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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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2
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [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: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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3
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Cui S, McGranahan N, Gao J, Chen P, Jiang W, Yang L, Ma L, Liao J, Xie T, Xie C, Enver T, Wu S. Tracking the evolution of esophageal squamous cell carcinoma under dynamic immune selection by multi-omics sequencing. Nat Commun 2023; 14:892. [PMID: 36807354 PMCID: PMC9938262 DOI: 10.1038/s41467-023-36558-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/06/2023] [Indexed: 02/19/2023] Open
Abstract
Intratumoral heterogeneity (ITH) has been linked to decreased efficacy of clinical treatments. However, although genomic ITH has been characterized in genetic, transcriptomic and epigenetic alterations are hallmarks of esophageal squamous cell carcinoma (ESCC), the extent to which these are heterogeneous in ESCC has not been explored in a unified framework. Further, the extent to which tumor-infiltrated T lymphocytes are directed against cancer cells, but how the immune infiltration acts as a selective force to shape the clonal evolution of ESCC is unclear. In this study, we perform multi-omic sequencing on 186 samples from 36 primary ESCC patients. Through multi-omics analyses, it is discovered that genomic, epigenomic, and transcriptomic ITH are underpinned by ongoing chromosomal instability. Based on the RNA-seq data, we observe diverse levels of immune infiltrate across different tumor sites from the same tumor. We reveal genetic mechanisms of neoantigen evasion under distinct selection pressure from the diverse immune microenvironment. Overall, our work offers an avenue of dissecting the complex contribution of the multi-omics level to the ITH in ESCC and thereby enhances the development of clinical therapy.
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Affiliation(s)
- Sijia Cui
- Department of Radiation and Medical Oncology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | | | - Jing Gao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Peng Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Wei Jiang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Lingrong Yang
- Hangzhou Cancer Institution, Hangzhou Cancer Hospital, Hangzhou, China
| | - Li Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Junfang Liao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Tian Xie
- College of Pharmacy, School of Medicine, Hangzhou Normal University, Hangzhou, China
| | - Congying Xie
- Department of Radiation and Medical Oncology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Tariq Enver
- Cancer Institute, University College London, London, UK.
| | - Shixiu Wu
- Department of Radiation and Medical Oncology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. .,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
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4
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Feinberg AP, Levchenko A. Epigenetics as a mediator of plasticity in cancer. Science 2023; 379:eaaw3835. [PMID: 36758093 PMCID: PMC10249049 DOI: 10.1126/science.aaw3835] [Citation(s) in RCA: 72] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 12/22/2022] [Indexed: 02/11/2023]
Abstract
The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.
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Affiliation(s)
- Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University Schools of Medicine, Biomedical Engineering, and Public Health, Baltimore, MD 21205, USA
| | - Andre Levchenko
- Yale Systems Biology Institute and Department of Biomedical Engineering, Yale University, West Haven, CT 06516, USA
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Raineri E, Alberola I Pla M, Dabad M, Heath S. cvlr: finding heterogeneously methylated genomic regions using ONT reads. BIOINFORMATICS ADVANCES 2023; 3:vbac101. [PMID: 36726731 PMCID: PMC9887406 DOI: 10.1093/bioadv/vbac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 01/25/2023]
Abstract
Summary Nanopore reads encode information on the methylation status of cytosines in CpG dinucleotides. The length of the reads makes it comparatively easy to look at patterns consisting of multiple loci; here, we exploit this property to search for regions where one can define subpopulations of molecules based on methylation patterns. As an example, we run our clustering algorithm on known imprinted genes; we also scan chromosome 15 looking for windows corresponding to heterogeneous methylation. Our software can also compute the covariance of methylation across these regions while keeping into account the mixture of different types of reads. Availability and implementation https://github.com/EmanueleRaineri/cvlr. Contact simon.heath@cnag.crg.eu. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Emanuele Raineri
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
| | - Mariona Alberola I Pla
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
| | - Marc Dabad
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
| | - Simon Heath
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
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6
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Benetatos L, Benetatou A, Vartholomatos G. Epialleles and epiallelic heterogeneity in hematological malignancies. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2022; 39:139. [PMID: 35834015 DOI: 10.1007/s12032-022-01737-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/22/2022] [Indexed: 10/17/2022]
Abstract
DNA methylation has a well-established role in the pathogenesis, prognosis, and response to treatment in all the spectra of hematological malignancies. However, most of the data reported involve average DNA methylation observed in a sample. The emergence of bisulfite sequencing methods such as enhanced reduced representation that permit analyze adjacent CpGs led to exciting findings. Among these are the epialleles shift and the resulting epigenetic heterogeneity observed in leukemias and lymphomas. Epialleles seem to have an influential role as the cause of mutations that characterize leukemias, may stratify groups with different prognosis and response to treatment, and may be redistributed in the genome at different time points of the disease promoting activation of alternate transcriptional networks. Epiallelic shift may be responsible for the intratumor heterogeneity observed within the cells of the same tumor which increases with disease aggressiveness. It may also responsible for the interpatient heterogeneity explaining why blood cancers exhibit different behavior among different patients. Understanding better epiallelic conformation and the consequent chromatin conformational changes and the pathways that may be affected will permit deeper understanding of hematological malignancies pathogenesis and treatment.
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Affiliation(s)
- Leonidas Benetatos
- Blood Bank, Preveza General Hospital, Selefkias 2, 48100, Preveza, Greece.
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7
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Jeong Y, de Andrade E Sousa LB, Thalmeier D, Toth R, Ganslmeier M, Breuer K, Plass C, Lutsik P. Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes. Brief Bioinform 2022; 23:6632618. [PMID: 35794707 PMCID: PMC9294431 DOI: 10.1093/bib/bbac248] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022] Open
Abstract
DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods—both reference-based and reference-free—generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.
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Affiliation(s)
- Yunhee Jeong
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.,Faculty of Mathematics and Informatics, Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, Germany
| | | | - Dominik Thalmeier
- Helmholtz AI, Helmholtz Zentrum München, Ingolstädter Landstraβ e 1, 85764, Neuherberg, Germany
| | - Reka Toth
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Marlene Ganslmeier
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Kersten Breuer
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
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8
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Challenges in promoter methylation analysis in the new era of translational oncology: a focus on liquid biopsy. Biochim Biophys Acta Mol Basis Dis 2022; 1868:166390. [PMID: 35296416 DOI: 10.1016/j.bbadis.2022.166390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/01/2022] [Accepted: 03/08/2022] [Indexed: 12/20/2022]
Abstract
Toward the discovery of novel reliable biomarkers, epigenetic alterations have been repeatedly proposed for the diagnosis and the development of therapeutic strategies against cancer. Indeed, for promoter methylation to actively become a tumor marker for clinical use, it must be combined with a highly informative technology evaluated in an appropriate biospecimen. Methodological standardization related to epigenetic research is, in fact, one of the most challenging tasks. Moreover, tissue-based biopsy is being complemented and, in some cases, replaced by liquid biopsy. This review will highlight the advancements made for both pre-analytical and analytical implementation for the prospective use of methylation biomarkers in clinical settings, with particular emphasis on liquid biopsy.
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9
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Sarnataro A, De Riso G, Cocozza S, Pezone A, Majello B, Amente S, Scala G. A novel workflow for the qualitative analysis of DNA methylation data. Comput Struct Biotechnol J 2022; 20:5925-5934. [PMID: 36382198 PMCID: PMC9636440 DOI: 10.1016/j.csbj.2022.10.027] [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: 07/20/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/30/2022] Open
Abstract
A novel R package (EpiStatProfiler) for the qualitative analysis of DNA methylation data. A novel workflow for the analysis of CG and non-CG epialleles starting from any type of bisulfite sequencing data. EpiStatProfiler can perform strand-specific characterization of epialleles composition. Important loci can be annotated along with their biological role and potential functions. EpiStatProfiler has the ability to identify loci whose epiallelic profile is associated with disease pathogenesis.
DNA methylation is an epigenetic modification that plays a pivotal role in major biological mechanisms, such as gene regulation, genomic imprinting, and genome stability. Different combinations of methylated cytosines for a given DNA locus generate different epialleles and alterations of these latter have been associated with several pathological conditions. Existing computational methods and statistical tests relevant to DNA methylation analysis are mostly based on the comparison of average CpG sites methylation levels and they often neglect non-CG methylation. Here, we present EpiStatProfiler, an R package that allows the analysis of CpG and non-CpG based epialleles starting from bisulfite sequencing data through a collection of dedicated extraction functions and statistical tests. EpiStatProfiler is provided with a set of useful auxiliary features, such as customizable genomic ranges, strand-specific epialleles analysis, locus annotation and gene set enrichment analysis. We showcase the package functionalities on two public datasets by identifying putative relevant loci in mice harboring the Huntington’s disease-causing Htt gene mutation and in Ctcf +/− mice compared to their wild-type counterparts. To our knowledge, EpiStatProfiler is the first package providing functionalities dedicated to the analysis of epialleles composition derived from any kind of bisulfite sequencing experiment.
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10
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Liang W, Chen Z, Li C, Liu J, Tao J, Liu X, Zhao D, Yin W, Chen H, Cheng C, Yu F, Zhang C, Liu L, Tian H, Cai K, Liu X, Wang Z, Xu N, Dong Q, Chen L, Yang Y, Zhi X, Li H, Tu X, Cai X, Jiang Z, Ji H, Mo L, Wang J, Fan JB, He J. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest 2021; 131:145973. [PMID: 33793424 PMCID: PMC8121527 DOI: 10.1172/jci145973] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/18/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUNDCurrent clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests.METHODWe developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules.RESULTSA 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones.CONCLUSIONThis study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions.FUNDINGThe National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.
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Affiliation(s)
- Wenhua Liang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Zhiwei Chen
- AnchorDx Medical Co., Guangzhou, China
- AnchorDx Inc., Fremont, California, USA
| | - Caichen Li
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Jun Liu
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | | | - Xin Liu
- AnchorDx Inc., Fremont, California, USA
| | | | - Weiqiang Yin
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Hanzhang Chen
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Chao Cheng
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fenglei Yu
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Chunfang Zhang
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Luxu Liu
- Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Kaican Cai
- Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou, China
| | - Xiang Liu
- Department of Thoracic Surgery, The Second Hospital, University of South China, Hengyang, China
| | - Zheng Wang
- Department of Thoracic Surgery, Shenzhen People’s Hospital, Shenzhen, China
| | - Ning Xu
- Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, China
| | - Qing Dong
- Department of Thoracic Surgery, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liang Chen
- Department of Thoracic Surgery, Jiangsu Province Hospital, Nanjing, China
| | - Yue Yang
- Department of Thoracic Surgery, Beijing Cancer Hospital, Beijing, China
| | - Xiuyi Zhi
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hui Li
- AnchorDx Medical Co., Guangzhou, China
| | | | - Xiangrui Cai
- College of Computer Science, Nankai University, Tianjin, China
| | | | - Hua Ji
- College of Computer Science, Nankai University, Tianjin, China
- Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, United Kingdom
| | - Lili Mo
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Jiaxuan Wang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Jian-Bing Fan
- AnchorDx Medical Co., Guangzhou, China
- Department of Pathology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Jianxing He
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
- Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou, China
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11
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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12
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Lam D, Luu PL, Song JZ, Qu W, Risbridger GP, Lawrence MG, Lu J, Trau M, Korbie D, Clark SJ, Pidsley R, Stirzaker C. Comprehensive evaluation of targeted multiplex bisulphite PCR sequencing for validation of DNA methylation biomarker panels. Clin Epigenetics 2020; 12:90. [PMID: 32571390 PMCID: PMC7310104 DOI: 10.1186/s13148-020-00880-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 06/04/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND DNA methylation is a well-studied epigenetic mark that is frequently altered in diseases such as cancer, where specific changes are known to reflect the type and severity of the disease. Therefore, there is a growing interest in assessing the clinical utility of DNA methylation as a biomarker for diagnosing disease and guiding treatment. The development of an accurate loci-specific methylation assay, suitable for use on low-input clinical material, is crucial for advancing DNA methylation biomarkers into a clinical setting. A targeted multiplex bisulphite PCR sequencing approach meets these needs by allowing multiple DNA methylated regions to be interrogated simultaneously in one experiment on limited clinical material. RESULTS Here, we provide an updated protocol and recommendations for multiplex bisulphite PCR sequencing (MBPS) assays for target DNA methylation analysis. We describe additional steps to improve performance and reliability: (1) pre-sequencing PCR optimisation which includes assessing the optimal PCR cycling temperature and primer concentration and (2) post-sequencing PCR optimisation to achieve uniform coverage of each amplicon. We use a gradient of methylated controls to demonstrate how PCR bias can be assessed and corrected. Methylated controls also allow assessment of the sensitivity of methylation detection for each amplicon. Here, we show that the MBPS assay can amplify as little as 0.625 ng starting DNA and can detect methylation differences of 1% with a sequencing coverage of 1000 reads. Furthermore, the multiplex bisulphite PCR assay can comprehensively interrogate multiple regions on 1-5 ng of formalin-fixed paraffin-embedded DNA or circulating cell-free DNA. CONCLUSIONS The MBPS assay is a valuable approach for assessing methylated DNA regions in clinical samples with limited material. The optimisation and additional quality control steps described here improve the performance and reliability of this method, advancing it towards potential clinical applications in biomarker studies.
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Affiliation(s)
- Dilys Lam
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia
| | - Phuc-Loi Luu
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, New South Wales, 2010, Australia
| | - Jenny Z Song
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia
| | - Wenjia Qu
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia
| | - Gail P Risbridger
- Monash Partners Comprehensive Cancer Consortium, Monash Biomedicine Discovery Institute Cancer Program, Prostate Cancer Research Group, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Mitchell G Lawrence
- Monash Partners Comprehensive Cancer Consortium, Monash Biomedicine Discovery Institute Cancer Program, Prostate Cancer Research Group, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Jennifer Lu
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Matt Trau
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Darren Korbie
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Susan J Clark
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia
- St Vincent's Clinical School, UNSW Sydney, Sydney, New South Wales, 2010, Australia
| | - Ruth Pidsley
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia.
- St Vincent's Clinical School, UNSW Sydney, Sydney, New South Wales, 2010, Australia.
| | - Clare Stirzaker
- Epigenetics Research Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, 2010, Australia.
- St Vincent's Clinical School, UNSW Sydney, Sydney, New South Wales, 2010, Australia.
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13
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Lee D, Lee S, Kim S. PRISM: methylation pattern-based, reference-free inference of subclonal makeup. Bioinformatics 2020; 35:i520-i529. [PMID: 31510697 PMCID: PMC6612819 DOI: 10.1093/bioinformatics/btz327] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Motivation Characterizing cancer subclones is crucial for the ultimate conquest of cancer. Thus, a number of bioinformatic tools have been developed to infer heterogeneous tumor populations based on genomic signatures such as mutations and copy number variations. Despite accumulating evidence for the significance of global DNA methylation reprogramming in certain cancer types including myeloid malignancies, none of the bioinformatic tools are designed to exploit subclonally reprogrammed methylation patterns to reveal constituent populations of a tumor. In accordance with the notion of global methylation reprogramming, our preliminary observations on acute myeloid leukemia (AML) samples implied the existence of subclonally occurring focal methylation aberrance throughout the genome. Results We present PRISM, a tool for inferring the composition of epigenetically distinct subclones of a tumor solely from methylation patterns obtained by reduced representation bisulfite sequencing. PRISM adopts DNA methyltransferase 1-like hidden Markov model-based in silico proofreading for the correction of erroneous methylation patterns. With error-corrected methylation patterns, PRISM focuses on a short individual genomic region harboring dichotomous patterns that can be split into fully methylated and unmethylated patterns. Frequencies of such two patterns form a sufficient statistic for subclonal abundance. A set of statistics collected from each genomic region is modeled with a beta-binomial mixture. Fitting the mixture with expectation-maximization algorithm finally provides inferred composition of subclones. Applying PRISM for two AML samples, we demonstrate that PRISM could infer the evolutionary history of malignant samples from an epigenetic point of view. Availability and implementation PRISM is freely available on GitHub (https://github.com/dohlee/prism). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Sangseon Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.,Bioinformatics Institute, Seoul National University, Seoul, Korea
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14
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Nieroda L, Maas L, Thiebes S, Lang U, Sunyaev A, Achter V, Peifer M. iRODS metadata management for a cancer genome analysis workflow. BMC Bioinformatics 2019; 20:29. [PMID: 30646845 PMCID: PMC6334444 DOI: 10.1186/s12859-018-2576-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 12/10/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date. RESULTS We describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information. CONCLUSIONS Integrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.
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Affiliation(s)
- Lech Nieroda
- Regional Computing Center (RRZK), University of Cologne, Cologne, 50931 Germany
| | - Lukas Maas
- Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, Cologne, 50931 Germany
| | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, 76133 Germany
| | - Ulrich Lang
- Regional Computing Center (RRZK), University of Cologne, Cologne, 50931 Germany
| | - Ali Sunyaev
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, 76133 Germany
| | - Viktor Achter
- Regional Computing Center (RRZK), University of Cologne, Cologne, 50931 Germany
| | - Martin Peifer
- Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, Cologne, 50931 Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, 50931 Germany
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15
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Assenov Y, Brocks D, Gerhäuser C. Intratumor heterogeneity in epigenetic patterns. Semin Cancer Biol 2018; 51:12-21. [PMID: 29366906 DOI: 10.1016/j.semcancer.2018.01.010] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 11/24/2017] [Accepted: 01/17/2018] [Indexed: 02/08/2023]
Abstract
Analogous to life on earth, tumor cells evolve through space and time and adapt to different micro-environmental conditions. As a result, tumors are composed of millions of genetically diversified cells at the time of diagnosis. Profiling these variants contributes to understanding tumors' clonal origins and might help to better understand response to therapy. However, even genetically homogenous cell populations show remarkable diversity in their response to different environmental stimuli, suggesting that genetic heterogeneity does not explain the full spectrum of tumor plasticity. Understanding epigenetic diversity across cancer cells provides important additional information about the functional state of subclones and therefore allows better understanding of tumor evolution and resistance to current therapies.
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Affiliation(s)
- Yassen Assenov
- Epigenomics and Cancer Risk Factors, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - David Brocks
- Epigenomics and Cancer Risk Factors, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Clarissa Gerhäuser
- Epigenomics and Cancer Risk Factors, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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