Global hypomethylation and regional hypermethylation are well-known epigenetic features of cancer; however, in chronic lymphocytic leukemia (CLL), studies on genome-wide epigenetic modifications are limited. Here, we analyzed the global methylation profiles in CLL, by applying high-resolution methylation microarrays (27 578 CpG sites) to 23 CLL samples, belonging to the immunoglobulin heavy-chain variable (IGHV) mutated (favorable) and IGHV unmutated/IGHV3-21 (poor-prognostic) subsets. Overall, results demonstrated significant differences in methylation patterns between these subgroups. Specifically, in IGHV unmutated CLL, we identified methylation of 7 known or candidate tumor suppressor genes (eg, VHL, ABI3, and IGSF4) as well as 8 unmethylated genes involved in cell proliferation and tumor progression (eg, ADORA3 and PRF1 enhancing the nuclear factor-B and mitogen-activated protein kinase pathways, respectively). In contrast, these latter genes were silenced by methylation in IGHV mutated patients. The array data were validated for selected genes using methylation-specific polymerase chain reaction, quantitative reverse transcriptasepolymerase chain reaction, and bisulfite sequencing. Finally, the significance of DNA methylation in regulating gene promoters was shown by reinducing 4 methylated tumor suppressor genes (eg, VHL and ABI3) in IGHV unmutated samples using the methyl-inhibitor 5-aza-2-deoxycytidine. Taken together, our data for the first time reveal differences in global methylation profiles between prognostic subsets of CLL, which may unfold epigenetic silencing mechanisms involved in CLL pathogenesis. (Blood. 2010;115:296-305)

BackgroundEpigenetic mechanisms such as DNA methylation and histone modifications are important regulators of gene expression and are frequently involved in silencing tumor suppressor genes.MethodsIn order to identify genes that are epigenetically regulated in neuroblastoma tumors, we treated four neuroblastoma cell lines with the demethylating agent 5-Aza-2'-deoxycytidine (5-Aza-dC) either separately or in conjunction with the histone deacetylase inhibitor trichostatin A (TSA). Expression was analyzed using whole-genome expression arrays to identify genes activated by the treatment. These data were then combined with data from genome-wide DNA methylation arrays to identify candidate genes silenced in neuroblastoma due to DNA methylation.ResultsWe present eight genes (KRT19, PRKCDBP, SCNN1A, POU2F2, TGFBI, COL1A2, DHRS3 and DUSP23) that are methylated in neuroblastoma, most of them not previously reported as such, some of which also distinguish between biological subsets of neuroblastoma tumors. Differential methylation was observed for the genes SCNN1A (p < 0.001), PRKCDBP (p < 0.001) and KRT19 (p < 0.01). Among these, the mRNA expression of KRT19 and PRKCDBP was significantly lower in patients that have died from the disease compared with patients with no evidence of disease (fold change -8.3, p = 0.01 for KRT19 and fold change -2.4, p = 0.04 for PRKCDBP).ConclusionsIn our study, a low methylation frequency of SCNN1A, PRKCDBP and KRT19 is significantly associated with favorable outcome in neuroblastoma. It is likely that analysis of specific DNA methylation will be one of several methods in future patient therapy stratification protocols for treatment of childhood neuroblastomas.

Summary. Background: Vitamin K epoxide reductase (VKORC1) is the site of inhibition by coumarins. Several reports have shown that mutations in the gene encoding VKORC1 affect the sensitivity of the enzyme for warfarin. Recently, three main haplotypes of VKORC1; *2, *3 and *4 have been observed, that explain most of the genetic variability in warfarin dose among Caucasians. Objectives: We have investigated the main haplotypes of the VKORC1 gene in a Swedish population. Additional objective was to screen the studied population for mutations in the coding region of VKORC1 gene. Patients/methods: Warfarin doses and plasma S-and R-warfarin of 98 patients [with a target International Normalized Ratio (INR) of 2.0-3.0] have been correlated to VKORC1 haplotypes. Controls of 180 healthy individuals have also been haplotyped. Furthermore, a retrospective analysis of case records was performed to find any evidence indicating influence of VKORC1 haplotypes on warfarin response in the first 4 weeks (initiation phase) and the latest 12 months of warfarin treatment. Results and conclusions: Our result shows that VKORC1*2 is the most important haplotype for warfarin dosage. Patients with VKORC1*2 haplotype had more frequent visits than patients with VKORC1*3 or *4 haplotypes, higher coefficient of variation (CV) of prothrombin time-INR and higher percentage of INR values outside the therapeutic interval (i.e. 2.0-3.0) than patients with VKORC1*3 or *4 haplotypes. Also, there was a statistically significant difference in warfarin dose (P < 0.001) and R-warfarin plasma levels (P < 0.01) between VKORC1*2 and VKORC1*3 or 4 haplotypes. Patients with VKORC1*2 haplotype seem to require much lower warfarin doses than other patients.

A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.