1
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Hou Y, Meng X, Zhou X. Systematically Evaluating Cell-Free DNA Fragmentation Patterns for Cancer Diagnosis and Enhanced Cancer Detection via Integrating Multiple Fragmentation Patterns. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308243. [PMID: 38881520 PMCID: PMC11321639 DOI: 10.1002/advs.202308243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/12/2024] [Indexed: 06/18/2024]
Abstract
Cell-free DNA (cfDNA) fragmentation patterns have immense potential for early cancer detection. However, the definition of fragmentation varies, ranging from the entire genome to specific genomic regions. These patterns have not been systematically compared, impeding broader research and practical implementation. Here, 1382 plasma cfDNA sequencing samples from 8 cancer types are collected. Considering that cfDNA within open chromatin regions is more susceptible to fragmentation, 10 fragmentation patterns within open chromatin regions as features and employed machine learning techniques to evaluate their performance are examined. All fragmentation patterns demonstrated discernible classification capabilities, with the end motif showing the highest diagnostic value for cross-validation. Combining cross and independent validation results revealed that fragmentation patterns that incorporated both fragment length and coverage information exhibited robust predictive capacities. Despite their diagnostic potential, the predictive power of these fragmentation patterns is unstable. To address this limitation, an ensemble classifier via integrating all fragmentation patterns is developed, which demonstrated notable improvements in cancer detection and tissue-of-origin determination. Further functional bioinformatics investigations on significant feature intervals in the model revealed its impressive ability to identify critical regulatory regions involved in cancer pathogenesis.
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Affiliation(s)
- Yuying Hou
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China
| | - Xiang‐Yu Meng
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China
- Health Science CenterHubei Minzu UniversityEnshi445000China
- Hubei Provincial Clinical Medical Research Center for NephropathyHubei Minzu UniversityEnshi445000China
| | - Xionghui Zhou
- Hubei Key Laboratory of Agricultural BioinformaticsCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China
- Key Laboratory of Smart Farming for Agricultural AnimalsMinistry of Agriculture and Rural AffairsWuhan430070China
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2
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Cornelli L, Van Paemel R, Ferro Dos Santos MR, Roelandt S, Willems L, Vandersteene J, Baert E, Mus LM, Van Roy N, De Wilde B, De Preter K. Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid. Clin Epigenetics 2024; 16:87. [PMID: 38970137 PMCID: PMC11225235 DOI: 10.1186/s13148-024-01696-w] [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: 04/04/2024] [Accepted: 06/17/2024] [Indexed: 07/07/2024] Open
Abstract
Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.
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Affiliation(s)
- Lotte Cornelli
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Biotechnology, VIB-UGent, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
| | - Ruben Van Paemel
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Pediatric Hematology, Oncology and Stem Cell Transplantation, Ghent University Hospital, Ghent, Belgium
| | - Maísa R Ferro Dos Santos
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Biotechnology, VIB-UGent, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
| | - Sofie Roelandt
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Biotechnology, VIB-UGent, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
| | - Leen Willems
- Department of Pediatric Hematology, Oncology and Stem Cell Transplantation, Ghent University Hospital, Ghent, Belgium
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | | | - Edward Baert
- Department of Neurosurgery, Ghent University Hospital, Ghent, Belgium
| | - Liselot M Mus
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Pediatric Hematology, Oncology and Stem Cell Transplantation, Ghent University Hospital, Ghent, Belgium
| | - Nadine Van Roy
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
| | - Bram De Wilde
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Pediatric Hematology, Oncology and Stem Cell Transplantation, Ghent University Hospital, Ghent, Belgium
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
- Center for Medical Biotechnology, VIB-UGent, Ghent, Belgium.
- Cancer Research Institute Ghent, Ghent, Belgium.
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3
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Abstract
This review delves into the rapidly evolving landscape of liquid biopsy technologies based on cell-free DNA (cfDNA) and cell-free RNA (cfRNA) and their increasingly prominent role in precision medicine. With the advent of high-throughput DNA sequencing, the use of cfDNA and cfRNA has revolutionized noninvasive clinical testing. Here, we explore the physical characteristics of cfDNA and cfRNA, present an overview of the essential engineering tools used by the field, and highlight clinical applications, including noninvasive prenatal testing, cancer testing, organ transplantation surveillance, and infectious disease testing. Finally, we discuss emerging technologies and the broadening scope of liquid biopsies to new areas of diagnostic medicine.
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Affiliation(s)
- Conor Loy
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA;
| | - Lauren Ahmann
- Department of Pathology, Stanford University, Stanford, California, USA;
| | - Iwijn De Vlaminck
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA;
| | - Wei Gu
- Department of Pathology, Stanford University, Stanford, California, USA;
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4
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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.
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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
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5
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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.
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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.
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6
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Yap CX, Vo DD, Heffel MG, Bhattacharya A, Wen C, Yang Y, Kemper KE, Zeng J, Zheng Z, Zhu Z, Hannon E, Vellame DS, Franklin A, Caggiano C, Wamsley B, Geschwind DH, Zaitlen N, Gusev A, Pasaniuc B, Mill J, Luo C, Gandal MJ. Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics. SCIENCE ADVANCES 2024; 10:eadn7655. [PMID: 38781333 PMCID: PMC11114225 DOI: 10.1126/sciadv.adn7655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.
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Affiliation(s)
- Chloe X. Yap
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel D. Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew G. Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cindy Wen
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuanhao Yang
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- The National Centre for Register-based Research, Aarhus University, Denmark
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Dorothea Seiler Vellame
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Brie Wamsley
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Geschwind
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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7
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Caggiano C, Morselli M, Qian X, Celona B, Thompson M, Wani S, Tosevska A, Taraszka K, Heuer G, Ngo S, Steyn F, Nestor P, Wallace L, McCombe P, Heggie S, Thorpe K, McElligott C, English G, Henders A, Henderson R, Lomen-Hoerth C, Wray N, McRae A, Pellegrini M, Garton F, Zaitlen N. Tissue informative cell-free DNA methylation sites in amyotrophic lateral sclerosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.08.24305503. [PMID: 38645132 PMCID: PMC11030489 DOI: 10.1101/2024.04.08.24305503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Cell-free DNA (cfDNA) is increasingly recognized as a promising biomarker candidate for disease monitoring. However, its utility in neurodegenerative diseases, like amyotrophic lateral sclerosis (ALS), remains underexplored. Existing biomarker discovery approaches are tailored to a specific disease context or are too expensive to be clinically practical. Here, we address these challenges through a new approach combining advances in molecular and computational technologies. First, we develop statistical tools to select tissue-informative DNA methylation sites relevant to a disease process of interest. We then employ a capture protocol to select these sites and perform targeted methylation sequencing. Multi-modal information about the DNA methylation patterns are then utilized in machine learning algorithms trained to predict disease status and disease progression. We applied our method to two independent cohorts of ALS patients and controls (n=192). Overall, we found that the targeted sites accurately predicted ALS status and replicated between cohorts. Additionally, we identified epigenetic features associated with ALS phenotypes, including disease severity. These findings highlight the potential of cfDNA as a non-invasive biomarker for ALS.
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Affiliation(s)
- C Caggiano
- Department of Neurology, UCLA, Los Angeles, California
- Institute of Genomic Health, Icahn School of Medicine at Mt Sinai, New York, New York
| | - M Morselli
- Department of Molecular, Cell, and Developmental Biology, UCLA; Los Angeles, California
- Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
| | - X Qian
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - B Celona
- Cardiovascular Research Institute, UCSF, San Francisco, California
| | - M Thompson
- Department of Neurology, UCLA, Los Angeles, California
- Systems and Synthetic Biology, Centre for Genomic Regulation, Barcelona, Spain
| | - S Wani
- Cardiovascular Research Institute, UCSF, San Francisco, California
| | - A Tosevska
- Department of Molecular, Cell, and Developmental Biology, UCLA; Los Angeles, California
- Department of Internal Medicine III, Division of Rheumatology, Medical University of Vienna, Vienna, Austria
| | - K Taraszka
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - G Heuer
- Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, California
| | - S Ngo
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - F Steyn
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - P Nestor
- Queensland Brain Institute, Unviversity of Queensland, Brisbane, Australia
- Mater Public Hospital, Brisbane, Australia
| | - L Wallace
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - P McCombe
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - S Heggie
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - K Thorpe
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | | | - G English
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - A Henders
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - R Henderson
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - C Lomen-Hoerth
- Department of Neurology, UCSF, San Francisco, California
| | - N Wray
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - A McRae
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - M Pellegrini
- Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
| | - F Garton
- Institute for Molecular Biology, University of Queensland, Brisbane, Australia
| | - N Zaitlen
- Department of Neurology, UCLA, Los Angeles, California
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California
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8
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Ferro dos Santos MR, Giuili E, De Koker A, Everaert C, De Preter K. Computational deconvolution of DNA methylation data from mixed DNA samples. Brief Bioinform 2024; 25:bbae234. [PMID: 38762790 PMCID: PMC11102637 DOI: 10.1093/bib/bbae234] [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: 02/07/2024] [Revised: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.
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Affiliation(s)
- Maísa R Ferro dos Santos
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Edoardo Giuili
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Andries De Koker
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Celine Everaert
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Katleen De Preter
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
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9
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Zhang Y, Liu R, Li J, Ma H, Bao W, Jiang J, Guo C, Tan D, Cheng X, Dai L, Ming Y. Circulating cell-free DNA as a biomarker for diagnosis of Schistosomiasis japonica. Parasit Vectors 2024; 17:114. [PMID: 38449022 PMCID: PMC10918879 DOI: 10.1186/s13071-024-06203-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: 10/07/2023] [Accepted: 02/16/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Schistosomiasis, a neglected tropical disease, remains an important public health problem. Although there are various methods for diagnosing schistosomiasis, many limitations still exist. Early diagnosis and treatment of schistosomiasis can significantly improve survival and prognosis of patients. METHODOLOGY Circulating cell-free (cf)DNA has been widely used in the diagnosis of various diseases. In our study, we evaluated the diagnostic value of circulating cfDNA for schistosomiasis caused by Schistosoma japonicum. We focused on the tandem sequences and mitochondrial genes of S. japonicum to identify highly sensitive and specific targets for diagnosis of Schistosomiasis japonica. RESULTS Through data screening and analysis, we ultimately identified four specific tandem sequences (TD-1, TD-2, TD-3. and TD-4) and six mitochondrial genes (COX1(1), COX1(2), CYTB, ATP6, COX3, and ND5). We designed specific primers to detect the amount of circulating cfDNA in S. japonicum-infected mouse and chronic schistosomiasis patients. Our results showed that the number of tandem sequences was significantly higher than that of the mitochondrial genes. A S. japonicum infection model in mice suggested that infection of S. japonicum can be diagnosed by detecting circulating cfDNA as early as the first week. We measured the expression levels of circulating cfDNA (TD-1, TD-2, and TD-3) at different time points and found that TD-3 expression was significantly higher than that of TD-1 or TD-2. We also infected mice with different quantities of cercariae (20 s and 80 s). The level of cfDNA (TD-3) in the 80 s infection group was significantly higher than in the 20 s infection group. Additionally, cfDNA (TD-3) levels increased after egg deposition. Meanwhile, we tested 42 patients with chronic Schistosomiasis japonica and circulating cfDNA (TD-3) was detected in nine patients. CONCLUSIONS We have screened highly sensitive targets for the diagnosis of Schistosomiasis japonica, and the detection of circulating cfDNA is a rapid and effective method for the diagnosis of Schistosomiasis japonica. The levels of cfDNA is correlated with cercariae infection severity. Early detection and diagnosis of schistosomiasis is crucial for patient treatment and improving prognosis.
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Affiliation(s)
- Yu Zhang
- Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
- Engineering and Technology Research Center for Transplantation Medicine, of National Health Commission, Changsha, Hunan, China
- Hunan Province Clinical Research Center for Infectious Diseases, Changsha, Hunan, China
| | - Rangjiao Liu
- Sanway Clinical Laboratories, Changsha, Hunan, China
| | - Junhui Li
- Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
- Engineering and Technology Research Center for Transplantation Medicine, of National Health Commission, Changsha, Hunan, China
- Hunan Province Clinical Research Center for Infectious Diseases, Changsha, Hunan, China
| | - Hongchang Ma
- Sansure Biotech Incoporation, Changsha, Hunan, China
| | - Wenjuan Bao
- Sanway Clinical Laboratories, Changsha, Hunan, China
| | - Jie Jiang
- Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
- Engineering and Technology Research Center for Transplantation Medicine, of National Health Commission, Changsha, Hunan, China
- Hunan Province Clinical Research Center for Infectious Diseases, Changsha, Hunan, China
| | - Chen Guo
- Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
- Engineering and Technology Research Center for Transplantation Medicine, of National Health Commission, Changsha, Hunan, China
- Hunan Province Clinical Research Center for Infectious Diseases, Changsha, Hunan, China
| | - Deyong Tan
- Sansure Biotech Incoporation, Changsha, Hunan, China
| | - Xing Cheng
- Sansure Biotech Incoporation, Changsha, Hunan, China
| | - Lizhong Dai
- Sansure Biotech Incoporation, Changsha, Hunan, China.
| | - Yingzi Ming
- Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Engineering and Technology Research Center for Transplantation Medicine, of National Health Commission, Changsha, Hunan, China.
- Hunan Province Clinical Research Center for Infectious Diseases, Changsha, Hunan, China.
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Theme 02 - Genetics and Genomics. Amyotroph Lateral Scler Frontotemporal Degener 2023; 24:99-114. [PMID: 37966317 DOI: 10.1080/21678421.2023.2260192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
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11
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Andargie TE, Roznik K, Redekar N, Hill T, Zhou W, Apalara Z, Kong H, Gordon O, Meda R, Park W, Johnston TS, Wang Y, Brady S, Ji H, Yanovski JA, Jang MK, Lee CM, Karaba AH, Cox AL, Agbor-Enoh S. Cell-free DNA reveals distinct pathology of multisystem inflammatory syndrome in children. J Clin Invest 2023; 133:e171729. [PMID: 37651206 PMCID: PMC10617770 DOI: 10.1172/jci171729] [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: 05/01/2023] [Accepted: 08/29/2023] [Indexed: 09/02/2023] Open
Abstract
Multisystem inflammatory syndrome in children (MIS-C) is a rare but life-threatening hyperinflammatory condition induced by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes pediatric COVID-19 (pCOVID-19). The relationship of the systemic tissue injury to the pathophysiology of MIS-C is poorly defined. We leveraged the high sensitivity of epigenomics analyses of plasma cell-free DNA (cfDNA) and plasma cytokine measurements to identify the spectrum of tissue injury and glean mechanistic insights. Compared with pediatric healthy controls (pHCs) and patients with pCOVID-19, patients with MIS-C had higher levels of cfDNA primarily derived from innate immune cells, megakaryocyte-erythroid precursor cells, and nonhematopoietic tissues such as hepatocytes, cardiac myocytes, and kidney cells. Nonhematopoietic tissue cfDNA levels demonstrated significant interindividual variability, consistent with the heterogenous clinical presentation of MIS-C. In contrast, adaptive immune cell-derived cfDNA levels were comparable in MIS-C and pCOVID-19 patients. Indeed, the cfDNA of innate immune cells in patients with MIS-C correlated with the levels of innate immune inflammatory cytokines and nonhematopoietic tissue-derived cfDNA, suggesting a primarily innate immunity-mediated response to account for the multisystem pathology. These data provide insight into the pathogenesis of MIS-C and support the value of cfDNA as a sensitive biomarker to map tissue injury in MIS-C and likely other multiorgan inflammatory conditions.
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Affiliation(s)
- Temesgen E. Andargie
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
- Department of Biology, Howard University, Washington DC, USA
| | - Katerina Roznik
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Neelam Redekar
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Tom Hill
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Weiqiang Zhou
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Zainab Apalara
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
| | - Hyesik Kong
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
| | - Oren Gordon
- Infectious Diseases Unit, Department of Pediatrics, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rohan Meda
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
| | - Woojin Park
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
| | - Trevor S. Johnston
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Yi Wang
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sheila Brady
- Section on Growth and Obesity, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, Maryland, USA
| | - Hongkai Ji
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jack A. Yanovski
- Section on Growth and Obesity, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, Maryland, USA
| | - Moon K. Jang
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
| | - Clarence M. Lee
- Department of Biology, Howard University, Washington DC, USA
| | - Andrew H. Karaba
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Andrea L. Cox
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Sean Agbor-Enoh
- Genomic Research Alliance for Transplantation (GRAfT) and Laboratory of Applied Precision Omics, National Heart, Lung, and Blood Institute (NHLBI), NIH, Bethesda, Maryland, USA. GFAfT is detailed in Supplemental Acknowledgments
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
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12
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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.
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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.
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13
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Nguyen VTC, Nguyen TH, Doan NNT, Pham TMQ, Nguyen GTH, Nguyen TD, Tran TTT, Vo DL, Phan TH, Jasmine TX, Nguyen VC, Nguyen HT, Nguyen TV, Nguyen THH, Huynh LAK, Tran TH, Dang QT, Doan TN, Tran AM, Nguyen VH, Nguyen VTA, Ho LMQ, Tran QD, Pham TTT, Ho TD, Nguyen BT, Nguyen TNV, Nguyen TD, Phu DTB, Phan BHH, Vo TL, Nai THT, Tran TT, Truong MH, Tran NC, Le TK, Tran THT, Duong ML, Bach HPT, Kim VV, Pham TA, Tran DH, Le TNA, Pham TVN, Le MT, Vo DH, Tran TMT, Nguyen MN, Van TTV, Nguyen AN, Tran TT, Tran VU, Le MP, Do TT, Phan TV, Nguyen HDL, Nguyen DS, Cao VT, Do TTT, Truong DK, Tang HS, Giang H, Nguyen HN, Phan MD, Tran LS. Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization. eLife 2023; 12:RP89083. [PMID: 37819044 PMCID: PMC10567114 DOI: 10.7554/elife.89083] [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] [Indexed: 10/13/2023] Open
Abstract
Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.
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14
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McNamara ME, Loyfer N, Kiliti AJ, Schmidt MO, Shabi-Porat S, Jain SS, Martinez Roth S, McDeed AP, Shahrour N, Ballew E, Lin YT, Li HH, Deslattes Mays A, Rudra S, Riegel AT, Unger K, Kaplan T, Wellstein A. Circulating cell-free methylated DNA reveals tissue-specific, cellular damage from radiation treatment. JCI Insight 2023; 8:e156529. [PMID: 37318863 PMCID: PMC10443812 DOI: 10.1172/jci.insight.156529] [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: 11/11/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023] Open
Abstract
Radiation therapy is an effective cancer treatment, although damage to healthy tissues is common. Here we analyzed cell-free, methylated DNA released from dying cells into the circulation to evaluate radiation-induced cellular damage in different tissues. To map the circulating DNA fragments to human and mouse tissues, we established sequencing-based, cell-type-specific reference DNA methylation atlases. We found that cell-type-specific DNA blocks were mostly hypomethylated and located within signature genes of cellular identity. Cell-free DNA fragments were captured from serum samples by hybridization to CpG-rich DNA panels and mapped to the DNA methylation atlases. In a mouse model, thoracic radiation-induced tissue damage was reflected by dose-dependent increases in lung endothelial and cardiomyocyte methylated DNA in serum. The analysis of serum samples from patients with breast cancer undergoing radiation treatment revealed distinct dose-dependent and tissue-specific epithelial and endothelial responses to radiation across multiple organs. Strikingly, patients treated for right-sided breast cancers also showed increased hepatocyte and liver endothelial DNA in the circulation, indicating the impact on liver tissues. Thus, changes in cell-free methylated DNA can uncover cell-type-specific effects of radiation and provide a readout of the biologically effective radiation dose received by healthy tissues.
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Affiliation(s)
- Megan E. McNamara
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Netanel Loyfer
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Amber J. Kiliti
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Marcel O. Schmidt
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Sapir Shabi-Porat
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sidharth S. Jain
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Sarah Martinez Roth
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - A. Patrick McDeed
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Nesreen Shahrour
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | | | - Yun-Tien Lin
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Heng-Hong Li
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | | | - Sonali Rudra
- Medstar Georgetown University Hospital, Washington DC, USA
| | - Anna T. Riegel
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
| | - Keith Unger
- Medstar Georgetown University Hospital, Washington DC, USA
| | - Tommy Kaplan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anton Wellstein
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC, USA
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Li S, Zeng W, Ni X, Liu Q, Li W, Stackpole ML, Zhou Y, Gower A, Krysan K, Ahuja P, Lu DS, Raman SS, Hsu W, Aberle DR, Magyar CE, French SW, Han SHB, Garon EB, Agopian VG, Wong WH, Dubinett SM, Zhou XJ. Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring. Proc Natl Acad Sci U S A 2023; 120:e2305236120. [PMID: 37399400 PMCID: PMC10334733 DOI: 10.1073/pnas.2305236120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/16/2023] [Indexed: 07/05/2023] Open
Abstract
Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.
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Affiliation(s)
- Shuo Li
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
| | - Weihua Zeng
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
| | - Xiaohui Ni
- EarlyDiagnostics Inc., Los Angeles, CA90095
| | - Qiao Liu
- Department of Statistics, Stanford University, Stanford, CA94305
| | - Wenyuan Li
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Institute for Quantitative & Computational Biosciences, University of California at Los Angeles, Los Angeles, CA90095
| | - Mary L. Stackpole
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- EarlyDiagnostics Inc., Los Angeles, CA90095
| | - Yonggang Zhou
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
| | - Arjan Gower
- Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
| | - Kostyantyn Krysan
- Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
- Veterans Administration (VA) Greater Los Angeles Health Care System, Los Angeles, CA90073
| | - Preeti Ahuja
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
| | - David S. Lu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
| | - Steven S. Raman
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
- Department of Surgery, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
| | - William Hsu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
| | - Denise R. Aberle
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Department of Bioengineering, University of California, Los Angeles, CA90095
| | - Clara E. Magyar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
| | - Samuel W. French
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
| | - Steven-Huy B. Han
- Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
| | - Edward B. Garon
- Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
| | - Vatche G. Agopian
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
- Department of Surgery, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
| | - Wing Hung Wong
- Department of Statistics, Stanford University, Stanford, CA94305
- Department of Biomedical Data Science, Stanford University, Stanford, CA94305
| | - Steven M. Dubinett
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Department of Medicine, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
- Veterans Administration (VA) Greater Los Angeles Health Care System, Los Angeles, CA90073
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA90095
| | - Xianghong Jasmine Zhou
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA90095
- Institute for Quantitative & Computational Biosciences, University of California at Los Angeles, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA90095
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Keukeleire P, Makrodimitris S, Reinders M. Cell type deconvolution of methylated cell-free DNA at the resolution of individual reads. NAR Genom Bioinform 2023; 5:lqad048. [PMID: 37274121 PMCID: PMC10236360 DOI: 10.1093/nargab/lqad048] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/28/2023] [Accepted: 05/19/2023] [Indexed: 06/06/2023] Open
Abstract
Cell-free DNA (cfDNA) are DNA fragments originating from dying cells that are detectable in bodily fluids, such as the plasma. Accelerated cell death, for example caused by disease, induces an elevated concentration of cfDNA. As a result, determining the cell type origins of cfDNA molecules can provide information about an individual's health. In this work, we aim to increase the sensitivity of methylation-based cell type deconvolution by adapting an existing method, CelFiE, which uses the methylation beta values of individual CpG sites to estimate cell type proportions. Our new method, CelFEER, instead differentiates cell types by the average methylation values within individual reads. We additionally improved the originally reported performance of CelFiE by using a new approach for finding marker regions that are differentially methylated between cell types. We show that CelFEER estimates cell type proportions with a higher correlation (r = 0.94 ± 0.04) than CelFiE (r = 0.86 ± 0.09) on simulated mixtures of cell types. Moreover, we show that the cell type proportion estimated by CelFEER can differentiate between ALS patients and healthy controls, between pregnant women in their first and third trimester, and between pregnant women with and without gestational diabetes.
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Affiliation(s)
| | | | - Marcel Reinders
- To whom correspondence should be addressed. Tel: +31 15 27 86424;
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17
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Gaitsch H, Franklin RJM, Reich DS. Cell-free DNA-based liquid biopsies in neurology. Brain 2023; 146:1758-1774. [PMID: 36408894 PMCID: PMC10151188 DOI: 10.1093/brain/awac438] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 11/10/2022] [Indexed: 11/22/2022] Open
Abstract
This article reviews recent developments in the application of cell-free DNA-based liquid biopsies to neurological diseases. Over the past few decades, an explosion of interest in the use of accessible biofluids to identify and track molecular disease has revolutionized the fields of oncology, prenatal medicine and others. More recently, technological advances in signal detection have allowed for informative analysis of biofluids that are typically sparse in cells and other circulating components, such as CSF. In parallel, advancements in epigenetic profiling have allowed for novel applications of liquid biopsies to diseases without characteristic mutational profiles, including many degenerative, autoimmune, inflammatory, ischaemic and infectious disorders. These events have paved the way for a wide array of neurological conditions to benefit from enhanced diagnostic, prognostic, and treatment abilities through the use of liquid biomarkers: a 'liquid biopsy' approach. This review includes an overview of types of liquid biopsy targets with a focus on circulating cell-free DNA, methods used to identify and probe potential liquid biomarkers, and recent applications of such biomarkers to a variety of complex neurological conditions including CNS tumours, stroke, traumatic brain injury, Alzheimer's disease, epilepsy, multiple sclerosis and neuroinfectious disease. Finally, the challenges of translating liquid biopsies to use in clinical neurology settings-and the opportunities for improvement in disease management that such translation may provide-are discussed.
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Affiliation(s)
- Hallie Gaitsch
- NIH-Oxford-Cambridge Scholars Program, Wellcome-MRC Cambridge Stem Cell Institute and Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 1TN, UK
| | | | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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18
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Moser T, Kühberger S, Lazzeri I, Vlachos G, Heitzer E. Bridging biological cfDNA features and machine learning approaches. Trends Genet 2023; 39:285-307. [PMID: 36792446 DOI: 10.1016/j.tig.2023.01.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/10/2023] [Accepted: 01/19/2023] [Indexed: 02/15/2023]
Abstract
Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.
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Affiliation(s)
- Tina Moser
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Stefan Kühberger
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Isaac Lazzeri
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Georgios Vlachos
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Ellen Heitzer
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria.
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19
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Nabais MF, Gadd DA, Hannon E, Mill J, McRae AF, Wray NR. An overview of DNA methylation-derived trait score methods and applications. Genome Biol 2023; 24:28. [PMID: 36797751 PMCID: PMC9936670 DOI: 10.1186/s13059-023-02855-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 01/17/2023] [Indexed: 02/18/2023] Open
Abstract
Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.
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Affiliation(s)
- Marta F Nabais
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Eilis Hannon
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Jonathan Mill
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
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20
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Novel CSF Biomarkers Tracking Autoimmune Inflammatory and Neurodegenerative Aspects of CNS Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010073. [PMID: 36611365 PMCID: PMC9818715 DOI: 10.3390/diagnostics13010073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
The accurate diagnosis of neuroinflammatory (NIDs) and neurodegenerative (NDDs) diseases and the stratification of patients into disease subgroups with distinct disease-related characteristics that reflect the underlying pathology represents an unmet clinical need that is of particular interest in the era of emerging disease-modifying therapies (DMT). Proper patient selection for clinical trials and identifying those in the prodromal stages of the diseases or those at high risk will pave the way for precision medicine approaches and halt neuroinflammation and/or neurodegeneration in early stages where this is possible. Towards this direction, novel cerebrospinal fluid (CSF) biomarker candidates were developed to reflect the diseased organ's pathology better. Μisfolded protein accumulation, microglial activation, synaptic dysfunction, and finally, neuronal death are some of the pathophysiological aspects captured by these biomarkers to support proper diagnosis and screening. We also describe advances in the field of molecular biomarkers, including miRNAs and extracellular nucleic acids known as cell-free DNA and mitochondrial DNA molecules. Here we review the most important of these novel CSF biomarkers of NIDs and NDDs, focusing on their involvement in disease development and emphasizing their ability to define homogeneous disease phenotypes and track potential treatment outcomes that can be mirrored in the CSF compartment.
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21
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Zhou X, Cheng Z, Dong M, Liu Q, Yang W, Liu M, Tian J, Cheng W. Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis. Nat Commun 2022; 13:7694. [PMID: 36509772 PMCID: PMC9744803 DOI: 10.1038/s41467-022-35320-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring.
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Affiliation(s)
- Xiao Zhou
- grid.12527.330000 0001 0662 3178Department of Automation, Tsinghua University, Beijing, 100084 China
| | - Zhen Cheng
- grid.12527.330000 0001 0662 3178Department of Automation, Tsinghua University, Beijing, 100084 China
| | - Mingyu Dong
- grid.12527.330000 0001 0662 3178Department of Automation, Tsinghua University, Beijing, 100084 China
| | - Qi Liu
- grid.12527.330000 0001 0662 3178Department of Automation, Tsinghua University, Beijing, 100084 China
| | - Weiyang Yang
- grid.12527.330000 0001 0662 3178Department of Automation, Tsinghua University, Beijing, 100084 China
| | - Min Liu
- grid.12527.330000 0001 0662 3178Department of Automation, Tsinghua University, Beijing, 100084 China ,grid.413405.70000 0004 1808 0686Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, 510317 China
| | - Junzhang Tian
- grid.413405.70000 0004 1808 0686Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, 510317 China
| | - Weibin Cheng
- grid.413405.70000 0004 1808 0686Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, 510317 China
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22
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Ahn J, Heo S, Ahn SJ, Bang D, Lee SH. Differentially hypomethylated cell-free DNA and coronary collateral circulation. Clin Epigenetics 2022; 14:140. [PMID: 36320085 PMCID: PMC9628091 DOI: 10.1186/s13148-022-01349-w] [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: 06/08/2022] [Accepted: 10/02/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND The factors affecting cardioprotective collateral circulation are still incompletely understood. Recently, characteristics, such as CpG methylation of cell-free DNA (cfDNA), have been reported as markers with clinical utility. The aim of this study was to evaluate whether cfDNA methylation patterns are associated with the grade of coronary collateral circulation (CCC). RESULT In this case-control study, clinical and angiographic data were obtained from 143 patients (mean age, 58 years, male 71%) with chronic total coronary occlusion. Enzymatic methyl-sequencing (EM-seq) libraries were prepared using the cfDNA extracted from the plasma. Data were processed to obtain the average methylation fraction (AMF) tables of genomic regions from which blacklisted regions were removed. Unsupervised analysis of the obtained AMF values showed that some of the changes in methylation were due to CCC. Through random forest preparation process, 256 differentially methylated region (DMR) candidates showing strong association with CCC were selected. A random forest classifier was then constructed, and the area under the curve of the receiver operating characteristic curve indicated an appropriate predictive function for CCC. Finally, 20 DMRs were identified to have significantly different AMF values between the good and poor CCC groups. Particularly, the good CCC group exhibited hypomethylated DMRs. Pathway analysis revealed five pathways, including TGF-beta signaling, to be associated with good CCC. CONCLUSION These data have demonstrated that differential hypomethylation was identified in dozens of cfDNA regions in patients with good CCC. Our results support the clinical utility of noninvasively obtained epigenetic signatures for predicting collateral circulation in patients with vascular diseases.
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Affiliation(s)
- Jongseong Ahn
- Department of Chemistry, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | | | - Soo-Jin Ahn
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, Korea
| | - Duhee Bang
- Department of Chemistry, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Sang-Hak Lee
- Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.,Pohang University of Science and Technology (POSTECH), Pohang, Korea
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23
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Krasic J, Skara L, Bojanac AK, Ulamec M, Jezek D, Kulis T, Sincic N. The utility of cfDNA in TGCT patient management: a systematic review. Ther Adv Med Oncol 2022; 14:17588359221090365. [PMID: 35656387 PMCID: PMC9152191 DOI: 10.1177/17588359221090365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/10/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Testicular germ cell tumors (TGCTs) are the most common young male malignancy with a steadily rising incidence. Standard clinical practice is radical orchidectomy of suspicious lumps followed by histopathological diagnosis and tumor subtyping. This practice can lead to complications and quality of life issues for the patients. Liquid biopsies, especially cell-free DNA (cfDNA), promised to be true surrogates for tissue biopsies, which are considered dangerous to perform in cases of testicular tumors. In this study, we have performed a systematic review on the potential of cfDNA in TGCT patient management, its potential challenges in translation to clinical application and possible approaches in further research. Materials & Methods: The review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines on EuropePMC and PUBMED electronic databases, with the last update being on October 21, 2021. Due to the high heterogeneity in identified research articles, we have performed an overview of their efficacy. Results: Eight original articles have been identified on cfDNA in TGCT patients published from 2004 to 2021, of which six had more than one TGCT patient enrolled and were included in the final analysis. Three studies investigated cfDNA methylation, one has investigated mutations in cfDNA, two have investigated cfDNA amount, and one has investigated cfDNA integrity in TGCT. The sensitivity of cfDNA for TGCT was found to be higher than in serum tumor markers and lower than miR-371a-3p, with comparable specificity. cfDNA methylation analysis has managed to accurately detect teratoma in TGCT patients. Conclusion: Potential challenges in cfDNA application to TGCT patient management were identified. The challenges relating to the biology of TGCT with its low mutational burden and low cfDNA amounts in blood plasma make next-generation sequencing (NGS) methods especially challenging. We have also proposed possible approaches to help find clinical application, including a focus on cfDNA methylation analysis, and potentially solving the challenge of teratoma detection.
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Affiliation(s)
- Jure Krasic
- Department of Medical Biology, School of Medicine, University of Zagreb, Zagreb, Croatia
- Group for Research on Epigenetic Biomarkers (Epimark), School of Medicine, University of Zagreb, Zagreb, Croatia
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Lucija Skara
- Department of Medical Biology, School of Medicine, University of Zagreb, Zagreb, Croatia
- Group for Research on Epigenetic Biomarkers (Epimark), School of Medicine, University of Zagreb, Zagreb, Croatia
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ana Katusic Bojanac
- Department of Medical Biology, School of Medicine, University of Zagreb, Zagreb, Croatia
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Monika Ulamec
- Group for Research on Epigenetic Biomarkers (Epimark), School of Medicine, University of Zagreb, Zagreb, Croatia
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
- Ljudevit Jurak Clinical Department of Pathology and Cytology, University Clinical Hospital Center Sestre Milosrdnice, Zagreb, Croatia
- Department of Pathology, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davor Jezek
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Histology and Embryology, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Tomislav Kulis
- Group for Research on Epigenetic Biomarkers (Epimark), School of Medicine, University of Zagreb, Zagreb, Croatia
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Urology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Nino Sincic
- Department of Medical Biology, School of Medicine, University of Zagreb, Šalata 3, Zagreb, 10 000, Croatia
- Group for Research on Epigenetic Biomarkers (Epimark), School of Medicine, University of Zagreb, Šalata 3, Zagreb, 10 000, Croatia
- Centre of Excellence for Reproductive and Regenerative Medicine, School of Medicine, University of Zagreb, Šalata 3, Zagreb, 10 000, Croatia
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24
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Neuberger EWI, Sontag S, Brahmer A, Philippi KFA, Radsak MP, Wagner W, Simon P. Physical activity specifically evokes release of cell-free DNA from granulocytes thereby affecting liquid biopsy. Clin Epigenetics 2022; 14:29. [PMID: 35193681 PMCID: PMC8864902 DOI: 10.1186/s13148-022-01245-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 02/07/2022] [Indexed: 11/28/2022] Open
Abstract
Physical activity impacts immune homeostasis and leads to rapid and marked increase in cell-free DNA (cfDNA). However, the origin of cfDNA during exercise remains elusive and it is unknown if physical activity could improve or interfere with methylation based liquid biopsy. We analyzed the methylation levels of four validated CpGs representing cfDNA from granulocytes, lymphocytes, monocytes, and non-hematopoietic cells, in healthy individuals in response to exercise, and in patients with hematological malignancies under resting conditions. The analysis revealed that physical activity almost exclusively triggered DNA release from granulocytes, highlighting the relevance as a pre-analytical variable which could compromise diagnostic accuracy.
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Affiliation(s)
- Elmo W I Neuberger
- Department of Sports Medicine, Rehabilitation and Disease Prevention, Faculty of Social Science, Media and Sport, Johannes Gutenberg-University Mainz, Albert-Schweitzerstr. 22, 55128, Mainz, Germany
| | - Stephanie Sontag
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Alexandra Brahmer
- Department of Sports Medicine, Rehabilitation and Disease Prevention, Faculty of Social Science, Media and Sport, Johannes Gutenberg-University Mainz, Albert-Schweitzerstr. 22, 55128, Mainz, Germany
| | - Keito F A Philippi
- Department of Sports Medicine, Rehabilitation and Disease Prevention, Faculty of Social Science, Media and Sport, Johannes Gutenberg-University Mainz, Albert-Schweitzerstr. 22, 55128, Mainz, Germany
| | - Markus P Radsak
- Department of Medicine III, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University Medical School, Aachen, Germany.,Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Perikles Simon
- Department of Sports Medicine, Rehabilitation and Disease Prevention, Faculty of Social Science, Media and Sport, Johannes Gutenberg-University Mainz, Albert-Schweitzerstr. 22, 55128, Mainz, Germany.
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25
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Liang R, Li X, Li W, Zhu X, Li C. DNA methylation in lung cancer patients: Opening a "window of life" under precision medicine. Biomed Pharmacother 2021; 144:112202. [PMID: 34654591 DOI: 10.1016/j.biopha.2021.112202] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/20/2022] Open
Abstract
DNA methylation is a work of adding a methyl group to the 5th carbon atom of cytosine in DNA sequence under the catalysis of DNA methyltransferase (DNMT) to produce 5-methyl cytosine. Some current studies have elucidated the mechanism of lung cancer occurrence and causes of lung cancer progression and metastasis from the perspective of DNA methylation. Moreover, many studies have shown that smoking can change the methylation status of some gene loci, leading to the occurrence of lung cancer, especially central lung cancer. This review mainly introduces the role of DNA methylation in the pathogenesis, early diagnosis and screening, progression and metastasis, treatment, and prognosis of lung cancer, as well as the latest progress. We point out that methylation markers, sample tests, and methylation detection limit the clinical application of DNA methylation. If the liquid biopsy is to become the main force in lung cancer diagnosis, it must make efficient use of limited samples and improve the sensitivity and specificity of the tests. In addition, we also put forward our views on the future development direction of DNA methylation.
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Affiliation(s)
- Runzhang Liang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou 310053, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Guangdong Medical University, Zhanjiang 524023, China
| | - Xiaosong Li
- Clinical Molecular Medicine Testing Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Weiquan Li
- Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Guangdong Medical University, Zhanjiang 524023, China
| | - Xiao Zhu
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou 310053, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Guangdong Medical University, Zhanjiang 524023, China.
| | - Chen Li
- Department of Biology, Chemistry, Pharmacy, Free University of Berlin, Berlin 14195, Germany.
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