1
|
Tsering T, Nadeau A, Wu T, Dickinson K, Burnier JV. Extracellular vesicle-associated DNA: ten years since its discovery in human blood. Cell Death Dis 2024; 15:668. [PMID: 39266560 PMCID: PMC11393322 DOI: 10.1038/s41419-024-07003-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 07/29/2024] [Accepted: 08/14/2024] [Indexed: 09/14/2024]
Abstract
Extracellular vesicles (EVs) have emerged as key players in intercellular communication, facilitating the transfer of crucial cargo between cells. Liquid biopsy, particularly through the isolation of EVs, has unveiled a rich source of potential biomarkers for health and disease, encompassing proteins and nucleic acids. A milestone in this exploration occurred a decade ago with the identification of extracellular vesicle-associated DNA (EV-DNA) in the bloodstream of a patient diagnosed with pancreatic cancer. Subsequent years have witnessed substantial advancements, deepening our insights into the molecular intricacies of EV-DNA emission, detection, and analysis. Understanding the complexities surrounding the release of EV-DNA and addressing the challenges inherent in EV-DNA research are pivotal steps toward enhancing liquid biopsy-based strategies. These strategies, crucial for the detection and monitoring of various pathological conditions, particularly cancer, rely on a comprehensive understanding of why and how EV-DNA is released. In our review, we aim to provide a thorough summary of a decade's worth of research on EV-DNA. We will delve into diverse mechanisms of EV-DNA emission, its potential as a biomarker, its functional capabilities, discordant findings in the field, and the hurdles hindering its clinical application. Looking ahead to the next decade, we envision that advancements in EV isolation and detection techniques, coupled with improved standardization and data sharing, will catalyze the development of novel strategies exploiting EV-DNA as both a source of biomarkers and therapeutic targets.
Collapse
Affiliation(s)
- Thupten Tsering
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Pathology, McGill University, Montreal, QC, Canada
| | - Amélie Nadeau
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Pathology, McGill University, Montreal, QC, Canada
| | - Tad Wu
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Pathology, McGill University, Montreal, QC, Canada
| | - Kyle Dickinson
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Julia V Burnier
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
- Department of Pathology, McGill University, Montreal, QC, Canada.
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
| |
Collapse
|
2
|
Xue R, Li X, Yang L, Yang M, Zhang B, Zhang X, Li L, Duan X, Yan R, He X, Cui F, Wang L, Wang X, Wu M, Zhang C, Zhao J. Evaluation and integration of cell-free DNA signatures for detection of lung cancer. Cancer Lett 2024; 604:217216. [PMID: 39233043 DOI: 10.1016/j.canlet.2024.217216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 09/06/2024]
Abstract
Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915-0.981), with a sensitivity of 79.3 % (95 % CI: 71.5-87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6-100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3-85.8 %) in stage I patients, 100 % (95 % CI: 100.0-100.0 %) in stage II patients, 100 % (95 % CI: 100.0-100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%-100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.
Collapse
Affiliation(s)
- Ruyue Xue
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaomin Li
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China; Cancer Center, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Lu Yang
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China; Cancer Center, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Meijia Yang
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bei Zhang
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China
| | - Xu Zhang
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lifeng Li
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoran Duan
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui Yan
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xianying He
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fangfang Cui
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Linlin Wang
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoqiang Wang
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China
| | - Mengsi Wu
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China
| | - Chao Zhang
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China
| | - Jie Zhao
- Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| |
Collapse
|
3
|
Li Y, Xie F, Zheng Q, Zhang Y, Li W, Xu M, He Q, Li Y, Sun J. Non-invasive diagnosis of pulmonary nodules by circulating tumor DNA methylation: A prospective multicenter study. Lung Cancer 2024; 195:107930. [PMID: 39146624 DOI: 10.1016/j.lungcan.2024.107930] [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: 04/03/2024] [Revised: 07/08/2024] [Accepted: 08/10/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs. METHODS A blood-based assay ("LUNG-TRAC") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort. RESULTS The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets. CONCLUSION The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer. CLINICAL TRIAL REGISTRATION www. CLINICALTRIALS gov (NCT03989219).
Collapse
Affiliation(s)
- Ying Li
- Department of Respiratory Endoscopy, Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
| | - Fangfang Xie
- Department of Respiratory Endoscopy, Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
| | - Qiang Zheng
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yujun Zhang
- Department of Respiratory Endoscopy, Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China
| | - Wei Li
- Singlera Genomics (Shanghai) Ltd., Shanghai, China
| | - Minjie Xu
- Singlera Genomics (Shanghai) Ltd., Shanghai, China
| | - Qiye He
- Singlera Genomics (Shanghai) Ltd., Shanghai, China
| | - Yuan Li
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jiayuan Sun
- Department of Respiratory Endoscopy, Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
| |
Collapse
|
4
|
Roznik K, Andargie TE, Johnston TS, Gordon O, Wang Y, Akindele NP, Persaud D, Antar AAR, Manabe YC, Zhou W, Ji H, Agbor-Enoh S, Karaba AH, Thompson EA, Cox AL. Emergency Myelopoiesis Distinguishes Multisystem Inflammatory Syndrome in Children From Pediatric Severe Coronavirus Disease 2019. J Infect Dis 2024; 230:e305-e317. [PMID: 38299308 PMCID: PMC11326850 DOI: 10.1093/infdis/jiae032] [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: 06/20/2023] [Revised: 12/18/2023] [Accepted: 01/25/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Multisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory condition caused by recent infection with severe acute respiratory syndrome coronavirus 2, but the underlying immunological mechanisms driving this distinct syndrome are unknown. METHODS We utilized high-dimensional flow cytometry, cell-free (cf) DNA, and cytokine and chemokine profiling to identify mechanisms of critical illness distinguishing MIS-C from severe acute coronavirus disease 2019 (SAC). RESULTS Compared to SAC, MIS-C patients demonstrated profound innate immune cell death and features of emergency myelopoiesis (EM), an understudied phenomenon observed in severe inflammation. EM signatures were characterized by fewer mature myeloid cells in the periphery and decreased expression of HLA-DR and CD86 on antigen-presenting cells. Interleukin 27 (IL-27), a cytokine known to drive hematopoietic stem cells toward EM, was increased in MIS-C, and correlated with immature cell signatures in MIS-C. Upon recovery, EM signatures decreased and IL-27 plasma levels returned to normal levels. Despite profound lymphopenia, we report a lack of cfDNA released by adaptive immune cells and increased CCR7 expression on T cells indicative of egress out of peripheral blood. CONCLUSIONS Immune cell signatures of EM combined with elevated innate immune cell-derived cfDNA levels distinguish MIS-C from SAC in children and provide mechanistic insight into dysregulated immunity contributing toward MIS-C, offering potential diagnostic and therapeutic targets.
Collapse
Affiliation(s)
- Katerina Roznik
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| | - Temesgen E Andargie
- Genomic Research Alliance for Transplantation and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- Department of Biology, Howard University, Washington, District of Columbia
| | - T Scott Johnston
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| | - Oren Gordon
- Infectious Diseases Unit, Department of Pediatrics, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Israel
- Department of Pediatrics, Johns Hopkins University School of Medicine
| | - Yi Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore
| | - Nadine Peart Akindele
- Department of Pediatrics, Johns Hopkins University School of Medicine
- Center for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland
| | - Deborah Persaud
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health
- Department of Pediatrics, Johns Hopkins University School of Medicine
| | - Annukka A R Antar
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| | - Weiqiang Zhou
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore
| | - Sean Agbor-Enoh
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
- Genomic Research Alliance for Transplantation and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - Andrew H Karaba
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| | - Elizabeth A Thompson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| | - Andrea L Cox
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore
| |
Collapse
|
5
|
Ryu H, Kim JH, Kim YJ, Jeon H, Kim BC, Jeon Y, Kim Y, Bak H, Kang Y, Kim C, Um H, Ahn JH, Hyun H, Kim BC, Song I, Jeon S, Bhak J, Han EC. Quantification method of ctDNA using cell-free DNA methylation profile for noninvasive screening and monitoring of colon cancer. Clin Epigenetics 2024; 16:95. [PMID: 39030645 PMCID: PMC11264732 DOI: 10.1186/s13148-024-01708-9] [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: 12/26/2023] [Accepted: 07/09/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Colon cancer ranks as the second most lethal form of cancer globally. In recent years, there has been active investigation into using the methylation profile of circulating tumor DNA (ctDNA), derived from blood, as a promising indicator for diagnosing and monitoring colon cancer. RESULTS We propose a liquid biopsy-based epigenetic method developed by utilizing 49 patients and 260 healthy controls methylation profile data to screen and monitor colon cancer. Our method initially identified 901 colon cancer-specific hypermethylated (CaSH) regions in the tissues of the 49 cancer patients. We then used these CaSH regions to accurately quantify the amount of circulating tumor DNA (ctDNA) in the blood samples of these same patients, utilizing cell-free DNA methylation profiles. Notably, the methylation profiles of ctDNA in the blood exhibited high sensitivity (82%) and specificity (93%) in distinguishing patients with colon cancer from the control group, with an area under the curve of 0.903. Furthermore, we confirm that our method for ctDNA quantification is effective for monitoring cancer patients and can serve as a valuable tool for postoperative prognosis. CONCLUSIONS This study demonstrated a successful application of the quantification of ctDNA among cfDNA using the original cancer tissue-derived CaSH region for screening and monitoring colon cancer.
Collapse
Affiliation(s)
- Hyojung Ryu
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | - Ji-Hoon Kim
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
- GenomeLab, Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Yeo Jin Kim
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | - Hahyeon Jeon
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | | | - Yeonsu Jeon
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | | | - Hyebin Bak
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | | | - Changjae Kim
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | - Hyojin Um
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | - Ji-Hye Ahn
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | - Hwi Hyun
- Clinomics, Inc., Ulsan, 44919, Republic of Korea
| | | | - Inho Song
- Division of Colorectal Surgery, Department of Surgery, Dongnam Institute of Radiological and Medical Sciences, Busan, 46033, Republic of Korea
| | - Sungwon Jeon
- Clinomics, Inc., Ulsan, 44919, Republic of Korea.
- Geromics Inc., Suwon, 16229, Republic of Korea.
| | - Jong Bhak
- Clinomics, Inc., Ulsan, 44919, Republic of Korea.
- GenomeLab, Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.
- Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.
- Geromics Inc., Suwon, 16229, Republic of Korea.
- Personal Genomics Institute (PGI), Genome Research Foundation (GRF), Cheongju, 28160, Republic of Korea.
| | - Eon Chul Han
- Division of Colorectal Surgery, Department of Surgery, Dongnam Institute of Radiological and Medical Sciences, Busan, 46033, Republic of Korea.
| |
Collapse
|
6
|
Xiong D, Han T, Li Y, Hong Y, Li S, Li X, Tao W, Huang YS, Chen W, Li C. TOTEM: a multi-cancer detection and localization approach using circulating tumor DNA methylation markers. BMC Cancer 2024; 24:840. [PMID: 39009999 PMCID: PMC11247868 DOI: 10.1186/s12885-024-12626-7] [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: 09/15/2023] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Detection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers. METHODS We performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach. RESULTS A total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort. CONCLUSIONS TOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.
Collapse
Affiliation(s)
- Dalin Xiong
- Department of Thoracic Surgery, Yan'an Hospital of Kunming Medical University, Kunming, 650051, China
| | - Tiancheng Han
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Yulong Li
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Yuanyuan Hong
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Suxing Li
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Xi Li
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Wenhui Tao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Yu S Huang
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Weizhi Chen
- Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China
| | - Chunguang Li
- Department of Colorectal and Anal Surgery/Hubei Key Laboratory of Intestinal and Colorectal Diseases, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
- Clinical Center of Intestinal and Colorectal Diseases of Hubei Province, Wuhan, 430071, China.
- Quality Control Center of Colorectal and Anal Surgery of Health Commission of Hubei Province, Wuhan, 430071, China.
| |
Collapse
|
7
|
Ma Y, Li Y, Wen Z, Lai Y, Kamila K, Gao J, Xu WY, Gong C, Chen F, Shi L, Zhang Y, Chen H, Zhu M. Genome wide identification of novel DNA methylation driven prognostic markers in colorectal cancer. Sci Rep 2024; 14:15654. [PMID: 38977698 PMCID: PMC11231291 DOI: 10.1038/s41598-024-60351-9] [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: 01/12/2024] [Accepted: 04/22/2024] [Indexed: 07/10/2024] Open
Abstract
Colorectal cancer (CRC) stands as a major contributor to cancer-related fatalities within China. There is an urgent need to identify accurate biomarkers for recurrence predicting in CRC. Reduced representation bisulfite sequencing was used to perform a comparative analysis of methylation profiles in tissue samples from 30 recurrence to 30 non-recurrence patients with CRC. Least absolute shrinkage and selection operator method was performed to select the differential methylation regions (DMRs) and built a DNA methylation classifier for predicting recurrence. Based on the identified top DMRs, a methylation classifier was built and consisted of eight hypermethylated DMRs in CRC. The DNA methylation classifier showed high accuracy for predicting recurrence with an area under the receiver operator characteristic curve of 0.825 (95% CI 0.680-0.970). The Kaplan-Meier survival analysis demonstrated that CRC patients with high methylation risk score, evaluated by the DNA methylation classifier, had poorer survival than low risk score (Hazard Ratio 4.349; 95% CI 1.783-10.61, P = 0.002). And only CRC patients with low methylation risk score could acquire benefit from adjuvant therapy. The DNA methylation classifier has been proved as crucial biomarkers for predicting recurrence and exhibited promising prognostic value after curative surgery in patients with CRC.
Collapse
Affiliation(s)
- Yuhua Ma
- Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay Central Hospital, Karamay, 834099, Xin Jiang, China
- Department of Pathology, Karamay Central Hospital, No. 67, Junggar Road, Karamay, 834099, Xin Jiang, China
| | - Yuanxin Li
- Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay Central Hospital, Karamay, 834099, Xin Jiang, China
- Department of Pathology, Karamay Central Hospital, No. 67, Junggar Road, Karamay, 834099, Xin Jiang, China
| | - Zhahong Wen
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201318, China
| | - Yining Lai
- Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay Central Hospital, Karamay, 834099, Xin Jiang, China
- Department of Pathology, Karamay Central Hospital, No. 67, Junggar Road, Karamay, 834099, Xin Jiang, China
| | - Kulaixijiang Kamila
- Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay Central Hospital, Karamay, 834099, Xin Jiang, China
- Department of Pathology, Karamay Central Hospital, No. 67, Junggar Road, Karamay, 834099, Xin Jiang, China
| | - Jing Gao
- Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay Central Hospital, Karamay, 834099, Xin Jiang, China
- Department of Pathology, Karamay Central Hospital, No. 67, Junggar Road, Karamay, 834099, Xin Jiang, China
| | - Wang-Yang Xu
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201318, China
| | | | - Feifan Chen
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201318, China
| | - Liuqing Shi
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201318, China
| | - Yunzhi Zhang
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201318, China
| | - Hanzhang Chen
- Department of Pathology, Zhabei Central Hospital of Shanghai, No. 619, Zhonghua New Road, Jing'an District, Shanghai, 200070, China.
| | - Min Zhu
- Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay Central Hospital, Karamay, 834099, Xin Jiang, China.
- Department of Pathology, Karamay Central Hospital, No. 67, Junggar Road, Karamay, 834099, Xin Jiang, China.
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
Zhu Q, Xie J, Mei W, Zeng C. Methylated circulating tumor DNA in hepatocellular carcinoma: A comprehensive analysis of biomarker potential and clinical implications. Cancer Treat Rev 2024; 128:102763. [PMID: 38763055 DOI: 10.1016/j.ctrv.2024.102763] [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: 02/12/2024] [Revised: 04/30/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
The intricate epigenetic landscape of hepatocellular carcinoma (HCC) is profoundly influenced by alterations in DNA methylation patterns. Understanding these alterations is crucial for unraveling the molecular mechanisms underlying HCC pathogenesis. Methylated circulating tumor DNA (ctDNA) presents itself as an encouraging avenue for biomarker discovery and holds substantial clinical implications in HCC management. This review comprehensively outlines the studies concerning DNA methylation in HCC and underscores the significance of methylated ctDNA within this context. Moreover, a variety of cfDNA methylation-based methodologies, such as 5hmC profiling, bisulfite-based, restriction enzyme-dependent, and enrichment-based methods, provide in-depth insights into the molecular pathology of HCC. Additionally, the integration of methylated ctDNA analysis into clinical practice represents a significant advancement in personalized HCC management. By facilitating cancer screening, prognosis assessment, and treatment response prediction, the utilization of methylated ctDNA signifies a pivotal stride toward enhancing patient care and outcomes in HCC.
Collapse
Affiliation(s)
- Qian Zhu
- Department of Gastroenterology, Shenzhen Longhua District Central Hospital, Shenzhen 518110, China
| | - Jiaqi Xie
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Wuxuan Mei
- Xianning Medical College, Hubei University of Science and Technology, Xianning 437100, China
| | - Changchun Zeng
- Department of Medical Laboratory, Shenzhen Longhua District Central Hospital, Guangdong Medical University, Shenzhen 518110, China.
| |
Collapse
|
10
|
Cheng JC, Swarup N, Morselli M, Huang WL, Aziz M, Caggiano C, Kordi M, Patel A, Chia D, Kim Y, Li F, Wei F, Zaitlen N, Krysan K, Dubinett S, Pellegrini M, Wong DW. Single-stranded pre-methylated 5mC adapters uncover the methylation profile of plasma ultrashort Single-stranded cell-free DNA. Nucleic Acids Res 2024; 52:e50. [PMID: 38797520 PMCID: PMC11194076 DOI: 10.1093/nar/gkae276] [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: 10/08/2023] [Revised: 03/21/2024] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Whole-genome bisulfite sequencing (BS-Seq) measures cytosine methylation changes at single-base resolution and can be used to profile cell-free DNA (cfDNA). In plasma, ultrashort single-stranded cfDNA (uscfDNA, ∼50 nt) has been identified together with 167 bp double-stranded mononucleosomal cell-free DNA (mncfDNA). However, the methylation profile of uscfDNA has not been described. Conventional BS-Seq workflows may not be helpful because bisulfite conversion degrades larger DNA into smaller fragments, leading to erroneous categorization as uscfDNA. We describe the '5mCAdpBS-Seq' workflow in which pre-methylated 5mC (5-methylcytosine) single-stranded adapters are ligated to heat-denatured cfDNA before bisulfite conversion. This method retains only DNA fragments that are unaltered by bisulfite treatment, resulting in less biased uscfDNA methylation analysis. Using 5mCAdpBS-Seq, uscfDNA had lower levels of DNA methylation (∼15%) compared to mncfDNA and was enriched in promoters and CpG islands. Hypomethylated uscfDNA fragments were enriched in upstream transcription start sites (TSSs), and the intensity of enrichment was correlated with expressed genes of hemopoietic cells. Using tissue-of-origin deconvolution, we inferred that uscfDNA is derived primarily from eosinophils, neutrophils, and monocytes. As proof-of-principle, we show that characteristics of the methylation profile of uscfDNA can distinguish non-small cell lung carcinoma from non-cancer samples. The 5mCAdpBS-Seq workflow is recommended for any cfDNA methylation-based investigations.
Collapse
Affiliation(s)
- Jordan C Cheng
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Neeti Swarup
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Marco Morselli
- Department of Molecular, Cell, and Developmental Biology, Life Sciences Division, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
| | - Wei-Lun Huang
- Center of Applied Nanomedicine, National Cheng Kung University, Tainan, Taiwan
| | - Mohammad Aziz
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christa Caggiano
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Misagh Kordi
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Abhijit A Patel
- Department of Therapeutic Radiology, Yale University, New Haven, CT, USA
| | - David Chia
- Department of Pathology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yong Kim
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Feng Li
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Fang Wei
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Noah Zaitlen
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kostyantyn Krysan
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Steve Dubinett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Matteo Pellegrini
- Department of Molecular, Cell, and Developmental Biology, Life Sciences Division, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - David T W Wong
- School of Dentistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| |
Collapse
|
11
|
Fu Y, Aganezov S, Mahmoud M, Beaulaurier J, Juul S, Treangen TJ, Sedlazeck FJ. MethPhaser: methylation-based long-read haplotype phasing of human genomes. Nat Commun 2024; 15:5327. [PMID: 38909018 PMCID: PMC11193733 DOI: 10.1038/s41467-024-49588-0] [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: 09/12/2023] [Accepted: 06/11/2024] [Indexed: 06/24/2024] Open
Abstract
The assignment of variants across haplotypes, phasing, is crucial for predicting the consequences, interaction, and inheritance of mutations and is a key step in improving our understanding of phenotype and disease. However, phasing is limited by read length and stretches of homozygosity along the genome. To overcome this limitation, we designed MethPhaser, a method that utilizes methylation signals from Oxford Nanopore Technologies to extend Single Nucleotide Variation (SNV)-based phasing. We demonstrate that haplotype-specific methylations extensively exist in Human genomes and the advent of long-read technologies enabled direct report of methylation signals. For ONT R9 and R10 cell line data, we increase the phase length N50 by 78%-151% at a phasing accuracy of 83.4-98.7% To assess the impact of tissue purity and random methylation signals due to inactivation, we also applied MethPhaser on blood samples from 4 patients, still showing improvements over SNV-only phasing. MethPhaser further improves phasing across HLA and multiple other medically relevant genes, improving our understanding of how mutations interact across multiple phenotypes. The concept of MethPhaser can also be extended to non-human diploid genomes. MethPhaser is available at https://github.com/treangenlab/methphaser .
Collapse
Affiliation(s)
- Yilei Fu
- Department of Computer Science, Rice University, Houston, TX, USA
| | | | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | | | - Sissel Juul
- Oxford Nanopore Technologies Inc, New York, NY, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX, USA.
- Department of Bioengineering, Rice University, Houston, TX, USA.
| | - Fritz J Sedlazeck
- Department of Computer Science, Rice University, Houston, TX, USA.
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
| |
Collapse
|
12
|
Yu Z, Li J, Deng Y, Li C, Ye M, Zhang Y, Huang Y, Wang X, Zhao X, Liu J, Liu Z, Yin X, Mei L, Hou Y, Hu Q, Huang Y, Wang R, Fu H, Qiu R, Xu J, Gong Z, Zhang D, Zhang X. Lung tumor discrimination by deep neural network model CanDo via DNA methylation in bronchial lavage. iScience 2024; 27:110079. [PMID: 38883836 PMCID: PMC11176796 DOI: 10.1016/j.isci.2024.110079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/02/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
Bronchoscopic-assisted discrimination of lung tumors presents challenges, especially in cases with contraindications or inaccessible lesions. Through meta-analysis and validation using the HumanMethylation450 database, this study identified methylation markers for molecular discrimination in lung tumors and designed a sequencing panel. DNA samples from 118 bronchial washing fluid (BWF) specimens underwent enrichment via multiplex PCR before targeted methylation sequencing. The Recursive Feature Elimination Cross-Validation and deep neural network algorithm established the CanDo classification model, which incorporated 11 methylation features (including 8 specific to the TBR1 gene), demonstrating a sensitivity of 98.6% and specificity of 97.8%. In contrast, bronchoscopic rapid on-site evaluation (bronchoscopic-ROSE) had lower sensitivity (87.7%) and specificity (80%). Further validation in 33 individuals confirmed CanDo's discriminatory potential, particularly in challenging cases for bronchoscopic-ROSE due to pathological complexity. CanDo serves as a valuable complement to bronchoscopy for the discriminatory diagnosis and stratified management of lung tumors utilizing BWF specimens.
Collapse
Affiliation(s)
- Zezhong Yu
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jieyi Li
- Jiaxing Key Laboratory of Precision Medicine and Companion Diagnostics, Jiaxing Yunying Medical Inspection Co., Ltd., Jiaxing 314033, China
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Yi Deng
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chun Li
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Maosong Ye
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yong Zhang
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yuqing Huang
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Xintao Wang
- Jiaxing Key Laboratory of Precision Medicine and Companion Diagnostics, Jiaxing Yunying Medical Inspection Co., Ltd., Jiaxing 314033, China
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Xiaokai Zhao
- Jiaxing Key Laboratory of Precision Medicine and Companion Diagnostics, Jiaxing Yunying Medical Inspection Co., Ltd., Jiaxing 314033, China
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Jie Liu
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Zilong Liu
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xia Yin
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Lijiang Mei
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yingyong Hou
- Department of Pathology Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Qin Hu
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yao Huang
- Department of Pulmonary, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210046, China
| | - Rongping Wang
- Department of Pulmonary, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210046, China
| | - Huiyu Fu
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Rumeng Qiu
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Jiahuan Xu
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
| | - Ziying Gong
- Jiaxing Key Laboratory of Precision Medicine and Companion Diagnostics, Jiaxing Yunying Medical Inspection Co., Ltd., Jiaxing 314033, China
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
- Department of R&D, Shanghai Yunying Biopharmaceutical Technology Co., Ltd., Shanghai 201612, China
| | - Daoyun Zhang
- Jiaxing Key Laboratory of Precision Medicine and Companion Diagnostics, Jiaxing Yunying Medical Inspection Co., Ltd., Jiaxing 314033, China
- Department of R&D, Zhejiang Yunying Medical Technology Co., Ltd., Jiaxing 314006, China
- Department of R&D, Shanghai Yunying Biopharmaceutical Technology Co., Ltd., Shanghai 201612, China
| | - Xin Zhang
- Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Department of Pulmonary, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210046, China
| |
Collapse
|
13
|
Gerke MB, Jansen CS, Bilen MA. Circulating Tumor DNA in Genitourinary Cancers: Detection, Prognostics, and Therapeutic Implications. Cancers (Basel) 2024; 16:2280. [PMID: 38927984 PMCID: PMC11201475 DOI: 10.3390/cancers16122280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
CtDNA is emerging as a non-invasive clinical detection method for several cancers, including genitourinary (GU) cancers such as prostate cancer, bladder cancer, and renal cell carcinoma (RCC). CtDNA assays have shown promise in early detection of GU cancers, providing prognostic information, assessing real-time treatment response, and detecting residual disease and relapse. The ease of obtaining a "liquid biopsy" from blood or urine in GU cancers enhances its potential to be used as a biomarker. Interrogating these "liquid biopsies" for ctDNA can then be used to detect common cancer mutations, novel genomic alterations, or epigenetic modifications. CtDNA has undergone investigation in numerous clinical trials, which could address clinical needs in GU cancers, for instance, earlier detection in RCC, therapeutic response prediction in castration-resistant prostate cancer, and monitoring for recurrence in bladder cancers. The utilization of liquid biopsy for ctDNA analysis provides a promising method of advancing precision medicine within the field of GU cancers.
Collapse
Affiliation(s)
- Margo B. Gerke
- Emory University School of Medicine, Atlanta, GA 30322, USA; (M.B.G.); (C.S.J.)
| | - Caroline S. Jansen
- Emory University School of Medicine, Atlanta, GA 30322, USA; (M.B.G.); (C.S.J.)
- Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
| | - Mehmet A. Bilen
- Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| |
Collapse
|
14
|
Cui XL, Nie J, Zhu H, Kowitwanich K, Beadell AV, West-Szymanski DC, Zhang Z, Dougherty U, Kwesi A, Deng Z, Li Y, Meng D, Roggin K, Barry T, Owyang R, Fefferman B, Zeng C, Gao L, Zhao CWT, Malina Y, Wei J, Weigert M, Kang W, Goel A, Chiu BCH, Bissonnette M, Zhang W, Chen M, He C. LABS: linear amplification-based bisulfite sequencing for ultrasensitive cancer detection from cell-free DNA. Genome Biol 2024; 25:157. [PMID: 38877540 PMCID: PMC11177480 DOI: 10.1186/s13059-024-03262-2] [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: 11/14/2023] [Accepted: 04/29/2024] [Indexed: 06/16/2024] Open
Abstract
Methylation-based liquid biopsies show promises in detecting cancer using circulating cell-free DNA; however, current limitations impede clinical application. Most assays necessitate substantial DNA inputs, posing challenges. Additionally, underrepresented tumor DNA fragments may go undetected during exponential amplification steps of traditional sequencing methods. Here, we report linear amplification-based bisulfite sequencing (LABS), enabling linear amplification of bisulfite-treated DNA fragments in a genome-wide, unbiased fashion, detecting cancer abnormalities with sub-nanogram inputs. Applying LABS to 100 patient samples revealed cancer-specific patterns, copy number alterations, and enhanced cancer detection accuracy by identifying tissue-of-origin and immune cell composition.
Collapse
Affiliation(s)
- Xiao-Long Cui
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ji Nie
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Houxiang Zhu
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Krissana Kowitwanich
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Alana V Beadell
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Diana C West-Szymanski
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Zhou Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Akushika Kwesi
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Zifeng Deng
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Yan Li
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Danqing Meng
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Kevin Roggin
- Department of Surgery, The University of Chicago, Chicago, IL, USA
| | - Teresa Barry
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Ryan Owyang
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Ben Fefferman
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Chang Zeng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Lu Gao
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Carolyn W T Zhao
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Yuri Malina
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Jiangbo Wei
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA
| | - Melanie Weigert
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, The University of Chicago, Chicago, IL, USA
| | - Wenjun Kang
- Department of Medicine, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Ajay Goel
- City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Brian C-H Chiu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Marc Bissonnette
- Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Mengjie Chen
- Department of Medicine, The University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA.
| | - Chuan He
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA.
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, USA.
| |
Collapse
|
15
|
Zhen L, Tang X, Xu Z, Huang Y, Qian X, Lin H, Li C, Cui R, Fang H, Yang H, Qiu J, Fang Z, Peng X, Jin Y, Nie J, Guo S, Wang Y, Zhong M, Gu H, Xu H. Early Diagnosis of Colorectal Cancer Based on Bisulfite-free Site-specific Methylation Identification PCR Strategy: High-Sensitivity, Accuracy, and Primary Medical Accessibility. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2401137. [PMID: 38868913 DOI: 10.1002/advs.202401137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/26/2024] [Indexed: 06/14/2024]
Abstract
Due to its decade-long progression, colorectal cancer (CRC) is most suitable for population screening to achieve a significant reduction in its incidence and mortality. DNA methylation has emerged as a potential marker for the early detection of CRC. However, the current mainstream methylation detection method represented by bisulfite conversion has issues such as tedious operation, DNA damage, and unsatisfactory sensitivity. Herein, a new high-performance CRC screening tool based on the promising specific terminal-mediated polymerase chain reaction (STEM-PCR) strategy is developed. CRC-related methylation-specific candidate CpG sites are first prescreened through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using self-developed bioinformatics. Next, 9 homebrew colorectal cancer DNA methylated STEM‒PCR assays (ColoC-mSTEM) with high sensitivity (0.1%) and high specificity are established to identify candidate sites. The clinical diagnostic performance of these selected methylation sites is confirmed and validated by a case-control study. The optimized diagnostic model has an overall sensitivity of 94.8% and a specificity of 95.0% for detecting early-stage CRC. Taken together, ColoC-mSTEM, based on a single methylation-specific site, is a promising diagnostic approach for the early detection of CRC which is perfectly suitable for the screening needs of CRC in primary healthcare institutions.
Collapse
Affiliation(s)
- Linqing Zhen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Xinlu Tang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhengguo Xu
- Medical community of Linhai First People's Hospital, Zhejiang, 317000, P. R. China
| | - Yizhou Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Xiaohua Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Haiping Lin
- Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Chao Li
- Medical community of Linhai First People's Hospital, Zhejiang, 317000, P. R. China
| | - Rong Cui
- Jiading Hospital of Traditional Chinese medicine, Shanghai, 201800, P. R. China
| | - Hongsheng Fang
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, P. R. China
| | - Hao Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Jiani Qiu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Zhaoqi Fang
- Shanghai Healzone Biotechnology Co., LTD, Shanghai, 200000, P. R. China
| | - Xiaohuan Peng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Yifeng Jin
- Jiading Hospital of Traditional Chinese medicine, Shanghai, 201800, P. R. China
| | - Jianing Nie
- Shanghai Healzone Biotechnology Co., LTD, Shanghai, 200000, P. R. China
| | - Shiwei Guo
- Medical community of Linhai First People's Hospital, Zhejiang, 317000, P. R. China
| | - Yuguang Wang
- Medical community of Linhai First People's Hospital, Zhejiang, 317000, P. R. China
| | - Ming Zhong
- Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Hongchen Gu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| | - Hong Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Hefei Cancer Early Screening Innovation Technology Institute, Anhui Province, China
| |
Collapse
|
16
|
Unterman I, Avrahami D, Katsman E, Triche TJ, Glaser B, Berman BP. CelFiE-ISH: a probabilistic model for multi-cell type deconvolution from single-molecule DNA methylation haplotypes. Genome Biol 2024; 25:151. [PMID: 38858759 PMCID: PMC11163775 DOI: 10.1186/s13059-024-03275-x] [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: 08/20/2023] [Accepted: 05/13/2024] [Indexed: 06/12/2024] Open
Abstract
Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and of tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.
Collapse
Affiliation(s)
- Irene Unterman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dana Avrahami
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Efrat Katsman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Timothy J Triche
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, USA
| | - Benjamin Glaser
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Benjamin P Berman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
| |
Collapse
|
17
|
He B, Yao H, Yi C. Advances in the joint profiling technologies of 5mC and 5hmC. RSC Chem Biol 2024; 5:500-507. [PMID: 38846078 PMCID: PMC11151843 DOI: 10.1039/d4cb00034j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/21/2024] [Indexed: 06/09/2024] Open
Abstract
DNA cytosine methylation, a crucial epigenetic modification, involves the dynamic interplay of 5-methylcytosine (5mC) and its oxidized form, 5-hydroxymethylcytosine (5hmC), generated by ten-eleven translocation (TET) DNA dioxygenases. This process is central to regulating gene expression, influencing critical biological processes such as development, disease progression, and aging. Recognizing the distinct functions of 5mC and 5hmC, researchers often employ restriction enzyme-based or chemical treatment methods for their simultaneous measurement from the same genomic sample. This enables a detailed understanding of the relationship between these modifications and their collective impact on cellular function. This review focuses on summarizing the technologies for detecting 5mC and 5hmC together but also discusses the limitations and potential future directions in this evolving field.
Collapse
Affiliation(s)
- Bo He
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing China
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies Chengdu China
| | - Haojun Yao
- College of Chemistry and Chemical Engineering, Hunan University Changsha China
| | - Chengqi Yi
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing China
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University Beijing China
- Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University Beijing China
| |
Collapse
|
18
|
Chen Y, Wang X, Luo S, Dai C, Wu Y, Zhao J, Liu W, Kong D, Yang Y, Geng L, Liu Y, Wei D. Electrically Oriented Antibodies on Transistor for Monitoring Several Copies of Methylated DNA. Anal Chem 2024; 96:8300-8307. [PMID: 38747393 DOI: 10.1021/acs.analchem.3c04670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
An antibody transistor is a promising biosensing platform for the diagnosis and monitoring of various diseases. Nevertheless, the low concentration and short half-life of biomarkers require biodetection at the trace-molecule level, which remains a challenge for existing antibody transistors. Herein, we demonstrate a graphene field-effect transistor (gFET) with electrically oriented antibody probes (EOA-gFET) for monitoring several copies of methylated DNA. The electric field confines the orientation of antibody probes on graphene and diminishes the distance between graphene and methylated DNAs captured by antibodies, generating more induced charges on graphene and amplifying the electric signal. EOA-gFET realizes a limit of detection (LoD) of ∼0.12 copy μL-1, reaching the lowest LoD reported before. EOA-gFET shows a distinguishable signal for liver cancer clinical serum samples within ∼6 min, which proves its potential as a powerful tool for disease screening and diagnosis.
Collapse
Affiliation(s)
- Yiheng Chen
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Xuejun Wang
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Shi Luo
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Changhao Dai
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Yungen Wu
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Junhong Zhao
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Wentao Liu
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Derong Kong
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Yuetong Yang
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| | - Li Geng
- Department of Special Treatment, Eastern Hepatobiliary Surgery Hospital, Shanghai, 200438, China
| | - Yunqi Liu
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
- Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Dacheng Wei
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Laboratory of Molecular Materials and Devices, Department of Material Science, Fudan University, Shanghai 200433, China
| |
Collapse
|
19
|
Karetnikov DI, Romanov SE, Baklaushev VP, Laktionov PP. Age Prediction Using DNA Methylation Heterogeneity Metrics. Int J Mol Sci 2024; 25:4967. [PMID: 38732187 PMCID: PMC11084170 DOI: 10.3390/ijms25094967] [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: 03/30/2024] [Revised: 04/27/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.
Collapse
Affiliation(s)
- Dmitry I. Karetnikov
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Stanislav E. Romanov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Vladimir P. Baklaushev
- Federal Center for Brain and Neurotechnologies, Federal Medical and Biological Agency of Russia, 117513 Moscow, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Department of Medical Nanobiotechnology, Medical and Biological Faculty, Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, 117997 Moscow, Russia
| | - Petr P. Laktionov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| |
Collapse
|
20
|
Zhou W, Johnson BK, Morrison J, Beddows I, Eapen J, Katsman E, Semwal A, Habib W, Heo L, Laird P, Berman B, Triche T, Shen H. BISCUIT: an efficient, standards-compliant tool suite for simultaneous genetic and epigenetic inference in bulk and single-cell studies. Nucleic Acids Res 2024; 52:e32. [PMID: 38412294 PMCID: PMC11014253 DOI: 10.1093/nar/gkae097] [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: 05/22/2023] [Revised: 01/23/2024] [Accepted: 02/08/2024] [Indexed: 02/29/2024] Open
Abstract
Data from both bulk and single-cell whole-genome DNA methylation experiments are under-utilized in many ways. This is attributable to inefficient mapping of methylation sequencing reads, routinely discarded genetic information, and neglected read-level epigenetic and genetic linkage information. We introduce the BISulfite-seq Command line User Interface Toolkit (BISCUIT) and its companion R/Bioconductor package, biscuiteer, for simultaneous extraction of genetic and epigenetic information from bulk and single-cell DNA methylation sequencing. BISCUIT's performance, flexibility and standards-compliant output allow large, complex experimental designs to be characterized on clinical timescales. BISCUIT is particularly suited for processing data from single-cell DNA methylation assays, with its excellent scalability, efficiency, and ability to greatly enhance mappability, a key challenge for single-cell studies. We also introduce the epiBED format for single-molecule analysis of coupled epigenetic and genetic information, facilitating the study of cellular and tissue heterogeneity from DNA methylation sequencing.
Collapse
Affiliation(s)
- Wanding Zhou
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Benjamin K Johnson
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Jacob Morrison
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Ian Beddows
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - James Eapen
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Efrat Katsman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Ayush Semwal
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Walid Abi Habib
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Lyong Heo
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Peter W Laird
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Benjamin P Berman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Timothy J Triche
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Hui Shen
- Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA
| |
Collapse
|
21
|
McNamara ME, Jain SS, Oza K, Muralidaran V, Kiliti AJ, McDeed AP, Patil D, Cui Y, Schmidt MO, Riegel AT, Kroemer AH, Wellstein A. Circulating, cell-free methylated DNA indicates cellular sources of allograft injury after liver transplant. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.04.588176. [PMID: 38617373 PMCID: PMC11014558 DOI: 10.1101/2024.04.04.588176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Post-transplant complications reduce allograft and recipient survival. Current approaches for detecting allograft injury non-invasively are limited and do not differentiate between cellular mechanisms. Here, we monitor cellular damages after liver transplants from cell-free DNA (cfDNA) fragments released from dying cells into the circulation. We analyzed 130 blood samples collected from 44 patients at different time points after transplant. Sequence-based methylation of cfDNA fragments were mapped to patterns established to identify cell types in different organs. For liver cell types DNA methylation patterns and multi-omic data integration show distinct enrichment in open chromatin and regulatory regions functionally important for the respective cell types. We find that multi-tissue cellular damages post-transplant recover in patients without allograft injury during the first post-operative week. However, sustained elevation of hepatocyte and biliary epithelial cfDNA beyond the first week indicates early-onset allograft injury. Further, cfDNA composition differentiates amongst causes of allograft injury indicating the potential for non-invasive monitoring and timely intervention.
Collapse
Affiliation(s)
- Megan E. McNamara
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Sidharth S. Jain
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Kesha Oza
- MedStar Georgetown Transplant Institute, MedStar Georgetown University Hospital and Center for Translational Transplant Medicine, Georgetown University Medical Center, Washington, DC, USA
- Department of General Surgery, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Vinona Muralidaran
- MedStar Georgetown Transplant Institute, MedStar Georgetown University Hospital and Center for Translational Transplant Medicine, Georgetown University Medical Center, Washington, DC, USA
| | - Amber J. Kiliti
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - A. Patrick McDeed
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Digvijay Patil
- MedStar Georgetown Transplant Institute, MedStar Georgetown University Hospital and Center for Translational Transplant Medicine, Georgetown University Medical Center, Washington, DC, USA
| | - Yuki Cui
- MedStar Georgetown Transplant Institute, MedStar Georgetown University Hospital and Center for Translational Transplant Medicine, Georgetown University Medical Center, Washington, DC, USA
| | - Marcel O. Schmidt
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Anna T. Riegel
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Alexander H.K. Kroemer
- MedStar Georgetown Transplant Institute, MedStar Georgetown University Hospital and Center for Translational Transplant Medicine, Georgetown University Medical Center, Washington, DC, USA
| | - Anton Wellstein
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| |
Collapse
|
22
|
Wang F, Zhao D, Xu WY, Liu Y, Sun H, Lu S, Ji Y, Jiang J, Chen Y, He Q, Gong C, Liu R, Su Z, Dong Y, Yan Z, Liu L. Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study. BMC Med 2024; 22:147. [PMID: 38561764 PMCID: PMC10986011 DOI: 10.1186/s12916-024-03368-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy. METHODS This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes. RESULTS The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies. CONCLUSIONS This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.
Collapse
Affiliation(s)
- Feihang Wang
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Danyang Zhao
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Wang-Yang Xu
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203, China
| | - Yiying Liu
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203, China
| | - Huiyi Sun
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Shanshan Lu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yuan Ji
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jingjing Jiang
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yi Chen
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
| | - Qiye He
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203, China
| | | | - Rui Liu
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203, China
| | - Zhixi Su
- Singlera Genomics (Shanghai) Ltd., Shanghai, 201203, China.
| | - Yi Dong
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
| | - Zhiping Yan
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
| | - Lingxiao Liu
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- National Clinical Research Center for Interventional Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
| |
Collapse
|
23
|
Zhao G, Jiang R, Shi Y, Gao S, Wang D, Li Z, Zhou Y, Sun J, Wu W, Peng J, Kuang T, Rong Y, Yuan J, Zhu S, Jin G, Wang Y, Lou W. Circulating cell-free DNA methylation-based multi-omics analysis allows early diagnosis of pancreatic ductal adenocarcinoma. Mol Oncol 2024. [PMID: 38561976 DOI: 10.1002/1878-0261.13643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 02/29/2024] [Accepted: 03/15/2024] [Indexed: 04/04/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a 5-year survival rate of 7.2% in China. However, effective approaches for diagnosis of PDAC are limited. Tumor-originating genomic and epigenomic aberration in circulating free DNA (cfDNA) have potential as liquid biopsy biomarkers for cancer diagnosis. Our study aims to assess the feasibility of cfDNA-based liquid biopsy assay for PDAC diagnosis. In this study, we performed parallel genomic and epigenomic profiling of plasma cfDNA from Chinese PDAC patients and healthy individuals. Diagnostic models were built to distinguish PDAC patients from healthy individuals. Cancer-specific changes in cfDNA methylation landscape were identified, and a diagnostic model based on six methylation markers achieved high sensitivity (88.7% for overall cases and 78.0% for stage I patients) and specificity (96.8%), outperforming the mutation-based model significantly. Moreover, the combination of the methylation-based model with carbohydrate antigen 19-9 (CA19-9) levels further improved the performance (sensitivity: 95.7% for overall cases and 95.5% for stage I patients; specificity: 93.3%). In conclusion, our findings suggest that both methylation-based and integrated liquid biopsy assays hold promise as non-invasive tools for detection of PDAC.
Collapse
Affiliation(s)
- Guochao Zhao
- Department of Pancreatic Surgery, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | | | - Ying Shi
- Envelope Health Biotechnology Co. Ltd., BGI-Shenzhen, China
| | - Suizhi Gao
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital Affiliated to Navy Medical University, Shanghai, China
| | - Dansong Wang
- Department of Pancreatic Surgery, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhilong Li
- Envelope Health Biotechnology Co. Ltd., BGI-Shenzhen, China
| | - Yuhong Zhou
- Department of Medical Oncology, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jianlong Sun
- Envelope Health Biotechnology Co. Ltd., BGI-Shenzhen, China
| | - Wenchuan Wu
- Department of Pancreatic Surgery, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jiaxi Peng
- Envelope Health Biotechnology Co. Ltd., BGI-Shenzhen, China
| | - Tiantao Kuang
- Department of Pancreatic Surgery, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yefei Rong
- Department of Pancreatic Surgery, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jie Yuan
- The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Shida Zhu
- BGI Genomics, BGI-Shenzhen, China
- Shenzhen Engineering Laboratory for Innovative Molecular Diagnostics, BGI-Shenzhen, China
| | - Gang Jin
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital Affiliated to Navy Medical University, Shanghai, China
| | - Yuying Wang
- Envelope Health Biotechnology Co. Ltd., BGI-Shenzhen, China
| | - Wenhui Lou
- Department of Pancreatic Surgery, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
24
|
Wang M, Bissonnette N, Laterrière M, Dudemaine PL, Gagné D, Roy JP, Sirard MA, Ibeagha-Awemu EM. DNA methylation haplotype block signatures responding to Staphylococcus aureus subclinical mastitis and association with production and health traits. BMC Biol 2024; 22:65. [PMID: 38486242 PMCID: PMC10941392 DOI: 10.1186/s12915-024-01843-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/09/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND DNA methylation has been documented to play vital roles in diseases and biological processes. In bovine, little is known about the regulatory roles of DNA methylation alterations on production and health traits, including mastitis. RESULTS Here, we employed whole-genome DNA methylation sequencing to profile the DNA methylation patterns of milk somatic cells from sixteen cows with naturally occurring Staphylococcus aureus (S. aureus) subclinical mastitis and ten healthy control cows. We observed abundant DNA methylation alterations, including 3,356,456 differentially methylated cytosines and 153,783 differential methylation haplotype blocks (dMHBs). The DNA methylation in regulatory regions, including promoters, first exons and first introns, showed global significant negative correlations with gene expression status. We identified 6435 dMHBs located in the regulatory regions of differentially expressed genes and significantly correlated with their corresponding genes, revealing their potential effects on transcriptional activities. Genes harboring DNA methylation alterations were significantly enriched in multiple immune- and disease-related pathways, suggesting the involvement of DNA methylation in regulating host responses to S. aureus subclinical mastitis. In addition, we found nine discriminant signatures (differentiates cows with S. aureus subclinical mastitis from healthy cows) representing the majority of the DNA methylation variations related to S. aureus subclinical mastitis. Validation of seven dMHBs in 200 cows indicated significant associations with mammary gland health (SCC and SCS) and milk production performance (milk yield). CONCLUSIONS In conclusion, our findings revealed abundant DNA methylation alterations in milk somatic cells that may be involved in regulating mammary gland defense against S. aureus infection. Particularly noteworthy is the identification of seven dMHBs showing significant associations with mammary gland health, underscoring their potential as promising epigenetic biomarkers. Overall, our findings on DNA methylation alterations offer novel insights into the regulatory mechanisms of bovine subclinical mastitis, providing further avenues for the development of effective control measures.
Collapse
Affiliation(s)
- Mengqi Wang
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
- Department of Animal Science, Laval University, Quebec, QC, Canada
| | - Nathalie Bissonnette
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
| | - Mario Laterrière
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec, QC, Canada
| | - Pier-Luc Dudemaine
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
| | - David Gagné
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec, QC, Canada
| | - Jean-Philippe Roy
- Department of Clinical Sciences, Université de Montréal, St-Hyacinthe, QC, Canada
| | | | - Eveline M Ibeagha-Awemu
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada.
| |
Collapse
|
25
|
Wang M, Yang N, Laterrière M, Gagné D, Omonijo F, Ibeagha-Awemu EM. Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis. J Anim Sci Biotechnol 2024; 15:46. [PMID: 38481273 PMCID: PMC10938844 DOI: 10.1186/s40104-024-00996-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/14/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes. RESULTS Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as 'Staphylococcus aureus infection pathway' and 'natural killer cell mediated cytotoxicity pathway', etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows. CONCLUSION The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.
Collapse
Affiliation(s)
- Mengqi Wang
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
- Department of Animal Science, Université Laval, Quebec City, QC, Canada
| | - Naisu Yang
- Department of Animal Science, Université Laval, Quebec City, QC, Canada
| | - Mario Laterrière
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec City, QC, Canada
| | - David Gagné
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec City, QC, Canada
| | - Faith Omonijo
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
| | - Eveline M Ibeagha-Awemu
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada.
| |
Collapse
|
26
|
Wang Z, Fu G, Ma G, Wang C, Wang Q, Lu C, Fu L, Zhang X, Cong B, Li S. The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction. Hum Genet 2024; 143:401-421. [PMID: 38507014 DOI: 10.1007/s00439-024-02659-0] [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: 09/12/2023] [Accepted: 02/13/2024] [Indexed: 03/22/2024]
Abstract
As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.
Collapse
Affiliation(s)
- Zhonghua Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Guangping Fu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Guanju Ma
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Chunyan Wang
- Physical Examination Center of Shijiazhuang People's Hospital, Shijiazhuang, 050011, Hebei, China
| | - Qian Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Chaolong Lu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Lihong Fu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Xiaojing Zhang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Shujin Li
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China.
| |
Collapse
|
27
|
Galant N, Nicoś M, Kuźnar-Kamińska B, Krawczyk P. Variant Allele Frequency Analysis of Circulating Tumor DNA as a Promising Tool in Assessing the Effectiveness of Treatment in Non-Small Cell Lung Carcinoma Patients. Cancers (Basel) 2024; 16:782. [PMID: 38398173 PMCID: PMC10887123 DOI: 10.3390/cancers16040782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Despite the different possible paths of treatment, lung cancer remains one of the leading causes of death in oncological patients. New tools guiding the therapeutic process are under scientific investigation, and one of the promising indicators of the effectiveness of therapy in patients with NSCLC is variant allele frequency (VAF) analysis. VAF is a metric characterized as the measurement of the specific variant allele proportion within a genomic locus, and it can be determined using methods based on NGS or PCR. It can be assessed using not only tissue samples but also ctDNA (circulating tumor DNA) isolated from liquid biopsy. The non-invasive characteristic of liquid biopsy enables a more frequent collection of material and increases the potential of VAF analysis in monitoring therapy. Several studies have been performed on patients with NSCLC to evaluate the possibility of VAF usage. The research carried out so far demonstrates that the evaluation of VAF dynamics may be useful in monitoring tumor progression, remission, and recurrence during or after treatment. Moreover, the use of VAF analysis appears to be beneficial in making treatment decisions. However, several issues require better understanding and standardization before VAF testing can be implemented in clinical practice. In this review, we discuss the difficulties in the application of ctDNA VAF analysis in clinical routine, discussing the diagnostic and methodological challenges in VAF measurement in liquid biopsy. We highlight the possible applications of VAF-based measurements that are under consideration in clinical trials in the monitoring of personalized treatments for patients with NSCLC.
Collapse
Affiliation(s)
- Natalia Galant
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-059 Lublin, Poland
| | - Marcin Nicoś
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-059 Lublin, Poland
| | - Barbara Kuźnar-Kamińska
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, 61-710 Poznan, Poland;
| | - Paweł Krawczyk
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, 20-059 Lublin, Poland
| |
Collapse
|
28
|
Zhang K, Fu R, Liu R, Su Z. Circulating cell-free DNA-based multi-cancer early detection. Trends Cancer 2024; 10:161-174. [PMID: 37709615 DOI: 10.1016/j.trecan.2023.08.010] [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: 07/03/2023] [Revised: 08/03/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023]
Abstract
Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.
Collapse
Affiliation(s)
- Kai Zhang
- Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Chaoyang District, Beijing 100021, China
| | - Ruiqing Fu
- Singlera Genomics Ltd, Shanghai 201203, China
| | - Rui Liu
- Singlera Genomics Ltd, Shanghai 201203, China
| | - Zhixi Su
- Singlera Genomics Ltd, Shanghai 201203, China.
| |
Collapse
|
29
|
Xu D, Lai Y, Liu H, Li H, Feng N, Liu Y, Gong C, Zhang Y, Zhou J, Shen Y. A diagnostic model based on DNA methylation haplotype block characteristics for identifying papillary thyroid carcinoma from thyroid adenoma. Transl Res 2024; 264:76-84. [PMID: 37863284 DOI: 10.1016/j.trsl.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/28/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023]
Abstract
Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer. Methylation of some genes plays a crucial role in the tendency to malignancy as well as poor prognosis of thyroid cancer, suggesting that methylation features can serve as complementary markers for molecular diagnosis. In this study, we aimed to develop and validate a diagnostic model for PTC based on DNA methylation markers. A total of 142 thyroid nodule tissue samples containing 84 cases of PTC and 58 cases of thyroid adenoma (TA) were collected for reduced representation bisulfite sequencing (RRBS) and subsequent analysis. The diagnostic model was constructed by the logistic regression (LR) method followed by 5-cross validation and based on 94 tissue methylation haplotype block (MHB) markers. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.974 (95% CI, 0.964-0.981) on 108 training samples and 0.917 (95% CI, 0.864-0.973) on 27 independent testing samples. The diagnostic model scores showed significantly high in males (P = 0.0016), age ≤ 45 years (P = 0.026), high body mass index (BMI) (P = 0.040), lymph node metastasis (P = 0.00052) and larger nodules (P = 0.0017) in the PTC group, and the risk score of this diagnostic model showed significantly high in recurrent PTC group (P = 0.0005). These results suggest that the diagnostic model can be expected to be a powerful tool for PTC diagnosis and there are more potential clinical applications of methylation markers to be excavated.
Collapse
Affiliation(s)
- Dong Xu
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160 Pujian Road, Shanghai, 200127, China
| | - Yi Lai
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160 Pujian Road, Shanghai, 200127, China; Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160 Pujian Road, Shanghai, 200127, China
| | - Hongmei Liu
- Singlera Genomics (Shanghai) Ltd., 8th Floor, Building 1, Lane 500, Furonghua Road, Pudong, Shanghai 201328, China
| | - He Li
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160 Pujian Road, Shanghai, 200127, China
| | - Ningning Feng
- Singlera Genomics (Shanghai) Ltd., 8th Floor, Building 1, Lane 500, Furonghua Road, Pudong, Shanghai 201328, China
| | - Yiying Liu
- Singlera Genomics (Shanghai) Ltd., 8th Floor, Building 1, Lane 500, Furonghua Road, Pudong, Shanghai 201328, China
| | - Chengxiang Gong
- Singlera Genomics (Shanghai) Ltd., 8th Floor, Building 1, Lane 500, Furonghua Road, Pudong, Shanghai 201328, China
| | - Yunzhi Zhang
- Singlera Genomics (Shanghai) Ltd., 8th Floor, Building 1, Lane 500, Furonghua Road, Pudong, Shanghai 201328, China
| | - Jiaqing Zhou
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160 Pujian Road, Shanghai, 200127, China.
| | - Yuling Shen
- Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160 Pujian Road, Shanghai, 200127, China.
| |
Collapse
|
30
|
Alekberli T, Ohana BL, Zemmour H, Khader R, Shemer R, Dor Y, Landesberg G. The correlation between high-sensitivity troponin-T and cell-free cardiac DNA in the blood of patients undergoing noncardiac, predominantly vascular surgery. J Int Med Res 2024; 52:3000605241229638. [PMID: 38340803 PMCID: PMC10859063 DOI: 10.1177/03000605241229638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/13/2024] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVE To present a novel method that uses an epigenetic fingerprint to measure changes in plasma concentrations of cardiac-specific cell-free DNA (CS-cfDNA) as a marker of myocardial cell death. METHODS This prospective, analytic, observational comparative study included patients with heart disease or multiple risk factors for heart disease undergoing major noncardiac, mostly vascular surgery, requiring an arterial-line, and at least 24 h hospitalization in the post anaesthesia care unit or critical care unit after surgery. Blood samples were collected at least four times per patient to measure troponin-T (via high-sensitivity troponin-T test) and CS-cfDNA pre- and postoperatively. RESULTS A total of 117 patients were included (group 1, 77 patients [66%] with low preoperative and postoperative troponin-T; group 2, 18 patients [15%] with low preoperative but increased postoperative troponin-T; group 3, 16 patients [14%] with high troponin-T both preoperatively and postoperatively; and group 4, six patients [5%] with elevated preoperative troponin-T that decreased postoperatively). The increase in CS-cfDNA after surgery was statistically significant only in group 2, which correlated with an increase in troponin-T in the same group. CONCLUSIONS CS-cfDNA increased early postoperatively, particularly in patients with silent postoperative troponin elevation, and was correlated with an increase in troponin-T. These results may suggest that, in the subgroup of patients with postoperative elevated troponin, cardiomyocyte death indeed occurred.
Collapse
Affiliation(s)
- Tural Alekberli
- Department of Anaesthesiology, Critical Care and Pain Medicine, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Anaesthesia, Edmundston Regional Hospital, Vitalite Health Network, University of Sherbrooke, Edmundston, NB, Canada
| | - Braha Lea Ohana
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hai Zemmour
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rudy Khader
- Department of Anaesthesiology, Critical Care and Pain Medicine, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ruth Shemer
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Giora Landesberg
- Department of Anaesthesiology, Critical Care and Pain Medicine, Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
31
|
Wang R, Yang Y, Lu T, Cui Y, Li B, Liu X. Circulating cell-free DNA-based methylation pattern in plasma for early diagnosis of esophagus cancer. PeerJ 2024; 12:e16802. [PMID: 38313016 PMCID: PMC10838104 DOI: 10.7717/peerj.16802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/26/2023] [Indexed: 02/06/2024] Open
Abstract
With the increased awareness of early tumor detection, the importance of detecting and diagnosing esophageal cancer in its early stages has been underscored. Studies have consistently demonstrated the crucial role of methylation levels in circulating cell-free DNA (cfDNA) in identifying and diagnosing early-stage cancer. cfDNA methylation pertains to the methylation state within the genomic scope of cfDNA and is strongly associated with cancer development and progression. Several research teams have delved into the potential application of cfDNA methylation in identifying early-stage esophageal cancer and have achieved promising outcomes. Recent research supports the high sensitivity and specificity of cfDNA methylation in early esophageal cancer diagnosis, providing a more accurate and efficient approach for early detection and improved clinical management. Accordingly, this review aims to present an overview of methylation-based cfDNA research with a focus on the latest developments in the early detection of esophageal cancer. Additionally, this review summarizes advanced analytical technologies for cfDNA methylation that have significantly benefited from recent advancements in separation and detection techniques, such as methylated DNA immunoprecipitation sequencing (MeDIP-seq). Recent findings suggest that biomarkers based on cfDNA methylation may soon find successful applications in the early detection of esophageal cancer. However, large-scale prospective clinical trials are required to identify the potential of these biomarkers.
Collapse
Affiliation(s)
- Rui Wang
- School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yue Yang
- Department of Thoracic Surgery, First Hospital of Jilin University, Changchun, Jilin, China
| | - Tianyu Lu
- Department of Thoracic Surgery, First Hospital of Jilin University, Changchun, Jilin, China
| | - Youbin Cui
- Department of Thoracic Surgery, First Hospital of Jilin University, Changchun, Jilin, China
| | - Bo Li
- School of Public Health, Jilin University, Changchun, Jilin, China
| | - Xin Liu
- Department of Thoracic Surgery, First Hospital of Jilin University, Changchun, Jilin, China
| |
Collapse
|
32
|
Nazer N, Sepehri MH, Mohammadzade H, Mehrmohamadi M. A novel approach toward optimal workflow selection for DNA methylation biomarker discovery. BMC Bioinformatics 2024; 25:37. [PMID: 38262949 PMCID: PMC10804576 DOI: 10.1186/s12859-024-05658-0] [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: 01/06/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024] Open
Abstract
DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.
Collapse
Affiliation(s)
- Naghme Nazer
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Hoda Mohammadzade
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mahya Mehrmohamadi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
| |
Collapse
|
33
|
Hong Y, Liu L, Feng Y, Zhang Z, Hou R, Xu Q, Shi J. mHapBrowser: a comprehensive database for visualization and analysis of DNA methylation haplotypes. Nucleic Acids Res 2024; 52:D929-D937. [PMID: 37831137 PMCID: PMC10767976 DOI: 10.1093/nar/gkad881] [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: 09/13/2023] [Accepted: 09/29/2023] [Indexed: 10/14/2023] Open
Abstract
DNA methylation acts as a vital epigenetic regulatory mechanism involved in controlling gene expression. Advances in sequencing technologies have enabled characterization of methylation patterns at single-base resolution using bisulfite sequencing approaches. However, existing methylation databases have primarily focused on mean methylation levels, overlooking phased methylation patterns. The methylation status of CpGs on individual sequencing reads represents discrete DNA methylation haplotypes (mHaps). Here, we present mHapBrowser, a comprehensive database for visualizing and analyzing mHaps. We systematically processed data of diverse tissues in human, mouse and rat from public repositories, generating mHap format files for 6366 samples. mHapBrowser enables users to visualize eight mHap metrics across the genome through an integrated WashU Epigenome Browser. It also provides an online server for comparing mHap patterns across samples. Additionally, mHap files for all samples can be downloaded to facilitate local processing using downstream analysis toolkits. The utilities of mHapBrowser were demonstrated through three case studies: (i) mHap patterns are associated with gene expression; (ii) changes in mHap patterns independent of mean methylation correlate with differential expression between lung cancer subtypes; and (iii) the mHap metric MHL outperforms mean methylation for classifying tumor and normal samples from cell-free DNA. The database is freely accessible at http://mhap.sibcb.ac.cn/.
Collapse
Affiliation(s)
- Yuyang Hong
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Leiqin Liu
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Feng
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Rui Hou
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qiong Xu
- Department of Respiratory Disease, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jiantao Shi
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
34
|
Gristina V, Malapelle U. Dissecting the nuances of cancer epigenomics in liquid biopsy. Epigenomics 2024; 16:79-83. [PMID: 38166432 DOI: 10.2217/epi-2023-0326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024] Open
Affiliation(s)
- Valerio Gristina
- Department of Surgical, Oncological & Oral Sciences, University of Palermo, Palermo, 90127, Italy
| | - Umberto Malapelle
- Department of Public Health, University Federico II of Naples, Naples, 80131, Italy
| |
Collapse
|
35
|
Chang L, Wang D, Han Y, Diao Z, Chen Y, Li J, Zhang R. External quality assessment for detection of colorectal cancer by Septin9 DNA methylation in clinical laboratories. Clin Chim Acta 2024; 552:117663. [PMID: 38008152 DOI: 10.1016/j.cca.2023.117663] [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: 06/25/2023] [Revised: 10/28/2023] [Accepted: 11/15/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND AIMS The incidence and mortality rate of colorectal cancer (CRC) are increasing worldwide. Septin9 methylated (mSEPT9) DNA in circulation can be used as a non-invasive detection method to assist in the early diagnosis of CRC; however, the detection methods and procedures are complicated. This study aimed to evaluate the ability of clinical laboratories to detect Septin9 methylation in plasma cell-free DNA (cfDNA). MATERIALS AND METHODS We prepared a sample panel consisting of positive and negative Septin9 methylation cells and CRC cells. Three positive samples with different methylation levels, one negative sample and one duplicate sample, two samples containing interference, three different CRC cell samples, and a fictitious case report were included. The panel was distributed to 59 laboratories for mSEPT9 analysis, result comparison, and scoring. RESULTS The sample panel, validated by National Medical Products Administration (NMPA)-approved tests and targeted bisulfite sequencing, met expectations and could be used for external quality assessment (EQA). Among the 59 laboratories, 55 (93.22%) correctly reported the mSEPT9 results for all samples, while four (6.79%) reported 15 false negatives and were considered improvable. All false negatives originated from four laboratories using laboratory-developed tests (LDTs), with three failing to detect weakly positive samples, samples containing interference, and samples from different CRC cells, and one reported erroneous results on all positive samples. CONCLUSION Our results illustrated that the detection of mSEPT9 in cfDNA is satisfactory in China. EQA is indispensable because it can help improve the diagnostic capability and quality management of the laboratories, and provide suggestions for the problems existing in mSEPT9 detection.
Collapse
Affiliation(s)
- Lu Chang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China
| | - Duo Wang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China
| | - Yanxi Han
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China
| | - Zhenli Diao
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China
| | - Yuqing Chen
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China.
| | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China; National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, P.R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing, P.R. China.
| |
Collapse
|
36
|
Feng Y, Zhang Z, Hong Y, Ding Y, Liu L, Gao S, Fang H, Shi J. A DNA methylation haplotype block landscape in human tissues and preimplantation embryos reveals regulatory elements defined by comethylation patterns. Genome Res 2023; 33:2041-2052. [PMID: 37940553 PMCID: PMC10760529 DOI: 10.1101/gr.278146.123] [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: 06/01/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023]
Abstract
DNA methylation and associated regulatory elements play a crucial role in gene expression regulation. Previous studies have focused primarily on the distribution of mean methylation levels. Advances in whole-genome bisulfite sequencing (WGBS) have enabled the characterization of DNA methylation haplotypes (MHAPs), representing CpG sites from the same read fragment on a single chromosome, and the subsequent identification of methylation haplotype blocks (MHBs), in which adjacent CpGs on the same fragment are comethylated. Using our expert-curated WGBS data sets, we report comprehensive landscapes of MHBs in 17 representative normal somatic human tissues and during early human embryonic development. Integrative analysis reveals MHBs as a distinctive type of regulatory element characterized by comethylation patterns rather than mean methylation levels. We show the enrichment of MHBs in open chromatin regions, tissue-specific histone marks, and enhancers, including super-enhancers. Moreover, we find that MHBs tend to localize near tissue-specific genes and show an association with differential gene expression that is independent of mean methylation. Similar findings are observed in the context of human embryonic development, highlighting the dynamic nature of MHBs during early development. Collectively, our comprehensive MHB landscapes provide valuable insights into the tissue specificity and developmental dynamics of DNA methylation.
Collapse
Affiliation(s)
- Yan Feng
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yuyang Hong
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yi Ding
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Leiqin Liu
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Siqi Gao
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiantao Shi
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China;
| |
Collapse
|
37
|
Melton CA, Freese P, Zhou Y, Shenoy A, Bagaria S, Chang C, Kuo CC, Scott E, Srinivasan S, Cann G, Roychowdhury-Saha M, Chang PY, Singh AH. A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns. Cancers (Basel) 2023; 16:82. [PMID: 38201510 PMCID: PMC10777919 DOI: 10.3390/cancers16010082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/07/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman's correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10-3 and 10-4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.
Collapse
|
38
|
Yang Y, Zeng Q, Liu G, Zheng S, Luo T, Guo Y, Tang J, Huang Y. Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer. BMC Bioinformatics 2023; 24:465. [PMID: 38066424 PMCID: PMC10709847 DOI: 10.1186/s12859-023-05529-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/12/2023] [Indexed: 12/18/2023] Open
Abstract
Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advantages, its application in predicting primary cancer is rare. To leverage the similarity of cancers and the specificity of methylation patterns among them, we developed the Cancer Hierarchy Classification Tool (CHCT) using the idea of hierarchical classification, with methylation data from 30 cancer types and 8239 methylome samples downloaded from publicly available databases (The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO)). We used unsupervised clustering to divide the classification subproblems and screened differentially methylated sites using Analysis of variance (ANOVA) test, Tukey-kramer test, and Boruta algorithms to construct models for each classifier module. After validation, CHCT accurately classified 1568 out of 1660 cases in the test set, with an average accuracy of 94.46%. We further curated an independent validation cohort of 677 cancer samples from GEO and assigned a diagnosis using CHCT, which showed high diagnostic potential with generally high accuracies (an average accuracy of 91.40%). Moreover, CHCT demonstrates predictive capability for additional cancer types beyond its original classifier scope as demonstrated in the medulloblastoma and pituitary tumor datasets. In summary, CHCT can hierarchically classify primary cancer by methylation profile, by splitting a large-scale classification of 30 cancer types into ten smaller classification problems. These results indicate that cancer hierarchical classification has the potential to be an accurate and robust cancer classification method.
Collapse
Affiliation(s)
- Youpeng Yang
- Medicine School, Sun Yat-sen University, Shenzhen, 518107, China
| | - Qiuhong Zeng
- Geneplus-Shenzhen Institute, Shenzhen, 518118, China
| | - Gaotong Liu
- Geneplus-Shenzhen Institute, Shenzhen, 518118, China
| | - Shiyao Zheng
- Medicine School, Sun Yat-sen University, Shenzhen, 518107, China
| | - Tianyang Luo
- Medicine School, Sun Yat-sen University, Shenzhen, 518107, China
| | - Yibin Guo
- Medicine School, Sun Yat-sen University, Shenzhen, 518107, China.
| | - Jia Tang
- NHC Key Laboratory of Male Reproduction and Genetics, Guangdong Provincial Reproductive Science Institute (Guangdong Provincial Fertility Hospital), Guangzhou, 510062, China.
- School of Medicine, Jinan University, Guangzhou, 510632, China.
| | - Yi Huang
- Geneplus-Shenzhen Institute, Shenzhen, 518118, China.
| |
Collapse
|
39
|
Drag MH, Debes KP, Franck CS, Flethøj M, Lyhne MK, Møller JE, Ludvigsen TP, Jespersen T, Olsen LH, Kilpeläinen TO. Nanopore sequencing reveals methylation changes associated with obesity in circulating cell-free DNA from Göttingen Minipigs. Epigenetics 2023; 18:2199374. [PMID: 37032646 PMCID: PMC10088973 DOI: 10.1080/15592294.2023.2199374] [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: 11/22/2022] [Revised: 01/29/2023] [Accepted: 03/08/2023] [Indexed: 04/11/2023] Open
Abstract
Profiling of circulating cell-free DNA (cfDNA) by tissue-specific base modifications, such as 5-methylcytosines (5mC), may enable the monitoring of ongoing pathophysiological processes. Nanopore sequencing allows genome-wide 5mC detection in cfDNA without bisulphite conversion. The aims of this study were: i) to find differentially methylated regions (DMRs) of cfDNA associated with obesity in Göttingen minipigs using Nanopore sequencing, ii) to validate a subset of the DMRs using methylation-specific PCR (MSP-PCR), and iii) to compare the cfDNA DMRs with those from whole blood genomic DNA (gDNA). Serum cfDNA and gDNA were obtained from 10 lean and 7 obese Göttingen Minipigs both with experimentally induced myocardial infarction and sequenced using Oxford Nanopore MinION. A total of 1,236 cfDNA DMRs (FDR<0.01) were associated with obesity. In silico analysis showed enrichment of the adipocytokine signalling, glucagon signalling, and cellular glucose homoeostasis pathways. A strong cfDNA DMR was discovered in PPARGC1B, a gene linked to obesity and type 2 diabetes. The DMR was validated using MSP-PCR and correlated significantly with body weight (P < 0.05). No DMRs intersected between cfDNA and gDNA, suggesting that cfDNA originates from body-wide shedding of DNA. In conclusion, nanopore sequencing detected differential methylation in minute quantities (0.1-1 ng/µl) of cfDNA. Future work should focus on translation into human and comparing 5mC from somatic tissues to pinpoint the exact location of pathology.
Collapse
Affiliation(s)
- Markus Hodal Drag
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Conservation, Copenhagen Zoo, Frederiksberg, Denmark
| | | | - Clara Sandkamm Franck
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Flethøj
- Research & Early Development, Novo Nordisk A/S, Måløv, Denmark
| | - Mille Kronborg Lyhne
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Eifer Møller
- Department of Cardiology, Copenhagen University Hospital and Odense University Hospital, Odense, Denmark
| | | | - Thomas Jespersen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lisbeth Høier Olsen
- Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
40
|
Li C, Shao J, Li P, Feng J, Li J, Wang C. Circulating tumor DNA as liquid biopsy in lung cancer: Biological characteristics and clinical integration. Cancer Lett 2023; 577:216365. [PMID: 37634743 DOI: 10.1016/j.canlet.2023.216365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 08/29/2023]
Abstract
Lung cancer maintains high morbidity and mortality rate globally despite significant advancements in diagnosis and treatment in the era of precision medicine. Pathological analysis of tumor tissue, the current gold standard for lung cancer diagnosis, is intrusive and intrinsically confined to evaluating the limited amount of tissues that could be physically extracted. However, tissue biopsy has several limitations, including the invasiveness of the procedure and difficulty in obtaining samples for patients at advanced stages., there Additionally,has been no major breakthrough in tumor biomarkers with high specificity and sensitivity, particularly for early-stage lung cancer. Liquid biopsy has been considered a feasible auxiliary tool for tearly dianosis, evaluating treatment responses and monitoring prognosis of lung cancer. Circulating tumor DNA (ctDNA), an ideal biomarker of liquid biopsy, has emerged as one of the most reliable tools for monitoring tumor processes at molecular levels. Herein, this review focuses on tumor heterogeneity to elucidate the superiority of liquid biopsy and retrospectively discussdeciphersolution. We systematically elaborate ctDNA biological characteristics, introduce methods for ctDNA detection, and discuss the current role of plasma ctDNA in lung cancer management. Finally, we summarize the drawbacks of ctDNA analysis and highlight its potential clinical application in lung cancer.
Collapse
Affiliation(s)
- Changshu Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Shao
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Peiyi Li
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jiaming Feng
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Jingwei Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
41
|
Lin PY, Chang YT, Huang YC, Chen PY. Estimating genome-wide DNA methylation heterogeneity with methylation patterns. Epigenetics Chromatin 2023; 16:44. [PMID: 37941029 PMCID: PMC10634068 DOI: 10.1186/s13072-023-00521-7] [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: 03/27/2023] [Accepted: 10/30/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment. RESULTS Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection. CONCLUSIONS We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.
Collapse
Affiliation(s)
- Pei-Yu Lin
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
| | - Ya-Ting Chang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
| | - Yu-Chun Huang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, 115, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei, 115, Taiwan
| | - Pao-Yang Chen
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan.
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, 115, Taiwan.
| |
Collapse
|
42
|
Deng Z, Ji Y, Han B, Tan Z, Ren Y, Gao J, Chen N, Ma C, Zhang Y, Yao Y, Lu H, Huang H, Xu M, Chen L, Zheng L, Gu J, Xiong D, Zhao J, Gu J, Chen Z, Wang K. Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network. Genome Med 2023; 15:93. [PMID: 37936230 PMCID: PMC10631027 DOI: 10.1186/s13073-023-01238-8] [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: 09/27/2022] [Accepted: 09/26/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC. METHODS We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth. RESULTS NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%). CONCLUSIONS By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.
Collapse
Affiliation(s)
- Zhenzhong Deng
- Department of Oncology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongkun Ji
- BamRock Research Department, Suzhou BamRock Biotechnology Ltd., Suzhou, Jiangsu Province, China
| | - Bing Han
- Division of Hepatobiliary and Transplantation Surgery, Department of General Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Department of Transplantation, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongming Tan
- Hepatobiliary Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Yuqi Ren
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jinghan Gao
- Department of Software Engineering, Tsinghua University, Beijing, China
| | - Nan Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Cong Ma
- Suzhou Known Biotechnology Ltd, Suzhou, Jiangsu Province, China
| | - Yichi Zhang
- Department of Transplantation, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yunhai Yao
- Infectious Disease Department, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Hong Lu
- Infectious Disease Department, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Heqing Huang
- Infectious Disease Department, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Midie Xu
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lei Chen
- Department of Pathology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Leizhen Zheng
- Department of Oncology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianchun Gu
- Department of Oncology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Deyi Xiong
- College of Intelligence and Computing, Tianjin University, Tianjin, China.
| | - Jianxin Zhao
- Department of Interventional Medicine, the affiliated hospital of infectious diseases of Soochow University, Suzhou, 215131, Jiangsu Province, China.
| | - Jinyang Gu
- Department of Transplantation, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Liver Transplantation Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
| | - Zutao Chen
- Infectious Disease Department, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.
- Suzhou Key Laboratory of Pathogen Bioscience and Anti-Infective Medicine, Suzhou, Jiangsu Province, China.
| | - Ke Wang
- Hepatobiliary Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.
| |
Collapse
|
43
|
Kim SY, Jeong S, Lee W, Jeon Y, Kim YJ, Park S, Lee D, Go D, Song SH, Lee S, Woo HG, Yoon JK, Park YS, Kim YT, Lee SH, Kim KH, Lim Y, Kim JS, Kim HP, Bang D, Kim TY. Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection. Exp Mol Med 2023; 55:2445-2460. [PMID: 37907748 PMCID: PMC10689759 DOI: 10.1038/s12276-023-01119-5] [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: 06/26/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 11/02/2023] Open
Abstract
Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.
Collapse
Affiliation(s)
| | | | | | - Yujin Jeon
- IMBdx Inc., Seoul, 08506, Republic of Korea
| | | | | | - Dongin Lee
- Department of Chemistry, Yonsei University, Seoul, 03722, Republic of Korea
| | - Dayoung Go
- IMBdx Inc., Seoul, 08506, Republic of Korea
| | - Sang-Hyun Song
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
| | - Sanghoo Lee
- Seoul Clinical Laboratories Healthcare Inc., Yongin-si, Gyenggi-do, 16954, Republic of Korea
| | - Hyun Goo Woo
- Department of Physiology, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Jung-Ki Yoon
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Young Sik Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Young Tae Kim
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, 03063, Republic of Korea
| | - Kwang Hyun Kim
- Department of Urology, Ewha Womans University Seoul Hospital, Seoul, 07804, Republic of Korea
| | - Yoojoo Lim
- IMBdx Inc., Seoul, 08506, Republic of Korea
| | - Jin-Soo Kim
- IMBdx Inc., Seoul, 08506, Republic of Korea
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, 07061, Republic of Korea
| | | | - Duhee Bang
- Department of Chemistry, Yonsei University, Seoul, 03722, Republic of Korea.
| | - Tae-You Kim
- IMBdx Inc., Seoul, 08506, Republic of Korea.
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea.
| |
Collapse
|
44
|
Zhang L, Li J. Unlocking the secrets: the power of methylation-based cfDNA detection of tissue damage in organ systems. Clin Epigenetics 2023; 15:168. [PMID: 37858233 PMCID: PMC10588141 DOI: 10.1186/s13148-023-01585-8] [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: 06/08/2023] [Accepted: 10/11/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Detecting organ and tissue damage is essential for early diagnosis, treatment decisions, and monitoring disease progression. Methylation-based assays offer a promising approach, as DNA methylation patterns can change in response to tissue damage. These assays have potential applications in early detection, monitoring disease progression, evaluating treatment efficacy, and assessing organ viability for transplantation. cfDNA released into the bloodstream upon tissue or organ injury can serve as a biomarker for damage. The epigenetic state of cfDNA, including DNA methylation patterns, can provide insights into the extent of tissue and organ damage. CONTENT Firstly, this review highlights DNA methylation as an extensively studied epigenetic modification that plays a pivotal role in processes such as cell growth, differentiation, and disease development. It then presents a variety of highly precise 5-mC methylation detection techniques that serve as powerful tools for gaining profound insights into epigenetic alterations linked with tissue damage. Subsequently, the review delves into the mechanisms underlying DNA methylation changes in organ and tissue damage, encompassing inflammation, oxidative stress, and DNA damage repair mechanisms. Next, it addresses the current research status of cfDNA methylation in the detection of specific organ tissues and organ damage. Finally, it provides an overview of the multiple steps involved in identifying specific methylation markers associated with tissue and organ damage for clinical trials. This review will explore the mechanisms and current state of research on cfDNA methylation-based assay detecting organ and tissue damage, the underlying mechanisms, and potential applications in clinical practice.
Collapse
Affiliation(s)
- Lijing Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, No. 1 Dahua Road, Dongdan, Beijing, 100730, People's Republic of China
- Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, People's Republic of China
- Beijing Engineering Research Center of Laboratory Medicine, Beijing, People's Republic of China
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, No. 1 Dahua Road, Dongdan, Beijing, 100730, People's Republic of China.
- Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, People's Republic of China.
- Beijing Engineering Research Center of Laboratory Medicine, Beijing, People's Republic of China.
| |
Collapse
|
45
|
Mattox AK, Douville C, Wang Y, Popoli M, Ptak J, Silliman N, Dobbyn L, Schaefer J, Lu S, Pearlman AH, Cohen JD, Tie J, Gibbs P, Lahouel K, Bettegowda C, Hruban RH, Tomasetti C, Jiang P, Chan KA, Lo YMD, Papadopoulos N, Kinzler KW, Vogelstein B. The Origin of Highly Elevated Cell-Free DNA in Healthy Individuals and Patients with Pancreatic, Colorectal, Lung, or Ovarian Cancer. Cancer Discov 2023; 13:2166-2179. [PMID: 37565753 PMCID: PMC10592331 DOI: 10.1158/2159-8290.cd-21-1252] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 12/16/2022] [Accepted: 08/09/2023] [Indexed: 08/12/2023]
Abstract
Cell-free DNA (cfDNA) concentrations from patients with cancer are often elevated compared with those of healthy controls, but the sources of this extra cfDNA have never been determined. To address this issue, we assessed cfDNA methylation patterns in 178 patients with cancers of the colon, pancreas, lung, or ovary and 64 patients without cancer. Eighty-three of these individuals had cfDNA concentrations much greater than those generally observed in healthy subjects. The major contributor of cfDNA in all samples was leukocytes, accounting for ∼76% of cfDNA, with neutrophils predominating. This was true regardless of whether the samples were derived from patients with cancer or the total plasma cfDNA concentration. High levels of cfDNA observed in patients with cancer did not come from either neoplastic cells or surrounding normal epithelial cells from the tumor's tissue of origin. These data suggest that cancers may have a systemic effect on cell turnover or DNA clearance. SIGNIFICANCE The origin of excess cfDNA in patients with cancer is unknown. Using cfDNA methylation patterns, we determined that neither the tumor nor the surrounding normal tissue contributes this excess cfDNA-rather it comes from leukocytes. This finding suggests that cancers have a systemic impact on cell turnover or DNA clearance. See related commentary by Thierry and Pisareva, p. 2122. This article is featured in Selected Articles from This Issue, p. 2109.
Collapse
Affiliation(s)
- Austin K. Mattox
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Christopher Douville
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Yuxuan Wang
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Maria Popoli
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Janine Ptak
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Natalie Silliman
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Lisa Dobbyn
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Joy Schaefer
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Steve Lu
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Alexander H. Pearlman
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Joshua D. Cohen
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Jeanne Tie
- Division of Systems Biology and Personalized Medicine, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Oncology, Western Health, St Albans, Victoria 3021, Australia
- Department of Medical Oncology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Peter Gibbs
- Division of Systems Biology and Personalized Medicine, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Oncology, Western Health, St Albans, Victoria 3021, Australia
- Department of Medical Oncology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Kamel Lahouel
- Division of Mathematics for Cancer Evolution and Early Detection, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010
| | - Chetan Bettegowda
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287
| | - Ralph H. Hruban
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Cristian Tomasetti
- Division of Mathematics for Cancer Evolution and Early Detection, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010
| | - Peiyong Jiang
- State Key Laboratory of Translational Oncology and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - K.C. Allen Chan
- State Key Laboratory of Translational Oncology and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Yuk Ming Dennis Lo
- State Key Laboratory of Translational Oncology and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Nickolas Papadopoulos
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Kenneth W. Kinzler
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Bert Vogelstein
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| |
Collapse
|
46
|
Kotanidou EP, Kosvyra A, Mouzaki K, Giza S, Tsinopoulou VR, Serbis A, Chouvarda I, Galli-Tsinopoulou A. Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease? Exp Ther Med 2023; 26:461. [PMID: 37664671 PMCID: PMC10469396 DOI: 10.3892/etm.2023.12160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 06/09/2023] [Indexed: 09/05/2023] Open
Abstract
DNA methylation of cytosine-guanine sites (CpGs) is associated with type 1 diabetes (T1D). The sequence of methylated and non-methylated sites in a specific genetic region constitutes its methyl-haplotype. The aim of the present study was to identify insulin gene promoter (IGP) methyl-haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl-haplotypes. A total of 40 individuals (20 T1D) participated. The IGP region from peripheral whole blood DNA of 40 participants (20 T1D) was sequenced using next-generation sequencing, sequences were read using FASTQ files and methylation status was calculated by python-based pipeline for targeted deep bisulfite sequenced amplicons (ampliMethProfiler). Methylation profile at 10 CpG sites proximal to transcription start site of the IGP was recorded and coded as 0 for unmethylation or 1 for methylation. A single read could result in '1111111111' methyl-haplotype (all methylated), '000000000' methyl-haplotype (all unmethylated) or any other combination. Principal component analysis was applied to the generated methyl-haplotypes for dimensionality reduction, and the first three principal components were employed as features with five different classifiers (random forest, decision tree, logistic regression, Naive Bayes, support vector machine). Naive Bayes was the best-performing classifier, with 0.9 accuracy. Predictive models were evaluated using receiver operating characteristics (AUC 0.96). Methyl-haplotypes '1111111111', '1111111011', '1110111111', '1111101111' and '1110101111' were revealed to be the most significantly associated with T1D according to the dimensionality reduction method. Methylation-based biomarkers such as IGP methyl-haplotypes could serve to identify individuals at high risk for T1D.
Collapse
Affiliation(s)
- Eleni P. Kotanidou
- Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Alexandra Kosvyra
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Konstantina Mouzaki
- Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Styliani Giza
- Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Vasiliki Rengina Tsinopoulou
- Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Anastasios Serbis
- Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece
- Department of Pediatrics, Faculty of Medicine, School of Health Sciences, University of Ioannina, University Hospital of Ioannina, 45500 Ioannina, Greece
| | - Ioanna Chouvarda
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Assimina Galli-Tsinopoulou
- Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece
| |
Collapse
|
47
|
Kerr L, Kafetzopoulos I, Grima R, Sproul D. Genome-wide single-molecule analysis of long-read DNA methylation reveals heterogeneous patterns at heterochromatin that reflect nucleosome organisation. PLoS Genet 2023; 19:e1010958. [PMID: 37782664 PMCID: PMC10569558 DOI: 10.1371/journal.pgen.1010958] [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: 05/01/2023] [Revised: 10/12/2023] [Accepted: 09/04/2023] [Indexed: 10/04/2023] Open
Abstract
High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome.
Collapse
Affiliation(s)
- Lyndsay Kerr
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Ioannis Kafetzopoulos
- MRC Human Genetics Unit and CRUK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
- Current address: Altos Labs Cambridge Institute, Cambridge, United Kingdom
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Duncan Sproul
- MRC Human Genetics Unit and CRUK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| |
Collapse
|
48
|
Varshavsky M, Harari G, Glaser B, Dor Y, Shemer R, Kaplan T. Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm. CELL REPORTS METHODS 2023; 3:100567. [PMID: 37751697 PMCID: PMC10545910 DOI: 10.1016/j.crmeth.2023.100567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 09/28/2023]
Abstract
Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.
Collapse
Affiliation(s)
- Miri Varshavsky
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; The Center for Computational Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gil Harari
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Benjamin Glaser
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Dor
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel; The Center for Computational Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ruth Shemer
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - Tommy Kaplan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel; The Center for Computational Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
| |
Collapse
|
49
|
Viswanathan R, Cheruba E, Wong PM, Yi Y, Ngang S, Chong DQ, Loh YH, Tan IB, Cheow LF. DARESOME enables concurrent profiling of multiple DNA modifications with restriction enzymes in single cells and cell-free DNA. SCIENCE ADVANCES 2023; 9:eadi0197. [PMID: 37713482 PMCID: PMC10881072 DOI: 10.1126/sciadv.adi0197] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/15/2023] [Indexed: 09/17/2023]
Abstract
5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are the most abundant DNA modifications that have important roles in gene regulation. Detailed studies of these different epigenetic marks aimed at understanding their combined effects and dynamic interconversion are, however, hampered by the inability of current methods to simultaneously measure both modifications, particularly in samples with limited quantities. We present DNA analysis by restriction enzyme for simultaneous detection of multiple epigenomic states (DARESOME), an assay based on modification-sensitive restriction digest and sequential tag ligation that can concurrently perform quantitative profiling of unmodified cytosine, 5mC, and 5hmC in CCGG sites genome-wide. DARESOME reveals the opposing roles of 5mC and 5hmC in gene expression regulation as well as their interconversion during aging in mouse brain. Implementation of DARESOME in single cells demonstrates pronounced 5hmC strand bias that reflects the semiconservative replication of DNA. Last, we showed that DARESOME enables integrative genomic, 5mC, and 5hmC profiling of cell-free DNA that uncovered multiomics cancer signatures in liquid biopsy.
Collapse
Affiliation(s)
- Ramya Viswanathan
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Elsie Cheruba
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Pui-Mun Wong
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
| | - Yao Yi
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore 138673, Singapore
| | - Shaun Ngang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| | - Dawn Qingqing Chong
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
| | - Yuin-Han Loh
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore 138673, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore 117543, Singapore
| | - Iain Beehuat Tan
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 169610, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
| | - Lih Feng Cheow
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
| |
Collapse
|
50
|
Zhang S, He S, Zhu X, Wang Y, Xie Q, Song X, Xu C, Wang W, Xing L, Xia C, Wang Q, Li W, Zhang X, Yu J, Ma S, Shi J, Gu H. DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues. Nat Commun 2023; 14:5686. [PMID: 37709764 PMCID: PMC10502058 DOI: 10.1038/s41467-023-41015-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/18/2023] [Indexed: 09/16/2023] Open
Abstract
Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).
Collapse
Affiliation(s)
- Shirong Zhang
- Translational Medicine Research Center, Hangzhou First People's Hospital, 310006, Hangzhou, Zhejiang Province, China
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou First People's Hospital, 310006, Hangzhou, Zhejiang Province, China
| | - Shutao He
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, 200031, Shanghai, China
- Institute of Biotechnology and Health, Beijing Academy of Science and Technology, 100089, Beijing, China
| | - Xin Zhu
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Cancer Hospital, 310022, Hangzhou, Zhejiang Province, China
| | - Yunfei Wang
- Zhejiang ShengTing Biotech Co. Ltd, 310018, Hangzhou, Zhejiang Province, China
| | - Qionghuan Xie
- Zhejiang ShengTing Biotech Co. Ltd, 310018, Hangzhou, Zhejiang Province, China
| | - Xianrang Song
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Chunwei Xu
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, 210002, Nanjing, Jiangshu Province, China
| | - Wenxian Wang
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Cancer Hospital, 310022, Hangzhou, Zhejiang Province, China
| | - Ligang Xing
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Chengqing Xia
- Zhejiang ShengTing Biotech Co. Ltd, 310018, Hangzhou, Zhejiang Province, China
| | - Qian Wang
- Department of Respiratory Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029, Nanjing, Jiangshu Province, China
| | - Wenfeng Li
- Department of Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, 325000, Wenzhou, Zhejiang Province, China
| | - Xiaochen Zhang
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 310006, Hangzhou, Zhejiang Province, China
| | - Jinming Yu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Shenglin Ma
- Translational Medicine Research Center, Hangzhou First People's Hospital, 310006, Hangzhou, Zhejiang Province, China.
- Department of Oncology, Hangzhou Cancer Hospital, 310006, Hangzhou, Zhejiang Province, China.
| | - Jiantao Shi
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, 200031, Shanghai, China.
| | - Hongcang Gu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 230031, Hefei, Anhui Province, China.
- Hefei Cancer Hospital, Chinese Academy of Sciences, 230031, Hefei, Anhui Province, China.
| |
Collapse
|