DNA Methylation Profiling of Sorted Peripheral Blood Cells Using Microarray and Next Generation Sequencing Reveals Distinct Molecular Signatures in the Polymorphonuclear and Mononuclear Cells of Patients with Essential Thrombocythemia, Polycythemia Vera and Primary Myelofibrosis

Background The past decade has witnessed a significant progress in the understanding of the molecular pathogenesis of myeloproliferative neoplasms (MPN). Mutations in a large number of genes have now been implicated in the pathogenesis of MPN but these do not yet explain the differentiation into the separate MPN syndromes and do not give full prediction of the wide variation in prognosis. We hypothesized that epigenetic mechanisms may help explain these phenomena at a cell-type specific level. Aim The aim of this study was to perform DNA methylation profiling on different cell types from patients with MPN in order to identify regulatory loci adjacent to genes whose differential expression could elucidate the pathogenesis and predict survival in patients with MPN in a multiracial country. Methods We performed DNA methylation profiling on normal controls (NC) and patients with MPN from 3 different races (Malay, Chinese and Indian) in Malaysia who were diagnosed with essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) according to the 2008 WHO diagnostic criteria for MPN. Two cohorts of patients, the patient and validation cohorts, from 3 tertiary-level hospitals were recruited prospectively over 3 years and informed consents were obtained. Peripheral blood samples were taken and sorted into polymorphonuclear cells (PMNs), mononuclear cells (MNCs) and T cells. DNA was extracted from each cell population. DNA methylation profiling was performed using the Illumina HumanMethylation450 Beadchip for microarray and subsequent confirmation was performed using the Fluidigm Access Array/Illumina Miseq next generation sequencing platform on the patient and validation cohorts respectively. Results Twenty-nine patients (11 ET, 11 PV and 7 PMF) and 11 NC were recruited into the patient cohort. Twelve patients (4 ET, 4 PV and 4 PMF) and 4 NC were recruited into the validation cohort. Methylation levels of the CpG sites for each cell type in each disease were compared with NC. In the patient cohort, the number of differentially methylated CpG sites in ET, PV and PMF was 1889, 6545 and 11,372 respectively for PMNs (p < 0.0001) and 732, 7700 and 49,219 respectively for MNCs (p < 0.0001). For T cells, the number of differentially methylated CpG sites in ET, PV and PMF were significantly less with 297, 1091 and 987 CpG sites respectively (p < 0.0001). Quantile-quantile plots showed a continuum of progressive skewness from ET to PV to MF for both PMNs and MNCs. However, this appearance was not seen in all 3 diseases for T cells. A total of 43 CpG sites showing the most significant difference in degrees of methylation from the different categories above were selected and all successfully validated using the Fluidigm/Miseq next generation sequencing platform on the validation cohort. For PMNs, 11 of the 14 CpG sites were associated with genes primarily involved in cell signaling pathways. For MNCs, 9 of the 15 CpG sites were associated with genes primarily involved in metabolic pathways and transcription regulation. Remarkably, there was no overlap between the validated PMN and MNC differentially methylated CpG sites or between disease subtypes. Fourteen differentially methylated CpG sites were validated in T cells. Conclusion This is the first study to use microarray and next generation sequencing platforms to compare cell type-specific methylation of CpG sites between different subtypes of MPN. The significantly lower differential methylation and the lack of skewness in the quantile-quantile plot in T cells validate the techniques used and indicate that they are not part of the neoplastic clone. The continuum of increasing number of differentially methylated CpG sites from ET to PV to MF in both PMNs and MNCs may be related to the increasing severity of the disease phenotypes. Differential methylation was greatest in PMF and was most markedly seen in MNCs which may be related to their more severe phenotype. The pattern of cell type-specific differentially methylated CpG sites and the lack of overlap between cell types and diseases provide further insight into the pathogenesis of MPN and into the mechanisms giving rise to the different disease subtypes. Differentially methylated CpG sites and the linked genes also indicate further routes of investigation and possible disease-specific targets for therapy not identified by mutation or gene expression analyses. Disclosures Aitman: Illumina: Honoraria.