1
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Lagarde CB, Kavalakatt J, Benz MC, Hawes ML, Arbogast CA, Cullen NM, McConnell EC, Rinderle C, Hebert KL, Khosla M, Belgodere JA, Hoang VT, Collins-Burow BM, Bunnell BA, Burow ME, Alahari SK. Obesity-associated epigenetic alterations and the obesity-breast cancer axis. Oncogene 2024; 43:763-775. [PMID: 38310162 DOI: 10.1038/s41388-024-02954-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/05/2024]
Abstract
Both breast cancer and obesity can regulate epigenetic changes or be regulated by epigenetic changes. Due to the well-established link between obesity and an increased risk of developing breast cancer, understanding how obesity-mediated epigenetic changes affect breast cancer pathogenesis is critical. Researchers have described how obesity and breast cancer modulate the epigenome individually and synergistically. In this review, the epigenetic alterations that occur in obesity, including DNA methylation, histone, and chromatin modification, accelerated epigenetic age, carcinogenesis, metastasis, and tumor microenvironment modulation, are discussed. Delineating the relationship between obesity and epigenetic regulation is vital to furthering our understanding of breast cancer pathogenesis.
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Affiliation(s)
- Courtney B Lagarde
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Joachim Kavalakatt
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Megan C Benz
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Mackenzie L Hawes
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Carter A Arbogast
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Nicole M Cullen
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Emily C McConnell
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Caroline Rinderle
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Katherine L Hebert
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Maninder Khosla
- Department of Biochemistry and Molecular Biology, LSU Health Science Center School of Medicine, New Orleans, LA, 70112, USA
| | - Jorge A Belgodere
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
- Department of Biological and Agricultural Engineering, Louisiana State University and Agricultural Center, Baton Rouge, LA, 70803, USA
| | - Van T Hoang
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Bridgette M Collins-Burow
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Bruce A Bunnell
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Matthew E Burow
- Department of Medicine, Section of Hematology and Oncology, Tulane University School of Medicine, New Orleans, LA, 70112, USA.
| | - Suresh K Alahari
- Department of Biochemistry and Molecular Biology, LSU Health Science Center School of Medicine, New Orleans, LA, 70112, USA.
- Stanley S. Scott Cancer Center, LSU Health Science Center School of Medicine, New Orleans, LA, 70112, USA.
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2
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Zhang M, Zhang X, Ma T, Wang C, Zhao J, Gu Y, Zhang Y. Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer. Comput Biol Med 2023; 163:107222. [PMID: 37413851 DOI: 10.1016/j.compbiomed.2023.107222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/18/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023]
Abstract
A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.
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Affiliation(s)
- Mengyan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xingda Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, China
| | - Te Ma
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Cong Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Jiyun Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yue Gu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China; College of Pathology, Qiqihar Medical University, Qiqihar, 161042, China.
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3
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de Luna FCF, Ferreira WAS, Casseb SMM, de Oliveira EHC. Anticancer Potential of Flavonoids: An Overview with an Emphasis on Tangeretin. Pharmaceuticals (Basel) 2023; 16:1229. [PMID: 37765037 PMCID: PMC10537037 DOI: 10.3390/ph16091229] [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: 07/26/2023] [Revised: 08/18/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
Natural compounds with pharmacological activity, flavonoids have been the subject of an exponential increase in studies in the field of scientific research focused on therapeutic purposes due to their bioactive properties, such as antioxidant, anti-inflammatory, anti-aging, antibacterial, antiviral, neuroprotective, radioprotective, and antitumor activities. The biological potential of flavonoids, added to their bioavailability, cost-effectiveness, and minimal side effects, direct them as promising cytotoxic anticancer compounds in the optimization of therapies and the search for new drugs in the treatment of cancer, since some extensively antineoplastic therapeutic approaches have become less effective due to tumor resistance to drugs commonly used in chemotherapy. In this review, we emphasize the antitumor properties of tangeretin, a flavonoid found in citrus fruits that has shown activity against some hallmarks of cancer in several types of cancerous cell lines, such as antiproliferative, apoptotic, anti-inflammatory, anti-metastatic, anti-angiogenic, antioxidant, regulatory expression of tumor-suppressor genes, and epigenetic modulation.
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Affiliation(s)
- Francisco Canindé Ferreira de Luna
- Laboratory of Cytogenomics and Environmental Mutagenesis, Environment Section (SEAMB), Evandro Chagas Institute (IEC), BR 316, KM 7, s/n, Levilândia, Ananindeua 67030-000, Brazil; (W.A.S.F.); (E.H.C.d.O.)
| | - Wallax Augusto Silva Ferreira
- Laboratory of Cytogenomics and Environmental Mutagenesis, Environment Section (SEAMB), Evandro Chagas Institute (IEC), BR 316, KM 7, s/n, Levilândia, Ananindeua 67030-000, Brazil; (W.A.S.F.); (E.H.C.d.O.)
| | | | - Edivaldo Herculano Correa de Oliveira
- Laboratory of Cytogenomics and Environmental Mutagenesis, Environment Section (SEAMB), Evandro Chagas Institute (IEC), BR 316, KM 7, s/n, Levilândia, Ananindeua 67030-000, Brazil; (W.A.S.F.); (E.H.C.d.O.)
- Faculty of Natural Sciences, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Rua Augusto Correa, 01, Belém 66075-990, Brazil
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4
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Ji Y, Dutta P, Davuluri R. Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes. BIOINFORMATICS ADVANCES 2023; 3:vbad075. [PMID: 37424943 PMCID: PMC10328436 DOI: 10.1093/bioadv/vbad075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/08/2023] [Indexed: 07/11/2023]
Abstract
Motivation Molecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches. Results We developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks. Availability and implementation Source codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Yanrong Ji
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Pratik Dutta
- Department of Biomedical Informatics, Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ramana Davuluri
- Department of Biomedical Informatics, Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook University, Stony Brook, NY 11794, USA
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5
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Choi JM, Chae H. moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks. BMC Bioinformatics 2023; 24:169. [PMID: 37101124 PMCID: PMC10131354 DOI: 10.1186/s12859-023-05273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Breast cancer is a highly heterogeneous disease that comprises multiple biological components. Owing its diversity, patients have different prognostic outcomes; hence, early diagnosis and accurate subtype prediction are critical for treatment. Standardized breast cancer subtyping systems, mainly based on single-omics datasets, have been developed to ensure proper treatment in a systematic manner. Recently, multi-omics data integration has attracted attention to provide a comprehensive view of patients but poses a challenge due to the high dimensionality. In recent years, deep learning-based approaches have been proposed, but they still present several limitations. RESULTS In this study, we describe moBRCA-net, an interpretable deep learning-based breast cancer subtype classification framework that uses multi-omics datasets. Three omics datasets comprising gene expression, DNA methylation and microRNA expression data were integrated while considering the biological relationships among them, and a self-attention module was applied to each omics dataset to capture the relative importance of each feature. The features were then transformed to new representations considering the respective learned importance, allowing moBRCA-net to predict the subtype. CONCLUSIONS Experimental results confirmed that moBRCA-net has a significantly enhanced performance compared with other methods, and the effectiveness of multi-omics integration and omics-level attention were identified. moBRCA-net is publicly available at https://github.com/cbi-bioinfo/moBRCA-net .
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Affiliation(s)
- Joung Min Choi
- Department of Computer Science, Virginia Tech, Blacksburg, USA
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women's University, Seoul, Republic of Korea.
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Choi JM, Park C, Chae H. meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles. BMC Bioinformatics 2023; 24:168. [PMID: 37101254 PMCID: PMC10131478 DOI: 10.1186/s12859-023-05272-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed. RESULTS In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets. CONCLUSIONS From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .
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Affiliation(s)
- Joung Min Choi
- Department of Computer Science, Virginia Tech, Blacksburg, USA
| | - Chaelin Park
- Division of Computer Science, Sookmyung Women's University, Seoul, Republic of Korea
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women's University, Seoul, Republic of Korea.
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7
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Romagnoli D, Nardone A, Galardi F, Paoli M, De Luca F, Biagioni C, Franceschini GM, Pestrin M, Sanna G, Moretti E, Demichelis F, Migliaccio I, Biganzoli L, Malorni L, Benelli M. MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples. Brief Bioinform 2023; 24:6991124. [PMID: 36653909 DOI: 10.1093/bib/bbad015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 01/20/2023] Open
Abstract
DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.
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Affiliation(s)
| | - Agostina Nardone
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Francesca Galardi
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Marta Paoli
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca De Luca
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Chiara Biagioni
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Gian Marco Franceschini
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Marta Pestrin
- Medical Oncology Unit, Azienda Sanitaria Universitaria Giuliano Isontina, 34170 Gorizia, Italy
| | - Giuseppina Sanna
- Medical Oncology, Ospedale Civile SS Annunziata, 07100 Sassari, Italy
| | - Erica Moretti
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Ilenia Migliaccio
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Laura Biganzoli
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Luca Malorni
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Matteo Benelli
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
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8
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Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility. Br J Cancer 2023; 128:1208-1222. [PMID: 36725920 PMCID: PMC10050410 DOI: 10.1038/s41416-023-02178-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 01/19/2023] [Accepted: 01/23/2023] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Oestrogen receptor (ER) signalling-dependent cancer cell growth is one of the major features of ER-positive breast cancer (BC). Inhibition of ER function is a standard and effective treatment for ER-positive tumours; however, ~20% of patients with ER-positive BC experience early or late recurrence. In this study, we examined intertumour heterogeneity from an epigenetic perspective based on the hypothesis that the intrinsic difference in epigenetic states around ER signalling pathway underlies endocrine therapy resistance. METHODS We performed transposase-accessible chromatin sequencing (ATAC-seq) analysis of 42 BC samples, including 35 ER-positive(+) human epidermal growth factor receptor 2 (HER2)-negative(-) and 7 triple-negative tumours. We also reanalysed ATAC-seq data of 45 ER + /HER2 - tumours in the Cancer Genome Atlas (TCGA) BC cohort to validate our observations. RESULTS We conducted a comprehensive analysis of cis-regulatory elements (CREs) using ATAC-seq, identifying three subgroups based on chromatin accessibility profiles. We identified a subgroup of ER-positive BCs with a distinctive chromatin accessibility pattern including reduced accessibility to ER-responsive elements (EREs). The same subgroup was also observed in TCGA BC cohort. Despite the reduced accessibility to EREs, the expression of ER and potential ER target genes were not decreased in these tumours. CONCLUSION Our findings highlight the existence of a subset of ER-positive BCs with unchanged ER expression but reduced EREs accessibility that cannot be distinguished by conventional immunostaining for ER. Future studies should determine whether these tumours are associated with resistance to endocrine therapy.
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Devall M, Soanes DM, Smith AR, Dempster EL, Smith RG, Burrage J, Iatrou A, Hannon E, Troakes C, Moore K, O'Neill P, Al-Sarraj S, Schalkwyk L, Mill J, Weedon M, Lunnon K. Genome-wide characterization of mitochondrial DNA methylation in human brain. Front Endocrinol (Lausanne) 2023; 13:1059120. [PMID: 36726473 PMCID: PMC9885148 DOI: 10.3389/fendo.2022.1059120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 01/17/2023] Open
Abstract
Background There is growing interest in the role of DNA methylation in regulating the transcription of mitochondrial genes, particularly in brain disorders characterized by mitochondrial dysfunction. Here, we present a novel approach to interrogate the mitochondrial DNA methylome at single base resolution using targeted bisulfite sequencing. We applied this method to investigate mitochondrial DNA methylation patterns in post-mortem superior temporal gyrus and cerebellum brain tissue from seven human donors. Results We show that mitochondrial DNA methylation patterns are relatively low but conserved, with peaks in DNA methylation at several sites, such as within the D-LOOP and the genes MT-ND2, MT-ATP6, MT-ND4, MT-ND5 and MT-ND6, predominantly in a non-CpG context. The elevated DNA methylation we observe in the D-LOOP we validate using pyrosequencing. We identify loci that show differential DNA methylation patterns associated with age, sex and brain region. Finally, we replicate previously reported differentially methylated regions between brain regions from a methylated DNA immunoprecipitation sequencing study. Conclusions We have annotated patterns of DNA methylation at single base resolution across the mitochondrial genome in human brain samples. Looking to the future this approach could be utilized to investigate the role of mitochondrial epigenetic mechanisms in disorders that display mitochondrial dysfunction.
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Affiliation(s)
- Matthew Devall
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Darren M Soanes
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Adam R Smith
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Emma L Dempster
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Rebecca G Smith
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Joe Burrage
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Artemis Iatrou
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Claire Troakes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Karen Moore
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Paul O'Neill
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Safa Al-Sarraj
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Leonard Schalkwyk
- School of Biological Sciences, University of Essex, Essex, United Kingdom
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Michael Weedon
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
| | - Katie Lunnon
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
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10
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Yang X, Zheng W, Li M, Zhang S. Somatic Super-Enhancer Epigenetic Signature for Overall Survival Prediction in Patients with Breast Invasive Carcinoma. Bioinform Biol Insights 2023; 17:11779322231162767. [PMID: 37020500 PMCID: PMC10068971 DOI: 10.1177/11779322231162767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 02/18/2023] [Indexed: 04/03/2023] Open
Abstract
To analyze genome-wide super-enhancers (SEs) methylation signature of breast invasive carcinoma (BRCA) and its clinical value. Differential methylation sites (DMS) between BRCA and adjacent tissues from The Cancer Genome Atlas (TCGA) database were identified by using ChAMP package in R software. Super-enhancers were identified sing ROSE software. Overlap analysis was used to assess the potential DMS in SEs region. Feature selection was performed by Cox regression and least absolute shrinkage and selection operator (LASSO) algorithm based on TCGA training cohort. Prognosis model validation was performed in TCGA training cohort, TCGA validation cohort, and gene expression omnibus (GEO) test cohort. The gene ontology and KEGG analysis revealed that SEs target genes were significantly enriched in cell-migration-associated processes and pathways. A total of 83 654 DMS were identified between BRCA and adjacent tissues. Around 2397 DMS in SEs region were identified by overlap study and used to feature selection. By using Cox regression and LASSO algorithm, 42 features were selected to develop a clinical prediction model (CPM). Both training (TCGA) and validation cohorts (TCGA and GEO) show that the CPM has ideal discrimination and calibration. The CPM based on DMS at SE regions has ideal discrimination and calibration, which combined with tumor node metastasis (TNM) stage could improve prognostication, and thus contribute to individualized medicine.
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Affiliation(s)
- Xu Yang
- Department of Urology, Fujian Medical
University Union Hospital, Fuzhou, P.R. China
| | - Wenzhong Zheng
- Department of Urology, Fujian Medical
University Union Hospital, Fuzhou, P.R. China
| | - Mengqiang Li
- Department of Urology, Fujian Medical
University Union Hospital, Fuzhou, P.R. China
| | - Shiqiang Zhang
- Department of Urology, Kidney and
Urology Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen,
P.R. China
- Shiqiang Zhang, Department of Urology,
Kidney and Urology Center, The Seventh Affiliated Hospital, Sun Yat-Sen
University, No.628, Zhenyuan Rd, Guangming (New) Dist., Shenzhen 518107, P.R.
China.
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11
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Nirgude S, Desai S, Choudhary B. Genome-wide differential DNA methylation analysis of MDA-MB-231 breast cancer cells treated with curcumin derivatives, ST08 and ST09. BMC Genomics 2022; 23:807. [PMID: 36474139 PMCID: PMC9727864 DOI: 10.1186/s12864-022-09041-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 10/17/2022] [Indexed: 12/12/2022] Open
Abstract
ST08 and ST09 are potent curcumin derivatives with antiproliferative, apoptotic, and migrastatic properties. Both ST08 and ST09 exhibit in vitro and in vivo anticancer properties. As reported earlier, these derivatives were highly cytotoxic towards MDA-MB-231 triple-negative breast cancer cells with IC50 values in the nanomolar (40-80nM) range.In this study,we performed whole-genome bisulfite sequencing(WGBS) of untreated (control), ST08 and ST09 (treated) triple-negative breast cancer cell line MDA-MB-231 to unravel epigenetic changes induced by the drug. We identified differentially methylated sites (DMSs) enriched in promoter regions across the genome. Analysis of the CpG island promoter methylation identified 12 genes common to both drugs, and 50% of them are known to be methylated in patient samples that were hypomethylated by drugs belonging to the homeobox family transcription factors.Methylation analysis of the gene body revealed 910 and 952 genes to be hypermethylatedin ST08 and ST09 treated MDA-MB-231 cells respectively. Correlation of the gene body hypermethylation with expression revealed CACNAH1 to be upregulated in ST08 treatment and CDH23 upregulation in ST09.Further, integrated analysis of the WGBS with RNA-seq identified uniquely altered pathways - ST08 altered ECM pathway, and ST09 cell cycle, indicating drug-specific signatures.
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Affiliation(s)
- Snehal Nirgude
- grid.418831.70000 0004 0500 991XInstitute of Bioinformatics and Applied Biotechnology, Electronic city phase 1, 560100 Bangalore, India ,grid.239552.a0000 0001 0680 8770Working at Division of Human Genetics, Children’s Hospital of Philadelphia, 19104 Philadelphia, PA USA
| | - Sagar Desai
- grid.418831.70000 0004 0500 991XInstitute of Bioinformatics and Applied Biotechnology, Electronic city phase 1, 560100 Bangalore, India
| | - Bibha Choudhary
- grid.418831.70000 0004 0500 991XInstitute of Bioinformatics and Applied Biotechnology, Electronic city phase 1, 560100 Bangalore, India
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12
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Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer. Int J Mol Sci 2022; 23:ijms232112747. [PMID: 36361541 PMCID: PMC9658742 DOI: 10.3390/ijms232112747] [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: 09/02/2022] [Revised: 10/03/2022] [Accepted: 10/20/2022] [Indexed: 11/05/2022] Open
Abstract
Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and “Boruta” feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.
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13
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Ma Z, Wang Y, Quan Y, Wang Z, Liu Y, Ding Z. Maternal obesity alters methylation level of cytosine in CpG island for epigenetic inheritance in fetal umbilical cord blood. Hum Genomics 2022; 16:34. [PMID: 36045397 PMCID: PMC9429776 DOI: 10.1186/s40246-022-00410-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 08/22/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Over the past few decades, global maternal obesity prevalence has rapidly increased. This condition may induce long-lasting pathophysiological effects on either fetal or infant health that could be attributable to unknown unique changes in the umbilical blood composition. METHODS A total of 34 overweight/obese and 32 normal-weight pregnant women were recruited. Fifteen umbilical blood samples including 8 overweight/obese subjects and 7 normal weight women were sequenced using Targeted Bisulfite Sequencing technology to detect the average methylation level of cytosine and identify the differentially methylated region (DMR). GO and KEGG analyses were then employed to perform pathway enrichment analysis of DMR-related genes and promoters. Moreover, the mRNA levels of methylation-related genes histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) were characterized in the samples obtained from these two groups. RESULTS Average methylated cytosine levels in both the CpG islands (CGI) and promoter significantly decreased in overweight/obese groups. A total of 1669 DMRs exhibited differences in their DNA methylation status between the overweight/obese and control groups. GO and KEGG analyses revealed that DMR-related genes and promoters were enriched in the metabolism, cancer and cardiomyopathy signaling pathways. Furthermore, the HDACs and DNMTs mRNA levels trended to decline in overweight/obese groups. CONCLUSIONS Decreased methylated cytosine levels in overweight/obese women induce the gene expression activity at a higher level than in the control group. DMRs between these two groups in the fetal blood may contribute to the changes in gene transcription that underlie the increased risk of metabolic disorders, cancers and cardiomyopathy in their offspring.
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Affiliation(s)
- Zhuoyao Ma
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China
| | - Yingjin Wang
- Department of Obstetrics and Gynecology, Shanghai Eighth People's Hospital, Shanghai, 200235, China
| | - Yanmei Quan
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China
| | - Zhijie Wang
- Department of Obstetrics and Gynecology, Shanghai Eighth People's Hospital, Shanghai, 200235, China.
| | - Yue Liu
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China.
| | - Zhide Ding
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China.
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14
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Yang J, Wang Q, Zhang ZY, Long L, Ezhilarasan R, Karp JM, Tsirigos A, Snuderl M, Wiestler B, Wick W, Miao Y, Huse JT, Sulman EP. DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas. Nat Commun 2022; 13:4410. [PMID: 35906213 PMCID: PMC9338285 DOI: 10.1038/s41467-022-31827-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 07/05/2022] [Indexed: 02/06/2023] Open
Abstract
Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies. No clinical assay currently exists to classify glioma tumours based on gene expression. Here, the authors develop a DNA methylation-based classifier, Unified Diagnostic Pipeline (UniD) that identifies genomic alterations and gene expression subtypes of gliomas.
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Affiliation(s)
- Jie Yang
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.,Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.,Quantitative Science Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Qianghu Wang
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Ze-Yan Zhang
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.,Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Lihong Long
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ravesanker Ezhilarasan
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.,Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Jerome M Karp
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA.,Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Aristotelis Tsirigos
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.,Applied Bioinformatics Laboratory, NYU Grossman School of Medicine, New York, NY, USA
| | - Matija Snuderl
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Wolfgang Wick
- German Cancer Research Center (DKFZ) and Department of Neurology and NCT Neurooncology Program, University of Heidelberg, Heidelberg, Germany
| | - Yinsen Miao
- Department of Statistics, Rice University, Houston, TX, USA
| | - Jason T Huse
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Erik P Sulman
- Department of Radiation Oncology, NYU Grossman School of Medicine, New York, NY, USA. .,Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
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15
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Kalinkin AI, Sigin VO, Ignatova EO, Frolova MA, Kuznetsova EB, Vinogradov IY, Vinogradov MI, Vinogradov II, Nemtsova MV, Zaletaev DV, Tanas AS, Strelnikov VV. Design of Marker Panels for Prediction of Neoadjuvant Chemotherapy Response of Triple-Negative Breast Tumors Based on the Results of Genome-Wide DNA Methylation Screening. RUSS J GENET+ 2022. [DOI: 10.1134/s1022795422070080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Pedersen CA, Cao MD, Fleischer T, Rye MB, Knappskog S, Eikesdal HP, Lønning PE, Tost J, Kristensen VN, Tessem MB, Giskeødegård GF, Bathen TF. DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival. Breast Cancer Res 2022; 24:43. [PMID: 35751095 PMCID: PMC9233373 DOI: 10.1186/s13058-022-01537-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 06/03/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer. METHODS DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT. RESULTS DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049). CONCLUSION Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.
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Affiliation(s)
- Christine Aaserød Pedersen
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
| | - Maria Dung Cao
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. .,Department of Nursing, Health and Laboratory Science, Østfold University College, Halden, Norway.
| | - Thomas Fleischer
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Morten B Rye
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,BioCore - Bioinformatics Core Facility, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Stian Knappskog
- K.G. Jebsen Centre for Genome-Directed Cancer Therapy, Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Oncology, Haukeland University Hospital, Bergen, Norway
| | - Hans Petter Eikesdal
- K.G. Jebsen Centre for Genome-Directed Cancer Therapy, Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Oncology, Haukeland University Hospital, Bergen, Norway
| | - Per Eystein Lønning
- K.G. Jebsen Centre for Genome-Directed Cancer Therapy, Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Oncology, Haukeland University Hospital, Bergen, Norway
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, Université Paris Saclay, 91000, Evry, France
| | - Vessela N Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - May-Britt Tessem
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Guro F Giskeødegård
- Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, and Nursing, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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17
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Gore S, Azad RK. CancerNet: a unified deep learning network for pan-cancer diagnostics. BMC Bioinformatics 2022; 23:229. [PMID: 35698059 PMCID: PMC9195411 DOI: 10.1186/s12859-022-04783-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 06/06/2022] [Indexed: 11/10/2022] Open
Abstract
Background Despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. Early detection of cancer and localization of the tissue of its origin are key to effective treatment. Here, we leverage technological advances in machine learning or artificial intelligence to design a novel framework for cancer diagnostics. Our proposed framework detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas (TCGA). Our model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, which arise early in carcinogenesis and differ remarkably between different cancer types or subtypes, thus holding a great promise in early cancer detection. Results Our comprehensive assessment of the proposed model on the 33 different tissues of origin demonstrates its ability to detect and classify cancers to a high accuracy (> 99% overall F-measure). Furthermore, our model distinguishes cancers from pre-cancerous lesions to metastatic tumors and discriminates between hypomethylation changes due to age related epigenetic drift and true cancer. Conclusions Beyond detection of primary cancers, our proposed computational model also robustly detects tissues of origin of secondary cancers, including metastatic cancers, second primary cancers, and cancers of unknown primaries. Our assessment revealed the ability of this model to characterize pre-cancer samples, a significant step forward in early cancer detection. Deployed broadly this model can deliver accurate diagnosis for a greatly expanded target patient population. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04783-y.
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Affiliation(s)
- Steven Gore
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, 76203, USA
| | - Rajeev K Azad
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, 76203, USA. .,Department of Mathematics, University of North Texas, Denton, TX, 76203, USA.
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18
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Fackler MJ, Tulac S, Venkatesan N, Aslam AJ, de Guzman T, Mercado-Rodriguez C, Cope LM, Downs BM, Vali AH, Ding W, Lehman J, Denbow R, Reynolds J, Buckley ME, Visvanathan K, Umbricht CB, Wolff AC, Stearns V, Bates M, Lai EW, Sukumar S. Development of an automated liquid biopsy assay for methylated markers in advanced breast cancer. CANCER RESEARCH COMMUNICATIONS 2022; 2:391-401. [PMID: 36046124 PMCID: PMC9426415 DOI: 10.1158/2767-9764.crc-22-0133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/03/2022] [Accepted: 05/03/2022] [Indexed: 11/16/2022]
Abstract
Current molecular liquid biopsy assays to detect recurrence or monitor response to treatment require sophisticated technology, highly trained personnel, and a turnaround time of weeks. We describe the development and technical validation of an automated Liquid Biopsy for Breast Cancer Methylation (LBx-BCM) prototype, a DNA methylation detection cartridge assay that is simple to perform and quantitatively detects nine methylated markers within 4.5 h. LBx-BCM demonstrated high interassay reproducibility when analyzing exogenous methylated DNA (75-300 DNA copies) spiked into plasma (Coefficient of Variation, CV = 7.1 - 10.9%) and serum (CV = 19.1 - 36.1%). It also demonstrated high interuser reproducibility (Spearman r = 0.887, P < 0.0001) when samples of metastatic breast cancer (MBC, N = 11) and normal control (N = 4) were evaluated independently by two users. Analyses of interplatform reproducibility indicated very high concordance between LBx-BCM and the reference assay, cMethDNA, among 66 paired plasma samples (MBC N = 40, controls N = 26; Spearman r = 0.891; 95% CI = 0.825 - 0.933, P< 0.0001). LBx-BCM achieved a ROC AUC = 0.909 (95% CI = 0.836 - 0.982), 83% sensitivity and 92% specificity; cMethDNA achieved a ROC AUC = 0.896 (95% CI = 0.817 - 0.974), 83% sensitivity and 92% specificity in test set samples. The automated LBx-BCM cartridge prototype is fast, with performance levels equivalent to the highly sensitive, manual cMethDNA method. Future prospective clinical studies will evaluate LBx-BCM detection sensitivity and its ability to monitor therapeutic response during treatment for advanced breast cancer.
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Affiliation(s)
- Mary Jo Fackler
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | | | | | | | | | - Leslie M. Cope
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Bradley M. Downs
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Abdul Hussain Vali
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Wanjun Ding
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei, P.R. China
| | - Jennifer Lehman
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Rita Denbow
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jeffrey Reynolds
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Morgan E. Buckley
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kala Visvanathan
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Antonio C. Wolff
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Vered Stearns
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | | | - Saraswati Sukumar
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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19
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Nguyen E, Richerolle A, Sánchez-Bellver J, Varennes J, Ségal-Bendirdjian E. hTERT DNA Methylation Analysis Identifies a Biomarker for Retinoic Acid-Induced hTERT Repression in Breast Cancer Cell Lines. Biomedicines 2022; 10:biomedicines10030695. [PMID: 35327497 PMCID: PMC8945736 DOI: 10.3390/biomedicines10030695] [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: 02/22/2022] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 11/16/2022] Open
Abstract
Telomerase reactivation is responsible for telomere preservation in about 90% of cancers, providing cancer cells an indefinite proliferating potential. Telomerase consists of at least two main subunits: a catalytic reverse transcriptase protein (hTERT) and an RNA template subunit. Strategies to inhibit hTERT expression seem promising for cancer treatment. Previous works showed that all-trans retinoic acid (ATRA) induces hTERT repression in acute promyelocytic leukemia cells, resulting in their death. Here, we investigated the effects of ATRA in a subset of breast cancer cell lines. The mutational status of hTERT promoter and the methylation patterns at a single CpG resolution were assessed. We observed an inverse relationship between hTERT expression after ATRA treatment and the methylation level of a specific CpG at chr5: 1,300,438 in a region of hTERT gene at −5 kb of the transcription initiation site. This observation highlighted the significance of this region, whose methylation profile could represent a promising biomarker to predict the sensitivity to ATRA-induced hTERT repression in specific breast cancer subtypes. As hTERT repression promotes drug-induced cell death, checking the methylation status of this unique region and the specific CpG included can help in decision-making to include ATRA in combination therapy and contributes to a better clinical outcome.
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Affiliation(s)
- Eric Nguyen
- Université Paris Cité, INSERM, CNRS, T3S “Environmental Toxicity, Therapeutic Targets, Cellular Signaling and Biomarkers”, F-75006 Paris, France; (E.N.); (A.R.); (J.V.)
| | - Andréa Richerolle
- Université Paris Cité, INSERM, CNRS, T3S “Environmental Toxicity, Therapeutic Targets, Cellular Signaling and Biomarkers”, F-75006 Paris, France; (E.N.); (A.R.); (J.V.)
- Ecole Pratique des Hautes Etudes, F-75014 Paris, France
| | | | - Jacqueline Varennes
- Université Paris Cité, INSERM, CNRS, T3S “Environmental Toxicity, Therapeutic Targets, Cellular Signaling and Biomarkers”, F-75006 Paris, France; (E.N.); (A.R.); (J.V.)
| | - Evelyne Ségal-Bendirdjian
- Université Paris Cité, INSERM, CNRS, T3S “Environmental Toxicity, Therapeutic Targets, Cellular Signaling and Biomarkers”, F-75006 Paris, France; (E.N.); (A.R.); (J.V.)
- Correspondence: ; Tel.: +33-1-42-86-22-46
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20
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Feng J, Zhao D, Lv F, Yuan Z. Epigenetic Inheritance From Normal Origin Cells Can Determine the Aggressive Biology of Tumor-Initiating Cells and Tumor Heterogeneity. Cancer Control 2022; 29:10732748221078160. [PMID: 35213254 PMCID: PMC8891845 DOI: 10.1177/10732748221078160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
The acquisition of genetic- and epigenetic-abnormalities during transformation has been recognized as the two fundamental factors that lead to tumorigenesis and determine the aggressive biology of tumor cells. However, there is a regularity that tumors derived from less-differentiated normal origin cells (NOCs) usually have a higher risk of vascular involvement, lymphatic and distant metastasis, which can be observed in both lymphohematopoietic malignancies and somatic cancers. Obviously, the hypothesis of genetic- and epigenetic-abnormalities is not sufficient to explain how the linear relationship between the cellular origin and the biological behavior of tumors is formed, because the cell origin of tumor is an independent factor related to tumor biology. In a given system, tumors can originate from multiple cell types, and tumor-initiating cells (TICs) can be mapped to different differentiation hierarchies of normal stem cells, suggesting that the heterogeneity of the origin of TICs is not completely chaotic. TIC’s epigenome includes not only genetic- and epigenetic-abnormalities, but also established epigenetic status of genes inherited from NOCs. In reviewing previous studies, we found much evidence supporting that the status of many tumor-related “epigenetic abnormalities” in TICs is consistent with that of the corresponding NOC of the same differentiation hierarchy, suggesting that they may not be true epigenetic abnormalities. So, we speculate that the established statuses of genes that control NOC’s migration, adhesion and colonization capabilities, cell-cycle quiescence, expression of drug transporters, induction of mesenchymal formation, overexpression of telomerase, and preference for glycolysis can be inherited to TICs through epigenetic memory and be manifested as their aggressive biology. TICs of different origins can maintain different degrees of innate stemness from NOC, which may explain why malignancies with stem cell phenotypes are usually more aggressive.
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Affiliation(s)
- Jiliang Feng
- Clinical-Pathology Center, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
| | - Dawei Zhao
- Medical Imaging Department, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
| | - Fudong Lv
- Clinical-Pathology Center, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
| | - Zhongyu Yuan
- Clinical-Pathology Center, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
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21
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Zhu C, Zhang S, Liu D, Wang Q, Yang N, Zheng Z, Wu Q, Zhou Y. A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer. Front Genet 2021; 12:742578. [PMID: 34956313 PMCID: PMC8693898 DOI: 10.3389/fgene.2021.742578] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022] Open
Abstract
Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC). Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database. Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset. Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.
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Affiliation(s)
- Chunmei Zhu
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shuyuan Zhang
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Di Liu
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Qingqing Wang
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Ningning Yang
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhewen Zheng
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Qiuji Wu
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yunfeng Zhou
- Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China.,Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
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22
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Pu W, Qian F, Liu J, Shao K, Xiao F, Jin Q, Liu Q, Jiang S, Zhang R, Zhang J, Guo S, Zhang J, Ma Y, Ju S, Ding W. Targeted Bisulfite Sequencing Reveals DNA Methylation Changes in Zinc Finger Family Genes Associated With KRAS Mutated Colorectal Cancer. Front Cell Dev Biol 2021; 9:759813. [PMID: 34778269 PMCID: PMC8581662 DOI: 10.3389/fcell.2021.759813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/11/2021] [Indexed: 11/24/2022] Open
Abstract
Background: Colorectal cancer (CRC) is a leading cause of cancer death, and early diagnosis of CRC could significantly reduce its mortality rate. Previous studies suggest that the DNA methylation status of zinc finger genes (ZFGs) could be of potential in CRC early diagnosis. However, the comprehensive evaluation of ZFGs in CRC is still lacking. Methods: We first collected 1,426 public samples on genome-wide DNA methylation, including 1,104 cases of CRC tumors, 54 adenomas, and 268 para-tumors. Next, the most differentially methylated ZFGs were identified and validated in two replication cohorts comprising 218 CRC patients. Finally, we compared the prediction capabilities between the ZFGs and the SEPT9 in all CRC patients and the KRAS + and KRAS- subgroup. Results: Five candidate ZFGs were selected: ESR1, ZNF132, ZNF229, ZNF542, and ZNF677. In particular, ESR1 [area under the curve (AUC) = 0.91] and ZNF132 (AUC = 0.93) showed equivalent or better diagnostic capability for CRC than SEPT9 (AUC = 0.91) in the validation dataset, suggesting that these two ZFGs might be of potential for CRC diagnosis in the future. Furthermore, we performed subgroup analysis and found a significantly higher diagnostic capability in KRAS + (AUC ranged from 0.97 to 1) than that in KRAS- patients (AUC ranged from 0.74 to 0.86) for all these five ZFGs, suggesting that these ZFGs could be ideal diagnostic markers for KRAS mutated CRC patients. Conclusion: The methylation profiles of the candidate ZFGs could be potential biomarkers for the early diagnosis of CRC, especially for patients carrying KRAS mutations.
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Affiliation(s)
- Weilin Pu
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, China
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Fei Qian
- Department of Gastrointestinal Surgery, Affiliated Hospital of Nantong University, Nantong, China
| | - Jing Liu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Keke Shao
- Department of Laboratory Medicine, The First People’s Hospital of Yancheng City, Yancheng, China
| | - Feng Xiao
- Department of Pathology, The Third People’s Hospital of Nantong City, Nantong, China
| | - Qin Jin
- Department of Pathology, Affiliated Hospital of Nantong University, Nantong, China
| | - Qingmei Liu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Shuai Jiang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Rui Zhang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Jun Zhang
- Department of Gastroenterology, Huashan Hospital, Fudan University, Shanghai, China
| | - Shicheng Guo
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Jianfeng Zhang
- Department of Gastroenterology, Affiliated Hospital of Nantong University, Nantong, China
| | - Yanyun Ma
- Human Phenome Institute, Fudan University, Shanghai, China
- Six Industrial Research Institute, Fudan University, Shanghai, China
| | - Shaoqing Ju
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, China
| | - Weifeng Ding
- Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, China
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23
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Yu W, Wu P, Wang F, Miao L, Han B, Jiang Z. Construction of Novel Methylation-Driven Gene Model and Investigation of PARVB Function in Glioblastoma. Front Oncol 2021; 11:705547. [PMID: 34568031 PMCID: PMC8461318 DOI: 10.3389/fonc.2021.705547] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/23/2021] [Indexed: 12/17/2022] Open
Abstract
Background Glioblastoma multiforme (GBM) is characterized by widespread genetic and transcriptional heterogeneity. Aberrant DNA methylation plays a vital role in GBM progression by regulating gene expression. However, little is known about the role of methylation and its association with prognosis in GBM. Our aim was to explore DNA methylation-driven genes (DMDGs) and provide evidence for survival prediction and individualized treatment of GBM patients. Methods Use of the MethylMix R package identified DMDGs in GBM. The prognostic signature of DMDGs based on the risk score was constructed by multivariate Cox regression analysis. Receiver operating characteristics (ROC) curve and C-index were applied to assess the predictive performance of the DMDG prognostic signature. The predictive ability of the multigene signature model was validated in TCGA and CGGA cohorts. Finally, the role of DMDG β-Parvin (PARVB) was explored in vitro. Results The prognostic signature of DMDGs was constructed based on six genes (MDK, NMNAT3, PDPN, PARVB, SERPINB1, and UPP1). The low-risk cohort had significantly better survival than the high-risk cohort (p < 0.001). The area under the curve of the ROC of the six-gene signature was 0.832, 0.927, and 0.980 within 1, 2, and 3 years, respectively. The C-index of 0.704 indicated superior specificity and sensitivity. The six-gene model has been demonstrated to be an independent prognostic factor for GBM. In addition, joint survival analysis indicated that the MDK, NMNAT3, PARVB, SERPINB1, and UPP1 genes were significantly associated with prognosis and therapeutic targets for GBM. Importantly, our DMDG prognostic model was more suitable and accurate for low-grade gliomas. Finally, we verified that PARVB induced epithelial-mesenchymal transition partially through the JAK2/STAT3 pathway, which in turn promoted GBM cell proliferation, migration, and invasion. Conclusion This study demonstrated the potential value of the prognostic signature of DMDGs and provided important bioinformatic and potential therapeutic target data to facilitate individualized treatment for GBM, and to elucidate the specific mechanism by which PARVB promotes GBM progression.
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Affiliation(s)
- Wanli Yu
- Department of Neurosurgery, Gaoxin Hospital of the First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pengfei Wu
- Department of Neurosurgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, China.,Anhui Key Laboratory of Brain Function and Diseases, Hefei, China
| | - Fang Wang
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Li Miao
- Central Laboratory, Gaoxin Hospital of the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Bo Han
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhiqun Jiang
- Department of Neurosurgery, Gaoxin Hospital of the First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
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24
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Dai YH, Wang YF, Shen PC, Lo CH, Yang JF, Lin CS, Chao HL, Huang WY. Gene-associated methylation status of ST14 as a predictor of survival and hormone receptor positivity in breast Cancer. BMC Cancer 2021; 21:945. [PMID: 34418985 PMCID: PMC8380334 DOI: 10.1186/s12885-021-08645-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/20/2021] [Indexed: 12/14/2022] Open
Abstract
Background Genomic profiles of specific gene sets have been established to guide personalized treatment and prognosis for patients with breast cancer (BC). However, epigenomic information has not yet been applied in a clinical setting. ST14 encodes matriptase, a proteinase that is widely expressed in BC with reported prognostic value. Methods In this present study, we evaluated the effect of ST14 DNA methylation (DNAm) on overall survival (OS) of patients with BC as a representative example to promote the use of the epigenome in clinical decisions. We analyzed publicly available genomic and epigenomic data from 1361 BC patients. Methylation was characterized by the β-value from CpG probes based on sequencing with the Illumina Human 450 K platform. Results A high mean DNAm (β > 0.6779) across 34 CpG probes for ST14, as the gene-associated methylation (GAM) pattern, was associated with a longer OS after adjusting age, stage, histology and molecular features in Cox model (p value < 0.001). A high GAM status was also associated with a higher XBP1 expression level and higher proportion of hormone-positive BC (p value < 0.001). Pathway analysis revealed that altered GAM was related to matrisome-associated pathway. Conclusions Here we show the potential role of ST14 DNAm in BC prognosis and warrant further study. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08645-3.
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Affiliation(s)
- Yang-Hong Dai
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan
| | - Ying-Fu Wang
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan
| | - Po-Chien Shen
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan
| | - Cheng-Hsiang Lo
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan
| | - Jen-Fu Yang
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan
| | - Chun-Shu Lin
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan
| | - Hsing-Lung Chao
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan.,Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Wen-Yen Huang
- Department of Radiation Oncology, Tri-Service General Hospital, National Defense Medical Center, No. 325, Chengong Rd., Sec. 2, Neihu, Taipei, 114, Taiwan. .,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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25
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The Roles of DNA Demethylases in Triple-Negative Breast Cancer. Pharmaceuticals (Basel) 2021; 14:ph14070628. [PMID: 34209564 PMCID: PMC8308559 DOI: 10.3390/ph14070628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/21/2021] [Accepted: 06/25/2021] [Indexed: 02/07/2023] Open
Abstract
Triple-negative breast cancers (TNBCs) are very heterogenous, molecularly diverse, and are characterized by a high propensity to relapse or metastasize. Clinically, TNBC remains a diagnosis of exclusion by the lack of hormone receptors (Estrogen Receptor (ER) and Progesterone Receptor (PR)) as well as the absence of overexpression and/or amplification of HER2. DNA methylation plays an important role in breast cancer carcinogenesis and TNBCs have a distinct DNA methylation profile characterized by marked hypomethylation and lower gains of methylations compared to all other subtypes. DNA methylation is regulated by the balance of DNA methylases (DNMTs) and DNA demethylases (TETs). Here, we review the roles of TETs as context-dependent tumor-suppressor genes and/or oncogenes in solid tumors, and we discuss the current understandings of the oncogenic role of TET1 and its therapeutic implications in TNBCs.
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26
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Schröder R, Illert AL, Erbes T, Flotho C, Lübbert M, Duque-Afonso J. The epigenetics of breast cancer - Opportunities for diagnostics, risk stratification and therapy. Epigenetics 2021; 17:612-624. [PMID: 34159881 PMCID: PMC9235902 DOI: 10.1080/15592294.2021.1940644] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The stage and molecular pathology-dependent prognosis of breast cancer, the limited treatment options for triple-negative carcinomas, as well as the development of resistance to therapies illustrate the need for improved early diagnosis and the development of new therapeutic approaches. Increasing data suggests that some answers to these challenges could be found in the area of epigenetics. In this study, we focus on the current research of the epigenetics of breast cancer, especially on the potential of epigenetics for clinical application in diagnostics, risk stratification and therapy. The differential DNA methylation status of specific gene regions has been used in the past to differentiate breast cancer cells from normal tissue. New technologies as detection of circulating nucleic acids including microRNAs to early detect breast cancer are emerging. Pattern of DNA methylation and expression of histone-modifying enzymes have been successfully used for risk stratification. However, all these epigenetic biomarkers should be validated in larger clinical studies. Recent preclinical and clinical studies show a therapeutic benefit of epigenetically active drugs for breast cancer entities that are still difficult to treat (triple negative, UICC stage IV). Remarkably, epigenetic therapies combined with chemotherapies or hormone-based therapies represent the most promising strategy. At the current stage, the integration of epigenetic substances into established breast cancer therapy protocols seems to hold the greatest potential for a clinical application of epigenetic research.
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Affiliation(s)
- Rieke Schröder
- Department for Pediatric Hematology and Oncology, Faculty of Medicine and University of Freiburg Medical Center, University of Freiburg, Freiburg, Germany
| | - Anna-Lena Illert
- Department of Hematology/Oncology/Stem Cell Transplantation, University of Freiburg, Freiburg, Germany
| | - Thalia Erbes
- Department of Gynecology, Faculty of Medicine and University of Freiburg Medical Center, University of Freiburg, Freiburg, Germany
| | - Christian Flotho
- Department for Pediatric Hematology and Oncology, Faculty of Medicine and University of Freiburg Medical Center, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (Deutsches Konsortium Für Translationale Krebsforschung, DKTK), Freiburg, Germany
| | - Michael Lübbert
- Department of Hematology/Oncology/Stem Cell Transplantation, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (Deutsches Konsortium Für Translationale Krebsforschung, DKTK), Freiburg, Germany
| | - Jesús Duque-Afonso
- Department of Hematology/Oncology/Stem Cell Transplantation, University of Freiburg, Freiburg, Germany
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27
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Liu M, Xu Y, Zhou Y, Lang R, Shi Z, Zhao J, Meng Y, Bao L. Integrated Analyses Reveal the Multi-Omics and Prognostic Characteristics of ATP5B in Breast Cancer. Front Genet 2021; 12:652474. [PMID: 34122507 PMCID: PMC8194306 DOI: 10.3389/fgene.2021.652474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
The beta subunit of F1Fo-ATP synthase (ATP5B) has been demonstrated to play an essential role in tumor progression and metastasis. However, there has been no comprehensive pan-cancer multi-omics analysis of ATP5B, while the clinical relevance of ATP5B and its potential mechanism in regulating breast cancer are still poorly understood. In this study, we demonstrated that ATP5B has a higher frequency of amplification than deletion in most cancer types, and the copy number variation (CNV) of ATP5B was significantly positively correlated with its mRNA expression level. DNA methylation analysis across pan-cancer also revealed a strong correlation between ATP5B expression and epigenetic changes. We identified 6 significant methylation sites involved in the regulation of ATP5B expression. Tissue microarrays (TMA) from 129 breast cancer samples, integrated with multiple additional breast cancer dataset, were used to evaluate the ATP5B expression and its correlation with prognosis. Higher levels of ATP5B expression were consistently associated with a worse OS in all datasets, and Cox regression analysis suggested that ATP5B expression was an independent prognostic factor. Gene enrichment analysis indicated that the gene signatures of DNA damage recognition, the E-cadherin nascent pathway and the PLK1 pathway were enriched in ATP5B-high patients. Moreover, somatic mutation analysis showed that a significant different mutation frequency of CDH1 and ADAMTSL3 could be observed between the ATP5B-high and ATP5B-low groups. In conclusion, this study reveals novel significance regarding the genetic characteristics and clinical value of ATP5B highlighted in predicting the outcome of breast cancer patients.
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Affiliation(s)
- Min Liu
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.,The Graduate School, Tianjin Medical University, Tianjin, China
| | - Yuxuan Xu
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.,The Graduate School, Tianjin Medical University, Tianjin, China
| | - Yaoyao Zhou
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.,The Graduate School, Tianjin Medical University, Tianjin, China
| | - Ronggang Lang
- Department of Breast Cancer Pathology and Research Laboratory, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zhenyu Shi
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jing Zhao
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuanyuan Meng
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.,The Graduate School, Tianjin Medical University, Tianjin, China
| | - Li Bao
- Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Accelerated aging in normal breast tissue of women with breast cancer. Breast Cancer Res 2021; 23:58. [PMID: 34022936 PMCID: PMC8140515 DOI: 10.1186/s13058-021-01434-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
Background DNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean. Methods We generated whole-genome DNA methylation profiles (GSE160233) on purified epithelial cells and used publicly available Infinium HumanMethylation 450K array datasets (TCGA, GSE88883, GSE69914, GSE101961, and GSE74214) for discovery and validation. Results We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39/427) were outliers for DNA methylation from 6 DNA methylation datasets. We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) than in normal samples (15/228, 5.2%). Additionally, we found significant differences between the predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, we found that the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer. Conclusions A subset of patients with breast cancer has severely altered epigenomes which are characterized by accelerated aging in their normal-appearing tissue. In the future, these DNA methylation sites should be studied further such as in cell-free DNA to determine their potential use as biomarkers for early detection of malignant transformation and preventive intervention in breast cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s13058-021-01434-7.
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29
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Ren C, Tang X, Lan H. Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer. PeerJ 2021; 9:e11377. [PMID: 33987034 PMCID: PMC8103922 DOI: 10.7717/peerj.11377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 04/08/2021] [Indexed: 11/20/2022] Open
Abstract
Background Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq. Methods We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset. Results We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.
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Affiliation(s)
- Chongyang Ren
- Department of Breast Cancer, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Xiaojiang Tang
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Haitao Lan
- Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
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30
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Aure MR, Fleischer T, Bjørklund S, Ankill J, Castro-Mondragon JA, Børresen-Dale AL, Tost J, Sahlberg KK, Mathelier A, Tekpli X, Kristensen VN. Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer. Genome Med 2021; 13:72. [PMID: 33926515 PMCID: PMC8086068 DOI: 10.1186/s13073-021-00880-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 03/26/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Abnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression. METHODS miRNA expression and DNA methylation data from two breast cancer cohorts, Oslo2 (n = 297) and The Cancer Genome Atlas (n = 439), were integrated through a correlation approach that we term miRNA-methylation Quantitative Trait Loci (mimQTL) analysis. Hierarchical clustering was used to identify clusters of miRNAs and CpGs that were further characterized through analysis of mRNA/protein expression, clinicopathological features, in silico deconvolution, chromatin state and accessibility, transcription factor binding, and long-range interaction data. RESULTS Clustering of the significant mimQTLs identified distinct groups of miRNAs and CpGs that reflect important biological processes associated with breast cancer pathogenesis. Notably, two major miRNA clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding CpGs associated with the estrogen signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2, and ER-alpha. Further, at the hub of the estrogen cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already present in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER-positive breast cancer. CONCLUSIONS We describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.
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Affiliation(s)
- Miriam Ragle Aure
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
| | - Thomas Fleischer
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
| | - Sunniva Bjørklund
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
| | - Jørgen Ankill
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
| | - Jaime A. Castro-Mondragon
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
- Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA–Institut de Biologie François Jacob, University Paris-Saclay, Evry, France
| | - Kristine K. Sahlberg
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
- Department of Research, Vestre Viken Hospital Trust, Drammen, Norway
| | - Anthony Mathelier
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Xavier Tekpli
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
| | - Vessela N. Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
- Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
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Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers. Biosci Rep 2021; 40:224161. [PMID: 32412047 PMCID: PMC7263199 DOI: 10.1042/bsr20201053] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/13/2020] [Accepted: 05/14/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.
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Scherer M, Schmidt F, Lazareva O, Walter J, Baumbach J, Schulz MH, List M. Machine learning for deciphering cell heterogeneity and gene regulation. NATURE COMPUTATIONAL SCIENCE 2021; 1:183-191. [PMID: 38183187 DOI: 10.1038/s43588-021-00038-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/08/2021] [Indexed: 12/14/2022]
Abstract
Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.
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Affiliation(s)
- Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
- Computational Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany
| | | | - Olga Lazareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jörn Walter
- Computational Biology Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Computational BioMedicine Lab, Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Marcel H Schulz
- Institute of Cardiovascular Regeneration, University Hospital and Goethe University Frankfurt, Frankfurt, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
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Su SF, Liu CH, Cheng CL, Ho CC, Yang TY, Chen KC, Hsu KH, Tseng JS, Chen HW, Chang GC, Yu SL, Li KC. Genome-Wide Epigenetic Landscape of Lung Adenocarcinoma Links HOXB9 DNA Methylation to Intrinsic EGFR-TKI Resistance and Heterogeneous Responses. JCO Precis Oncol 2021; 5:PO.20.00151. [PMID: 34036228 PMCID: PMC8140798 DOI: 10.1200/po.20.00151] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 10/15/2020] [Accepted: 01/08/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) show efficacy in treating patients with lung adenocarcinoma with EGFR-activating mutations. However, a significant subset of targeted patients fail to respond. Unlike acquired resistance (AR), intrinsic resistance (IR) remains poorly understood. We investigated whether epigenomic factors contribute to patient-to-patient heterogeneity in the EGFR-TKI response and aimed to characterize the IR subpopulation that obtains no benefit from EGFR-TKIs. PATIENTS AND METHODS We conducted genome-wide DNA methylation profiling of 79 tumors sampled from patients with advanced lung adenocarcinoma before they received EGFR-TKI treatment and analyzed the patient responses. Pyrosequencing was performed in a validation cohort of 163 patients with EGFR-activating mutations. RESULTS A DNA methylation landscape of 216 CpG sites with differential methylation was established to elucidate the association of DNA methylation with the characteristics and EGFR-TKI response status of the patients. Functional analysis of 37 transcription-repressive sites identified the enrichment of transcription factors, notably homeobox (HOX) genes. DNA methylation of HOXB9 (cg13643585) in the enhancer region yielded 88% sensitivity for predicting drug response (odds ratio [OR], 6.64; 95% CI, 1.98 to 25.23; P = .0009). Pyrosequencing validated that HOXB9 gained methylation in patients with a poor EGFR-TKI response (OR, 3.06; 95% CI, 1.13 to 8.19; P = .019). CONCLUSION Our data suggest that homeobox DNA methylation could be a novel tumor cellular state that can aid the precise categorization of tumor heterogeneity in the study of IR to EGFR-TKIs. We identified, for the first time, an epigenomic factor that can potentially complement DNA mutation status in discriminating patients with lung adenocarcinoma who are less likely to benefit from EGFR-TKI treatment, thereby leading to improved patient management in precision medicine.
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Affiliation(s)
- Sheng-Fang Su
- Institute of Statistical Sciences, Academia Sinica, Taipei, Taiwan.,Graduate Institute of Oncology, National Taiwan University, College of Medicine, Taipei, Taiwan.,YongLin Institute of Health, YongLin Scholar, National Taiwan University, Taipei, Taiwan
| | - Chia-Hsin Liu
- Institute of Statistical Sciences, Academia Sinica, Taipei, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan.,Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Chiou-Ling Cheng
- NTU Centers for Genomic and Precision Medicine, National Taiwan University, College of Medicine, Taipei, Taiwan
| | - Chao-Chi Ho
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University, College of Medicine, Taipei, Taiwan
| | - Tsung-Ying Yang
- Department of Internal Medicine, Division of Chest Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Kun-Chieh Chen
- Department of Internal Medicine, Division of Chest Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Applied Chemistry, National Chi Nan University, Nantou, Taiwan
| | - Kuo-Hsuan Hsu
- Institute of Biomedical Sciences, National Chung Hsing University, Taichung, Taiwan.,Internal Medicine, Division of Critical Care and Respiratory Therapy, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jeng-Sen Tseng
- Department of Internal Medicine, Division of Chest Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Biomedical Sciences, National Chung Hsing University, Taichung, Taiwan
| | - Huei-Wen Chen
- Graduate Institute of Toxicology, National Taiwan University, College of Medicine, Taipei, Taiwan
| | - Gee-Chen Chang
- Department of Internal Medicine, Division of Chest Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Institute of Biomedical Sciences, National Chung Hsing University, Taichung, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.,Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.,School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Sung-Liang Yu
- NTU Centers for Genomic and Precision Medicine, National Taiwan University, College of Medicine, Taipei, Taiwan.,Department of Clinical Laboratory Sciences and Medical Biotechnology, National Taiwan University, College of Medicine, Taipei, Taiwan.,Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan.,Department of Pathology and Graduate Institute of Pathology, National Taiwan University, College of Medicine, Taipei, Taiwan.,Institute of Medical Device and Imaging, National Taiwan University, College of Medicine, Taipei, Taiwan.,Graduate Institute of Clinical Medicine, National Taiwan University, College of Medicine, Taipei, Taiwan
| | - Ker-Chau Li
- Institute of Statistical Sciences, Academia Sinica, Taipei, Taiwan.,Department of Statistics, University of California, Los Angeles, Los Angeles, CA
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El-Fattah AAA, Sadik NAH, Shaker OG, Mohamed Kamal A, Shahin NN. Serum Long Non-Coding RNAs PVT1, HOTAIR, and NEAT1 as Potential Biomarkers in Egyptian Women with Breast Cancer. Biomolecules 2021; 11:301. [PMID: 33670447 PMCID: PMC7922136 DOI: 10.3390/biom11020301] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/29/2022] Open
Abstract
Long non-coding RNAs play an important role in tumor growth, angiogenesis, and metastasis in several types of cancer. However, the clinical significance of using lncRNAs as biomarkers for breast cancer diagnosis and prognosis is still poorly investigated. In this study, we analyzed the serum expression levels of lncRNAs PVT1, HOTAIR, NEAT1, and MALAT1, and their associated proteins, PAI-1, and OPN, in breast cancer patients compared to fibroadenoma patients and healthy subjects. Using quantitative real-time PCR (qRT-PCR), we compared the serum expression levels of the four circulating lncRNAs in patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The serum levels of PAI-1 and OPN were measured using ELISA. Receiveroperating-characteristic (ROC) analysis and multivariate logistic regression were used to evaluate the diagnostic value of the selected parameters. The serum levels of HOTAIR, PAI-1, and OPN were significantly higher in breast cancer patients compared to controls and fibroadenoma patients. The serum level of PVT1 was significantly higher in breast cancer patients than in the controls, while that of NEAT1 was significantly lower in breast cancer patients compared to controls and fibroadenoma patients. Both ROC and multivariate logistic regression analyses revealed that PAI-1 has the greatest power in discriminating breast cancer from the control, whereas HOTAIR, PAI-1, and OPN have the greatest power in discriminating breast cancer from fibroadenoma patients. In conclusion, our data suggest that the serum levels of PVT1, HOTAIR, NEAT1, PAI-1, and OPN could serve as promising diagnostic biomarkers for breast cancer.
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Affiliation(s)
- Amal Ahmed Abd El-Fattah
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Einy Street, Cairo 11562, Egypt; (A.A.A.E.-F.); (N.A.H.S.); (N.N.S.)
| | - Nermin Abdel Hamid Sadik
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Einy Street, Cairo 11562, Egypt; (A.A.A.E.-F.); (N.A.H.S.); (N.N.S.)
| | - Olfat Gamil Shaker
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Cairo University, Cairo 11562, Egypt;
| | - Amal Mohamed Kamal
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Einy Street, Cairo 11562, Egypt; (A.A.A.E.-F.); (N.A.H.S.); (N.N.S.)
| | - Nancy Nabil Shahin
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Einy Street, Cairo 11562, Egypt; (A.A.A.E.-F.); (N.A.H.S.); (N.N.S.)
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35
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A Histone Acetylation Modulator Gene Signature for Classification and Prognosis of Breast Cancer. ACTA ACUST UNITED AC 2021; 28:928-939. [PMID: 33617509 PMCID: PMC7985767 DOI: 10.3390/curroncol28010091] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/07/2021] [Accepted: 02/12/2021] [Indexed: 02/08/2023]
Abstract
Regulators of histone acetylation are promising epigenetic targets for therapy in breast cancer. In this study, we comprehensively analyzed the expression of histone acetylation modulator genes in breast cancer using TCGA data sources. A gene signature composed of eight histone acetylation modulators (HAMs) was found to be effective for the classification and prognosis of breast cancers, especially in the HER2-enriched and basal-like molecular subtypes. The eight genes consist of two histone acetylation writers (GTF3C4 and CLOCK), two erasers (HDAC2 and SIRT7) and four readers (BRD4, BRD7, SP100, and BRWD3). Both histone acetylation writer genes and eraser genes were found to be differentially expressed between the two groups indicating a close relationship exists between overall histone acetylation level and prognosis of breast cancer in HER2-enriched and basal-like breast cancer.
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36
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Cappetta M, Fernandez L, Brignoni L, Artagaveytia N, Bonilla C, López M, Esteller M, Bertoni B, Berdasco M. Discovery of novel DNA methylation biomarkers for non-invasive sporadic breast cancer detection in the Latino population. Mol Oncol 2021; 15:473-486. [PMID: 33145876 PMCID: PMC7858097 DOI: 10.1002/1878-0261.12842] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/19/2020] [Accepted: 10/23/2020] [Indexed: 01/06/2023] Open
Abstract
Human diversity is one of the main pitfalls in the development of robust worldwide biomarkers in oncology. Epigenetic variability across human populations is associated with different genetic backgrounds, as well as variable lifestyles and environmental exposures, each of which should be investigated. To identify potential non-invasive biomarkers of sporadic breast cancer in the Uruguayan population, we studied genome-wide DNA methylation using Illumina methylation arrays in leukocytes of 22 women with sporadic breast cancer and 10 healthy women in a case-control study. We described a panel of 38 differentially methylated CpG positions that was able to cluster breast cancer patients (BCP) and controls, and that also recapitulated methylation differences in 12 primary breast tumors and their matched normal breast tissue. Moving forward, we simplified the detection method to improve its applicability in a clinical setting and used an independent well-characterized cohort of 80 leukocyte DNA samples from BCP and 80 healthy controls to validate methylation results at specific cancer-related genes. Our investigations identified methylation at CYFIP1 as a novel epigenetic biomarker candidate for sporadic breast cancer in the Uruguayan population. These results provide a proof-of-concept for the design of larger studies aimed at validating biomarker panels for the Latin American population.
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Affiliation(s)
- Mónica Cappetta
- Departamento de GenéticaFacultad de MedicinaUniversidad de la RepúblicaMontevideoUruguay
| | - Lucía Fernandez
- Departamento de GenéticaFacultad de MedicinaUniversidad de la RepúblicaMontevideoUruguay
| | - Lucía Brignoni
- Departamento de GenéticaFacultad de MedicinaUniversidad de la RepúblicaMontevideoUruguay
| | - Nora Artagaveytia
- Departamento Básico de MedicinaFacultad de MedicinaUniversidad de la RepúblicaMontevideoUruguay
| | - Carolina Bonilla
- Departamento de Medicina PreventivaFacultad de MedicinaUniversidad de São PauloBrazil
- Population Health SciencesBristol Medical SchoolUniversity of BristolUK
| | - Miguel López
- Cancer Epigenetics and Biology Program (PEBC)Bellvitge Biomedical Research Institute (IDIBELL)BarcelonaSpain
- Epigenetic Therapies Group, Experimental and Clinical Hematology Program (PHEC)Josep Carreras Leukaemia Research Institute (IJC)BadalonaSpain
| | - Manel Esteller
- Cancer Epigenetics Group, Cancer and Leukemia Epigenetics and Biology Program (PEBCL)Josep Carreras Leukaemia Research Institute (IJC)BadalonaSpain
- Physiological Sciences DepartmentSchool of Medicine and Health SciencesUniversity of BarcelonaSpain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC)MadridSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Bernardo Bertoni
- Departamento de GenéticaFacultad de MedicinaUniversidad de la RepúblicaMontevideoUruguay
| | - María Berdasco
- Cancer Epigenetics and Biology Program (PEBC)Bellvitge Biomedical Research Institute (IDIBELL)BarcelonaSpain
- Epigenetic Therapies Group, Experimental and Clinical Hematology Program (PHEC)Josep Carreras Leukaemia Research Institute (IJC)BadalonaSpain
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Cui J, Wang L, Zhong W, Chen Z, Chen J, Yang H, Liu G. Development and Validation of Epigenetic Signature Predict Survival for Patients with Laryngeal Squamous Cell Carcinoma. DNA Cell Biol 2021; 40:247-264. [PMID: 33481663 DOI: 10.1089/dna.2020.5789] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Establishing epigenetic signature to improve the accuracy of survival prediction and optimize therapeutic strategies for laryngeal squamous cell carcinoma (LSCC) by a genome-wide integrated analysis of methylation and the transcriptome. LSCC DNA methylation datasets and RNA sequencing datasets were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs), which developed an epigenetic signature. The predictive accuracy and clinical value of the epigenetic signature were evaluated by receiver operating characteristic and decision curve analysis, and compared with tumor-node-metastasis (TNM) stage system. In addition, prognostic value of the epigenetic signature was validated by external Gene Expression Omnibus (GEO) database. According to five MDGs of epigenetic signature, the candidate small molecules for LSCC were screen out by the CMap database. A total of 88 DNA MDGs were identified, five of which (MAGEB2, SUSD1, ZNF382, ZNF418, and ZNF732) were chosen to construct an epigenetic signature. The epigenetic signature can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.8 (5-year overall survival [OS]) and AUC of 0.745 (3-year OS). Stratification analysis affirmed that the epigenetic signature was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated that the efficacy of epigenetic signature appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the epigenetic signature was superior to traditional TNM stage. In addition, the epigenetic signature was confirmed in external LSCC cohorts from GEO. Finally, CMap matched the 10 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. An epigenetic signature, with five DNA MDGs, was identified and validated in LSCC patients by integrating multidimensional genomic data, which may offer novel research directions and prospects for individualized treatment of patients with LSCC.
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Affiliation(s)
- Jie Cui
- Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, PR China
| | - Liping Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, PR China
| | - Waisheng Zhong
- Department of Head Neck Surgery, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, PR China
| | - Zhen Chen
- Department of Intensive Care Unit, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, PR China
| | - Jie Chen
- Department of Head Neck Surgery, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, PR China
| | - Hong Yang
- Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, PR China
| | - Genglong Liu
- Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, PR China
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Griess B, Klinkebiel D, Kueh A, Desler M, Cowan K, Fitzgerald M, Teoh-Fitzgerald M. Association ofSOD3 promoter DNA methylation with its down-regulation in breast carcinomas. Epigenetics 2020; 15:1325-1335. [PMID: 32508251 PMCID: PMC7678930 DOI: 10.1080/15592294.2020.1777666] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 04/29/2020] [Accepted: 05/11/2020] [Indexed: 12/29/2022] Open
Abstract
Superoxide dismutase 3 (SOD3) is a secreted antioxidant enzyme that regulates reactive oxygen species in the microenvironment. It is also a potential tumour suppressor gene that is significantly downregulated in breast cancer. We have previously shown that its mRNA expression is inversely correlated with relapse free survival in breast cancer patients. This study aimed to investigate the correlation of SOD3 promoter DNA methylation with its expression in different molecular subtypes of breast carcinoma. We found that SOD3 expression was significantly reduced in breast carcinoma samples compared to normal tissues with the lowest levels observed in Luminal B subtype. Pyrosequencing analysis showed significant increase in methylation levels in the SOD3 promoter region (-108 and -19 from the TSS) in tumours vs normal tissues (53.6% vs 25.2%). The highest degree of correlation between methylation and SOD3 expression levels was observed in Luminal B subtype (Spearman's R = -0.540, P < 0.00093). In this subtype, the -78 CpG position is the most significantly methylated site. The Spearman's coefficient analysis also indicated the most significant correlation of DNA methylation at this site with SOD3 gene expression levels in tumours vs. normal tissues (R = -0.5816, P < 6.9E-12). Moreover, copy number variation analysis of TCGA database revealed that the more aggressive Triple Negative and Her2+ subtypes had higher levels of SOD3 gene deletion. The predominantly down-regulated expression pattern of SOD3 and the various genetic and epigenetic deregulations of its expression suggest that loss of this antioxidant promotes an advantageous tumour-promoting microenvironment in breast cancer.
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Affiliation(s)
- Brandon Griess
- Department of Biochemistry and Molecular Biology, Fred and Pamela Buffett Cancer Center, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - David Klinkebiel
- Department of Biochemistry and Molecular Biology, Fred and Pamela Buffett Cancer Center, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Alice Kueh
- Eppley Institute for Cancer Research, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Michelle Desler
- Eppley Institute for Cancer Research, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Kenneth Cowan
- Eppley Institute for Cancer Research, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Matthew Fitzgerald
- College of Nursing, University of Nebraska Medical Center, Omaha, NE, USA
| | - Melissa Teoh-Fitzgerald
- Department of Biochemistry and Molecular Biology, Fred and Pamela Buffett Cancer Center, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
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Identification and validation of methylation-driven genes prognostic signature for recurrence of laryngeal squamous cell carcinoma by integrated bioinformatics analysis. Cancer Cell Int 2020; 20:472. [PMID: 33005105 PMCID: PMC7526132 DOI: 10.1186/s12935-020-01567-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 09/23/2020] [Indexed: 02/07/2023] Open
Abstract
Background Recurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methylation-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC. Methods LSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, five genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH). Results A total of 88 DNA MDGs were identified, and five RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratification analysis affirmed that the MDGPS was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated the efficacy of MDGPS appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confirmed in external LSCC cohorts from GEO. CMap matched the 9 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has significantly higher expression of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have significantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results. Conclusion A MDGPS, with five DNA MDGs, was identified and validated in LSCC patients by combining transcriptome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe offer novel research directions and prospects for individualized treatment of patients with LSCC.
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Brägelmann J, Lorenzo Bermejo J. A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets. Brief Bioinform 2020; 20:2055-2065. [PMID: 30099476 PMCID: PMC6954449 DOI: 10.1093/bib/bby068] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/11/2018] [Accepted: 07/06/2018] [Indexed: 12/26/2022] Open
Abstract
Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.
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Affiliation(s)
- Johannes Brägelmann
- University Hospital of Cologne, Germany.,Departement of medical biometry and biostatistics, University of Heidelberg, Germany
| | - Justo Lorenzo Bermejo
- Statistical Genetics Group, Institute of Medical Biometry and Informatics, University of Heidelberg, Germany
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Soleimani Dodaran M, Borgoni S, Sofyalı E, Verschure PJ, Wiemann S, Moerland PD, van Kampen AHC. Candidate methylation sites associated with endocrine therapy resistance in ER+/HER2- breast cancer. BMC Cancer 2020; 20:676. [PMID: 32684154 PMCID: PMC7368985 DOI: 10.1186/s12885-020-07100-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 03/23/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Estrogen receptor (ER) positive breast cancer is often effectively treated with drugs that inhibit ER signaling, i.e., tamoxifen (TAM) and aromatase inhibitors (AIs). However, 30% of ER+ breast cancer patients develop resistance to therapy leading to tumour recurrence. Changes in the methylation profile have been implicated as one of the mechanisms through which therapy resistance develops. Therefore, we aimed to identify methylation loci associated with endocrine therapy resistance. METHODS We used genome-wide DNA methylation profiles of primary ER+/HER2- tumours from The Cancer Genome Atlas in combination with curated data on survival and treatment to predict development of endocrine resistance. Association of individual DNA methylation markers with survival was assessed using Cox proportional hazards models in a cohort of ER+/HER2- tumours (N = 552) and two sub-cohorts corresponding to the endocrine treatment (AI or TAM) that patients received (N = 210 and N = 172, respectively). We also identified multivariable methylation signatures associated with survival using Cox proportional hazards models with elastic net regularization. Individual markers and multivariable signatures were compared with DNA methylation profiles generated in a time course experiment using the T47D ER+ breast cancer cell line treated with tamoxifen or deprived from estrogen. RESULTS We identified 134, 5 and 1 CpGs for which DNA methylation is significantly associated with survival in the ER+/HER2-, TAM and AI cohorts respectively. Multi-locus signatures consisted of 203, 36 and 178 CpGs and showed a large overlap with the corresponding single-locus signatures. The methylation signatures were associated with survival independently of tumour stage, age, AI treatment, and luminal status. The single-locus signature for the TAM cohort was conserved among the loci that were differentially methylated in endocrine-resistant T47D cells. Similarly, multi-locus signatures for the ER+/HER2- and AI cohorts were conserved in endocrine-resistant T47D cells. Also at the gene set level, several sets related to endocrine therapy and resistance were enriched in both survival and T47D signatures. CONCLUSIONS We identified individual and multivariable DNA methylation markers associated with therapy resistance independently of luminal status. Our results suggest that these markers identified from primary tumours prior to endocrine treatment are associated with development of endocrine resistance.
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MESH Headings
- Antineoplastic Agents, Hormonal/pharmacology
- Antineoplastic Agents, Hormonal/therapeutic use
- Aromatase Inhibitors/pharmacology
- Aromatase Inhibitors/therapeutic use
- Biomarkers, Tumor/genetics
- Breast Neoplasms/drug therapy
- Breast Neoplasms/genetics
- Breast Neoplasms/mortality
- Breast Neoplasms/pathology
- Carcinoma, Ductal, Breast/drug therapy
- Carcinoma, Ductal, Breast/genetics
- Carcinoma, Ductal, Breast/mortality
- Carcinoma, Ductal, Breast/pathology
- Cohort Studies
- CpG Islands/genetics
- DNA Methylation
- Drug Resistance, Neoplasm/genetics
- Female
- Gene Expression Profiling
- Gene Expression Regulation, Neoplastic
- Humans
- Receptor, ErbB-2/metabolism
- Receptors, Estrogen/metabolism
- Survival Analysis
- Tamoxifen/pharmacology
- Tamoxifen/therapeutic use
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Affiliation(s)
- Maryam Soleimani Dodaran
- Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Amsterdam Public Health research institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, AZ, 1105, The Netherlands
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Simone Borgoni
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
- Faculty of Biosciences, University Heidelberg, 69120, Heidelberg, Germany
| | - Emre Sofyalı
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
- Faculty of Biosciences, University Heidelberg, 69120, Heidelberg, Germany
| | - Pernette J Verschure
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Stefan Wiemann
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany
- Faculty of Biosciences, University Heidelberg, 69120, Heidelberg, Germany
| | - Perry D Moerland
- Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Amsterdam Public Health research institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, AZ, 1105, The Netherlands.
| | - Antoine H C van Kampen
- Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Amsterdam Public Health research institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam, AZ, 1105, The Netherlands.
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands.
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Wu J, Mamidi TKK, Zhang L, Hicks C. Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer. Cancers (Basel) 2020; 12:cancers12061559. [PMID: 32545594 PMCID: PMC7352267 DOI: 10.3390/cancers12061559] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 01/01/2023] Open
Abstract
Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.
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Affiliation(s)
- Jiande Wu
- Health Sciences Center, Department of Genetic, Louisiana State University School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA;
| | - Tarun Karthik Kumar Mamidi
- Center for Computational Genomics and Data Science, Departments of Pediatrics and Pathology, University of Alabama–Birmingham School of Medicine, Birmingham, AL 35233, USA;
| | - Lu Zhang
- Department of Public Health Sciences, Clemson University, 513 Edwards Hall, Clemson, SC 29634, USA;
| | - Chindo Hicks
- Health Sciences Center, Department of Genetic, Louisiana State University School of Medicine, 533 Bolivar Street, New Orleans, LA 70112, USA;
- Correspondence: ; Tel.: +1-504-568-2657
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DNA methylation markers panel can improve prediction of response to neoadjuvant chemotherapy in luminal B breast cancer. Sci Rep 2020; 10:9239. [PMID: 32514046 PMCID: PMC7280523 DOI: 10.1038/s41598-020-66197-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 05/18/2020] [Indexed: 12/31/2022] Open
Abstract
Despite the advantages of neoadjuvant chemotherapy (NACT), associated toxicity is a serious complication that renders monitoring of the patients' response to NACT highly important. Thus, prediction of tumor response to treatment is imperative to avoid exposure of potential non-responders to deleterious complications. We have performed genome-wide analysis of DNA methylation by XmaI-RRBS and selected CpG dinucleotides differential methylation of which discriminates luminal B breast cancer samples with different sensitivity to NACT. With this data, we have developed multiplex methylation sensitive restriction enzyme PCR (MSRE-PCR) protocol for determining the methylation status of 10 genes (SLC9A3, C1QL2, DPYS, IRF4, ADCY8, KCNQ2, TERT, SYNDIG1, SKOR2 and GRIK1) that distinguish BC samples with different NACT response. Analysis of these 10 markers by MSRE-PCR in biopsy samples allowed us to reveal three top informative combinations of markers, (1) IRF4 and C1QL2; (2) IRF4, C1QL2, and ADCY8; (3) IRF4, C1QL2, and DPYS, with the areas under ROC curves (AUCs) of 0.75, 0.78 and 0.74, respectively. A classifier based on IRF4 and C1QL2 better meets the diagnostic panel simplicity requirements, as it consists of only two markers. Diagnostic accuracy of the panel of these two markers is 0.75, with the sensitivity of 75% and specificity of 75%.
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Wong KK. DNMT1: A key drug target in triple-negative breast cancer. Semin Cancer Biol 2020; 72:198-213. [PMID: 32461152 DOI: 10.1016/j.semcancer.2020.05.010] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/04/2020] [Accepted: 05/18/2020] [Indexed: 02/06/2023]
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Altered epigenetics regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. In this review, the oncogenic functions rendered by DNMT1 in TNBCs, and DNMT1 inhibitors targeting TNBC cells are presented and discussed. In summary, DNMT1 expression is associated with poor breast cancer survival, and it is overexpressed in TNBC subtype. The oncogenic roles of DNMT1 in TNBCs include: (1) Repression of estrogen receptor (ER) expression; (2) Promotion of epithelial-mesenchymal transition (EMT) required for metastasis; (3) Induces cellular autophagy and; (4) Promotes the growth of cancer stem cells in TNBCs. DNMT1 confers these phenotypes by hypermethylating the promoter regions of ER, multiple tumor suppressor genes, microRNAs and epithelial markers involved in suppressing EMT. DNMT1 inhibitors exert anti-tumorigenic effects against TNBC cells. This includes the hypomethylating agents azacitidine, decitabine and guadecitabine that might sensitize TNBC patients to immune checkpoint blockade therapy. DNMT1 represents an epigenetic target for TNBC cells destruction as well as to derail their metastatic and aggressive phenotypes.
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Affiliation(s)
- Kah Keng Wong
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.
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McCartney A, Benelli M, Di Leo A. Estimating the magnitude of clinical benefit from (neo)adjuvant chemotherapy in patients with ER-positive/HER2-negative breast cancer. Breast 2020; 48 Suppl 1:S81-S84. [PMID: 31839168 DOI: 10.1016/s0960-9776(19)31130-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
Gene-expression assays were originally validated retrospectively as tools of prognostication, with evidence emerging from more recent prospectively-conducted studies such as MINDACT and TAILORx supporting their clinical validity and utility as biomarkers in identifying patients with luminal breast cancer who might be spared chemotherapy. However, these assays still do not have the ability to identify all patients who may safely avoid chemotherapy, and may over-estimate the risk of relapse in some cases. Future studies should aim to prospectively integrate contemporary approaches that assume a theoretical risk of relapse (based on pathological and/or genomic evaluation of the primary tumour), with new tools that can detect signals of active micro-metastatic disease. Until current methods of estimating prognosis and predicting benefit from adjuvant chemotherapy are significantly refined, estimating and improving the true magnitude of benefit derived from chemotherapy remains a challenge for clinicians.
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Affiliation(s)
- Amelia McCartney
- "Sandro Pitigliani" Department of Medical Oncology, Hospital of Prato, Prato, Italy
| | | | - Angelo Di Leo
- "Sandro Pitigliani" Department of Medical Oncology, Hospital of Prato, Prato, Italy.
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Stastny I, Zubor P, Kajo K, Kubatka P, Golubnitschaja O, Dankova Z. Aberrantly Methylated cfDNA in Body Fluids as a Promising Diagnostic Tool for Early Detection of Breast Cancer. Clin Breast Cancer 2020; 20:e711-e722. [PMID: 32792225 DOI: 10.1016/j.clbc.2020.05.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 03/29/2020] [Accepted: 05/11/2020] [Indexed: 12/24/2022]
Abstract
Breast malignancies are the leading type of cancer among women. Its prevention and early detection, particularly in young women, remains challenging. To this end, cell-free DNA (cfDNA) detected in body fluids demonstrates great potential for early detection of tissue transformation and altered molecular setup, such as epigenetic profiles. Aberrantly methylated cfDNA in body fluids could therefore serve as a potential diagnostic and prognostic tool in breast cancer management. Abnormal methylation may lead to both an activation of oncogenes via hypomethylation and an inactivation of tumor suppressor genes by hypermethylation. We update the state of the art in the area of aberrant cfDNA methylation analyses as a diagnostic and prognostic tool in breast cancer, report on the main technological challenges, and provide an outlook for advancing the overall management of breast malignancies based on cfDNA as a target for diagnosis and tailored therapies.
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Affiliation(s)
- Igor Stastny
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic; Department of Obstetrics and Gynaecology, Martin University Hospital and Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic.
| | - Pavol Zubor
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic; Department of Gynecologic Oncology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
| | - Karol Kajo
- Department of Pathology, St Elizabeth Cancer Institute Hospital, Bratislava, Slovak Republic; Biomedical Research Centre, Slovak Academy of Sciences, Bratislava, Slovak Republic
| | - Peter Kubatka
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic; Department of Medical Biology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovak Republic
| | - Olga Golubnitschaja
- Radiological Hospital, Rheinische, Excellence University of Bonn, Bonn, Germany; Breast Cancer Research Centre, Rheinische, Excellence University of Bonn, Bonn, Germany; Centre for Integrated Oncology, Cologne-Bonn, Excellence University of Bonn, Bonn, Germany
| | - Zuzana Dankova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
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Ding H, Sharpnack M, Wang C, Huang K, Machiraju R. Integrative cancer patient stratification via subspace merging. Bioinformatics 2020; 35:1653-1659. [PMID: 30329022 DOI: 10.1093/bioinformatics/bty866] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 06/09/2018] [Accepted: 10/15/2018] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease. RESULTS Our proposed approach creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more informative clusters by preserving the complementary information from each level of omics data. We applied our approach to The Cancer Genome Atlas (TCGA) breast cancer dataset and show that by integrating gene expression, microRNA and DNA methylation data, our proposed method can produce clinically useful subtypes of breast cancer. We then investigate the molecular characteristics underlying these subtypes. We discover a highly expressed cluster of genes on chromosome 19p13 that strongly correlates with survival in TCGA breast cancer patients and validate these results in three additional breast cancer datasets. We also compare our approach with previous integrative clustering approaches and obtain comparable or superior results. AVAILABILITY AND IMPLEMENTATION https://github.com/michaelsharpnack/GrassmannCluster. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hao Ding
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - Michael Sharpnack
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Chao Wang
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.,Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Kun Huang
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.,Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Raghu Machiraju
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.,Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
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Lu A, Wang W, Wang-Renault SF, Ring BZ, Tanaka Y, Weng J, Su L. 5-Aza-2'-deoxycytidine advances the epithelial-mesenchymal transition of breast cancer cells by demethylating Sipa1 promoter-proximal elements. J Cell Sci 2020; 133:jcs.236125. [PMID: 32193333 PMCID: PMC7240297 DOI: 10.1242/jcs.236125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 03/02/2020] [Indexed: 12/17/2022] Open
Abstract
Human breast cancer cells exhibit considerable diversity in the methylation status of genomic DNA CpGs that regulate metastatic transcriptome networks. In this study, we identified human Sipa1 promoter-proximal elements that contained a CpG island and demonstrated that the methylation status of the CpG island was inversely correlated with SIPA1 protein expression in cancer cells. 5-Aza-2′-deoxycytidine (5-Aza-CdR), a DNA methyltransferase inhibitor, promoted the expression of Sipa1 in the MCF7 breast cancer cells with a low level of SIPA1 expression. On the contrary, in MDA-MB-231 breast cancer cells with high SIPA1 expression levels, hypermethylation of the CpG island negatively regulated the transcription of Sipa1. In addition, the epithelial–mesenchymal transition (EMT) was reversed after knocking down Sipa1 in MDA-MB-231 cells. However, the EMT was promoted in MCF7 cells with over-expression of SIPA1 or treated with 5-Aza-CdR. Taken together, hypomethylation of the CpG island in Sipa1 promoter-proximal elements could enhance SIPA1 expression in breast cancer cells, which could facilitate EMT of cancer cells, possibly increasing a risk of cancer cell metastasis in individuals treated with 5-Aza-CdR. Summary: Hypomethylation by 5-Aza-CdR upregulates the SIPA1 expression and promotes epithelial–mesenchymal transition in breast cancer cells, possibly increasing the risk of cancer cell metastasis.
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Affiliation(s)
- Ang Lu
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wei Wang
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Shu-Fang Wang-Renault
- INSERM UMR-S1147, CNRS SNC5014; Paris Descartes University, Equipe Labellisée Ligue Nationale Contre le Cancer, Paris 75006, France
| | - Brian Z Ring
- Institute of Genomic and Personalized Medicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yoshimasa Tanaka
- Center for Medical Innovation, Nagasaki University, 1-7-1, Sakamoto, Nagasaki, 852-8588, Japan
| | - Jun Weng
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Li Su
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China .,Research Institute of Huazhong University of Science and Technology in Shenzhen, Shenzhen, 518063, China
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Coyne GO'S, Wang L, Zlott J, Juwara L, Covey JM, Beumer JH, Cristea MC, Newman EM, Koehler S, Nieva JJ, Garcia AA, Gandara DR, Miller B, Khin S, Miller SB, Steinberg SM, Rubinstein L, Parchment RE, Kinders RJ, Piekarz RL, Kummar S, Chen AP, Doroshow JH. Intravenous 5-fluoro-2'-deoxycytidine administered with tetrahydrouridine increases the proportion of p16-expressing circulating tumor cells in patients with advanced solid tumors. Cancer Chemother Pharmacol 2020; 85:979-993. [PMID: 32314030 PMCID: PMC7188725 DOI: 10.1007/s00280-020-04073-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 04/06/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE Following promising responses to the DNA methyltransferase (DNMT) inhibitor 5-fluoro-2'-deoxycytidine (FdCyd) combined with tetrahydrouridine (THU) in phase 1 testing, we initiated a non-randomized phase 2 study to assess response to this combination in patients with advanced solid tumor types for which tumor suppressor gene methylation is potentially prognostic. To obtain pharmacodynamic evidence for DNMT inhibition by FdCyd, we developed a novel method for detecting expression of tumor suppressor protein p16/INK4A in circulating tumor cells (CTCs). METHODS Patients in histology-specific strata (breast, head and neck [H&N], or non-small cell lung cancers [NSCLC] or urothelial transitional cell carcinoma) were administered FdCyd (100 mg/m2) and THU (350 mg/m2) intravenously 5 days/week for 2 weeks, in 28-day cycles, and progression-free survival (PFS) rate and objective response rate (ORR) were evaluated. Blood specimens were collected for CTC analysis. RESULTS Ninety-three eligible patients were enrolled (29 breast, 21 H&N, 25 NSCLC, and 18 urothelial). There were three partial responses. All strata were terminated early due to insufficient responses (H&N, NSCLC) or slow accrual (breast, urothelial). However, the preliminary 4-month PFS rate (42%) in the urothelial stratum exceeded the predefined goal-though the ORR (5.6%) did not. An increase in the proportion of p16-expressing cytokeratin-positive CTCs was detected in 69% of patients evaluable for clinical and CTC response, but was not significantly associated with clinical response. CONCLUSION Further study of FdCyd + THU is potentially warranted in urothelial carcinoma but not NSCLC or breast or H&N cancer. Increase in the proportion of p16-expressing cytokeratin-positive CTCs is a pharmacodynamic marker of FdCyd target engagement.
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Affiliation(s)
- Geraldine O 'Sullivan Coyne
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Lihua Wang
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Jennifer Zlott
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Lamin Juwara
- Clinical Monitoring Research Program, Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Joseph M Covey
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Jan H Beumer
- Department of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
| | - Mihaela C Cristea
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Edward M Newman
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | | | - Jorge J Nieva
- University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Agustin A Garcia
- University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA
- Louisiana State University, New Orleans, LA, 70112, USA
| | - David R Gandara
- University of California Davis Cancer Center, Sacramento, CA, USA
| | - Brandon Miller
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sonny Khin
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sarah B Miller
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Seth M Steinberg
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Larry Rubinstein
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Ralph E Parchment
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Robert J Kinders
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Richard L Piekarz
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Shivaani Kummar
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - Alice P Chen
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA
| | - James H Doroshow
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, 31 Center Drive, Bldg. 31 Room 3A-44, Bethesda, MD, 20892, USA.
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
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Kaur J, Daoud A, Eblen ST. Targeting Chromatin Remodeling for Cancer Therapy. Curr Mol Pharmacol 2020; 12:215-229. [PMID: 30767757 PMCID: PMC6875867 DOI: 10.2174/1874467212666190215112915] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 01/25/2019] [Accepted: 01/31/2019] [Indexed: 12/31/2022]
Abstract
Background: Epigenetic alterations comprise key regulatory events that dynamically alter gene expression and their deregulation is commonly linked to the pathogenesis of various diseases, including cancer. Unlike DNA mutations, epigenetic alterations involve modifications to proteins and nucleic acids that regulate chromatin structure without affecting the underlying DNA sequence, altering the accessibility of the transcriptional machinery to the DNA, thus modulating gene expression. In cancer cells, this often involves the silencing of tumor suppressor genes or the increased expression of genes involved in oncogenesis. Advances in laboratory medicine have made it possible to map critical epigenetic events, including histone modifications and DNA methylation, on a genome-wide scale. Like the identification of genetic mutations, mapping of changes to the epigenetic landscape has increased our understanding of cancer progression. However, in contrast to irreversible genetic mutations, epigenetic modifications are flexible and dynamic, thereby making them promising therapeutic targets. Ongoing studies are evaluating the use of epigenetic drugs in chemotherapy sensitization and immune system modulation. With the preclinical success of drugs that modify epigenetics, along with the FDA approval of epigenetic drugs including the DNA methyltransferase 1 (DNMT1) inhibitor 5-azacitidine and the histone deacetylase (HDAC) inhibitor vorinostat, there has been a rise in the number of drugs that target epigenetic modulators over recent years. Conclusion: We provide an overview of epigenetic modulations, particularly those involved in cancer, and discuss the recent advances in drug development that target these chromatin-modifying events, primarily focusing on novel strategies to regulate the epigenome.
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Affiliation(s)
- Jasmine Kaur
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina, United States
| | - Abdelkader Daoud
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina, United States
| | - Scott T Eblen
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina, United States
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