Some of the strengths of this study include the rigorous quality control steps, the use of a powerful DNA methylation platform on specially designed high density oligonucleotide microarrays, the use of primary patient materials, the performance of the three different assays using the same type of 50-mer high density oligonucleotide arrays in multiple replicates and the extensive single locus validation. Recently DNA methylation microarrays have been used to study acute leukemias as well as other malignancies. Groups of hypermethylated genes have been identified by such studies in AML cell lines as well as in ALL patient samples. Thus, the integration of genetic and epigenetic platforms seems only natural, since individually both types of platforms have proven to capture biologically relevant information. Along these lines, some groups have begun to investigate the potential to be found in the combination of information from different microarray platforms. Shi et. al. used a CpG Monoammoniumglycyrrhizinate island microarray containing 1507 expressed CpG island sequence tags to carry out a triple analysis of histone acetylation, DNA methylation and gene expression in an ovarian cancer cell line treated with trichostatin A and 59deoxyazacytidine. While they were able to detect a functional interaction between histone acetylation and DNA methylation, they could not demonstrate an overall correlation between changes in epigenetic modifications and changes in expression levels. Wu et. al. used a combination of Albaspidin-AA ChIP-chip for H3K9 modifications and Differential Methylation Hybridization on a 9.2 K mouse promoter array and showed an inverse correlation between H3K9 acetylation and DNA methylation, while no significant correlation could be found between DNA methylation and H3 dimethyl-K9 at the promoter level. In our study, however, we propose the use of a combination of three different high-density genomic and epigenomic platforms for the 
in-depth analysis of their relationship in the context of human cancer specimens. Posing a simple biological question�Cthat is the differentiation between cell types in a sample set�Cwe determined first by carrying out a systematic unsupervised clustering analysis that epigenomic platforms can be readily used for profiling and classification of leukemia clinical samples. Moreover, the combination of DNA methylation and H3K9 acetylation to gene expression data resulted in a significantly larger number of genes being identified that distinguished ALL from AML samples. Since each one of these platforms is affected by unique technical limitations, it is not surprising that they would result in the detection of only partially overlapping cohorts of genes. The existence of such limitations was confirmed by the fact that restricting the analysis to the subset of genes that displayed high signal to noise ratios on any two platforms resulted in a high degree of correlation between the different measures. Furthermore, we hypothesized that this technical limitation due to the presence of noise in gene expression arrays was significantly affecting our ability to detect genuine differences in mRNA levels. By looking at a group of genes that displayed a significant difference between ALL and AML in either H3K9 acetylation or DNA methylation levels but did not display significant differences on gene expression arrays we found that when the mRNA levels of these genes were measured by qRT-PCR, an underlying difference in gene expression could be readily detected. Thus, we were able to confirm that there is an important degree of loss of information when carrying out genome-wide studies by gene expression microarrays alone.