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Georgiadis P, Liampa I, Hebels DG, Krauskopf J, Chatziioannou A, Valavanis I, de Kok TM, Kleinjans JC, Bergdahl IA, Melin B, Spaeth F, Palli D, Vermeulen R, Vlaanderen J, Chadeau-Hyam M, Vineis P, Kyrtopoulos SA. Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis. BMC Genomics 2017; 18:728. [PMID: 28903739 PMCID: PMC5598006 DOI: 10.1186/s12864-017-4117-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 09/05/2017] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance. RESULTS We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0-15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expressed genes include, among others, multiple genes related to WNT signaling as well as the miRNAs miR-150-5p and miR-155-5p. CONCLUSIONS Our findings demonstrate the presence in prediagnostic blood of future CLL patients, more than 10 years before diagnosis, of CLL-like cells which evolve as preclinical disease progresses, and point to early molecular alterations with a pathogenetic potential.
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MESH Headings
- Biomarkers, Tumor/genetics
- DNA Methylation
- Gene Expression Profiling
- Gene Expression Regulation, Neoplastic
- Leukemia, Lymphocytic, Chronic, B-Cell/blood
- Leukemia, Lymphocytic, Chronic, B-Cell/diagnosis
- Leukemia, Lymphocytic, Chronic, B-Cell/genetics
- MicroRNAs/genetics
- Prognosis
- Time Factors
- Humans
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Affiliation(s)
- Panagiotis Georgiadis
- Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, 48, Vassileos Constantinou Avenue, 11635 Athens, Greece
| | - Irene Liampa
- Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, 48, Vassileos Constantinou Avenue, 11635 Athens, Greece
| | - Dennie G. Hebels
- Department of Toxicogenomics, Maastricht University, 6229 Maastricht, ER Netherlands
| | - Julian Krauskopf
- Department of Toxicogenomics, Maastricht University, 6229 Maastricht, ER Netherlands
| | - Aristotelis Chatziioannou
- Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, 48, Vassileos Constantinou Avenue, 11635 Athens, Greece
| | - Ioannis Valavanis
- Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, 48, Vassileos Constantinou Avenue, 11635 Athens, Greece
| | - Theo M.C.M. de Kok
- Department of Toxicogenomics, Maastricht University, 6229 Maastricht, ER Netherlands
| | - Jos C.S. Kleinjans
- Department of Toxicogenomics, Maastricht University, 6229 Maastricht, ER Netherlands
| | - Ingvar A. Bergdahl
- Department of Biobank Research, and Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, 901 87 Umeå, Sweden
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology, Umeå University, 901 87 Umeå, Sweden
| | - Florentin Spaeth
- Department of Radiation Sciences, Oncology, Umeå University, 901 87 Umeå, Sweden
| | - Domenico Palli
- The Institute for Cancer Research and Prevention, 50141 Florence, Italy
| | - R.C.H. Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - J. Vlaanderen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College, London, W2 1PG UK
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College, London, W2 1PG UK
| | - Soterios A. Kyrtopoulos
- Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, 48, Vassileos Constantinou Avenue, 11635 Athens, Greece
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Hardt C, Beber ME, Rasche A, Kamburov A, Hebels DG, Kleinjans JC, Herwig R. ToxDB: pathway-level interpretation of drug-treatment data. Database (Oxford) 2016; 2016:baw052. [PMID: 27074805 PMCID: PMC4830474 DOI: 10.1093/database/baw052] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 03/17/2016] [Indexed: 01/05/2023]
Abstract
Motivation: Extensive drug treatment gene expression data have been generated in order to identify biomarkers that are predictive for toxicity or to classify compounds. However, such patterns are often highly variable across compounds and lack robustness. We and others have previously shown that supervised expression patterns based on pathway concepts rather than unsupervised patterns are more robust and can be used to assess toxicity for entire classes of drugs more reliably. Results: We have developed a database, ToxDB, for the analysis of the functional consequences of drug treatment at the pathway level. We have collected 2694 pathway concepts and computed numerical response scores of these pathways for 437 drugs and chemicals and 7464 different experimental conditions. ToxDB provides functionalities for exploring these pathway responses by offering tools for visualization and differential analysis allowing for comparisons of different treatment parameters and for linking this data with toxicity annotation and chemical information. Database URL:http://toxdb.molgen.mpg.de
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Affiliation(s)
- C Hardt
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, D-14195 Berlin, Germany
| | - M E Beber
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, D-14195 Berlin, Germany
| | - A Rasche
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, D-14195 Berlin, Germany
| | - A Kamburov
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, D-14195 Berlin, Germany
| | - D G Hebels
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, Md 6200, The Netherlands Department of Cell Biology-Inspired Tissue Engineering, MERLN Institute, Maastricht University, Universiteitssingel 40, Maastricht, Er 6229, The Netherlands
| | - J C Kleinjans
- Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, Maastricht, Md 6200, The Netherlands
| | - R Herwig
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, D-14195 Berlin, Germany
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