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Nguyen TH, Doan NNT, Tran TH, Huynh LAK, Doan PL, Nguyen THH, Nguyen VTC, Nguyen GTH, Nguyen HN, Giang H, Tran LS, Phan MD. Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks. J Transl Med 2024; 22:618. [PMID: 38961476 PMCID: PMC11223394 DOI: 10.1186/s12967-024-05416-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.
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Affiliation(s)
| | | | - Trung Hieu Tran
- Medical Genetics Institute, Gene Solutions, Ho Chi Minh, Vietnam
| | - Le Anh Khoa Huynh
- Medical Genetics Institute, Gene Solutions, Ho Chi Minh, Vietnam
- Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, USA
| | - Phuoc Loc Doan
- Medical Genetics Institute, Gene Solutions, Ho Chi Minh, Vietnam
| | | | | | | | | | - Hoa Giang
- Medical Genetics Institute, Gene Solutions, Ho Chi Minh, Vietnam
| | - Le Son Tran
- Medical Genetics Institute, Gene Solutions, Ho Chi Minh, Vietnam
| | - Minh Duy Phan
- Medical Genetics Institute, Gene Solutions, Ho Chi Minh, Vietnam.
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2
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Chen K, He Y, Wang W, Yuan X, Carbone DP, Yang F. Development of new techniques and clinical applications of liquid biopsy in lung cancer management. Sci Bull (Beijing) 2024; 69:1556-1568. [PMID: 38641511 DOI: 10.1016/j.scib.2024.03.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/12/2023] [Accepted: 01/17/2024] [Indexed: 04/21/2024]
Abstract
Lung cancer is an exceedingly malignant tumor reported as having the highest morbidity and mortality of any cancer worldwide, thus posing a great threat to global health. Despite the growing demand for precision medicine, current methods for early clinical detection, treatment and prognosis monitoring in lung cancer are hampered by certain bottlenecks. Studies have found that during the formation and development of a tumor, molecular substances carrying tumor-related genetic information can be released into body fluids. Liquid biopsy (LB), a method for detecting these tumor-related markers in body fluids, maybe a way to make progress in these bottlenecks. In recent years, LB technology has undergone rapid advancements. Therefore, this review will provide information on technical updates to LB and its potential clinical applications, evaluate its effectiveness for specific applications, discuss the existing limitations of LB, and present a look forward to possible future clinical applications. Specifically, this paper will introduce technical updates from the prospectives of engineering breakthroughs in the detection of membrane-based LB biomarkers and other improvements in sequencing technology. Additionally, it will summarize the latest applications of liquid biopsy for the early detection, diagnosis, treatment, and prognosis of lung cancer. We will present the interconnectedness of clinical and laboratory issues and the interplay of technology and application in LB today.
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Affiliation(s)
- Kezhong Chen
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China
| | - Yue He
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China
| | - Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China
| | - Xiaoqiu Yuan
- Peking University Health Science Center, Beijing 100191, China
| | - David P Carbone
- Thoracic Oncology Center, Ohio State University, Columbus 43026, USA.
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute & Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Beijing 100044, China.
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3
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Hashimoto T, Nakamura Y, Oki E, Kobayashi S, Yuda J, Shibuki T, Bando H, Yoshino T. Bridging horizons beyond CIRCULATE-Japan: a new paradigm in molecular residual disease detection via whole genome sequencing-based circulating tumor DNA assay. Int J Clin Oncol 2024; 29:495-511. [PMID: 38551727 PMCID: PMC11043144 DOI: 10.1007/s10147-024-02493-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 02/16/2024] [Indexed: 04/26/2024]
Abstract
Circulating tumor DNA (ctDNA) is the fraction of cell-free DNA in patient blood that originates from a tumor. Advances in DNA sequencing technologies and our understanding of the molecular biology of tumors have increased interest in exploiting ctDNA to facilitate detection of molecular residual disease (MRD). Analysis of ctDNA as a promising MRD biomarker of solid malignancies has a central role in precision medicine initiatives exemplified by our CIRCULATE-Japan project involving patients with resectable colorectal cancer. Notably, the project underscores the prognostic significance of the ctDNA status at 4 weeks post-surgery and its correlation to adjuvant therapy efficacy at interim analysis. This substantiates the hypothesis that MRD is a critical prognostic indicator of relapse in patients with colorectal cancer. Despite remarkable advancements, challenges endure, primarily attributable to the exceedingly low ctDNA concentration in peripheral blood, particularly in scenarios involving low tumor shedding and the intrinsic error rates of current sequencing technologies. These complications necessitate more sensitive and sophisticated assays to verify the clinical utility of MRD across all solid tumors. Whole genome sequencing (WGS)-based tumor-informed MRD assays have recently demonstrated the ability to detect ctDNA in the parts-per-million range. This review delineates the current landscape of MRD assays, highlighting WGS-based approaches as the forefront technique in ctDNA analysis. Additionally, it introduces our upcoming endeavor, WGS-based pan-cancer MRD detection via ctDNA, in our forthcoming project, SCRUM-Japan MONSTAR-SCREEN-3.
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Affiliation(s)
- Tadayoshi Hashimoto
- Translational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Yoshiaki Nakamura
- Translational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Eiji Oki
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shin Kobayashi
- Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Junichiro Yuda
- Department of Hematology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Taro Shibuki
- Translational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan
| | - Hideaki Bando
- Translational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Takayuki Yoshino
- Department of Gastroenterology and Gastrointestinal Oncology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
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Bao Y, Zhang D, Guo H, Ma W. Beyond blood: Advancing the frontiers of liquid biopsy in oncology and personalized medicine. Cancer Sci 2024; 115:1060-1072. [PMID: 38308498 PMCID: PMC11007055 DOI: 10.1111/cas.16097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/04/2024] Open
Abstract
Liquid biopsy is emerging as a pivotal tool in precision oncology, offering a noninvasive and comprehensive approach to cancer diagnostics and management. By harnessing biofluids such as blood, urine, saliva, cerebrospinal fluid, and pleural effusions, this technique profiles key biomarkers including circulating tumor DNA, circulating tumor cells, microRNAs, and extracellular vesicles. This review discusses the extended scope of liquid biopsy, highlighting its indispensable role in enhancing patient outcomes through early detection, continuous monitoring, and tailored therapy. While the advantages are notable, we also address the challenges, emphasizing the necessity for precision, cost-effectiveness, and standardized methodologies in its broader application. The future trajectory of liquid biopsy is set to expand its reach in personalized medicine, fueled by technological advancements and collaborative research.
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Affiliation(s)
- Ying Bao
- Key Laboratory for Translational MedicineThe First Hospital Affiliated with Huzhou UniversityHuzhouChina
| | - Dejing Zhang
- Department of General SurgeryPuyang Oilfield General HospitalPuyangChina
| | - Huihui Guo
- Key Laboratory for Translational MedicineThe First Hospital Affiliated with Huzhou UniversityHuzhouChina
| | - Wenxue Ma
- Department of Medicine, Moores Cancer Center, and Sanford Stem Cell InstituteUniversity of California San DiegoLa JollaCaliforniaUSA
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Wu X, Li W, Tu H. Big data and artificial intelligence in cancer research. Trends Cancer 2024; 10:147-160. [PMID: 37977902 DOI: 10.1016/j.trecan.2023.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.
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Affiliation(s)
- Xifeng Wu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
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Li M, Zhou T, Han M, Wang H, Bao P, Tao Y, Chen X, Wu G, Liu T, Wang X, Lu Q, Zhu Y, Lu ZJ. cfOmics: a cell-free multi-Omics database for diseases. Nucleic Acids Res 2024; 52:D607-D621. [PMID: 37757861 PMCID: PMC10767897 DOI: 10.1093/nar/gkad777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.
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Affiliation(s)
- Mingyang Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100084, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Tianxiu Zhou
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Mingfei Han
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Hongke Wang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Pengfei Bao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Yuhuan Tao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Xiaoqing Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Guansheng Wu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Tianyou Liu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaojuan Wang
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, No. 168, Litang Road, Changping District, Beijing 102218, China
| | - Qian Lu
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, No. 168, Litang Road, Changping District, Beijing 102218, China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Zhi John Lu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
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7
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Fan R, Chen L, Zhao S, Yang H, Li Z, Qian Y, Ma H, Liu X, Wang C, Liang X, Bai J, Xie J, Fan X, Xie Q, Hao X, Wang C, Yang S, Gao Y, Bai H, Dou X, Liu J, Wu L, Jiang G, Xia Q, Zheng D, Rao H, Xia J, Shang J, Gao P, Xie D, Yu Y, Yang Y, Gao H, Liu Y, Sun A, Jiang Y, Yu Y, Niu J, Sun J, Wang H, Hou J. Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures. J Hepatol 2023; 79:933-944. [PMID: 37302583 DOI: 10.1016/j.jhep.2023.05.039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/09/2023] [Accepted: 05/23/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND & AIMS Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures. METHODS A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development. RESULTS We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p = 0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001). CONCLUSIONS aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance. IMPACT AND IMPLICATIONS In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.
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Affiliation(s)
- Rong Fan
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lei Chen
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Institute/hospital, Shanghai, China
| | - Siru Zhao
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hao Yang
- Berry Oncology Corporation, Beijing, China
| | | | - Yunsong Qian
- Hepatology Department, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Hong Ma
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiaolong Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Chuanxin Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xieer Liang
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jian Bai
- Berry Oncology Corporation, Beijing, China
| | - Jianping Xie
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaotang Fan
- Department of Hepatology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Qing Xie
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Hao
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | | | - Song Yang
- Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yanhang Gao
- The First Hospital of Jilin University, Changchun, China
| | - Honglian Bai
- The Department of Infectious Disease, The First People's Hospital of Foshan, Foshan, China
| | - Xiaoguang Dou
- Department of Infectious Diseases, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jingfeng Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Lin Wu
- Berry Oncology Corporation, Beijing, China
| | - Guoqing Jiang
- Department of Hepatobiliary Surgery, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Qi Xia
- Department of Infectious Diseases, Zhejiang University 1st Affiliated Hospital, Hangzhou, China
| | - Dan Zheng
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huiying Rao
- Peking University Hepatology Institute, Peking University People's Hospital, Beijing, China
| | - Jie Xia
- Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jia Shang
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Pujun Gao
- The First Hospital of Jilin University, Changchun, China
| | - Dongying Xie
- Department of Infectious Diseases, Sun Yat-Sen University 3rd Affiliated Hospital, Guangzhou, China
| | - Yanlong Yu
- Chifeng Clinical Medical School of Inner, Mongolia Medical University, Chifeng, China
| | | | | | - Yali Liu
- Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Aimin Sun
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Yongfang Jiang
- Liver Disease Research Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yanyan Yu
- Department of Infectious Diseases, First Hospital of Peking University, Beijing, China
| | - Junqi Niu
- The First Hospital of Jilin University, Changchun, China
| | - Jian Sun
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Hongyang Wang
- International Cooperation Laboratory on Signal Transduction, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Institute/hospital, Shanghai, China.
| | - Jinlin Hou
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
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Xue R, Yang L, Yang M, Xue F, Li L, Liu M, Ren Y, Qi Y, Zhao J. Circulating cell-free DNA sequencing for early detection of lung cancer. Expert Rev Mol Diagn 2023; 23:589-606. [PMID: 37318381 DOI: 10.1080/14737159.2023.2224504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
INTRODUCTION Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing. AREAS COVERED In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions. EXPERT OPINION Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.
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Affiliation(s)
- Ruyue Xue
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lu Yang
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing, Jiangsu, China
- Nanjing Simcere Medical Laboratory Science Co, Ltd, Nanjing, Jiangsu, China
| | - Meijia Yang
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Fangfang Xue
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing, Jiangsu, China
- Nanjing Simcere Medical Laboratory Science Co, Ltd, Nanjing, Jiangsu, China
| | - Lifeng Li
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Manjiao Liu
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing, Jiangsu, China
- Nanjing Simcere Medical Laboratory Science Co, Ltd, Nanjing, Jiangsu, China
| | - Yong Ren
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd, Nanjing, Jiangsu, China
- Nanjing Simcere Medical Laboratory Science Co, Ltd, Nanjing, Jiangsu, China
| | - Yu Qi
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jie Zhao
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Wang W, He Y, Yang F, Chen K. Current and emerging applications of liquid biopsy in pan-cancer. Transl Oncol 2023; 34:101720. [PMID: 37315508 DOI: 10.1016/j.tranon.2023.101720] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023] Open
Abstract
Cancer morbidity and mortality are growing rapidly worldwide and it is urgent to develop a convenient and effective method that can identify cancer patients at an early stage and predict treatment outcomes. As a minimally invasive and reproducible tool, liquid biopsy (LB) offers the opportunity to detect, analyze and monitor cancer in any body fluids including blood, complementing the limitations of tissue biopsy. In liquid biopsy, circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are the two most common biomarkers, displaying great potential in the clinical application of pan-cancer. In this review, we expound the samples, targets, and newest techniques in liquid biopsy and summarize current clinical applications in several specific cancers. Besides, we put forward a bright prospect for further exploring the emerging application of liquid biopsy in the field of pan-cancer precision medicine.
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Affiliation(s)
- Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, 11 Xizhimen South Street, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute, Beijing 100044, China
| | - Yue He
- Department of Thoracic Surgery, Peking University People's Hospital, 11 Xizhimen South Street, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute, Beijing 100044, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, 11 Xizhimen South Street, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute, Beijing 100044, China
| | - Kezhong Chen
- Department of Thoracic Surgery, Peking University People's Hospital, 11 Xizhimen South Street, Beijing 100044, China; Peking University People's Hospital Thoracic Oncology Institute, Beijing 100044, China.
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Rolfo C, Russo A, Malapelle U. The next frontier of early lung cancer and minimal residual disease detection: is multiomics the solution? EBioMedicine 2023; 92:104605. [PMID: 37156171 PMCID: PMC10195843 DOI: 10.1016/j.ebiom.2023.104605] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/10/2023] Open
Affiliation(s)
- Christian Rolfo
- Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | | | - Umberto Malapelle
- Department of Public Health, University of Naples Federico II, Naples, Italy
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Souza VGP, Forder A, Brockley LJ, Pewarchuk ME, Telkar N, de Araújo RP, Trejo J, Benard K, Seneda AL, Minutentag IW, Erkan M, Stewart GL, Hasimoto EN, Garnis C, Lam WL, Martinez VD, Reis PP. Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis. Int J Mol Sci 2023; 24:ijms24108894. [PMID: 37240238 DOI: 10.3390/ijms24108894] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.
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Affiliation(s)
- Vanessa G P Souza
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Molecular Oncology Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
| | - Aisling Forder
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Liam J Brockley
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | | | - Nikita Telkar
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- British Columbia Children's Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Rachel Paes de Araújo
- Molecular Oncology Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
| | - Jessica Trejo
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Katya Benard
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Ana Laura Seneda
- Molecular Oncology Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
| | - Iael W Minutentag
- Molecular Oncology Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
| | - Melis Erkan
- Department of Pathology and Laboratory Medicine, IWK Health Centre, Halifax, NS B3K 6R8, Canada
- Department of Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS B3K 6R8, Canada
- Beatrice Hunter Cancer Research Institute, Halifax, NS B3H 4R2, Canada
| | - Greg L Stewart
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Erica N Hasimoto
- Department of Surgery and Orthopedics, Faculty of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
| | - Cathie Garnis
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Division of Otolaryngology, Department of Surgery, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Wan L Lam
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Victor D Martinez
- Department of Pathology and Laboratory Medicine, IWK Health Centre, Halifax, NS B3K 6R8, Canada
- Department of Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS B3K 6R8, Canada
- Beatrice Hunter Cancer Research Institute, Halifax, NS B3H 4R2, Canada
| | - Patricia P Reis
- Molecular Oncology Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
- Department of Surgery and Orthopedics, Faculty of Medicine, São Paulo State University (UNESP), Botucatu, SP 18618-687, Brazil
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