy. Whether additional chemotherapy was necessary for patients with detectable posttreatment EBV DNA remains controversial, we are anticipating the results of NRG-HN001 trial (NCT02135042). This trial divided NPC patients into low-risk and high-risk groups according to post-radiotherapy EBV DNA levels and was initiated to test the possibility of eliminating unnecessary adjuvant chemotherapy for low-risk patients while introducing intensified adjuvant chemotherapy for high-risk patients. Another three ongoing trials (NCT02363400 by Taiwan National Health Research Institute, NCT02874651 by Sun Yat-sen University, NCT00370890 by Chinese University of Hong Kong) also tried to explore the necessity of adjuvant chemotherapy for NPC patients with detectable post-radiotherapy EBV DNA levels. Taiwan National Health Research Institute (NCT03544099) also initiated a phase II trial to examine the efficacy of pembrolizumab for prolonging the one-year disease-free survival in nasopharyngeal carcinoma patients with solely detectable EBV DNA after curative chemoradiation. In addition, pre- or mid-treatment EBV DNA levels may also be valuable in guiding treatment. An ongoing Phase II clinical trial (NCT02871518) was designed to compare the efficacy of two-cycle and three-cycle cisplatin-based concurrent chemotherapy for low-risk patients (stage III-IVB, AJCC 7 edition, identified with pretreatment plasma EBV DNA 0.05); but ARB group, LAD, LVD decreased significantly (P < 0.05). And ACEI can increase AF cardioversion rate from 76.1% in the control group to 77.2% (P ¼ 0.62), ARB to 81.6% (P ¼ 0.02). Conclusion: It does improve AF cardioversion rate after radiofrequency catheter ablation that the precise anti-structural remodeling drugs continuous therapy was adopted based on the pathogenesis of AF. Key words Atrial fibrillation, Pathogenesis, Radiofrequency catheter ablation, Drug therapy, Structural remodeling


Introduction Atrial fibrillation (AF) is one of the most common arrhythmias in the clinic, and the risk of developing AF is one in four for patients aged 40 years and above [1]. Valvular heart disease is often associated with AF, especially in patients with mitral valve disease. About 64% of mitral valve patients suffer from AF which increases the risk of stroke, heart failure, and death [2–5]. Current

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Tao Li and Yongjun Qian

treatments for AF include two methods: (1) controlling ventricular rate (i.e., rate control) without trying to stop or prevent AF; (2) trying to obtain and maintain sinus rhythm (i.e., rhythm control) [6]. Although large-scale randomized trials have not shown that rhythm is better than rate control [7], it is generally believed that the failure to benefit from more circadian rhythm control is due to the limited and poor efficacy of antiarrhythmic drugs maintained by sinus rhythm reaction. Radiofrequency ablation is now widely used to treat AF of valve disease in clinical practice, the rate of maintenance of sinus rhythm is about 75–85% after 6 months, only 62.3% reported recently [3–5, 8]. The maintenance rate of sinus rhythm still falls far short of what doctors and patients expects. Can the rate of AF conversion be further increased? Through a series of studies of the AF pathogenesis, our research group found that the structural remodeling of AF related to valvular heart disease, and that the degree of structural remodeling in different types of mitral valve lesions was different, and that the mechanism of AF structural remodeling induced by Reninangiotensin-aldosterone system (RAAS) was not the same [9– 12]. Therefore, we assume that: (1) After the radiofrequency ablation of AF relating to valvular heart disease, the treatment with continuous anti-structural remodeling drugs may further improve the cardioversion rate of AF; (2) With regard to different types of mitral valve disease associated with AF, using different kinds of antistructural remodeling drugs for precise treatment after radiofrequency ablation may have different effects of AF cardioversion.


Data and Methods

2.1 Subjects and Groups

There was a total of 1334 patients who underwent mitral valve replacement with bipolar radiofrequency ablation in virtue of mitral valve disease with AF from West China Hospital Sub-Database of Chinese Adult Cardiac Surgical Database from June 2011 to July 2017. Inclusion criteria: (1) The MS or MR patients who needed mitral valve replacement plus concomitant AF radiofrequency ablation, and whose aortic valve is mild below or without stenosis or regurgitation and does not require surgical intervention, and whose tricuspid valves are moderate or less regurgitation or without valvuloplasty; (2) The patients who completed follow-up of the outpatient department or telephone for 6 months and persisted in using anti-structural remodeling-related drugs; (3) The patients who were no complications of anticoagulation, membrane dysfunction, leakage of the valve, tricuspid moderate or more regurgitation, and surgery-related cardiac. Exclusion criteria: (1) The patients who existed two degree or more conduction block or implanted permanent pacemaker after operation; (2) The patients with aortic valve lesions of moderate or

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . .


The patients of meeting the inclusion criteria from West China Hospital Sub-Database of Chinese Adult Cardiac Surgical Database from June 2011 to July 2017 (1334 cases) During follow-up, patients who failed to complete drug treatment, died and did not complete 6 months follow-up (-172 cases)

87% of patients who completed treatment or did not undergo anti-structural remodeling treatment completed follow-up as required (1662 cases)

MS Group (825 cases)

ACEI Group (256 cases)

ARB Group (91 cases)

MR Group (337 cases)

Control Group (479 cases)

ACEI Group (108 cases)

ARB Group (67 cases)

Control Group (162 cases)

Fig. 1 Group of included patients diagram

more or requiring surgical treatment; (3) The patients with tricuspid moderate or more regurgitation or no tricuspid valvuloplasty and requiring tricuspid valve replacement; (4) Other cases, such as preoperative infective endocarditis, hyperthyroidism, hypertensive heart disease, hyperlipidemia, coronary heart disease and diabetes, an AF duration of less than half a year, complications of anticoagulation, and re-admission during the follow-up period. Grouping: First, the patients were divided into two groups (group MS and group MR) according to the valve lesion, and each group was divided into three groups according to the purpose of the study, and anti-structural remodeling drugs used by the patients: group ACEI (take ACEI), group ARB (take ARB), and control group (unused any anti-structural remodeling drugs). There are six groups, as shown in Fig. 1. 2.2


2.2.1 Collection Clinical Material

The data of clinical and related laboratory examinations at discharge and follow-up were recorded, including gender, age, duration of atrial fibrillation, electrocardiogram, and heart color Doppler ultrasound data, etc. The mitral valve replacement was performed by routine sternotomy, median incision, extracorporeal circulation, mechanical valve replacement, and warfarin anticoagulation for a lifetime, with a strength of INR maintained at 1.5–2.5.


Tao Li and Yongjun Qian

Radiofrequency catheter ablation of atrial fibrillation during mechanical mitral valve replacement was performed using the Medtronic bipolar radiofrequency ablation clamp in accordance with the standard COX-IV Operation line. 2.2.2 Medication and Follow-up

All patients underwent cardiac ultrasonography at discharge and were treated with or without ACEI and ARB drugs according to their condition and doctor’s willingness. ACEI was prescribed Capoten 12.5 mg, once daily, and ARB was 75 mg, once a day. Other drugs that do not affect the research purposes can be used routinely to regulate heart function and cardiac rhythm. The patients needed to see a doctor at outpatient service after operation every month to adjust the oral medication and monitor blood coagulate functions and understand the heart rate and rhythm of the patient. The dosage of amiodarone, metoprolol, and other drugs were adjusted according to the ventricular rate and heart rate of the patients. Clinicians recorded the duration of ACEI and ARB at outpatient department and observed side effects of medications such as cough and pruritus to assess whether patients need to adjust the dosages of ACEI and ARB or discontinue ACEI and ARB medications.

2.2.3 Assessment of Sinus Rhythm

Eligible patients were recommended for 24 h Holter, but the cardiac rhythm of the patients after 6 months was also assessed comprehensively by monthly electrocardiogram and cardiac ultrasound according to the current situation of clinical work and the feasibility of large sample data acquisition. AF was diagnosed according as all the 12 lead ECG duration 10 s of atrial fibrillation rhythm. Comprehensive evaluation was completed independently by three different professional physicians from departments of cardiac ultrasonography, internal medicine-cardiovascular and cardiac surgery, and only three doctors have determined that the patient had sinus rhythm to assess the patient’s sinus rhythm.

2.3 Statistical Analysis

In this study, the data were expressed as mean  standard deviation (X  s), the number of cases, age, AF duration, LAD, LVD, RA, RVD, LVEF, and LVFS between groups of patients were compared by t test, comparison of sex between two groups of patients using X2 test. Statistical analysis was performed with SPSS Ver17.0 statistical software, p < 0.05 was considered statistically significant.



3.1 Patient Grouping Results

During the study, west China hospital data of Chinese adult cardiac surgical database, a total of 1334 cases of patients with mitral valve disease with AF underwent mitral valve replacement concomitant radiofrequency ablation. During the 6 months of follow-up, there

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . .


were a total of 172 patients of failing to adhere to the completion of drug treatment, lung infection, and other re-admission, again cardiac surgery or other surgery, electric cardioversion in 6 months, death and incomplete 6 months follow-up, etc. 87% of patients, a total of 1162 cases, were followed up, including 825 cases of MS group, 337 cases of MR group. Each group was further divided into three groups according to the use of anti-structural reconstruction drugs of different types and unused drugs, and the number of groups and cases in each group was shown in Fig. 1. 3.2 The Comparisons of General Clinical Data at the Time of Discharge and AF Cardioversion Using Different Anti-structural Drugs of Three Groups of Patients in MS Group

There was no statistically significant difference in age, gender, AF duration, LAD, LVD, RAD, RVD, LVEF, and LVFS in the three groups (P > 0.05), as shown in Table 1.

3.2.1 The Comparison of General Clinical Data of Three Groups of Patients in MS Group at the Time of Discharge 3.2.2 The Comparison of Cardiac Color Doppler Data Compared with the Control Group in Three Groups of Patients with MS Group After 6 Months Follow-up

With ACEI and ARB Group, LAD and LVD were significantly reduced, the difference was statistically significant (P < 0.05), but there was no statistically significant difference between RAD, RVD, LVEF, and LVFS (P > 0.05), as shown in Fig. 2. Both ACEI and ARB can significantly reduce the size of left atrial and left ventricle in MS patients, while the improvement of right atrial, right ventricle, LVEF, and LVFS is not obvious. ACEI group and ARB group in the improvement of left atrial and left ventricle were equivalent.

3.2.3 The Comparison of Effects of ACEI and ARB on the Rate of AF Conversion in MS Group Using Different Anti-structural Remodeling Drugs

Different drugs can improve the cardioversion rate of AF in patients with MS, but the degree of improvement is different. ACEI and ARB were able to increase the cardioversion rate from 79.1% in the control group to 83.7% and 82.8%, respectively (P ¼ 0.03 and P ¼ 0.04), and the difference was statistically significant, while the comparison of the cardioversion of AF between ACEI and ARB was not statistically significant in 83.7 vs. 82.8% (P ¼ 0.21), as shown in Fig. 3. Can be seen in MS patients, ACEI and ARB can significantly improve the cardioversion rate of AF, while the effect between the two was no difference.


Tao Li and Yongjun Qian

Table 1 Comparison of general clinical data of three groups of patients in MS group at the time of discharge (X  s) ACEI group (N ¼ 256)

ARB group (N ¼ 91)

Control group (N ¼ 479)


Age (years)

51.3  11.2

50.9  13.2

52.1  12.6







AF duration (months)

11.1  4.5

13.6  4.8

12.7  6.1



53.6  12.1

52.9  10.7

52.3  15.4



44.4  6.3

48.6  9.5

46.1  7.8



48.9  14.2

47.6  11.4

49.2  14.9



24.5  5.6

27.3  7.9

25.6  5.7



58.7  9.3

57.6  7.3

56.8  6.2



33.1  4.3

34.6  6.7

35.8  5.8


60 55 50 45 40 35 30 25 20 LAD (mm)

LVD (mm)

RAD (mm)

RVD (mm)



ACEI Group







ARB Group







Control Group







Fig. 2 Comparison of cardiac color Doppler data in three groups of patients with MS Group after 6 months follow-up (mean)

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . .


85.0% P=0.03




AF Conversion Rate

83.0% 82.0% 81.0% 80.0%




78.0% 79.1% 77.0% 76.0% AF Conversion Rate

ACEI Group

ARB Group

Control Group




Fig. 3 Comparison of effects of ACEI and ARB on the cardioversion rate of AF in MS group using different antistructural remodeling drugs 3.3 The Comparisons of General Clinical Data at the Time of Discharge and AF Cardioversion Using Different Anti-structural Drugs of Patients in MR Group

There was no statistically significant difference in age, gender, AF duration, LAD, LVD, RAD, RVD, LVEF, and LVFS in the three groups (P > 0.05), as shown in Table 2.

3.3.1 The Comparison of General Clinical Data of Three Groups of Patients in MR Group at the Time of Discharge 3.3.2 The Comparison of Cardiac Color Doppler Data Compared with the Control Group in Three Groups of Patients with MR Group After 6 Months Follow-up

Compared with the control group, the differences of LAD, LVD, RAD, RVD, LVEF, and LVFS were statistically insignificant in patients with ACEI (P > 0.05), while in patients with ARB group, LAD and LVD were significantly reduced, the difference was statistically significant (P < 0.05), but there was no statistically significant difference between RAD, RVD, LVEF, and LVFS (P > 0.05). Compared with the patients in ARB group, the LAD and LVD of patients in ACEI group were significantly reduced and the difference was statistically significant (P < 0.05), but there was


Tao Li and Yongjun Qian

Table 2 Comparison of general clinical data of three groups of patients in MR group at the time of discharge (X  s) ACEI group (N ¼ 108)

ARB group (N ¼ 67)

Control group (N ¼ 162)


Age (years)

56.1  12.2

54.4  13.6

55.3  10.7







AF duration (months)

10.9  6.8

12.6  5.7

11.2  9.2



55.9  14.2

54.7  9.8

55.3  10.7



42.5  9.1

46.3  10.5

44.4  8.4



47.1  9.7

44.7  14.5

46.5  12.6



25.6  6.7

28.5  9.1

23.6  8.8



55.5  10.3

53.6  8.4

54.8  9.7



31.1  5.5

32.5  4.9

34.2  6.7


60 55 50 45 40 35 30 25 20







ACEI Group







ARB Group







Control Group







Fig. 4 Comparison of cardiac color Doppler data in three groups of patients with MR group after 6 months follow-up (mean)

no statistically significant difference between RAD, RVD, LVEF, and LVFS (P > 0.05), as shown in Fig. 4. It can be seen that the long-term use of ACEI did not improve the left atrium, left ventricle, right atrium, and right ventricular of patients with MR and did not improve LVEF and LVFS. ARB significantly improved the left atrium and left ventricle of MR patients, while the right atrium, right ventricle, LVEF, and LVFS were not improved significantly. The improvement of left atrium and left ventricular in ARB group was superior to ACEI in patients

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . .


83.0% P=0.62




81.0% AF Conversion Rate

80.0% 79.0% 78.0% 81.6%

77.0% 76.0% 77.2%


76.1% 74.0% 73.0% AF Conversion Rate

ACEI Group

ARB Group

Control Group




Fig. 5 Comparison of effects of ACEI and ARB on the rate of AF conversion in MR group using different antistructural remodeling drugs

with MR, but there was no significant difference in other indicators of cardiac color Doppler ultrasonography. 3.3.3 The Comparison of Effects of ACEI and ARB on the Rate of AF Conversion in MR Group Using Different Anti-structural Remodeling Drugs


Different drugs can improve the conversion rate of AF in patients with MR, but the degree of improvement is also different. ACEI can increase the conversion rate of 76.1% in the control group to 77.2% and P ¼ 0.62, but the difference was not statistically significant. ARB could increase the conversion rate of 76.1% in the control group to 81.6%, P ¼ 0.02, the difference was statistically significant; ACEI group and ARB group between the AF conversion rate was 81.6 vs. 77.2%, P ¼ 0.03 difference was statistically significant, see Fig. 5. Obviously, in patients with MR, ACEI can also improve the AF conversion rate, but there is no statistically difference with the control group, and ARB can obviously improve the rate of AF cardioversion.

Discussion At present, more and more patients with AF, the goal of treatment of AF is to improve the quality of life to prevent complications and death [6]. AF treatment strategies include heart rate control and cardiac rhythm control, the former can improve the quality of life of patients, but cannot improve the effective prevention of complications and death, while cardiac rhythm control is the high goal of


Tao Li and Yongjun Qian

atrial fibrillation treatment. Recently, cardiac surgery at the same time radiofrequency ablation of AF is the most important method of AF treatment to control cardiac rhythm. However, recurrence of atrial fibrillation after radiofrequency ablation is quite common, resulting in poor clinical treatment satisfaction of patients. The recurrence rate of paroxysmal atrial fibrillation with the fastest response can reach 60% within 1.5 years [13, 14]. In addition, long-term recurrence is frequent even after initial successful surgery, possibly due to underlying disease progression [15]. Further improvement of the AF conversion rate is the bottleneck of AF therapy. Breakthrough current status of using IC drugs in the same period of pulmonary vein isolation, individualized treatment for atrial fibrillation occurrence and maintenance mechanism is the direction of AF treatment in the next 20 years [16, 17]. Heart rate control is an indispensable part of the treatment of patients with atrial fibrillation, which can usually relieve the symptoms related to atrial fibrillation. Compared with stroke prevention and rhythm control, there is little conclusive evidence of the optimal type and intensity of rate control therapy, with most of the data coming from short-term crossover trials and observational studies [18, 19]. Pharmacological control of acute or long-term frequency can be achieved by using β-blockers, digoxin, calcium channel blockers diltiazem, and verapamil or combination therapy. Some antiarrhythmic drugs also reduce heart rate (amiodarone, tenidarone, sotalol and, to some extent, propafenone), but they can only be used in patients who need rhythm control therapy [19]. However, clinical studies have shown that there is no significant difference in a series of clinical events, cardiac function classification, and hospitalization between strict heart rate control and loose heart rate control. It is also worth noting that many patients with “adequately controlled heart rate” (resting heart rate 60–100 bpm) have severe symptoms and need further management [20]. The success of rhythm control therapy depends on a variety of factors, including the number, type, and severity of underlying disease, age, sex, compliance with antiarrhythmic drug therapy, and factors related to the quality of atrial fibrillation ablation surgery [21]. Amiodarone is more effective in maintaining sinus rhythm than other antiarrhythmic drugs [19], but antiarrhythmic drugs (AAD) are associated with a variety of potential adverse reactions. A recent Cochrane collaborative analysis examined the outcomes of AAD to maintain sinus rhythm after cardioversion. The results of blind trials showed that the total mortality rate of AAD tended to be higher, and the adverse event withdrawal rate of almost all drugs was higher than that of the control group [22]. At present, it is considered that the occurrence and maintenance of AF are closely related to atrial remodeling, which mainly includes atrial electrical remodeling and atrial structural remodeling [6]. The short-term atrial electrical remodeling after continuous AF

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . .


cardioversion can disappear completely, but its structural remodeling still exists, while AF is still easy to recur at this time [23, 24]. This phenomenon suggests that atrial structural remodeling rather than atrial electrical remodeling may be the key factor in the occurrence and maintenance of AF. Atrial structural remodeling slows localized conduction and increases conduction heterogeneity. Abnormal conduction provides conditions for unidirectional conduction block and reentry, which provide the basis for AF, and even in a small area of myocardial structural remodeling can occur AF [25]. Komatsu et al. found that the use of drugs to prevent atrial remodeling can not only increase the effect of AF electrocardiogram, but also prevent the recurrence of AF [26]. Our research team has noticed the importance of structural remodeling of AF and took the lead in using drugs for structural remodeling (vitamin c + captopril + simvastatin) to cardioverter 49 patients with AF after mitral valve replacement in outpatient clinic. Compared with the traditional cardioversion therapy, this method reduced the adverse drug reactions and achieved a certain clinical effect. About 35% of the patients recovered sinus rhythm, while only 6% of the control group recovered sinus rhythm, suggesting that blocking structural remodeling therapy for AF is effective [27]. At home and abroad, our research group took the lead in a series of studies on pathogenesis and maintenance mechanism of AF in different types of valvular diseases. Through the clinical observation, we found that mitral stenosis and mitral regurgitation with AF has the different incidence, and that mitral stenosis is more prone to AF, about 64% patients with AF, and that mitral regurgitation is associated with less AF. Through a series of studies, we confirmed that there is a beautiful circle in the pathogenesis of valvular disease with AF, as shown in Fig. 6 [9]. The circle mainly consists of three parts: (1) The left part of the circle proved that different types of mitral valve disease show different pathogenesis of AF, MS patients with AF are associated with angiotensinconverting enzyme (ACE) and angiotensin II (Ang II), whereas in MR patients with AF only associated with Ang II [10]. (2) The right part of the circle proved that there is structural remodeling in valvular disease with AF, and that left atrial structural remodeling is not the same in patients with different types of mitral valve disease. Compared with MR patients, the remodeling of left atrial structure in MS patients is more pronounced [12]. On the basis of the above two studies, the key step of the research team to complete this circle is to demonstrate that Ang II and ACE are connected to RAAS and structural remodeling, and ultimately point out that there is a beautiful circle in the pathogenesis of valvular disease with AF from RAAS to structural reconstruction [9]. A series of pathogenesis studies suggest that preventing the structural remodeling of AF may further improve the conversion rate of AF. Different types of valvular disease need to use different drugs, which is the theoretical


Tao Li and Yongjun Qian

Fig. 6 The beautiful circle in the pathogenesis of valvular disease with AF from RAAS to structural remodeling

basis of this study. To the best of our knowledge, this study is the largest sample of retrospective studies on the choices of using structural remodeling drug therapy after AF radiofrequency ablation for the pathogenesis. The preliminary basic studies determine the type of drug selected in this study. The studies found that the pathogenesis of AF was different in different types of mitral valve lesions, and AF was most related to with ACE and AngII in MS patients, while AF was only associated with Ang II in MR patients [10]. Therefore, this study selected ACEI or ARB drugs to intervene AF. The drugs have been commonly used in cardiovascular clinical drugs because of the use of safety and less side effects. And follow-up found that the main side effects are cough and skin itching, etc., which often can resume medication after suspended for a week, while a small number of patients need to stop continuing medication. In addition, this study using ACEI or ARB dose is small, which has less impact on patients with blood pressure, and there is no case of stopping medication due to obvious fluctuations in blood pressure. Because of the low side effects of the drugs in this study and the low incidence rate and its large sample size, so the study expels the patients with intermittent medication or terminate the drug. This study found that ACEI and ARB can significantly improve the conversion rate of AF in MS patients, while the effect between the two was no difference, and that in patients with MR, ARB can obviously improve the rate of AF cardioversion, while ACEI can also improve the AF conversion rate, but there is no statistically difference with the control group. The results of this clinical study

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . .


coincide with previous basic studies, which further prove that AF is associated with ACE and AngII in patients with MS, and using ACEI or ARB can further improve the cardioversion rate of AF, and that AF only with Ang II in MR patients, and only ARB can further improve the cardioversion rate of AF, while the effect of ACEI is not obvious. The indexes of evaluating structure remodeling were selected from the heart color Doppler ultrasound, mainly due to previous studies that found that cardiac structural remodeling mainly manifested as myocardial ultrastructural changes, myocardial type I and type III fibrillary collagen changes, and changes in cardiac size [12]. Myocardial ultrastructural changes and fibrillary collagen changes are invasive, difficult to achieve in clinical follow-up, and they are not conducive to large sample size study, so this study selected the heart size provided by cardiac color Doppler ultrasound to evaluate structural remodeling. It is suggested by the cardiac color Doppler ultrasound data that in patients with MS, whether ACEI or ARB intervention can improve the left atrial and left ventricular as the representative of the structural remodeling, whereas in MR patients only ARB intervention can improve the left atrial and left ventricular structure remodeling, and ACEI intervention could not improve its structural remodeling. The structural remodeling of different drug treatments is consistent with its corresponding cardioversion rate of AF, which sufficiently demonstrates that the series of basic studies of AF structural remodeling can guide the clinical treatment of AF. Researches showed that after the successful cardioversion of radiofrequency ablation of persistent AF, the atrial electrical remodeling can be completely stopped in a short time, but the myocardial structural remodeling is still ongoing, and at the same time the AF is still easy to relapse [24]. It can be seen that the structural remodeling of the heart is the material basis of AF recurrence, not the electrical remodeling. Based on the studies of AF pathogenesis, our research team continues to deepen and think about whether the use of structural remodeling drugs after the cessation of electrical remodeling in radiofrequency ablation of valvular disease with AF. Although a study has shown that RAAS blockers can reduce new onset AF of 21%, while ARB can reduce 22%, but the study is significantly different from ours because of its too general subjects and AF patients with the miscellaneous underlying disease without radiofrequency ablation [16]. Based on the study of the pathogenesis of AF, this study only chooses ACEI and ARB in RAAS blockers for accurate targeted therapy and also carries out fine grouping of subjects. From the results of the study, combined with the different underlying diseases and different pathogenesis of AF, further application of precision drug continuous therapy of AF after radiofrequency ablation is necessary and effective. This scheme, adopting the individualized “precision treatment“according to the study of


Tao Li and Yongjun Qian

fine pathogenesis of AF, can further improve the cardioversion rate on the basis of radiofrequency ablation of AF. In a word, there is still structural remodeling after radiofrequency ablation of valvular disease with AF, and different types of valvular lesion have different pathogenesis of AF, different drugs, and different effects. Individualized, precise anti-structural remodeling drug continuous therapy can further improve the cardioversion rate of AF after radiofrequency ablation. References 1. Camm AJ, Savelieva I, Potpara T et al (2016) The changing circumstance of atrial fibrillation - progress towards precision medicine. J Intern Med 279(5):412–427 2. Pitt B, Remme W, Zannad F et al (2003) Eplerenone, a selective aldosterone blocker, in patients with left ventricular dysfunction after myocardial infarction. N Engl J Med 348 (14):1309–1321 3. Schaff HV (2015) Surgical ablation of atrial fibrillation--when, why, and how? N Engl J Med 372(15):1465–1467 4. Gillinov AM, Gelijns AC, Parides MK et al (2015) Surgical ablation of atrial fibrillation during mitral-valve surgery. N Engl J Med 372(15):1399–1409 5. Zhu X, Li Q, Li Y et al (2016) Analysis of bipolar radiofrequency ablation in treatment of atrial fibrillation associated with rheumatic heart disease. PLoS One 11(3):e0151248 6. Heijman J, Guichard JB, Dobrev D et al (2018) Translational challenges in atrial fibrillation. Circ Res 122(5):752–773 7. Roy D, Talajic M, Nattel S et al (2008) Rhythm control versus rate control for atrial fibrillation and heart failure. N Engl J Med 358 (25):2667–2677 8. Chen L, Xiao Y, Ma R et al (2014) Bipolar radiofrequency ablation is useful for treating atrial fibrillation combined with heart valve diseases. BMC Surg 14:32 9. Yongjun Q, Huanzhang S, Wenxia Z et al (2015) From changes in local RAAS to structural remodeling of the left atrium: a beautiful cycle in atrial fibrillation. Herz 40(3):514–520 10. Qian Y, Liu Y, Tang H et al (2013) Circulating and local renin-angiotensin-aldosterone system express differently in atrial fibrillation patients with different types of mitral valvular disease. J Renin-Angiotensin-Aldosterone Syst 14 (3):204–211 11. Qian Y, Shao H, Luo T et al (2008) Plasma angiotensin converting enzyme level and permanent atrial fibrillation with mitral valvular

disease. Chin J Clin Thorac Cardiovasc Surg 39(11):674–677 12. Qian Y, Meng J, Tang H et al (2010) Different structural remodelling in atrial fibrillation with different types of mitral valvular diseases. Europace 12(3):371–377 13. Macle L, Khairy P, Weerasooriya R et al (2015) Adenosine-guided pulmonary vein isolation for the treatment of paroxysmal atrial fibrillation: an international, multicentre, randomised superiority trial. Lancet 386(9994):672–679 14. Verma A, Jiang CY, Betts TR et al (2015) Approaches to catheter ablation for persistent atrial fibrillation. N Engl J Med 372 (19):1812–1822 15. Schreiber D, Rostock T, Frohlich M et al (2015) Five-year follow-up after catheter ablation of persistent atrial fibrillation using the stepwise approach and prognostic factors for success. Circ Arrhythm Electrophysiol 8 (2):308–317 16. Khatib R, Joseph P, Briel M et al (2013) Blockade of the renin-angiotensin-aldosterone system (RAAS) for primary prevention of non-valvular atrial fibrillation: a systematic review and meta analysis of randomized controlled trials. Int J Cardiol 165(1):17–24 17. Gillis AM, Krahn AD, Skanes AC et al (2013) Management of atrial fibrillation in the year 2033: new concepts, tools, and applications leading to personalized medicine. Can J Cardiol 29(10):1141–1146 18. Al-Khatib SM, Allen LaPointe NM, Chatterjee R et al (2014) Rate- and rhythm-control therapies in patients with atrial fibrillation: a systematic review. Ann Intern Med 160(11):760–773 19. Kirchhof P, Benussi S, Kotecha D et al (2016) 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Europace 18(11):1609–1678 20. Kirchhof P, Ammentorp B, Darius H et al (2014) Management of atrial fibrillation in seven European countries after the publication of the 2010 ESC Guidelines on atrial

Precise Drug Sequential Therapy Can Improve the Cardioversion Rate. . . fibrillation: primary results of the PREvention oF thromboemolic events—European Registry in Atrial Fibrillation (PREFER in AF). Europace 16(1):6–14 21. Kirchhof P, Breithardt G, Aliot E et al (2013) Personalized management of atrial fibrillation: proceedings from the fourth Atrial Fibrillation competence NETwork/European Heart Rhythm Association consensus conference. Europace 15(11):1540–1556 22. Lafuente-Lafuente C, Valembois L, Bergmann JF et al (2015) Antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation. Cochrane Database Syst Rev (3): Cd005049 23. Ausma J, van der Velden HM, Lenders MH et al (2003) Reverse structural and gap-junctional remodeling after prolonged atrial fibrillation in the goat. Circulation 107 (15):2051–2058 24. Shenasa M, Soleimanieh M, Shenasa F (2012) Individualized therapy in patients with atrial


fibrillation: new look at atrial fibrillation. Europace 14(Suppl 5):v121–v124 25. Roshanali F, Mandegar MH, Yousefnia MA et al (2009) Prevention of atrial fibrillation after coronary artery bypass grafting via atrial electromechanical interval and use of amiodarone prophylaxis. Interact Cardiovasc Thorac Surg 8(4):421–425 26. Komatsu T, Tachibana H, Sato Y et al (2009) Long-term efficacy of upstream therapy using angiotensin-converting enzyme inhibitors and statins in combination with antiarrhythmic agents for the treatment of paroxysmal atrial fibrillation. Int Heart J 50(4):465–476 27. Qian YJ, Xiao XJ, Yuan HS et al (2008) Combination pharmacological cardioversion of permanent atrial fibrillation in post-prosthetic mitral valve replacement outpatients: a novel approach for the treatment of atrial fibrillation. J Int Med Res 36(3):537–543

Chapter 14 Precision Medicine and Dilated Cardiomyopathy Xiang Li and Wenyan Zhu Abstract As the most common cardiomyopathy, dilated cardiomyopathy (DCM) is currently defined as a heart muscle disease which is characterized by left ventricular (LV) or biventricular dilation and systolic dysfunction at the exclusion of either pressure or volume overload or severe coronary artery disease sufficient to explain the dysfunction. For established DCM patients, treatment is directed at the major clinical manifestations of heart failure and arrhythmias, including pharmacological treatment, device therapies, and heart transplantation. But this traditional strategy is incompletely effective and untenable for the consistently high morbidity and mortality of DCM. Implementation of precision medicine in the field of DCM is expected to greatly improve the prognosis of patients and reduce the cost by shifting the current focus on disease treatment to prevention and individualized treatment. This chapter intends to summarize the progress of accurate medical diagnosis and treatment of dilated heart disease. Key words Dilated cardiomyopathy, Precision medicine, Pathogenic gene, Gene mutation, Genetic detection


Introduction As the most common cardiomyopathy, dilated cardiomyopathy (DCM) is currently defined as a heart muscle disease which is characterized by left ventricular (LV) or biventricular dilation and systolic dysfunction at the exclusion of either pressure or volume overload or severe coronary artery disease sufficient to explain the dysfunction [1]. The clinical manifestations of DCM are heart failure, arrhythmia, thromboembolism, syncope, and even sudden death, which make a poor prognosis and impose a heavy social and economic burden with an estimated prevalence of 40/100000 individuals and an annual incidence of 7/100000 individuals [2]. For established DCM patients, treatment is directed at the major clinical manifestations of heart failure and arrhythmias, including pharmacological treatment, device therapies, and heart transplantation [3]. But this traditional strategy is incompletely effective and untenable for the consistently high morbidity and mortality of DCM.

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Xiang Li and Wenyan Zhu

To investigate the possible reason, we believe that these traditional treatments come from the guidance of clinical guidelines, which are based on evidence-based medicine represented by largescale randomized controlled trials. However, the existing clinical trials usually have strict inclusion criteria and withdrawal criteria, and only a small number of patients who meet the conditions can be included in the study, which make the race and specific population are significant limitations. While the guidelines based on clinical studies lack individual attention on the pathophysiological process, clinical manifestation, and treatment response of patients. In this context, the concept of precision medicine was brought forward and gradually began to be widely used in the diagnosis and treatment of cardiovascular diseases in recent years. The concept of precision medicine was first put forward by the United States in 2011, which refers to a large sample population study of diseases, based on individual genomic information, combined with proteomics [4], metabonomics [5], transcriptome [6], epigenetics [7], and other combinatorial studies to find biomolecule markers related to diseases, so as to accurately find the causes of diseases and therapeutic targets. Precision medicine aims to design the best treatment plan for patients, and finally achieve the goal of personalized and accurate treatment for diseases and specific patients, and improve the efficiency of disease diagnosis and prevention [8]. However, the brightest spotlight was provided in 2015 by President Obama in his State of the Union address where he laid out a vision for a national Precision Medicine Initiative in the United States [9]. Since then, China, the United Kingdom and many other countries have launched the precision medicine plan one after another, and precision medicine has become an important direction of medical treatment in the future [10]. At present, precision medicine has been widely used in the field of cardiovascular disease and has made gratifying achievements. For example, in the establishment of a large cohort study, the CHANCE study led by Professor Wang Yongjun of China verified the effectiveness of accurate dual antiplatelet therapy after treating more than 5100 patients [11]. In the aspect of cardiovascular disease genomics research, scientists have designed small molecule MYK-461 to successfully inhibit the progress of hypertrophic cardiomyopathy (HCM) in animal experiments [12], which is expected to become a targeted drug for the treatment of HCM and also become a classic example of precision treatment based on the genetic characteristics of cardiovascular disease. Professor Huo Yong’s team in China has developed enalapril maleate folate tablets for patients with “High Hcy hypertension” through cardiovascular pharmacogenomics research, which is the only innovative drug in the world that is allowed to control two major risk factors for stroke (hypertension and high Hcy) at the same time [13]. The achievement of these results indicates that the implementation of precision medicine in the field of DCM may also benefit.

Precision Medicine and Dilated Cardiomyopathy


Implementation of precision medicine in the field of DCM is expected to greatly improve the prognosis of patients and reduce the cost by shifting the current focus on disease treatment to prevention and individualized treatment. The central principle supporting this emerging area is that a detailed understanding of each person’s unique genetic variation, environment, and lifestyle factors will allow for a subtle assessment of disease risk and personalized interventions. This chapter intends to summarize the progress of accurate medical diagnosis and treatment of dilated heart disease.


Precision Medicine in the Diagnosis and Treatment of DCM

2.1 The Etiology and Common Pathogenic Genes of DCM

The etiology of DCM can be divided into hereditary or non-hereditary factors. Idiopathic DCM refers to hereditary cardiomyopathy, in which familial hereditary cardiomyopathy is the main type of idiopathic DCM with accounting for about 50% of idiopathic DCM [14]. At present, more than 60 pathogenic genes of idiopathic DCM have been reported, most of which affect sarcomere, nucleic acid protein, cardiomyocyte ion channel, cardiac development, and so on. About 25% of patients with DCM are caused by genetic factors of gene mutation [15], suggesting that genetic defects play an important role in the pathogenesis of dilated cardiomyopathy. At present, the known pathogenic genes of DCM include coding sarcomere, Z-line, cytoskeleton, mitochondria, RNA-binding protein, sarcoplasmic reticulum, and nuclear membrane, among which sarcomere and cytoskeleton proteins are the most common mutation targets in DCM [16]. The following we mainly introduce several gene mutations that affect sarcomere. TTN is the most common pathogenic gene of DCM, accounting for about 25% of DCM pathogenic genes. Patients usually develop typical clinical symptoms before the age of 40 [17]. Titin protein encoded by TTN gene is the largest protein (4200 kDa) and the third most abundant muscle protein in human body. Myosin plays an important role in assembling sarcomere, senses mechanical stimulation, and converts it into biochemical signal, provides passive tension in striated muscle, and mediates active contractile force of sarcomere [18, 19]. After selective splicing, TTN produces a variety of skeletal muscle and myocardial subtypes, among which the heart subtypes include N2B, N2BA, and NOVEX-3, which are regulated by the RNA-binding motif protein (RBM) gene [17, 20]. TTN mutations can cause DCM [21]. The types of TTN mutations recorded in OMIM, GHMD, and LOVD databases include nonsense mutations, frame shift mutations, missense mutations, and splice site mutations. Among them, TTN mutations associated with DCM, including 29 nonsense mutations, 17 frame shift mutations, 18 mutations affecting TTN gene editing, and 7 missense mutations [22].


Xiang Li and Wenyan Zhu

RNA-binding motif protein 20 (RBM20) gene is located on human chromosome 10 and contains 14 exons with a relative molecular weight of 1,400,000. It is mainly expressed in cardiomyocytes and skeletal muscle cells, but is not expressed or rarely expressed in non-muscle tissues. It regulates the splicing of mRNA more than 30 genes [23, 24]. Current studies have shown that RBM20 is closely related to familial DCM. It has been reported that as many as dozens of RBM20 gene mutations can lead to DCM [25], including RBM20 can regulate the alternative splicing of TTN gene to make it transform to different subtypes [26, 27]. RBM20 gene mutation can not only lead to DCM, but also cause early clinical symptoms in patients with DCM, increase the risk of early systolic dysfunction and heart failure, further increase mortality and affect prognosis. So far, the pathogenic genes related to familial DCM that affect sarcomere function include MYBPC3, MYH7, TNNC1, TNNI3, TNNT2, TPM1, LDB3, and TTR [28]. 2.2 Precise Diagnosis and Treatment of DCM Under the Guidance of Genetic Information

Different from the application of precision medicine in tumor and other fields, precision medicine with gene detection as the main means rarely produce direct diagnosis and treatment effect in the field of DCM, such as developing direct targeted drugs to treat DCM, changing the existing clinical treatment of patients with DCM, and so on. But the genetic test results are of great significance in assessing the future risk of the disease among asymptomatic family members. Family members with positive genotype and negative phenotype need regular follow-up echocardiography, which can detect systolic dysfunction early before clinical symptoms appear [29]. It can be more targeted regular follow-up and even necessary preventive treatment for those patients. For example, patients with DCM caused by mutations in the LMNA gene are often associated with malignant bradycardia or heart failure [30, 31]. In the LMNA heterozygous deficiency mouse model, early use of the β-blocker carvedilol attenuated DCM development [32], and preliminary human data showed a beneficial ventricular remodeling effect [33]. If genetic tests identify LMNA mutations at an early stage, this population can obtain lifestyle guidance, such as avoiding competitive sports [34], and may also benefit from prophylactic implantable cardioverter defibrillator devices and early heart transplants [30]. In addition, mutations in the SCN5A gene may also be associated with a particularly severe arrhythmic DCM [35–38]. Compared with the relative inefficacy of standard heart failure therapy, taking drugs with sodium channel blocking can significantly improve ventricular systolic function and reduce the burden of arrhythmias [35, 37, 38]. In addition, although precision medicine with genetic detection as the main means has a certain application in patients with DCM, its yield is relatively low, which also lacks of specific treatment. In most cases, even if there are positive results from genetic

Precision Medicine and Dilated Cardiomyopathy


test, it rarely can be fully used and affect the clinical treatment. This situation also hinders the application and promotion of gene detection in the diagnosis and treatment of DCM [39]. It can be expected that if there is a significant impact on clinical practice, clinicians’ demand for and interest in genetic information will be greatly increased. Precision medicine based on genetic knowledge are promising to meet this clinical need. But if this is to be met, the first challenge is to increase the number of individuals with positive genetic results. More people need to be tested. In addition, it is necessary to confirm as many as possible that gene mutations have functional alterations associated with DCM. This work needs to be done by functional genomics analysis, in which mice are preferred as the animal model for the study [40]. In addition, zebrafish can be selected as a model to evaluate the role of genes in cardiac development, cardiomyopathy and arrhythmias [41, 42], but there are some limitations in its research value because of its important differences with humans in anatomy and cardiovascular hemodynamics [43, 44]. At present, transgenic animals are increasingly used for disease modeling and can be used to evaluate heart function in adults [45]. Once the functional change effects of new genes and new variants are determined, feedback can be used to guide clinical treatment. The great progress made in noninvasive imaging and other cardiovascular diagnostic methods can be used as another part of precision medicine to help early diagnosis and guide the intervention of DCM. For example, the reduction of left ventricular ejection fraction by transthoracic echocardiography is the most widely used method for the diagnosis of dilated cardiomyopathy, but it is usually insensitive, so it can identify diseases relatively accurately. Cardiac imaging techniques, including the assessment of myocardial strain on speckle-tracking echocardiography, are producing more sensitive and specific markers of systolic dysfunction [46], which are expected to predict the prognosis of patients with symptomatic DCM and identify early ventricular dysfunction in asymptomatic variant carriers [47–49]. In addition, in evaluating the efficacy and prognosis of DCM, blood biomarkers are expected to be used as alternative indicators of ventricular dysfunction, such as circulating clones of bone marrow-derived hematopoietic cells containing somatic mutations have recently been added to the list of cardiac biomarkers, which have been confirmed that it is significantly associated with age and progression of heart failure [50].


Controversies and Prospect Precision medicine is a systematic project that integrates research, prevention, and treatment, which has a broader application prospect than the traditional medical model. With the progress and development of current science and technology, many gratifying


Xiang Li and Wenyan Zhu

achievements have been made in the clinical application of precision medicine. However, there are more limitations or even challenges: although the current technology has been able to collect individual genes, transcripts, proteins, and phenotypes on a large scale, the huge data scale will magnify the bias of bioinformation collection [51]. More importantly, the existing research experience still lacks the ability to interpret most of the information of the human genome, while epigenetics, transcriptome, proteomics, and microbiology started even later. More research is needed to explain the specific significance of the information contained in genomics [52]. In addition, with the advent of the era of precision medicine, the collection of patient information will be more intensive. How to reasonably protect patients’ privacy, avoid information leakage, avoid genetic discrimination, and so on, which poses a great challenge to personal privacy and medical ethics on the big data environment [53, 54]. While carrying out a large number of information collection and information system construction, the corresponding medical expenditure will also increase significantly. In terms of drug enterprises, accurate medical care means that fewer people benefit from the same drug, and the cost of drug development increases. The higher the unit price of the drug will be [54]. Therefore, how to reasonably integrate the existing resources, ensure the practice of precision medicine on the basis of acceptable expenditure, and further reduce the costs of sequencing, research, and development are all problems that need to be solved. At present, it has made a lot of gratifying progress in the field of diagnosis and treatment of DCM by precision medicine, but there are still many unsolved problems needed further research. For example, the potential use of stem cells to improve prognosis of patients with congestive heart failure and reduced left ventricular ejection fraction (LVEF) is a topic of considerable interest [55]. The preclinical studies of stem cell therapies in DCM have been limited by the small number of suitable experimental models. In addition, no clinical studies have shown that stem cell therapies can improve the clinical outcome of patients with DCM [56]. In addition, although human genome sequence data are now readily available, it will be necessary to expand the implementation of genetic testing and combine effective strategies to identify harmful variants and explain their prognostic significance. More researches are needed to determine when and how to treat patients and relatives who carry harmful genetic mutations, as well as how to monitor the therapeutic efficacy [3]. Many interesting questions are still related to the genetic basis and clinical treatment of DCM, and many cases of idiopathic and familial diseases have not been explained yet. The discovery of new pathogenic gene mutations and the possible use of whole genome sequencing should help to improve the diagnosis rate of patients with DCM. It remains to be seen whether patients with preclinical disease will benefit from

Precision Medicine and Dilated Cardiomyopathy


early pharmacological (or any other) interventions to prevent or delay the onset of clinical cardiomyopathy. Despite the knowledge gap, precise medicine in cardiology is no longer a theoretical vision, but a real opportunity to treat patients with DCM in the future. Despite these challenges, the successful combination of comprehensive sequence analysis and detailed phenotypic analysis should create a new clinical framework for future evidence-based and personalized DCM therapy. Cardiovascular disease, represented by DCM, seriously affects the health of our people, resulting in a heavy economic and social burden. Thanks to the progress of multi-biomics research represented by genomics and the rapid progress of data acquisition and processing technology, precision medicine has initially shown great application value in the field of cardiovascular diseases. It is applied to basic research to better understand the biological and environmental factors of the occurrence and development of various cardiovascular diseases. In clinical practice, precision medicine has further promoted the individualized progress of cardiovascular disease risk assessment, improved existing diagnostic strategies, and expanded cardiovascular disease intervention methods, which provides an important support for further reducing the occurrence of cardiovascular disease and improving the prognosis of cardiovascular disease. At present, the application of precision medicine in the field of cardiovascular diseases is still in its infancy. In the scientific research and clinical practice of cardiovascular disease, we need to establish a thinking model of precision medicine and carry out intensive research on accurate prevention and control technology, the discovery of molecular markers for diagnosis and prognosis, as well as clinical accurate treatment by using modern technical means such as genomics and big data analysis to seek accurate intervention targets. It aims to achieve early detection, early diagnosis, and early treatment of cardiovascular diseases, meanwhile maximize individual and social health benefits with effective, safe, and economical medical services and promote China’s medical and health security level to the forefront of the world. References 1. Elliott P, Andersson B, Arbustini E, Bilinska Z, Cecchi F, Charron P, Dubourg O, Ku¨hl U, Maisch B, McKenna WJ, Monserrat L, Pankuweit S, Rapezzi C, Seferovic P, Tavazzi L, Keren A (2008) Classification of the cardiomyopathies: a position statement from the European Society Of Cardiology Working Group on Myocardial and Pericardial Diseases. Eur Heart J 29(2):270–276. https://

2. Weintraub RG, Semsarian C, Macdonald P (2017) Dilated cardiomyopathy. Lancet (London, England) 390(10092):400–414. 31713-5 3. Fatkin D, Huttner IG, Kovacic JC, Seidman JG, Seidman CE (2019) Precision medicine in the management of dilated cardiomyopathy: JACC state-of-the-art review. J Am Coll


Xiang Li and Wenyan Zhu

Cardiol 74(23):2921–2938. 10.1016/j.jacc.2019.10.011 4. Lindsey ML, Mayr M, Gomes AV, Delles C, Arrell DK, Murphy AM, Lange RA, Costello CE, Jin YF, Laskowitz DT, Sam F, Terzic A, Van Eyk J, Srinivas PR (2015) Transformative impact of proteomics on cardiovascular health and disease: a scientific statement from the American Heart Association. Circulation 132 (9):852–872. 0000000000000226 5. Shah SH, Newgard CB (2015) Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ Cardiovasc Genet 8 (2):410–419. cgenetics.114.000223 6. Wende AR (2016) Post-translational modifications of the cardiac proteome in diabetes and heart failure. Proteomics Clin Appl 10 (1):25–38. 201500052 7. Fox CS, Hall JL, Arnett DK, Ashley EA, Delles C, Engler MB, Freeman MW, Johnson JA, Lanfear DE, Liggett SB, Lusis AJ, Loscalzo J, MacRae CA, Musunuru K, Newby LK, O’Donnell CJ, Rich SS, Terzic A (2015) Future translational applications from the contemporary genomics era: a scientific statement from the American Heart Association. Circulation 131(19):1715–1736. 10.1161/cir.0000000000000211 8. Natarajan P, O’Donnell CJ (2016) Reducing cardiovascular risk using genomic information in the era of precision medicine. Circulation 133(12):1155–1159. 1161/circulationaha.116.021765 9. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372 (9):793–795. NEJMp1500523 10. Siva N (2015) UK gears up to decode 100,000 genomes from NHS patients. Lancet (London, England) 385(9963):103–104. https://doi. org/10.1016/s0140-6736(14)62453-3 11. Wang Y, Wang Y, Zhao X, Liu L, Wang D, Wang C, Wang C, Li H, Meng X, Cui L, Jia J, Dong Q, Xu A, Zeng J, Li Y, Wang Z, Xia H, Johnston SC (2013) Clopidogrel with aspirin in acute minor stroke or transient ischemic attack. N Engl J Med 369(1):11–19. https:// 12. Green EM, Wakimoto H, Anderson RL, Evanchik MJ, Gorham JM, Harrison BC, Henze M, Kawas R, Oslob JD, Rodriguez HM, Song Y, Wan W, Leinwand LA, Spudich JA, McDowell RS, Seidman JG, Seidman CE (2016) A smallmolecule inhibitor of sarcomere contractility

suppresses hypertrophic cardiomyopathy in mice. Science (New York, NY) 351 (6273):617–621. science.aad3456 13. Huo Y, Li J, Qin X, Huang Y, Wang X, Gottesman RF, Tang G, Wang B, Chen D, He M, Fu J, Cai Y, Shi X, Zhang Y, Cui Y, Sun N, Li X, Cheng X, Wang J, Yang X, Yang T, Xiao C, Zhao G, Dong Q, Zhu D, Wang X, Ge J, Zhao L, Hu D, Liu L, Hou FF (2015) Efficacy of folic acid therapy in primary prevention of stroke among adults with hypertension in China: the CSPPT randomized clinical trial. JAMA 313(13):1325–1335. 10.1001/jama.2015.2274 14. de Gonzalo-Calvo D, Quezada M, Campuzano O, Perez-Serra A, Broncano J, Ayala R, Ramos M, Llorente-Cortes V, Blasco-Turrio´n S, Morales FJ, Gonzalez P, Brugada R, Mangas A, Toro R (2017) Familial dilated cardiomyopathy: a multidisciplinary entity, from basic screening to novel circulating biomarkers. Int J Cardiol 228:870–880. 045 15. Petretta M, Pirozzi F, Sasso L, Paglia A, Bonaduce D (2011) Review and metaanalysis of the frequency of familial dilated cardiomyopathy. Am J Cardiol 108(8):1171–1176. https:// 16. Hershberger RE, Siegfried JD (2011) Update 2011: clinical and genetic issues in familial dilated cardiomyopathy. J Am Coll Cardiol 57 (16):1641–1649. jacc.2011.01.015 17. Herman DS, Lam L, Taylor MR, Wang L, Teekakirikul P, Christodoulou D, Conner L, DePalma SR, McDonough B, Sparks E, Teodorescu DL, Cirino AL, Banner NR, Pennell DJ, Graw S, Merlo M, Di Lenarda A, Sinagra G, Bos JM, Ackerman MJ, Mitchell RN, Murry CE, Lakdawala NK, Ho CY, Barton PJ, Cook SA, Mestroni L, Seidman JG, Seidman CE (2012) Truncations of titin causing dilated cardiomyopathy. N Engl J Med 366 (7):619–628. NEJMoa1110186 18. Voelkel T, Linke WA (2011) Conformationregulated mechanosensory control via titin domains in cardiac muscle. Pflugers Archiv 462(1):143–154. s00424-011-0938-1 19. Tharp CA, Haywood ME, Sbaizero O, Taylor MRG, Mestroni L (2019) The giant protein titin’s role in cardiomyopathy: genetic, transcriptional, and post-translational modifications of TTN and their contribution to

Precision Medicine and Dilated Cardiomyopathy cardiac disease. Front Physiol 10:1436. 20. Beqqali A, Bollen IA, Rasmussen TB, van den Hoogenhof MM, van Deutekom HW, Schafer S, Haas J, Meder B, Sørensen KE, van Oort RJ, Mogensen J, Hubner N, Creemers EE, van der Velden J, Pinto YM (2016) A mutation in the glutamate-rich region of RNA-binding motif protein 20 causes dilated cardiomyopathy through missplicing of titin and impaired Frank-Starling mechanism. Cardiovasc Res 112(1):452–463. https://doi. org/10.1093/cvr/cvw192 21. Gerull B, Gramlich M, Atherton J, McNabb M, Trombita´s K, Sasse-Klaassen S, Seidman JG, Seidman C, Granzier H, Labeit S, Frenneaux M, Thierfelder L (2002) Mutations of TTN, encoding the giant muscle filament titin, cause familial dilated cardiomyopathy. Nat Genet 30(2):201–204. https:// 22. Chauveau C, Rowell J, Ferreiro A (2014) A rising titan: TTN review and mutation update. Hum Mutat 35(9):1046–1059. https://doi. org/10.1002/humu.22611 23. Wells QS, Becker JR, Su YR, Mosley JD, Weeke P, D’Aoust L, Ausborn NL, Ramirez AH, Pfotenhauer JP, Naftilan AJ, Markham L, Exil V, Roden DM, Hong CC (2013) Whole exome sequencing identifies a causal RBM20 mutation in a large pedigree with familial dilated cardiomyopathy. Circ Cardiovasc Genet 6(4):317–326. 1161/circgenetics.113.000011 24. Guo W, Zhu C, Yin Z, Wang Q, Sun M, Cao H, Greaser ML (2018) Splicing factor RBM20 regulates transcriptional network of titin associated and calcium handling genes in the heart. Int J Biol Sci 14(4):369–380. 25. Ellinor PT, Sasse-Klaassen S, Probst S, Gerull B, Shin JT, Toeppel A, Heuser A, Michely B, Yoerger DM, Song BS, Pilz B, Krings G, Coplin B, Lange PE, Dec GW, Hennies HC, Thierfelder L, MacRae CA (2006) A novel locus for dilated cardiomyopathy, diffuse myocardial fibrosis, and sudden death on chromosome 10q25-26. J Am Coll Cardiol 48 (1):106–111. jacc.2006.01.079 26. Guo W, Schafer S, Greaser ML, Radke MH, Liss M, Govindarajan T, Maatz H, Schulz H, Li S, Parrish AM, Dauksaite V, Vakeel P, Klaassen S, Gerull B, Thierfelder L, RegitzZagrosek V, Hacker TA, Saupe KW, Dec GW, Ellinor PT, MacRae CA, Spallek B, Fischer R, ¨ zcelik C, Saar K, Hubner N, GotPerrot A, O thardt M (2012) RBM20, a gene for hereditary


cardiomyopathy, regulates titin splicing. Nat Med 18(5):766–773. 1038/nm.2693 27. Li S, Guo W, Dewey CN, Greaser ML (2013) Rbm20 regulates titin alternative splicing as a splicing repressor. Nucleic Acids Res 41 (4):2659–2672. nar/gks1362 28. Pe´rez-Serra A, Toro R, Sarquella-Brugada G, de Gonzalo-Calvo D, Cesar S, Carro E, Llorente-Cortes V, Iglesias A, Brugada J, Brugada R, Campuzano O (2016) Genetic basis of dilated cardiomyopathy. Int J Cardiol 224:461–472. ijcard.2016.09.068 29. Hershberger RE, Givertz MM, Ho CY, Judge DP, Kantor PF, McBride KL, Morales A, Taylor MRG, Vatta M, Ware SM (2018) Genetic evaluation of cardiomyopathy-A Heart Failure Society of America Practice Guideline. J Card Fail 24(5):281–302. 1016/j.cardfail.2018.03.004 30. Kumar S, Baldinger SH, Gandjbakhch E, Maury P, Sellal JM, Androulakis AF, Waintraub X, Charron P, Rollin A, Richard P, Stevenson WG, Macintyre CJ, Ho CY, Thompson T, Vohra JK, Kalman JM, Zeppenfeld K, Sacher F, Tedrow UB, Lakdawala NK (2016) Long-term arrhythmic and nonarrhythmic outcomes of lamin A/C mutation carriers. J Am Coll Cardiol 68 (21):2299–2307. jacc.2016.08.058 31. Fatkin D, MacRae C, Sasaki T, Wolff MR, Porcu M, Frenneaux M, Atherton J, Vidaillet HJ Jr, Spudich S, De Girolami U, Seidman JG, Seidman C, Muntoni F, Mu¨ehle G, Johnson W, McDonough B (1999) Missense mutations in the rod domain of the lamin A/C gene as causes of dilated cardiomyopathy and conduction-system disease. N Engl J Med 341(23):1715–1724. 1056/nejm199912023412302 32. Chandar S, Yeo LS, Leimena C, Tan JC, Xiao XH, Nikolova-Krstevski V, Yasuoka Y, Gardiner-Garden M, Wu J, Kesteven S, Karlsdotter L, Natarajan S, Carlton A, Rainer S, Feneley MP, Fatkin D (2010) Effects of mechanical stress and carvedilol in lamin A/ C-deficient dilated cardiomyopathy. Circ Res 106(3):573–582. circresaha.109.204388 33. Yeoh T, Hayward C, Benson V, Sheu A, Richmond Z, Feneley MP, Keogh AM, Macdonald P, Fatkin D (2011) A randomised, placebo-controlled trial of carvedilol in early familial dilated cardiomyopathy. Heart Lung


Xiang Li and Wenyan Zhu

Circ 20(9):566–573. 1016/j.hlc.2011.06.004 34. Pelliccia A, Solberg EE, Papadakis M, Adami PE, Biffi A, Caselli S, La Gerche A, Niebauer J, Pressler A, Schmied CM, Serratosa L, Halle M, Van Buuren F, Borjesson M, Carre` F, Panhuyzen-Goedkoop NM, Heidbuchel H, Olivotto I, Corrado D, Sinagra G, Sharma S (2019) Recommendations for participation in competitive and leisure time sport in athletes with cardiomyopathies, myocarditis, and pericarditis: position statement of the Sport Cardiology Section of the European Association of Preventive Cardiology (EAPC). Eur Heart J 40 (1):19–33. eurheartj/ehy730 35. Mann SA, Castro ML, Ohanian M, Guo G, Zodgekar P, Sheu A, Stockhammer K, Thompson T, Playford D, Subbiah R, Kuchar D, Aggarwal A, Vandenberg JI, Fatkin D (2012) R222Q SCN5A mutation is associated with reversible ventricular ectopy and dilated cardiomyopathy. J Am Coll Cardiol 60 (16):1566–1573. jacc.2012.05.050 36. McNair WP, Sinagra G, Taylor MR, Di Lenarda A, Ferguson DA, Salcedo EE, Slavov D, Zhu X, Caldwell JH, Mestroni L (2011) SCN5A mutations associate with arrhythmic dilated cardiomyopathy and commonly localize to the voltage-sensing mechanism. J Am Coll Cardiol 57(21):2160–2168. 37. Laurent G, Saal S, Amarouch MY, Be´ziau DM, Marsman RF, Faivre L, Barc J, Dina C, Bertaux G, Barthez O, Thauvin-Robinet C, Charron P, Fressart V, Maltret A, Villain E, Baron E, Me´rot J, Turpault R, Coudie`re Y, Charpentier F, Schott JJ, Loussouarn G, Wilde AA, Wolf JE, Baro´ I, Kyndt F, Probst V (2012) Multifocal ectopic Purkinje-related premature contractions: a new SCN5A-related cardiac channelopathy. J Am Coll Cardiol 60 (2):144–156. jacc.2012.02.052 38. Zakrzewska-Koperska J, Franaszczyk M, Bilin´ska Z, Truszkowska G, Karczmarz M, Szumowski Ł, Zielin´ski T, Płoski R, Bilin´ska M (2018) Rapid and effective response of the R222Q SCN5A to quinidine treatment in a patient with Purkinje-related ventricular arrhythmia and familial dilated cardiomyopathy: a case report. BMC Med Genet 19(1):94. 39. Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, Calkins H, Camm AJ, Ellinor PT, Gollob M, Hamilton R, Hershberger RE,

Judge DP, Le Marec H, McKenna WJ, Schulze-Bahr E, Semsarian C, Towbin JA, Watkins H, Wilde A, Wolpert C, Zipes DP (2011) HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies: this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA). Europace 13(8):1077–1109. 40. van der Weyden L, White JK, Adams DJ, Logan DW (2011) The mouse genetics toolkit: revealing function and mechanism. Genome Biol 12 (6):224. 41. Huttner IG, Wang LW, Santiago CF, Horvat C, Johnson R, Cheng D, von Frieling-Salewsky M, Hillcoat K, Bemand TJ, Trivedi G, Braet F, Hesselson D, Alford K, Hayward CS, Seidman JG, Seidman CE, Feneley MP, Linke WA, Fatkin D (2018) A-band titin truncation in zebrafish causes dilated cardiomyopathy and hemodynamic stress intolerance. Circ Genom Precis Med 11(8):e002135. 002135 42. Shi X, Chen R, Zhang Y, Yun J, BrandArzamendi K, Liu X, Wen XY (2018) Zebrafish heart failure models: opportunities and challenges. Amino Acids 50(7):787–798. https:// 43. Wilkinson RN, Jopling C, van Eeden FJ (2014) Zebrafish as a model of cardiac disease. Prog Mol Biol Transl Sci 124:65–91. https://doi. org/10.1016/b978-0-12-386930-2.00004-5 44. Bakkers J (2011) Zebrafish as a model to study cardiac development and human cardiac disease. Cardiovasc Res 91(2):279–288. https:// 45. Bartlett HL, Escalera RB 2nd, Patel SS, Wedemeyer EW, Volk KA, Lohr JL, Reinking BE (2010) Echocardiographic assessment of cardiac morphology and function in Xenopus. Comp Med 60(2):107–113 46. Collier P, Phelan D, Klein A (2017) A test in context: myocardial strain measured by speckle-tracking echocardiography. J Am Coll Cardiol 69(8):1043–1056. 10.1016/j.jacc.2016.12.012 47. Potter E, Marwick TH (2018) Assessment of left ventricular function by echocardiography: the case for routinely adding global longitudinal strain to ejection fraction. J Am Coll Cardiol Img 11(2 Pt 1):260–274. https://doi. org/10.1016/j.jcmg.2017.11.017

Precision Medicine and Dilated Cardiomyopathy 48. van der Bijl P, Bootsma M, Hiemstra YL, Ajmone Marsan N, Bax JJ, Delgado V (2019) Left ventricular 2D speckle tracking echocardiography for detection of systolic dysfunction in genetic, dilated cardiomyopathies. Eur Heart J Cardiovasc Imaging 20(6):694–699. https:// 49. Verdonschot JAJ, Merken JJ, Brunner-La Rocca HP, Hazebroek MR, Eurlings C, Thijssen E, Wang P, Weerts J, van Empel V, Schummers G, Schreckenberg M, van den Wijngaard A, Lumens J, Brunner HG, Heymans SRB, Krapels IPC, Knackstedt C (2020) Value of speckle tracking-based deformation analysis in screening relatives of patients with asymptomatic dilated cardiomyopathy. J Am Coll Cardiol Img 13(2 Pt 2):549–558. 032 50. Dorsheimer L, Assmus B, Rasper T, Ortmann CA, Ecke A, Abou-El-Ardat K, Schmid T, Bru¨ne B, Wagner S, Serve H, Hoffmann J, Seeger F, Dimmeler S, Zeiher AM, Rieger MA (2019) Association of mutations contributing to clonal hematopoiesis with prognosis in chronic ischemic heart failure. JAMA Cardiol 4(1):25–33. jamacardio.2018.3965 51. Goldfeder RL, Priest JR, Zook JM, Grove ME, Waggott D, Wheeler MT, Salit M, Ashley EA (2016) Medical implications of technical accuracy in genome sequencing. Genome Med 8 (1):24. 52. Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Lepe MP, O’Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M,


Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, M€agi R, Stanton A, Connell J, Bakker SJ, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JM, Tobin MD, Munroe PB, Ehret GB, Wain LV (2017) Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet 49(3):403–415. 10.1038/ng.3768 53. Liddicoat J, Skopek JM, Liddell K (2017) Precision medicine: legal and ethical challenges. University of Cambridge Faculty of Law Research Paper No 64/2017. doi:https://doi. org/10.2139/ssrn.3070388 54. Joyner MJ, Paneth N (2015) Seven Questions for Personalized Medicine. JAMA 314 (10):999–1000. jama.2015.7725 55. Bolli R, Ghafghazi S (2015) Current status of cell therapy for non-ischaemic cardiomyopathy: a brief overview. Eur Heart J 36 (42):2905–2908. eurheartj/ehv454 56. Martino H, Brofman P, Greco O, Bueno R, Bodanese L, Clausell N, Maldonado JA, Mill J, Braile D, Moraes J Jr, Silva S, Bozza A, Santos B, Campos de Carvalho A (2015) Multicentre, randomized, double-blind trial of intracoronary autologous mononuclear bone marrow cell injection in non-ischaemic dilated cardiomyopathy (the dilated cardiomyopathy arm of the MiHeart study). Eur Heart J 36 (42):2898–2904. eurheartj/ehv477

Chapter 15 Research Progress in Pathogenesis of Total Anomalous Pulmonary Venous Connection Xin Shi, Yanan Lu, and Kun Sun Abstract Congenital heart defect (CHD) is one of the most common birth defects and the leading course of infant mortality. Total anomalous pulmonary venous connection (TAPVC) is a rare type of cyanotic which accounting for approximately 1–3% of congenital heart disease cases. Based on where the anomalous veins drain, TAPVC can be divided into four subtypes: supracardiac, cardiac, infracardiac, and mixed. In TAPVC, all pulmonary veins fail to link to the left atrium correctly but make abnormal connections to the right atrium or systemic venous system. The mortality of TAPVC patients without proper intervention is nearly 80% in the first year of life and 50% of them died within 3 months after birth. However, the pathogenesis and mechanism of TAPVC remains elusive. In this chapter, we systematically review the epidemiology, anatomy, and pathophysiology of TAPVC and give an overview of the research progress of TAPVC pathogenesis. Key words Congenital heart disease, Total anomalous pulmonary venous connection, Genetics, Genome


TAPVC Epidemiology Congenital heart defect (CHD) is one of the most common birth defects and the leading course of infant mortality [1, 2]. Total anomalous pulmonary venous connection (TAPVC) is recognized as a rare and severe cyanotic CHD, affecting about 1–3% of infants with cardiovascular malformations [3]. In birth defect data from population-based birth defects surveillance programs across the United States from 2005 to 2009, researchers estimated that each year about 1 in every 10,000 infants born with TAPVC [4]. An international population-based study from 1998 to 2004 showed a higher incidence of TAPVC than previous studies at 7.1 per 100,000 live births [5]. A large retrospective study reviewed the medical records of 377 children with TAPVC between 1946 and 2005 [4]. In this study, the anomalous venous connection was supracardiac in 44%, infracardiac in 26%, cardiac in 21%, and

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Xin Shi et al.

mixed in 9% [6]. Most TAPVC cases exist independently, but some of them can occur in conjunction with a wide variety of cardiac and extracardiac anomalies and complicated syndrome, such as SchmidFraccaro syndrome, Holt-Oram syndrome, Asplenia syndrome, and Fryns syndrome [7, 8]. Despite great care with case identification, it is still likely that a small number of cases were missed, and in some, a diagnosis may never have been made, either in life or death.


Development of Pulmonary Veins At early gestational periods, pulmonary vein emerged from a big venous plexus located within the splanchnic mesoderm, in contrast, systemic venous tributaries develop laterally on the junction between the splanchnic and somatic mesoderm by muscularization of the mesenchyme that surrounds the common cardinal veins. After the development of vascular channels within the lung buds, connection with the pharyngeal mesenchyme will develop as the portal of entry for the pulmonary vein. However, whether the pulmonary vein as a branch from the left atrium obtains a connection to the lung plexus or the pulmonary vein forms as a solitary vessel in the dorsal mesocardium and is only secondarily incorporated into the atrium remains controversial. Previous studies suggest that TAPVC occurs when the midpharyngeal endothelial strand (MES), which is the precursor of the common pulmonary vein, arrange at the improper location [9]. According to this suggestion, TAPVC is due to the defects in the formation or maintenance of the MES [10]. Recently, highresolution three-dimensional reconstructions of avian embryos have helped clarify that the pulmonary vein derives from a greater vascular plexus within the splanchnic mesoderm [11]. Incomplete remodeling of this plexus with failure of separation into distinct pulmonary and systemic vascular zones has been postulated to be a developmental defect that could result in anomalous pulmonary venous connection. In pathology research of TAPVC, no vessel wall was found in the smooth-walled left atrial body and myocardial layer was structurally unnatural. No myocardial layer was organized around the pulmonary veins. An open connection to the left atrial is mandatory for proper development of the left atrial vascular wall and myocardium and the pulmonary venous muscular sleeve. The posterior heart field is suggested to be responsible for the abnormal memorialization and smooth muscle cell formation of the left atrial dorsal wall and pulmonary veins in TAPVC [12].

Research Progress in Pathogenesis of Total Anomalous Pulmonary Venous Connection



TAPVC Anatomy In TAPVC, none pulmonary veins link to the left atrium correctly but make abnormal connections to the right atrium or systemic venous system. The abnormal connection of pulmonary veins delivers oxygen-rich blood to the right side of the heart. To survive, a patent foramen ovale (PFO) or an atrial septal defect (ASD) always exists, so that oxygen-rich blood which entered the right atrium from the pulmonary vein could go across to the left atrium and out to the body. Different subtypes of TAPVC are recognized depending on anatomical position of the pulmonary veins drain to the heart: supracardiac, cardiac, infracardiac, and mixed. 1. Supracardiac form: The pulmonary veins come together and form an abnormal connection above the heart to the superior vena cava, which is a main blood vessel that brings oxygen-poor blood from the upper part of the body to the heart. 2. Cardiac form: The pulmonary veins meet behind the heart and connect to the right atrium. The coronary sinus, which helps bring oxygen-poor blood from the heart muscle back to the heart, helps connect the pulmonary veins to the right atrium. 3. Infracardiac form: The pulmonary veins come together and form abnormal connections below the heart. Blood returns to the right atrium from the veins of the liver and the inferior vena cava, which is the main blood vessel that brings oxygen-poor blood from the lower part of the body to the heart. 4. Mixed form: This form of TAPVC may consist of any of the above connections. The repair of mixed type TAPVC involved a combination of the above approaches as dictated by the specific anatomy of the lesion.


Gene Mutations in TAPVC Patients Because most TAPVC is sporadic, and its mortality is extremely high without proper intervention, and other factors, the previous studies mainly based on clinical data, few researches depend on the pathogenesis of TAPVC. They were summarized in Table 1. Bleyl et al. revealed that 4q12 is related to TAPVC using genetic linkage analysis, and further study showed that the candidate genes in this region include vascular endothelial growth factor receptor 2 (VEGFR2) and platelet-derived growth factor receptor 2 (PDGFR2) [13, 14]. In mouse and chick embryos for both the PDGFRA receptor and its ligand PDGFRA show temporal and spatial patterns consistent with a role in pulmonary vein development. These data supported a role for PDGF-signaling in


Xin Shi et al.

Table 1 Gene mutations in TAPVC patients Gene

Gene name




ACVRL1 Activin A receptor-like type 1

p.T217G; p.W217X; p.G219H

Li et al. Oncotarget (2017)


ANKRD1 Ankyrin repeat domain 1


Cinquetti et al. Hum Mutat (2008)


Thr116Met GJA1

Gap junction protein alpha 1

p.G717A; p.C555T

Huang et al. J Cardiovasc Dis 22135478 Res. (2011)


Kinase insert domain receptor


Bleyl et al. Am J Med Genet A 17036341 12112663 (2006); Gutierrez et al. Hum Mutat (2002)


NK2 homeobox 5


Ye et al. Development. (2015) 26138475


Neuropilin 1


AghajanianJ et al. Biol Chem (2014)


PDGFRA Platelet-derived growth factor receptor alpha


Bleyl et al. Hum Mol Genet (2010)



p.E70Q; p.Glu70Gln

Nash et al. Plos One (2015)


SEMA3D Semaphorin 3D

P.F602L; S65P

Degenhardt et al. Nature Medecine (2013)



Sarcoglycan delta


Li et al. Oncotarget (2017)



SMAD family member 1


Fahed et al.Perros et al. Circulation (2015)



Zic family member 3


Circ Res (2013)


Retinol binding protein 5

pulmonary veins development and suggest that dysregulation of the PDGFRA gene confers susceptibility to the occurrence of TAPVC [15]. Previously reported a TAPVC patient bearing a de novo 10;21 balanced translocation. Cinquetti et al. then cloned both translocation breakpoints from this patient and mapped the ANKRD1 gene, encoding a cardiac transcriptional regulator, in situ hybridization analysis performed on murine embryos showed ANKRD1 expression in the developing pulmonary veins. in lymphoblastoid cell lines derived from TAPVR patient, ANKRD1 expression levels were found to be highly increased. In vitro calpain-mediated degradation assays, mutation from TAPVC patients enhances both the stability of the ANKRD1/CARP protein and its transcriptional repression activity upon the cardiac-specific atrial natriuretic factor (ANF) promoter. Taken together, these results indicated ANKRD1 as a possible candidate gene for TAPVR pathogenesis [16–18].

Research Progress in Pathogenesis of Total Anomalous Pulmonary Venous Connection


Karl et al. found without semaphorin 3D (SEMA3D), endothelial tubes form in a region that is normally avascular, resulting in aberrant connections. SEMA3D provides a repulsive cue to endothelial cells in this area, establishing a boundary. Sequencing of SEMA3D in individuals with anomalous pulmonary veins identified a phenylalanine-to-leucine substitution that adversely affects SEMA3D function. They demonstrated SEMA3D to be a crucial gene in pulmonary venous connection because SEMA3D / mice displayed the TAPVC or partial APVC (PAPVC) phenotype [19]. Li et al. analyzed WES data from six sporadic TAPVC cases, providing evidence for ACVRL1 as a known causative gene and for SGCD as a candidate TAPVC gene [20]. Nash et al. used WGS analysis to identify a nonsynonymous variant predicted to be deleterious and overrepresented in TAPVC. This variant lies in the shared segment in the retinol binding protein 5 (RBP5) gene [17]. Shi et al. used WES data from 178 TAPVC cases and filtered three novel candidate genes (SNAI1, HMGA2, and VAV2) which have not previously been reported in either humans or animals, and this is the largest series of WES in TAPVC cases reported to date [21]. Besides, based on the results of CNV discovery in a casecontrol cohort Shi et al. found evidence that CNVs of 7 candidate genes (PCSK7, RRP7A, SERHL, TARP, TTN, SERHL2, NBPF3) could contribute to the genetic etiology of TAPVC [22]. These candidate genes open new fields of investigation into TAPVC pathology and provide novel insights into pulmonary vein development. Up to now, only a few genes have been identified as candidate genes for TAPVC pathogenesis. These candidate genes explain only a small fraction of the molecular mechanism underlying TAPVC pathogenesis, and comprehensive genomic data are still lacking.


Conclusions TAPVC is a rare and severe heart defect, the pathogenesis of TAPVC remains complicated and undiscovered. Through the past decades, some copy number variants and gene mutations have been considered to associated with TAPVC. It has reached a consensus that the accumulation of copy number variants, gene mutations, and environmental factors may play an important role in the development of pulmonary vein. However, because of the great heterogeneity, the genetic molecular mechanism of TAPVC is still unclear. More information is required to illustrate the relationship between genotype and phenotype. Furthermore, with the progress in genetic testing technologies and the use of next-generation sequencing, candidate gene, and underlying mechanism of TAPVC will open new fields of investigation into TAPVC pathology and provide novel insights into pulmonary vein development.


Xin Shi et al.

References 1. Pediatric Cardiac Genomics C, Gelb B, Brueckner M et al (2013) The congenital heart disease genetic network study: rationale, design, and early results. Circ Res 112 (4):698–706 2. Jin SC, Homsy J, Zaidi S et al (2017) Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands. Nat Genet 49(11):1593–1601 3. Shi G, Zhu Z, Chen J et al (2017) Total anomalous pulmonary venous connection: the current management strategies in a pediatric cohort of 768 patients. Circulation 135 (1):48–58 4. Mai CT, Riehle-Colarusso T, O’Halloran A et al (2012) Selected birth defects data from population-based birth defects surveillance programs in the United States, 2005-2009: featuring critical congenital heart defects targeted for pulse oximetry screening. Birth Defects Res A Clin Mol Teratol 94 (12):970–983 5. Seale AN, Uemura H, Webber SA et al (2010) Total anomalous pulmonary venous connection: morphology and outcome from an international population-based study. Circulation 122(25):2718–2726 6. Nurkalem Z, Gorgulu S, Eren M, Bilal MS (2006) Total anomalous pulmonary venous return in the fourth decade. Int J Cardiol 113 (1):124–126 7. Correa-Villasenor A, Ferencz C, Boughman JA, Neill CA (1991) Total anomalous pulmonary venous return: familial and environmental factors. The Baltimore-Washington Infant Study Group. Teratology 44(4):415–428 8. Siddharth CB, Yadav M, Bhoje A, Hote MP (2018) Dual drainage total anomalous pulmonary venous connection: a rare mixed variant. Asian Cardiovasc Thorac Ann 26(4):305–307 9. van den Berg G, Moorman AF (2011) Development of the pulmonary vein and the systemic venous sinus: an interactive 3D overview. PLoS One 6(7):e22055 10. DeRuiter MC, Gittenberger-De Groot AC, Wenink AC, Poelmann RE, Mentink MM (1995) In normal development pulmonary veins are connected to the sinus venosus segment in the left atrium. Anat Rec 243 (1):84–92 11. Douglas YL, Jongbloed MR, Deruiter MC, Gittenberger-de Groot AC (2011) Normal and abnormal development of pulmonary

veins: state of the art and correlation with clinical entities. Int J Cardiol 147(1):13–24 12. Douglas YL, Jongbloed MR, den Hartog WC et al (2009) Pulmonary vein and atrial wall pathology in human total anomalous pulmonary venous connection. Int J Cardiol 134 (3):302–312 13. Bleyl SB, Botto LD, Carey JC et al (2006) Analysis of a Scottish founder effect narrows the TAPVR-1 gene interval to chromosome 4q12. Am J Med Genet A 140(21):2368–2373 14. Bleyl S, Nelson L, Odelberg SJ et al (1995) A gene for familial total anomalous pulmonary venous return maps to chromosome 4p13q12. Am J Hum Genet 56(2):408–415 15. Bleyl SB, Saijoh Y, Bax NA et al (2010) Dysregulation of the PDGFRA gene causes inflow tract anomalies including TAPVR: integrating evidence from human genetics and model organisms. Hum Mol Genet 19(7):1286–1301 16. Cinquetti R, Badi I, Campione M et al (2008) Transcriptional deregulation and a missense mutation define ANKRD1 as a candidate gene for total anomalous pulmonary venous return. Hum Mutat 29(4):468–474 17. Nash D, Arrington CB, Kennedy BJ et al (2015) Shared segment analysis and nextgeneration sequencing implicates the retinoic acid signaling pathway in total anomalous pulmonary venous return (TAPVR). PLoS One 10 (6):e0131514 18. Arimura T, Bos JM, Sato A et al (2009) Cardiac ankyrin repeat protein gene (ANKRD1) mutations in hypertrophic cardiomyopathy. J Am Coll Cardiol 54(4):334–342 19. Degenhardt K, Singh MK, Aghajanian H et al (2013) Semaphorin 3d signaling defects are associated with anomalous pulmonary venous connections. Nat Med 19(6):760–765 20. Li J, Yang S, Pu Z et al (2017) Whole-exome sequencing identifies SGCD and ACVRL1 mutations associated with total anomalous pulmonary venous return (TAPVR) in Chinese population. Oncotarget 8(17):27812–27819 21. Shi X, Huang T, Wang J et al (2018) Nextgeneration sequencing identifies novel genes with rare variants in total anomalous pulmonary venous connection. EBioMedicine 38:217–227 22. Shi X, Cheng L, Jiao X et al (2018) Rare copy number variants identify novel genes in sporadic total anomalous pulmonary vein connection. Front Genet 9:559

Part IV Precision Medicine of Other Complex Diseases

Chapter 16 Airway Inflammation Biomarker for Precise Management of Neutrophil-Predominant COPD Xue Liang, Ting Liu, Zhiming Zhang, and Ziyu Yu Abstract Chronic obstructive pulmonary disease (COPD) course can be divided into stable stage and acute exacerbation. Deepen the understanding to the function and role of airway inflammatory cells in stable COPD is important for developing new therapies to restore airway dysfunction and preventing stable stage COPD progress to acute exacerbation COPD. Neutrophil is a feature of lower airways and lung inflammation in majority COPD patients at stable stage and increased neutrophils usually means COPD patients are in a more serious stage. Neutrophil-predominant COPD always accompanied by increased numbers of macrophages, lymphocytes, and dendritic cells. The composition proportion of different inflammatory cells are changed with disease severity. Recently, neutrophilic inflammation has been proved to be correlated with the disturbance of airway resident microbiota, which promote neutrophil influx and exacerbates inflammation. Consequently, understanding the details of increased neutrophils and dysbacteriosis in COPD is necessary for making precise management strategy against neutrophil-associated COPD. Key words Neutrophil, COPD, Precision medicine, Inflammation, Diagnosis, Resident microbiota, Dysbacteriosis


Introduction Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality in the world, mainly caused by air pollution or cigarette smoking. Airway inflammation is a consistent feature of COPD, but still have heterogeneity. COPD patients according to different airway inflammation phenotyping can be divided into four patterns, including inflammasomeneutrophil-predominant bacterial-associated COPD, T2-eosinophil-predominant COPD, T1-viral-associated COPD, and pro-inflammatory bacterial-associated COPD [1]. Neutrophilassociated COPD is the most common inflammatory phenotype in COPD (Fig. 1), which is characterized by the activation of the inflammation. However, more evidence suggests that neutrophilic inflammation as well as the concomitant inflammasome activation

Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Xue Liang et al.

Fig. 1 The inflammatory phenotype pattern of COPD

are probably driven by the dysbiosis of airway colony microbiota. Eosinophil-associated COPD probably occupy 10–40% of COPD, which is characterized by increased eosinophilic inflammation in the sputum or blood, in asthma-COPD overlap patients, the concomitant elevated neutrophils and eosinophils are together contributed to a greater future risk of severe exacerbation [2]. Airway inflammation, one main symptom of COPD airway, is implicated in the pathogenesis of COPD. While, COPD with different phenotype shows different inflammation heterogeneity, and for neutrophil-predominant COPD, activation of the inflammasome is its main feature [3]. Unfortunately, the therapies target neutrophilic inflammation or inflammasome have been proved ineffective. Therefore, ascertaining the causing of dominant neutrophil features of airway is essential to developing an effective and accurate management. Recent studies show the neutrophil-related inflammation may induced by dysbacteriosis, which may result in failure in treatment directly target neutrophilic inflammation [3, 4]. For instance, smoking as a risk factor of COPD can promote neutrophilic inflammation, and also impair its antibacterial defense, leading to the disturbance of airway resident microbiota, instead, dysbacteriosis in turn promotes neutrophil influx and exacerbates inflammation [5, 6]. In neutrophil-predominant COPD patients,

Airway Inflammation Biomarker for Precise Management of Neutrophil. . .


there is an increase in the abundance of the bacterial phylum Proteobacteria and H. Influenzae, so that the ration of gammaproteobacteria to firmicutes (γP:F) increase [7–9]. Lung microbiome can vary even in stable stage; therefore, the microbial changes from stable stage to exacerbations contains two sides: the regular temporal perturbations of lung resident microbiota, and the disease-associated disruption of lung resident microbiota. Therefore, examining the baseline variability should be the first step to more precisely assess the dysbiosis during different disease stage. Here, we reviewed the inflammatory and immunological features of neutrophil-predominant COPD, and how these immune events crosstalk with the resident microbiota, aim to find a way out to COPD precise management.


Inflammatory and Immunological Features of COPD In COPD, chronic inflammation process is triggered by external causes and internal causes, afterwards, exists throughout every stage of COPD [10]. Distinguishing the inflammatory phenotypes of different stage COPD is vital to promoting precise clinical management. The progression of COPD is a long course with progressive and irreversible airflow limitation, according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD), airflow limitation of COPD should be comply with a forced expiratory volume in the first second (FEV1) to forced vital capacity (FVC) ratio of less than 0.7 [11]. COPD can be categorized into four stages based on the different extent of airflow limitation, symptoms like shortness of breath, and frequency of exacerbation [12]. Immune response is also correlated with COPD progression [13, 14]. Neutrophil numbers in sputum and bronchoalveolar lavage fluid (BALF) of COPD patients is positively correlated with the disease severity. This is especially true in process of COPD exacerbation, which is usually complicated by respiratory infections when increasing amounts of neutrophils migrating into airway or lung [15–17]. COPD patients at GOLD stage I, who usually have sustained airflow limitation, however, the majority do not progress to advanced stages [18]. Chronic bronchitis and asthma are also risk factors for pulmonary emphysema, thus it can be included into this stage (Fig. 2). The initial steps that activating and spreading of the innate and adaptive immune responses in early COPD are still not very clear. At first, it was thought that innate immune inflammation was predominant in the mild stage of COPD. But now, it is believed that innate immune and adaptive immune may act simultaneously since individual susceptibility to several risk factors is different, that is the reason that young population also could develop airflow obstruction [19, 20]. Both innate


Xue Liang et al.

Fig. 2 The symptoms and inflammatory phenotype in different COPD stage

immunity and adaptive immunity events are involved in mild COPD. Innate lymphoid cells (ILCs), alveolar macrophages, neutrophils, dendritic cells, and CD8+ T cells are the original immunologic events induced by external triggers (Fig. 2). All these immunity responses belong to Th1 type immune response and protect lungs against bacteria, viruses, or other intracellular microbes. Th1 cells express chemokine receptors such as CCR5 and CXCR3 and are responsible for secreting chemokine such as IL-2, IFN-γ, and also [21, 22]. Emphysema is the symptom of III or IV stage COPD, contributing to the loss of lung function. Patients with emphysema were proved to have high percent of CD4+ and CD8+ T lymphocytes which expressed the T helper 1 cell (Th1) marker such as CCR5, CXCR3, and secreted more interferon gamma (IFNγ), interferon-inducible protein 10 (IP-10), and monokine induced by interferon gamma (MIG), which means Th1 type immune response was predominant to regulate migration of immune cells to specific loci [13, 23]. In addition to Th1 type immune response, Th17 type immune response is also important in COPD. Th17 type immune cells including CD4+ Th17 cells, CD8 + Tc17 cells, and group 3 innate lymphoid cell (ILC3s) are stimulated by IL-6 and IL-23 and differentiated from Th0 cells. Th17 cells can secrete IL-17, TNF-α, and IL-22, which can collectively recruit and activate neutrophils [24] and protect lung against extracellular bacteria and fungi [25].

Airway Inflammation Biomarker for Precise Management of Neutrophil. . .



Inflammatory and Immunological Features of Neutrophil-Predominant COPD The main pathologic events during COPD process includes airway lumens remodeling, airway epithelial junction impairation and loss of lung function. Airway damage leads to a pro-inflammatory when various pro-inflammatory mediators including IL-6, IL-8, tumor necrosis factor (TNF)-α, tumor necrosis factor (TNF), and vascular endothelial growth factor (VEGF) are released by damaged airway epithelial cells. Immune cells like neutrophils were first recruited to release more pro-inflammation chemokines to attract more innate cells and trigger subsequent adaptive immunity. Neutrophil numbers and neutrophil-derived proteins like neutrophil MPO increase depending on the inflammatory mediator levels such as IL-6, IL-1β, TNFα, GM-CSF, and so on. A cohort study showed that a significant positive correlation was found between the sputum neutrophil numbers and faster FEV1%Pred decline in patients with moderate-to-severe COPD, while no significant correlation was found between macrophage or lymphocyte numbers and rapid lung function decline in the same patients [26]. Therefore, neutrophils rather than other immune cells in COPD are suitable for assessing the COPD status. In neutrophil-predominant COPD patients, neutrophil counts have been increased in both blood and sputum. COPD in mild stage sustains a low level of slightly increased neutrophils and macrophages, while COPD patients in a more serious stage are usually accompanied by abundant neutrophils and B lymphocytes infiltration [27]. In neutrophilpredominant COPD, inflammatory mediators such as IL-6, TNFα, IL-1β, CXCL8, GM-CSF, and CRP are responsible for neutrophil priming and subsequently activating more immune cells in circulating systerm. In peripheral blood of COPD patients, several abnormal alterations in neutrophils function have been identified, including expression of cell surface receptors, degranulation, phagocytosis, and chemotaxis. In COPD, neutrophils show enhanced chemotaxis [28], increased degranulation as evidenced by increased MPO and neutrophil elastase [16, 29]. Thus, the neutrophils in COPD appear to be functionally primed and immune response enhanced, suggesting that chronic neutrophil activation increases its effector responses as well as worsen systemic inflammation, both of which contribute to the COPD pathogenesis. Various inflammatory cells also have been proved to express distinct chemokine receptors by which they can gather around invading pathogens, regulating immune responses. Neutrophils are firstly recruited to damaged sites of where its immune phagocytosis is triggered, then various antimicrobial products are released to recruit more neutrophils and other immune cells [30]. Neutrophils express several kinds of surface receptors to recognize invaded


Xue Liang et al.

pathogen, including G-protein coupled chemokine and chemoattractant receptors, Fc-receptors, innate immune receptors like Tolllike receptors and C-type lectins, as well as cytokine receptors [31]. G-protein coupled receptors expressed on neutrophils include formyl-peptide receptors such as FPR1, FPR2, and FPR3, classical chemoattractant receptors such as BLT1, BLT2, PAFR, and C5aR, and chemokine receptors such as CXCR1, CXCR2, CCR1, and CCR2. NOD-like receptors such as NOD2 and NLRP3 and RIG-like receptors such as RIG-1 MDA5 are also expressed on neutrophils. In addition, although some chemokine receptors like CXCR4 does not express on neutrophils, it can still play roles in neutrophils mobilization [32]. The G-protein coupled formyl peptide receptors on neutrophils can directly mediate its phagocytosis, by which neutrophils catch and engulf bacteria [33]. In COPD airway, immunologic events such as innate immune deficit in bacterial phagocytosis and progressive decrease in lung microbial diversity also occurred [34]. Recent study proved that this was related to the formation of neutrophil extracellular traps (NETs) and increased NET complexes will reduce airway neutrophil phagocytosis [35]. Formation of neutrophil extracellular traps (NETs) was first discovered in 2004. During NETs formation, numerous proteins such as myeloperoxidase (MPO), neutrophil elastase (NE), and histones were released, all of which attribute antimicrobial properties to NETs [36, 37]. In patients of severe stage COPD, NETs are more abundant and are associated with reduced microbiota diversity and increased abundance of Haemophilus species [35].


Crosstalk Between Bacteria and Neutrophil in COPD Accumulating evidence links microbiota to pulmonary disease. Bacterial pathogens can reach and colonize lower airways of COPD or asthma patients through respiration [38, 39]. Microbiota plays a vital part in pathogen-associated molecular pattern (PAMP) shaping immune function and response to pathogenic stress [40]. Insights into airway inflammation and the reduced microbiome diversity have been derived from lung specimens and sputum. When compared to healthy individuals, individuals with COPD show decreased pulmonary bacterial diversity and gene richness [35]. Airways of COPD patients are colonized by commensal bacterial microbiota containing gram-negative pathogenic Proteobacteria, Bacteroidetes, Actinobacteria, Firmicutes, Streptococcus, Prevotella, Moraxella, Haemophilus, Acinetobacter, Fusobacterium, Veillonella, and Neisseria [39, 41, 42]. Among these microbes, H. influenzae was the most prevalent and the only pathogen persistently exists in patients with stable COPD [7].

Airway Inflammation Biomarker for Precise Management of Neutrophil. . .


Neutrophilic inflammation is the most common inflammatory phenotype in COPD. Following exposure to the external triggers of COPD such as smoking, there is airway damage and onset of damage-associated molecular patterns (DAMPs). Murine cigarette smoke-induced COPD models show that H. influenzae enhances airway inflammation characterized by producing pro-inflammatory mediators TNF-α, IL-6, and IL-1β, and inducing airway neutrophilia infiltration [43]. Prevotella species are common colonizers of airway, which have weak innate stimulatory properties and their colonization in airway is tolerable by the respiratory immune system [44]. Proteobacteria are pathogenic bacteria of airway and have innate stimulatory capacity to drive COPD progression [44]. Bacteria contain several compounds including the microbeassociated molecular pattern (MAMP), which can activate immune response. LPS is a cell membrane constituent of Gram-negative bacteria and is considered to be a potent MAMP. Toll-like receptor is the major innate receptor of LPS, and most pro-inflammatory innate response of human leucocytes to pathogenic gram-negative bacteria is also TLR4 and TLR2 dependent [45]. Different bacteria trigger different innate respiratory immune responses. Non-typeable Haemophilus influenza induced severe Toll-like receptor 2 (TLR2)-independent inflammation which is characterized by predominant airway neutrophil and neutrophilic cytokine/ chemokine expression [44]. While Prevotella nanceiensis, one of the most common bacteria in airway, can diminish neutrophilic airway inflammation via TLR2 [41, 44]. Haemophilus influenzae can trigger innate immune response as well as mobilizing intrapulmonary neutrophils via TLR4 [46]. Moraxella catarrhalis, another common gram-negative bacteria which can as a cause of respiratory tract infections, can trigger inflammatory immune response via TLR2 [47]. Gram-negative anaerobic Prevotella species were reduced in COPD and asthma, most Prevotella species rarely cause respiratory infections and as we have mentioned above, Prevotella nanceiensis could diminish neutrophilic airway inflammation [41, 48]. However, Proteobacteria, which has a increasing relative abundance in COPD, was considered as a co-drivers of COPD besides Prevotella species. Also, it could potently mediated airway neutrophilia production (Fig. 3). Meanwhile, metabolites from activated immune cells also could promote growth of Proteobacteria [49]. Veillonella species are also associated with increasing host inflammation during COPD progress to later stages. Haemophilus species, one important specific Proteobacteria in COPD, whose relative abundance was positively associated with an increased volume fraction of neutrophils in lung [50]. Moraxella species, another specific Proteobacteria in COPD, whose abundance in airway is also associated with increased airway neutrophils [51].


Xue Liang et al.

Fig. 3 Microbes of neutrophil-predominant COPD

The role of bacterial metabolites within the lung has not yet been studied well, but there is some indication that unique metabolic milieu which was created by lung microbiota will promote neutrophil-mediated inflammation and Th17 type immune response (Fig. 3) [52]. However, there are still bioactive bacterial metabolites like glycolic acid and indol-3-acetate, both of which have anti-inflammatory effects [53]. These metabolites provides alternative options to alleviate the inflammation of COPD through remeding dysbacteriosis.


Summary Neutrophil-predominant COPD is a borderless stage between moderate and extremely severe stage. Neutrophil in COPD also works as a link point between DAMP, PAMP, and MAMP. Lung microbiota is associated with the phagocytosis, degranulation, and chemotaxis of neutrophil, and holds the potential to distinguish the

Airway Inflammation Biomarker for Precise Management of Neutrophil. . .


endotypes of COPD as new biomarker. Considering the changes of multiple genera, colonization of airway could no doubt add additional accuracy to disease progression risk assessment. With the development of technology, detecting the microbiome profiles by amplifying bacterial 16S rRNA genes and sequencing them is very convenient and costless. Also sputum qPCR has proved to be a sensitive way to measure airway bacteria and identify neutrophilic inflammation in stable COPD patients [7]. Therefore, comprehensive consideration of the counts of neutrophil in peripheral blood and sputum, as well as the resident microbiota profiles in sputum or BALF, developing a personalized anti-dysbacteriosis strategy may play a role in COPD management. References 1. Bafadhel M et al (2011) Acute exacerbations of chronic obstructive pulmonary disease: identification of biologic clusters and their biomarkers. Am J Respir Crit Care Med 184 (6):662–671 2. Bafadhel M et al (2018) Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials. Lancet Respir Med 6(2):117–126 3. Brightling C, Greening N (2019) Airway inflammation in COPD: progress to precision medicine. Eur Respir J 54(2):1900651 4. Ghebre MA et al (2018) Biological exacerbation clusters demonstrate asthma and chronic obstructive pulmonary disease overlap with distinct mediator and microbiome profiles. J Allergy Clin Immunol 141(6):2027–2036.e12 5. Willemse BWM et al (2005) Effect of 1-year smoking cessation on airway inflammation in COPD and asymptomatic smokers. Eur Respir J 26(5):835–845 6. Morris A et al (2013) Comparison of the respiratory microbiome in healthy nonsmokers and smokers. Am J Respir Crit Care Med 187 (10):1067–1075 7. Bafadhel M et al (2015) Airway bacteria measured by quantitative polymerase chain reaction and culture in patients with stable COPD: relationship with neutrophilic airway inflammation, exacerbation frequency, and lung function. Int J Chron Obstruct Pulmon Dis 10:1075–1083 8. Hussain M et al (2018) Enzalutamide in men with nonmetastatic, castration-resistant prostate cancer. N Engl J Med 378(26):2465–2474 9. Wang Z et al (2016) Lung microbiome dynamics in COPD exacerbations. Eur Respir J 47 (4):1082–1092

10. Singh D et al (2019) Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the GOLD science committee report 2019. Eur Respir J 53(5):1900164 11. Menezes AM et al (2017) The PLATINO study: description of the distribution, stability, and mortality according to the Global Initiative for Chronic Obstructive Lung Disease classification from 2007 to 2017. Int J Chron Obstruct Pulmon Dis 12:1491–1501 12. Han MK et al (2013) GOLD 2011 disease severity classification in COPDGene: a prospective cohort study. Lancet Respir Med 1 (1):43–50 13. Grumelli S et al (2004) An immune basis for lung parenchymal destruction in chronic obstructive pulmonary disease and emphysema. PLoS Med 1(1):e8–e8 14. Baines KJ, Simpson JL, Gibson PG (2011) Innate immune responses are increased in chronic obstructive pulmonary disease. PLoS One 6(3):e18426–e18426 15. Barnes PJ (2008) Immunology of asthma and chronic obstructive pulmonary disease. Nat Rev Immunol 8(3):183–192 16. Quint JK, Wedzicha JA (2007) The neutrophil in chronic obstructive pulmonary disease. J Allergy Clin Immunol 119(5):1065–1071 17. Sta˘nescu D et al (1996) Airways obstruction, chronic expectoration, and rapid decline of FEV1 in smokers are associated with increased levels of sputum neutrophils. Thorax 51 (3):267–271 18. Vestbo J, Lange P (2002) Can GOLD Stage 0 provide information of prognostic value in chronic obstructive pulmonary disease? Am J Respir Crit Care Med 166(3):329–332


Xue Liang et al.

19. Suzuki M et al (2017) The cellular and molecular determinants of emphysematous destruction in COPD. Sci Rep 7(1):9562–9562 20. Martinez FD (2016) Early-Life Origins of Chronic Obstructive Pulmonary Disease. N Engl J Med 375(9):871–878 21. Luther SA, Cyster JG (2001) Chemokines as regulators of T cell differentiation. Nat Immunol 2(2):102–107 22. Loetscher P et al (1998) CCR5 is characteristic of Th1 lymphocytes. Nature 391 (6665):344–345 23. Panina-Bordignon P et al (2001) The C-C chemokine receptors CCR4 and CCR8 identify airway T cells of allergen-challenged atopic asthmatics. J Clin Invest 107(11):1357–1364 24. Pedraza-Zamora CP et al (2017) Th17 cells and neutrophils: close collaborators in chronic Leishmania mexicana infections leading to disease severity. Parasite Immunol 39(4). https:// 25. Annunziato F, Romagnani C, Romagnani S (2015) The 3 major types of innate and adaptive cell-mediated effector immunity. J Allergy Clin Immunol 135(3):626–635 26. Donaldson GC et al (2005) Airway and systemic inflammation and decline in lung function in patients with COPD. Chest 128 (4):1995–2004 27. Caramori G et al (2016) COPD immunopathology. Semin Immunopathol 38(4):497–515 28. Burnett D et al (1987) Neutrophils from subjects with chronic obstructive lung disease show enhanced chemotaxis and extracellular proteolysis. Lancet (London, England) 2 (8567):1043–1046 29. Noguera A et al (2001) Enhanced neutrophil response in chronic obstructive pulmonary disease. Thorax 56(6):432–437 30. Kolaczkowska E, Kubes P (2013) Neutrophil recruitment and function in health and inflammation. Nat Rev Immunol 13(3):159–175 31. Futosi K, Fodor S, Mo´csai A (2013) Neutrophil cell surface receptors and their intracellular signal transduction pathways. Int Immunopharmacol 17(3):638–650 32. De Filippo K, Rankin SM (2018) CXCR4, the master regulator of neutrophil trafficking in homeostasis and disease. Eur J Clin Invest 48 (Suppl 2):e12949–e12949 33. Wen X et al (2019) G-protein-coupled formyl peptide receptors play a dual role in neutrophil chemotaxis and bacterial phagocytosis. Mol Biol Cell 30(3):346–356 34. Soriano JB, Polverino F, Cosio BG (2018) What is early COPD and why is it important? Eur Respir J 52(6):1801448

35. Dicker AJ et al (2018) Neutrophil extracellular traps are associated with disease severity and microbiota diversity in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol 141(1):117–127 36. Brinkmann V et al (2004) Neutrophil extracellular traps kill bacteria. Science (New York, N. Y.) 303(5663):1532–1535 37. Remijsen Q et al (2011) Dying for a cause: NETosis, mechanisms behind an antimicrobial cell death modality. Cell Death Differ 18 (4):581–588 38. Erb-Downward JR et al (2011) Analysis of the lung microbiome in the “healthy” smoker and in COPD. PLoS One 6(2):e16384–e16384 39. Wang L et al (2017) Role of the lung microbiome in the pathogenesis of chronic obstructive pulmonary disease. Chin Med J 130 (17):2107–2111 40. Maynard CL et al (2012) Reciprocal interactions of the intestinal microbiota and immune system. Nature 489(7415):231–241 41. Cabrera-Rubio R et al (2012) Microbiome diversity in the bronchial tracts of patients with chronic obstructive pulmonary disease. J Clin Microbiol 50(11):3562–3568 42. Caverly LJ, Huang YJ, Sze MA (2019) Past, present, and future research on the lung microbiome in inflammatory airway disease. Chest 156(2):376–382 43. Moghaddam SJ et al (2008) Haemophilus influenzae lysate induces aspects of the chronic obstructive pulmonary disease phenotype. Am J Respir Cell Mol Biol 38(6):629–638 44. Larsen JM et al (2015) Chronic obstructive pulmonary disease and asthma-associated Proteobacteria, but not commensal Prevotella spp., promote Toll-like receptor 2-independent lung inflammation and pathology. Immunology 144(2):333–342 45. Elson G et al (2007) Contribution of Toll-like receptors to the innate immune response to Gram-negative and Gram-positive bacteria. Blood 109(4):1574–1583 46. Wang X et al (2002) Toll-like receptor 4 mediates innate immune responses to Haemophilus influenzae infection in mouse lung. J Immunol 168(2):810–815 47. Slevogt H et al (2007) Moraxella catarrhalis is internalized in respiratory epithelial cells by a trigger-like mechanism and initiates a TLR2and partly NOD1-dependent inflammatory immune response. Cell Microbiol 9 (3):694–707 48. Nagy E (2010) Anaerobic infections: update on treatment considerations. Drugs 70 (7):841–858

Airway Inflammation Biomarker for Precise Management of Neutrophil. . . 49. Scales BS, Dickson RP, Huffnagle GB (2016) A tale of two sites: how inflammation can reshape the microbiomes of the gut and lungs. J Leukoc Biol 100(5):943–950 50. Sze MA et al (2015) Host response to the lung microbiome in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 192 (4):438–445 51. Robinson PFM et al (2019) Lower airway microbiota associates with inflammatory


phenotype in severe preschool wheeze. J Allergy Clin Immunol 143(4):1607–1610.e3 52. Segal LN et al (2016) Enrichment of the lung microbiome with oral taxa is associated with lung inflammation of a Th17 phenotype. Nat Microbiol 1:16031–16031 53. Segal LN et al (2017) Randomised, doubleblind, placebo-controlled trial with azithromycin selects for anti-inflammatory microbial metabolites in the emphysematous lung. Thorax 72(1):13–22

Chapter 17 Genome Variation and Precision Medicine in Systemic Lupus Erythematosus Ru Yang, Yaqi Hu, and Lin Bo Abstract Systemic lupus erythematosus (SLE) is a complex autoimmune disease which is facing the difficulties in treatment. Genetics play an important role in SLE. Several studies have shown that genetic factors not only affect the development of SLE, but also affect its clinical progress. In this review article, we focus on exploring the influence of genetics on different aspects of SLE pathogenesis, clinical course, and treatment and will provide some references in further precision medicine for SLE patients. The coming era of precision medicine, SLE patients will be stratified by genetic profiling. This will enable us to make more effective and precise choices of treatment plan. Key words SLE, Genome variation, GWAS, Precision medicine


Introduction Systemic lupus erythematosus (SLE) is a complex autoimmune disease, which is characterized by the destruction of immune tolerance, which promotes the formation of auto-reactive B and T cells, the abnormal production of cytokines, and the subsequent production of autoantibodies against DNA- and RNA-based self-antigens [1, 2]. It has been proposed treat to target in immunotherapy of SLE, with the goal of clinical remission or low disease activity that predicts favorable long-term outcomes [3]. However, only a small number of patients can achieve the goal of prolonged remission or low disease activity [4, 5]. Therefore, immunotherapy is facing the difficulties of unpredictable and frequent relapses of active SLE, flares and maintenance of remission. Previous studies have proved that genetics play an important role in SLE. Several studies have shown that genetic factors not only

Ru Yang and Yaqi Hu contributed equally to this work. Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Ru Yang et al.

affect the development of SLE, but also affect its clinical progress. Especially, the research of genome-wide association (GWAS) has increased dramatically, and more than 100 loci with significant GWAS with SLE have been identified [6]. In this review article, we focus on exploring the influence of genetics on different aspects of SLE pathogenesis, clinical course, and treatment and will provide some references in further precision medicine for SLE patients.


Genetic Pathogenesis of SLE In the past few years, humans have made great progress in the pathophysiological identification of SLE, but there are still many problems. It is generally believed that SLE is caused by the recognition of nuclear antigen by the immune system. Some environmental factors, such as ultraviolet (UV) light, toxins, and infection, lead to cellular apoptosis and damage in clearance of apoptotic bodies, leading them to be recognized by both innate and adaptive immune system [7]. Autoantibodies against these nuclear antigens lead to the construction of immune complex (IC), which deposits on susceptible organs in susceptible organs (such as glomerulus) in SLE patients because of the defective IC clearance mechanism. The interaction of immune cells with these ICs leads to clinical manifestations and further tissue damage in SLE patients. But there is still little evidence that can link the pathophysiology to the clinical course of the disease. It enhances the difficulties in diagnosis and treatment of SLE. According to recent studies, the differences in the prevalence of SLE in different ethnicities support the important role of genetics in this disease [8, 9]. Consistent with this, a high prevalence of SLE in affected individuals and monozygotic twins further supports this issue [10]. According to a population-based study of 23 million participants in Taiwan, the relative risk of SLE was 315.94 for twins and 23.68 for siblings [11]. These results suggest that genetics play an important role in SLE susceptibility.


Monogenic SLE

Identification of causal mutations in monogenic SLE or lupus-like autoimmune syndrome provides important clues for the pathogenesis of SLE. Lupus-prone families with primary defects of classical complement pathways (C1q, C1r/s, C2, C4A and C4B) reveal the importance of this pathway in the pathogenesis of lupus [12]. Complement is essential for the regulation and clearance of immune complex and apoptotic cells. In addition, mutations in genes involved in DNA processing during apoptosis lead to lupus-like systemic autoimmunity [13]. In 20 patients with SLE, there were 2 cases with decreased DNASE1 activity due to heterozygous nonsense DNASE1 mutation [14]. DNASE1L3 mutation resulted in familial SLE with positive ANA, anti-dsDNA antibody, anti-neutrophil cytoplasmic antibody (ANCA) and low C3/C4 [15].

Genome Variation and Precision Medicine in Systemic Lupus Erythematosus


Furthermore, it was reported that a homozygous missense mutation of PRKCD homozygote encoding protein kinase (PKC) in a juvenile-onset SLE family [16]. PKC is a serine/threonine kinase, which is related to the negative selection of B cells and control of cell proliferation [17, 18]. These insights highlight the central role of complement pathway, nucleic acid metabolism, and self-reactive B cells in the pathogenesis of human SLE. 2.2

Polygenic SLE

The GWAS study includes the screening of the association between loci and common multifactor diseases (such as SLE). More than 100 SNPs have been proved to be closely related to SLE, most of which are located in the noncoding region and affect gene expression through transcription or epigenetic modification [19, 20]. Some reported genes are related to abnormal recognition of self-nucleic acids (Ncf1, Ncf2, FCGR2A, ITGAM, etc.), type I IFN overproduction/TLR signaling (IFIH1, IRF5, TNFAIP3, etc.), and defective immune cell signaling (BLK, TNFSF13B, etc.). Human leukocyte antigen (HLA) was involved in antigen presentation, complement components C2 and C4, and cytokines TNF-α [6, 19]. Most of the genes in this region are related to immunity. In different ethnic populations, HLA-DR and—DQ loci have the same correlation with SLE [20–22]. GWAS results also strongly support the participation of non-HLA III region genes in SLE. SLE-GWAS SNPs are enriched in B cell- and T cellspecific gene expression and epigenetic enhancer markers [22, 23]. Additionally, SLE is a kind of clinical heterogeneous disease. Some phenotype-related loci have been reported, such as PDGRFA in lupus nephritis and ITGAM in arthritis [24, 25]. However, the genetic structure of SLE subtypes has not been fully elucidated. The further analysis of GWAS and clinical subphenotypes can identify new sites of association.

2.3 SLE-GWAS Candidate Gene

Here, we introduce some SLE-GWAS candidate gene. The confirmed loci are organized by the dominant pathway they are involved in.

2.3.1 MHC Gene

Human leukocyte antigen (HLA) is involved in the process of T cell antigen presentation. Therefore, HLA polymorphism is related to a variety of autoimmune diseases including SLE. Genetic analysis supports the association between HLA class II polymorphism and SLE [26]. Several studies showed that HLA-DRB1*0301 allele, HLA-DR3-DQ2 haplotype, HLA-DR2, HLA-DR3, DR9, DR15, HLA-DRB1*0301, HLA-DRB1*0801, and HLA-DQA1*0102 alleles were related to disease susceptibility, while HLA-DRB1*1101allele, HLA-DR4, HLA-DR5, DR11, DR14 alleles had protective effects [27]. In addition, HLA-DR4 and DR11 alleles are protective variants of lupus nephritis (LN), while HLA-DR3 and DR15 alleles are related to renal involvement.


Ru Yang et al.

The relationship between MHC gene polymorphism and SLE susceptibility was also found in GWAS. According to the European studies, the relationship between HLA-B*0801, HLA-DQA1*0501, HLA-DQB1*0201, HLA-DRB1*0301, HLA-DRB3*01, HLA-DRB3*02 alleles, DR17 (broad antigen DR3), B8 antigen, and SLE was reported. However, HLADQB1*0301, DQ7 (broad antigen DQ3) seem to have protective effects [22, 28–30]. The relationship between MHC gene polymorphism and clinical manifestations of SLE has not been studied in detail, but there is limited evidence that these genetic variations can also affect the progress of SLE. A study of Chinese Han population showed that rs3077 and rs9277535 in HLA-DP gene were related to the risk of SLE. In addition, rs3077 polymorphism was associated with cutaneous vasculitis, serum IL17, and INF-γ levels. Another study in Hungarian patients with SLE showed that the HLA-DRB1*03 and DRB1*07 alleles are associated with LN, while HLA-DRB1*1501 appears to be a protective allele [31]. In addition, HLA-DRB1*07 was positively correlated with serositis, severe renal, and cardiopulmonary damage, and seemed to be related to fatal manifestations. In addition, HLA DRB1*04 and DRB1*1112 alleles were associated with drug-resistant leukopenia and discoid lupus erythematosus (DLE), respectively. In another study of patients with Arabian SLE, HLA-DRB1*10, DRB1*11, DQB1*03, and DRB1*15 alleles were found to be related to hematology, neurology, skin and kidney diseases, respectively [32]. In addition, serositis is related to HLA-DRB3 and DRB1*11 alleles. In Portuguese SLE patients, the risk of nervous system involvement in HLA-DRB1*08 carriers was almost four times higher, and HLA DRB1*03 allele was positively correlated with LN [33]. In addition, HLA-DRB1*01 alleles have been reported to be significantly overexpressed in patients with SLE neurological disease [34]. In general, the association between MHC gene variation and SLE clinical manifestations has been reported almost all over the world, but these associations seem to be very different in the population. 2.3.2 Non-MHC Gene IRF5

IRF5 interferon regulatory factor 5 (IRF5) is one of the most important genes related to SLE. The increase of IFN-α and its gene transcripts in blood cells are related to the severity of the disease. The IRF5 gene encodes a transcription factor that induces the expression of IFN-related proteins and type I interferon [35]. The single nucleotide of IRF5 gene was studied in four independent SLE case-control cohorts [36]. It has been reported that T allele of the rs2004640 SNP of IRF5 haplotypes contribute to the expression of multiple IRF5 subtypes, increased expression of IRF5, and increased risk of SLE susceptibility [36]. Another study found that multiple over transmitted haplotypes may be

Genome Variation and Precision Medicine in Systemic Lupus Erythematosus


associated with the involvement of IRF5 in SLE [37]. Further casecontrol analysis showed that single nucleotide polymorphisms of IRF5 gene, such as rs2070197, were strongly associated with SLE risk, especially in the Latin American population [38]. Several years later, 3230 common variants of IRF5-TNPO3 were studied in more than 8000 SLE patients and 7000 healthy controls of different races supports the hypothesis that risk variation of IRF5 gene increases gene expression [39]. A recent study has shown that different functional SNPs contribute to the establishment of dangerous haplotypes and have a cumulative effect on the risk of SLE [40]. STAT4

STAT4 transcription 4 (STAT4) gene signaling and activators were first proposed in 2007 to be associated with SLE [41]. Further studies confirmed that STAT4 rs3821236, rs3024866, rs7574865, rs3024896, and rs7601754 SNPs were associated with SLE. In addition, STAT4 rs7582694 CC and CG genotypes are associated with SLE in adolescents in the Egyptian population [42]. Another study in patients of European descent suggested that STAT4 polymorphism might be associated with LN [43], another group examined the association between STAT4 gene and LN in two Swedish cohorts by GWAS [44]. In addition, STAT4 rs7582694 SNPs were associated with severe renal complications [44]. The study also showed that in SLE patients, STAT4 susceptible sites are closely related to the presence of anti-dsDNA [43]. Based on these findings, the association of STAT4 rs7582694 CC genotype with skin manifestations, proteinuria, Ana, and anti-dsDNA positivity was reported in Egyptian adolescents with SLE [42]. Further study of the animal model may help to elucidate the role of STAT4 and its signaling pathway in the pathogenesis of SLE.


There was a significant correlation between polymorphisms of integrin alpha M (ITGAM) and SLE. The alleles rs1143678, rs4548893, rs9888739, rs1143679, and rs1143683 were identified by GWAS in European women with SLE, which were related to disease susceptibility [45]. There is limited data on the association of ITGAM polymorphism with clinical manifestations of SLE. A study of patients of European descent showed that the secondary allele of rs1143679 polymorphism was positively correlated with LN, discoid rash, and immune performance (including antidsDNA and anti-ribonucleoprotein antibody (anti-nRNP) [46]. In another study of Asian patients, 13 SNPs, including rs1143679, were confirmed to be associated with LN. On the contrary, they were negatively correlated with discoid rash, but anti-SM antibody was positive. In Hong Kong and Thailand SLE patients, the small alleles of rs1143679 and rs1143663 polymorphism were linked with LN, and the small alleles of rs1143680 and


Ru Yang et al.

rs1143678 SNP were positively correlated with neural invasion [47]. Therefore, these polymorphisms have an important impact on the development of LN. BANK1


The relationship between B cell scaffold protein with ankyrin repeats (bank1) and SLE susceptibility was also studied. Nine SNPs were identified in Swedish SLE patients: rs4522865 (A), rs4572885 (T), rs10516487 (G), rs10516486 (C), rs17200824 (A), rs6849308 (C), rs10516482 (C), rs10516483 (C), and rs2631271 (G) [48]. The association of rs10516487, rs17266594, and rs3733197 was replicated in Argentina, Germany, Italy, and Spain, with a combined odds ratio of 1.38, 1.42, and 1.23, respectively. The association of rs10028805 with SLE was previously reported in another GWAS for European patients [49]. The clinical significance of BANK1 gene polymorphism has not been studied in detail, but there is limited evidence, suggesting that the single nucleotide polymorphism of BANK1 may affect the disease subtype. A European cohort study of SLE reported that different BANK1 SNPs were associated with different SLE-specific autoantibodies and clinical manifestations [50]. In the SNPs mentioned earlier, rs17266594 and rs10516487 were negatively correlated with immune disorders and kidney involvement. In addition, risk variants of BANK1 are described to be associated with antidsDNA-positive SLE cases, but not with their anti-dsDNA-negative counterparts [51].

Potential Clinical Value of Genetic in SLE It is still a long way in the era of using personalized treatments to cure SLE patients. Nevertheless, some cases have been reported that this approach has made gratifying progress. For example, a recent study reported a 4-year-old SLE patient with Malayan rash, arthritis, high ANA titer, anti-dsDNA, anticardiolipin antibody, leukopenia, and Coombs-positive anemia at 3 years old [52]. At 4 years old, she developed right hemiplegia and was diagnosed with SLE vasculitis/encephalitis based on irregular medium-size vessels in MRA. It was identified an R97H homozygous mutation in three prime repair exonuclease 1 (TREX1) gene. TREX1 is an extracellular cleavage enzyme, which can increase serum IFN-α level. Therefore, this patient is suitable for the treatment with anti-IFN-α antibody, and genetic technology is helpful to find a suitable therapeutic target. Recent studies proved that genetic polymorphism can well predict the clinical progression of SLE [53]. According to the meta-analysis, the polymorphism of immune complex clearance pathway-related genes was significantly associated with

Genome Variation and Precision Medicine in Systemic Lupus Erythematosus


neuropsychiatric lupus erythematosus (NPSLE) [54]. In addition, individuals with FCGR3A 158ff, Fcgr3B Na1/2, and itgamrs1143679 HH genotypes were more likely to develop neuropsychiatric symptoms of their disease [55]. Patients with the wild type rs7925662 TT of TRCP6 gene had a greater risk of NPSLE [55]. In vivo and in vitro studies have shown that anti-NMDA antibodies can cause neuronal apoptosis and remodeling in SLE patients, leading to neuropsychiatric symptoms [56]. These insights imply that this pathway may be a novel candidate treatment strategy for NPSLE. Genetic polymorphism can also predict the outcome of treatment in SLE patients. The relationship between HSP90AA1 gene polymorphism and clinical response to glucocorticoid therapy in SLE patients was studied [57]. The results showed that rs7160651, rs10873531, and rs2298877 polymorphisms were related to glucocorticoid response. A study also showed that CCCGAACATCCC haplotypes of HSP90B1 gene were associated with glucocorticoid efficacy [58]. In addition, it had been proved that the patients with the AA/AG genotype in the 1082 position of the IL10 gene and the AA/GA genotype in the 308 of the TNFA gene had the best response to antimalarial drugs [59]. These SNPs are located in the promoter regulatory region of related genes and can regulate the basal concentrations of IL-10 and TNF-α. The relationship between genetic polymorphism and LN treatment outcome was also studied. In one study, it was found that the CT genotype of glutathione S-transferase (GST) A1 gene during cyclophosphamide therapy had a lower LN remission rate compared to CC carriers [60]. In line with previous studies, ile105val genotype of GSTP1 gene is an independent factor of renal dysfunction in LN patients treated with cyclophosphamide, and the null genotype of GSTM1 gene is related to adverse drug reactions [61]. Another study showed that the genetic polymorphism of Fc fragment of IgG receptor (FCGR) gene cluster had an effect on renal outcome after cyclophosphamide treatment. The results showed that the carriers of rs6697139, rs10917686, and rs10917688 alleles were located between FCGR2B and Fc receptor like a (FCRLA) genes and had a low response to treatment [62]. Therefore, drug therapy may be affected by gene mutation.


Future Perspectives and Conclusion Since the first description, SLE has been recognized as an autoimmune disease that could affect nearly everybody organ. Its physiopathology is complex. Environmental factors, such as ultraviolet light, can induce cell apoptosis and subsequent nuclear autoantigen exposure to the immune system. The destruction of apoptotic body clearing mechanism leads to its accumulation, which leads to the


Ru Yang et al.

destruction of tolerance of autoantibodies secreted by self-reactive B cells. The formation of IC and its poor clearance lead to their deposition in organs of anatomically susceptible people. ICs can further activate adaptive and innate immune pathways, leading to tissue damage and organ failure. More and more evidences have suggested the footprint of genetic polymorphism is evident in all of the above pathways. In addition, with the help of GWAS, some SNPs are considered play impression roles in the pathogenesis of SLE. SLE has an extremely unpredictable clinical process. Every physician cannot avoid facing life-threatening cases or treatmentresistant cases although not every patient presents this type of aggressive manifestation. Therefore, treatment of SLE is still facing severe challenges and difficulties. However, genetic variation and genetic pathway have contributed to a clearer understanding of the role of these SNPs in the pathogenesis, clinical course, and treatment of SLE and bring new chances. As mentioned before, some SNPs have been detected that can predict the therapeutic response to some SLE drugs. In addition, the association of these polymorphisms with specific clinical and serologic manifestations has also been reported. Although all the studies are generally proved in theory, and the conclusions have little practical application at patient bedside, we still hope that future genetic research will eventually become a tool that can better diagnosis and differential diagnosis SLE patients. The coming era of precision medicine, SLE patients will be stratified by genetic profiling. Each patient will be longitudinally evaluated and predict the natural course of the disease. This will enable us to make more effective and precise choices of treatment plan. References 1. Rahman A, Isenberg DA (1976) Systemic lupus erythematosus. Ryoikibetsu Shokogun Shirizu 358(9281):585–585 2. Domeier PP, Schell SL, Rahman ZS (2017) Spontaneous germinal centers and autoimmunity. Autoimmunity 50(1):4–18 3. Van VRF, Mosca M, Bertsias G et al (2014) Treat-to-target in systemic lupus erythematosus: recommendations from an international task force. Ann Rheum Dis 73(6):958–967 4. Zen M, Iaccarino L, Gatto M et al (2015) Prolonged remission in Caucasian patients with SLE: prevalence and outcomes. Ann Rheumatic Dis 74:2117–2122. https://doi. org/10.1136/annrheumdis-2015-207347 5. Urowitz MB, Feletar M, Bruce IN et al (2005) Prolonged remission in systemic lupus erythematosus. J Rheumatol 32(8):1467–1472

6. Cui Y, Sheng Y, Zhang X (2013) Genetic susceptibility to SLE: recent progress from GWAS. J Autoimmun 41(Complete):25–33 7. Tsokos GC, Lo MS, Reis PC et al (2016) New insights into the immunopathogenesis of systemic lupus erythematosus. Nat Rev Rheumatol 12(12):716–730 8. Rees F, Doherty M, Grainge MJ et al (2017) The worldwide incidence and prevalence of systemic lupus erythematosus: a systematic review of epidemiological studies. Rheumatology 56:1945 9. Lim SS, Bayakly AR, Helmick CG et al (2014) The incidence and prevalence of systemic lupus erythematosus, 2002-2004: the Georgia Lupus Registry. Arthritis Rheumatol 66 (2):357–368

Genome Variation and Precision Medicine in Systemic Lupus Erythematosus 10. Aslani S, Rezaei R, Jamshidi A et al (2018) Genetic and epigenetic etiology of autoimmune diseases: lessons from twin studies. Rheumatol Res 3:45–57 11. Kuo CF, Grainge MJ, Valdes AM et al (2015) Familial aggregation of systemic lupus erythematosus and coaggregation of autoimmune diseases in affected families. JAMA Intern Med 175(9):1518–1526 12. Ghodke-Puranik Y, Niewold TB (2015) Immunogenetics of systemic lupus erythematosus: a comprehensive review. J Autoimmun 64:125–136 13. Saeed M (2017) Lupus pathobiology based on genomics. Immunogenetics 69(1):1–12 14. Yasutomo K, Horiuchi T, Kagami S et al (2001) Mutation of DNASE1 in people with systemic lupus erythematosus. Nat Genet 28 (4):313–314 15. Al-Mayouf SM, Sunker A, Abdwani R et al (2011) Loss-of-function variant in DNASE1L3 causes a familial form of systemic lupus erythematosus. Nat Genet 43(12):1186–1188 16. Belot A, Kasher PR, Trotter EW et al (2013) Protein kinase Cdelta deficiency causes mendelian systemic lupus erythematosus with B celldefective apoptosis and hyperproliferation. Arthritis Rheum 65(8):2161–2171 17. Mecklenbrauker I, Saijo K, Zheng NY et al (2002) Protein kinase Cdelta controls selfantigen-induced B-cell tolerance. Nature 416 (6883):860–865 18. Miyamoto A, Nakayama K, Imaki H et al (2002) Increased proliferation of B cells and auto-immunity in mice lacking protein kinase Cdelta. Nature 416(6883):865–869 19. Deng Y, Tsao BP (2017) Updates in lupus genetics. Curr Rheumatol Rep 19(11):68 20. Bentham J, Morris DL, Graham C, Deborah S et al (2015) Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat Genet 47 (12):1457–1464 21. Sun C, Molineros JE, Looger LL et al (2016) High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry. Nat Genet 48 (3):323–330 22. Armstrong DL, Zidovetzki R, Alarco´nRiquelme ME et al (2014) GWAS identifies novel SLE susceptibility genes and explains the association of the HLA region. Genes Immun 15(6):347–354 23. Farh KH, Marson A, Zhu J et al (2014) Genetic and epigenetic fine mapping of causal


autoimmune disease variants. Nature 518 (7539):337–343 24. Chung SA, Brown EE, Williams AH et al (2014) Lupus nephritis susceptibility loci in women with systemic lupus erythematosus. J Am Soc Nephrol 25(12):2859–2870 25. Taylor KE, Chung SA, Graham RR et al (2011) Risk alleles for systemic lupus erythematosus in a large case-control collection and associations with clinical subphenotypes. PLoS Genet 7(2): e1001311 26. Relle M, Schwarting A (2012) Role of MHC-linked susceptibility genes in the pathogenesis of human and murine lupus. Clin Dev Immunol 2012(2):584374 27. Castano-Rodriguez N, Diaz-Gallo LM, Pineda-Tamayo R et al (2008) Meta-analysis of HLA-DRB1 and HLA-DQB1 polymorphisms in Latin American patients with systemic lupus erythematosus. Autoimmun Rev 7 (4):322–330 28. Niu Z, Zhang P, Tong Y (2014) Value of HLA-DR genotype in systemic lupus erythematosus and lupus nephritis: a meta-analysis. Int J Rheum Dis 18(1):17–28 29. Morris DL, Taylor KE, Fernando MM et al Unraveling multiple MHC gene associations with systemic lupus erythematosus: model choice indicates a role for HLA alleles and non-HLA genes in Europeans. Am J Hum Genet 91(5):778–793 30. Zhang J, Zhan W, Yang B et al (2017) Genetic polymorphisms of rs3077 and rs9277535 in HLA-DP associated with systemic lupus erythematosus in a Chinese population. Sci Rep 7:39757 31. Endreffy E (2003) HLA class II allele polymorphism in Hungarian patients with systemic lupus erythematosus. Ann Rheum Dis 62 (10):1017–1018 32. Wadi W, Elhefny NEAM, Mahgoub EH et al (2014) Relation between HLA typing and clinical presentations in systemic lupus erythematosus patients in Al-Qassim region, Saudi Arabia. Int J Health Sci 8(2):159–165 33. Vasconcelos C, Carvalho C, Leal B et al (2009) HLA in portuguese systemic lupus erythematosus patients and their relation to clinical features. Ann N Y Acad Sci 1173(1):575–580 34. Hachicha H, Kammoun A, Mahfoudh N et al (2018) Human leukocyte antigens-DRB1*03 is associated with systemic lupus erythematosus and anti-SSB production in South Tunisia. Int J Health Sci 12(1):21–27 35. Ro¨nnblom L, Eloranta ML, Alm GV (2006) The type I interferon system in systemic lupus


Ru Yang et al.

erythematosus. Arthritis Rheum 54 (2):408–420 36. Graham RR, Kozyrev SV, Baechler EC et al (2006) A common haplotype of interferon regulatory factor 5 (IRF5) regulates splicing and expression and is associated with increased risk of systemic lupus erythematosus. Nat Genet 38 (5):550–555 37. Graham DSC, Manku H, Wagner S et al (2006) Association of IRF5 in UK SLE families identifies a variant involved in polyadenylation. Hum Mol Genet 16(6):579–591 38. Reddy MVPL, Vela´zquez-Cruz R, Baca V et al (2007) Genetic association ofIRF5with SLE in Mexicans: higher frequency of the risk haplotype and its homozygozity than Europeans. Hum Genet 121(6):721–727 39. Kottyan LC, Zoller EE, Bene J et al (2015) The IRF5-TNPO3 association with systemic lupus erythematosus has two components that other autoimmune disorders variably share. Hum Mol Genet 24(2):582–596 40. Raj P, Rai E, Song R et al (2016) Regulatory polymorphisms modulate the expression of HLA class II molecules and promote autoimmunity. eLife 5:e12089 41. Remmers EF, Plenge RM, Lee AT et al (2007) STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus. N Engl J Med 357(10):977–986 42. Nageeb RS, Omran AA, Nageeb GS (et al, 2018) STAT4 gene polymorphism in two major autoimmune diseases (multiple sclerosis and juvenile-onset systemic lupus erythematosus) and its relation to disease severity. Egypt J Neurol Psychiatr Neurosurg 54(1):16 43. Chung SA, Criswell LA (2008) PTPN22: its role in SLE and autoimmunity. Autoimmunity 40(8):582–590 44. Bolin K, Sandling JK, Zickert A et al (2013) Association of STAT4 polymorphism with severe renal insufficiency in lupus nephritis. PLoS One 8(12):e84450 45. Harley JB, Alarco´n-Riquelme ME, Criswell LA et al (2008) Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat Genet 40(2):204–210 46. Kimhoward X, Maiti AK, Anaya JM et al (2010) ITGAM coding variant (rs1143679) influences the risk of renal disease, discoid rash and immunological manifestations in patients with systemic lupus erythematosus with European ancestry. Ann Rheum Dis 69 (7):1329

47. Yang W, Zhao M, Hirankarn N et al (2009) ITGAM is associated with disease susceptibility and renal nephritis of systemic lupus erythematosus in Hong Kong Chinese and Thai. Hum Mol Genet 18(11):2063–2070 48. Kozyrev SV, Abelson AK, Wojcik J et al (2008) Functional variants in the B-cell gene BANK1 are associated with systemic lupus erythematosus. Nat Genet 40(2):211–216 49. Bentham J, Morris DL, Cunninghame Graham DS et al (2015) Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat Genet 47(12):1457–1464 50. Guo L, Deshmukh H, Lu R et al (2009) Replication of the BANK1 genetic association with systemic lupus erythematosus in a Europeanderived population. Genes Immun 10 (5):531–538 51. Chung SA, Taylor KE, Graham RR et al (2011) Differential genetic associations for systemic lupus erythematosus based on anti–dsDNA Autoantibody Production. PLoS Genet 7(3): e1001323 52. Ellyard JI, Jerjen R, Martin JL et al (2016) Identification of a pathogenic variant in TREX1 in early-onset cerebral SLE by wholeexome sequencing. Pathology 48:S47 53. Sarah MG, Stewart W, Joanna W et al (2018) Neurological disease in lupus: toward a personalized medicine approach. Front Immunol 9:1146 54. Ho RC, Ong H, Thiaghu C, Lu Y et al (2016) Genetic variants that are associated with neuropsychiatric systemic lupus erythematosus. J Rheumatol 43(3):541–551 55. Ramirez GA, Lanzani C, Bozzolo EP et al (2015) TRPC6 gene variants and neuropsychiatric lupus. J Neuroimmunol 288:21–24 56. Arinuma Y (2018) Antibodies and the brain: anti-N-methyl-D-aspartate receptor antibody and the clinical effects in patients with systemic lupus erythematosus. Curr Opin Neurol 31:294 57. Zou YF, Xu JH, Gu YY et al (2016) Single nucleotide polymorphisms of HSP90AA1 gene influence response of SLE patients to glucocorticoids treatment. Springerplus 5(1):222 58. Sun XX, Li SS, Zhang M et al (2017) Association of HSP90B1 genetic polymorphisms with efficacy of glucocorticoids and improvement of HRQoL in systemic lupus erythematosus patients from Anhui Province. Am J Clin Exp Immunol 7(2):27–39

Genome Variation and Precision Medicine in Systemic Lupus Erythematosus 59. Lo´pez P, Go´mez J, Mozo L et al (2006) Cytokine polymorphisms influence treatment outcomes in SLE patients treated with antimalarial drugs. Arthritis Res Ther 8(2):R42 60. Wang HN, Zhu XY, Zhu Y et al (2015) The GSTA1 polymorphism and cyclophosphamide therapy outcomes in lupus nephritis patients. Clin Immunol 160(2):342–348 61. Audemard-Verger A, Silva NM, Verstuyft C et al (2016) Glutathione S transferases


polymorphisms are independent prognostic factors in lupus nephritis treated with cyclophosphamide. PLoS One 11(3):e0151696 62. Kim K, Bang SY, Joo YB et al (2016) Response to intravenous cyclophosphamide treatment for lupus nephritis associated with polymorphisms in the FCGR2B-FCRLA locus. J Rheumatol 43(6):1045–1049

Part V Engineering and Surgical Developments in Precision Medicine

Chapter 18 Precision Medicine in Tissue Engineering on Bone Bingkun Zhao, Qian Peng, Rong Zhou, Haixia Liu, Shengcai Qi, and Raorao Wang Abstract With the rapidly development of clinical treatments, precision medicine has come to people eyes with the requirement according to different people and different disease situation. So precision medicine is called personalized medicine which is a new frontier of healthcare. Bone tissue engineering developed from traditional bone graft to precise medicine era. So scientists seek approaches to harness stem cells, scaffolds, growth factors, and extracellular matrix to promise enhanced and more reliable bone formation. This review provides an overview of novel developments on precision medicine in tissue engineering of bone hoping it can open new perspectives of strategies on bone treatment. Key words Precision medicine, Tissue engineering, Bone


Introduction Tissue engineering precision medicine needs to combine a variety of fields. This area aims to treat patients on an individual basis according to their specific characters and disease state. Scientists defined precise therapies as those in which the therapeutic strategy are individualized to patient’s needs [1]. Since biomaterial have been developed in various applications about scaffolds, drug delivery, and immunomodulation. However, what makes these material limited on theory level lies in their ignorance in realistic disease mode making it lack specific target. Different people always at different age, different basic disease, and tissue-specific microenvironments which raise the requirement of tissue engineering developing into more precisely times. At present, more than half a million patients in the USA have received functional bone implants, with a total cost higher than $2.5 billion in 2012, which is expected to double by 2020

Bingkun Zhao and Qian Peng contributed equally to this work. Tao Huang (ed.), Precision Medicine, Methods in Molecular Biology, vol. 2204,, © Springer Science+Business Media, LLC, part of Springer Nature 2020



Bingkun Zhao et al.

[2]. Although bone tissue engineering has developed for a long period, there remain many challenges on it and lack a perfect way to treat bone injury. So different kinds of strategies spring out and was invented by scientists aimed to improve bone healing. In this study, we cited some novel research and classified them in order to present the precision medicine views in bone tissue engineering.


Traditional Consideration on Bone Precise Medicine

2.1 Traditional Physical Property

Human bone has their nature physical property and different part has their own mechanical characters. Long bone (tensile testing: ultimate strength ¼ 133 MPa, Young’s moduli ¼ 17GPa, Compressive testing: ultimate strength ¼ 193 MPa, Young’s moduli ¼ 18.2GPa) need to bear the gravity of body, and they perform better in tensile and compressive tests compared to cranial bone (tensile testing: ultimate strength ¼ 43.4 MPa, Young’s moduli ¼ 5.4GPa, Compressive testing: ultimate strength ¼ 96.5 MPa, Young’s moduli ¼ 5.6GPa) [3]. But mouse bone are much weaker than human because of the body weight and activity behavior (Cranial bone Young’s modulus 1.26  0.29 GPa), which also need to be taken into consideration in designing the model [4]. So in future, we need to take this into bone material design thinking the different situation between animal model and human. The work should satisfy this basic mechanical property.

2.2 Stimulus Sensitivity

There are some stimuli factors that scientists used to trigger structural or chemical proper changes in designed biomaterials to meet different task. So proper stimulus is indispensable factor in design. There are some stimuli that are often used in design bone tissue biomaterial like: pH [5], temperature [6], magnetic fields [7], light [8], and even self-remodeling material [9]. Among them FDA has approved the use of a thermos-sensitive liposome with hyperthermia-triggered release of chemotherapeutics for prostate cancer treatment [10].


However, some research point out that material properties cannot accurately correspond to the steps of the material–host response due to its complex biological nature [11]. In bone healing, it consists of hematoma formation stage, inflammation stage, callus formation stage, and bone remodeling stage [12]. These steps conduct orderly and many scientists begin focus their material targeting one or two steps in contribution to the bone healing.


Precision Medicine in Tissue Engineering on Bone



Bone Tissue Engineering Target Different Healing Stage

3.1 Strategies on Inflammation Stage

Acute inflammatory stage peaks at 24 h [13]. This stage is a key step to create a proper microenvironment to initiate the tissue repair. But exacerbated and/or chronic inflammation will do negative function on bone healing [14]. So how to moderate inflammation stage according to different people in different disease situation attract scientist’s attention. Hassan Rammal’s work built bioactive and osteoinductive calcium phosphate/chitosan/hyaluronic acid substrate (CaP-CHI-HA) system to regulate inflammation factor (TNF-α, MCP-1, IL-6, IL-8, and IL-10) and growth factor (VEGF and TGF-β) manipulate macrophage between pro-inflammatory (M1) phenotype and anti-inflammatory (M2) phenotype, which give out a good result in bone healing [15]. In order to control the inflammation harmony and prevent overload of inflammation factors, Basu S established a shear-thinning DNA two-dimensional silicate nanodisks (nSi) hydrogel aiming modulate the release of a model osteogenic drug dexamethasone (Dex) and can own the properties of rapid self-healing [16]. This method can control the drug delivery and moderate inflammation response more precisely. Won JE engineered hierarchically structured microchanneled scaffolds [17]. This scaffold was made of biocompatible polymer polycaprolactone and designed on a 3D printable platform. Besides regulating immune/inflammatory responses, the highlight of this scaffold lies in the ability in contribution angiogenesis, and stem cell recruitment. Old people always suffer from osteoporosis which delayed bone formation because of chronic low levels of inflammation [18]. Therefore, precision medicine of tissue engineering also puts much efforts on the older group. Alhamdi JR created a biomimetic calcium phosphate coating to serve as a highly localized delivery system to guide macrophage phenotype transitions which may get a good results in helping bone formation in the elder [19]. More remarkable, inflammation response, which plays a significant role in the process of bone injury healing, needs to be thoroughly investigated in the future in order for future clinical application and theory based on inflammation of bone healing still remain the major limitation currently.

3.2 Strategies on Callus Formation Stage and Bone Remodeling Stage

Callus formation and bone remodeling stage happen simultaneously, and these steps need a balance of operation of two stage causing the formation and reconstruction of bone. Wang T made a novel layer-by-layer engineering platform (PCL/collagen/HAp composite nanofibrous mesh) for construction of a versatile biomimetic periosteum, enabling further assembly of a multicomponent and multifunctional periosteum replacement for bone defect repair and reconstruction [20]. They point out that this multiple layer structure can combine with various natural and synthetic structural


Bingkun Zhao et al.

bone graft materials to repair defects of any size and shape and further permits controlled insertion of multiple functional components. Jiang N created a Ti-inplants (Micro/Nanoscaled Hierarchical Ti Phosphate/Ti Oxide Hybrid Coating) which exhibit osseointegration performance and have a strong influence on the cell behaviors, such as proliferation, adhesion, and differentiation [21]. Growth factors act throughout the two stages [22]. So scientists always focus on it. Fujioka-Kobayashi M delivered bone healing growth factor BMP2 and FGF18 by using cholesteryl groupand acryloyl group-bearing pullulan (CHPOA) nanogels and this hydrogel strongly enhanced and stabilized the BMP2-dependent bone repair, inducing osteoprogenitor cell infiltration reach an efficient bone tissue engineering [23]. Seeherman HJ improved retention of BV256, a BMP-2/BMP-6/activin A chimera, by A composite matrix (CM) which contains calcium-deficient hydroxyapatite granules suspended in a macroporous, fenestrated, polymer mesh-reinforced recombinant human type I collagen matrix [24]. This research paved a better way to apply BMP-2 in clinical trials which is far more efficient than single BMP-2 application.


Tissue Location-Specific Consideration Injury always occurs at the interface between bone and other tissues like bone–cartilage structure. Mature articular cartilage is an avascular tissue which is different from bone tissue. So in osteochondral system, this special structure always add much difficulties in healing and always accompanied by the poor prognosis due to the limited ability to heal itself [25]. So more and more scientists focus their attention on these challenges and make bone tissue developed into precision medicine times. Based on this special structure, researcher provide multiple different layers to deal with this structure. Jiang j created two layers with different material to facilitate both bone and cartilage region regeneration [26]. After that professor Nukavarapu pointed out this structure’s short comings that the interface of two different materials may cause the delayed healing in the future because the barrier will impede the interactions of cells in two scaffolds and further lead to the poor interfacial integration between the new formed cartilage and bone [27]. In order to overcome this disadvantages, Deng T used a novel threedimensional (3D) heterogeneous/bilayered scaffold [28]. This scaffold has to distinct materials, but they are integrated which corresponds to the interface of cartilage and bone interface. A silk fibroin (SF) layer and a silk-nano calcium phosphate (silk-nanoCaP) layer was created by Le-Ping Yan [29]. Differ from previous study, this scaffolds realized fully integrated and homogeneous porosity distribution. Based on widely used growth factor in tissue engineering, BMP-2 was also applied in osteochondral for more precise

Precision Medicine in Tissue Engineering on Bone


tissue engineering [30]. Recently, Yanlun Zhu created an injectable continuous structurally and functionally biomimetic sodium alginate (SA)/bioglass (BG) composite hydrogel [31], which has proper swelling properties and not only stimulate healing of an entire osteochondral unit but also promote the integration between the newly formed tissues and the host tissue. Another important interface of bone structure is bone–tendon structure which differ from bone–cartilage structure. This structure consists compositionally and mechanically graded structure with bone and tendon properties. In another words, bone–tendon interface is also functionally and compositionally graded structure [32]. In mechanical level, it needs to be considered more about stability in cycles of physiological tensile loading with the repair site over the long term. Chen CH tried to use periosteum to envelop the injured tendon to improve tendon–bone healing [33]. This research outlines the tendon–bone incorporation repair. Some scientists also apply growth factor to increase the self-healing of bone–tendon interface [34, 35]. To increase the firm tendon–bone anchoring and with more stability in knee, Mutsuzaki H use calcium phosphate (CaP)-hybridized attach to the tendon graft which successfully simulated the nature state of tendon-bone connection [36]. Since Li D pointed out that nanofibers can be easily assembled into a range of arrays or hierarchically structured films by manipulating their alignment, stacking, and/or folding [37]. This special property was regarded as an ideal material to contribute to tendon–bone interface repair. Liu W combined poly (lactic acid) (PLA) (a material that has been approved by the FDA for tissue engineering applications) with nanofibers, which exhibit good biocompatibility and inherent biodegradability [38]. Ker D created functionally graded material called QHM polymer (quadrol (Q), hexamethylene diisocyanate (H), and methacrylic anhydride (M)) which is crosslinked by UV. This study finds a new way to mimic tendon–bone interface structure and improved a lot in mechanical property compared to prior nanofiber material [39]. From the above research, they performed some strategies contributing to bone–tendon conjunction repair. But tendon is a unique tissue that not only need to bear the gravity but also need to bear tensile-loose cycle. This require the material to have high standards about mechanical property which is still the main problem and challenge of tissue engineering on bone–tendon interface repair.


3D Printing-Based Strategy 3D printing recently is an eye-catching technology to mimic the human tissue and recently scientists have successfully print the heart and pulmonary which has shocked the world making the 3D


Bingkun Zhao et al.

printing an ideal option on tissue engineering [40]. Therefore, scientists also try different methods on bone tissue engineering research, especially making the material more precise for different situation. So many scientists put much efforts on improving and polishing the 3D printing technology making it more suitable for bone tissue engineering. The Silvan Gantenbein and their team established a three-dimensional (3D) printing approach to generate recyclable lightweight structures with hierarchical architectures, complex geometries, and unprecedented stiffness and toughness which can be applied as lightweight biological materials such as bone [41]. Notably, as microfluidics tissue engineering can control the chemical environment on a micrometer scale within a macroscopic scaffold could aid in engineering complex tissues [42]. Marco Costantini combined the 3D printing and microfluidics by using valve-based flow-focusing chip to establish a predefined 3D geometry and a controlled, spatially varying internal porous architecture, such as a model of a bone [43]. Besides the breakthrough of the structure simulation of bone, the microenvironments of ECM (extracellular matrix) of bone tissues simulation also regarded as an important step in more precise tissue engineering. A leading strategy in tissue engineering is the design of biomimetic scaffolds that stimulate the body’s repair mechanisms through the recruitment of endogenous stem cells to sites of injury [44]. The extracellular matrix (ECM) plays a crucial role in governing organ branching during tissue regenera tion. It not only provides structural integrity for tissue elasticity [45], but also many ECM components are locally synthesized and continuously reorganized to modulate diverse cellular processes [46]. So some scientists try to pave the way on the combination of 3D printing and ECM. AlaaMansour coated synthetic dicalcium phosphate (DCP) bioceramics with the bone ECM and proved that to enhance the integration of synthetic biomaterials and improve their ability to regenerate bone probably by modulating the host immune reaction [47]. Linxiao Wu’s work used 3D printing to remodel elastomer nanohybrid to mimic ECM and regulate chondrogenesis and osteogenesis [46]. Chen et al. pointed out that autogenous ECM is biocompatible, bioactive, and bio-safe, making it an optimal choice for future clinical applications of 3D printing and they combined ECM and 3D printing together successfully to repair skull defect [48]. Shi W used silk fibroin with gelatin in combination with BMSC-specific affinity peptide using 3D printing (3DP) technology to establish a suitable 3D microenvironment for BMSC proliferation, differentiation, and extracellular matrix production which is a promising biomaterial for knee cartilage repair [49]. Pati et al. enhanced the osteogenic potential of 3D-printed PCL/PLGA/β-TCP scaffolds by using human nasal inferior turbinate tissue-derived mesenchymal stromal cells to deposit bone-like ECM [50]. This is the application of autologous cell

Precision Medicine in Tissue Engineering on Bone


transplantation to form the osteogenetic environments, but the method has still limited that the patients’ situation is proper. Demirci U teamwork established a 3D with neural crest cells in 3-D microenvironments [51]. In their study, Col II expression (chondrogenesis marker) did not increase in osteogenic conditions and aggrecan expression did not alter significantly. These findings indicate that there is no chondrogenic tissue formation and calcium deposition was all from the intramembranous. This method enables precise control over the microenvironment which is more suitable for primary embryonic craniofacial bone disorders. From the above research, we can see that there is a trend that takes both advantages of ECM and 3D printing. It is obvious that 3D printing has its own special advantages on simulation of the bone structure. But it is important to make it specific and proper to different situation which needs other complements.


Summary From developmental view, precise tissue engineering strategy has its indispensable advantages that it can treat the diseases according to different functional requirement, different place, and different personal situation. Bone tissue engineering moving from bulk material to more precise and personalized material is still in urgent need.

Acknowledgements Bingkun Zhao and Qian Peng contributed equally to this work. References 1. Oliva N, Unterman S, Zhang Y, Conde J, Song HS, Artzi N (2015) Personalizing biomaterials for precision nanomedicine considering the local tissue microenvironment. Adv Healthc Mater 4:1584 2. Bose S, Sarkar N (2019) Natural medicinal compounds in bone tissue engineering. Trends Biotechnol 3. Zhao J, Shen G, Liu C, Wang S, Zhang W, Zhang X, Zhang X, Ye D, Wei J, Zhang Z, Jiang X (2012) Enhanced healing of rat calvarial defects with sulfated chitosan-coated calcium-deficient hydroxyapatite/bone morphogenetic protein 2 scaffolds. Tissue Eng Part A 18:185 4. Schenk MPAP (2014) Determining mechanical properties of adult murine calvaria. 60th Annual Meeting of the Orthopaedic Research Society 2014

5. Barros J, Melo L, Silva RA, Ferraz MP, Azeredo J, Pinheiro V, Colaco B, Fernandes MH, Gomes PS, Monteiro FJ (2019) Encapsulated bacteriophages in alginatenanohydroxyapatite hydrogel as a novel delivery system to prevent orthopedic implantassociated infections. Nanomedicine-Uk 24:102145 6. Wasupalli GK, Verma D (2020) Injectable and thermosensitive nanofibrous hydrogel for bone tissue engineering. Mater Sci Eng C Mater Biol Appl 107:110343 7. Bianchi M, Boi M, Sartori M, Giavaresi G, Lopomo N, Fini M, Dediu A, Tampieri A, Marcacci M, Russo A (2015) Nanomechanical mapping of bone tissue regenerated by magnetic scaffolds. J Mater Sci Mater Med 26:5363 8. Celikkin N, Mastrogiacomo S, Jaroszewicz J, Walboomers XF, Swieszkowski W (2018)


Bingkun Zhao et al.

Gelatin methacrylate scaffold for bone tissue engineering: the influence of polymer concentration. J Biomed Mater Res A 106:201 9. Meng L, Shao C, Cui C, Xu F, Lei J, Yang J (2020) Autonomous self-healing silk fibroin injectable hydrogels formed via surfactant-free hydrophobic association. ACS Appl Mater Interfaces 12:1628 10. Arias JL, Clares B, Morales ME, Gallardo V, Ruiz MA (2011) Lipid-based drug delivery systems for cancer treatment. Curr Drug Targets 12:1151 11. Cooper DK, Novitzky D, Wicomb WN (1988) Hormonal therapy in the brain-dead experimental animal. Transplant Proc 20:51 12. Ono T, Takayanagi H (2017) Osteoimmunology in bone fracture healing. Curr Osteoporos Rep 15:367 13. Einhorn TA, Gerstenfeld LC (2015) Fracture healing: mechanisms and interventions. Nat Rev Rheumatol 11:45 14. Mountziaris PM, Mikos AG (2008) Modulation of the inflammatory response for enhanced bone tissue regeneration. Tissue Eng Part B Rev 14:179 15. Rammal H, Bour C, Dubus M, Entz L, Aubert L, Gangloff SC, Audonnet S, Bercu NB, Boulmedais F, Mauprivez C, Kerdjoudj H (2018) Combining calcium phosphates with polysaccharides: a bone-inspired material modulating monocyte/macrophage early inflammatory response. Int J Mol Sci 19:3458 16. Basu S, Pacelli S, Feng Y, Lu Q, Wang J, Paul A (2018) Harnessing the noncovalent interactions of DNA backbone with 2D silicate nanodisks to fabricate injectable therapeutic hydrogels. ACS Nano 12:9866 17. Won JE, Lee YS, Park JH, Lee JH, Shin YS, Kim CH, Knowles JC, Kim HW (2020) Hierarchical microchanneled scaffolds modulate multiple tissue-regenerative processes of immune-responses, angiogenesis, and stem cell homing. Biomaterials 227:119548 18. Franceschi C, Bonafe M, Valensin S, Olivieri F, De Luca M, Ottaviani E, De Benedictis G (2000) Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci 908:244 19. Alhamdi JR, Peng T, Al-Naggar IM, Hawley KL, Spiller KL, Kuhn LT (2019) Controlled M1-to-M2 transition of aged macrophages by calcium phosphate coatings. Biomaterials 196:90 20. Wang T, Zhai Y, Nuzzo M, Yang X, Yang Y, Zhang X (2018) Layer-by-layer nanofiberenabled engineering of biomimetic periosteum

for bone repair and reconstruction. Biomaterials 182:279 21. Jiang N, Guo Z, Sun D, Li Y, Yang Y, Chen C, Zhang L, Zhu S (2018) Promoting osseointegration of Ti implants through micro/nanoscaled hierarchical Ti phosphate/Ti oxide hybrid coating. ACS Nano 12:7883 22. Siddiqui JA, Partridge NC (2016) Physiological bone remodeling: systemic regulation and growth factor involvement. Physiology (Bethesda) 31:233 23. Fujioka-Kobayashi M, Ota MS, Shimoda A, Nakahama K, Akiyoshi K, Miyamoto Y, Iseki S (2012) Cholesteryl group- and acryloyl group-bearing pullulan nanogel to deliver BMP2 and FGF18 for bone tissue engineering. Biomaterials 33:7613 24. Seeherman HJ, Berasi SP, Brown CT, Martinez RX, Juo ZS, Jelinsky S, Cain MJ, Grode J, Tumelty KE, Bohner M, Grinberg O, Orr N, Shoseyov O, Eyckmans J, Chen C, Morales PR, Wilson CG, Vanderploeg EJ, Wozney JM (2019, 11) A BMP/activin A chimera is superior to native BMPs and induces bone repair in nonhuman primates when delivered in a composite matrix. Sci Transl Med:eaar4953 25. Huang BJ, Hu JC, Athanasiou KA (2016) Cellbased tissue engineering strategies used in the clinical repair of articular cartilage. Biomaterials 98:1 26. Jiang J, Tang A, Ateshian GA, Guo XE, Hung CT, Lu HH (2010) Bioactive stratified polymer ceramic-hydrogel scaffold for integrative osteochondral repair. Ann Biomed Eng 38:2183 27. Nukavarapu SP, Dorcemus DL (2013) Osteochondral tissue engineering: current strategies and challenges. Biotechnol Adv 31:706 28. Deng T, Lv J, Pang J, Liu B, Ke J (2014) Construction of tissue-engineered osteochondral composites and repair of large joint defects in rabbit. J Tissue Eng Regen Med 8:546 29. Yan LP, Silva-Correia J, Oliveira MB, Vilela C, Pereira H, Sousa RA, Mano JF, Oliveira AL, Oliveira JM, Reis RL (2015) Bilayered silk/ silk-nanoCaP scaffolds for osteochondral tissue engineering: in vitro and in vivo assessment of biological performance. Acta Biomater 12:227 30. Vayas R, Reyes R, Rodriguez-Evora M, Del RC, Delgado A, Evora C (2017) Evaluation of the effectiveness of a bMSC and BMP-2 polymeric trilayer system in cartilage repair. Biomed Mater 12:45001 31. Zhu Y, Kong L, Farhadi F, Xia W, Chang J, He Y, Li H (2019) An injectable continuous stratified structurally and functionally

Precision Medicine in Tissue Engineering on Bone biomimetic construct for enhancing osteochondral regeneration. Biomaterials 192:149 32. Lu HH, Thomopoulos S (2013) Functional attachment of soft tissues to bone: development, healing, and tissue engineering. Annu Rev Biomed Eng 15:201 33. Chen CH, Chen WJ, Shih CH, Chou SW (2004) Arthroscopic anterior cruciate ligament reconstruction with periosteum-enveloping hamstring tendon graft. Knee Surg Sports Traumatol Arthrosc 12:398 34. Yamazaki S, Yasuda K, Tomita F, Tohyama H, Minami A (2005) The effect of transforming growth factor-beta1 on intraosseous healing of flexor tendon autograft replacement of anterior cruciate ligament in dogs. Arthroscopy 21:1034 35. Chen CH, Liu HW, Tsai CL, Yu CM, Lin IH, Hsiue GH (2008) Photoencapsulation of bone morphogenetic protein-2 and periosteal progenitor cells improve tendon graft healing in a bone tunnel. Am J Sports Med 36:461 36. Mutsuzaki H, Sakane M, Hattori S, Kobayashi H, Ochiai N (2009) Firm anchoring between a calcium phosphate-hybridized tendon and bone for anterior cruciate ligament reconstruction in a goat model. Biomed Mater 4:45013 37. Li D, Ouyang G, McCann JT, Xia Y (2005) Collecting electrospun nanofibers with patterned electrodes. Nano Lett 5:913 38. Liu W, Lipner J, Moran CH, Feng L, Li X, Thomopoulos S, Xia Y (2015) Generation of electrospun nanofibers with controllable degrees of crimping through a simple, plasticizer-based treatment. Adv Mater 27:2583 39. Ker D, Wang D, Behn AW, Wang E, Zhang X, Zhou BY, Mercado-Pagan AE, Kim S, Kleimeyer J, Gharaibeh B, Shanjani Y, Nelson D, Safran M, Cheung E, Campbell P, Yang YP (2018) Functionally graded, boneand tendon-like polyurethane for rotator cuff repair. Adv Funct Mater 28:1707107 40. Grigoryan B, Paulsen SJ, Corbett DC, Sazer DW, Fortin CL, Zaita AJ, Greenfield PT, Calafat NJ, Gounley JP, Ta AH, Johansson F, Randles A, Rosenkrantz JE, Louis-Rosenberg JD, Galie PA, Stevens KR, Miller JS (2019) Multivascular networks and functional intravascular topologies within biocompatible hydrogels. Science 364:458 41. Gantenbein S, Masania K, Woigk W, Sesseg J, Tervoort TA, Studart AR (2018) Threedimensional printing of hierarchical liquidcrystal-polymer structures. Nature 561:226


42. Choi NW, Cabodi M, Held B, Gleghorn JP, Bonassar LJ, Stroock AD (2007) Microfluidic scaffolds for tissue engineering. Nat Mater 6:908 43. Costantini M, Jaroszewicz J, Kozon L, Szlazak K, Swieszkowski W, Garstecki P, Stubenrauch C, Barbetta A, Guzowski J (2019) 3D-printing of functionally graded porous materials using on-demand reconfigurable microfluidics. Angew Chem Int Ed Engl 58:7620 44. Pacelli S, Basu S, Whitlow J, Chakravarti A, Acosta F, Varshney A, Modaresi S, Berkland C, Paul A (2017) Strategies to develop endogenous stem cell-recruiting bioactive materials for tissue repair and regeneration. Adv Drug Deliv Rev 120:50 45. Cox TR, Erler JT (2011) Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer. Dis Model Mech 4:165 46. Wu L, Magaz A, Wang T, Liu C, Darbyshire A, Loizidou M, Emberton M, Birchall M, Song W (2018) Stiffness memory of indirectly 3D-printed elastomer nanohybrid regulates chondrogenesis and osteogenesis of human mesenchymal stem cells. Biomaterials 186:64 47. Mansour A, Abu-Nada L, Al-Waeli H, Mezour MA, Abdallah MN, Kinsella JM, KortMascort J, Henderson JE, Ramirez-Garcialuna JL, Tran SD, Elkashty OA, Mousa A, El-Hadad AA, Taqi D, Al-Hamad F, Alageel O, Kaartinen MT, Tamimi F (2019) Bone extracts immunomodulate and enhance the regenerative performance of dicalcium phosphates bioceramics. Acta Biomater 89:343 48. Chen H, Zhang J, Li X, Liu L, Zhang X, Ren D, Ma C, Zhang L, Fei Z, Xu T (2019) Multi-level customized 3D printing for autogenous implants in skull tissue engineering. Biofabrication 11:45007 49. Shi W, Sun M, Hu X, Ren B, Cheng J, Li C, Duan X, Fu X, Zhang J, Chen H, Ao Y (2017) Structurally and functionally optimized silkfibroin-gelatin scaffold using 3d printing to repair cartilage injury in vitro and in vivo. Adv Mater 29:1701089 50. Pati F, Song TH, Rijal G, Jang J, Kim SW, Cho DW (2015) Ornamenting 3D printed scaffolds with cell-laid extracellular matrix for bone tissue regeneration. Biomaterials 37:230 51. Namkoong B, Guven S, Ramesan S, Liaudanskaya V, Abzhanov A, Demirci U (2016) Recapitulating cranial osteogenesis with neural crest cells in 3-D microenvironments. Acta Biomater 31:301

Chapter 19 Progress of Clinical Application for Ex Vivo Lung Perfusion (EVLP) in Lung Transplantation Chang Gu, Xufeng Pan, and Jianxin Shi Abstract In recent years, medical advances make lung transplantation become a standard treatment for terminal lung diseases (such as emphysema, pulmonary fibrosis, pulmonary cystic fibrosis, and pulmonary arterial hypertension) that cannot be cured by drugs or surgery (Lund et al., J Heart Lung Transplant 34:1244, 2015). However, the current number of donor lungs that meet the transplant criteria is no longer sufficient for transplanting, causing some patients to die while waiting for a suitable lung. Current methods for improving the situation of shortage of lung transplant donors include the use of donation after cardiac death (DCD) donors, smoker donors, and Ex Vivo Lung Perfusion (EVLP). Among them, EVLP is a technique for extending lung preservation time and repairing lung injury in the field of lung transplantation. By continuously assessing and improving the function of marginal donor lungs, EVLP increases the number of lungs that meet the transplant criteria and, to some extent, alleviates the current situation of shortage of donor lungs. This chapter reviews the clinical application and research progress of EVLP in the field of lung transplantation. Key words Ex vivo lung perfusion, Lung transplantation, Emphysema, Pulmonary fibrosis, Pulmonary cystic fibrosis, Pulmonary arterial hypertension


The Current Situation of Lung Transplant Donor Shortage Since Hardy successfully implemented the first human lung transplant in 1963, the medical level has been increasing and lung transplantation has become the standard treatment for terminalstage lung disease that cannot be treated by drugs or surgery. The current ideal donor lung criteria include: (1) age < 55 years of age; (2) smoking index 60 min in cardiac death donation [16]. The criteria for inclusion of “marginal donor lungs” after 4–6 h of clinical EVLP were: (1) oxygenation index >400 mmHg, (2) stable or improved pulmonary artery pressure, (3) stable or improved airway pressure, (4) stable or improved lung compliance; exclusion criteria were: (1) oxygenation index