DNA-methylation in C1R is a prognostic biomarker for acute myeloid leukemia
- Tanja Božić†1, 2,
- Qiong Lin†1, 2,
- Joana Frobel1, 2,
- Stefan Wilop3,
- Melanie Hoffmann3,
- Carsten Müller-Tidow4,
- Tim H. Brümmendorf3,
- Edgar Jost3 and
- Wolfgang Wagner1, 2Email author
© Božić et al. 2015
Received: 28 August 2015
Accepted: 29 October 2015
Published: 4 November 2015
Epigenetic aberrations play a central role in the pathophysiology of acute myeloid leukemia (AML). It has been shown that molecular signatures based on DNA-methylation (DNAm) patterns can be used for classification of the disease. In this study, we followed the hypothesis that DNAm at a single CpG site might support risk stratification in AML.
Using DNAm profiles of 194 patients from The Cancer Genome Atlas (TCGA), we identified a CpG site in complement component 1 subcomponent R (C1R) as best suited biomarker: patients with higher methylation at this CpG site (>27 % DNAm) reveal significantly longer overall survival (53 versus 11 months; P < 0.0001). This finding was validated in an independent set of 62 DNAm profiles of cytogenetically normal AML patients (P = 0.009) and with a region-specific pyrosequencing assay in 84 AML samples (P = 0.012). DNAm of C1R correlated with genomic DNAm and gene expression patterns, whereas there was only moderate association with gene expression levels of C1R. These results indicate that DNAm of C1R is a biomarker reflecting chromatin reorganization rather than being of pathophysiological relevance per se. Notably, DNAm of C1R was associated with occurrence of specific genomic mutations that are traditionally used for risk stratification in AML. Furthermore, DNAm of C1R correlates also with overall survival in several other types of cancer, but the prognostic relevance was less pronounced than in AML.
Analysis of DNAm at C1R provides a simple, robust, and cost-effective biomarker to further complement risk assessment in AML.
KeywordsDNA-methylation Epigenetic Leukemia AML C1R Survival Biomarker Prognosis TCGA
Risk assessment is relevant for the choice of therapeutic regimen in acute myeloid leukemia (AML). It is usually based on many parameters including age, white blood cell count, cytogenetic abnormalities, and specific mutations . Epigenetic modifications, such as DNA-methylation (DNAm) changes, seem to play a critical role in pathogenesis of AML . Various studies demonstrated that DNAm patterns can discriminate subgroups of patients with different clinical outcomes [3–7]. So far, these approaches utilized a combination of many differentially methylated regions and therefore require DNAm profiles based on microarray or sequencing technology. In contrast, a region-specific assay would be much faster, economic, and easier to interpret. We have recently described that aberrant hypermethylation at a specific region of the de novo methyltransferase DNMT3A is associated with poor prognosis in AML, but this association was only significant for patients without genomic mutations in DNMT3A because both modifications seem to have a similar molecular and clinical sequel . This exemplifies that identification of simple epigenetic markers is hampered by the high molecular and clinical heterogeneity of the disease. Despite these hurdles, we followed the hypothesis that DNAm at a unique CpG site might provide a robust biomarker to further support risk assessment of AML.
Selection of CpG sites that are indicative for overall survival in AML
Epigenetic co-regulation of C1R with other genomic regions and correlation with gene expression
Association of DNA-methylation at C1R with clinical parameters
DNAm levels in C1R were subsequently compared with clinical parameters of the TCGA dataset : there was no clear association with blast counts (R = 0.0046; Fig. 2d) and no gender difference (Mann-Whitney P = 0.82; Fig. 2e). AML samples with favorable cytogenetic risk score (Fig. 2f) and AML subtype M3 (Fig. 2g) revealed significantly higher DNAm in C1R. Multivariate analysis of OS considering age, gender, bone marrow blast count, and FAB classification demonstrated that DNAm at C1R can support risk stratification (Additional file 1: Table S2). If alternatively only DNAm in C1R, cytogenetic risk score, and molecular risk score were included as parameters for multivariate analysis, the relevance of C1R was also significant (Additional file 1: Table S3). However, if all parameters were combined into one multivariate model, only age and molecular risk score were classified as relevant parameters (Additional file 1: Table S4). To further evaluate if the prognostic relevance of DNAm in C1R is independent from cytogenetic risk groups, we performed K-M analysis within individual cytogenetic groups. DNAm at C1R revealed significant association with OS in the intermediate group of TCGA (K-M analysis: P = 0.015), and the same trend was also observed in the other groups (in TCGA and pyrosequencing datasets; Additional file 1: Figure S8). These results indicate that DNAm in C1R might be of independent prognostic relevance, but this needs to be further validated in larger cohorts. Furthermore, higher methylation at C1R was associated with significantly less mutations in TP53, RUNX1, TET2, and DNMT3A, whereas mutations in CEBPA and WT1 and the translocations PML-PARA and MYH11-CBFB were significantly enriched (Fig. 2h; Additional file 1: Figure S9). Thus, it can be speculated that either DNAm at C1R is influenced by these mutations or vice versa [12, 17]. Because of the strong interaction of DNAm at C1R with prognostic relevant mutations, we performed an additional Kaplan-Meier analysis for patients without the above mentioned mutations. In this subset of patients, DNAm of C1R was also indicative for OS (P = 0.036; Additional file 1: Figure S10).
Analysis of DNA-methylation at C1R in various other types of cancer
To estimate if DNAm in C1R might also be a suitable biomarker for OS in other types of cancer, we utilized 5699 DNAm profiles of 25 different types of tumor from TCGA. In fact, significant association in COX analysis was also observed for kidney renal papillary carcinoma (P = 0.00009), low-grade glioma (P = 0.0002), skin cutaneous melanoma (P = 0.002), hepatocellular carcinoma (P = 0.026), and glioblastoma (P = 0.042; Additional file 1: Table S5). These results indicate similar association of DNAm in C1R with OS in other tumors, but the prognostic relevance was found to be particularly predominant in patients with AML.
In this study, we demonstrate that DNAm at a single CpG site in C1R is indicative for overall survival in AML. In contrast to previously described epigenetic signatures [3, 7, 18], our method is based on only one CpG site. It is unclear if C1R plays a functional role for leukemia development or if it is differentially methylated in leukemia initiating cells, but this is not a prerequisite for biomarkers. Our data indicate that DNAm of C1R may reflect global chromatin reorganization in a subset of AML patients, and this may contribute to specific genomic mutations or vice versa [12, 17]. We have demonstrated that DNAm of C1R was also of prognostic relevance in cytogenetic normal AMLs, in subsets with defined cytogenetic risk score, and in subsets without specific genomic mutations. These results suggest that DNAm in C1R is of independent prognostic significance, but further validation with larger datasets is required to ultimately substantiate the clinical potential. Many cytogenetic aberrations are routinely analyzed in AML diagnostics, but the importance of epigenetic biomarkers should not be neglected, considering that AML develops by means of genetic and epigenetic changes as well. Complementation of epigenetic biomarkers to existing genetic biomarkers could provide a balanced and more accurate diagnostic approach. Furthermore, it is conceivable that DNAm in C1R is also indicative for treatment response to specific drugs, particularly for demethylating agents, and this needs to be addressed in future studies. Either way, analysis of DNAm at a unique CpG site provides a very simple and robust biomarker to complement risk assessment in AML.
DNAm profiles and bioinformatics analysis
We used publically available DNAm profiles (all based on HumanMethylation450K BeadChip) of AML patients from The Cancer Genome Atlas (TCGA)  and a study of Qu et al. (GSE58477) , of healthy individuals (GSE40279, GSE35069) [10, 19], and of 25 other types of cancer (TCGA). For Kaplan-Meier estimation of overall survival, we stratified samples by median DNAm levels and adjusted the results for multiple testing (log-rank test calculated in R). Alternatively, we calculated COX regression with OS for each individual CpG site in R.
Blood samples and pyrosequencing
Blood samples were taken from 40 healthy donors and 84 AML patients after written consent according to the guidelines of the ethics committee of the Medical Faculty of the RWTH Aachen (Permit Number: EK206/09). Patient characteristics are summarized in Additional file 1: Table S6. Genomic DNA was isolated from blood, bisulfite converted, and analyzed by pyrosequencing (primers are provided in Additional file 1: Table S7). More detailed methods are described in the supplemental document.
Availability of supporting data
The data sets supporting the results of this article are available at The Cancer Genome Atlas portal (https://tcga-data.nci.nih.gov/tcga/)  and at Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under the accession numbers GSE58477 , GSE40279 , and GSE35069 .
Acute myeloid leukemia
Complement component 1 subcomponent R
French-American-British classification of AML
The Cancer Genome Atlas
We thank researchers of TCGA and the Karolinska Institute for making their DNAm and sequencing data of AML patients available. This work was supported by the Else Kröner-Fresenius Stiftung (2014_A193), the German Research Foundation (WA/1706/2-1), and the Interdisciplinary Center for Clinical Research (IZKF) within the Faculty of Medicine at the RWTH Aachen University (O1-1).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Patel JP, Gonen M, Figueroa ME, Fernandez H, Sun Z, Racevskis J, et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med. 2012;366:1079–89.PubMed CentralView ArticlePubMedGoogle Scholar
- Shih AH, Abdel-Wahab O, Patel JP, Levine RL. The role of mutations in epigenetic regulators in myeloid malignancies. Nat Rev Cancer. 2012;12:599–612.View ArticlePubMedGoogle Scholar
- Bartholdy B, Christopeit M, Will B, Mo Y, Barreyro L, Yu Y, et al. HSC commitment-associated epigenetic signature is prognostic in acute myeloid leukemia. J Clin Invest. 2014;124:1158–67.PubMed CentralView ArticlePubMedGoogle Scholar
- Christopeit M, Bartholdy B. Epigenetic signatures as prognostic tools in acute myeloid leukemia and myelodysplastic syndromes. Epigenomics. 2014;6:371–4.View ArticlePubMedGoogle Scholar
- Figueroa ME, Lugthart S, Li Y, Erpelinck-Verschueren C, Deng X, Christos PJ, et al. DNA methylation signatures identify biologically distinct subtypes in acute myeloid leukemia. Cancer Cell. 2010;17:13–27.PubMed CentralView ArticlePubMedGoogle Scholar
- Deneberg S, Guardiola P, Lennartsson A, Qu Y, Gaidzik V, Blanchet O, et al. Prognostic DNA methylation patterns in cytogenetically normal acute myeloid leukemia are predefined by stem cell chromatin marks. Blood. 2011;118:5573–82.View ArticlePubMedGoogle Scholar
- Marcucci G, Yan P, Maharry K, Frankhouser D, Nicolet D, Metzeler KH, et al. Epigenetics meets genetics in acute myeloid leukemia: clinical impact of a novel seven-gene score. J Clin Oncol. 2014;32:548–56.PubMed CentralView ArticlePubMedGoogle Scholar
- Jost E, Lin Q, Ingrid WC, Wilop S, Hoffmann M, Walenda T, et al. Epimutations mimic genomic mutations of DNMT3A in acute myeloid leukemia. Leukemia. 2014;28:1227–34.PubMed CentralView ArticlePubMedGoogle Scholar
- Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368:2059–74.View ArticleGoogle Scholar
- Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlen SE, Greco D, et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS ONE. 2012;7:e41361.PubMed CentralView ArticlePubMedGoogle Scholar
- Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15:R24.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin Q, Wagner W. Epigenetic aging signatures are coherently modified in cancer. PLoS Genet. 2015;11:e1005334.PubMed CentralView ArticlePubMedGoogle Scholar
- Qu Y, Lennartsson A, Gaidzik VI, Deneberg S, Karimi M, Bengtzen S. Differential methylation in CN-AML preferentially targets non-CGI regions and is dictated by DNMT3A mutational status and associated with predominant hypomethylation of HOX genes. Epigenetics. 2014;9:1108–19.PubMed CentralView ArticlePubMedGoogle Scholar
- Wijeyewickrema LC, Yongqing T, Tran TP, Thompson PE, Viljoen JE, Coetzer TH, et al. Molecular determinants of the substrate specificity of the complement-initiating protease, C1r. J Biol Chem. 2013;288:15571–80.PubMed CentralView ArticlePubMedGoogle Scholar
- Shukla S, Kavak E, Gregory M, Imashimizu M, Shutinoski B, Kashlev M, et al. CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing. Nature. 2011;479:74–9.View ArticlePubMedGoogle Scholar
- Ong CT, Corces VG. CTCF: an architectural protein bridging genome topology and function. Nat Rev Genet. 2014;15:234–46.PubMed CentralView ArticlePubMedGoogle Scholar
- Wagner W, Weidner CI, Lin Q. Do age-associated DNA methylation changes increase the risk of malignant transformation? Bioessays. 2015;37:20–4.View ArticlePubMedGoogle Scholar
- Bullinger L, Ehrich M, Dohner K, Schlenk RF, Dohner H, Nelson MR, et al. Quantitative DNA methylation predicts survival in adult acute myeloid leukemia. Blood. 2010;115:636–42.View ArticlePubMedGoogle Scholar
- Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:459–367.View ArticleGoogle Scholar
- Ernst J, Kellis M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol. 2010;28:817–25.PubMed CentralView ArticlePubMedGoogle Scholar